Dataset Information

A three-microRNA signature predicts responses to platinum-based doublet chemotherapy in patients with lung adenocarcinoma.

ABSTRACT:

Purpose

To examine the clinical utility of intratumor microRNAs (miRNA) as a biomarker for predicting responses to platinum-based doublet chemotherapy in patients with recurring lung adenocarcinoma (LADC).

Experimental design

The expression of miRNAs was examined in LADC tissues surgically resected from patients treated with platinum-based doublet chemotherapy at the time of LADC recurrence. Microarray-based screening of 904 miRNAs followed by quantitative reverse transcription-PCR-based verification in 40 test cohort samples, including 16 (40.0%) responders, was performed to identify miRNAs that are differentially expressed in chemotherapy responders and nonresponders. Differential expression was confirmed in a validation cohort (n = 63 samples), including 18 (28.6%) responders. An miRNA signature that predicted responses to platinum-based doublet chemotherapy was identified and its accuracy was examined by principal component and support vector machine analyses. Genotype data for the TP53-Arg72Pro polymorphism, which is associated with responses to platinum-based doublet chemotherapy, were subsequently incorporated into the prediction analysis.

Results

A signature comprising three miRNAs (miR1290, miR196b, and miR135a*) enabled the prediction of a chemotherapeutic response (rather than progression-free and overall survival) with high accuracy in both the test and validation cohorts (82.5% and 77.8%). Examination of the latter was performed using miRNAs extracted from archived formalin-fixed paraffin-embedded tissues. Combining this miRNA signature with the TP53-Arg72Pro polymorphism genotype marginally improved the predictive power.

Conclusion

The three-miRNA signature in surgically resected primary LADC tissues may by clinically useful for predicting responsiveness to platinum-based doublet chemotherapy in patients with LADC recurrence.

SUBMITTER: Saito M

PROVIDER: S-EPMC6329384 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Publications

A three-microRNA signature predicts responses to platinum-based doublet chemotherapy in patients with lung adenocarcinoma.

Saito Motonobu M Shiraishi Kouya K Matsumoto Kenji K Schetter Aaron J AJ Ogata-Kawata Hiroko H Tsuchiya Naoto N Kunitoh Hideo H Nokihara Hiroshi H Watanabe Shun-Ichi S Tsuta Koji K Kumamoto Kensuke K Takenoshita Seiichi S Yokota Jun J Harris Curtis C CC Kohno Takashi T

Clinical cancer research : an official journal of the American Association for Cancer Research 20140820 18

<h4>Purpose</h4>To examine the clinical utility of intratumor microRNAs (miRNA) as a biomarker for predicting responses to platinum-based doublet chemotherapy in patients with recurring lung adenocarcinoma (LADC).<h4>Experimental design</h4>The expression of miRNAs was examined in LADC tissues surgically resected from patients treated with platinum-based doublet chemotherapy at the time of LADC recurrence. Microarray-based screening of 904 miRNAs followed by quantitative reverse transcription-PC ...[more]

PMID: 25142144

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Project description:We developed a computational platform including machine learning and a mechanistic mathematical model to find the optimal protocol for administration of platinum-doublet chemotherapy in a palliative setting. The platform has been applied to advanced metastatic non-small cell lung cancer (NSCLC). The 42 NSCLC patients treated with palliative intent at Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, were collected from a retrospective cohort of patients diagnosed in 2004-2014. Patients were followed-up, for three years. Clinical data collected include complete information about the clinical course of the patients including treatment schedule, response according to RECIST classification, and survival. The core of the platform is the mathematical model, in the form of a system of ordinary differential equations, describing dynamics of platinum-sensitive and platinum-resistant cancer cells and interactions reflecting competition for space and resources. The model is simulated stochastically by sampling the parameter values from a joint probability distribution function. The machine learning model is applied to calibrate the mathematical model and to fit it to the overall survival curve. The model simulations faithfully reproduce the clinical cohort at three levels long-term response (OS), the initial response (according to RECIST criteria), and the relationship between the number of chemotherapy cycles and time between two consecutive chemotherapy cycles. In addition, we investigated the relationship between initial and long-term response. We showed that those two variables do not correlate which means that we cannot predict patient survival solely based on the initial response. We also tested several chemotherapy schedules to find the best one for patients treated with palliative intent. We found that the optimal treatment schedule depends, among others, on the strength of competition among various subclones in a tumor. The computational platform developed allows optimizing chemotherapy protocols, within admissible limits of toxicity, for palliative treatment of metastatic NSCLC. The simplicity of the method allows its application to chemotherapy optimization in different cancers.

Dataset Information

A three-microRNA signature predicts responses to platinum-based doublet chemotherapy in patients with lung adenocarcinoma.

Purpose

Experimental design

Results

Conclusion

Publications

A three-microRNA signature predicts responses to platinum-based doublet chemotherapy in patients with lung adenocarcinoma.

Similar Datasets

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