A new computational biomarker uses existing biopsy slides to identify which patients will benefit from standard treatment before therapy begins.
RT’s Three Key Takeaways:
- Treatment Prediction: The Ai-powered tool, PhenopyCell, can predict if a patient with extensive-stage small cell lung cancer will respond to platinum-based chemotherapy using existing diagnostic biopsy slides.
- Immune Cell Analysis: Research indicates that tumors with organized groups of immune cells surrounding tumor clusters correlate with better treatment outcomes, a pattern identified through the Ai analysis.
- Clinical Efficiency: Because the tool utilizes existing pathology slides, it eliminates the need for additional biopsies, costs, or invasive procedures for patients with rapidly progressing disease.
Results of a new study suggest that a pathology tool powered by artificial intelligence can predict whether a patient with extensive-stage small cell lung cancer (SCLC) will respond to platinum-based chemotherapy before treatment has begun, according to data published in the journal npj Precision Oncology.
The tool, called PhenopyCell, allows patients to avoid treatments that are unlikely to be effective, providing an opportunity to enroll earlier in clinical trials for newer medications. The tool was developed by a research team co-led by Prantesh Jain, MD, FACP, thoracic oncologist at Roswell Park, and Anant Madabhushi, PhD, of Winship Cancer Institute of Emory University.
“We are entering an era where we will have more tools than ever to offer people with small cell lung cancer,” said Jain. “But knowing which tool is right for which patient requires biological markers, and right now we don’t have them.”
Addressing the Lack of SCLC Biomarkers
Approximately 70% of patients with SCLC have extensive-stage disease at the time of their initial diagnosis. Because the disease is often rapidly progressing and has spread to other parts of the body, most patients survive only 12 to 13 months. Currently, there is no method to distinguish between different subtypes of SCLC, meaning all patients typically receive a standard treatment of platinum-based chemotherapy and immunotherapy.
While new SCLC treatments have recently been approved by the Food and Drug Administration (FDA), they are effective in only a small subset of patients. Unlike other cancers where biological markers in blood or tissue can indicate treatment response, no such biomarkers have been identified for SCLC until now.
PhenopyCell serves as a computational biomarker by combining data from pathology slides and medical records to determine how the information corresponds to patient outcomes.
Improving Accuracy Without New Biopsies
In a retrospective study, the research team used the tool to analyze standard pathology slides from 281 patients with SCLC treated at Roswell Park, Winship, and University Hospitals Cleveland Medical Center. By analyzing immune cells within tissue samples from the diagnostic biopsy, the tool predicted chemotherapy responses with greater accuracy than manual analysis, according to the study.
The Ai analysis revealed that patients with better outcomes had tumors containing more immune cells organized in groups surrounding tumor clusters. Conversely, patients with poor outcomes had fewer immune cells that appeared in disorganized groups further from the tumor. These specific arrangements were only visible through the use of the AI tool.
“Every patient with small cell lung cancer already has a pathology slide from their diagnostic biopsy,” said Jain. “This system works from that existing slide. There’s no need for additional procedures or tissue collection, and no added cost. In a disease where survival is measured in months and re-biopsy is rarely possible this, has the potential to become a uniquely powerful tool.”
The study included researchers from University Hospitals Cleveland Medical Center, Case Western Reserve University, City of Hope, and Penn State Cancer Institute.
