Effectiveness of gene expression profiling for response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy

Abstract

Purpose: There is a wide spectrum of tumor responsiveness of rectal adenocarcinomas to preoperative chemoradiotherapy ranging from complete response to complete resistance. This study aimed to investigate whether parallel gene expression profiling of the primary tumor can contribute to stratification of patients into groups of responders or nonresponders.

Patients and methods: Pretherapeutic biopsies from 30 locally advanced rectal carcinomas were analyzed for gene expression signatures using microarrays. All patients were participants of a phase III clinical trial (CAO/ARO/AIO-94, German Rectal Cancer Trial) and were randomized to receive a preoperative combined-modality therapy including fluorouracil and radiation. Class comparison was used to identify a set of genes that were differentially expressed between responders and nonresponders as measured by T level downsizing and histopathologic tumor regression grading.

Results: In an initial set of 23 patients, responders and nonresponders showed significantly different expression levels for 54 genes (P < .001). The ability to predict response to therapy using gene expression profiles was rigorously evaluated using leave-one-out cross-validation. Tumor behavior was correctly predicted in 83% of patients (P = .02). Sensitivity (correct prediction of response) was 78%, and specificity (correct prediction of nonresponse) was 86%, with a positive and negative predictive value of 78% and 86%, respectively.

Conclusion: Our results suggest that pretherapeutic gene expression profiling may assist in response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy. The implementation of gene expression profiles for treatment stratification and clinical management of cancer patients requires validation in large, independent studies, which are now warranted.

Conflict of interest statement

Authors’ Disclosures of Potential Conflicts of Interest

The authors indicated no potential conflicts of interest.

Figures

Fig 1

Pictorial presentation of specimen accrual, clinical diagnosis, and experimental design. cUICC refers to pretherapeutic clinical staging of tumors. ypUICC refers to histopathologic assessment of the resected specimens after completion of preoperative therapy. UICC, International Union Against Cancer; FU, fluorouracil.

Fig 2

Flow of statistical analyses for cDNA and oligonucleotide data. Gray ovals represent data, rectangles represent statistical procedures, and round-edged boxes indicate results. The figure shows the three main statistical analyses performed. Class prediction (red) and class comparison (blue) for cDNA data and cross-platform normalization for the classification of oligonucleotide data (green) is shown.

Fig 3

Hierarchical cluster analysis of 23 patients based on the 54 most significantly changed genes (P < .001) when using T level downsizing. Red indicates increased expression, and green indicates decreased expression. Gene symbols and fold change between the groups are listed to the right. Values less than 1 reflect downregulation in the class of responders, whereas values more than 1 reflect upregulation. P, patient.