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Genetic Landscape of Cancer Drug

Base editing screens define the genetic landscape of cancer drug resistance mechanisms


Drug resistance is a principal limitation to the long-term efficacy of cancer therapies. Cancer genome sequencing can retrospectively delineate the genetic basis of drug resistance, but this requires large numbers of post-treatment samples to nominate causal variants. Here we prospectively identify genetic mechanisms of resistance to ten oncology drugs from CRISPR base editing mutagenesis screens in four cancer cell lines using a guide RNA library predicted to install 32,476 variants in 11 cancer genes. We identify four functional classes of protein variants modulating drug sensitivity and use single-cell transcriptomics to reveal how these variants operate through distinct mechanisms, including eliciting a drug-addicted cell state. We identify variants that can be targeted with alternative inhibitors to overcome resistance and functionally validate an epidermal growth factor receptor (EGFR) variant that sensitizes lung cancer cells to EGFR inhibitors. Our variant-to-function map has implications for patient stratification, therapy combinations and drug scheduling in cancer treatment.

Main

Resistance to molecularly targeted anti-cancer treatments remains a major clinical challenge1. Drug resistance is frequently caused by DNA single nucleotide variants (SNVs) in the cancer genome2, leading to point mutations in the drug target or proteins within the same signaling pathway3. The study of drug resistance can inform drug mechanism of action, the design of second-generation inhibitors targeting drug-resistant proteins, the development of combination therapies and patient stratification for second-line therapies. Current approaches often depend on sequencing tumor biopsies from patients that relapse on treatment. These are challenging samples to acquire, meaning it can take years to accrue enough to infer variant function. These analyses are generally restricted to frequently observed variants and must be individually experimentally validated to establish a causal link to drug resistance. This is a slow process that does not allow for the direct comparison of different variant effects. These challenges limit the interpretation of cancer biopsy and circulating tumor DNA sequencing data. Rapid, prospective and systematic functional annotation of variants would accelerate the discovery of drug resistance mechanisms.

CRISPR-based gene editing approaches such as base editing can be used to directly interpret the function of variants of unknown significance (VUS)4,5,6,7,8,9,10,11,12 and study genetic mechanisms of resistance to cytokines and inhibitors8,13,14. Cytidine and adenine base editors use a Cas9 nickase fused to a deaminase, facilitating the programmed installation of C>T and A>G SNVs in the genome at high efficiency in physiologically relevant cell types9,10,11,12,15,16,17,18. Here we use base editing at scale to investigate genetic mechanisms of acquired resistance to molecularly targeted cancer therapies, identifying VUS conferring drug resistance and drug sensitization in cancer cells. We classify cancer variants modulating drug sensitivity into four functional classes, thus providing a systematic framework for interpreting drug resistance mechanisms.

Results

Base editing screens map functional domains in cancer genes


We investigated drug resistance to ten molecularly targeted cancer drugs that are currently approved by the United States Food and Drug Administration (FDA) or are under clinical investigation (Fig. 1a). We selected four cancer cell lines that are sensitive to these agents19 and harbor diverse oncogenic drivers20: H23 (lung; KRAS-G12C), PC9 (lung; EGFR amplification and exon19 deletion), HT-29 (colon; BRAF V600E) and MHH-ES-1 (Ewing sarcoma; EWS-FLI1 fusion). We mutagenized 11 cancer genes that encode common drug targets or genes within the same signaling pathway for interrogation with a guide RNA (gRNA) library (n = 22,816), tiling these genes and their 5′ and 3′ untranslated regions (UTRs). As controls, we included nontargeting gRNAs (NT gRNAs) (n = 57), intergenic-targeting gRNAs (n = 168) and gRNAs predicted to introduce splice variants21 in nonessential (n = 87) and essential (n = 316) genes22,23 (Supplementary Table 1). To maximize the saturation of targeted mutagenesis, the gRNA library was introduced into cancer cell lines expressing doxycycline-inducible cytidine base editor (CBE) or adenine base editor (ABE)8 with relaxed PAM requirements (Cas9–NGN)24. We analyzed the potential functional effects of thousands of gene variants on drug resistance in parallel by performing base editing screens with a proliferation read-out in the presence of targeted anti-cancer drugs from 46 independent pooled genetic screens (Fig. 1a and Supplementary Table 2). Base editing screen replicates were highly correlated (Extended Data Fig. 1), and control gRNAs targeting essential genes were depleted, indicating efficient base editing (Fig. 1b and Extended Data Fig. 2a).
genetic landscape, cancer, drug response, genetic mutations, drug resistance, cancer therapies, tumor growth, personalized treatment, drug metabolism, next-generation sequencing, molecular profiling, actionable mutations, precision oncology, treatment efficacy, adverse effects, genetic variations, targeted therapies, therapeutic resistance, cancer genomics, molecular oncology,

#CancerGenetics, #DrugResponse, #GeneticMutations, #CancerTherapy, #DrugResistance, #TumorGrowth, #PersonalizedTreatment, #DrugMetabolism, #NextGenSequencing, #MolecularProfiling, #ActionableMutations, #PrecisionOncology, #TreatmentEfficacy, #GeneticVariations, #TargetedTherapies, #TherapeuticResistance, #CancerGenomics, #MolecularOncology, #CancerResearch, #OncologyBreakthroughs

International Conference on Genetics and Genomics of Diseases 




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