Factors predictive of second‐line chemotherapy in soft tissue sarcoma: An analysis of the National Genomic Profiling Database

Abstract

AbstractOf the drugs used in second‐line chemotherapy for soft tissue sarcoma (STS), trabectedin is effective for liposarcoma and leiomyosarcoma (L‐sarcoma), eribulin for liposarcoma, and pazopanib for non‐liposarcoma. The indications for these drugs in STS other than L‐sarcoma have not been established. Here we explored the prognosis, mutation profiles, and drug‐response factors in STS using real‐world big data. Clinicogenomic data on 1761 patients with sarcoma who underwent FoundationOne CDx were obtained from a national database in Japan. Patients with TP53 and KDM2D mutations had a significantly shorter survival period of 253 (95% CI, 99–404) and 330 (95% CI, 20–552) days, respectively, than those without mutations. Non‐supervised clustering based on mutation profiles generated 13 tumor clusters. The response rate (RR) to trabectedin was highest in an MDM2‐amplification cluster (odds ratio [OR]: 2.2; p = 0.2). The RR was lowest for eribulin in an MDM2‐amplification cluster (OR: 0.4; p = 0.03) and highest in a TERT‐mutation cluster (OR: 3.0; p = 0.03). The RR was highest for pazopanib in a PIK3CA/PTEN‐wild type cluster (OR: 2.1; p = 0.03). In particular, patients harboring mutations in genes regulating the PI3K/Akt/mTOR pathway had a lower RR than patients without mutations (OR: 0.3; p = 0.04). In STS, mutation profiles were more useful in predicting the drug response than histology. The present study demonstrated the potential of tailored therapy guided by mutation profiles established by comprehensive genomic profiling testing in optimizing second‐line chemotherapy for STS. The findings of this study will hopefully contribute some valuable insights into enhancing STS treatment strategies and outcomes.