![Predictive Biomarker for Chemotherapy in Colon Cancer Unveiled Predictive Biomarker for Chemotherapy in Colon Cancer Unveiled](https://images.medindia.net/health-images/1200_1000/cancer-intestine.jpg)
Discovery and validation of a 10-gene predictive signature for response to adjuvant chemotherapy in stage II and III colon cancer
Go to source). A secondary finding from the study could also lead to further research and application. Researchers found that the gene signature could potentially also predict whether immunotherapy would help some patients – important because there are not yet clear guidelines on which colon cancer patients might benefit from immunotherapy.
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The five-year survival rate for localized colon cancer is about 90%. #coloncancer #biomarker #chemotherapy ’
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The study, led by Steven Chen, Ph.D., a researcher at the Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine, lays the foundation for further research that could someday allow patients and their doctors to make personalized treatment decisions. The five-year survival rate for localized colon cancer is about 90%. #coloncancer #biomarker #chemotherapy ’
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“When you’re talking about precision oncology, it means you use an individual patient’s information — here we are particularly talking about biomarkers from the patient — to guide the doctor in making a clinical decision about what kind of treatment is best for the patient,” Chen said.
“Ideally, we only want to apply adjuvant chemotherapy to the patients who will benefit from it. For patients who don’t respond, we still need to find other effective treatments.”
As a data scientist, Chen applies machine learning and artificial intelligence to cancer research, primarily focusing on colorectal and breast cancer.
New Biomarker for Colon Cancer Therapy
Scientists have previously found biomarkers that help doctors predict a patient’s survival curve or understand how aggressive a cancer is. These are useful, Chen said, but don’t help guide treatment.Advertisement
Colon cancer patients’ tumors have many different genomic profiles, so the team aggregated gene expression profiles from six publicly available sources to create a 933-patient data set, making it one of the largest gene expression datasets for stage 2 and 3 colon cancer.
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They also wanted the gene signature to be practical, with a small number of genes. They used machine learning to build a network of thousands of potentially relevant genes, which they narrowed down first to an 18-gene network and then to 10 genes.
Once they were confident the 10-gene network was biologically relevant, they built a model that analyzes the gene signature to predict which patients would benefit from adjuvant chemotherapy.
Next, the team wanted to test their gene signature’s accuracy. Having an interdisciplinary team was crucial to this step, Chen said. “Working closely with surgeons, oncologists, and biologists ensures that our findings are robust, clinically relevant, and can be effectively translated into practice.”
Revolutionizing Cancer Care
The team’s data scientists tested the gene signature’s predictive power by comparing it to results from thousands of random 5- to 15-gene networks. It was dramatically better at predicting whether a patient would benefit from chemotherapy.The surgeons and oncologists collected tumor tissue samples from 109 stage 2 and 3 colon cancer patients, along with information about the patient’s responses to adjuvant chemotherapy.
Tests using these samples further verified the model: Patients predicted to benefit from chemotherapy based on the gene signature “had significantly better survival outcomes than those predicted not to benefit,” the study found.
Chen hopes the team’s biomarker will be used to help patients someday, but several more steps are needed before it can be used clinically.
“To be really clinically applicable, we need to go through prospective clinical trials,” he said. “It means we recruit patients and apply this biomarker to see if it’s really effective.”
The study was supported by funding from Sylvester and was motivated by Chen’s goal of translating data science into real-world benefits.
“I hope every research finding we discover can improve clinical decisions and eventually help cancer patients,” he said.
Reference:
- Discovery and validation of a 10-gene predictive signature for response to adjuvant chemotherapy in stage II and III colon cancer - (https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(24)00375-6)
Source-Eurekalert