RESUMEN
Analysis of premalignant tissue has identified the typical order of somatic events leading to invasive tumors in several cancer types. For other cancers, premalignant tissue is unobtainable, leaving genetic progression unknown. Here, we demonstrate how to infer progression from exome sequencing of primary tumors. Our computational method, PhylogicNDT, recapitulated the previous experimentally determined genetic progression of human papillomavirus-negative (HPV-) head and neck squamous cell carcinoma (HNSCC). We then evaluated HPV+ HNSCC, which lacks premalignant tissue, and uncovered its previously unknown progression, identifying early drivers. We converted relative timing estimates of driver mutations and HPV integration to years before diagnosis based on a clock-like mutational signature. We associated the timing of transitions to aneuploidy with increased intratumor genetic heterogeneity and shorter overall survival. Our approach can establish previously unknown early genetic progression of cancers with unobtainable premalignant tissue, supporting development of experimental models and methods for early detection, interception and prognostication.
Asunto(s)
Carcinoma de Células Escamosas , Neoplasias de Cabeza y Cuello , Infecciones por Papillomavirus , Lesiones Precancerosas , Humanos , Carcinoma de Células Escamosas de Cabeza y Cuello/genética , Carcinoma de Células Escamosas/genética , Carcinoma de Células Escamosas/patología , Neoplasias de Cabeza y Cuello/genética , Lesiones Precancerosas/genéticaRESUMEN
Immune checkpoint blockade (CPB) improves melanoma outcomes, but many patients still do not respond. Tumor mutational burden (TMB) and tumor-infiltrating T cells are associated with response, and integrative models improve survival prediction. However, integrating immune/tumor-intrinsic features using data from a single assay (DNA/RNA) remains underexplored. Here, we analyze whole-exome and bulk RNA sequencing of tumors from new and published cohorts of 189 and 178 patients with melanoma receiving CPB, respectively. Using DNA, we calculate T cell and B cell burdens (TCB/BCB) from rearranged TCR/Ig sequences and find that patients with TMBhigh and TCBhigh or BCBhigh have improved outcomes compared to other patients. By combining pairs of immune- and tumor-expressed genes, we identify three gene pairs associated with response and survival, which validate in independent cohorts. The top model includes lymphocyte-expressed MAP4K1 and tumor-expressed TBX3. Overall, RNA or DNA-based models combining immune and tumor measures improve predictions of melanoma CPB outcomes.
