Abstract
Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients’ sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION. Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.
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Data availability
The entire collection of the processed datasets used in this manuscript, including preclinical models of cancer cell lines and PDCs, can be accessed in the Zenodo repository (https://zenodo.org/record/7860559)58. We collected the bulk-expression and drug response profiles generated in cancer cell lines curated from the DepMap portal (https://depmap.org/portal/download) (version 20Q1). The sc-expression of 205 cancer cell lines was generated in a previous study34 and was downloaded from https://singlecell.broadinstitute.org/single_cell/study/SCP542/pan-cancer-cell-line-heterogeneity#study-download. The sc-expression profiles of patients with multiple myeloma were downloaded from the original study (their supplementary Table 2; https://static-content.springer.com/esm/art%3A10.1038%2Fs41591-021-01232-w/MediaObjects/41591_2021_1232_MOESM3_ESM.xlsx); data from patients with breast cancer were downloaded from GEO (GSE158724) and data from patients with NSCLC were provided by the original study authors41.
Code availability
The scripts to replicate each step of results and plots can be accessed in a GitHub repository (https://github.com/ruppinlab/SCPO_submission). We used open-source R versions 4.0 through 4.2 to generate the figures. Wherever required, commercially available Adobe Illustrator was used to create the figure grids.
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Acknowledgements
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.
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Contributions
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.
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Competing interests
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.
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