Approach predicts response to cisplatin among patients with cancer
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Key takeaways:
- Only about 7% of patients with cancer in the United States may benefit from genome-driven care.
- A novel model predicted response to cisplatin in cell lines and clinical trends in tumor samples.
A gene signature model appeared to predict response to cisplatin chemotherapy among tumor samples from patients with muscle-invasive bladder cancer, according to early-phase study results.
In addition, the model appeared to retrospectively predict survival outcomes, researchers noted.
Rationale
“This research stemmed from seeing that, despite the huge leaps we’ve made in chemotherapeutic development, personalized medicine in oncology primarily helps only patients with targetable mutations,” Jessica A. Scarborough, PhD, student in Case Western Reserve University's Medical Scientist Training Program, told Healio. “Unfortunately, most patients don’t have these targetable mutations, and in 2020, it was estimated that only 7.04% of patients with cancer in the United States may benefit from genome-driven care. We wanted to expand precision medicine into the world of conventional cytotoxic chemotherapies, and gene signatures that predict therapeutic response is our way of pushing that needle.”
Researchers used a novel extraction method to develop the Cisplatin Response Signature (CisSig) to assess how two cohorts of pretreatment tumor samples respond to cisplatin.
Findings
Results showed the signature could predict response to cisplatin in cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) database. Moreover, signature expression aligned with clinical trends observed in independent datasets of human tissue samples from The Cancer Genome Atlas and Total Cancer Care databases, researchers wrote.
“We also demonstrated that a CisSig-trained model can retrospectively predict survival outcomes in a small cohort of patients with muscle-invasive bladder cancer who received cisplatin-containing chemotherapy.”
Using this method, signatures can be extracted for hundreds of chemotherapeutic agents, which may lead to a significant expansion of precision medicine in the world of medical oncology, Scarborough added.
“Further, our demonstration that the CisSig model can predict outcomes in patients with muscle-invasive bladder cancer is in a small cohort, but it’s a promising first step to translating CisSig for use in a clinical setting,” she said.
Future research
Scarborough said that the CisSig model will need further validation in a much larger cohort of patients.
“We are currently working on procuring tissue samples from patients with muscle-invasive bladder cancer and HPV-positive head and neck cancer — both cancers where cisplatin is commonly used,” she said. “Furthermore, our team is working on expanding this signature extraction methodology to combine signatures of individual drug sensitivity to predict response to drug combinations, as this is how chemotherapy is most commonly provided in clinical practice today.”
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