Systematic identification of biomarker-driven drug combinations to overcome resistance

Rees, Matthew G.; Brenan, Lisa; Carmo, Mariana Do; Duggan, Patrick; Bajrami, Besnik; Arciprete, Michael; Boghossian, Andrew S.; Ferrara, Steven; Lewis, Timothy A.; Rosenberg, Danny; Sangpo, Tenzin; Roth, Jennifer A.; Kaushik, Virendar K.; Piccioni, Federica; Doench, John G.; Root, David E.; Johannessen, Cory M.

doi:10.1038/s41589-022-00996-7

Cited by 14 publications

(16 citation statements)

References 55 publications

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“…A recent study proved drug sensitivity could be inferred through expression profiles. 38 We asked whether one nonlinear regression model named random forest could discover drugs, with similar functional mechanisms. Interestingly, PLK1 inhibitors and EGFR inhibitors show a high degree of similarity (about 20%-50%).…”

Section: Resultsmentioning

confidence: 99%

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RNA sequencing identifies lung cancer lineage and facilitates drug repositioning

Zeng

Zhang

et al. 2023

Preprint

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Breakthrough therapies recently improve survival in lung adenocarcinoma (LUAD), yet we still lack a paradigm to support prospective confirmation. To classify three clusters including bronchioid, neuroendocrine, and squamoid, the non-negative matrix factorization (NMF) algorithm was first performed at The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), Gene Expression Omnibus (GEO) and preclinical models. In terms of prognostic value, the squamoid cluster is of poor prognostic factor. The neuroendocrine cluster is characterized by STK11 mutations and 14q13.3 amplifications. From an immunological perspective, the bronchioid cluster is considered an immune activation because of the highest immune-related genetic perturbation. Further analysis is the estimation of the relative cell abundance of the tumor microenvironment (TME), specific cell types can be reflected among three clusters. Meanwhile, the higher portion of immune cell infiltration belonged to bronchioid and squamous, not neuroendocrine cluster. Taken together, the neuroendocrine cluster show resistance to PD-L1 blockade. While pemetrexed or platinum-based therapies are suitable for bronchioid and squamoid clusters, respectively. Our emphasis was on phenotype-based action to explore compounds. Large-scale drug sensitivity databases including ConnectivityMap (CMAP), Cancer Cell Line Encyclopedia (CCLE), and Genomics of Drug Sensitivity in Cancer (GDSC) were analyzed. MEK inhibitors exhibited resistance in the bronchioid, while sensitive to the squamous cluster. Dinaciclib and alvocidib showed similar activity and sensitivity in the neuroendocrine cluster. A lineage factor named KLF5 recognized by two networks could be suppressed by verteporfin. This work adds to the knowledge of the lung cancer lineage and facilitates drug repositioning.

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Section: Resultsmentioning

confidence: 99%

“…In addition, we use the manuscript’s orthogonal discovery method, called a random forest, which can effectively capture potential gene profiles regarding drug sensitivity. 38…”

Section: Methodsmentioning

confidence: 99%

RNA sequencing identifies lung cancer lineage and facilitates drug repositioning

Zeng

Zhang

et al. 2023

Preprint

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“…It requires a pre-defined collection of Genomic Profiles of Drug Sensitivity (GPDS); these are lists of genes ranked according to their contribution in correctly predicting the effect of a small molecule. To build GPDS, we integrated several publicly available RNA-seq datasets and cell-line viability screens, including the following datasets: GDSC (8), CTRP2 (13, 24) and PRISM (25) (Figure 1A).…”

Section: Resultsmentioning

confidence: 99%

Predicting drug response from single-cell expression profiles of tumours

Pellecchia

Viscido

Gambardella

2023

Preprint

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Drug response prediction at the single cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens and functional enrichment analysis to predict single cell drug sensitivity from transcriptomic data. We extensively tested DREEP on several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations. DREEP provides an in-silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerate drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP.

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“…This capability is especially noteworthy, given that most patients receive multiple drugs at once. There are many more examples of computational prediction models, all using various models and unique approaches [ 61 , 62 , 63 , 64 , 65 ]. With any given method of drug response prediction, it will be key to adjust the regimen as treatment proceeds, based on what a tumor’s molecular profile reflects as it continues to evolve and change.…”

Section: Predicting Drug Sensitivity To Improve Treatment Planningmentioning

confidence: 99%

Evolution-Informed Strategies for Combating Drug Resistance in Cancer

Lin‐Rahardja

Weaver

Scarborough

et al. 2023

IJMS

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The ever-changing nature of cancer poses the most difficult challenge oncologists face today. Cancer’s remarkable adaptability has inspired many to work toward understanding the evolutionary dynamics that underlie this disease in hopes of learning new ways to fight it. Eco-evolutionary dynamics of a tumor are not accounted for in most standard treatment regimens, but exploiting them would help us combat treatment-resistant effectively. Here, we outline several notable efforts to exploit these dynamics and circumvent drug resistance in cancer.

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Systematic identification of biomarker-driven drug combinations to overcome resistance

Cited by 14 publications

References 55 publications

RNA sequencing identifies lung cancer lineage and facilitates drug repositioning

RNA sequencing identifies lung cancer lineage and facilitates drug repositioning

Predicting drug response from single-cell expression profiles of tumours

Evolution-Informed Strategies for Combating Drug Resistance in Cancer

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