Transformative PGA Technology May Help Treatment Selection for Lung Cancer
Liquid biopsy-based patient-derived gene expression signature could be used to predict the most suitable treatment for patients with lung cancer.
- (1888PressRelease) May 12, 2023 - Patient-derived gene expression signature derived from plasma samples—could be used to predict the most suitable treatment for patients with non-small cell lung cancer (NSCLC), suggests a pilot trial that will be submitted for publication soon.
NSCLC is relatively common and typically have a poor prognosis because they are frequently diagnosed at a late stage. The 5-year survival rate is around 20%. At the time of diagnosis, the disease is often too advanced for patients to be eligible for surgery, leaving targeted therapy, immunotherapy or chemotherapy as the only option. However, patients show variable responses and inevitably develop resistance to targeted therapy and immunotherapy.
Plasma cell-free messenger RNA (cfmRNA) profiling is a valuable tool for studying cancer biology and developing personalized cancer treatments because gene expression patterns mimic the characteristics of the tumors they are derived from, including key genetic, pathway, functional and phenotypic features. Most importantly, it will also capture non-tumor signals from tumor microenvironment.
OncoDxRx team then compared PGA (Patient-derived Gene expression-informed Anti-cancer drug efficacy) findings with data from patients who had gone through routine workup without PGA information. They found that the treatment response in the PGA group significantly outperformed that in the placebo group in terms of PFS and OS, which indicated “the clinical utility of drug efficacy prediction using PGA.”
The team used a pre-established lung cancer-specific gene expression panel on each individual patient to obtain personalized signature to predict drug efficacy. OncoDxRx’s patent-pending, one-of-a-kind PGA can provide comprehensive anti-cancer drug efficacy data within 5 days and improve the clinical-benefit rate for cancer patients. The firm added that PGA’s unprecedented potential of selecting optimal treatment regimen among hundreds of anti-cancer drugs for individual patients based on patients’ own gene expression profiles will impact the cancer genetic care profoundly.
The company concluded: “PGA could be a good clinical decision support tool for the NSCLC patients and their doctors.”
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