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Oncology
Sarcoma

Tailored therapy for soft tissue sarcoma: Real-world big data unveils key insights

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Real-world big data analysis of soft tissue sarcoma (STS) patients has provided significant insights into the effectiveness of second-line chemotherapy drugs based on mutation profiles. The study emphasizes that mutation profiles, rather than traditional histological classifications, are more predictive of drug response in STS. Specific mutations, such as TP53 and KDM2D, were associated with shorter survival periods, while distinct mutation clusters influenced the response rates of different chemotherapy drugs.

The study, based on clinicogenomic data from 1761 patients with sarcoma who underwent FoundationOne CDx testing, found that among the drugs used in second-line chemotherapy, trabectedin proves effective for liposarcoma and leiomyosarcoma (L-sarcoma), eribulin for liposarcoma, and pazopanib for non-liposarcoma. Notably, the indications for these drugs in STS other than L-sarcoma had not been established previously.

Patients with TP53 and KDM2D mutations were found to have significantly shorter survival periods—253 days (95% CI, 99-404) and 330 days (95% CI, 20-552), respectively—compared to those without mutations.

The study’s non-supervised clustering based on mutation profiles yielded 13 distinct tumor clusters. Of particular significance was the observation that mutation profiles were more predictive of drug response than histology in STS.

Trabectedin demonstrated the highest response rate (RR) in an MDM2-amplification cluster. Conversely, eribulin showed the lowest RR in an MDM2-amplification cluster. Pazopanib, on the other hand, exhibited the highest RR in a PIK3CA/PTEN-wild type cluster.

A key insight emerged for patients with mutations in genes regulating the PI3K/Akt/mTOR pathway, who exhibited a lower RR compared to patients without such mutations. This underscores the potential of tailored therapy guided by mutation profiles, as established through comprehensive genomic profiling testing, in optimizing second-line chemotherapy for STS.

Reference
Mochizuki T, Ikegami M, Akiyama T. Factors predictive of second-line chemotherapy in soft tissue sarcoma: An analysis of the National Genomic Profiling Database. Cancer Sci. 2023;doi: 10.1111/cas.16050. Epub ahead of print. PMID: 38115234.

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