Original source (on modern site)
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Sinha, S. Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors (0.1) [Data set]. Zenodo https://doi.org/10.5281/zenodo.7860559 (2022). Download references This research was supported in part by the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute (NCI), NIH grants R01CA231300 (T.G.B.), R01CA204302 (T.G.B.), R01CA211052 (T.G.B.), R01CA169338 (T.G.B.) and U54CA224081 (T.G.B.). This work used the computational resources of the NIH High-Performance Computing Biowulf cluster (http://hpc.nih.gov). We acknowledge and thank the NCI for providing financial and infrastructural support. Thanks to K. Wang, S. Rajagopal and Z. Ronai for their valuable feedback and discussion. Special thanks to J. I. Griffiths and A. H. Bild for clarifying the patient response data in reference 40 and for their helpful feedback. Author notes Sanju Sinha Present address: NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA, USA These authors contributed equally: Sanju Sinha, Rahulsimham Vegesna. Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA Sanju Sinha, Rahulsimham Vegesna, Sumit Mukherjee, Ashwin V. Kammula, Saugato Rahman Dhruba, Nishanth Ulhas Nair, Peng Jiang, Alejandro A. Schäffer & Eytan Ruppin University of Maryland, College Park, MD, USA Ashwin V. Kammula Department of Medicine, University of California, San Francisco, San Francisco, CA, USA Wei Wu, D. Lucas Kerr, Collin M. Blakely & Trever G. Bivona Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA Matthew G. Jones & Nir Yosef Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA Matthew G. Jones & Nir Yosef Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA Matthew G. Jones Whitehead Institute, Cambridge, MA, USA Matthew G. Jones Rancho BioSciences, San Diego, CA, USA Oleg V. Stroganov & Ivan Grishagin Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA Ivan Grishagin & Craig J. Thomas Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA Kenneth D. Aldape Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA Collin M. Blakely & Trever G. Bivona Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA Craig J. Thomas Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Cyril H. Benes Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA Trever G. Bivona Chan Zuckerberg Biohub Investigator, San Francisco, CA, USA Trever G. Bivona S.S., R.V., A.A.S. and E.R. conceived the framework of the analysis. E.R. and A.A.S. mentored and guided the study. S.S. and R.V. led the analysis of the development of the models and most of the testing. A.A.S., A.V.K., R.V. and S.S. performed the analysis related to clinical trials curation and data analysis. A.A.S., S.M., S.R.D, N.U.N, M.G.J. and N.Y worked on the revisions for model validation and further testing and development of the software. W.W., D.L.K, C.M.B. and T.G.B. provided the lung cancer data and aided in its analysis. O.V.S., I.G., K.D.A., C.M.B. and C.J.T. contributed to finding relevant dosages to translate in vitro to in vivo results. S.S., R.V., A.A.S., E.R., P.J., C.H.B. and T.G.B. wrote the initial draft of the manuscript; S.S., S.M., A.A.S. and E.R. carried out the revisions. S.S., R.V., A.A.S. and E.R. are inventors on a provisional patent application covering the methods in PERCEPTION. E.R. is a co-founder of Medaware, Metabomed and Pangea Biomed (divested from the latter). E.R. serves as a non-paid scientific consultant to Pangea Biomed, a company developing a precision oncology SL-based multi-omics approach, with emphasis on bulk tumor transcriptomics. T.G.B. is an advisor to Array/Pfizer, Revolution Medicines, Springworks, Jazz Pharmaceuticals, Relay Therapeutics, Rain Therapeutics and Engine Biosciences, and receives research funding from Novartis, Strategia, Kinnate and Revolution Medicines. The work in the laboratory of C.H.B. was funded in part by Amgen and Novartis. The other authors declare no competing interests. Nature Cancer thanks Federica Eduati and Tuomas Tammela for their contribution to the peer review of this work. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. A) Cancer type distribution of the 318 cell lines used during the bulk expression training of PERCEPTION (step 1). B) Similarly, showing the cancer type distribution of the 169 cell lines used during the sc-expression training of PERCEPTION (step 2) C) The performance of PERCEPTION in predicting response in unseen cell lines when built via (1) pan-cancer models: all available cell lines (N = 169) are used for training the model, (2) Cancer-type specific: trained only on cell lines of the same cancer type as those used in the testing (N = 16 melanoma cell lines, 37 lung cancer cell lines and 15 breast cancer cell lines, as we used the PERCEPTION to predict the patient's treatment response in three clinical trial cohorts from skin, lung, and breast cancer, we compared the pan-cancer model with these three individual cancer-type models). No statistical test was performed to compare groups. Error bars indicate the standard error of the mean (SEM), reflecting data variability. D) Major classes of mechanism of action of the 133 FDA-approved drugs that were studied here. No statistical test was performed to compare between groups. E) Top pathways enriched in frequently appearing features/genes in the PERCEPTION models. This is computed using a GSEA rank test across all hallmark pathways. To assess the statistical significance of these scores, a permutation test was performed. A) The top-most panel visualizes the PERCEPTION predicted killing by nutlin-3, a canonical MDM2 antagonist and the expression of MDM2 for every single cell (each point) in the top and bottom tSNE plot, respectively. The intensity of the color denotes the extent of predicted killing in the right panel and measured MDM2 expression in the left panel. 3566 single-cells from nine p53 WT lung cancer cell lines are depicted. The tSNE clustering is performed using the expression of all the genes. B) A similar display visualizes PERCEPTION's predicted killing and the EGFR pathway signature expression across 12,482 individual lung cancer cells. C) The four panels visualize predicted killing by four EGFR inhibitors, afatinib, icotinib, lapatinib, osimertinib, in every single cell (each point) via a tSNE plot, respectively. Here, the color of each point denotes the extent of predicted killing. In this figure, we provide data on 12,482 individual lung cancer cells. The tSNE clustering is performed using the expression profiles of all the genes. D) We present here the correlation between the predicted killing effect of nutlin-3 from the PERCEPTION prediction of each cell (x-axis) and the MDM2 gene expression in that single cell, where they are found to be strongly correlated. "MDM2 Activity" on the y-axis denotes MDM2 gene expression. A) A) Correlation Analysis: Examines the relationships across three platforms - "GDSC vs. PRISM", "PRISM vs. PERCEPTION" (cross-validation), and "GDSC vs. PERCEPTION". Drug response predictions at single-cell resolution were aggregated to represent overall cell line responses. B) These cross-platform correlations are provided at a drug level. Significance of correlations assessed using Pearson's r test. C) Monotherapy Predictions by PERCEPTION: Showcases the predicted viability of monotherapies based on cell line-specific sc-expression, comparing resistant (N = 72) and sensitive (N = 84) lines using boxplots. Significance determined by one-tailed Wilcoxon rank-sum test. D) Sensitivity-Specificity Analysis: The receiver operator curve illustrates the balance between sensitivity and specificity in distinguishing between sensitive and resistant cell lines. Area under the curve (AUC) values are noted, with the dashed line representing random-model performance. E) & F) Drug Combination Response Predictions: Depict PERCEPTION's predictions for drug combination responses in resistant (N = 28) vs. sensitive (N = 24) cell lines. G) Single-cell vs. Pseudo-bulk Level Analysis in PRISM Screens: Extends the analysis in panel A to single-cell and pseudo-bulk levels, highlighting the improved performance in pseudo-bulk data. The comparison includes predicted AUC values at both levels and experimental AUC values in PRISM for dabrafenib, AZD-7762, and trametinib, covering both testing (N = 80) and training cell lines (N = 318). H-K) Patient-Derived H&N Primary Cell Analysis: H) Prediction of Monotherapy Response: PERCEPTION's predicted viability in resistant (n = 16) vs. sensitive (n = 16) lines. I) ROC Curve Analysis: Illustrates model's prediction capability (sensitivity and specificity) for resistant vs. sensitive lines. AUC values are presented. J) & K) Combination Treatment Response: Similar analysis for combination treatments, comparing resistant (12) to sensitive (12) lines. All box plots show median, 25th/75th percentiles, and range. A) Concordance between Lung Cancer and PRISM Screens: Illustrates the correlation (Rho on x-axis) and significance (y-axis) between our lung cancer screen and PRISM. Focuses on cell lines showing significantly positive correlation, as indicated by Pearson's r test p-value. B) Predicted vs. Observed Viability Comparison: Analyzes the correlation between predicted and observed cell viability (N = 94 viability observations each, both centered and scaled). Pearson correlation and significance are noted. A best fit line with a 95% confidence interval is shown. C) Viability Prediction in Top vs. Bottom 50% Cell Lines: Compares predicted viability in resistant (N = 11, bottom 50%) versus sensitive (N = 10, top 50%) cell lines for each drug. Uses one-tailed Wilcoxon rank-sum test for statistical significance, presented for each drug. D) Combination Response Prediction in 21 Lung Cancer Cell Lines: Similar to panel B, this compares predicted versus observed combination viability (N = 49 viability observations each), with Pearson correlation and significance provided. A best fit line with a 95% confidence interval is included. E) Combination Viability Prediction in Top vs. Bottom 50% Cell Lines: Analyzes predicted combination viability (centered and scaled) for resistant (N = 11) and sensitive (N = 10) cell lines (based on observed viability) across 7 drug pairs. Uses one-tailed Wilcoxon rank-sum test for significance, presented for each combination. F) Consolidated Analysis of Monotherapies and Combinations: Integrates data from distinct drugs in panel E for combined analysis of monotherapies (N = 188) and drug combinations (N = 98). All box plots show median, 25th/75th percentiles, and range. Each scatter plot compares the experimentally observed cell viability (x-axis; at median IC50 concentration) to the predicted viability (y-axis; rescaled AUC value) for the four drugs docetaxel, epothilone-b, gefitinib, and vorinostat (top four) and the pairwise combinations among {docetaxel, epothilone-b, gefitinib} (bottom three). Each dot represents the response of patient-derived cell lines (N = 5, color coded) for the drugs they were screened with. The Spearman rank correlation (cor) is provided at the bottom of each plot. These plots are provided for the following treatment concentrations - A) median IC50 B) one-third of median IC50. The error bands in all panels of this figure show 95% confidence interval of the fit. Each scatter plot compares experimental cell viability (N = 20, x-axis; scaled per drug treatment) with predicted viability (N = 20, y-axis; rescaled AUC value). Points represent patient-derived cell line responses, color-coded by line and shape-coded by drug. Pearson correlation (R) is noted in each plot's lower right corner. All panels feature error bands showing the 95% confidence interval of the fit. A) Monotherapy Response at Median IC50: Relation between monotherapy response and experimental response (N = 20 each). B) Combination Therapy Response at Median IC50: Similar analysis for combination therapy (N = 15 each). C) Monotherapy Response at 3x Median IC50: Examines monotherapy response at higher concentration (N = 20 each). D) Combination Therapy at 3x Median IC50: Analyzes combination therapy response at increased concentration (N = 15 each). E-G) Monotherapy and Combination Response Prediction in Lung Cancer Cell Lines: E) UMAP Clustering: Represents 53,514 cells from 199 cell lines (~300 cells/line) using sc-expression, identifying 29 clusters with cells from four unique sub-clones. F-G) Predicted Viability Based on Most-Resistant Clone: Viability predictions for 21 lung cancer cell lines (N = 11 resistant & 10 sensitive cell ines), considering the most resistant clone. Statistical significance assessed with two-sided Wilcoxon rank-sum test. H-I) Monotherapy and Combination Response Prediction in Patient-Derived HNSC Primary Cells (N = 5): H) Monotherapy Response Based on Most-Resistant Clone: Presents PERCEPTION predicted viability and resistance vs. sensitivity stratification (N = 2 resistant & 3 sensitive). Includes drugs docetaxel, epothilone-b, gefitinib, and vorinostat. I) Combination Response: Similar analysis for combination treatments. Both panels include a left-side plot for predicted viability in resistant (N = 2) vs. sensitive (N = 3) lines and a right-side ROC plot showing prediction power (sensitivity and specificity). AUC values are provided, with the dashed line indicating random-model performance. Statistical analysis performed with two-sided Wilcoxon rank-sum test. All box plots depict median, 25th/75th percentiles, and range. A) tSNE Transcriptional Clustering: Displays 36 transcriptional tumor clusters identified in the trial, integrating cells from 34 patients at three time points. Clusters, color-coded and defined in the legend, were derived using Seurat package. B) Malignant Sub-Clone Abundance: Shows the distribution of malignant sub-clones (y-axis) in breast cancer samples (x-axis), based on sc-expression. Different sub-clones are color-coded in the legend. Sample labels on the x-axis indicate patient id and time point of collection ("_S" - day 0, "_M" - day 14, "_E" - day 180). C) Pre-Treatment Clone-Level Response in Arms B and C: Predicted ribociclib viability (y-axis) versus various clones in pre-treatment samples (x-axis). Response status is displayed at the top of each column, with sample names below. Dot sizes represent the proportion of each cluster/clone, with a color scale indicating predicted viability (dark blue for low, yellow for high). D-E) Stratification Power of PERCEPTION vs Published Models: D) Bulk Expression-Based Models: Compares PERCEPTION with models trained only on bulk expression (N = 7 responders and 7 non-responders). E) Models Not Tuned on sc-Expression: PERCEPTION compared against models without sc-expression tuning (N = 7 responders and 7 non-responders). Both panels include deterministic model generation (seed=1) for training and test sets. Left-side plots present PERCEPTION predicted viability in responders vs. non-responders. Right-side ROC plots depict prediction power (sensitivity and specificity), with AUC values near the lower right corner. The dashed diagonal line indicates performance of a random model. Statistical significance assessed using two-sided Wilcoxon rank-sum test. F) Stratification Using Average sc-Viability: Stratifies responders (N = 7) vs. non-responders (N = 7) in combination therapy arms using average sc-viability in the FELINE trial. Statistical significance evaluated by two-sided Wilcoxon rank-sum test. Box plots show median, 25th/75th percentiles, and range. (A) A UMAP of 3671 malignant cells derived from 25 patients with 26,485 genes are clustered using Seurat considering the first 10 axes with the most variance. Each clone (a transcriptional cluster) output is annotated using a color where the legend is provided on the right. (B) The proportions of these clones (y-axis) are provided in each patient (x-axis) faceted by the time point at which these biopsies are collected. (C-F) Predicted viability of the four tyrosine kinase inhibitors: erlotinib, dabrafenib, osimertinib, and trametinib, in respective order, is provided at a clonal level for each patient where response status is provided at the bottom of each facet. In A-D), The extent of resistance to a treatment from the baseline (x-axis) is correlated with the treatment elapsed time (Number of days from the start of the treatment before the biopsy was taken) (y-axis). (A) The points and line colors denote the treatment administered to the patients listed by the right legend. B) Color denotes prior treatment. C) Color denotes the patient's ID. D) Color denotes whether the disease is metastatic or primary at the time of biopsy. E) Extent of Resistance was calculated using bulk-expression of the tumor, where the increase with "Treatment Elapsed time" is positive, however, insignificant, and weaker than when the patient response is taken as the most-resistant clone available response. The error bands in all panels of this figure show 95% confidence interval of the fit. A) Median Disjoint Killing Score (DKS) in Myeloma: For 94 drug pairs with positive DKS, the median DKS (y-axis) is plotted against each pair (x-axis). Color intensity denotes the proportion of patients (N = 12) with DKS > 0, with the top pairs labeled. Legend for color intensity is at the top. B) DKS for Triplets: Similar analysis for drug triplets. C) Clone-Level Disjoint Killing for Top Pairs: Viability profiles of clones for top pairs from C are shown for each patient (facet), with color intensity indicating post-treatment viability of each clone (x-axis) for a given drug (y-axis). Legend on the right. D) Clone-Level Disjoint Killing for Triplets: Analogous to C, but for drug triplets (N = 86, Triplets with DKS > 0). E-L) Analysis in Lung Cancer: E) Correlation in Clinical Trials: Examines the correlation between response difference of combination vs monotherapy (x-axis) and observed survival difference in combination vs single-treatment arms. Dot size represents patient numbers, with a best-fit line shown. Legend for dot sizes and error bands showing 95% confidence interval are at the top. Weighted Pearson's r test p-value denotes correlation significance. F-H) Repeated for progression-free survival, overall survival, and erlotinib combinations. I) DKS for Lung Cancer Drug Combinations: Median DKS (y-axis) for 31 positive pairs plotted against each pair (x-axis). Color intensity shows proportion of patients with positive DKS, top pairs labeled, legend at the top. J-K) Disjoint Killing by Drug Class and Mechanism: Compares DKS (log10 value on y-axis) by general drug classes (N = 3 chemo+chemo, 7 chemo+targeted, 5 targeted+ targeted) (J) and mechanisms of action (N = 3 each MOA) (K). Evaluated by two-sided Wilcoxon rank-sum test. Box plots show median, 25th/75th percentiles, and range. L) Clone-Level Response in Lung Cancer: Shows post-treatment viability for top effective combinations, one facet per patient. Color intensity indicates clone viability (x-axis) for each drug (y-axis), for the top three patients ranked by highest DKS score per drug. Sinha, S., Vegesna, R., Mukherjee, S. et al. PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors.
Nat Cancer (2024). https://doi.org/10.1038/s43018-024-00756-7 Download citation Received: 20 June 2023 Accepted: 08 March 2024 Published: 18 April 2024 DOI: https://doi.org/10.1038/s43018-024-00756-7Data availability
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Extended Data Fig. 1 Overview of PERCEPTION model's training data and features.
Extended Data Fig. 2 Visualization of PERCEPTION's ability to predict viability at four recent EGFR inhibitors vs the EGFR pathway activity at single-cell resolution.
Extended Data Fig. 3 Evaluating PERCEPTION's Efficacy in Unseen Lung Cancer Cell Line Screens.
Extended Data Fig. 4 Quality Control and Predictive Analyses in Lung Cancer Cell Line Screens.
Extended Data Fig. 5 The predicted vs. experimental correlations obtained for individual treatments.
Extended Data Fig. 6 Correlation of Predicted and Observed Viability in Monotherapies and Combination Treatments in Cell Lines.
Extended Data Fig. 7 Comparing PERCEPTION with Existing Bulk Response Models in a Breast Cancer Clinical Trial.
Extended Data Fig. 8 Pre-processing and predicting clone level response in lung cancer patient cohort.
Extended Data Fig. 9 Correlation between the elapsed treatment time and estimated resistance holds true across different conditions.
Extended Data Fig. 10 Identifying Optimal Drug Combinations for Multiple Myeloma and Lung Cancer Patients.
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