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Changes in L-phenylalanine concentration reflect and predict response to anti-PD-1 treatment combined with chemotherapy in patients with non-small cell lung cancer

ABSTRACT: Chemotherapy combined with checkpoint blockade antibodies targeting programmed cell death protein (PD-1) has achieved remarkable success in non-small cell lung cancer (NSCLC). However, few patients benefit from long-term treatment. Therefore, biomarkers capable of guiding the optimal therapeutic selection and reducing unnecessary toxicity are of pressing importance. In our research, we gathered serial blood samples from two groups of NSCLC patients: 49 patients received a combination of therapies, and 34 patients went under chemotherapy alone. Utilizing non-targeted metabolomic analysis, we examined different metabolites’ disparity. Among the lot, L-phenylalanine emerged as a significant prognostic in the combination treatment of NSCLC patients, interestingly absent in patients under sole chemotherapy. The reduced ratio of L-phenylalanine concentration (two cycles treatment vs. pre-treatment) was associated with improved progression-free survival (hazard ratio [HR] = 1.8000, 95% confidence interval [CI], 0.8566‒3.7820, p < 0.0001) and overall survival (HR = 1.583, 95% CI, 0.7416‒3.3800, p < 0.005). We further recruited two validation cohorts (cohort 1:40 patients and cohort 2:30 patients) to validate the sensitivity and specificity of L-phenylalanine prediction. Our results demonstrate that a model based on L-phenylalanine variations could serve as an early risk-assessment tool for NSCLC patients undergoing treatment, potentially facilitating strategic clinical decision-making.

INSTRUMENT(S): Liquid Chromatography MS - positive - hilic, Liquid Chromatography MS - negative - hilic

PROVIDER: MTBLS11232 | MetaboLights | 2025-06-16

REPOSITORIES: MetaboLights

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Changes in L-phenylalanine concentration reflect and predict response to anti-PD-1 treatment combined with chemotherapy in patients with non-small cell lung cancer.

Liu Yaqing Y Ping Yu Y Zhang Liubo L Zhao Qitai Q Huo Yachang Y Li Congcong C Shan Jiqi J Qi Yanwen Y Wang Liping L Zhang Yi Y

MedComm 20250217 3

Chemotherapy combined with checkpoint blockade antibodies targeting programmed cell death protein (PD-1) has achieved remarkable success in non-small cell lung cancer. However, few patients benefit from long-term treatment. Therefore, biomarkers capable of guiding the optimal therapeutic selection and reducing unnecessary toxicity are of pressing importance. In our research, we gathered serial blood samples from two groups of non-small cell lung cancer patients: 49 patients received a combinatio ...[more]

PMID: 39968502

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Project description:Introduction: Effective predictive biomarkers for selection of patients benefiting from adjuvant platinum-based chemotherapy in non-small cell lung cancer (NSCLC) are needed. Based on a previously validated methodology, molecular profiles of predicted sensitivity in two patient cohorts are presented. Methods: The profiles are correlations between in vitro sensitivity to cisplatin and vinorelbine and baseline mRNA expression of the 60 cell lines in the National Cancer Institute panel. An applied clinical samples filter focused the profiles to clinically relevant genes. The profiles were tested on 1) snap-frozen tumors from 133 patients with completely resected stage 1B-2 NSCLC randomized to adjuvant cisplatin and vinorelbine (ACV, n=71) or no adjuvant treatment (OBS, n=62) [GSE14814] and 2) formalin-fixed paraffin-embedded (FFPE) pre-treatment tumors from 95 patients with completely resected stage 1A-3B NSCLC receiving adjuvant cisplatin and vinorelbine. Results: The combined cisplatin and vinorelbine profiles showed: 1) univariate Hazard Ratio (HR) for sensitive versus resistant of 0.265 (95% CI:0.079-0.889, p=0.032) in the ACV cohort and a HR of 0.28 in a multivariate model (95% CI:0.08-1.04, p=0.0573); 2) significant prediction at 3 year survival from surgery in univariate (HR=0.138 (95% CI:0.035-0.537), p=0.004) and multivariate analysis (HR=0.14 (95% CI:0.030-0.6), p=0.0081). No discrimination was found in the OBS cohort (HR=1.328, p=0.60). The cisplatin predictor alone had similar figures with 1) univariate HR of 0.37 (95% CI:0.12-1.15, p=0.09) in the ACV cohort and 2) univariate HR of 0.14 (95% CI:0.03-0.59, p=0.0076) to three years. Functional analysis on the cisplatin profile revealed a group of upregulated genes related to RNA splicing as a part of DNA damage repair and apoptosis. Conclusions: Profiles derived from snap-frozen and FFPE NSCLC tissue were prognostic and predictive in the patients that received cisplatin and vinorelbine but not in the cohort that did not receive adjuvant treatment.

Project description:summary Ovarian cancer is the leading cause of death among gynecologic malignancies. This is partly due to a non-durable response to chemotherapy. Prediction of resistance to chemotherapy could be a key role in more personalized treatment. In the current study we aimed to examine if microRNA based predictors could predict resistance to chemotherapy in ovarian cancer, and to investigate if the predictors could be prognostic factors for progression free and overall survival. Methods Predictors of chemotherapy-resistance were developed based on correlation between miRNA expression and differences in measured growth inhibition in a variety of human cancer cell lines in the presence of Carboplatin, Paclitaxel and Docetaxel (deposited under accession number E-MTAB-327 in the ArrayExpress database of the European Bioinformatics Institute, 2012). These predictors were then, retrospectively, blindly validated in a cohort of 170 epithelial ovarian cancer patients treated with Carboplatin and Paclitaxel or Docetaxel as first line treatment. Results In a multivariate cox proportional analysis the predictors of chemotherapy-resistance were not able to predict time to progression after end of chemotherapy (hazard ratio: 0.64, 95% CI: 0.36 – 1.12, P=0.117). However, in a multivariate logistic analysis, where time to progression was considered as either more or less than 6 months, the predictors match clinical observed chemotherapy-resistance (odds ratio: 0.19, 95% CI: 0.05-0.73, P=0.015). Neither univariate nor multivariate, time-dependent, cox analysis for progression free survival (PFS) or overall survival (OS) in all 170 patients showed to match predicted resistance to chemotherapy (PFS: hazard ratio: 0.69, 95% CI: 0.40-1.19, P=0.183, OS: hazard ratio: 0.76, 95% CI: 0.42-1.40, P=0.386). Conclusion In the current study, microRNA based predictors of chemotherapy-resistance did not demonstrate any convincing correlation to clinical observed chemotherapy-resistance, progression free survival, or overall survival, in patients with epithelial ovarian cancer. However the predictors did reflect relapse more or less than 6 months.

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