EGFR kinase inhibitors and gastric acid suppressants in EGFR-mutant NSCLC: A retrospective database analysis of potential drug interaction

10.18632/oncotarget.13458Oncotarget75185542-8555

Cost of Medical Care of Patients with Advanced Serious Illness in Singapore (COMPASS): Prospective cohort study protocol

10.1186/s12885-018-4356-zBMC Cancer18145

Limb hypothermia for preventing paclitaxel-induced peripheral neuropathy in breast cancer patients: A pilot study

10.3389/fonc.2016.00274Frontiers in Oncology6JAN27

Low Levels of NDRG1 in nerve tissue are predictive of severe paclitaxel-induced neuropathy

10.1371/journal.pone.0164319PLoS ONE1110e016431

Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial

Objective To date, there are no predictive biomarkers to guide selection of patients with gastric cancer (GC) who benefit from paclitaxel. Stomach cancer Adjuvant Multi-Institutional group Trial (SAMIT) was a 2x2 factorial randomised phase III study in which patients with GC were randomised to Pac-S-1 (paclitaxel +S-1), Pac-UFT (paclitaxel +UFT), S-1 alone or UFT alone after curative surgery. Design The primary objective of this study was to identify a gene signature that predicts survival benefit from paclitaxel chemotherapy in GC patients. SAMIT GC samples were profiled using a customised 476 gene NanoString panel. A random forest machine-learning model was applied on the NanoString profiles to develop a gene signature. An independent cohort of metastatic patients with GC treated with paclitaxel and ramucirumab (Pac-Ram) served as an external validation cohort. Results From the SAMIT trial 499 samples were analysed in this study. From the Pac-S-1 training cohort, the random forest model generated a 19-gene signature assigning patients to two groups: Pac-Sensitive and Pac-Resistant. In the Pac-UFT validation cohort, Pac-Sensitive patients exhibited a significant improvement in disease free survival (DFS): 3-year DFS 66% vs 40% (HR 0.44, p=0.0029). There was no survival difference between Pac-Sensitive and Pac-Resistant in the UFT or S-1 alone arms, test of interaction p<0.001. In the external Pac-Ram validation cohort, the signature predicted benefit for Pac-Sensitive (median PFS 147 days vs 112 days, HR 0.48, p=0.022). Conclusion Using machine-learning techniques on one of the largest GC trials (SAMIT), we identify a gene signature representing the first predictive biomarker for paclitaxel benefit