Researchers develop model to predict treatment response in gastric
cancer

Date:
February 14, 2022
Source:
Mayo Clinic
Summary:
A study is validating the use of genomic sequencing to predict the
likelihood that patients with gastric cancer will derive benefit
from chemotherapy or from immunotherapy.



FULL STORY ==========================================================================
A study by researchers at Mayo Clinic Cancer Center in Florida is
validating the use of genomic sequencing to predict the likelihood that patients with gastric cancer will derive benefit from chemotherapy or
from immunotherapy. The study is published inNature Communications.


========================================================================== "Gastric cancer is among the leading causes of cancer-related death, worldwide," says Tae Hyun Hwang, Ph.D., the Florida Department of Health
cancer chair at Mayo Clinic Cancer Center in Florida.

Dr. Hwang says most patients with gastric cancer are treated with
chemotherapy, and sometimes immunotherapy, as part of their treatment
plan. However, not all patients derive benefit from these therapies.

"We sought to use genomic sequencing to build a model that predicts the likelihood that a patient will derive benefit from chemotherapy or from immunotherapy," says Dr. Hwang.

To build this model, Dr. Hwang and his team developed and implemented a
machine learning algorithm that integrated genetic data from more than
5,000 patients.

Then the team developed a molecular signature consisting of 32 genes
that could be used to guide patient care decisions.

"We were pleased that our 32-gene signature provided not only prognostic information, but also predicted patient benefit from chemotherapy and immunotherapy," says Dr. Hwang. "In particular, we were surprised that
the 32- gene signature we identified was able to predict a patient's
response to immunotherapy because identifying reliable biomarkers
for immunotherapy response in patients with gastric cancer has been a
challenge for the field." Dr. Hwang says the 32-gene molecular signature
still needs prospective validation, but he believes it eventually will be
able to identify patients who are likely to respond to chemotherapy and immunotherapy. "Similarly, we would also be able to identify patients
who are unlikely to benefit from chemotherapy and immunotherapy,
thereby sparing them the potential side effects of these therapies,"
says Dr. Hwang.

Dr. Hwang and his team also are working to develop new assays
based on the expression level of a single -- or several -- genes
to make biomarkers more accessible and easily deployed in the
clinical setting. "We are working on artificial intelligence
algorithms that utilize diagnostic histopathology images to
identify patients most likely to derive benefit from immunotherapy,"
says Dr. Hwang. "We are also studying the molecular mechanisms of
immunotherapy resistance made available by the machine learning and
artificial intelligence approaches that we have developed in our lab." ========================================================================== Story Source: Materials provided by Mayo_Clinic. Original written by
Joe Dangor. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Jae-Ho Cheong, Sam C. Wang, Sunho Park, Matthew R. Porembka,
Alana L.

Christie, Hyunki Kim, Hyo Song Kim, Hong Zhu, Woo Jin Hyung, Sung
Hoon Noh, Bo Hu, Changjin Hong, John D. Karalis, In-Ho Kim, Sung Hak
Lee, Tae Hyun Hwang. Development and validation of a prognostic
and predictive 32- gene signature for gastric cancer. Nature
Communications, 2022; 13 (1) DOI: 10.1038/s41467-022-28437-y ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220214183317.htm

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