Pretreatment Transcriptional Profiling for Predicting Response to Neoadjuvant Chemoradiotherapy in Rectal Adenocarcinoma

Abstract

Purpose: Patients presenting with locally advanced rectal cancer currently receive preoperative radiotherapy with or without chemotherapy. Although pathologic complete response is achieved for approximately 10% to 30% of patients, a proportion of patients derive no benefit from this therapy while being exposed to toxic side effects of treatment. Therefore, there is a strong need to identify patients who are unlikely to benefit from neoadjuvant therapy to help direct them toward alternate and ultimately more successful treatment options.Experimental Design: In this study, we obtained expression profiles from pretreatment biopsies for 51 rectal cancer patients. All patients underwent preoperative chemoradiotherapy, followed by resection of the tumor 6 to 8 weeks posttreatment. Gene expression and response to treatment were correlated, and a supervised learning algorithm was used to generate an original predictive classifier and validate previously published classifiers.Results: Novel predictive classifiers based on Mandard's tumor regression grade, metabolic response, TNM (tumor node metastasis) downstaging, and normal tissue expression profiles were generated. Because there were only 7 patients who had minimal treatment response (>80% residual tumor), expression profiles were used to predict good tumor response and outcome. These classifiers peaked at 82% sensitivity and 89% specificity; however, classifiers with the highest sensitivity had poor specificity, and vice versa. Validation of predictive classifiers from previously published reports was attempted using this cohort; however, sensitivity and specificity ranged from 21% to 70%.Conclusions: These results show that the clinical utility of microarrays in predictive medicine is not yet within reach for rectal cancer and alternatives to microarrays should be considered for predictive studies in rectal adenocarcinoma. Clin Cancer Res; 17(9); 3039-47. Ó2011 AACR.

Comprehensive evaluation of the effectiveness of gene expression signatures to predict complete response to neoadjuvant chemoradiotherapy and guide surgical intervention in rectal cancer

“…Six different studies have previously attempted to identify gene expression signatures capable of predicting response to nCRT in rectal cancer patients using microarrays [12][13][14][15][16][17]. Gene lists for five of these six studies were available for testing on the RNA-Seq data generated for the 25 patients in our cohort (Table 4) The gene signature identified by Gahdimi et al [12] showed the highest accuracy (76%) in our cohort, with 66.7% sensitivity and 81.2% specificity.…”

“…Many studies have attempted to identify a clinically useful and reproducible gene expression signature capable of predicting response to nCRT using microarrays [12][13][14][15][16][17]. Most studies have focused on the identification of predictive signatures to distinguish "good" responders from "bad" responders and were primarily interested in the identification of patients that would benefit the most from nCRT and spare others from the potential toxicity of CRT.…”

“…Most studies have focused on the identification of predictive signatures to distinguish "good" responders from "bad" responders and were primarily interested in the identification of patients that would benefit the most from nCRT and spare others from the potential toxicity of CRT. However, definition of "good" response to nCRT M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 4 may not be straightforward and sometimes grouped together pCR and residual ypT3 or ypN1 when Tumor Regression Grade (TRG) alone was used for classification [12][13][14][15][16][17][18].…”

Biomarkers and Molecular Imaging as Predictors of Response to Neoadjuvant Chemoradiotherapy in Patients With Locally Advanced Rectal Cancer

“…In fact, the authors not only obtained limited sensitivity and specificity in their cohort, but also failed to confirm the power of previously published predictive classifiers, possibly as a result of the different biological characteristics of the sample sets or of transcription changes that were too small to be detected. 40 However, as highlighted by Watanabe et al, the use of qRT-PCR to construct a prediction model based on expression evaluation of a small number of significant genes selected in microarray studies is more feasible in clinical practice and allows for accurate and reproducible quantification of mRNA obtained from even small amounts of fresh-frozen or paraffin-embedded tissue. 39 …”

Organ-preservation strategies in rectal cancer: advances and challenges

“…Several studies have attempted to establish a clinically useful gene signature capable of predicting response to nCRT [40][41][42][43]. Even though more than 17 signatures have been reported with nearly 1000 genes, there is minimal overlap between genes in all of these signatures.…”