Predicting drug response from single-cell expression profiles of tumours

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.

[1]  G. Gambardella,et al.  Single cell lineage tracing reveals subclonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer , 2023, bioRxiv.

[2]  G. Gambardella,et al.  Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data , 2022, bioRxiv.

[3]  James M. McFarland,et al.  Computational estimation of quality and clinical relevance of cancer cell lines , 2022, Molecular systems biology.

[4]  D. di Bernardo,et al.  A single-cell analysis of breast cancer cell lines to study tumour heterogeneity and drug response , 2022, Nature Communications.

[5]  John G Doench,et al.  Systematic identification of biomarker-driven drug combinations to overcome resistance , 2022, Nature Chemical Biology.

[6]  S. Theocharis,et al.  Histone Deacetylase Inhibitors: A Promising Therapeutic Alternative for Endometrial Carcinoma , 2021, Disease markers.

[7]  P. Musich,et al.  Receptor tyrosine kinases as druggable targets in glioblastoma: Do signaling pathways matter? , 2021, Neuro-oncology advances.

[8]  Song He,et al.  Machine learning methods, databases and tools for drug combination prediction , 2021, Briefings Bioinform..

[9]  F. Al-Shahrour,et al.  Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data , 2021, Genome Medicine.

[10]  A. Nica,et al.  PI3K/AKT/mTOR signalling pathway involvement in renal cell carcinoma pathogenesis (Review) , 2021, Experimental and therapeutic medicine.

[11]  Z. Dong,et al.  Targeting AURKA in Cancer: molecular mechanisms and opportunities for Cancer therapy , 2021, Molecular cancer.

[12]  Qiming Wang,et al.  MEK inhibitors for the treatment of non-small cell lung cancer , 2021, Journal of Hematology & Oncology.

[13]  Rafael Peres da Silva,et al.  Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures , 2020, Genome Medicine.

[14]  James M. McFarland,et al.  Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity , 2020, Nature Genetics.

[15]  J. Pérez-Fidalgo,et al.  Aurora kinases in ovarian cancer , 2020, ESMO Open.

[16]  A. Goldenberg,et al.  Machine learning approaches to drug response prediction: challenges and recent progress , 2020, npj Precision Oncology.

[17]  A. Dricu,et al.  Receptor tyrosine kinase targeting in glioblastoma: performance, limitations and future approaches , 2020, Contemporary oncology.

[18]  Jing-Yuan Fang,et al.  Comprehensive review of targeted therapy for colorectal cancer , 2020, Signal Transduction and Targeted Therapy.

[19]  Wenge Zhu,et al.  Combining DNMT and HDAC6 inhibitors increases anti-tumor immune signaling and decreases tumor burden in ovarian cancer , 2020, Scientific Reports.

[20]  R. Fisher,et al.  Head and Neck Squamous Cell Carcinoma , 2020, Definitions.

[21]  Joshua A. Bittker,et al.  Discovering the anticancer potential of non-oncology drugs by systematic viability profiling , 2020, Nature Cancer.

[22]  B. Rueda,et al.  Understanding and Targeting Apoptotic Pathways in Ovarian Cancer , 2019, Cancers.

[23]  Gennady Korotkevich,et al.  Fast gene set enrichment analysis , 2019, bioRxiv.

[24]  Zeping Han,et al.  Drugs and Clinical Approaches Targeting the Antiapoptotic Protein: A Review , 2019, BioMed research international.

[25]  Diego di Bernardo,et al.  A Tool for Visualization and Analysis of Single-Cell RNA-Seq Data Based on Text Mining , 2019, Front. Genet..

[26]  H. Samadi kafil,et al.  DNA damage response and repair in ovarian cancer: Potential targets for therapeutic strategies. , 2019, DNA repair.

[27]  O. Kallioniemi,et al.  Clonal heterogeneity influences drug responsiveness in renal cancer assessed by ex vivo drug testing of multiple patient‐derived cancer cells , 2018, International journal of cancer.

[28]  Sourav Bandyopadhyay,et al.  Aurora kinase A drives the evolution of resistance to third generation EGFR inhibitors in lung cancer , 2018, Nature Medicine.

[29]  Haifa Shen,et al.  Sensitizing non-small cell lung cancer to BCL-xL-targeted apoptosis , 2018, Cell Death & Disease.

[30]  Joshua M. Dempster,et al.  Genetic and transcriptional evolution alters cancer cell line drug response , 2018, Nature.

[31]  Hayley E. Francies,et al.  Organoid cultures recapitulate esophageal adenocarcinoma heterogeneity providing a model for clonality studies and precision therapeutics , 2018, Nature Communications.

[32]  Demetrios A. Spandidos,et al.  Cutaneous melanoma: From pathogenesis to therapy (Review) , 2018, International journal of oncology.

[33]  G. Mills,et al.  Systems analysis of apoptotic priming in ovarian cancer identifies vulnerabilities and predictors of drug response , 2017, Nature Communications.

[34]  Sydney M. Shaffer,et al.  Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance , 2017, Nature.

[35]  Emanuel J. V. Gonçalves,et al.  A Landscape of Pharmacogenomic Interactions in Cancer , 2016, Cell.

[36]  Dennis Wang,et al.  Combenefit: an interactive platform for the analysis and visualization of drug combinations , 2016, Bioinform..

[37]  Charles H. Yoon,et al.  Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq , 2016, Science.

[38]  Arindam Ghosh,et al.  Genetics of renal cancer: focus on MTOR , 2016, Aging.

[39]  Joshua A. Bittker,et al.  Correlating chemical sensitivity and basal gene expression reveals mechanism of action , 2015, Nature chemical biology.

[40]  Marc Bissonnette,et al.  Colon cancer and the epidermal growth factor receptor: Current treatment paradigms, the importance of diet, and the role of chemoprevention. , 2015, World journal of clinical oncology.

[41]  Steven J. M. Jones,et al.  Comprehensive genomic characterization of head and neck squamous cell carcinomas , 2015, Nature.

[42]  D. Aoki,et al.  Aurora kinase A has a significant role as a therapeutic target and clinical biomarker in endometrial cancer , 2015, International journal of oncology.

[43]  C. Prabhakar.  Epidermal growth factor receptor in non-small cell lung cancer. , 2015, Translational lung cancer research.

[44]  A. Avan,et al.  EGFR as a potential target for the treatment of pancreatic cancer: dilemma and controversies. , 2014, Current drug targets.

[45]  Laura M. Heiser,et al.  A community effort to assess and improve drug sensitivity prediction algorithms , 2014, Nature Biotechnology.

[46]  A. Godwin,et al.  Aurora kinase A mediates epithelial ovarian cancer cell migration and adhesion , 2014, Oncogene.

[47]  Laura M. Heiser,et al.  Modeling precision treatment of breast cancer , 2013, Genome Biology.

[48]  G. Netto,et al.  High epidermal growth factor receptor immunohistochemical expression in urothelial carcinoma of the bladder is not associated with EGFR mutations in exons 19 and 21: a study using formalin-fixed, paraffin-embedded archival tissues. , 2012, Human pathology.

[49]  A. Sivachenko,et al.  A Landscape of Driver Mutations in Melanoma , 2012, Cell.

[50]  Joshua M. Stuart,et al.  Subtype and pathway specific responses to anticancer compounds in breast cancer , 2011, Proceedings of the National Academy of Sciences.

[51]  A. McKenna,et al.  The Mutational Landscape of Head and Neck Squamous Cell Carcinoma , 2011, Science.

[52]  Ben S. Wittner,et al.  A Chromatin-Mediated Reversible Drug-Tolerant State in Cancer Cell Subpopulations , 2010, Cell.

[53]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[54]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[55]  S. Fosså,et al.  Expression of the epidermal growth factor receptor family in normal and malignant urothelium , 2005, BJU international.

[56]  H. Koeffler,et al.  Histone Deacetylase Inhibitors Have a Profound Antigrowth Activity in Endometrial Cancer Cells , 2004, Clinical Cancer Research.

[57]  H. Iwase,et al.  [Breast cancer]. , 2006, Nihon rinsho. Japanese journal of clinical medicine.