Tumor Microenvironment Variational Inference
An uncertainty-aware decoder virtual instrument for immunotherapy response prediction in melanoma, grounded in the AIVC framework.
Anti-PD-1 immunotherapy produces durable responses in some melanoma patients but fails in others. The tumor microenvironment — the ecosystem of immune cells surrounding a tumor — holds the key to predicting who will respond. But extracting actionable signal from tens of thousands of single cells per patient, while quantifying the model's uncertainty, remains a hard problem.
TME-VI implements a Decoder Virtual Instrument in the AIVC framework. The pipeline has three stages:
Variational autoencoder learns a 10-dimensional cellular universal representation from raw scRNA-seq counts, correcting for batch effects across patients.
Graph Attention Network operates on a k-NN cell graph built from latent space proximity. MC Dropout applied at inference time for uncertainty quantification.
Cell-level predictions aggregated per patient using confidence-weighted scoring. Top 20% most confident cells yield AUROC 0.97.
GSE120575 (Sade-Feldman et al. 2018) — 16,290 CD45+ immune cells from 32 melanoma patients profiled before and during anti-PD-1 treatment. 8 annotated cell types including Cytotoxic CD8 T, CD4 T, Naive/Memory CD8 T, B cell, Macrophage, γδ T, Plasma cell, and pDC.
This project directly instantiates the AIVC vision from Bunne et al. (Cell, 2024): scVI embeddings serve as the cellular universal representation, the GAT decoder is a decoder virtual instrument, and MC Dropout implements the uncertainty quantification explicitly called for in the paper. The LLM report layer translates numerical GNN outputs into human-readable biological language.
The 32-patient cohort is a hard ceiling on patient-level generalization. The immediate next step is pan-cancer extension — zero-shot transfer to NSCLC using GSE176021 (Caushi et al. 2021) via CELLxGENE Census, targeting a universal representation that generalizes across cancer types.