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

Suphavilai, Chayaporn; Chia, Shumei; Sharma, Ankur; Tu, Lei; Silva, Da; Mongia, Aanchal; DasGupta, Ramanuj; Nagarajan, Niranjan

doi:10.1101/2020.11.23.389676

Cited by 3 publications

(6 citation statements)

References 58 publications

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“…Moving from cancer cell-line cells to patient tumor cells, we next tested the ability of PERCEPTION to predict response in patient-derived primary cells (PDC). We used SC-expression of head and neck cancer primary cells derived from five different patients treated with eight different drugs at two concentrations ( Table S6 ), including both monotherapy and combination therapies (Suphavilai et al 2020). We were able to build predictive PERCEPTION response models for 4 out of the 8 drugs tested (docetaxel, epothilone-b, gefitinib, and vorinostat; Pearson R threshold > 0.25, Methods ) and focused our analysis on these drugs.…”

Section: Resultsmentioning

confidence: 99%

“…The single-cell expression of the five 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: Methodsmentioning

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 - Beyondcell , that identifies tumor cell subpopulations with distinct drug responses from single-cell RNA-seq data for proposing cancer-specific treatments, have focused on preclinical models and lacks validation in patients at the clinical level (Kim et al 2016, Suphavilai et al 2020, Fustero-Torre et al 2021). Additional efforts to identify biomarkers of response and resistance at the patient level using single-cell expression are rapidly emerging for both targeted- and immuno-therapies, with remarkable results (Cohen et al 2021, Ledergor et al 2018, Sade-Feldman et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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

Sinha

Vegesna

Dhruba

et al. 2022

Preprint

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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). 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 patient single-cell (SC) tumor transcriptomics. 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 patient tumor 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 patient cohort. Notably, PERCEPTION outperforms state-of-the-art bulk expression-based predictors in all three clinical cohorts. 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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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Sinha

Vegesna

Dhruba

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…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%

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors

Sinha,

Vegesna,

Mukherjee

et al. 2024

Nat Cancer

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Tailoring the best treatments for individual cancer patients is an important open challenge. Here, we build a precision oncology computational pipeline for PERsonalized single-Cell Expression-based Planning for Treatments In ONcology (PERCEPTION). Our approach capitalizes on recently published matched bulk and single-cell (SC) transcriptome pro les of large-scale cell-line drug screens to build treatment response models from patients' SC tumor transcriptomics. We start by showing that PERCEPTION successfully predicts the response to monotherapy and combination treatments in screens performed in cancer and patient-tumor-derived primary cells based on their SC-expression pro les. Our key result is that PERCEPTION successfully strati es responders to combination therapy based on the patients' tumor's SC-expression, as tested in two recently published clinical trials, including multiple myeloma and breast cancer. Thirdly, studying the emergence of resistance via a recent SC non-small cell lung cancer (NSCLC) patients' cohort, we show that PERCEPTION successfully captures and quanti es the development of patients' resistance during treatment with tyrosine kinase inhibitors. Notably, PERCEPTION predictions markedly outperform that of bulk expression-based predictors in all these cohorts. In sum, this study provides a rst-of-its-kind conceptual and computational method demonstrating the feasibility of predicting patients' response from SC gene expression of their tumors.

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“…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, 19 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 generalise the prediction model.…”

Section: Introductionmentioning

confidence: 99%

Gene expression based inference of drug resistance in cancer

Chawla¹,

Rockstroh²,

Lehman³

et al. 2021

Preprint

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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 large-scale drug screening datasets has provided an opportunity for predicting appropriate patient-tailored therapies by employing machine learning approaches. In this study, we report a predictive modeling approach to infer treatment response in cancers using gene expression data. In particular, we demonstrate the benefits of considering integrated chemogenomics approach, utilizing the molecular drug descriptors and pathway activity information as opposed to gene expression levels. We performed extensive validation of our approach on tissue-derived single-cell and bulk expression data. Further, we constructed several prostate cancer cell lines and xenografts, exposed to differential treatment conditions to assess the predictability of the outcomes. Our approach was further assessed on pan-cancer RNA-sequencing data from The Cancer Genome Atlas (TCGA) archives, as well as an independent clinical trial study describing the treatment journey of three melanoma patients. To summarise, we benchmarked the proposed approach on cancer RNA-seq data, obtained from cell lines, xenografts, as well as humans. We concluded that pathway-activity patterns in cancer cells are reasonably indicative of drug resistance, and therefore can be leveraged in personalized treatment recommendations.

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

Cited by 3 publications

References 58 publications

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

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

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors

Gene expression based inference of drug resistance in cancer

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