Expression of vascular endothelial growth factor as a predictor of complete response for preoperative chemoradiotherapy in rectal cancer

The Association of the Epidermal Growth Factor Receptor (EGFR) Immunoexpression With Prognostic Parameters in Adenocarcinoma Patients Receiving Neoadjuvant Treatment

The epidermal growth factor receptor (EGFR) expression is considered to play an essential role in the pathogenesis of colorectal adenocarcinoma. This study assessed the expression and predictive/prognostic value of EGFR expression in pre-op biopsy and post-op resection specimens in patients receiving neoadjuvant radiotherapy/neoadjuvant chemoradiotherapy (NRT/NCRT). Thirty-four consecutive patients were included in this study. The association between the prognostic features and EGFR immunohistochemical expression was analyzed in pre- (n=34) and post-treatment (n=22) tissue samples in cases with available tissue blocks. Of 34, 23 (67.6%) were men. The median age was 60.50 ± 10.69 (range, 31-84) years. EGFR expression was detected in 88.2% of biopsy specimens and in 91.2% of surgical specimens. There was only slight agreement between pre-op and post-op EGFR expression scores (kappa value 0.11). There was no significant correlation between pre-op and post-op EGFR expression scores (p>0.05). Although pre-op EGFR positivity and higher pre-op EGFR scores seemed to indicate a worse prognosis, this association between pre-op EGFR expression and overall survival (OS) or disease-specific survival (DSS) did not reach statistical significance (p>0.05). The only case with a post-op EGFR score of three who died of the disease experienced local recurrence and had distant metastasis. In conclusion, EGFR positivity in pre-op biopsy samples seems to be associated with shorter survival, and increased EGFR expression in post-treatment resection specimens predicts aggressive behavior in patients with rectal adenocarcinoma who received NRT/NCRT. However, due to the molecular heterogeneity, EGFR expression status should be evaluated in resection specimens rather than in pre-op biopsy samples for optimal prognosis prediction.

Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learning

OBJECTIVE

In this study, we aimed to investigate the predictive efficacy of magnetic resonance imaging (MRI) radiomics features at different time points of neoadjuvant therapy for rectal cancer in patients with pathological complete response (pCR). Furthermore, we aimed to develop and validate a radiomics space-time model (RSTM) using machine learning for artificial intelligence interventions in predicting pCR in patients.

METHODS

Clinical and imaging data of 83 rectal cancer patients were retrospectively analyzed, and the patients were classified as pCR and non-pCR patients according to their postoperative pathological results. All patients received one MRI examination before and after neoadjuvant therapy to extract radiomics features, including pre-treatment, post-treatment, and delta features. Delta features were defined by the ratio of the difference between the pre- and the post-treatment features to the pre-treatment feature. After feature dimensionality reduction based on the above three feature types, the RSTM was constructed using machine learning methods, and its performance was evaluated using the area under the curve (AUC).

RESULTS

The AUC values of the individual basic models constructed by pre-treatment, post-treatment, and delta features were 0.771, 0.681, and 0.871, respectively. Their sensitivity values were 0.727, 0.864, and 0.909, respectively, and their specificity values were 0.803, 0.492, and 0.656, respectively. The AUC, sensitivity, and specificity values of the combined basic model constructed by combining pre-treatment, post-treatment, and delta features were 0.901, 0.909, and 0.803, respectively. The AUC, sensitivity, and specificity values of the RSTM constructed using the K-Nearest Neighbor (KNN) classifier on the basis of the combined basic model were 0.944, 0.871, and 0.983, respectively. The Delong test showed that the performance of RSTM was significantly different from that of pre-treatment, post-treatment, and delta models (P < 0.05) but not significantly different from the combined basic model of the three (P > 0.05).

CONCLUSIONS

The RSTM constructed using the KNN classifier based on the combined features of before and after neoadjuvant therapy and delta features had the best predictive efficacy for pCR of neoadjuvant therapy. It may emerge as a new clinical tool to assist with individualized management of rectal cancer patients.

Can histologic features predict neoadjuvant therapy response in rectal adenocarcinoma?

Current standard therapy for locally advanced rectal cancer (LARC) is neoadjuvant therapy followed by surgical resection; however, treatment response is variable among patients. This study aimed to identify histologic features that predict tumor response. This retrospective study included 105 patients with LARC, all of whom underwent biopsy followed by neoadjuvant therapy and subsequent surgical resection. Each patient's initial biopsy was evaluated for tumor grade, tumor budding, intraepithelial lymphocytes, intraepithelial neutrophils, desmoplasia, apoptosis, adjacent stromal lymphocytes, signet ring cells, mucinous features, tumoral Paneth cells, dirty necrosis, microscopic ulceration, and prominent lymphoid aggregates. These histologic features, along with patient age at diagnosis and tumor microsatellite status, were compared to tumor regression grades from the respective resection specimens. No histologic factors in tumor biopsies predictive of treatment response in post-therapy resection specimens were identified. Histologic features in pre-therapy biopsy samples of LARC do not predict subsequent response to neoadjuvant therapy. Effective and reliable methods to predict response to neoadjuvant therapy in rectal cancer remain elusive.

High-Risk Clinicopathological and Genetic Features and Outcomes in Patients Receiving Neoadjuvant Radiochemotherapy for Locally Advanced Rectal Cancer

Administering preoperative radiochemotherapy (RCT) in stage II-III tumors to locally advanced rectal carcinoma patients has proved to be effective in a high percentage of cases. Despite this, 20-30% of patients show no response or even disease progression. At present, preoperative response is assessed by a combination of imaging and tumor regression on histopathology, but recent studies suggest that various genetic abnormalities may be associated with the sensitivity or resistance of rectal cancer tumor cells to neoadjuvant therapy. In the present study we investigated the relationship between genetic lesions detected by high-density single-nucleotide polymorphisms (SNP) arrays 6.0 and response to neoadjuvant RCT, evaluated according to Dworak criteria in 39 rectal cancer tumors before treatment. The highest frequency of copy-number (CN) losses detected corresponded to chromosomes 18q (n = 27; 69%), 1p (n = 22; 56%), 15q (n = 19; 49%), 8p (n = 18; 48%), 4q (n = 17; 46%), and 22q (n = 17; 46%); in turn, CN gains more frequently involved chromosomes 20p (n = 22; 56%), 8p (n = 20; 51%), and 15q (n = 16; 41%). There was a significant association between alterations in the 1p, 3q, 7q, 12p, 17q, 20p, and 22q chromosomal regions and the degree of response to therapy prior to surgery. However, 4q, 15q11.1, and 15q14 chromosomal region alterations were identified as important by five prediction algorithms, i.e., those with the greatest influence on predicting the tumor response to treatment with preoperative RCT. Multivariate analysis of prognostic factors showed that gains on 15q11.1 and carcinoembryonic antigen (CEA) levels serum at diagnosis were the only independent variables predicting disease-free survival (DFS). Lymph node involvement also showed a prognostic impact on overall survival (OS) in the multivariate analysis. A deep-learning-based algorithm showed a 100% success rate in predicting both DFS and OS at 60 months after diagnosis of the disease. In summary, our results indicate the existence of an association between tumor genetic abnormalities at diagnosis, response to neoadjuvant therapy, and survival of patients with locally advanced rectal cancer. In addition to the clinical and biological characteristics of locally advanced rectal cancer patients, these could be used in the future as therapeutic and prognostic biomarkers, to identify patients sensitive or resistant to preoperative treatment, helping guide therapeutic decision-making. Additional prospective studies in larger series of patients are required to confirm the clinical utility of the newly identified biomarkers.