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Simple Summary: A subset of patients with rectal cancer are treated before surgery with chemoradiation. Unfortunately, this neoadjuvant chemoradiotherapy does not have the preferred effect of tumor downstaging in all patients, but does bring substantial side effects and possible complications. A pre-treatment prediction based on available parameters might provide a means to better select therapy for individual patients. Genomic mutational status of pre-treatment biopsies may provide prognostic information, however, it also might be influenced by tumor heterogeneity. This study investigates whether pre-treatment biopsy material is a reliable way of defining mutational status in rectal cancer. Neoadjuvant therapy before surgical resection is indicated for patients with locally advanced rectal cancer. However, a significant number of patients show minimal or no response to neoadjuvant therapy. Unfortunately, we are currently unable to predict response and identify non-responding patients before neoadjuvant treatment is given. Genomic mutational status might provide valuable prognostic information. However, it is unclear whether predictions based on genomic mutational status in single preoperative biopsies are reliable due to intra-tumoral heterogeneity. In this study we aim to investigate the reliability of genomic mutations found in single pre-operative biopsies by comparing genomic mutations to four other locations within the same tumor using next generation sequencing. Rectal cancer patients undergoing primary resection without neoadjuvant therapy were included. From each patient, one biopsy, two deep and two superficial samples were obtained and sequenced using a targeted next generation sequencing gene panel. Concordance between these five samples was assessed. In this feasibility study we included 11 patients. In 7 out of 11 (64%) patients, all 5 samples showed concordant mutations. In 4 out of 11 patients (36%) discordant mutations were observed. In conclusion, assessment of mutational status on a single pre-operative biopsy shows discordance with tumor tissue from other locations in 36% of cases. These results warrant careful interpretation of biopsy material analysis, as these might be influenced by tumor heterogeneity. [ABSTRACT FROM AUTHOR]

Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform.