Our objective is to develop a PCR-based ~10-gene signature, through gene expression analyses, that can predict all three subtypes of pathologic responses (with high accuracy) following chemoradiation therapy in patients with esophageal cancer who undergo chemoradiation followed by surgery (Tri-modality [TM] therapy). The three pathologic subtypes are: pathologic complete response (pathCR), partial response, and extreme chemoradiation-resistance (exCRTR). One can conceive a therapeutic approach suited for each outcome (e.g., avoid chemoradiation in patients whose cancer has an exCRTR). Today however, there are no tools to optimize therapy for these outcomes since we cannot predict them before therapy. A predictive signature that has a high level (=80%) of specificity and a reasonable level of sensitivity (=45%) would be an advance. Our hypothesis is that a practical molecular signature can be established through gene expression profiling to predict three subgroups prior to TM therapy. In our 19-patient gene expression profiling study, the unsupervised hierarchical cluster analysis segregated cancers into two subtypes. Five of 6 pathCR patients clustered in subtype I and one pathCR patient clustered in subtype II. We discovered that Sonic Hedgehog and NF-kB-related genes appear to mediate chemoradiation-resistance. We were able to independently validate this. In a gene expression analysis of 47 TM patients (Specific Aim 0), we used 17 genes (10% false-discovery rate) to construct a multivariate model to predict response. For each gene g, we first computed the residuals Rg,i from a linear model of the form , where Yg,i is the expression of gene g in sample i, t(i) is the subtype of sample i, and Sg,t(i) is the mean expression of gene g in samples of that subtype. We then used the residuals as predictors in an ordinal regression model to predict the outcome categories. We used the Akaike Information Criterion (AIC) to remove unnecessary variables from the model. The final model involved 7 genes: RiskScore=1.59 TMEM46 + 0.68 THBS1 -1.52 LOC442578 - 2.14 SRM 1.16 CHST4 + 0.83 DES + 1.14 SDS, with a cutoff between pathCR and partial response at -1.56 and a cutoff between partial response and exCRTR at 3.72. Four of these seven genes are related to Sonic Hedgehog pathway and 2 are NF-kB targets. In this proposal, data from 120 TM patients to be analyzed through a funded grant (R21CA127612) will be added to a new cohort of 120 TM patients (Specific Aim 1) to establish a large (n=240) training (discovery) set. We will identify best performing ~100 genes through microfluidic card technology.
Specific Aim 2 will validate ~100 best genes and refine the model to select ~10 best performing genes for predicting three outcomes.
Specific Aim 3 will prospectively validate the ~10-gene signature. A continuous """"""""risk score"""""""" for the outcome will be computed. Specificity and sensitivity will be determined by generating receiver-operating (ROC) curves for optimizing the prediction boundaries.

Public Health Relevance

This proposal is an early attempt to individualize therapy based on molecular biology for patients with esophageal cancer. Our goal is to pave the way for a strategy in the future that will allow administration of effective therapy, improve safety, and preserve the esophagus in some patients.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Cancer Biomarkers Study Section (CBSS)
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Thurin, Magdalena
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University of Texas MD Anderson Cancer Center
Internal Medicine/Medicine
Other Domestic Higher Education
United States
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Harada, Kazuto; Wang, Xuemei; Shimodaira, Yusuke et al. (2018) Early Metabolic Change after Induction Chemotherapy Predicts Histologic Response and Prognosis in Patients with Esophageal Cancer: Secondary Analysis of a Randomized Trial. Target Oncol 13:99-106
Amlashi, Fatemeh G; Wang, Xuemei; Davila, Raquel E et al. (2018) Barrett's Esophagus after Bimodality Therapy in Patients with Esophageal Adenocarcinoma. Oncology 95:81-90
Elimova, Elena; Wang, Xuemei; Qiao, Wei et al. (2018) Actionable Locoregional Relapses after Therapy of Localized Esophageal Cancer: Insights from a Large Cohort. Oncology 94:345-353
Song, Shumei; Xie, Min; Scott, Ailing W et al. (2018) A Novel YAP1 Inhibitor Targets CSC-Enriched Radiation-Resistant Cells and Exerts Strong Antitumor Activity in Esophageal Adenocarcinoma. Mol Cancer Ther 17:443-454
Harada, Kazuto; Pool Pizzi, Melissa; Baba, Hideo et al. (2018) Cancer stem cells in esophageal cancer and response to therapy. Cancer 124:3962-3964
Harada, Kazuto; Song, Shumei; Ajani, Jaffer A (2018) Attenuation of YAP1 can potentially target cancer stem cells to overcome drug resistance. Oncoscience 5:214-215
Mizrak Kaya, Dilsa; Nogueras-González, Graciela M; Harada, Kazuto et al. (2018) Risk of peritoneal metastases in patients who had negative peritoneal staging and received therapy for localized gastric adenocarcinoma. J Surg Oncol 117:678-684
Harada, Kazuto; Mizrak Kaya, Dilsa; Baba, Hideo et al. (2018) Immune checkpoint blockade therapy for esophageal squamous cell carcinoma. J Thorac Dis 10:699-702
Harada, Kazuto; Mizrak Kaya, Dilsa; Shimodaira, Yusuke et al. (2017) Translating genomic profiling to gastrointestinal cancer treatment. Future Oncol 13:919-934
Elimova, Elena; Slack, Rebecca S; Chen, Hsiang-Chun et al. (2017) Patterns of relapse in patients with localized gastric adenocarcinoma who had surgery with or without adjunctive therapy: costs and effectiveness of surveillance. Oncotarget 8:81430-81440

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