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The aim of this study was to identify an easily reliable prognostic score that selects the subset of advanced soft tissue sarcoma (ASTS) patients with a higher benefit with trabectedin in terms of time to progression and overall survival. A retrospective series of 357 patients with ASTS treated with trabectedin as second- or further-line in 19 centers across Spain was analyzed. First, it was confirmed that patients with high growth modulation index (GMI > 1.33) were associated with the better clinical outcome. Univariate and multivariate analyses were performed to identify factors associated with a GMI > 1.33. Thus, GEISTRA score was based on metastasis free-interval (MFI ≤ 9.7 months), Karnofsky < 80%, Non L-sarcomas and better response in the previous systemic line. The median GMI was 0.82 (0–69), with 198 patients (55%) with a GMI < 1, 41 (11.5%) with a GMI 1–1.33 and 118 (33.1%) with a GMI > 1.33. The lowest GEISTRA score showed a median of time-to-progression (TTP) and overall survival (OS) of 5.7 and 19.5 months, respectively, whereas it was 1.8 and 3.1 months for TTP and OS, respectively, for the GEISTRA 4 score. This prognostic tool can contribute to better selecting candidates for trabectedin treatment in ASTS.

Predictive biomarkers of trabectedin represent an unmet need in advanced soft‐tissue sarcomas (STS). DNA damage repair (DDR) genes, involved in homologous recombination or nucleotide excision repair, had been previously described as biomarkers of trabectedin resistance or sensitivity, respectively. The majority of these studies only focused on specific factors (ERCC1, ERCC5, and BRCA1) and did not evaluate several other DDR‐related genes that could have a relevant role for trabectedin efficacy. In this retrospective translational study, 118 genes involved in DDR were evaluated to determine, by transcriptomics, a predictive gene signature of trabectedin efficacy. A six‐gene predictive signature of trabectedin efficacy was built in a series of 139 tumor samples from patients with advanced STS. Patients in the high‐risk gene signature group showed a significantly worse progression‐free survival compared with patients in the low‐risk group (2.1 vs 6.0 months, respectively). Differential gene expression analysis defined new potential predictive biomarkers of trabectedin sensitivity (PARP3 and CCNH) or resistance (DNAJB11 and PARP1). Our study identified a new gene signature that significantly predicts patients with higher probability to respond to treatment with trabectedin. Targeting some genes of this signature emerges as a potential strategy to enhance trabectedin efficacy.