Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

Suphavilai, Chayaporn; Chia, Shumei; Sharma, Ankur; Tu, Lorna; Silva, Rafael Peres da; Mongia, Aanchal; DasGupta, Ramanuj; Nagarajan, Niranjan

doi:10.1186/s13073-021-01000-y

Cited by 32 publications

(50 citation statements)

References 82 publications

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“…To this end, we split the dataset based on the cell lines so that no cell line is common among the training, validation, and test sets. We compared our framework, named Precily, with two well-cited methods — Cancer Drug Response prediction using a Recommender System for single-cell RNA-seq (CaDRReS-Sc) by Suphavilai, Chayaporn, et al 21 and another method by Sakellaropoulos, Theodore, et al 16 . Both the methods utilize gene expression profiles for drug response prediction.…”

Section: Resultsmentioning

confidence: 99%

“…Pathway projections of scRNA-seq/bulk RNA-seq profiles reasonably alleviate this problem. Notable in this regard is the work by Suphavilai, Chayaporn, et al, 21 that describes a drug response prediction approach in head and neck cancer, leveraging scRNA-seq profiles. The authors, however, did not explore the utility of drug descriptors to generalize the prediction model.…”

Section: Introductionmentioning

confidence: 99%

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Gene expression based inference of cancer drug sensitivity

et al. 2022

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Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of high-throughput screening datasets has paved the way for machine learning based personalized therapy recommendations using the molecular profiles of cancer specimens. In this study, we introduce Precily, a predictive modeling approach to infer treatment response in cancers using gene expression data. In this context, we demonstrate the benefits of considering pathway activity estimates in tandem with drug descriptors as features. We apply Precily on single-cell and bulk RNA sequencing data associated with hundreds of cancer cell lines. We then assess the predictability of treatment outcomes using our in-house prostate cancer cell line and xenografts datasets exposed to differential treatment conditions. Further, we demonstrate the applicability of our approach on patient drug response data from The Cancer Genome Atlas and an independent clinical study describing the treatment journey of three melanoma patients. Our findings highlight the importance of chemo-transcriptomics approaches in cancer treatment selection.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gene expression based inference of cancer drug sensitivity

et al. 2022

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“…The single-cell expression of the ve head and neck squamous cell cancer (HNSC) patient-derived cell lines and their treatment response for eight drugs and combination therapy at two different dosages were obtained from Suphavilai et al 2020. For these drugs, PERCEPTION was unable to build drug response models with Spearman correlation between their predicted vs. experimental viability greater than 0.3 using PRISM screens.…”

Section: Perception's Prediction In Patient-derived Head and Neck Can...mentioning

confidence: 99%

“…However, building such predictors of drug response at a single cell (SC) resolution is currently challenging due to the paucity of large-scale preclinical or clinical training datasets. Previous efforts, including a recent computational method termed Beyondcell that identi es tumor cell subpopulations with distinct drug responses from single-cell RNA-seq data for proposing cancer-speci c treatments, have focused on preclinical models but lack validation in patients at the clinical level (Kim et al 2016, Suphavilai et al 2020, Fustero-Torre et al 2021, Ianevski et al 2021.…”

Section: Introductionmentioning

confidence: 99%

Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors.

Sinha

Vegesna

Dhruba

et al. 2022

JCO

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e20540 Background: Tailoring the best treatments to cancer patients is an important open challenge. Here, we build a precision oncology data science and software framework for PERsonalized single-Cell Expression-based Planning for Treatments In Oncology (PERCEPTION). Methods: Our approach capitalizes on recently published matched bulk and single-cell transcriptome profiles of large-scale cell-line drug screens to build treatment response models from patients’ single-cell (SC) tumor transcriptomics. Our approach is a two-step process: first, the prediction models are trained on large-scale bulk-expression profiles of cancer cell lines and then, in a second step, the models’ performance is further optimized by training on SC-expression profiles of cancer cell lines. Results: First, we show that PERCEPTION successfully predicts the response to monotherapy and combination treatments in screens performed in cancer and patient-tumor-derived primary cells based on SC-expression profiles. Second, it successfully stratifies responders to combination therapy based on the patients’ tumor’s SC-expression in two very recent multiple myeloma and breast cancer clinical trials. Thirdly, it captures the development of clinical resistance to five standard tyrosine kinase inhibitors using tumor SC-expression profiles obtained during treatment in a lung cancer patients’ cohort. Notably, PERCEPTION outperforms state-of-the-art bulk expression-based predictors in all three clinical cohorts. Conclusions: In sum, this study provides a first-of-its-kind conceptual and computational method that is predictive of response to therapy in patients, based on the clonal SC gene expression of their tumors.

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“…Tumor tissues consist of a mix of malignant and non-malignant cells; the non-malignant cells referred to as the tumor microenvironment (TME) include a diverse set of immune cells, fibroblasts, endothelial cells etc. Although much research has focused on the TME heterogeneity, recently, waxing interest has been allocated to the diversity of cancer cells, leading to a number of findings of so-called "signatures" describing unique cell states [3][4][5][6][7][8][9][10][11][12][13] . Cell states are pivotal to the study of cancer as they can influence the maintenance and progression of tumors 2 .…”

Section: Introductionmentioning

confidence: 99%

CanSig: discovery of shared transcriptional states across cancer patients from single-cell RNA sequencing data

Yates

Barkmann

Czyz

et al. 2022

Preprint

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Human tumors are highly heterogeneous in their cell composition; specifically, they exhibit heterogeneity in transcriptional states of malignant cells, as has been recently discovered through single-cell RNA sequencing (scRNA-seq). Distinct states of malignant cells have been linked to variability in tumorigenic properties and resistance to anti-cancer treatment. Despite the fact that scRNA-seq data contain all necessary information to uncover shared transcriptional states of malignant cells in tumors, jointly analyzing cells from multiple cancer patients comes with its set of challenges including batch correction and accounting for patient-specific genetic background driving differences between gene expression vectors. We propose CanSig, an easy-to-use approach designed to discover known and de novo shared signatures in cancer single cells. CanSig preprocesses, integrates and analyzes scRNA-seq data to provide new signatures of shared transcriptional states and links these states to known pathways. We show that CanSig successfully rediscovers ground truth pathways determining shared transcriptional states in two simulated and three experimental datasets; the latter spanning 135 patients and 72,000 cells. We then illustrate CanSig's investigative potential by discovering novel signatures in esophageal squamous cell carcinoma possibly linked to targeted patient treatment; we also point out a de novo signature in breast cancer predictive of patients' survival. In the cancer types studied, we juxtapose copy number variation with discovered shared transcriptional states and uncover a genetic component predisposing cancer cells to activation of specific transcriptional programs. In sum, CanSig, specifically developed to analyze shared transcriptional heterogeneity of malignant cells of different genetic backgrounds, can greatly facilitate the exploratory analysis of scRNA-seq cancer data and efficiently identify novel transcriptional signatures linked to known biological pathways.

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Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

Cited by 32 publications

References 82 publications

Gene expression based inference of cancer drug sensitivity

Gene expression based inference of cancer drug sensitivity

Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors.

CanSig: discovery of shared transcriptional states across cancer patients from single-cell RNA sequencing data

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