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

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^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

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.

Original language	English
Journal	Nature Cancer
DOIs	https://doi.org/10.1038/s43018-024-00756-7
Publication status	Accepted/In press - 2024
Externally published	Yes

Bibliographical note

Publisher Copyright:
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024.

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1038/s43018-024-00756-7

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Sinha, S., Vegesna, R., Mukherjee, S., Kammula, A. V., Dhruba, S. R., Wu, W., Kerr, D. L., Nair, N. U., Jones, M. G., Yosef, N., Stroganov, O. V., Grishagin, I., Aldape, K. D., Blakely, C. M., Jiang, P., Thomas, C. J., Benes, C. H., Bivona, T. G., Schäffer, A. A., & Ruppin, E. (Accepted/In press). PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors. Nature Cancer. https://doi.org/10.1038/s43018-024-00756-7

@article{6cf0b50b4850482586d82f0fc94c3aaf,

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

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{\textquoteright} 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.",

author = "Sanju Sinha and Rahulsimham Vegesna and Sumit Mukherjee and Kammula, {Ashwin V.} and Dhruba, {Saugato Rahman} and Wei Wu and Kerr, {D. Lucas} and Nair, {Nishanth Ulhas} and Jones, {Matthew G.} and Nir Yosef and Stroganov, {Oleg V.} and Ivan Grishagin and Aldape, {Kenneth D.} and Blakely, {Collin M.} and Peng Jiang and Thomas, {Craig J.} and Benes, {Cyril H.} and Bivona, {Trever G.} and Sch{\"a}ffer, {Alejandro A.} and Eytan Ruppin",

note = "Publisher Copyright: {\textcopyright} This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024.",

year = "2024",

doi = "10.1038/s43018-024-00756-7",

language = "English",

journal = "Nature Cancer",

issn = "2662-1347",

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Sinha, S, Vegesna, R, Mukherjee, S, Kammula, AV, Dhruba, SR, Wu, W, Kerr, DL, Nair, NU, Jones, MG, Yosef, N, Stroganov, OV, Grishagin, I, Aldape, KD, Blakely, CM, Jiang, P, Thomas, CJ, Benes, CH, Bivona, TG, Schäffer, AA & Ruppin, E 2024, 'PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors', Nature Cancer. https://doi.org/10.1038/s43018-024-00756-7

TY - JOUR

T1 - PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors

AU - Sinha, Sanju

AU - Vegesna, Rahulsimham

AU - Mukherjee, Sumit

AU - Kammula, Ashwin V.

AU - Dhruba, Saugato Rahman

AU - Wu, Wei

AU - Kerr, D. Lucas

AU - Nair, Nishanth Ulhas

AU - Jones, Matthew G.

AU - Yosef, Nir

AU - Stroganov, Oleg V.

AU - Grishagin, Ivan

AU - Aldape, Kenneth D.

AU - Blakely, Collin M.

AU - Jiang, Peng

AU - Thomas, Craig J.

AU - Benes, Cyril H.

AU - Bivona, Trever G.

AU - Schäffer, Alejandro A.

AU - Ruppin, Eytan

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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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U2 - 10.1038/s43018-024-00756-7

DO - 10.1038/s43018-024-00756-7

M3 - Article

AN - SCOPUS:85190804654

SN - 2662-1347

JO - Nature Cancer

JF - Nature Cancer

ER -

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

Abstract

Bibliographical note

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