A precision oncology framework to predict patient response to cancer therapy from the tumor transcriptome

Even if the same cancer treatment were to be given to similar patients, some may have a full recovery and others may not. The differences in a patient can begin at the nutritional level with their eating habits or it may begin with the differences in their lifestyle patterns; however, it is the genetic background of the cancer patients which plays a decisive role. Therefore, personalized medicine for cancer patients is on the rise. Selecting the most suitable anticancer treatment which is based on the patient’s genetic data is essential to recovery.

A research team led by researchers from the National Cancer Institute and Sungkyunkwan University demonstrated that they can identify which therapies may be particularly beneficial for individual patients by analyzing their molecular markup before treatment. When applied to data from a wide panel of different cancer targeted and immunotherapy clinical trials, the approach – termed SELECT – was successfully predictive of patient responses to these therapies in about 80 percent of the trials.

Within the cell, one gene interacts closely in-sync with many other genes. The researchers selected synthetic lethal interactions that have a fatal effect on the survival of cancer cells, which are directly linked to cancer treatment, among these gene interactions and used them for personalized anticancer treatments. Currently, uncovering genetic networks that can directly aid in the treatment of cancer patients using experimental methods is not straightforward. Therefore, researchers are analyzing a large amount of cancer patient genetic data to uncover a genetic network that can predict the treatment’s effect of each anticancer drug. The genetic network, which will be uncovered by this process, will enable an examination for the best anticancer drugs to treat individual patients. Through this genetic network, the use of unnecessary treatments can be prevented and the most efficient treatment can be provided to the individualized patient.

In this most recent study, the research team assembled a broad collection of 35 published transcriptomic datasets from cancer targeted and immunotherapy clinical trials across 10 different cancer types. They applied SELECT to predict the treatment response of the patients. Based on the molecular data of tumors which they were given, the SELECT signatures were found to be highly accurate in 80 percent of the trials.

Prof. Joo-sang Lee stated, “To the best of our knowledge, SELECT is the first approach that systematically achieves these moderate, but helpful levels of accuracy across many different therapies and cancer types.”

Looking ahead, further investigation is now underway in collaboration with several clinical teams at the NIH Clinical Center to bring SELECT into clinics. The team hopes these prospective studies will further improve SELECT in the next few years. Establishing SELECT as a complementary precision oncology approach for enhancing cancer-patient care will lead to better treatments and saving lives.

SourceSungkyunkwan University

Lee JS, Nair NU, Dinstag G, Chapman L, Chung Y, Wang K, Sinha S, Cha H, Kim D, Schperberg AV, Srinivasan A, Lazar V, Rubin E, Hwang S, Berger R, Beker T, Ronai Z, Hannenhalli S, Gilbert MR, Kurzrock R, Lee SH, Aldape K, Ruppin E. (2021) Synthetic lethality-mediated precision oncology via the tumor transcriptome. Cell 184(9):2487-2502.e13. [abstract]

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