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.
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.
|Song, Shumei; Ajani, Jaffer A; Honjo, Soichiro et al. (2014) Hippo coactivator YAP1 upregulates SOX9 and endows esophageal cancer cells with stem-like properties. Cancer Res 74:4170-82|
|Shiozaki, Hironori; Sudo, Kazuki; Xiao, Lianchun et al. (2014) Distribution and timing of distant metastasis after local therapy in a large cohort of patients with esophageal and esophagogastric junction cancer. Oncology 86:336-9|
|Skinner, Heath D; Lee, Jeffrey H; Bhutani, Manoop S et al. (2014) A validated miRNA profile predicts response to therapy in esophageal adenocarcinoma. Cancer 120:3635-41|
|Ajani, J A; Wang, X; Song, S et al. (2014) ALDH-1 expression levels predict response or resistance to preoperative chemoradiation in resectable esophageal cancer patients. Mol Oncol 8:142-9|
|Song, Shumei; Ajani, Jaffer A (2013) The role of microRNAs in cancers of the upper gastrointestinal tract. Nat Rev Gastroenterol Hepatol 10:109-18|
|Sudo, Kazuki; Taketa, Takashi; Correa, Arlene M et al. (2013) Locoregional failure rate after preoperative chemoradiation of esophageal adenocarcinoma and the outcomes of salvage strategies. J Clin Oncol 31:4306-10|
|Wadhwa, Roopma; Song, Shumei; Lee, Ju-Seog et al. (2013) Gastric cancer-molecular and clinical dimensions. Nat Rev Clin Oncol 10:643-55|
|Xu, Enping; Gong, Yilei; Gu, Jian et al. (2013) Risk assessment of esophageal adenocarcinoma using ýý-H2AX assay. Cancer Epidemiol Biomarkers Prev 22:1797-804|
|Cho, Jae Yong; Lim, Jae Yun; Cheong, Jae Ho et al. (2011) Gene expression signature-based prognostic risk score in gastric cancer. Clin Cancer Res 17:1850-7|
|Suzuki, Akihiro; Xiao, Lianchun; Hayashi, Yuki et al. (2011) Prognostic significance of baseline positron emission tomography and importance of clinical complete response in patients with esophageal or gastroesophageal junction cancer treated with definitive chemoradiotherapy. Cancer 117:4823-33|
Showing the most recent 10 out of 11 publications