Ashir is an ambitious and emerging Machine Learning Scientist with a keen interest in applying cutting-edge technology to the field of biotechnology. His background in machine learning and artificial intelligence has enabled him to work on various projects, including setting up technological stacks for startups and exploring novel drug targets using CRISPR and other data types. In the early stages of his career, Ashir contributed to assisting startups in developing their tech infrastructure, helping them create and launch minimum viable products that secured essential funding. Following this, he shifted his focus to the biotechnology sector by working on multiple projects that identified new drug targets, some of which are now in preclinical trials. Ashir is pursuing his Ph.D. in Biological and Medical Informatics (Bioinformatics) at the University of California, San Francisco (UCSF). His research centers on using machine learning techniques and wet lab tools to study cancer biology. By fusing advanced computational methods with traditional research approaches, Ashir aims to unravel the intricacies of cancer and contribute to the development of innovative treatments. As an enthusiastic and driven professional, Ashir is dedicated to leveraging his unique combination of technical and analytical skills to make a lasting impact in the field of biotechnology and improve the lives of patients around the world.
For more information, have a look at my curriculum vitae .
Systematic identification of signaling pathways required for the fitness of cancer cells will facilitate the development of new cancer therapies. We used gene essentiality measurements in 1,086 cancer cell lines to identify selective coessentiality modules and found that a ubiquitin ligase complex composed of UBA6, BIRC6, KCMF1, and UBR4 is required for the survival of a subset of epithelial tumors that exhibit a high degree of aneuploidy. Suppressing BIRC6 in cell lines that are dependent on this complex led to a substantial reduction in cell fitness in vitro and potent tumor regression in vivo. Mechanistically, BIRC6 suppression resulted in selective activation of the integrated stress response (ISR) by stabilization of the heme-regulated inhibitor, a direct ubiquitination target of the UBA6/BIRC6/KCMF1/UBR4 complex. These observations uncover a novel ubiquitination cascade that regulates ISR and highlight the potential of ISR activation as a new therapeutic strategy.
Paper linkIn high-throughput functional genomic screens, each gene product is commonly assumed to exhibit a singular biological function within a defined protein complex or pathway. In practice, a single gene perturbation may induce multiple cascading functional outcomes, a genetic principle known as pleiotropy. Here, we model pleiotropy in fitness screen collections by representing each gene perturbation as the sum of multiple perturbations of biological functions, each harboring independent fitness effects inferred empirically from the data. Our approach (Webster) recovered pleiotropic functions for DNA damage proteins from genotoxic fitness screens, untangled distinct signaling pathways upstream of shared effector proteins from cancer cell fitness screens, and predicted the stoichiometry of an unknown protein complex subunit from fitness data alone. Modeling compound sensitivity profiles in terms of genetic functions recovered compound mechanisms of action. Our approach establishes a sparse approximation mechanism for unraveling complex genetic architectures underlying high-dimensional gene perturbation readouts.
Paper linkPrognostically relevant RNA expression states exist in pancreatic ductal adenocarcinoma (PDAC), but our understanding of their drivers, stability, and relationship to therapeutic response is limited. To examine these attributes systematically, we profiled metastatic biopsies and matched organoid models at single-cell resolution. In vivo, we identify a new intermediate PDAC transcriptional cell state and uncover distinct site- and state-specific tumor microenvironments (TMEs). Benchmarking models against this reference map, we reveal strong culture-specific biases in cancer cell transcriptional state representation driven by altered TME signals. We restore expression state heterogeneity by adding back in vivo-relevant factors and show plasticity in culture models. Further, we prove that non-genetic modulation of cell state can strongly influence drug responses, uncovering state-specific vulnerabilities. This work provides a broadly applicable framework for aligning cell states across in vivo and ex vivo settings, identifying drivers of transcriptional plasticity and manipulating cell state to target associated vulnerabilities.
Paper linkThe RecQ DNA helicase WRN is a synthetic lethal target for cancer cells with microsatellite instability (MSI), a form of genetic hypermutability that arises from impaired mismatch repair. Depletion of WRN induces widespread DNA double-strand breaks in MSI cells, leading to cell cycle arrest and/or apoptosis. However, the mechanism by which WRN protects MSI-associated cancers from double-strand breaks remains unclear. Here we show that TA-dinucleotide repeats are highly unstable in MSI cells and undergo large-scale expansions, distinct from previously described insertion or deletion mutations of a few nucleotides5. Expanded TA repeats form non-B DNA secondary structures that stall replication forks, activate the ATR checkpoint kinase, and require unwinding by the WRN helicase. In the absence of WRN, the expanded TA-dinucleotide repeats are susceptible to cleavage by the MUS81 nuclease, leading to massive chromosome shattering. These findings identify a distinct biomarker that underlies the synthetic lethal dependence on WRN, and support the development of therapeutic agents that target WRN for MSI-associated cancers.
Paper linkPlease feel free to contact me if you have any questions or would like to collaborate on a project!