Development of a long non-coding RNA signature for prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal adenocarcinoma

Abstract

Standard treatment for locally advanced rectal adenocarcinoma (LARC) includes a combination of chemotherapy with pyrimidine analogues, such as capecitabine, and radiation therapy, followed by surgery. Currently no clinically useful genomic predictors of benefit from neoadjuvant chemoradiotherapy (nCRT) exist for LARC. In this study we assessed the expression of 8,127 long noncoding RNAs (lncRNAs), poorly studied in LARC, to infer their ability in classifying patients' pathological complete response (pCR). We collected and analyzed, using lncRNA-specific Agilent microarrays a consecutive series of 61 LARC cases undergoing nCRT. Potential lncRNA predictors in responders and non-responders to nCRT were identified with LASSO regression, and a model was optimized using k-fold cross-validation after selection of the three most informative lncRNA. 11 lncRNAs were differentially expressed with false discovery rate < 0.01 between responders and non-responders to NACT. We identified lnc-KLF7-1, lnc-MAB21L2-1, and LINC00324 as the most promising variable subset for classification building. Overall sensitivity and specificity were 0.91 and 0.94 respectively, with an AUC of our ROC curve = 0.93. Our study shows for the first time that lncRNAs can accurately predict response in LARC undergoing nCRT. Our three-lncRNA based signature must be independently validated and further analyses must be conducted to fully understand the biological role of the identified signature, but our results suggest lncRNAs may be an ideal biomarker for response prediction in the studied setting.

Fig 1. Consort-like flow diagram of the study.

Fig 2

(A) principal component analysis shows the position of each sample on a bi-dimensional graph. The x- and y-axes represent the first (PC1) and the second (PC2) principal component respectively. Red dots are responder patients, whereas blue dots are non-responder patients as defined in the Methods section. (B) Heatmap plot identifies gene expression patters of 11 lncRNAs in 30 LARC samples. The x-axis represents the class of samples (nr = non-responder, R = responder). The y-axis represents transcript expression. Each cell shows a color that ranges from dark blue to yellow. The darker the blue is, the lower the expression is and vice versa.

Fig 3

Comparison of gene expression profiles of the two transcripts, (A) LINC00261 and (B) LINC00324. Y-axis represents log2-transformed normalized expression, whereas x-axis show the responder and non-responder groups.

Fig 4

(A) needle plot of the SVM based on polynomial kernels. The graph shows feature importance calculated determining AUC for each predictor. The ranking allows to assess the minimum number of features to design an optimized simpler classifier. (B) ROC curve shows the diagnostic performance of lncRNAs signature. Green and blue areas represent partial area under the curves (pAUC), corresponding to clinically relevant regions of sensibility and specificity.