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Synthetic lethality-mediated precision oncology via the tumor transcriptome.

Joo Sang Lee1, Nishanth Ulhas Nair2, Gal Dinstag3

  • 1Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; Next Generation Medicine Lab, Department of Artificial Intelligence & Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea; Department of Digital Health & Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Samsung Medical Center, Sungkyunkwan University, Seoul 06351, Republic of Korea.

Cell
|April 15, 2021
PubMed
Summary

SELECT, a new precision oncology framework, uses tumor transcriptome data to predict cancer treatment response. This approach shows 80% accuracy across 35 clinical trials, expanding personalized cancer therapy options.

Keywords:
cancer immunotherapypatient stratificationprecision oncologysynthetic lethalitysynthetic rescuestranscriptomics

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Area of Science:

  • Oncology
  • Genomics
  • Computational Biology

Background:

  • Precision oncology currently focuses on targeting mutations in cancer driver genes.
  • Exploring the tumor transcriptome offers new avenues for guiding cancer patient treatment.
  • Genetic interactions within tumors are increasingly recognized as crucial for therapeutic response.

Purpose of the Study:

  • To introduce SELECT (synthetic lethality and rescue-mediated precision oncology via the transcriptome), a novel framework for predicting cancer therapy response.
  • To leverage tumor transcriptome data and genetic interactions for enhanced treatment selection.
  • To validate the SELECT framework across diverse cancer types and clinical trials.

Main Methods:

  • Development of the SELECT framework integrating genetic interactions and transcriptome data.
  • Application of SELECT to analyze data from 35 published targeted and immunotherapy clinical trials.
  • Testing SELECT's predictive performance across 10 different cancer types.
  • Validation using data from the multi-arm WINTHER trial.

Main Results:

  • SELECT demonstrated predictive capability in 80% of the analyzed clinical trials.
  • The framework successfully predicted patient response in the WINTHER trial.
  • Predictive signatures and analysis code were generated and made publicly available.

Conclusions:

  • SELECT provides a robust method for predicting patient response to cancer therapies using tumor transcriptome data.
  • The framework enhances precision oncology by incorporating genetic interactions.
  • SELECT's public availability supports further research and prospective clinical studies in personalized cancer treatment.