Predicting Targeted and Immunotherapeutic Response Outcomes in Melanoma With Single-Cell Raman Spectroscopy and Artificial Intelligence.
TL;DR
This work presents a nondestructive, single-cell approach combining Raman spectroscopy and machine learning that enables rapid cell profiling and therapeutic response prediction and may improve first- and second-line therapy selection assessments for precision medicine.
Abstract
Purpose
Identifying reliable predictors of immunotherapeutic response in melanoma remains an outstanding challenge. Existing transcriptomic and proteomic profiling methods for the tumor-immune microenvironment are costly and may not faithfully capture modifications actively affecting tumor behavior. Here, we present a nondestructive, single-cell approach combining Raman spectroscopy and machine learning (ML) that enables rapid cell profiling and therapeutic response prediction.
Methods
We analyzed single-cell Raman spectra of mouse and human melanoma cell lines alongside nine samples derived from patients with melanoma with known resistance profiles to targeted and immunotherapeutic inhibitors bemcentinib, cabozantinib, dabrafenib, and nivolumab and a combination of nivolumab and relatlimab. We assessed cell phenotyping classification and treatment resistance using random forests and feature importance analysis. For patient samples, we constructed a two-stage evaluation workflow to determine clinical drug resistance through aggregated single-cell predictions and identified corresponding highly variant spectral signatures using computational methods adapted from single-cell RNA sequencing methods.
Results
In cell lines, our approach achieved >96% differentiation accuracy across tumor microenvironment cell types and induced functional phenotypes. Persistent (drug-resistant) cells formed subclusters based on genetic mutations rather than sample origin, with Raman signatures reflecting biochemical changes relevant to therapeutic pathways. For patient samples, our workflow correctly inferred resistance likelihoods for 30 of 33 clinically relevant patient-drug combinations (91% accuracy).
Conclusion
Single-cell Raman spectroscopy combined with ML offers a scalable, prognostic platform to predict therapeutic resistance likelihood, with further potential to advance clinical, multiomic biomarker efforts for melanoma. Our approach may improve first- and second-line therapy selection assessments for precision medicine by providing rapid, nondestructive prediction of therapeutic response based on cellular spectral profiles.