[Construction of artificial neural network model for predicting the efficacy of first-line FOLFOX chemotherapy for metastatic colorectal cancer]

Objective: To explore and establish an artificial neural network (ANN) model for predicting the efficacy of first-line FOLFOX chemotherapy for metastatic colorectal cancer. Methods: A set of FOLFOX chemotherapy data from a group of patients with metastatic colorectal cancer (mCRC) (GSE104645) was downloaded from the GEO database as a training set. According to the FOLFOX protocol, the efficacy was divided into two groups: the chemo-sensitive group (including complete response and partial response) and the chemo-resistant group (including stable disease and progressive disease), including 31 cases in the sensitive group and 23 in the resistant group. Then, chip data (accessible number: GSE69657) from Fujian Medical University Union Hospital were chosen as a test set. A total of 30 patients were enrolled in the study, including 13 in the sensitive group and 17 in the resistant group. The batch effect correction was performed on the expression values of the two sets of matrices using the R 3.5.1 software Combat package. The gene expression difference of sensitive and resistant group in GSE104645 was analyzed by the GEO2R platform. P<0.05 and the absolute value of log(2)FC>0.33 (FC abbreviation of fold change) were used as the threshold value to screen the drug resistance and sensitive genes of the FOLFOX regimen. An ANN was constructed using the multi-layer perceptron (MLP) to perform the FOLFOX regimen on the GSE104645 dataset. The GSE69657 expression matrix and clinical efficacy parameters were then used for retrospective verification. Receiver operating characteristic(ROC) curves were used to evaluate the test results and predictive power. Results: A total of 2, 076 differentially expressed genes in GSE104645 were selected, of which 822 genes were up-regulated and 1, 254 genes were down-regulated in the chemo-resistance group. The down-regulated genes were sensitive genes. GO analysis of the biological processes in which the differentially expressed genes were involved, revealed that they were mainly involved in the regulation of substance metabolism. A total of 39 genes were included in the final model construction. This was a neural network model with two hidden layers. The accuracy of predicting training samples and test samples was 75.7% and 76.5%, respectively, and the area under the ROC curve was 0.875. The chip data set of our department (GSE69657) was set as the test set, and the area under the ROC curve was 0.778. Conclusions: In this study, an artificial neural network model is successfully constructed to predict the efficacy of first-line FOLFOX regimen for metastatic colorectal cancer based on the microarray, and an independent external verification is also conducted. The model has good stability and well prediction efficiency. Besides, the results of this study suggest that the gene functions related to oxaliplatin resistance are mainly enriched in the regulation process of substance metabolism.

[A prognostic nomogram for metastasized colorectal cancer patients treated with cetuximab]

Objective: To identify the prognostic factors in metastatic colorectal cancer (mCRC) patients treated with cetuximab and establish a prognostic nomogram and validate its accuracy. Methods: A retrospective case-control study was conducted. Patients were selected as following criteria: patients with metastatic colorectal cancer(mCRC), which primary site confirmed by pathology and metastatic lesions confirmed by CT or MRI with at least one measurable and evaluable target lesion; patients' expected survival longer than 3 months; Eastern Cooperative Oncology Group (ECOG) score between 0 to 2; patients have signed informed consent; both KRAS and NRAS genes were wild-type; and at least 2 cycles of cetuximab combined with chemotherapy as the first-line regimen. Patients who met the following criteria were excluded: patients with incomplete clinicopathological and follow-up data; patients with severe diseases of vital organs such as heart, brain, lung, kidney, or other advanced malignant tumors; patients without informed consent. According to the above criteria, clinicopathological data of 95 patients with mCRC admitted in the Department of Medical Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine for first-line treatment with cetuximab from January 2010 to January 2017 were analyzed retrospectively. The Cox proportional hazards model was used to analyze the clinicopathological factors to determine the independent prognostic factors for progression-free survival(PFS). The R software was adopted to establish a prognostic nomogram model. Then, the nomograms of 6-month, 12-month and 18-month progression-free survivals (PFS) were drawn, and compared with the reality. The internal validation and accuracy of the nomogram were determined by the Bootstrap method and also the calculated concordance index (C-index). Results: The median follow-up time was 16.5 (2-43) months and the median PFS was 8.5 months. PFS at 6-,12- and 18-month was 73.7%, 35.8%, and 17.9%, respectively. ECOG score of 1-2 (HR=5.733, 95% CI:2.408-13.649, P<0.001), primary tumor was located in the ileocecal region (HR=5.880, 95% CI:1.645-21.023, P=0.006), Ki-67 index ≥45% (HR=3.574,95% CI:1.403-9.108,P=0.008), baseline D-dimer level ≥345 mg/L (HR=2.536,95% CI:1.531-7.396, P=0.012), NLR≥2.8 (HR=5.573,95% CI:2.107-14.740,P=0.001) and the combined treatment for FOLFOX (HR=0.465, 95% CI: 0.265-0.817, P=0.008) were independent risk factors for PFS of mCRC patients (all P<0.05). These independent risk factors were taken into account to construct a nomogram prediction model. The bootstrap method was used to perform internal validation, and the C-index of the nomogram prediction model in this study was 0.67 (95% CI: 0.64~0.71). The 6-, 12- and 18-month PFS predicted by the nomogram were consistent with the actual values. Conclusion: The nomogram model constructed by ECOG score, primary tumor site, Ki-67 index, baseline D-dimer level, baseline NLR and chemotherapy regimen may predict the prognosis of mCRC patients treated with cetuximab more accurately and individually, which can assist clinicians in making treatment decisions.