Human Genomic Data Research Articles

Genetic resiliency associated with dominant lethal TPM1 mutation causing atrial septal defect with high heritability

SummaryAnalysis of large-scale human genomic data has yielded unexplained mutations known to cause severe disease in healthy individuals. Here, we report the unexpected recovery of a rare dominant lethal mutation in TPM1, a sarcomeric actin-binding protein, in eight individuals with large atrial septal defect (ASD) in a five-generation pedigree. Mice with Tpm1 mutation exhibit early embryonic lethality with disrupted myofibril assembly and no heartbeat. However, patient-induced pluripotent-stem-cell-derived cardiomyocytes show normal beating with mild myofilament defect, indicating disease suppression. A variant in TLN2, another myofilament actin-binding protein, is identified as a candidate suppressor. Mouse CRISPR knock-in (KI) of both the TLN2 and TPM1 variants rescues heart beating, with near-term fetuses exhibiting large ASD. Thus, the role of TPM1 in ASD pathogenesis unfolds with suppression of its embryonic lethality by protective TLN2 variant. These findings provide evidence that genetic resiliency can arise with genetic suppression of a deleterious mutation.

Interoperability of COVID-19 Clinical Phenotype Data with Host and Viral Genetics Data

The outbreak of the COVID-19 epidemic has focused enormous attention on the genetics of viral infection and related disease. Since the beginning of the pandemic, we focused on the collection and integration of SARS-CoV-2 databases, which contain information on the structure of the virus and on its ability to spread, mutate, and evolve; data are made available from several open-source databases. In the past, we gathered experience on human genomics data by building models and integrated databases of genomic datasets (representing, e.g., mutations, gene expression profiles, epigenetic signals). We also coordinated the development of a data dictionary describing the clinical phenotype of the COVID19 disease, in the context of a very large consortium. The main objective of this paper is to describe the content of the data dictionary and the process of data collection and organization. We also argue that—in the context of the COVID-19 disease—interoperability between the three domains of viral genomics, clinical phenotype, and human host genomics is essential for empowering important analysis processes and results. We call for actions that could be performed to link these data.

Next-generation diagnostic pathology

With the technological progresses and applications of human genome sequencing, bioinformatics analysis and data mining, and molecular pathology and artificial intelligence-assisted pathological diagnosis, the development of clinical medicine is moving towards the era of precision diagnosis and treatment. In the context of this era, the traditional diagnostic pathology is facing unprecedented opportunities and challenges in our history and is striving towards the "next-generation diagnostic pathology" (NGDP). NGDP is based on histomorphology and clinical data, and characterized by the combination of molecular detection and bioinformatics analysis, intelligent sampling and process quality control, intelligent diagnosis and remote consultation, lesion visualization and "non-invasive" pathology as well as other innovative cutting edge interdisciplinary technologies. The NGDP reports will include the results from multi-omics and cross-scale integrated diagnosis for final diagnosis. NGDP will also be applied for predicting disease progression and outcomes, and determining optional therapeutics as well as assessing treatment responses, so that a novel "golden standard" of disease diagnosis can be established. In the near fature, it is necessary to stimulate the innovative vitality of pathology disciplines, accelerate the maturity and application for NGDP, update the theory and technical system of pathology, and perform its important applicable role in the prevention, diagnosis, treatment of diseases so that the futher development of clinical medicine will be promoted and the strategy for maintenance of being healthy in China will be served.

Co-Inference of Data Mislabelings Reveals Improved Models in Genomics and Breast Cancer Diagnostics

Mislabeling of cases as well as controls in case–control studies is a frequent source of strong bias in prognostic and diagnostic tests and algorithms. Common data processing methods available to the researchers in the biomedical community do not allow for consistent and robust treatment of labeled data in the situations where both, the case and the control groups, contain a non-negligible proportion of mislabeled data instances. This is an especially prominent issue in studies regarding late-onset conditions, where individuals who may convert to cases may populate the control group, and for screening studies that often have high false-positive/-negative rates. To address this problem, we propose a method for a simultaneous robust inference of Lasso reduced discriminative models and of latent group-specific mislabeling risks, not requiring any exactly labeled data. We apply it to a standard breast cancer imaging dataset and infer the mislabeling probabilities (being rates of false-negative and false-positive core-needle biopsies) together with a small set of simple diagnostic rules, outperforming the state-of-the-art BI-RADS diagnostics on these data. The inferred mislabeling rates for breast cancer biopsies agree with the published purely empirical studies. Applying the method to human genomic data from a healthy-ageing cohort reveals a previously unreported compact combination of single-nucleotide polymorphisms that are strongly associated with a healthy-ageing phenotype for Caucasians. It determines that 7.5% of Caucasians in the 1000 Genomes dataset (selected as a control group) carry a pattern characteristic of healthy ageing.

Implementation of international standards of human genome, embryo, and genetic data security in the Russian criminal law

Implementation of international standards of human genome, embryo, and genetic data security in the Russian criminal law

The emerging complexity of Open Science: assessing Intelligent Data Openness in Genomic Anthropology and Human Genomics.

In recent decades, the scientific community has become aware of the importance of science being effectively open in order to speed up scientific and technological progress. In this context, the achievement of a robust, effective and responsible form of data sharing is now widely acknowledged as a fundamental part of the research process. The production and resolution of human genomic data has steadily increased in recent years, mainly due to technological advances and decreasing costs of DNA genotyping and sequencing. There is, however, a downside to this process due to the huge increase in the complexity of the data and related metadata. This means it is advisable to go beyond traditional forms of sharing analysis, which have focused on data availability only. Here we present a pilot study that aims to complement a survey on the availability of data related to peer-reviewed publications with an analysis of their findability, accessibility, useability and assessability (according to the "intelligent data openness" scheme). Sharing rates in genomic anthropology (73.0%) were found to be higher than human genomics (32.4%), but lower than closely related research fields (from 96.8% to 79.2% for paleogenetics and evolutionary genetics, respectively). We discuss the privacy and methodological issues that could be linked to this finding. Comparisons of sharing rates across a wide range of disciplines has suggested that the idea of human genomics as a forerunner for the open data movement should be questioned. Finally, both in genomic anthropology and human genomics, findability and useability were found to be compliant with the expectations of an intelligent data openness, whereas only a minor part of studies met the need to make the data completely assessable.

Ribosomal protein S6 kinase beta-1 gene variants cause hypertrophic cardiomyopathy

BackgroundHypertrophic cardiomyopathy (HCM) is a genetic heart muscle disease with preserved or increased ejection fraction in the absence of secondary causes. Mutations in the sarcomeric protein-encoding genes predominantly cause HCM....

SnpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data.

Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of different bioinformatics tools. Software incompatibilities, and inconsistencies across computing environments, are recurrent challenges, leading to poor reproducibility. Existing semi-automated or automated solutions lack comprehensive quality checks, flexible workflow architecture, and user control. To address these challenges, we have developed snpQT: a scalable, stand-alone software pipeline using nextflow and BioContainers, for comprehensive, reproducible and interactive quality control of human genomic data. snpQT offers some 36 discrete quality filters or correction steps in a complete standardised pipeline, producing graphical reports to demonstrate the state of data before and after each quality control procedure. This includes human genome build conversion, population stratification against data from the 1,000 Genomes Project, automated population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used, and a synthetic dataset and comprehensive online tutorial are provided for testing, educational purposes, and demonstration. The snpQT pipeline is designed to run with minimal user input and coding experience; quality control steps are implemented with numerous user-modifiable thresholds, and workflows can be flexibly combined in custom combinations. snpQT is open source and freely available at https://github.com/nebfield/snpQT. A comprehensive online tutorial and installation guide is provided through to GWAS (https://snpqt.readthedocs.io/en/latest/), introducing snpQT using a synthetic demonstration dataset and a real-world Amyotrophic Lateral Sclerosis SNP-array dataset.

Abstract 10150: Distinct Gut Microbiota Composition Among Older Adults with Impaired Left Ventricular Diastolic Function

Introduction: Recent research has demonstrated shifts in intestinal microbial composition in response to cardiovascular disease (CVD) development. Left ventricular (LV) diastolic dysfunction is an early manifestation of myocardial ageing. We aimed to examine whether gut microbial composition is associated with LV diastolic function among healthy older adults. Methods: Among community older adults, 15 subjects with preserved LV ejection fraction on echocardiography underwent gut microbial composition examination in this proof-of-concept cross-sectional study. Myocardial relaxation (diastolic function) was impaired in those subjects with lower ratios of peak early (E) to late diastolic (A, atrial contraction) velocities. Metagenomic reads from stool samples were analyzed after excluding human reference genome data, and classified into various microbial taxonomic groups using lowest-common-ancestor algorithm. Statistical analysis was performed using linear discriminant analysis (LDA). Results: The subjects (53% males, mean age 75±4 years) were stratified into Groups 1 (n=8) and 2 (n=7) with more severe and lesser diastolic dysfunction, respectively, based on E/A ratios (0.77 vs. 1.28, p<0.0001). In Group 1, there was significantly higher prevalence of certain bacteria (p<0.05), including Bacteroides sp, Ruminococcus sp., and Paraprevotella (Figure 1). The largest relative effect size was with Bacteroides sp., including Bacteroides xylanisolvens (logarithmic LDA score 3.0). In Group 2, there was significantly higher prevalence (p<0.05) of Oscillibacter sp., Peptostreptococcaceae, Clostridioides difficile, Prevotella timonensis, and Blautia producta. Relative effect size was largest with Oscillibacter sp. (log 2.8). Conclusions: Distinct gut microbiota were associated with more severe vs. lesser LV diastolic dysfunction. Future validation in larger cohorts is warranted for targeting the microbiome in myocardial ageing.

Voices of GA4GH members: Collaborating in technology and policy development

The Global Alliance for Genomics and Health (GA4GH) is organized around eight Work Streams, where members develop technology standards and policy frameworks to enable the responsible sharing of human genomic and health-related data. These are implemented across 24 Driver Projects, reflecting real-world genomics initiatives. In this Voices, Cell Genomics asked GA4GH members to reflect on their engagement with GA4GH and how this has driven progress in open science and genomic medicine.

SnpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data

Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of different bioinformatics tools. Software incompatibilities, and inconsistencies across computing environments, are recurrent challenges, leading to poor reproducibility. Existing semi-automated or automated solutions lack comprehensive quality checks, flexible workflow architecture, and user control. To address these challenges, we have developed snpQT: a scalable, stand-alone software pipeline using nextflow and BioContainers, for comprehensive, reproducible and interactive quality control of human genomic data. snpQT offers some 36 discrete quality filters or correction steps in a complete standardised pipeline, producing graphical reports to demonstrate the state of data before and after each quality control procedure. This includes human genome build conversion, population stratification against data from the 1,000 Genomes Project, automated population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used, and a synthetic dataset and comprehensive online tutorial are provided for testing, educational purposes, and demonstration. The snpQT pipeline is designed to run with minimal user input and coding experience; quality control steps are implemented with numerous user-modifiable thresholds, and workflows can be flexibly combined in custom combinations. snpQT is open source and freely available at https://github.com/nebfield/snpQT. A comprehensive online tutorial and installation guide is provided through to GWAS (https://snpqt.readthedocs.io/en/latest/), introducing snpQT using a synthetic demonstration dataset and a real-world Amyotrophic Lateral Sclerosis SNP-array dataset.

Human Genomics and Drug Development.

Insights into the genetic basis of human disease are helping to address some of the key challenges in new drug development including the very high rates of failure. Here we review the recent history of an emerging, genomics-assisted approach to pharmaceutical research and development, and its relationship to Mendelian randomization (MR), a well-established analytical approach to causal inference. We demonstrate how human genomic data linked to pharmaceutically relevant phenotypes can be used for (1) drug target identification (mapping relevant drug targets to diseases), (2) drug target validation (inferring the likely effects of drug target perturbation), (3) evaluation of the effectiveness and specificity of compound-target engagement (inferring the extent to which the effects of a compound are exclusive to the target and distinguishing between on-target and off-target compound effects), and (4) the selection of end points in clinical trials (the diseases or conditions to be evaluated as trial outcomes). We show how genomics can help identify indication expansion opportunities for licensed drugs and repurposing of compounds developed to clinical phase that proved safe but ineffective for the original intended indication. We outline statistical and biological considerations in using MR for drug target validation (drug target MR) and discuss the obstacles and challenges for scaled applications of these genomics-based approaches.

Detecting Selection in Multiple Populations by Modeling Ancestral Admixture Components.

One of the most powerful and commonly used approaches for detecting local adaptation in the genome is the identification of extreme allele frequency differences between populations. In this article, we present a new maximum likelihood method for finding regions under positive selection. It is based on a Gaussian approximation to allele frequency changes and it incorporates admixture between populations. The method can analyze multiple populations simultaneously and retains power to detect selection signatures specific to ancestry components that are not representative of any extant populations. Using simulated data, we compare our method to related approaches, and show that it is orders of magnitude faster than the state-of-the-art, while retaining similar or higher power for most simulation scenarios. We also apply it to human genomic data and identify loci with extreme genetic differentiation between major geographic groups. Many of the genes identified are previously known selected loci relating to hair pigmentation and morphology, skin, and eye pigmentation. We also identify new candidate regions, including various selected loci in the Native American component of admixed Mexican-Americans. These involve diverse biological functions, such as immunity, fat distribution, food intake, vision, and hair development.

TIGER: The gene expression regulatory variation landscape of human pancreatic islets.

SUMMARYGenome-wide association studies (GWASs) identified hundreds of signals associated with type 2 diabetes (T2D). To gain insight into their underlying molecular mechanisms, we have created the translational human pancreatic islet genotype tissue-expression resource (TIGER), aggregating >500 human islet genomic datasets from five cohorts in the Horizon 2020 consortium T2DSystems. We impute genotypes using four reference panels and meta-analyze cohorts to improve the coverage of expression quantitative trait loci (eQTL) and develop a method to combine allele-specific expression across samples (cASE). We identify >1 million islet eQTLs, 53 of which colocalize with T2D signals. Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identify eight cASE colocalizations, among which we found a T2D-associated SLC30A8 variant. We make all data available through the TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translates into therapeutic insight and precision medicine for T2D.

Deep learning-based prediction of the T cell receptor-antigen binding specificity.

Neoantigens play a key role in the recognition of tumor cells by T cells. However, only a small proportion of neoantigens truly elicit T cell responses, and fewer clues exist as to which neoantigens are recognized by which T cell receptors (TCRs). We built a transfer learning-based model, named pMHC-TCR binding prediction network (pMTnet), to predict TCR-binding specificities of neoantigens, and T cell antigens in general, presented by class I major histocompatibility complexes (pMHCs). pMTnet was comprehensively validated by a series of analyses, and showed advance over previous work by a large margin. By applying pMTnet in human tumor genomics data, we discovered that neoantigens were generally more immunogenic than self-antigens, but HERV-E, a special type of self-antigen that is re-activated in kidney cancer, is more immunogenic than neoantigens. We further discovered that patients with more clonally expanded T cells exhibiting better affinity against truncal, rather than subclonal, neoantigens, had more favorable prognosis and treatment response to immunotherapy, in melanoma and lung cancer but not in kidney cancer. Predicting TCR-neoantigen/antigen pairs is one of the most daunting challenges in modern immunology. However, we achieved an accurate prediction of the pairing only using the TCR sequence (CDR3β), antigen sequence, and class I MHC allele, and our work revealed unique insights into the interactions of TCRs and pMHCs in human tumors using pMTnet as a discovery tool.

Susceptibility to Neurodegenerative Disorders: Insights from Paleogenomic Data

abstract: Ancient human genome data accumulated in recent years can be employed to establish the spatiotemporal trajectories of genetic variants associated with human diseases. Such knowledge might illuminate if and how past adaptations impact contemporary human health and medicine. Scarcely any studies have yet attempted to evaluate genetic susceptibility to neurodegenerative disorders in ancient human communities. Using publicly available ancient human genome-wide data, this study evaluated the molecular predisposition to neurodegenerative disorders in ancient human communities. Ancient genome-wide data was screened for the presence of variants unequivocally associated with neuro-degenerative disorders in modern populations, identifying two rare variants in the LRRK2 gene associated with Mendelian Parkinson’s disease, a pathogenic variant in the CRH gene associated with an uncommon form of epilepsy, and a rare variant in the TREM2 gene that is a possible risk modifier associated with Alzheimer’s disease. Their historical and geographic prevalence was also assessed, indicating differing spatiotemporal frequency dynamics for these clinically significant variants. Neurodegenerative disorders often have poorly understood pathogenesis that might be elucidated by studying the interaction of past genetic variability with ecological and evolutionary factors such as adverse environmental conditions, specific selective pressures, periods of population isolation, and admixture processes. Data on molecular predisposition to neurodegenerative disorders in ancient genomes is instructive to modern medical diagnostic and therapeutic practices.

SnpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data.

Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of different bioinformatics tools. Dependency hell and reproducibility are recurrent challenges. Existing semi-automated or automated solutions lack comprehensive quality checks, flexible workflow architecture, and user control. To address these challenges, we have developed snpQT: a scalable, stand-alone software pipeline using nextflow and BioContainers, for comprehensive, reproducible and interactive quality control of human genomic data. snpQT offers some 36 discrete quality filters or correction steps in a complete standardised pipeline, producing graphical reports to demonstrate the state of data before and after each quality control procedure. This includes human genome build conversion, population stratification against data from the 1,000 Genomes Project, automated population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used, and a synthetic dataset and comprehensive online tutorial are provided for testing, educational purposes, and demonstration. The snpQT pipeline is designed to run with minimal user input and coding experience; quality control steps are implemented with default thresholds which can be modified by the user, and workflows can be flexibly combined in custom combinations. snpQT is open source and freely available at https://github.com/nebfield/snpQT. A comprehensive online tutorial and installation guide is provided through to GWAS (https://snpqt.readthedocs.io/en/latest/), introducing snpQT using a synthetic demonstration dataset and a real-world Amyotrophic Lateral Sclerosis SNP-array dataset.

Haplotype-based membership inference from summary genomic data.

MotivationThe availability of human genomic data, together with the enhanced capacity to process them, is leading to transformative technological advances in biomedical science and engineering. However, the public dissemination of such data has been difficult due to privacy concerns. Specifically, it has been shown that the presence of a human subject in a case group can be inferred from the shared summary statistics of the group, e.g. the allele frequencies, or even the presence/absence of genetic variants (e.g. shared by the Beacon project) in the group. These methods rely on the availability of the target’s genome, i.e. the DNA profile of a target human subject, and thus are often referred to as the membership inference method.ResultsIn this article, we demonstrate the haplotypes, i.e. the sequence of single nucleotide variations (SNVs) showing strong genetic linkages in human genome databases, may be inferred from the summary of genomic data without using a target’s genome. Furthermore, novel haplotypes that did not appear in the database may be reconstructed solely from the allele frequencies from genomic datasets. These reconstructed haplotypes can be used for a haplotype-based membership inference algorithm to identify target subjects in a case group with greater power than existing methods based on SNVs.Availability and implementationThe implementation of the membership inference algorithms is available at https://github.com/diybu/Haplotype-based-membership-inferences.

Abstract 2481: Time-resolved transcriptome analysis of murine TH-MYCN driven neuroblastoma identifies MEIS2 as early initiating factor and novel core gene regulatory circuitry constituent

Abstract Introduction: Neuroblastoma (NB) is a pediatric malignancy arising from peripheral neuronal sympathoblasts and exhibiting remarkable clinical and genetic heterogeneity. Patients older than 18 months have a poor prognosis with tumors presenting with highly recurrent segmental copy number alterations and MYCN amplification in half of these high-risk cases. The mechanism by which MYCN contributes to the development of neuroblastoma is unresolved and direct targeting of this key oncogene is not currently possible. Experimental Procedures: Our discovery efforts focused on identifying cooperating interactors and vulnerabilities in the MYCN regulatory network. MYCN-driven NBs can be modeled in mice with morphologic and genomic features that recapitulate human MYCN amplified NBs. Thus, this model serves as a valid tool for cross-species genomic analysis. Using this model, we performed a time-resolved analysis of the dynamic transcriptional changes of protein coding genes during murine TH-MYCN driven neuroblastoma development, focusing on timepoints representing tumor initiation and early tumor growth. We triangulated expression changes of key genes with publicly available exome-wide CRISPR-cas9 knockout analyses on a panel of human neuroblastoma cell lines and patient survival data. This unique data resource uncovered the relevance of MEIS2 as putative early cooperating initiating factor for neuroblastoma. Analysis of the genome-wide binding profile of MEIS2 in MYCN-amplified NB cell lines showed a striking overlap with enhancer-driven gene expression in regions of open chromatin, providing evidence that MEIS2 is a novel member of the adrenergic neuroblastoma core-regulatory circuitry. CRISPR-Cas9 mediated deletion of MEIS2 in animal models suppresses establishment of neuroblastoma tumors, indicating its putative requirement for tumor initiation. MEIS2, as a member of the CRC binds to several master regulators of gene expression, including the FOXM1 locus. Summary and conclusion: In conclusion, we present an in-depth characterization of the dynamic transcriptome profiles of TH-MYCN driven murine premalignant and established tumors and integrate with both primary human neuroblastoma tumor expression data, epigenetic and functional genomics data to identify and validate candidate cooperating dependencies suitable for targeting as a precision medicine approach in neuroblastoma. Citation Format: Kaat Durinck, Mark Zimmerman, Nina Weichert-Leahey, Jolien Dewyn, Wouter Van Loocke, Carolina Nunes, Anneleen Beckers, Bieke Decaesteker, Pieter-Jan Volders, Christophe Van Neste, Belamy Cheung, Daniel Carter, Thomas A. Look, Glenn Marshall, Katleen De Preter, Adam Durbin, Franki Speleman. Time-resolved transcriptome analysis of murine TH-MYCN driven neuroblastoma identifies MEIS2 as early initiating factor and novel core gene regulatory circuitry constituent [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2481.

Abstract LB108: Validation of computationally predicted synthetic lethal interactions for KRAS

Abstract Despite dedicated research in precision medicine, identification of therapeutic strategies for oncogenic drivers remains a major challenge as many mutations are difficult to target directly. Synthetic lethality-based target prediction becomes a powerful approach for drug discovery. We previously developed a computational platform, Mining Synthetic Lethals (MiSL), that can output a list of possible synthetic lethal partners of a specific gene when fed with primary human tumor genomic and transcriptomic data. The principal logic of the MiSL algorithm is based on the Boolean implication that the SL partners of a mutation will never be deleted in the presence of the mutation in the primary tumors. High frequency of KRAS mutation in lung and colon cancers makes it an important therapeutic target. While drugs directly targeting KRAS are yet to be fully developed, we applied the principle of synthetic lethality using the MiSL platform to predict potential KRAS specific therapeutic targets. We identified 556 putative SL partners of KRAS mutations combining lung and colon cancers. To efficiently test all predictions, we used customized shRNA and CRISPR lentiviral libraries to validate all the predicted SL interactions for KRAS in colon cancer cell lines HT29 (KRAS WT) and SW620 (KRAS G12V mutant). Eight shRNAs and four sgRNAs were designed for each gene in the shRNA and CRISPR libraries. The shRNAs and sgRNAs expressed at baseline and at the end of each screen were analyzed by barcode sequencing. Synthetic lethal interactions with KRAS were determined by identifying individual sh/sgRNAs whose mean values at the end relative to the start of the screen were significantly reduced in KRAS Mut cell lines compared to WT. We also compared the results of the in vitro to an in vivo screen of the focused libraries in human tumors xenografted into mice using the same cell lines. We compared the top 50 SL candidates from each screen as ranked by MAGeCK (Model-based Genome-wide Analysis of CRISPR/Cas9 KO) and found that only 19 were shared between the in vitro and in vivo screens. Further validation of several top SL candidates coming from both in vitro and in vivo screens was performed by testing individual shRNAs/sgRNAs for growth inhibition in KRAS Mut (SW620) vs. KRAS WT (HT29) cell lines in vitro and in vivo. Individual in vitro CRISPR/Cas9 knock out of two of the candidate SL partners reduced cell viability by 50% and 57% respectively in KRAS mutant cells compared to KRAS WT cells. Furthermore, induced individual KD or KO of four SL partners resulted in inhibition of tumor growth by 84%, 68%, 95% and 60%, respectively. In summary, MiSL has shown to be a powerful platform for discovering novel targets for specified cancer types. This study has identified and validated several genes as promising targets of KRAS dependent colon cancer for further therapeutic investigation. Citation Format: He Gong, Claire Repellin, Puja Patel, Merrill Knapp, Mingze He, Jun Li, Lisa Wu, Xiaohe Liu, Subarna Sinha, Lidia Sambucetti. Validation of computationally predicted synthetic lethal interactions for KRAS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB108.