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Identification of Novel Biomarkers in Pancreatic Tumor Tissue to Predict Response to Neoadjuvant Chemotherapy.

ABSTRACT: Background: Neoadjuvant chemotherapy (NAC) has been of recent interest as an alternative to upfront surgery followed by adjuvant chemotherapy in patients with pancreatic ductal adenocarcinoma (PDAC). However, a subset of patients does not respond to NAC and may have been better managed by upfront surgery. Hence, there is an unmet need for accurate biomarkers for predicting NAC response in PDAC. We aimed to identify upregulated proteins in tumor tissue from poor- and good-NAC responders. Methods: Tumor and adjacent pancreas tissue samples were obtained following surgical resection from NAC-treated PDAC patients. SWATH-MS proteomic analysis was performed to identify and quantify proteins in tissue samples. Statistical analysis was performed to identify biomarkers for NAC response. Pathway analysis was performed to characterize affected canonical pathways in good- and poor-NAC responders. Results: A total of 3,156 proteins were identified, with 19 being were significantly upregulated in poor-responders compared to good-responders (log2 ratio > 2, p < 0.05). Those with the greatest ability to predict poor-NAC response were GRP78, CADM1, PGES2, and RUXF. Notably, canonical pathways that were significantly upregulated in good-responders included acute phase signaling and macrophage activation, indicating a heightened immune response in these patients. Conclusion: A novel biomarker signature for poor-NAC response in PDAC was identified.

SUBMITTER: Sahni S

PROVIDER: S-EPMC7064619 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of Novel Biomarkers in Pancreatic Tumor Tissue to Predict Response to Neoadjuvant Chemotherapy.

Sahni Sumit S Nahm Christopher C Krisp Christoph C Molloy Mark P MP Mehta Shreya S Maloney Sarah S Itchins Malinda M Pavlakis Nick N Clarke Stephen S Chan David D Gill Anthony J AJ Howell Viive M VM Samra Jaswinder J Mittal Anubhav A

Frontiers in oncology 20200304

<b>Background:</b> Neoadjuvant chemotherapy (NAC) has been of recent interest as an alternative to upfront surgery followed by adjuvant chemotherapy in patients with pancreatic ductal adenocarcinoma (PDAC). However, a subset of patients does not respond to NAC and may have been better managed by upfront surgery. Hence, there is an unmet need for accurate biomarkers for predicting NAC response in PDAC. We aimed to identify upregulated proteins in tumor tissue from poor- and good-NAC responders. < ...[more]

PMID: 32195182

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Project description:BackgroundAccurate evaluation of the response to neoadjuvant chemotherapy (NAC) provides important information about systemic therapies for breast cancer, which implies pharmacological response, prognosis, and guide further therapy. Gene profiles overcome the shortcomings of the relatively limited detection indicators of the classical pathological evaluation criteria and the subjectivity of observation, but are complicated and expensive. Therefore, it is essential to develop a more accurate, repeatable, and economical evaluation approach for neoadjuvant chemotherapy responses.MethodsWe analyzed the transcriptional profiles of chemo-resistant breast cancer cell lines and tumors of chemo-resistant breast cancer patients in the GSE25066 dataset. We preliminarily screened out common significantly differentially expressed genes and constructed a NAC response risk model using LASSO regression and univariate and multivariate analyses. The differences in bioinformatic features of tumor cells, immune characteristics, and prognosis were compared between high and low-risk group. The potential drugs that could reverse chemotherapy resistance in breast cancer were screened by the CMap database.ResultsThirty-six genes were commonly up/down-regulated in both NAC chemo-resistant tumors and cells compared to the sensitive tumors and wild-type cells. Through LASSO regression, we obtained a risk model composed of 12 genes. The risk model divided patients into high and low-risk groups. Univariate and multivariate Cox regression analyses suggested that the risk score is an independent prognostic factor for evaluating NAC response in breast cancer. Tumors in risk groups exhibited significant differences in molecular biological characteristics, tumor-infiltrating lymphocytes, and immunosuppressive molecule expression. Our results suggested that the risk score was also a good prognostic factor for breast cancer. Finally, we screened potential drugs that could reverse chemotherapy resistance in breast cancer.ConclusionA novel 12 gene-signature could be used to predict NAC response and predict prognosis in breast cancer.

Dataset Information

Identification of Novel Biomarkers in Pancreatic Tumor Tissue to Predict Response to Neoadjuvant Chemotherapy.

Publications

Identification of Novel Biomarkers in Pancreatic Tumor Tissue to Predict Response to Neoadjuvant Chemotherapy.

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