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Babak, T.

Publications and source records attributed to Babak, T..

3 recordsLinked to original sources

Challenges and opportunities for drug repurposing in cancers based on synthetic lethality induced by tumor suppressor gene mutations

Although two-thirds of cancers arise from loss-of-function mutations in tumor suppressor genes, there are few approved targeted therapies linked to these alterations. Synthetic lethality offers a promising strategy to treat such cancers by targeting vulnerabilities unique to cancer cells with these mutations. To identify clinically relevant synthetic lethal interactions, we analyzed genome-wide CRISPR/Cas9 knock-out (KO) viability screens from the Cancer Dependency Map and evaluated their clinical relevance in patient tumors through mutual exclusivity, a pattern indicative of synthetic lethality. Indeed, we found significant enrichment of mutual exclusivity for interactions involving cancer driver genes compared to non-driver mutations. To identify therapeutic opportunities, we integrated drug sensitivity data to identify inhibitors that mimic the effects of CRISPR-mediated KO. This approach revealed potential drug repurposing opportunities, including BRD2 inhibitors for bladder cancers with ARID1A mutations and SIN3A-mutated cell lines showing sensitivity to nicotinamide phosphoribosyltransferase (NAMPT) inhibitors. However, we discovered that pharmacological inhibitors often fail to phenocopy KO of matched drug targets, with only a small fraction of drugs inducing similar effects. This discrepancy reveals fundamental differences between pharmacological and genetic perturbations, emphasizing the need for approaches that directly assess the interplay of loss-of-function mutations and drug activity in cancer models. Author SummarySynthetic lethality is an emerging approach for targeting a biological dependency in cancer cells that does not harm normal cells. This strategy is particularly valuable for targeting loss-of-function mutations in tumor suppressor genes, which are more challenging to directly target. In an effort to accelerate treatments for cancer patients, we aimed to map out these dependencies and overlap them with responses to available drugs. We discovered different outcomes when a protein is targeted by a drug versus when that same target is disrupted genetically. Thus, if a drug is to be effectively repurposed as synthetic lethal agent, feasibility studies must capture drug biology, ideally by test the drug empirically in relevant cancer models. A second notable discovery is that in vitro synthetic lethal interactions involving cancer driver genes are significantly more likely to exhibit consistent patterns, such as mutual exclusivity in human tumor samples. This is important since selection of relevant cell lines is often critical in drug development to maximize potential for translation to clinical responses.

cancer biology↗

Pharmacogenomic discovery of genetically targeted cancer therapies optimized against clinical outcomes

Despite the clinical success of dozens of genetically targeted cancer therapies, the vast majority of patients with tumors caused by loss-of-function (LoF) mutations do not have access to these treatments. This is primarily due to the challenge of developing a drug that treats a disease caused by the absence of a protein target. The success of PARP inhibitors has solidified synthetic lethality (SL) as a means to overcome this obstacle. Recent mapping of SL networks using pooled CRISPR-Cas9 screens is a promising approach for expanding this concept to treating cancers driven by additional LoF drivers. In practice, however, translating signals from cell lines, where these screens are typically conducted, to patient outcomes remains a challenge. We developed a pharmacogemic (PGx) approach called "Clinically Optimized Driver Associated PGx" (CODA-PGx) that accurately predicts genetically targeted therapies with clinical-stage efficacy in specific LoF driver contexts. Using approved targeted therapies and cancer drugs with available real-world evidence and molecular data from hundreds of patients, we discovered and optimized the key screening principles predictive of efficacy and overall patient survival. In addition to establishing basic technical conventions, such as drug concentration and screening kinetics, we found that replicating the driver perturbation in the right context, as well as selecting patients where those drivers are genuine founder mutations, were key to accurate translation. We used CODA-PGX to screen a diverse collection of clinical stage drugs and report dozens of novel LoF genetically targeted opportunities; many validated in xenografts and by real-world evidence. Notable examples include treating STAG2-mutant tumors with Carboplatin, SMARCB1-mutant tumors with Oxaliplatin, and TP53BP1-mutant tumors with Etoposide or Bleomycin. One Sentence SummaryWe identified principles of pharmacogenomic screening that predict clinical efficacy in cancer patients with specific driver mutations.

cancer biology↗

A catalog of cis-regulatory mutations in 12 major cancer types

Despite the recent availability of complete genome sequences of tumors from thousands of patients, isolating disease-causing (driver) non-coding mutations from the plethora of somatic variants is notoriously challenging, and only a handful of validated examples exist. By integrating whole-genome sequencing, gene expression, chromatin accessibility, and genetic data from TCGA, we identified 301 non-coding somatic mutations that affect gene expression in cis. These mutations cluster into 36 hotspot regions with diverse molecular mechanisms of gene expression regulation. We further show that these mutations have hallmark features of noncoding drivers; namely, that they confer a positive selection on growth, functionally disrupt transcription factor binding sites, and contribute to disease progression reflected in decreased overall patient survival.

genomics↗