Category Archives: Gene Medicine

Dr Niti Raizada, Director-Medical Oncology & Hemato Oncology, Fortis Group of Hospitals, Bangalore talks about lung cancer management and role of precision medicine

The World Health Organisation mentions cancer to be the leading cause of mortality across the world, with around 1.93 crore new cancer cases every year. While this data is from the International Agency for Research on Cancer, which collates information gathered from 183 countries, not all countries have centralised cancer statistics that represent the real numbers. Therefore, its safe to assume that the numbers could be much more than what is identified globally. Of these cancers, breast cancer topped the list in women, while lung cancer topped the list for men. Lung cancer remained the leading cause of cancer death, with an estimated 18 lakh deaths globally.

In India, around 13.2 Lakh new cancer cases are found every year. The total number of lung cancer cases in 2020 is approximately 72.5K, and the majority of these were men. The leading cause behind lung cancer is smoking and certain occupational hazards like exposure to radiation, chemical factories without proper protection gear, to mention a few. The symptoms include difficulty breathing, coughing up blood, pain in the chest, fluid accumulation in the chest area, etc. Therefore, the most successful way to prevent the disease is to stop smoking, avoid second-hand smoke, avoid pollution through preventive measures like masks, prevent occupational hazards by using appropriate safety gear, etc.

Though multiple awareness communication from the government and private bodies related to the prevention of smoking has been ongoing, the habit has been on an uptick. The main reason behind the higher death rate in such males is due to late identification of the disease resulting in a lack of options to manage the disease. Diagnosis for lung cancer includes X-rays, CT Scans to provide insights into small lump growths or cuts in the lungs. In case of such mass or lesions are identified, a tissue biopsy is the next step that can confirm if the patient has cancer.

The management of lung cancer has seen significant improvement over the last three decades, from the patients life expectancy going from less than six months to around ten years. The usual treatment procedure in the case of cancer includes radiation therapy, surgical removal of the affected section, chemotherapy, mostly in combination depending on the stage of the disease. The latest that medicine has to offer to manage lung cancer includes personalised precision treatment based on the genetic profile of the patient. This has shown success in around 60% of the patients in whom the cancer-specific genes could be identified, and the treatment is modified to suit the patient. However, a precise diagnosis requires advanced technology, and next-generation sequencing (NGS) is a powerful approach in recent clinical practice. Whole Exome Sequencing (WES) identifies alterations within the cancer genome, especially unravelling our coding regions prone to mutations. Since alterations in the genome are sometimes related to tumour progression, WES remains an advanced solution to detect cancer predispositions, mutations associated with disease progression that facilitates access to targeted therapies.

Extensive collaborative genome-wide association studies have identified common gene variants involved in lung cancer has been identified as three separate loci, namely 5p15, 6p21, and 15q25. In India specific studies, it has been observed that patients that carry the cancer genes show a three three-fold increase in the incidence of cancer. The most significant risk for LC (OR = 3.2; 95% CI: 0.7-3.8) was found in the association of the homozygous variant of the CYP1A1 gene. Precision medicine and personalised treatment have come a long way to tackle such cases with positive results.

An example of precision treatment in such cases with drugs like Gefitinib in patients with epidermal growth factor receptor (EGFR) mutation induces dramatic clinical responses in non-small cell lung cancers (NSCLCs). Such drugs can specifically target cells that have EGFR mutation and destroy them while the remaining cells remain unaffected. This significantly reduces the cost of treatment and the side effect profile from multiple cycles of chemotherapy and radiation therapies. Today with the availability of an Indian genetic profile based gene panel that supports the identification of different types of cancer mutation, precision treatment of conditions like lung cancer can get easier access and faster turnaround time for results.

While the advancement of medicine and science has taken a leap in the right direction in the management of lung cancer in India, the best approach is and will always remain prevention. Therefore, it is always important to ensure that one must always go for regular health checkups and stop habits like smoking that result in detrimental effects on the health of the individual.

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Lung cancer management in the era of precision medicine - Express Healthcare

Significance

Gene-based tests are important tools for elucidating the genetic basis of complex traits. Despite substantial recent efforts in this direction, the existing tests are still limited, owing to low power and detection of false-positive signals due to the confounding effects of linkage disequilibrium. In this paper, we describe a gene-based test that attempts to address these limitations by incorporating data on long-range chromatin interactions, several recent technical advances for region-based testing, and the knockoff framework for synthetic genotype generation. Through extensive simulations and applications to multiple diseases and traits, we show that the proposed test increases the power over state-of-the-art gene-based tests and provides a narrower focus on the possible causal genes involved at a locus.

Gene-based tests are valuable techniques for identifying genetic factors in complex traits. Here, we propose a gene-based testing framework that incorporates data on long-range chromatin interactions, several recent technical advances for region-based tests, and leverages the knockoff framework for synthetic genotype generation for improved gene discovery. Through simulations and applications to genome-wide association studies (GWAS) and whole-genome sequencing data for multiple diseases and traits, we show that the proposed test increases the power over state-of-the-art gene-based tests in the literature, identifies genes that replicate in larger studies, and can provide a more narrow focus on the possible causal genes at a locus by reducing the confounding effect of linkage disequilibrium. Furthermore, our results show that incorporating genetic variation in distal regulatory elements tends to improve power over conventional tests. Results for UK Biobank and BioBank Japan traits are also available in a publicly accessible database that allows researchers to query gene-based results in an easy fashion.

Gene-based association tests are commonly used to identify genetic factors in complex traits. Relative to individual variant or window-based tests, they have appealing features, including improved functional interpretation and potentially higher power due to lower penalty for multiple testing. Due to the recent advances in massively parallel sequencing technologies, a large number of gene-based tests have been proposed in the literature to test for association with genetic variation identified in sequencing studies (16). One important limitation of the current gene-based tests is that they often fail to incorporate the epigenetic context in noncoding regions. Moreover, how to best analyze the noncoding part of the genome to increase power remains unclear. Recently, several sliding window approaches have been proposed to scan the genome with flexible window sizes and appropriate adjustments for multiple testing, while accounting for correlations among test statistics (7, 8). However, these approaches are essentially scanning the genome in a one-dimensional (1D) fashion and fail to take into account the three-dimensional (3D) structure of the genome (9). Furthermore, because they scan the genome agnostically, the burden of multiple testing is high, which may lead to low power to identify true associations. These 1D approaches also suffer from interpretability issues similar to genome-wide association studies (GWAS) and therefore require follow-up analyses to be performed in order to identify the target genes. Several existing tests, such as multimarker analysis of genomic annotation (MAGMA), high-throughput chromosome conformation capture (Hi-C)-coupled MAGMA (H-MAGMA), and an omnibus test in the variant-set test for association using annotation information framework (STAAR-O) (1012), attempt to link variants to their cognate genes based on physical proximity or chromatin-interaction data. We will compare our proposed tests to these existing approaches both conceptually and empirically, and we will show that our tests are more flexible and powerful than these existing tests. Furthermore, when individual-level data are available, the proposed tests can produce a more narrow list of associated genes at a locus by reducing the confounding effect of linkage disequilibrium (LD), a unique aspect of our gene-based test.

A related and popular gene-based strategy is the transcriptome-wide association studies (TWASs) (13, 14) that use GWAS data for a specific trait combined with genetic-variation gene-expression repositories, such as GTEx (15), to perform gene-based association tests. However, TWASs are limited to expression quantitative trait loci (eQTLs) being present in the reference datasets, and the majority of genetic associations cannot be clearly assigned to existing eQTLs (16, 17). Therefore, they may have reduced power to identify the relevant genes for the trait of interest.

Regulatory elements, including enhancers and promoters, play an important role in controlling when, where, and to what degree genes will be expressed. Most of the disease-associated variants in GWAS lie in noncoding regions of the genome, and it is believed that a majority of causal noncoding variants reside in enhancers (18). However, identifying enhancers and linking them to the genes they regulate is challenging. A number of methods have emerged in recent years to identify promoterenhancer interactions. These techniques range from chromatin conformation capture (3C), which is limited to the detection of a single interaction, to circular chromosome conformation capture (which can detect all loci that interact with a single locus), to many-to-many mapping technologies possible using targeted enrichment. Hi-C maps the complete DNA interactome and elucidates the spatial organization of the human genome (1921). Hi-C provides direct physical evidence of interactions that may mediate gene-regulatory relationships and can aid in identifying putative regulatory elements for a gene of interest. However, due to the prohibitive sequencing costs of the Hi-C experimental technique, it is challenging to obtain high-resolution (e.g., 1 Kb) Hi-C data in a large number of cell types and tissues at multiple developmental times.

We propose here comprehensive gene-based association tests for common and rare genetic variation in both coding and noncoding regions, putative regulatory elements, and which incorporate several recent advances for region-based tests, including 1) scanning the genic and regulatory regions with varied window sizes; 2) the aggregated Cauchy association test (ACAT) to combine P values from single-variant, burden, and dispersion (sequence kernel association test [SKAT]) tests; 3) incorporation of multiple functional annotations; and 4) the saddlepoint approximation for unbalanced case-control data (2225). To further improve the power and the ability to prioritize putative causal genes at significant loci when individual-level data are available, we leverage a recent development in statistics, namely, the knockoff framework for knockoff genotype generation (26) that helps control the false discovery rate (FDR) under arbitrary correlation structure and attenuates the confounding effect of LD. One can think of the knockoff genotypes as synthetic, noisy copies of the original genotypes, which resemble the original data in terms of LD structure, but are conditionally independent of the trait of interest, given the original genotypes. Although conventional methods, such as the BenjaminiHochberg (BH) procedure, are also designed to control the FDR (27), they cannot guarantee FDR control at the target level with arbitrarily correlated P values. Furthermore, unlike the knockoff framework implemented here, the conventional methods do not naturally account for correlations due to LD. The proposed gene-based test is related to a recently proposed window-based test, KnockoffScreen (8). Specifically, we employ the knockoff generation algorithm for genotype data that we have introduced in KnockoffScreen (8) and develop knockoff-based inference for gene-based tests. We demonstrate below that the proposed test has important advantages compared with the window-based test KnockoffScreen in terms of controlling the FDR at gene level. While KnockoffScreen can identify significant windows with valid FDR control at window level, functional interpretation of significant windows is still needed, which means that post hoc analyses need to be done to link those windows to relevant genes. However, as we show in simulations, this procedure can lead to highly inflated FDR at gene level.

We evaluate the performance relative to existing methods using simulations and applications to multiple studies, including GWAS studies for neuropsychiatric and neurodegenerative diseases, whole-genome sequencing studies for Alzheimers disease (AD) from the Alzheimers Disease Sequencing Project (ADSP), and for lung function from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program. We also provide results of applications to UK Biobank and BioBank Japan binary and continuous traits.

We provide here a brief overview of the proposed gene-based tests that aim to comprehensively evaluate the effects of common and rare, coding, and proximal and distal regulatory variation on a trait of interest. A workflow depicting the overall gene-based testing approach proposed here is shown in Fig.1. Briefly, we build our final test, GeneScan3DKnock, progressively, starting with a test focused on scanning the gene body region (i.e., the interval between the transcription start site [TSS] and the end of the 3 untranslated region [UTR]) with varied window sizes. We refer to this test as GeneScan1D. We extend this test by incorporating genetic variants residing in putative regulatory elements, such as promoters and enhancers. In particular, we use chromatin immunoprecipitation sequencing (ChIP-seq) peak data extracted from the ChIP-Atlas database to identify promoter regions and data from the GeneHancer database to link enhancers to their target genes (28). We also use the activity-by-contact (ABC) model to predict functional enhancergene connections for five cell types and tissues (29). This is the GeneScan3D test. Finally, when individual-level data are available, we implement the knockoff framework for a more powerful gene-discovery and fine-mapping approach and refer to this test as GeneScan3DKnock.

Workflow of the proposed gene-based tests. (A) GeneScan1D, a 1D scan of the gene and buffer region. (B) GeneScan3D, a 3D scan of the gene and regulatory elements linked to it. (C) GeneScan3DKnock, the knockoff-enhanced test, implementing a knockoff-based version of GeneScan3D.

We take advantage of recent advances in region-based tests for sequencing data (4, 22, 24) to perform computationally efficient and comprehensive tests with genetic variation in a gene (including variants located in proximal and distal regulatory elements), while scanning the gene with a range of window sizes for improved power. The framework allows for the incorporation of a variety of functional genomics annotations as weights for individual variants included in the tests. Furthermore, an aspect of our testing framework is the derivation of knockoff statistics based on the generation of knockoff (synthetic) genetic data that resemble the original genotypes in terms of correlation structure, but are conditionally independent of the outcome variable given the true genotypes (8, 26, 30). The knockoff genotypes are essentially noisy copies of the original genotypes and serve as negative controls for the original genotype data; they help to select important genes while controlling the FDR. GeneScan3DKnock computes for each gene a knockoff statistic W that measures the importance of each gene (similar to a P value) and then uses the knockoff filter to detect genes that are significant at a specified FDR target level (26). We also compute a q value for each gene. A q value is similar to a P value, except that it measures significance in terms of FDR rather than family-wise error rate (FWER) and already incorporates correction for multiple testing. The knockoff version of the gene-based test has unique features relative to the standard gene-based tests, including improved power and the ability to prioritize causal genes over associations due to LD. The details on these specific tests can be found in Materials and Methods.

We compare with the nearest competitor gene-based tests in the literature, namely, MAGMA/H-MAGMA (10, 11), TWAS/FUSION (14), and STAAR-O (12). We also show comparisons with the recently proposed window-based test KnockoffScreen (8).

We conducted simulation studies in order to 1) examine the type I error rates of the proposed tests, GeneScan1D and GeneScan3D, under different significance levels; and (2) evaluate the potential power gain by incorporating the regulatory elements. For power comparisons, we considered the nearest competitor gene-based tests, MAGMA/H-MAGMA and STAAR-O.

For type I error-rate simulations, we used real Genetic Epidemiology of COPD (COPDGene) TOPMed whole-genome sequencing data, with n=5,593 for a continuous trait and n=4,450 for a binary trait. We randomly selected 10 genes (average gene length 25 Kb), and for each selected gene, we randomly selected R = 2 GeneHancer and ABC enhancers (average enhancer length 1.35 Kb). For each selected gene and the corresponding enhancers, we used the real genotype data, while the phenotype data are simulated as below:

For a continuous trait: Yi=Zi+i,

For a binary trait: logit(P(Yi=1|Zi))=0+Zi,

where ZiN(0,1) is a covariate and iN(0,1) is the standard normal error; Zi and i are independent. For the binary trait, an equal number of cases and controls were simulated. For GeneScan1D and GeneScan3D, we used two window sizes, 1 Kb and 5 Kb, to scan the gene region. All variants and common variants only were considered in the type I error-rate simulation studies.

To evaluate power and compare with existing tests such as MAGMA/H-MAGMA and STAAR-O, we used the same whole-genome sequencing data. We randomly selected 10 genes (average length 25 Kb), and, for each selected gene, we randomly selected R = 10 GeneHancer and ABC enhancers (average enhancer length 1.87 Kb). Power was computed for each gene separately, and the average over the 10 genes was reported. We made use of the real genotypes for the selected genes plus and minus a 5-Kb buffer region and for the corresponding enhancers. For each gene, the phenotype data were generated as follows:

For a continuous trait: Yi=1Gi1+sGis+Zi+i,

For a binary trait: logit(P(Yi=1|Zi,Gi))=0+Zi+1Gi1+sGis,

where Gij denotes the genotypes of randomly selected causal variants and j s are the corresponding effect sizes. For the binary trait, an equal number of cases and controls were simulated. We set 2% of the variants in the gene and buffer region to be causal, all within a 2-Kb signal window. For each enhancer, we set 2% (uniformly distributed) variants to be causal. The effect size of the causal variant j was assumed to be j=c|log10MAFj|, where MAF is the minor allele frequency. We assumed c = 0.25 for the continuous trait and c = 0.6 (e.g., OR=6.05, when MAF=0.001) for the binary trait.

For GeneScan1D and GeneScan3D, we used three window sizes for scanning: 1, 5, and 10 Kb. We applied MAGMA on the gene plus and minus the 5-Kb buffer region. For GeneScan3D and H-MAGMA, we incorporated R={2,5,10} enhancers. We also conducted STAAR-O gene-centric analyses on 1) the entire gene body and 2) the same R={2,5,10} enhancers, and then we combined the STAAR-O P values for these elements using the Cauchy combination method. As detailed in SI Appendix, to allow for fair comparisons, for STAAR-O we used the same weighting and MAF/minor allele count (MAC) thresholds, as used for the proposed tests. For the sake of completeness, we also ran the default setting of STAAR-O gene-centric analyses focused on rare variants. Finally, we adjusted for 10 principal components of ancestry.

We conducted 107 replications to examine the empirical type I error rate under both continuous and binary traits (SI Appendix, Table S1). For continuous traits, the type I error rates were well controlled in all analyses under moderate significance levels 103,104, and 105. Even for a stringent significance level of 2.5106, the type I error rates fell within the 95% CI: (1.52106,3.48106). For binary phenotypes, the type I error rates were slightly conservative at different levels.

We evaluated the empirical power at significance level 2.5106 using 10,000 replications (Fig.2A and SI Appendix, Fig. S1A). As shown, GeneScan3D and STAAR-O have important power advantages relative to H-MAGMA, likely due to their better tolerance of noisy variation, as also demonstrated below. GeneScan3D also exhibited higher power than STAAR-O, likely due to the sliding window scanning implemented in GeneScan3D. The 3D tests overall tended to be more powerful than the 1D tests, with the relative benefits diminishing as the number of signal enhancers decreased. STAAR-O with the default settings (focused on rare variants only) had lower-power performance (SI Appendix, Fig. S2A), as expected, given that our simulations included common causal variants, in addition to rare causal variants.

Power and FDR of the proposed gene-based tests and binary and continuous traits with tests including only common variants. (A) Power and robustness to noisy enhancers. A, Upper shows power for the GeneScan3D, GeneScan1D, H-MAGMA, MAGMA, and STAAR-O tests. The number of enhancers (R) ranges from 2 to 10. A, Lower shows power for the GeneScan3D, H-MAGMA, and STAAR-O tests, assuming causal variants in R={2,5} causal enhancers. Power is compared between using only the R={2,5} causal enhancers (the oracle approach) vs. using all 10 enhancers (including noisy enhancers). (B) Power and FDR for GeneScan3DKnock using different numbers of knockoffs and the BH procedure for GeneScan3D, STAAR-O, and H-MAGMA.

When performing the 3D analyses, it is likely that some of the putative regulatory elements do not contain any signal variants. We conducted additional power simulation studies to evaluate the performance when only R={2,5} enhancers of a total of 10 enhancers for a gene contained any signal variants. We compared with the power of the oracle approach, i.e., when only the signal-containing enhancers were included in the analyses (Fig.2A and SI Appendix, Figs. S1A and S2A). GeneScan3D and STAAR-O exhibited negligible power loss, suggesting that they are robust to inclusion of noisy enhancers, unlike H-MAGMA, which is less robust in such realistic settings. This empirical observation is consistent with the theoretical expectation: While GeneScan3D/STAAR-O combined signal from individual enhancers using the Cauchy P value combination method and, hence, are expected to maintain strong power in the presence of noisy enhancers, H-MAGMA is based on a principal component regression approach and, hence, combines genetic variants across multiple enhancers, rendering it less robust in the presence of noisy enhancers.

One aspect of the proposed knockoff-based test is that it allows for selecting significant genes by controlling the FDR in the presence of complex correlations due to LD. For the knockoff-based test, GeneScan3DKnock, we evaluated the empirical FDR and power, assuming multiple causal and noise genes. We randomly selected 10 causal genes and 250 noise genes (gene length 10 to 100 Kb, average length 39 Kb) as follows. Among the noise genes, some were selected to be physically close to the causal genes, i.e., within 2-Mb region, and others were randomly selected across the genome. For each gene, we only included the corresponding GeneHancer and ABC enhancers that fell within a 150-Kb region ( 75 Kb from the gene midpoint). This restriction to a 150-Kb region was done for computational reasons and only for these power simulations. On average, there were 10 enhancers for each gene, with an average length of 1.25 Kb. We generated multiple knockoff genotypes for 250-Kb regions spanning each gene ( 50 Kb on either side of the 150-Kb region), as detailed in Materials and Methods. Note that to avoid enhancer sharing across genes and too-strong LD among causal and noise genes (which leads to false discoveries for all the statistical tests considered here), we selected the genes such that the corresponding 150-Kb regions were disjoint.

For each replicate, we randomly selected a 10-Kb causal window in each causal gene 5-Kb buffer region and set 3.5% variants in the window to be causal. We also set 3.5% variants in all enhancers to be causal. We generated the continuous/binary traits using the selected causal variants as follows:

For a continuous trait: Yi=Zi+1Gi1++sGis+i,

For a binary trait: logit(P(Yi=1|Zi,Gi))=0+Zi+1Gi1++sGis.

As above, ZiN(0,1) is a covariate, and iN(0,1) is the standard normal error; Zi and i are independent; 0 is chosen such that the prevalence is 10%. Again, we set the effect size j=c|log10MAFj| for the j-th causal variant, with c = 0.2 for the continuous trait and c = 0.6 for the binary trait.

The empirical power and FDR for GeneScan3DKnock were averaged over 100 replicates. We present results for single knockoffs, as well as multiple knockoffs (M = 3 and 5). We calculated the original and knockoff P values from the GeneScan3D test (for all variants and common variants), adjusting for 10 principal components of ancestry. We computed q values for 10 causal and 250 noise genes in order to identify significant genes using the GeneScan3DKnock test at different target FDR levels, up to 0.15. The empirical power was defined as the proportion of causal genes being identified; the empirical FDR was defined as the proportion of detected genes that are noise. We show that GeneScan3DKnock can control the FDR at the target level and that using multiple knockoffs can improve power substantially, especially at lower levels of target FDR, where the single-knockoff approach has very low power, as expected (Fig.2B and SI Appendix, Fig. S1B).

For comparisons, we evaluated the empirical power and FDR for competitor methods, including STAAR-O and H-MAGMA, using the standard BH procedure for FDR control. A gene is significant if the corresponding q value is the target FDR level. The results show that the conventional BH procedure may not control the FDR at the target level, and, therefore, the proposed knockoff-based approach provides a valid alternative when FDR control is desirable, such as for polygenic traits with multiple underlying causal genes (Fig.2B and SI Appendix, Figs. S1B and S2B).

Although our main comparisons are with gene-based tests, we performed additional comparisons with the recently proposed window-based method KnockoffScreen (8) in order to illustrate the need for a gene-based knockoff filter when our interest is controlling the FDR at the gene level. We applied KnockoffScreen by scanning each 150-Kb region using several window sizes (1, 5, and 10 Kb), and we computed the empirical power and FDR of KnockoffScreen at both gene level and window level (SI Appendix). Although KnockoffScreen can control the window-level FDR, as shown in SI Appendix, Fig. S3, the empirical gene-level FDR can be quite high, suggesting that the proposed framework designed to control the FDR at the gene level is more appropriate for gene discovery. Essentially, as a window-based test, KnockoffScreen leads to a larger number of rejections, i.e., higher power, but also higher FDR at gene level (SI Appendix, Fig. S3).

We present results from an application to whole-genome sequencing data from the ADSP. The data include 3,085 whole genomes from the ADSP Discovery Extension Study and 809 whole genomes from the Alzheimers Disease Neuroimaging Initiative, for a total of 3,894 whole genomes (more details are available in SI Appendix). We adjusted for age, age2, gender, ethnic group, sequencing center, and the leading 10 principal components of ancestry. Seven tissue/cell-type specific GenoNet functional scores (31) related to brain were incorporated, including E071 (brain hippocampus [HIP] middle), E074 (brain substantia nigra), E073 (brain dorsolateral prefrontal cortex [DLPFC]), E068 (brain anterior caudate), E067 (brain angular gyrus), E069 (brain cingulate gyrus), and E072 (brain inferior temporal lobe).

We show results for several tests, including GeneScan1D and GeneScan3D tests; the proposed knockoff-based approach, GeneScan3DKnock, based on five random knockoffs; as well as existing tests, including MAGMA/H-MAGMA, STAAR-O, and TWAS. We do not include the results from KnockoffScreen since we have shown in the simulations that at the gene level, it can have inflated FDR. For all the tests except for the knockoff-based GeneScan3DKnock test, we identified significant genes using the Bonferroni method for FWER control since, as shown in the simulations, the conventional BH procedure does not control the FDR at the target level. For GeneScan3DKnock, we used the implemented knockoff filter procedure to identify significant genes at an FDR threshold of 10%. We present results for common variants only (those with MAF >1/2n, where n is the sample size) and all variants (rare variant-only analyses are not well powered at these sample sizes).

Overall, all tests considered identify the well-known signal at the apolipoprotein E (APOE) locus (Fig.3 and SI Appendix, Table S2 and Fig. S4). The GeneScan3D, H-MAGMA, and STAAR-O tests detected additional significant genes on chromosome 19 (chr19), mostly due to signals residing in the promoters and/or enhancers overlapping genes at the APOE locus (Fig.4). These results suggest that the APOE region is a central nucleating point for loops that regulate expression of potentially AD-associated genes. Therefore, it is possible that the strong signal observed at the APOE locus can be linked to genes that are farther away (Fig.4 and SI Appendix, Table S3).

Applications to ADSP whole-genome sequencing data, common variants only. (AF) Manhattan plots of MAGMA, H-MAGMA, TWAS, STAAR-O, GeneScan1D, and GeneScan3D results, respectively. (G) Manhattan plot of GeneScan3DKnock results. Genes within the zinc-finger-containing (ZNF) gene cluster on chr19 are unlabeled and shown in blue in H-MAGMA, GeneScan3D, and GeneScan3DKnock analyses for clear visualization. G, Right shows a heatmap with P values of the GeneScan3D test (truncated at 1020) for all genes passing the FDR = 0.1 threshold and the corresponding q values that already incorporate correction for multiple testing. The genes are shown in descending order of the knockoff statistics. (H) Scatterplot of W knockoff statistics (GeneScan3DKnock) vs. log10 (P value) (GeneScan3D) for common variants. Each dot represents a gene. The dashed lines show the significance threshold defined by Bonferroni correction (for P values) and the data-adaptive threshold for FDR control (for W statistic).

Visualization of promoterenhancer interactions of significant genes at the APOE locus. (A) The promoterenhancer links are shown for the significant genes in the GeneScan3D analyses for common variants, where arcs in blue point to those genes identified by the knockoff procedure only. Genes with a signal enhancer are shown in green; those with no signal enhancer are in orange (A, Middle). The APOE locus is the location of a tight cluster of several enhancers (shown in purple in A, Bottom), with arcs connecting the enhancers to many different gene promoters. (B) The LD structure in the APOE region. The locations of the enhancers in the region are also shown.

False-positive signals can arise due to possible coregulation of multiple genes by the same causal enhancers, or simply due to LD among causal and noncausal variants in genes or associated regulatory elements. The knockoff-based test GeneScan3DKnock cannot help eliminate false positives due to coregulation, but can attenuate the effect of LD-induced confounding. We computed the knockoff statistic W and the q value for each gene. A scatterplot of genome-wide W knockoff statistics vs. log10(P values) based on the GeneScan3D test illustrates how almost half of the significant genes at the APOE locus based on GeneScan3D (using the conventional FWER control) are no longer significant in the GeneScan3DKnock test, despite the less stringent FDR control (Fig.3 and SI Appendix, Fig. S4 and Tables S2 and S4). Similarly, GeneScan3DKnock identified a lower number of significant genes relative to STAAR-O with stringent FWER control. These include a large number of genes linked to GH19F044889 and several overlapping ABC enhancers, which contain variants in high LD with variants in the APOE gene (Fig.4). Indeed, we obtained a narrower list of significant genes on chr19 related to the APOE locus, which includes the main genes from the 1D tests, i.e., APOE, TOMM40, APOC1, and NECTIN2, but other interesting genes as well, including BCAM, RELB, and QPCTL. For example, rare variants in BCAM and RELB have recently been identified to be associated with AD and neuroimaging biomarkers of AD after adjusting for APOE genotypes (32, 33). QPCT, an important paralog of QPCTL, has been shown to be involved in AD pathogenesis and cognitive decline by glutaminyl cyclase-catalyzed pGlu-A formation (34). This ability to remove a substantial proportion of false-positive signals due to LD is a unique and appealing feature of the proposed GeneScan3DKnock test.

Interestingly, GeneScan3DKnock detected several associations outside chr19 that were missed by the competitor gene-based tests. These include NECTIN1, ZNF843, ZNF646, and PPP1R17 (for common variants) and HIPK3 (for all variants), which were previously found to be involved in AD-related pathophysiology. Nectin-1 is a member of the immunoglobulin superfamily and a Ca(2+)-independent adherens junction protein involved in synapse formation (35). The important role of nectin in synaptic development and maintenance can explain how genetic variation in NECTIN1 can perturb synaptic activity and play a role in AD. ZNF646 lies within the KAT8 locus, recently identified in two large-scale GWAS studies focused on clinically diagnosed AD and AD-by-proxy individuals (36, 37). Furthermore, ZNF646 was prioritized at the KAT8 locus based on high posterior probability for the colocalization between AD GWAS single-nucleotide polymorphisms (SNPs) at the KAT8 locus and eQTLs from both brain (DLPFC) and microglia (38). Similarly, PPP1R17 was found to be significantly underexpressed in the brains of 14-mo-old Sgo1/+ mice (a murine AD model of chromosome instability with chromosomal and centrosomal cohesinopathy) compared with age-matched wild-type animals (39). The protein encoded by this gene is found primarily in Purkinje cell bodies and projections in the cerebellum and subsets of neurons in the hypothalamus. An SNP located in the promoter of PPP1R17 was previously found to be associated with hypercholesterolemia (40). Finally, homeodomain-interacting protein kinase 3 (HIPK3) belongs to a group of HIPKs, including HIPK2, which is down-regulated by elevated amount of Amyloid (A), a hallmark of AD (41). Protein HIPK3 levels were also found to be significantly different between individuals with mild cognitive impairment that converted to AD vs. the nonconverters (42).

To provide more objective evidence of replication, we leveraged a large meta-analysis of clinical AD and AD-by-proxy studies [71,880 AD or proxy cases and 383,378 controls (36)] and performed gene-based tests (GeneScan1D and GeneScan3D) using the available GWAS summary statistics for 58 significant genes identified for AD across all the different tests, including the BH-adjusted tests. We constructed 3D windows using the same procedure as before (Materials and Methods). For each 3D window, we then applied the ACAT procedure (22) to combine P values for single variants within the 3D window. Since we did not have access to individual-level data, we did not conduct Burden and SKAT tests for these replication studies. Results are shown in SI Appendix, Table S5. Note that most of the genes identified by GeneScan3DKnock at FDR 10%, including genes at the APOE locus, ZNF646 and ZNF843, had a replication P value based on GeneScan3D <0.05/58=8.62104, while genes identified by conventional BH controlling procedures (SI Appendix, Figs. S5 and S6) failed to replicate for the most part, concordant with simulation studies showing that the BH procedure can result in inflated empirical FDR values, and, therefore, it is not a rigorous procedure to identify significant genes at a desired FDR level.

The COPDGene study includes chronic obstructive pulmonary disease (COPD) cases, controls, and additional smokers with varied lung function. In addition to COPD case/control status, lung-function measurements are also available, including forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and their ratio (FEV1/FVC). We analyzed whole-genome sequencing data from the TOPMed freeze5b dataset, which includes a subset of 5,593 Non-Hispanic White individuals for continuous traits and 4,450 individuals for COPD case/control binary trait. We present results from the application to FEV1, adjusting for sequencing center, 10 principal components of ancestry, age, age2, gender, height, height2, smoking pack-years, and current smoking. We incorporated five tissue/cell-type specific GenoNet functional scores (31) related to lung, namely: E017 (IMR90), E088 (fetal lung), E096 (lung), E114 (A549), and E128 (normal human lung fibroblast).

As with the AD example, GeneScan3DKnock identified significant genes that were missed by the other tests. Specifically, we identified a cluster of significant genes on chromosome 12 that included FRS2, CCT2, RAB3IP, LRRC10, and BEST3 and that was missed by all the other gene-based tests considered (Fig.5). Notably, an intronic SNP (rs10444582) in FRS2 was identified to be significantly associated with FEV1 in the UK Biobank and SpiroMeta (P=1.21010, n = 396, 723) (43). This locus was not included in the final list of loci released by Shrine et al. (43), as the P value in the replication cohort (SpiroMeta) was only 3.5103, above the predetermined significance threshold. As COPDGene was not part of the Shrine et al. study (43), our findings on chromosome 12 provide additional, independent evidence for this signal. Another significant and potentially interesting gene is RAB7A. A common SNP (rs9847178) residing in the promoter-flanking region of RAB7A has been found to be genome-wide significant in a recent large GWAS study on smoking (44). A nearby SNP (rs7650872) in the same promoter-flanking region has been found to be genome-wide significant with eosinophil counts in the UK Biobank (45). High eosinophil counts predict decline in FEV1 (46). Interestingly, a recent study has shown that loss of RAB7A confers resistance to SARSCoV-2 by reducing ACE2 levels (47), concordant with reports in the literature of loci associated with susceptibility and/or response to infection that have been previously associated with lung-function phenotypes (48).

Applications to COPDGene whole-genome sequencing data (trait FEV1) for common variants only. (AF) Manhattan plots of MAGMA, H-MAGMA, TWAS, STAAR-O, GeneScan1D, and GeneScan3D results, respectively. (G) Manhattan plot of GeneScan3DKnock results. G, Right shows a heatmap with P values of GeneScan3D test for all genes passing the FDR = 0.1 threshold and the corresponding q values that already incorporate correction for multiple testing. The genes are shown in descending order of the knockoff statistics. (H) Scatterplot of W knockoff statistics (GeneScan3DKnock) vs. log10 (P value) (GeneScan3D) for common variants. Each dot represents a gene. The dashed lines show the significance threshold defined by Bonferroni correction (for P values) and the data-adaptive threshold for FDR control (for W statistic).

We performed similar replication studies for 40 significant genes identified for FEV1 across all the different tests, including the BH-adjusted tests, using 383,471 European individuals with FEV1 measurements in the UK Biobank (SI Appendix, Table S6). The covariates adjusted for in the analyses include 10 principal components of ancestry, age, age2, gender, age gender, and age2 gender. Note that the number of available covariates in the UK Biobank is limited, and some important covariates for FEV1, such as height and smoking, are not adjusted for in these analyses. Despite this caveat, most of the genes identified as significantly associated with FEV1 in our COPDGene study replicated in the UK Biobank study (replication P value based on GeneScan3D <0.05/40=1.25103).

We applied the different gene-based tests to summary statistics from nine GWAS studies of brain disorders, including five neuropsychiatric traits: attention-deficit/hyperactivity disorder (ADHD) (49), autism spectrum disorder (ASD) (50), bipolar disorder (51), schizophrenia (52), and major depressive disorder (53); and four neurodegenerative traits: AD (36), Parkinsons disease (54), amyotrophic lateral sclerosis (ALS) (55), and multiple sclerosis (MS) (56). We do not include STAAR-O here since the current implementation is not applicable to summary statistics.

Since these applications focus on brain disorders, we leveraged two existing Hi-C human brain datasets for the DLPFC in adult brain (57) and for the germinal zone (GZ) and cortical and subcortical plate (CP) in fetal brain (58) and used the Fit-Hi-C method to identify statistically significant promoterenhancer interactions from these data (59) (SI Appendix). Similarly, we applied MAGMA/H-MAGMA (11) to the same datasets using the same significant Hi-C interactions. We also applied TWAS/FUSION (14) based on 13 brain regions from GTEx version 7 (amygdala [AMY], anterior cingulate cortex, caudate, cerebellar hemisphere, cerebellum, cortex, frontal cortex, HIP, hypothalamus, nucleus accumbens, putamen, spinal cord, and substantia nigra). The Cauchy P value combination method was used to combine TWAS P values from different brain regions.

We used a liberal significance threshold (103) to select genes from these analyses (because some of the GWAS studies, e.g., AD, ADHD, and MS, are underpowered) and investigated their expression patterns using spatiotemporal and single-cell transcriptomics data, as described below. The number of significant genes at this threshold and the overlap across the different tests are shown in SI Appendix, Table S7 and Fig. S11. Compared with 1D analyses (GeneScan1D and MAGMA), GeneScan3D and H-MAGMA detected a much larger number of disease-associated genes, as expected, given that they incorporate signals from distal regulatory elements. GeneScan3D and H-MAGMA also detected a substantially higher number of significant genes relative to TWAS, a possible reflection of the limitation of eQTL-based approaches to discover significant associations that cannot be explained by eQTLs in the reference datasets.

We used spatiotemporal transcriptomic data from embryonic and adult brains measured at 15 time periods, ranging from four postconceptional weeks to age 60 y (60). The gene-expression data are available for six brain regions: neocortex (NCX), mediodorsal nucleus of the thalamus, cerebellar cortex, HIP, AMY, and striatum. We focused here on the cortical expression profiles (NCX area), with 410 samples for the prenatal stages (periods 1 to 7) and 526 samples for the postnatal stages (periods 8 to 15). We centered the developmental expression matrix to the mean expression level for each sample.

We computed the average expression values across the significant genes for each brain sample and then compared the values for prenatal and postnatal brain samples. For psychiatric diseases, the genes detected by GeneScan3D tended to have significantly higher expression in prenatal relative to postnatal periods, as expected, and the trajectories highlight developmental windows in early or midgestation periods (Fig.6A). For neurodegenerative diseases, the pattern was reversed, with higher expression in the postnatal periods (except for MS), concordant with the expectation that genes for neurodegenerative disorders have increased expression with aging. The results for H-MAGMA suggest similar patterns, but with reversed patterns for ASD and ALS and less significant differences for AD (SI Appendix, Fig. S12A). Results for GeneScan1D, MAGMA, and TWAS showed similar patterns (SI Appendix, Figs. S13S15), although there were some discrepancies, including the higher postnatal expression vs. prenatal expression for the ASD-significant genes and significantly higher prenatal expression for ALS-significant genes (MAGMA).

(A) Human brain developmental expression of GeneScan3D significant genes for each brain disorder, combined Hi-C for adult brain and fetal brain GZ and CP layers. P values of Wilcoxon rank sum tests are shown in the boxplots to compare independent prenatal and postnatal samples. (B) Cell-type expression profiles of GeneScan3D significant genes. BD, bipolar disorder; MDD, major depressive disorder; PD, Parkinsons disease; SCZ, schizophrenia.

Additionally, we also leveraged existing single-cell expression profiles (57) on 285 single cells from six adult brain-cell types, including neurons (131 cells), astrocytes (62 cells), microglia (16 cells), endothelial (20 cells), oligodendrocytes (38 cells), and oligodendrocyte progenitor cells (18 cells). For each single cell, we centered the expression data to the mean level of genes and then computed the average across the significant genes for a given disease. For each specific cell type, we averaged across the multiple cells in this cell type. We computed standardized expression levels (i.e., subtracted the mean and divided by the SD) for the six adult cell types. Genes identified by GeneScan3D for psychiatric disorders tended to show higher expression levels primarily in neurons and, to some extent, in astrocytes compared to other cell types, whereas genes for neurodegenerative disorders tended to show higher expression levels primarily in microglia (Fig.6B). In particular, genes significant for ADHD showed the highest expression in astrocytes, consistent with recent evidence suggesting a key role of astrocytes in the regulation of attention-deficit disorder and hyperactivity (61).

Results for the other tests showed similar overall patterns as the GeneScan3D (SI Appendix, Figs. S12S15), although with some differences, including less pronounced evidence for the role of astrocytes in ADHD (except for TWAS). These results serve as a proof of concept for the proposed 3D test, showing that genes identified by GeneScan3D and other existing tests exhibit expression patterns consistent with existing literature, i.e., an important role for neurons for neuropsychiatric diseases and microglia for neurodegenerative diseases (62).

Browser for results on UK/Japan Biobank data.

We applied GeneScan3D to 1,403 UK Biobank binary phecodes and 827 continuous phenotypes using summary statistics on 28 million imputed variants. We have created a browser that displays phenome-wide results for a given gene and genome-wide gene-based results for a given trait and provides summary tables for significant genes. These gene-based results for the UK Biobank traits complement existing databases for single-variant tests (63) and rare variant-focused tests, such as SAIGE-GENE, a scalable generalized mixed-model region-based association test (6).

For non-European populations, we applied GeneScan3D to BioBank Japan binary phenotypes using available case-control GWAS summary statistics on 8,712,794 autosomal variants and 207,198 chromosome variants with 212,453 Japanese individuals across 42 diseases (64). Results can be queried using the aforementioned browser.

We propose gene-based tests that integrate genetic variation residing in putative regulatory regions and implement the knockoff framework for increased power and improved causal gene prioritization. This framework provides a rich toolkit for the analysis of GWAS and whole-genome sequencing data with applications to gene discovery and fine mapping. Based on empirical studies, we show that the proposed gene-based tests are more powerful and help attenuate the confounding effect of LD relative to state-of-the-art gene-based tests. They also have distinct advantages compared with the recently proposed window-based test, KnockoffScreen, in terms of functional interpretation and appropriate FDR control at the gene level. Indeed, our simulation results suggest that the knockoff filter procedure needs to be performed at the gene, rather than window, level if our interest is in identifying genes and controlling FDR at the gene level.

Our gene-based tests can be seen as complementary to the TWAS approach. Like TWAS, they attempt to incorporate the effect of distal regulatory elements into the test. TWAS, however, is limited to common eQTLs detectable in reference datasets, which appear to account for a minority of GWAS signal (16, 17). In contrast, our approach has the ability to assess the effects of coding, noncoding, rare, and common variants, including those with no detectable effects on gene expression, and can scan the gene with varied window sizes. Furthermore, the knockoff framework can attenuate the confounding effect of LD and is able to produce a narrower list of possible causal genes, likely removing some of the false gene discoveries.

In this paper, we have focused on using existing external data on geneenhancer links, and we recognize the limitations of these databases, both in terms of the accuracy of these links and the number of cell types with available data. Single-cell Hi-C is an emerging technology that could help overcome issues of tissue heterogeneity and expand these maps across many more cell types (65).

Like all gene-based tests that incorporate genetic variation in distal regulatory elements, our tests are also susceptible to false-positive associations due to, for example, causal variants residing in putative enhancers that may show significant interactions with promoters of multiple genes based on Hi-C data. Identifying the actual causal gene(s) requires follow-up experimental studies, such as CRISPR gene-perturbation experiments (66).

In summary, we propose comprehensive gene-based tests for common and rare variation, both coding and regulatory variation, that are more powerful than competitor gene-based tests in the literature. The GeneScan3DKnock approach is implemented in a computationally efficient R package.

We describe here the details of the proposed gene-based test that aims to comprehensively evaluate the effects of rare and common, coding, and proximal and distal regulatory variation on a trait of interest. Details of existing gene-based association tests and additional tests for comparison, including GeneScan1D, MAGMA/H-MAGMA, and STAAR-O, as well as KnockoffScreen, are in SI Appendix.

For a fixed window , we incorporated several recent advances for association tests for sequencing studies to compute the corresponding P value p, as follows. For each window, we conducted:

a. Burden and SKAT tests for common and low-frequency variants (MAF1/2n) with Beta(MAFj;1,25) weights. These tests aimed to detect the combined effect of common and low-frequency variants.

b. Burden and SKAT tests for rare variants (MAF<1/2n and MAC t) with Beta(MAFj;1,25) weights. These tests aimed to detect the combined effect of rare variants.

c. Burden and SKAT tests for rare variants, weighted by cell-type-specific functional annotations. These tests aimed to utilize functional annotations for improved power (24, 67).

d. Burden tests for aggregation of ultrarare variants (MAC

e. Single-variant score tests for common, low-frequency, and rare variants (MACt) in the window.

We then applied the ACAT (22) to combine P values from tests in ae to compute P values of each 1D window for all variants, including common and rare variants. Note that for the current analyses, we used MAC threshold 10.

For a given gene G, we considered the gene body (i.e., the interval between the TSS and the end of 3 UTR) 5-Kb buffer regions and integrated a single ChIP-seq promoter and R putative enhancers into the analyses (Fig.1). A set of overlapping 1D windows m,m=1,,M with window sizes 1 Kb, 5 Kb, and 10 Kb were generated to scan the gene and buffer regions together (each 1D window was overlapping with half of its adjacent windows for a given window size). Then, we constructed 3D windows for the gene by adding a ChIP-seq promoter and R putative enhancers to each 1D window m,m=1,,M as follows (details on how to identify the regulatory elements for each gene are in SI Appendix):m,03D=m+ChIP-seqpromoterm,13D=m+ChIP-seqpromoter+Enhancer13Dwindows:m,R3D=m+ChIP-seqpromoter+EnhancerR.

For each such 3D window, we computed a P value pm,r3D, with 1mM,0rR using the proposed combined test for a window. Finally, we computed a gene-level P value pG by combining the (1+R)M P values using the Cauchys combination method (22), as follows:Q=r=0Rm=1Mtan[(0.5pm,r3D)](1+R)M.

The P value of the Cauchy statistic is pG=1/2arctan(Q)/.

Note that the GeneScan3D analysis can be easily adapted for GWAS summary statistics by applying the ACAT procedure to combine P values for single variants within the 3D window.

An advantage of the proposed GeneScan3D test is that it allows the discovery of multiple possible causal genes by incorporating information from proximal and distal regulatory elements. However, it is likely that some of those genes are false positives, owing to confounding due to LD and/or coregulation. Extensive LD at a locus of interest can confound the results and lead to many genes being significant. For example, if the LD region overlaps several enhancers, all genes regulated by such enhancers may show a significant signal. The knockoff framework (26), a recent advance in statistics, can be leveraged to reduce the effect of LD in such cases and can help prioritize a narrow list of potential causal genes. Furthermore, the knockoff-based test is of independent interest, as by design, it controls the FDR at a target level under arbitrary correlation structure and can have higher power to identify additional significant genes that are missed by the conventional gene-based test, as we show empirically in the applications. The knockoff-based test has two steps: the knockoff generation and the filtering of the results using the knockoff filter.

The idea of the knockoff-based procedure is to generate artificial or knockoff genotypes G such that for any subset K of variants the distribution of (G,G) is invariant when swapping GK and GK, i.e., (G,G)swap(K)=d(G,G). Additionally the knockoff genotypes have the property that GY|G. Note that the well-known permutation procedure that permutes the samples does not guarantee these exchangeability properties between the original and knockoff genotypes. To generate valid knockoff genotypes, we can use a sequential model for knockoff generation that leverages the local patterns of LD, as previously proposed based on the Hidden Markov Models (HMMs) (30, 68), or an auto-regressive model (8), in such a way that the knockoff genotypes are exchangeable with the original (true) genotypes G, but are independent of the phenotype conditional on the original genotypes. The knockoff genotypes serve as negative controls and are designed to mimic the correlation or LD structure found within the original genotypes. Specifically, we sequentially sampled for each variant j the corresponding knockoff genotype L(Gj|Gj,G1(j1)), independent of the observed value of Gj. Because of the HMMs significant computational complexity with unphased genetic data, in order to generate knockoff genotypes, we relied on a recently introduced, computationally efficient, auto-regressive model that follows from the assumption that genotypes can be approximately modeled by a multivariate normal distribution:Gj=+kjkGk+kj1kGk+j,where j is a random error term. (Note that we can leverage the approximate block structure for LD in the genome to only include variants in a neighborhood of the current variant j.) We estimated (,,) by minimizing the mean squared loss. We calculated the residual j^=GjGj^ and its permutation j^*, and then we defined the knockoff feature as Gj=Gj^+j^*. More details on this knockoff-generation procedure and its theoretical and empirical properties can be found in ref. 8.

Once the knockoff genotypes G were generated, the knockoff filter was used to select significant genes. Specifically, we performed a gene-based test, as described above (GeneScan3D), in both the original cohort and the knockoff one. Let pG and pG be the resulting P values. We defined a feature statistic by contrasting the observed P value for each gene to its counterpart based on the knockoff data. More precisely, the feature statistic for a gene G is defined as WG=TGTG, where TG=log10(pG) and TG=log10(pG) are the importance scores for gene G in the original and knockoff cohort, respectively. This feature statistic has the flip-sign property, meaning that swapping the genetic variants in gene G with their knockoff counterparts changes the sign of WG. A data-adaptive threshold for WG can be determined by the knockoff filter (26) so that the FDR is controlled at the nominal level q, as follows:=min{t>0:1+#{G:WGt}#{G:WGt}q}.

We selected all genes with WG since genes with large feature statistics are more likely to be causal (nonnull) genes. This follows from the exchangeability property between the original and the knockoff genotypes, which ensures that the importance scores (TG and TG) for the null genes are exchangeable, and therefore the feature statistic WG is symmetric around zero for the null genes, but tends to be larger than zero for nonnull genes.

We additionally computed the corresponding q value for a gene, qG. The q value already incorporates correction for multiple testing and is defined as the minimum FDR that can be attained when all tests showing evidence against the null hypothesis at least as strong as the current one are declared as significant. In particular, we define the q value for a gene G with feature statistic WG>0 asqG=mintWG1+#{G:WGt}#{G:WGt},where 1+#{G:WGt}#{G:WGt} is an estimate of the proportion of false discoveries if we were to select all genes with feature statistic >t (with t > 0). For genes with feature statistic WG0, we set qG = 1.

To improve the power and stability of the knockoff procedure, we implemented a multiple-knockoff procedure (8, 69), where the inference is based on generating multiple, independent knockoff datasets. Gimenez and Zou (69) proposed an extension of the sequential model for knockoff generation to multiple knockoffs and showed the validity of the multiple-knockoff-generation procedure in controlling the FDR. We implemented this procedure here to generate multiple independent knockoff datasets. Briefly, we sequentially sample for each variant j: Gj1,,GjM from L(Gj|Gj,G1j11,,G1j1M), where M is the number of knockoffs. With multiple knockoffs, the feature statistic for a gene G is defined asWG=(TGmedianTGm)ITGmax1mMTGm,where TGm is the gene-importance score for gene G in the m-th knockoff replicate, and I is an indicator function. We define=min{t>0:1M+1M#{G:G1,Gt}#{G:G=0,Gt}q},where G=argmax0mMTGm (note that TG0=TG) and G=TGmedianTGm. We selected genes with WG, i.e., those genes that have importance scores greater than any of those corresponding to the M knockoffs (G=0) and for which the difference from the median importance score is above some threshold (G). A q value for a gene G can be computed for the multiple-knockoff scenario, similar to the single-knockoff case.

The multiple-knockoff procedure helps improve power because at a target FDR of q, the single-knockoff approach needs to make a minimum of 1/q discoveries, while a multiple-knockoff approach with M knockoffs decreases this detection threshold to 1/Mq. Therefore, in situations where the signal is sparse and the target FDR level q is low, a single-knockoff procedure will have very low power. In such cases, the multiple-knockoff procedure will tend to improve power. Furthermore, the multiple-knockoff procedure also helps with improving the stability of the selected genes, given that each knockoff generation is random, and, therefore, the results from a single knockoff can be unstable.

We used data from existing studies from COPDGene (TopMED; Database of Genotypes and Phenotypes [dbGaP] accession no. phs000951.v4.p4) and the ADSP (dbGaP accession no. phs000572.v8.p4)and summary-level GWAS results on neuropsychiatric and neurodegenerative traits are available from refs. 36 and 4956. Details of web-based resources are in SI Appendix. All study data are included in the article and/or supporting information. We have implemented GeneScan3DKnock in a computationally efficient R package, available on GitHub (https://github.com/Iuliana-Ionita-Laza/GeneScan3DKnock), that can be applied generally to the analysis of other whole-genome sequencing or GWAS studies. Details of software implementation are in SI Appendix.

This research was supported by NIH/National Institute of Mental Health Awards MH106910 and MH095797 (to I.I.-L.); and NIH/National Institute on Aging Award AG066206 (to Z.H.). We gratefully acknowledge the studies and participants who provided biological samples and data for the ADSP and TOPMed projects. The full study-specific acknowledgments are detailed in SI Appendix.

Author contributions: S.M. and I.I.-L. designed research; S.M. and I.I.-L. performed research; J.D., L.L., R.G., J.D.B., E.K.S., M.H.C., and Z.H. contributed new reagents/analytic tools; S.M., J.D., J.L., C.W., J.D.B., W.K.C., H.A., E.K.S., M.H.C., Z.H., and I.I.-L. analyzed data; and S.M. and I.I.-L. wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission. M.P.E. is a guest editor invited by the Editorial Board.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2105191118/-/DCSupplemental.

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Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes - pnas.org

BEDFORD, Mass.--(BUSINESS WIRE)--Stoke Therapeutics, Inc. (Nasdaq: STOK), a biotechnology company dedicated to addressing the underlying cause of severe diseases by upregulating protein expression with RNA-based medicines, today announced that five abstracts related to the Companys work in Dravet syndrome will be presented at the American Epilepsy Society (AES) 2021 Annual Meeting, taking place December 3 7, 2021 in Chicago.

At Stoke, our goal is to develop the first medicine to target the underlying cause of Dravet syndrome, a severe and progressive genetic epilepsy, said Barry Ticho, M.D., Ph.D., Chief Medical Officer of Stoke Therapeutics. In addition to our progress in the clinic with STK-001, we continue to build a strong foundational understanding of the non-seizure aspects of the disease with data from our BUTTERFLY Observational study to help evaluate the potential of disease-modifying medicines. We look forward to sharing updates on our progress at this years AES annual meeting.

Details for the Companys presentations at AES are as follows:

About Dravet Syndrome

Dravet syndrome is a severe and progressive genetic epilepsy characterized by frequent, prolonged and refractory seizures, beginning within the first year of life. Dravet syndrome is difficult to treat and has a poor long-term prognosis. Complications of the disease often contribute to a poor quality of life for patients and their caregivers. The effects of the disease go beyond seizures and often include intellectual disability, developmental delays, movement and balance issues, language and speech disturbances, growth defects, sleep abnormalities, disruptions of the autonomic nervous system and mood disorders. The disease is classified as a developmental and epileptic encephalopathy due to the developmental delays and cognitive impairment associated with the disease. Compared with the general epilepsy population, people living with Dravet syndrome have a higher risk of sudden unexpected death in epilepsy, or SUDEP. There are no approved disease modifying therapies for people living with Dravet syndrome. One out of 16,000 babies are born with Dravet syndrome, which is not concentrated in a particular geographic area or ethnic group.

About STK-001

STK-001 is an investigational new medicine for the treatment of Dravet syndrome currently being evaluated in ongoing clinical trials. Stoke believes that STK-001, a proprietary antisense oligonucleotide (ASO), has the potential to be the first disease-modifying therapy to address the genetic cause of Dravet syndrome. STK-001 is designed to upregulate NaV1.1 protein expression by leveraging the non-mutant (wild-type) copy of the SCN1A gene to restore physiological NaV1.1 levels, thereby reducing both occurrence of seizures and significant non-seizure comorbidities. Stoke has generated preclinical data demonstrating proof-of-mechanism and proof-of-concept for STK-001. STK-001 has been granted orphan drug designation by the FDA as a potential new treatment for Dravet syndrome.

About Phase 1/2a MONARCH Study (United States)

The MONARCH study is a Phase 1/2a open-label study of children and adolescents ages 2 to 18 who have an established diagnosis of Dravet syndrome and have evidence of a pathogenic genetic mutation in the SCN1A gene. The primary objectives for the study are to assess the safety and tolerability of STK-001, as well as to determine the pharmacokinetics in plasma and exposure in cerebrospinal fluid. A secondary objective is to assess the efficacy as an adjunctive antiepileptic treatment with respect to the percentage change from baseline in convulsive seizure frequency over a 12-week treatment period. Stoke also intends to measure non-seizure aspects of the disease, such as quality of life, as secondary endpoints. Stoke plans to enroll approximately 90 patients in the study across 20 sites in the United States. Additional information about the MONARCH study can be found at https://www.monarchstudy.com/.

Patients who participated in the MONARCH study are eligible to continue treatment in SWALLOWTAIL, an open label extension (OLE) study designed to evaluate the long-term safety and tolerability of repeat doses of STK-001. Enrollment and dosing in SWALLOWTAIL are underway.

About Phase 1/2a ADMIRAL Study (United Kingdom)

The ADMIRAL study is a Phase 1/2a open-label study of children and adolescents ages 2 to <18 who have an established diagnosis of Dravet syndrome and have evidence of a genetic mutation in the SCN1A gene. The primary objectives for the study are to assess the safety and tolerability of multiple doses of STK-001, as well as to determine the pharmacokinetics in plasma and exposure in cerebrospinal fluid. A secondary objective is to assess the effect of multiple doses of STK-001 as an adjunctive antiepileptic treatment with respect to the percentage change from baseline in convulsive seizure frequency over a 24-week treatment period. Stoke also intends to measure non-seizure aspects of the disease, such as overall clinical status and quality of life, as secondary endpoints. Stoke plans to enroll up to 60 patients in the study across multiple sites in the United Kingdom. Additional information about the ADMIRAL study can be found at https://www.admiralstudy.com.

About TANGO

TANGO (Targeted Augmentation of Nuclear Gene Output) is Stokes proprietary research platform. Stokes initial application for this technology are diseases in which one copy of a gene functions normally and the other is mutated, also called haploinsufficiencies. In these cases, the mutated gene does not produce its share of protein, resulting in disease. Using the TANGO approach and a deep understanding of RNA science, Stoke researchers design antisense oligonucleotides (ASOs) that bind to pre-mRNA and help the functional (or wild-type) genes produce more protein. TANGO aims to restore missing proteins by increasing or stoking protein output from healthy genes, thus compensating for the mutant copy of the gene.

About Stoke Therapeutics

Stoke Therapeutics (Nasdaq: STOK), is a biotechnology company dedicated to addressing the underlying cause of severe diseases by upregulating protein expression with RNA-based medicines. Using the Companys proprietary TANGO (Targeted Augmentation of Nuclear Gene Output) approach, Stoke is developing antisense oligonucleotides (ASOs) to selectively restore protein levels. The Companys first compound, STK-001, is in clinical testing for the treatment of Dravet syndrome, a severe and progressive genetic epilepsy. Dravet syndrome is one of many diseases caused by a haploinsufficiency, in which a loss of ~50% of normal protein levels leads to disease. The Company is pursuing treatment for a second haploinsufficient disease, autosomal dominant optic atrophy (ADOA), the most common inherited optic nerve disorder. Stokes initial focus is haploinsufficiencies and diseases of the central nervous system and the eye, although proof of concept has been demonstrated in other organs, tissues, and systems, supporting the Companys belief in the broad potential for its proprietary approach. Stoke is headquartered in Bedford, Massachusetts with offices in Cambridge, Massachusetts. For more information, visit https://www.stoketherapeutics.com/ or follow the Company on Twitter at @StokeTx.

Cautionary Note Regarding Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the "safe harbor" provisions of the Private Securities Litigation Reform Act of 1995, including, but not limited to: Stokes ability to use study data to advance the development of STK-001, the ability of STK-001 to treat the underlying causes of Dravet syndrome and reduce seizures, and the ability of TANGO to design medicines to increase protein production and the expected benefits thereof. Statements including words such as plan, potential, will, continue, expect, or similar words and statements in the future tense are forward-looking statements. These forward-looking statements involve risks and uncertainties, as well as assumptions, which, if they do not fully materialize or prove incorrect, could cause our results to differ materially from those expressed or implied by such forward-looking statements. Forward-looking statements are subject to risks and uncertainties that may cause the companys actual activities or results to differ significantly from those expressed in any forward-looking statement, including risks and uncertainties related to the Companys ability to advance its product candidates, obtain regulatory approval of and ultimately commercialize its product candidates, the timing and results of preclinical and clinical trials, the risk that positive results in a clinical trial may not be replicated in subsequent trials or success in early stage clinical trials may not be predictive of results in later stage trials, the Companys ability to fund development activities and achieve development goals, the Companys ability to protect intellectual property, the risks associated with the direct and indirect impacts of the ongoing COVID-19 pandemic on our business, and other risks and uncertainties described under the heading Risk Factors in documents the Company files from time to time with the Securities and Exchange Commission. These forward-looking statements speak only as of the date of this press release, and the Company undertakes no obligation to revise or update any forward-looking statements to reflect events or circumstances after the date hereof.

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Stoke Therapeutics Announces Presentations from the Company's Dravet Syndrome Program at the American Epilepsy Society 2021 Annual Meeting - Business...

DetailsCategory: Small MoleculesPublished on Sunday, 21 November 2021 12:44Hits: 843

BASEL, Switzerland I November 19, 2021 I Roche (SIX: RO, ROG; OTCQX: RHHBY) today announced that the European Commission (EC) has granted conditional marketing authorisation for Gavreto (pralsetinib) as a monotherapy for the treatment of adults with rearranged during transfection (RET) fusion-positive advanced non-small cell lung cancer (NSCLC) not previously treated with a RET inhibitor. Gavreto is the first and only precision medicine approved in the European Union (EU) for the first-line treatment of people with RET fusion-positive advanced NSCLC.1

Todays approval represents an important step forward in delivering precision medicine to people with RET fusion-positive advanced non-small cell lung cancer, for whom treatment options have historically been limited," said Levi Garraway, M.D., Ph.D., Roches Chief Medical Officer and Head of Global Product Development. By using cancer genomic profiling upfront, healthcare professionals may identify specific genetic alterations that predict clinical benefit of targeted treatment options like Gavreto in the first-line setting.

The approval is based on results of the ongoing phase I/II ARROW study, in which Gavreto led to durable responses in people with advanced RET fusion-positive NSCLC.2 In 75 treatment-nave patients, Gavreto demonstrated an overall response rate (ORR) of 72.0% (95% CI: 60.4%, 81.8%), and median duration of response (DOR) was not reached (NR) (95% CI: 9.0 months, NR).2 In 136 patients who had previously received platinum-based chemotherapy, Gavreto demonstrated an ORR of 58.8% (95% CI: 50.1%, 67.2%), and median DOR was 22.3 months (95% CI: 15.1 months, NR).2 Gavreto was also generally well-tolerated, with a low rate of treatment discontinuation; common grade 3-4 adverse reactions were neutropenia (reported in 20.1% of patients), anaemia (17.6%) and hypertension (16.1%).2

Approximately 37,500 people are diagnosed with RET fusion-positive NSCLC worldwide each year; the disease often affects people with minimal to no history of smoking, and who are typically younger than the average person diagnosed with lung cancer.3,4,5 Roche is committed to providing a tailored treatment option for every person with lung cancer, no matter how rare or difficult-to-treat their type of disease. Gavreto in RET fusion-positive advanced NSCLC, along with Alecensa (alectinib) in ALK-positive advanced NSCLC and Rozlytrek (entrectinib) in ROS1-positive advanced NSCLC, is part of Roches growing portfolio of precision medicines. Together, they offer personalised treatment options for almost one in ten people with advanced NSCLC, and biomarker testing is the most effective way to identify those people who may benefit.6

Beyond NSCLC, RET alterations are also key disease drivers in other cancer types, such as thyroid cancers. Gavreto has shown activity across multiple solid tumour types, reflecting tumour-agnostic potential.7 It is approved by the U.S. Food and Drug Administration (FDA) for the treatment of adults with metastatic RET fusion-positive NSCLC, and for the treatment of adult and paediatric patients 12 years of age and older with advanced RET-altered thyroid cancers. Gavreto is also approved in Canada, mainland China and Switzerland. In the EU, a submission for RET-altered thyroid cancers is planned. Regulatory submissions for advanced RET fusion-positive NSCLC and RET-altered thyroid cancers are also underway in multiple countries worldwide.

Blueprint Medicines and Roche are co-developing Gavreto globally, with the exception of certain territories in Asia, including China.* Blueprint Medicines and Genentech, a wholly owned member of the Roche Group, are commercialising Gavreto in the US and Roche has exclusive commercialisation rights for Gavreto outside of the US, with the exception of certain territories in Asia, including China.*

About the ARROW study8ARROW is an ongoing phase I/II, open-label, first-in-human study designed to evaluate the safety, tolerability and efficacy of Gavreto, administered orally in people with rearranged during transfection (RET) fusion-positive non-small cell lung cancer (NSCLC), RET-mutant medullary thyroid cancer, RET fusion-positive thyroid cancer and other RET-altered solid tumours. ARROW is being conducted at multiple sites across the United States, Europe and Asia.

About rearranged during transfection (RET)-altered cancersRET gene alterations, such as fusions and mutations, are key disease drivers in many types of cancer, including non-small cell lung cancer (NSCLC) and several types of thyroid cancer. There are approximately 2.21 million cases of lung cancer diagnosed each year worldwide,3 of which approximately 1.8 million are NSCLC and RET fusions are present in approximately 1-2% of these patients,4,5 meaning RET fusion-positive NSCLC affects up to 37,500 people each year. Additionally, approximately 10-20% of people with papillary thyroid cancer (the most common type of thyroid cancer) have RET fusion-positive tumours,9 and roughly 90% of people with advanced medullary thyroid cancer (a less prevalent form of thyroid cancer) carry RET mutations.10 Oncogenic RET fusions also are observed at low frequencies in other cancers, including cholangiocarcinoma, colorectal, neuroendocrine, ovarian, pancreatic and thymus cancers.

About Gavreto (pralsetinib)Gavreto is a once-daily, oral precision medicine designed to selectively target rearranged during transfection (RET) alterations, including fusions and mutations, regardless of the tissue of origin. Preclinical data have shown that Gavreto inhibits primary RET fusions and mutations that cause cancer in subsets of patients, as well as secondary RET mutations predicted to drive resistance to treatment. Blueprint Medicines and Roche are co-developing Gavreto for the treatment of people with various types of RET-altered cancers.

About Roche in lung cancerLung cancer is a major area of focus and investment for Roche, and we are committed to developing new approaches, medicines and tests that can help people with this deadly disease. Our goal is to provide an effective treatment option for every person diagnosed with lung cancer. We currently have six approved medicines to treat certain kinds of lung cancer, and a pipeline of investigational medicines to target the most common genetic drivers of lung cancer, or to boost the immune system to combat the disease.

About RocheRoche is a global pioneer in pharmaceuticals and diagnostics focused on advancing science to improve peoples lives. The combined strengths of pharmaceuticals and diagnostics, as well as growing capabilities in the area of data-driven medical insights help Roche deliver truly personalised healthcare. Roche is working with partners across the healthcare sector to provide the best care for each person.

Roche is the worlds largest biotech company, with truly differentiated medicines in oncology, immunology, infectious diseases, ophthalmology and diseases of the central nervous system. Roche is also the world leader in in vitro diagnostics and tissue-based cancer diagnostics, and a frontrunner in diabetes management. In recent years, the company has invested in genomic profiling and real-world data partnerships and has become an industry-leading partner for medical insights.

Founded in 1896, Roche continues to search for better ways to prevent, diagnose and treat diseases and make a sustainable contribution to society. The company also aims to improve patient access to medical innovations by working with all relevant stakeholders. More than thirty medicines developed by Roche are included in the World Health Organization Model Lists of Essential Medicines, among them life-saving antibiotics, antimalarials and cancer medicines. Moreover, for the thirteenth consecutive year, Roche has been recognised as one of the most sustainable companies in the pharmaceutical industry by the Dow Jones Sustainability Indices (DJSI).

The Roche Group, headquartered in Basel, Switzerland, is active in over 100 countries and in 2020 employed more than 100,000 people worldwide. In 2020, Roche invested CHF 12.2 billion in R&D and posted sales of CHF 58.3 billion. Genentech, in the United States, is a wholly owned member of the Roche Group. Roche is the majority shareholder in Chugai Pharmaceutical, Japan. For more information, please visit http://www.roche.com.

*CStone Pharmaceuticals retains all rights to the development and commercialisation of Gavreto in these territories (mainland China, Taiwan, Hong Kong and Macau) under its existing collaboration with Blueprint Medicines.

References[1] Gavreto, Summary of Product Characteristics. 2021.[2] Roche data on file.[3] World Health Organization. Cancer [Internet; cited 2021 Nov]. Available from: https://www.who.int/news-room/fact-sheets/detail/cancer%5B4%5D American Cancer Society. Key Statistics for Lung Cancer [Internet; cited 2021 Nov]. Available from: https://www.cancer.org/cancer/lung-cancer/about/key-statistics.html%5B5%5D Drilon A, et al. Brief Report: Frequency of Brain Metastases and Multikinase Inhibitor Outcomes in Patients With RET-Rearranged Lung Cancers. J Thorac Oncol. 2018;13:1595-601. [6] Pakkala S, Ramalingam SS. Personalized therapy for lung cancer: striking a moving target. JCI Insight. 2018;3(15):e120858.[7] Subbiah V, et al. Clinical activity and safety of the RET inhibitor pralsetinib in patients with RET fusion-positive solid tumors: Update from the ARROW trial. Presented at the American Society of Clinical Oncology (ASCO) Annual Meeting 2021; 04-08 Jun, 2021. Abstract #3079.[8] ClinicalTrials.gov. Phase 1/2 Study of the Highly-selective RET Inhibitor, Pralsetinib (BLU-667), in Patients With Thyroid Cancer, Non-Small Cell Lung Cancer, and Other Advanced Solid Tumors (ARROW) [Internet; cited 2021 Nov]. Available from: https://clinicaltrials.gov/ct2/show/NCT03037385%5B9%5D Santoro M, et al. RET Gene Fusions in Malignancies of the Thyroid and Other Tissues. Genes. 2020;11(4):424.[10] Romei C, et al. RET mutation heterogeneity in primary advanced medullary thyroid cancers and their metastases. Oncotarget. 2018;9(11):9875-84.

SOURCE: Roche

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European Commission approves Roche's Gavreto (pralsetinib) for the treatment of adults with RET fusion-positive advanced non-small cell lung cancer |...

Significance

Cannabis use is becoming more prevalent, including during developmentally sensitive periods such as pregnancy. Here we find that maternal cannabis use is associated with increased cortisol, anxiety, aggression, and hyperactivity in young children. This corresponded with widespread reductions in immune-related gene expression in the placenta which correlated with anxiety and hyperactivity. Future studies are needed to examine the effects of cannabis on immune function during pregnancy as a potential regulatory mechanism shaping neurobehavioral development.

While cannabis is among the most used recreational drugs during pregnancy, the impact of maternal cannabis use (mCB) on fetal and child development remains unclear. Here, we assessed the effects of mCB on psychosocial and physiological measures in young children along with the potential relevance of the in utero environment reflected in the placental transcriptome. Children (3 to 6 y) were assessed for hair hormone levels, neurobehavioral traits on the Behavioral Assessment System for Children (BASC-2) survey, and heart rate variability (HRV) at rest and during auditory startle. For a subset of children with behavioral assessments, placental specimens collected at birth were processed for RNA sequencing. Hair hormone analysis revealed increased cortisol levels in mCB children. In addition, mCB was associated with greater anxiety, aggression, and hyperactivity. Children with mCB also showed a reduction in the high-frequency component of HRV at baseline, reflecting reduced vagal tone. In the placenta, there was reduced expression of many genes involved in immune system function including type I interferon, neutrophil, and cytokine-signaling pathways. Finally, several of these mCB-linked immune genes organized into coexpression networks that correlated with child anxiety and hyperactivity. Overall, our findings reveal a relationship between mCB and immune response gene networks in the placenta as a potential mediator of risk for anxiety-related problems in early childhood.

Author contributions: G.R., Y.N., and Y.L.H. designed research, performed research, analyzed data, and wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission. T.B. is a guest editor invited by the Editorial Board.

See online for related content such as Commentaries.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2106115118/-/DCSupplemental.

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Maternal cannabis use is associated with suppression of immune gene networks in placenta and increased anxiety phenotypes in offspring - pnas.org

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With this reimbursement agreement approximately 700 people with cystic fibrosis now have access to a CFTR modulator therapy for the first time

LONDON--(BUSINESS WIRE)--Vertex Pharmaceuticals Incorporated (Nasdaq: VRTX) today announced that the Spanish government has approved terms for the national reimbursement of KAFTRIO (ivacaftor/tezacaftor/elexacaftor) in a combination regimen with ivacaftor for the treatment of cystic fibrosis (CF) for eligible patients. The agreement covers people with CF ages 12 years and older who have at least one copy of the F508del mutation, regardless of the other mutation type in the CFTR gene. KAFTRIO (ivacaftor/tezacaftor/elexacaftor) in a combination regimen with ivacaftor can be prescribed to eligible patients by treating physicians once the medicine is listed in the national Official Gazette Nomenclator.

The formalized agreement is an important milestone for people living with cystic fibrosis in Spain. We are pleased that the Ministry of Health recognized the value of KAFTRIO (ivacaftor/tezacaftor/elexacaftor) in a combination regimen with ivacaftor. Todays announcement means that the medicine can be prescribed to eligible patients in Spain when listed, said Ludovic Fenaux, Senior Vice President, Vertex International. We would like to thank the Ministry of Health for its collaborative approach, as well as the CF community for their important input during this process.

With this agreement, Spain joins the group of over 25 countries such as Ireland, Germany, Austria, Slovenia, Croatia, Luxembourg, Denmark, Finland, France, Portugal, Italy, Switzerland and the U.K. where eligible patients with CF have access to the triple combination therapy.

About Cystic Fibrosis

Cystic fibrosis (CF) is a rare, life-shortening genetic disease affecting more than 83,000 people globally. CF is a progressive, multi-organ disease that affects the lungs, liver, pancreas, GI tract, sinuses, sweat glands and reproductive tract. CF is caused by a defective and/or missing CFTR protein resulting from certain mutations in the CFTR gene. Children must inherit two defective CFTR genes one from each parent to have CF, and these mutations can be identified by a genetic test. While there are many different types of CFTR mutations that can cause the disease, the vast majority of people with CF have at least one F508del mutation. CFTR mutations lead to CF by causing CFTR protein to be defective or by leading to a shortage or absence of CFTR protein at the cell surface. The defective function and/or absence of CFTR protein results in poor flow of salt and water into and out of the cells in a number of organs. In the lungs, this leads to the buildup of abnormally thick, sticky mucus, chronic lung infections and progressive lung damage that eventually leads to death for many patients. The median age of death is in the early 30s.

About KAFTRIO (ivacaftor/tezacaftor/elexacaftor) in A Combination Regimen With Ivacaftor

In people with certain types of mutations in the CFTR gene, the CFTR protein is not processed or folded normally within the cell, and this can prevent the CFTR protein from reaching the cell surface and functioning properly. KAFTRIO (ivacaftor/tezacaftor/elexacaftor) in combination with ivacaftor is an oral medicine designed to increase the quantity and function of the CFTR protein at the cell surface. Elexacaftor and tezacaftor work together to increase the amount of mature protein at the cell surface by binding to different sites on the CFTR protein. Ivacaftor, which is known as a CFTR potentiator, is designed to facilitate the ability of CFTR proteins to transport salt and water across the cell membrane. The combined actions of ivacaftor, tezacaftor and elexacaftor help hydrate and clear mucus from the airways.

KAFTRIO (ivacaftor/tezacaftor/elexacaftor) in combination with ivacaftor is approved in the European Union for the treatment of cystic fibrosis (CF) in patients ages 12 years and older who have at least one copy of the F508del mutation in the CFTR gene.

For complete product information, please see the Summary of Product Characteristics that can be found on http://www.ema.europa.eu.

About Vertex

Vertex is a global biotechnology company that invests in scientific innovation to create transformative medicines for people with serious diseases. The company has multiple approved medicines that treat the underlying cause of cystic fibrosis (CF) a rare, life-threatening genetic disease and has several ongoing clinical and research programs in CF. Beyond CF, Vertex has a robust pipeline of investigational small molecule medicines in other serious diseases where it has deep insight into causal human biology, including pain, alpha-1 antitrypsin deficiency and APOL1-mediated kidney diseases. In addition, Vertex has a rapidly expanding pipeline of cell and genetic therapies for diseases such as sickle cell disease, beta thalassemia, Duchenne muscular dystrophy and type 1 diabetes mellitus.

Founded in 1989 in Cambridge, Mass., Vertex's global headquarters is now located in Boston's Innovation District and its international headquarters is in London. Additionally, the company has research and development sites and commercial offices in North America, Europe, Australia and Latin America. Vertex is consistently recognized as one of the industry's top places to work, including 12 consecutive years on Science magazine's Top Employers list, one of the 2021 Seramount (formerly Working Mother Media) 100 Best Companies, and a best place to work for LGBTQ equality by the Human Rights Campaign. For company updates and to learn more about Vertex's history of innovation, visit http://www.vrtx.com or follow us on Facebook, Twitter, LinkedIn, YouTube and Instagram.

Special Note Regarding Forward-Looking Statements

This press release contains forward-looking statements as defined in the Private Securities Litigation Reform Act of 1995, including, without limitation, statements made by Ludovic Fenaux, Senior Vice President, Vertex International, in this press release and statements regarding our beliefs about the eligible patient population that will have access to KAFTRIO in combination with ivacaftor, including patients that will now have access to a CFTR modulator therapy for the first time in Spain and our beliefs regarding the benefits of our medicines. While Vertex believes the forward-looking statements contained in this press release are accurate, these forward-looking statements represent the company's beliefs only as of the date of this press release and there are a number of risks and uncertainties that could cause actual events or results to differ materially from those indicated by such forward-looking statements. Those risks are listed under the heading Risk Factors in Vertex's annual report and in subsequent filings filed with the Securities and Exchange Commission and available through the company's website at http://www.vrtx.com and http://www.sec.gov. You should not place undue reliance on these statements. Vertex disclaims any obligation to update the information contained in this press release as new information becomes available.

(VRTX-GEN)

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Vertex Pharmaceuticals IncorporatedInvestors:InvestorInfo@vrtx.comor617-961-7163

Media:mediainfo@vrtx.comorInternational: +44 20 3204 5275orU.S.: 617-341-6992

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Vertex Announces Reimbursement Agreement in Spain for KAFTRIO (ivacaftor/tezacaftor/elexacaftor) in Combination With Ivacaftor to Treat People With...

Introduction

Prostate cancer is the second most common and fifth most aggressive cancer among men worldwide.1 According to one estimate, 1 in 7 US men and 1 in 25 men worldwide will be diagnosed with prostate cancer during his lifetime.1 Despite the advanced screening methods available, such as measurement of prostate-specific antigen levels, the incidence of metastatic disease remains as high as 20%.2 The best-known risk factors for prostate cancer are race (ie, African American descent), obesity, and genetics (eg, BRCA1/2 mutations). Gleason scoring is commonly used for histopathologic evaluation and for clinical and pathologic staging of disease. Patients with high-risk disease are treated with prostatectomy and/or external beam radiotherapy followed by androgen deprivation therapy (ADT) as maintenance therapy. If disease progression occurs while the patient is receiving ADT, the disease is noted to be castration-resistant prostate cancer (CRPC). Unfortunately, the majority of patients with prostate cancer progress to castration-resistant disease within 2 to 3 years.3

For decades the standard-of-care (SOC) treatment for metastatic CRPC (mCRPC) has been composed of cytotoxic agents, including taxanes (docetaxel or cabazitaxel [Jevtana]), and second-generation antihormonal agents (antihormonal therapy; AHT) such as abiraterone (Zytiga) or enzalutamide (Xtandi). Previously, CRPC was called androgen-independent prostate cancer and hormone-refractory prostate cancer.4 Subsequently, results of several studies showed that intratumoral (intracrine and paracrine) androgen production plays a significant role in the development of resistance among prostate cancer cells to testosterone suppression therapy.5

Other treatment options include pembrolizumab (Keytruda) for PD-L1positive and microsatellite instability (MSI)high disease, and radium-223 (Xofigo) for bone metastasis. PARP1 (or PARP) inhibitors are used in patients with mutations in homologous recombination repair (HRR) genes (most commonly, BRCA1/2). Recent study results indicate that the androgen receptor (AR) regulates the DNA repair pathways, and reciprocally, several enzymes involved in DNA repair can moderate AR activity.6-8 An example of such an enzyme is PARP1, which is involved in identifying single-stranded DNA breaks and their repair through the base excision method.9 Several cancers, including prostate cancer, exhibit increased PARP1 activity or expression.9-11 The mechanism of action of PARP inhibitors includes physical obstruction of the replication fork (PARP trapping), which affects HRR, resulting in DNA double-strand breaks.12 Previous study results have shown that these PARP inhibitors are synergistic when used with agents affecting the AR pathway regardless of HRR mutation status.9,13 In 2020, for the first time, the FDA approved PARP inhibitors for use in mCRPC.

The aim of this meta-analysis is to analyze the efficacy of these drugs in the treatment of mCRPC in terms of progression-free survival (PFS) and overall survival (OS), using the results of completed trials.

The authors followed PRISMA guidelines.

The databases accessed were Cochrane Central Registry of Clinical Trials, Embase, and PubMed. Search terms used were PARP inhibitors, prostate cancer, prostate neoplasm, olaparib (Lynparza), veliparib.

Papers had no restrictions in terms of date or status ofpublications. To be included, however, the papers had to report on RCTs that:

1. compared PARP inhibitors against SOC in patients with mCRPC;

2. reported PFS and OS;

3. included only patients 18 years or older; and

4. were available in the English language.

Papers that did not meet the above criteria were excluded.

Three authors independently reviewed all articles and abstracts and excluded the irrelevant trials. Risk of bias for selected papers was assessed using the Cochrane Collaboration tool and then classified as high, uncertain, or low.

Information was extracted using a prespecified extraction table. Information was extracted from the papers by reading through the main texts and tables, and a second author reviewed the information collected to ensure its accuracy. The extracted data included HRs for PFS and OS.

The meta-analysis was performed using Comprehensive Meta-analysis software version 3. HRs were calculated for PFS and OS . For effect sizes, a 95% CI was used, and a P value of less than .05 indicated statistical significance. Analysis was done using random and fixed models and both were reported. Heterogeneity was evaluated using I2 statistic and categorized as low (<40), moderate (40-60), and high (>60). Where a median was used, it was assumed to be equivalent to the mean, and SDs were estimated by dividing interquartile differences by 1.35. Fixed effectanalysis is usually adapted in cases where I2 value is 50%; otherwise, random effect model is used.

The initial search identified 351 articles; removal of duplicates left 322. The first screening excluded 262 articles. The full texts of the remaining 60 articles were analyzed. Thirty-seven articles were excluded because the trials described were incomplete; 13 were review articles; 2 described trials that were terminated; 4 were about single-arm studies; and 1 articles study did not have relevant intervention. Ultimately, articles describing 3 RCTs were included, and these trials had a total of 682 patients. The PRISMA flow diagram is shown in Figure 1, and main characteristics of RCTs are listed in the Table.

Risk of bias

The results of risk of bias are shown in Figure 2 and Figure 3.

Overall survival

Two studies reported OS when patients usedPARP inhibitors compared with SOC.14,15 The difference was statistically significant when calculated using the fixed model (HR, 0.751; 95% CI, 5.82-0.968; P = .027), and I2 = 23.23. When calculated using the random model, there was a strong deviation favoringPARP inhibitors,but it did not reach statistical significance (HR, 0.758; 95% CI, 0.565-1.017; P = .064)(Figure 4).

Progression-free survival

Three studies reported PFS when patients usedPARP inhibitorscompared with SOC.14-16 The difference was statistically significant when calculated using the fixed model (HR, 0.626; 95% CI, 0.521-0.752; P <.001), and I2 = 80.240. When calculated using the random model, there was a strong deviation favoringPARP inhibitors, but it did not reach statistical significance (HR, 0.674; 95% CI, 0.437-1.039; P = .074)(Figure 5).

Overall results of this meta-analysis indicate that patients with mCRPC experience survival benefits when treated with PARP inhibitors as compared with placebo or SOC chemotherapy. OS was better in the PARP inhibitor group under fixed effect (HR, 0.751; 95% CI,0.582-0.968; P = .027) and under random effect (HR, 0.758; 95% CI, 0.565-1.017; P = .064). PFS was improved in the PARP inhibitor group (HR, 0.626; 95% CI, 0.521-0.752; P <.001) compared with the chemotherapy group when analyzed under fixed effect.

In 2020, the FDA approved 2 PARP inhibitorsrucaparib (Rubraca) and olaparibto treat mCRPC in patients harboring somatic and/or germline mutations in BRCA1 or BRCA2 as well as in ATM genes. This decision was based upon the data from the multicenter, single-arm TRITON2 clinical trial (NCT02952534), in which rucaparib was used in patients with mCRPC positive for BRCA mutations. Currently, numerous clinical trials are ongoing to determine the efficacy of PARP inhibitors in mCRPC. Ongoing clinical trials with olaparib, veliparib, rucaparib, niraparib (Zejula), and talazoparib (Talzenna) in mCRPC were searched on the clinicaltrials.gov website. Currently, 37 trials are ongoing, 2 have been terminated, and 3 trials have been completed.

In 2 trials, olaparib was the study drug.14,15 In Clarke et al, patients were randomized into the treatment group (abiraterone plus olaparib) or the control group (abiraterone alone) irrespective of any genetic mutations or biomarker criteria. The study showed statistically significant improvement in both PFS and OS in the treatment group, indicating that a broader population, regardless of HRR mutation status, can benefit from the synergy of PARP inhibitors and AR inhibitors. However, men with HRR mutations derive the greatest benefit from these medications.

Our literature review also shows that PARP inhibitors are much more effective in patients with HRR or ATM mutations. In the PROfound study (NCT02987543; de Bono et al), patients were divided into 2 cohorts: Cohort A included patients with 1 or more of 3 mutations (BRCA1/2; ATM), and Cohort B included patients with a mutation in any of 12 other prespecified genes. Each cohort was divided into a treatment arm (olaparib) and a control arm (enzalutamide or abiraterone). OS was prolonged when measured together for cohorts A and B: 17.5 months with PARP inhibitor vs 14.4 months with chemotherapy (HR for death, 0.67; 95% CI, 0.49-0.93). The PFS for cohort A vs cohort B was 7.4 months vs 3.6 months, respectively; HR for progression or death, 0.34; 95% CI, 0.25-0.47; P < .001. The results for Cohort A and B combined showed median PFS to be 5.8 months vs 3.5 months (HR, 0.49; 95% CI, 0.38-0.63; P <.001). Once again, these results indicate that PARP inhibitors are effective in patients with mCRPC regardless of genetic mutation status, although responses are much more evident in the population with BRCA and ATM alterations.

In the third trial (NCT01576172; Hussain et al), veliparib was compared with a control regimen of abiraterone plus prednisone.16 The investigators primary objective was to see if ETS fusion status (related to a family of transcription factors) has a role in the tumor response to the treatment. Patients were first divided according to ETS fusion status (positive or negative) and then equally distributed in the case and control cohorts. Surprisingly, there was no difference in PFS between the treatment arms, regardless of ETS status, with overall PFS of 11 months (95% CI, 8.1-13.6) in the treatment group vs 10.1 months (95% CI, 8.2-13.8) in the control group (P = .99). However, a significant finding was that DNA repair status (ie, DRD gene mutation) was associated with statistically significant improvement in PFS regardless of treatment status: 14.5 months (abnormal DRD gene) vs 8 months (normal DRD gene) (HR, 0.52; 95% CI, 0.29-0.93; P = .02). This study was not included in the forest plot for OS because OS was not calculated in this group.

This study, as a meta-analysis, remains a retrospective chart review; the possibility for biases exists. Fewer trials and smaller study populations lead to publication bias. We made our best effort to locate all relevant published studies, randomize them, and complete data extraction and analysis. Another major limitation of this trial is the difficulty in performing stratified pool analysis for each PARP inhibitor drug; only a very limited number of completed phase 2/3 RCTs have currently available results. Other potential contributors to bias for this meta-analysis include heterogeneous population and inclusion criteria (with different first-line therapy patients and BRCA or other gene mutations). Another limitation is the inability to compare the adverse effect profiles between the PARP inhibitor and chemotherapy groups.

Conversely, a strength of this analysis is that it includes all phase 2/3 RCTs evaluating the efficacy of PARP inhibitors in mCRPC that have been completed and published, to date.

This meta-analysis shows that PARP inhibitors can prolong PFS or OS compared to SOC treatment in patients with mCRPC irrespective of HRR or other genetic mutation status. Longer PFS and OS were seen when PARP inhibitors were used alone or in combination with AHT therapies like abiraterone or enzalutamide. The effect was more significant when examined with a fixed model analysis. Although there was a significant deviation towards an increase in PFS and OS in the random model analysis, the effect was not statistically significant, and it was likely secondary to a relatively small patient population in the meta-analysis. Although, at baseline, there was heterogeneity among the populations participating in these trials, in terms of genetic alterations, the results of all the trials showed better outcomes in their intervention arms. This heterogeneity can be dealt with by incorporating more RCTs into meta-analyses going forward. More studies can further magnify these results once they are published.

DECLARATIONS

Ethics Approval and Consent to Participate: The data extracted and manuscript were reviewed with the Research Department and Ethics Committee of Department of Medicine, Staten Island University Hospital/Northwell. No experimental intervention was performed, and no specifications of guidelines, legislations, or permissions were required.

Availability of data and materials: Data are available in Excel files. Patient identifying information was removed at all stages in all the studies included.

Competing interests: No competing financial or personal interests are involved for all the authors.

Funding: No funding was obtained from any organization or personnel during any stage of manuscript writing or submission.

Authors contributions: Manuscript written and data obtained by M.R.K.N, A.J, S.S, S.B.S. Proofreading and literature review done by D.A and A.B.

AUTHOR AFFILIATIONS:

1. Department of Internal Medicine, Zucker School of Medicine at Hofstra/Northwell at Staten Island University Hospital, Staten Island, NY, USA.

2. Division of Hematology and Medical Oncology, Zucker School of Medicine at Hofstra/Northwell at Staten Island University Hospital, Staten Island, NY, USA.

1. Barsouk A, Padala SA, Vakiti A, et al. Epidemiology, staging and management of prostate cancer. Med Sci (Basel). 2020;8(3):28. doi:10.3390/medsci8030028

2. Studer UE, Hauri D, Hanselmann S, et al. Immediate versus deferred hormonal treatment for patients with prostate cancer who are not suitable for curative local treatment: results of the randomized trial SAKK 08/88. J Clin Oncol. 2004;22(20):4109-4118. doi:10.1200/jco.2004.11.514

3. Harris WP, Mostaghel EA, Nelson PS, Montgomery B. Androgen deprivation therapy: progress in understanding mechanisms of resistance and optimizing androgen depletion. Nat Clin Pract Urol. 2009;6(2):76-85. doi:10.1038/ncpuro1296

4. Ahmad K. New progress in treatment of hormone-refractory prostate cancer. Lancet Oncol. 2004;5(12):706. doi:10.1016/s1470-2045(04)01641-9

5. Montgomery RB, Mostaghel EA, Vessella R, et al. Maintenance of intratumoral androgens in metastatic prostate cancer: a mechanism for castration-resistant tumor growth. Cancer Res. 2008;68(11):4447-4454. doi:10.1158/0008-5472.Can-08-0249

6. Haffner MC, Aryee MJ, Toubaji A, et al. Androgen-induced TOP2B-mediated double-strand breaks and prostate cancer gene rearrangements. Nat Genet. 2010;42(8):668-675. doi:10.1038/ng.613

7. Schiewer MJ, Knudsen KE. Linking DNA damage and hormone signaling pathways in cancer. Trends Endocrinol Metab. 2016;27(4):216-225 .doi:10.1016/j.tem.2016.02.004

8. Ta HQ, Gioeli D. The convergence of DNA damage checkpoint pathways and androgen receptor signaling in prostate cancer. Endocr Relat Cancer. 2014;21(5):R395-R407. doi:10.1530/erc-14-0217

9. Schiewer MJ, Goodwin JF, Han S, et al. Dual roles of PARP-1 promote cancer growth and progression. Cancer Discov. 2012;2(12):1134-1149. doi:10.1158/2159-8290.Cd-12-0120

10. Hirai K, Ueda K, Hayaishi O. Aberration of poly(adenosine diphosphate-ribose) metabolism in human colon adenomatous polyps and cancers. Cancer Res. 1983;43(7):3441-3446.

11. Fukushima M, Kuzuya K, Ota K, Ikai K. Poly(ADP-ribose) synthesis in human cervical cancer cell -diagnostic cytological usefulness. Cancer Lett. 1981;14(3):227-236. doi:10.1016/0304-3835(81)90148-8

12. OConnor MJ. Targeting the DNA damage response in cancer. Mol Cell. 2015;60(4):547-560. doi:10.1016/j.molcel.2015.10.040

13. Asim M, Tarish F, Zecchini HI, et al. Synthetic lethality between androgen receptor signalling and the PARP pathway in prostate cancer. Nat Commun. 2017;8(1):374. doi:10.1038/s41467-017-00393-y

14. de Bono J, Mateo J, Fizazi K, et al. Olaparib for metastatic castration-resistant prostate cancer. N Engl J Med. 2020;382(22):2091-2102. doi:10.1056/NEJMoa1911440

15. Clarke N, Wiechno P, Alekseev B, et al. Olaparib combined with abiraterone in patients with metastatic castration-resistant prostate cancer: a randomised, double-blind, placebo-controlled, phase 2 trial. Lancet Oncol. 2018;19(7):975-986. doi:10.1016/s1470-2045(18)30365-6

16. Hussain M, Daignault-Newton S, Twardowski PW, et al. Targeting androgen receptor and DNA repair in metastatic castration-resistant prostate cancer: results from NCI 9012. J Clin Oncol. 2018;36(10):991-999. doi:10.1200/jco.2017.75.7310

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Efficacy of PARP Inhibitors as Maintenance Therapy for Metastatic Castration-Resistant Prostate Cancer: A Meta-Analysis of Randomized Controlled...

Introduction

The cells of a human being or other organism have parts called genes that control the chemical reactions in the cell that make it grow and function and ultimately determine the growth and function of the organism. An organism inherits some genes from each parent and thus the parents pass on certain traits to their offspring.

Gene therapy and genetic engineering are two closely related technologies that involve altering the genetic material of organisms.The distinction between the two is based on purpose.Gene therapy seeks to alter genes to correct genetic defects and thus prevent or cure genetic diseases.Genetic engineering aims to modify the genes to enhance the capabilities of the organism beyond what is normal.

Ethical controversy surrounds possible use of the both of these technologies in plants, nonhuman animals, and humans. Particularly with genetic engineering, for instance, one wonders whether it would be proper to tinker with human genes to make people able to outperform the greatest Olympic athletes or much smarter than Einstein.

If genetic engineering is meant in a very broad sense to include any intentional genetic alteration, then it includes gene therapy. Thus one hears of therapeutic genetic engineering (gene therapy) and negative genetic engineering (gene therapy), in contrast with enhancement genetic engineering and positive genetic engineering (what we call simply genetic engineering).

We use the phrase genetic engineering more narrowly for the kind of alteration that aims at enhancement rather than therapy. We use the term gene therapy for efforts to bring people up to normalcy and genetic engineering or enhancement genetic engineering for efforts to enhancement peoples capabilities beyond normalcy.

Two fundamental kinds of cell are somatic cells and reproductive cells. Most of the cells in our bodies are somatic cells that make up organs like skin, liver, heart, lungs, etc., and these cells vary from one another. Changing the genetic material in these cells is not passed along to a persons offspring. Reproductive cells are sperm cells, egg cells, and cells from very early embryos. Changes in the genetic make-up of reproductive cells would be passed along to the persons offspring. Those reproductive cell changes could result in different genetics in the offsprings somatic cells than otherwise would have occurred because the genetic makeup of somatic cells is directly linked to that of the germ cells from which they are derived.

Two problems must be confronted when changing genes. The first is what kind of change to make to the gene. The second is how to incorporate that change in all the other cells that are must be changed to achieve a desired effect.

There are several options for what kind of change to make to the gene. DNA in the gene could be replaced by other DNA from outside (called homologous replacement). Or the gene could be forced to mutate (change structure selective reverse mutation.) Or a gene could just be added. Or one could use a chemical to simply turn off a gene and prevent it from acting.

There are also several options for how to spread the genetic change to all the cells that need to be changed. If the altered cell is a reproductive cell, then a few such cells could be changed and the change would reach the other somatic cells as those somatic cells were created as the organism develops. But if the change were made to a somatic cell, changing all the other relevant somatic cells individually like the first would be impractical due to the sheer number of such cells. The cells of a major organ such as the heart or liver are too numerous to change one-by-one. Instead, to reach such somatic cells a common approach is to use a carrier, or vector, which is a molecule or organism. A virus, for example, could be used as a vector. The virus would be an innocuous one or changed so as not to cause disease. It would be injected with the genetic material and then as it reproduces and infects the target cells it would introduce the new genetic material. It would need to be a very specific virus that would infect heart cells, for instance, without infecting and changing all the other cells of the body. Fat particles and chemicals have also been used as vectors because they can penetrate the cell membrane and move into the cell nucleus with the new genetic material.

Gene therapy is often viewed as morally unobjectionable, though caution is urged. The main arguments in its favor are that it offers the potential to cure some diseases or disorders in those who have the problem and to prevent diseases in those whose genes predisposed them to those problems. If done on reproductive cells, gene therapy could keep children from carrying such genes (for unfavorable genetic diseases and disorders) that the children got from their patients.

Genetic engineering to enhance organisms has already been used extensively in agriculture, primarily in genetically modified (GM) crops (also known as GMO --genetically modified organisms). For example, crops and stock animals have been engineered so they are resistant to herbicides and pesticides, which means farmers can then use those chemicals to control weeds and insects on those crops without risking harming those plants. In the future genetic enhancement could be used to create crops with greater yields of nutritional value and selective breeding of farm stock, race horses, and show animals.

Genetically engineered bacteria and other microorganisms are currently used to produce human insulin, human growth hormone, a protein used in blood clotting, and other pharmaceuticals, and the number of such compounds could increase in the future.

Enhancing humans is still in the future, but the basic argument in favor of doing so is that it could make life better in significant ways by enhancing certain characteristics of people. We value intelligence, beauty, strength, endurance, and certain personality characteristics and behavioral tendencies, and if these traits were found to be due to a genetic component we could enhance people by giving them such features. Advocates of genetic engineering point out that many people try to improve themselves in these ways already by diet, exercise, education, cosmetics, and even plastic surgery. People try to do these things for themselves, and parents try to provide these things for their children. If exercising to improve strength, agility, and overall fitness is a worthwhile goal, and if someone is praised for pursuing education to increase their mental capabilities, then why would it not be worthwhile to accomplish this through genetics?

Advocates of genetic engineering also see enhancement as a matter of basic reproductive freedom. We already feel free to pick a mate partly on the basis of the possibility of providing desirable children. We think nothing is wrong with choosing a mate whom we hope might provide smart, attractive kids over some other mate who would provide less desirable children. Choosing a mate for the type of kids one might get is a matter of basic reproductive freedom and we have the freedom to pick the best genes we can for our children. Why, the argument goes, should we have less freedom to give our children the best genes we can through genetic enhancement?

Those who advocate making significant modification of humans through technology such as genetic engineering are sometimes called transhumanists.

Three arguments sometimes raised against gene therapy are that it is technically too dangerous, that it discriminates or invites discrimination against persons with disabilities, and that it may be becoming increasingly irrelevant in some cases.

The danger objection points out that a few recent attempts at gene therapy in clinical trials have made headlines because of the tragic deaths of some of the people participating in the trials. It is not fully known to what extent this was due to the gene therapy itself, as opposed to pre-existing conditions or improper research techniques, but in the light of such events some critics have called for a stop to gene therapy until more is known. We just do not know enough about how gene therapy works and what could go wrong. Specific worries are that

The discrimination objection is as follows. Some people who are physically, mentally, or emotionally impaired are so as the result of genetic factors they have inherited. Such impairment can result in disablement in our society. People with disabilities are often discriminated against by having fewer opportunities than other people. Be removing genetic disorders, and resulting impairment, it is true that gene therapy could contribute to removing one of the sources of discrimination and inequality in society. But the implicit assumption being made, the objection claims, is that people impaired through genetic factors need to be treated and made normal. The objection sees gene therapy as a form of discrimination against impaired people and persons with disabilities.

The irrelevance objection is that gene therapy on reproductive cells may in some cases already be superseded by in-vitro fertilization and selection of embryos. If a genetic disorder is such that can be detected in an early embryo, and not all embryos from the parent couple would have it, then have parents produce multiple embryos through in-vitro fertilization and implant only those free from the disorder. In such a case gene therapy would be unnecessary and irrelevant.

Ethicists have generally been even more concerned about possible problems with and implications of enhancement genetic engineering than they have been about gene therapy. First, there are worries similar to those about gene therapy that not enough is known and there may be unforeseen dangerous consequences. These worries may be even more serious given that the attempts are made not just toward normalcy but into strange new territory where humans have never gone before. We just do not know what freakish creatures might result from experiments gone awry.

Following are some other important objections:

Gene therapy is becoming a reality as you read this. Genetic engineering for enhancement is still a ways off. Plenty of debate is sure to occur over both issues.

Read the rest here:
Gene Therapy and Genetic Engineering - MU School of Medicine

Genome editing allows scientists to alter the DNA in an organism, whether through adding, subtracting, or changing the genetic code at a specific location. There are many methods for editing DNA, but themost commonly mentioned are CRISPR-Cas9 and TALENs.

CRISPRs are repeated sequences of DNA interspersed with unique sequences of spacers. CRISPRs are naturally occurring, used by bacteria and archaea to fight off pathogens by slicing up the intruders genetic material and adding these slices to its own genome as a sort of library.

Since the pathogens genes become a part of the bacteriums genes, the bacteria can remember the pathogen and better fight it in the future.

Molecular biologists use CRISPR to study relationships between genes and how living things look and function. In medicine, this technology gives hope for creating new treatments to cure diseases that are currently incurable.

One way of using gene editing is to identify and deactivate genes that are causing diseases. That includes genes that increase the risk of a disease, or normal genes that, when mutated or dysfunctional, cause genetic diseases.

The immune system, however, can also interfere with gene editing and disturb treatment.

TALENs is another method being used for efficient gene editing. Xanthomonas genus bacteria wreak havoc on plants, injecting a protein called TAL that can shut down a plants genes. This protein might be bad for plants, but for scientists, its opened up the world of gene editing even more. TAL is made up of sections that can identify certain DNA nucleotides, and tinkering with these sections allows scientists to locate genes they want to edit.

Is CRISPR flawed?

A recent study has flagged a new safety signal that could potentially hurt the drug developers focused on CRISPRCas9 gene editing.

The condition known as chromothripsis has the potential to cause cancer eventually, according to the study conducted by St. Jude Childrens Research Hospital, the DanaFarber Cancer Institute, and Harvard Medical School.

When double-strand DNA breaks during CRISPR editing, there could be chromothripsis, a condition that results from the shattering of individual chromosomes and the haphazard rearrangement of genetic material subsequently.

According to an article published inNatureBiotechnology, none of the companies advancing the CRISPR-based therapies have considered the issue.

Is gene editing even ethical?

During the Olympics, the physiological prowess of elite athletes is clear, whether its the long-limbed volleyball players or the muscular weightlifters. Unsurprisingly, physiological advantages vary by sport, but theres a number of genetic advantages that can arise.

Lance Armstrong even without performance-enhancing drugs, still had a genetically powerful build for cycling: he has a higher maximum oxygen consumption than the average person and this is associated with genetics.

Michael Phelps, the most decorated Olympian of all time, naturally produceshalf the lactic acidof other Olympic swimmers. When we perform high-energy activities, the body switches from generating energy aerobically (with oxygen) to generating energy anaerobically (without oxygen). During this process, the body breaks down a substance called pyruvate into lactic acid. Thislactic acidtires out muscles, leaving them with that all-too-familiar burning sensation when you exercise. Since Phelps doesnt have as much lactic acid, hes able to recover from high-intensity activity quickly.

Could we create designer elite athletes using genome editing?

The US National Academy of Sciences and National Academy of Medicine have hosted an interdisciplinary committee to outline the regulatory standards and ethics of human gene modification. The very first of these regulations was that genome editing can occur if it is restricted topreventing the transmissionof a serious disease or condition.

The World Anti-Doping Agency recently placed gene editing on theirlist of prohibited practices and substances. Theres just one problem: Its extremely difficult to determine if someone has modified their genome.

In theory, we could genetically engineer children to grow into better athletes: a runner with stronger leg muscles, a taller volleyball or basketball player, an archer with pinpoint vision.

Moderna gets a jump on gene editing

Moderna has found a direction to volley their mountain of COVID-19 vaccine cash: gene editing.

Executives revealed during a second-quarter earnings call recently that Moderna is ready to expand its horizons with external technologies or products.

Modernas pulled in billions with its COVID-19 vaccine. The shot, which the company now aims to market as Spikevax, is expected to bring in about $20 billion this year, based on existing orders.

Moderna is interested in new opportunities in nucleic acid technologies, gene therapy, gene editing and mRNA, CEO Stphane Bancel said during the conference call.

Most likely, Moderna will start with hematopoietic stem cells, which is the companys bread-and-butter delivery method. Other companies working on gene editing include CRISPR Therapeutics, Precision Biosciences, Beam Therapeutics, and Sangamo Therapeutics.

Gene editing applications

The Global genome editing market is expected to reach $8.7 billion by 2026, according to Reportlinker.com.

Genome editing finds application in a large number of areas, such as mutation, therapeutics, and agriculture biotechnology. The rise in the number of chronic and infectious diseases is likely to expand the scope of genome editing in the coming years.

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Gene editing: Great for medicine but ethical issues arise

Chroma Medicine thinks it can silence and activate genes via epigenetic editing, and the biotech uncovered itself with $125 million in financing to test the epigenome's potential in helping treat various diseases.

The biotech is working to create epigenetic editors that can turn on or turn off genesor perform a combination of the twoto regulate gene expression.

The epigenome is the system that informs cells how to comprehend DNA. Think of DNA as the hardware and the epigenome as the software that directs which genes are expressed and which are silenced, explained CEO Catherine Stehman-Breen, M.D., in an interview with Fierce Biotech.

Chroma's epigenetic editors are a "small tweak to the software package that sits on top of the hardware of the genome," said Vic Myer, Ph.D., president and chief scientific officer, in the joint interview. The "beauty of this system" is that Chroma's editor is only needed for a brief moment to change the local epigenetic mark set, he added.

This is done without nicks or cuts to the DNA itself, Stehman-Breen said.

RELATED:GV-backed Cambridge Epigenetix lines up $88M to roll out genetic sequencing technology

With the money in hand, Chroma will move the technology into in vivo proof-of-concept studies in mice and build out manufacturing capabilities, Myer said. The biotech has already reproduced the "critical experiments" conducted by the scientific founders, he added.

The executives declined to disclose which diseases or areas they'd tackle first. The funding will provide runway for a "couple of years," at which point Chroma will have the data to support the next round of financing, Stehman-Breen said.

The one-year-old startup has already combined forces with another biotech via its acquisition of Epsilen Bio, a Milan, Italy-based company working on a "somewhat parallel path,"Stehman-Breen said. Together, the companies are a "powerhouse in terms of epigenetic editing therapeutics," the CEO added.

Chroma's scientific co-founders include a team of epigenetic editing, gene editing and cell therapy experts, Stehman-Breen said. The groundwork was laid by Angelo Lombardo, Ph.D.,and Luigi Naldini, M.D., Ph.D., at the San Raffaele Telethon Institute for Gene Therapy. Chroma's other scientificoriginators include the University of California, San Francisco'sLuke Gilbert, Ph.D., Massachusetts General Hospital's Keith Joung, M.D., Ph.D., the Broad Institute's David Liu, Ph.D., and the Whitehead Institute's Jonathan Weissman, Ph.D.

For her part, Stehman-Breen'sresume includes stints as chief medical officer at Sarepta Therapeutics, chief R&D officer at Obsidian Therapeutics and vice president of global development at both Regeneron and Amgen. Prior to Chroma, Myer held interim C-suite roles at Korro Bio and Obsidian and was chief technology officer at gene-editing pioneer Editas Medicine from 2015 to 2019.

Atlas Venture and Newpath Partners provided seed capital for Chroma last year with Sofinnova Partners. Cormorant, Casdin Capital, Janus Henderson, Omega Funds, T. Rowe Price andWellington Management joined for the series A.

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Gene on, gene off: Chroma Medicine turns lights on with $125M to control gene expression with ex-Editas, Regeneron leaders - FierceBiotech