Biology subjects

Inam, H.

Publications and source records attributed to Inam, H..

4 recordsLinked to original sources

bioRxiv2025

Characterizing Variants of Uncertain Drug Resistance (VUDRs) Using Quantitative Measurements at Clinical Exposures

Somatic missense mutations in oncogenes drive resistance to anticancer drugs, yet many variants remain clinically uncharacterized. Analogous to Variants of Uncertain Significance (VUS) in genetic disorders, these Variants of Uncertain Drug Resistance (VUDRs) in cancer lack the functional annotation needed to guide clinical management. Here, we applied a standards-driven deep mutational scanning platform that connects quantitative concentration-response measurements to human pharmacokinetics across 4922 missense variants (>96% coverage) at approved (400 mg QD) and investigational (400 mg and 500 mg BID) human doses of imatinib. Resistance phenotypes for 18 standards spanning 2 orders of magnitude of drug sensitivity showed strong quantitative performance and clinical concordance. Analyzing 257 clinical VUDRs, >10% conferred modest levels of resistance that might be overcome by dose escalation with generic imatinib instead of a branded alternative, potentially alleviating financial toxicity. Integration with global germline data also revealed ancestry-specific variants with the potential to create private VUDRs. These preclinical data establish the first generalizable framework for high throughput resistance variant classification directly tied to known human doses.

bioengineering↗

bioRxiv2024

A side-by-side comparison of variant function measurements using deep mutational scanning and base editing

Variant annotation is a crucial objective in mammalian functional genomics. Deep Mutational Scanning (DMS) is a well-established method for annotating human gene variants, but CRISPR base editing (BE) is emerging as an alternative. However, questions remain about how well high-throughput base editing measurements can annotate variant function and the extent of downstream experimental validation required. This study presents the first direct comparison of DMS and BE in the same lab and cell line. Results indicate that focusing on the most likely edits and highest efficiency sgRNAs enhances the agreement between a "gold standard" DMS dataset and a BE screen. A simple filter for sgRNAs making single edits in their window could sufficiently annotate a large proportion of variants directly from sgRNA sequencing of large pools. When multi-edit guides are unavoidable, directly measuring the variants created in the pool, rather than sgRNA abundance, can recover high-quality variant annotation measurements in multiplexed pools. Taken together, our data show a surprising degree of correlation between base editor data and gold standard deep mutational scanning.

genomics↗

bioRxiv2019

Pairwise tests for conditional selection use evolutionary logic to predict intrinsic drug resistance in ALK alterations

Genomic data can facilitate personalized treatment decisions by enabling therapeutic hypotheses in individual patients. Conditional selection, which includes mutual exclusivity, is a signal that has been empirically useful for identifying mutations that may be sensitive to single agent targeted therapies. However, a low mutation frequency can underpower this signal for rare variants and prevent robust conclusions from genomic data. We develop a resampling based method for the direct pairwise comparison of conditional selection between sets of gene pairs. This effectively creates positive control guideposts of mutual exclusivity in known driver genes that normalizes differences in mutation abundance. We applied this method to a transcript variant of anaplastic lymphoma kinase (ALK) in melanoma, termed ALKATI, which has been the subject of a recent controversy in the literature. We reproduced some of the original cell transformation experiments, performed rescue experiments, and analyzed drug response data to revisit the original ALKATI findings. We found that ALKATI is not as mutually exclusive with BRAF or NRAS as BRAF and NRAS genes are with each other. We performed in vitro transformation assays and rescue assays that suggested that alternative transcript initiation in ALK is not likely to be sufficient for cellular transformation or growth and it does not predict single agent therapeutic dependency. Our work strongly disfavors the role of ALKATI as a targetable oncogenic driver that might be sensitive to single agent ALK treatment. The progress of other experimental agents in late-stage melanoma and our experimental and computational re-analysis led us to conclude that further single agent testing of ALK inhibitors in patients with ALKATI should be limited to cases where no other treatment hypotheses can be identified.

bioengineering↗

bioRxiv2019

Exploiting the 'survival of the likeliest' to enable evolution-guided drug design

Theoretical treatments of evolutionary dynamics tend to model the probability that a single "resistant" species will arise in a population. However, experimental studies have identified a diversity of mutations that can lead to genetic resistance. By quantitatively predicting mutations that occur across an entire drug target during treatment, we identify and bridge a fundamental gap in drug resistance theory: that nucleotide/codon substitution biases can dictate which resistant variants arise in the clinic. We find that the likeliest mutation can beat the most resistant mutation. This creates a new paradigm in drug resistance that we term "survival of the likeliest". We use epidemiological evidence in leukemia, isogenic experiments, stochastic dynamics, and large-scale simulations to support this theory. In addition, this work has strong implications for drug design because not all resistance liabilities are created equal. In pathogenic populations that exhibit survival of the likeliest, exploiting the least likely evolutionary path can minimize resistance across a population during widespread drug use, even when a vulnerability-free molecule or combination cannot be made. Data and Code Availabilityhttps://github.com/pritchardlabatpsu/SurvivalOfTheLikeliest/

evolutionary biology↗