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

Sanju Sinha; Rahulsimham Vegesna; Sumit Mukherjee; Ashwin V Kammula; Saugato Rahman Dhruba; Wei Wu; D Lucas Kerr; Nishanth Ulhas Nair; Matthew G Jones; Nir Yosef; Oleg V Stroganov; Ivan Grishagin; Kenneth D Aldape; Collin M Blakely; Peng Jiang; Craig J Thomas; Cyril H Benes; Trever G Bivona; Alejandro A Schäffer; Eytan Ruppin

doi:10.1038/s43018-024-00756-7

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

Nat Cancer. 2024 Apr 18. doi: 10.1038/s43018-024-00756-7. Online ahead of print.

Authors

Sanju Sinha^#^{1

2}, Rahulsimham Vegesna^#³, Sumit Mukherjee³, Ashwin V Kammula^{3

4}, Saugato Rahman Dhruba³, Wei Wu⁵, D Lucas Kerr⁵, Nishanth Ulhas Nair³, Matthew G Jones^{6

7

8

9}, Nir Yosef^{6

7}, Oleg V Stroganov¹⁰, Ivan Grishagin^{10

11}, Kenneth D Aldape¹², Collin M Blakely^{5

13}, Peng Jiang³, Craig J Thomas^{11

14}, Cyril H Benes¹⁵, Trever G Bivona^{5

13

16

17}, Alejandro A Schäffer³, Eytan Ruppin¹⁸

Affiliations

¹ Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA. ssinha@sbpdiscovery.org.
² NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA, USA. ssinha@sbpdiscovery.org.
³ Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
⁴ University of Maryland, College Park, MD, USA.
⁵ Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
⁶ Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
⁷ Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA.
⁸ Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA.
⁹ Whitehead Institute, Cambridge, MA, USA.
¹⁰ Rancho BioSciences, San Diego, CA, USA.
¹¹ Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA.
¹² Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
¹³ Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
¹⁴ Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
¹⁵ Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
¹⁶ Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
¹⁷ Chan Zuckerberg Biohub Investigator, San Francisco, CA, USA.
¹⁸ Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA. eytan.ruppin@nih.gov.

^# Contributed equally.

PMID: 38637658
DOI: 10.1038/s43018-024-00756-7

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.