Integrating epidemiology and genomics data to estimate the prevalence of acquired cysteine drug targets in the U.S. cancer patient population
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Personalized prediction of anticancer potential of non-oncology drugs through learning from genome derived molecular pathways
Advances in cancer genomics have significantly expanded our understanding of cancer biology. However, the high cost of drug development limits our ability to translate this knowledge into precise treatments. Approved non-oncology drugs, comprising a large repository of chemical entities, offer a promising avenue for repurposing in cancer therapy. Herein we present CHANCE, a supervised machine learning model designed to predict the anticancer activities of non-oncology drugs for specific patients by simultaneously considering personalized coding and non-coding mutations. Utilizing protein–protein interaction networks, CHANCE harmonizes multilevel mutation annotations and integrates pharmacological information across different drugs into a single model. We systematically benchmarked the performance of CHANCE and show its predictions are better than previous model and highly interpretable. Applying CHANCE to approximately 5000 cancer samples indicated that >30% might respond to at least one non-oncology drug, with 11% non-oncology drugs predicted to have anticancer activities. Moreover, CHANCE predictions suggested an association between SMAD7 mutations and aspirin treatment response. Experimental validation using tumor cells derived from seven patients with pancreatic or esophageal cancer confirmed the potential anticancer activity of at least one non-oncology drug for five of these patients. To summarize, CHANCE offers a personalized and interpretable approach, serving as a valuable tool for mining non-oncology drugs in the precision oncology era.
Modeling critical dosing strategies for stromal-induced resistance to cancer therapy
Complex interactions between stromal cells, tumor cells and therapies can influence environmental factors that in turn impact anticancer treatment efficacy. Disentangling these phenomena is critical for understanding treatment response and designing effective dosing strategies. We propose a mathematical model for a common tumor-stromal interaction motif where stromal cells secrete factors that promote drug resistance. We demonstrate that the presence of this interaction modulates the therapeutic dose window of efficacy and can lead to nonmonotonic treatment response. We consider combination strategies that target stromal cells and their secretome, and identify strategies that constrain drug concentrations within the efficacious window for long-term response. We explore an experimental dataset from colorectal cancer cells treated with anti-EGFR targeting therapy, cetuximab, where cancer-associated fibroblasts increase epidermal growth factor secretion under treatment. We apply our general approach to identify a critical drug concentration threshold and study effective dosing regimens for single-drug and combination therapies.
Systematic identification of cancer pathways and potential drugs for intervention through multi-omics analysis
The pathogenesis of cancer is complicated, and different types of cancer often exhibit different gene mutations resulting in different omics profiles. The purpose of this study was to systematically identify cancer-specific biological pathways and potential cancer-targeting drugs. We collectively analyzed the transcriptomics and proteomics data from 16 common types of human cancer to study the mechanism of carcinogenesis and seek potential treatment. Statistical approaches were applied to identify significant molecular targets and pathways related to each cancer type. Potential anti-cancer drugs were subsequently retrieved that can target these pathways. The number of significant pathways linked to each cancer type ranged from four (stomach cancer) to 112 (acute myeloid leukemia), and the number of therapeutic drugs that can target these cancer related pathways, ranged from one (ovarian cancer) to 97 (acute myeloid leukemia and non-small-cell lung carcinoma). As a validation of our method, some of these drugs are FDA approved therapies for their corresponding cancer type. Our findings provide a rich source of testable hypotheses that can be applied to deconvolute the complex underlying mechanisms of human cancer and used to prioritize and repurpose drugs as anti-cancer therapies.
Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence
Much research in the behavioral sciences aims to characterize the “typical” person. A statistically significant group-averaged effect size is often interpreted as evidence that the typical person shows an effect, but that is only true under certain distributional assumptions for which explicit evidence is rarely presented. Mean effect size varies with both within-participant effect size and population prevalence (proportion of population showing effect). Few studies consider how prevalence affects mean effect size estimates and existing estimators of prevalence are, conversely, confounded by uncertainty about effect size. We introduce a widely applicable Bayesian method, the p-curve mixture model, that jointly estimates prevalence and effect size by probabilistically clustering participant-level data based on their likelihood under a null distribution. Our approach, for which we provide a software tool, outperforms existing prevalence estimation methods when effect size is uncertain and is sensitive to differences in prevalence or effect size across groups or conditions.
Enhancer reprogramming: critical roles in cancer and promising therapeutic strategies
Transcriptional dysregulation is a hallmark of cancer initiation and progression, driven by genetic and epigenetic alterations. Enhancer reprogramming has emerged as a pivotal driver of carcinogenesis, with cancer cells often relying on aberrant transcriptional programs. The advent of high-throughput sequencing technologies has provided critical insights into enhancer reprogramming events and their role in malignancy. While targeting enhancers presents a promising therapeutic strategy, significant challenges remain. These include the off-target effects of enhancer-targeting technologies, the complexity and redundancy of enhancer networks, and the dynamic nature of enhancer reprogramming, which may contribute to therapeutic resistance. This review comprehensively encapsulates the structural attributes of enhancers, delineates the mechanisms underlying their dysregulation in malignant transformation, and evaluates the therapeutic opportunities and limitations associated with targeting enhancers in cancer.
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