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Background and Objective: Treatment approaches for colorectal cancer (CRC) are highly dependent on the molecular subtype, as immunotherapy has shown efficacy in cases with microsatellite instability (MSI) but is ineffective for the microsatellite stable (MSS) subtype. There is promising potential in utilizing deep neural networks (DNNs) to automate the differentiation of CRC subtypes by analyzing hematoxylin and eosin (H&E) stained whole-slide images (WSIs). Due to the extensive size of WSIs, multiple instance learning (MIL) techniques are typically explored. However, existing MIL methods focus on identifying the most representative image patches for classification, which may result in the loss of critical information. Additionally, these methods often overlook clinically relevant information, like the tendency for MSI class tumors to predominantly occur on the proximal (right side) colon., Methods: We introduce 'CIMIL-CRC', a DNN framework that: (1) solves the MSI/MSS MIL problem by efficiently combining a pre-trained feature extraction model with principal component analysis (PCA) to aggregate information from all patches, and (2) integrates clinical priors, particularly the tumor location within the colon, into the model to enhance patient-level classification accuracy. We assessed our CIMIL-CRC method using the average area under the receiver operating characteristic curve (AUROC) from a 5-fold cross-validation experimental setup for model development on the TCGA-CRC-DX cohort, contrasting it with a baseline patch-level classification, a MIL-only approach, and a clinically-informed patch-level classification approach., Results: Our CIMIL-CRC outperformed all methods (AUROC: 0.92±0.002 (95% CI 0.91-0.92), vs. 0.79±0.02 (95% CI 0.76-0.82), 0.86±0.01 (95% CI 0.85-0.88), and 0.87±0.01 (95% CI 0.86-0.88), respectively). The improvement was statistically significant. To the best of our knowledge, this is the best result achieved for MSI/MSS classification on this dataset., Conclusion: Our CIMIL-CRC method holds promise for offering insights into the key representations of histopathological images and suggests a straightforward implementation., Competing Interests: Declaration of competing interest We affirm that there exist no financial or personal affiliations with individuals or organizations that could unjustly sway our research. Further, we have no professional or personal interests, of any nature, in any products, services, and/or companies that could be interpreted as influencing the stance taken in, or the review of, the manuscript under consideration., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While convolutional neural networks (CNNs) have recently been introduced for automated CRC subtype identification using H&E stained histopathological images, the correlation between CRC subtype genomic variants and their corresponding cellular morphology expressed by their imaging phenotypes is yet to be fully explored. The goal of this study was to determine such correlations by incorporating genomic variants in CNN models for CRC subtype classification from H&E images. We utilized the publicly available TCGA-CRC-DX dataset, which comprises whole slide images from 360 CRC-diagnosed patients (260 for training and 100 for testing). This dataset also provides information on CRC subtype classifications and genomic variations. We trained CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology patterns. We assessed the interplay between CRC subtypes' genomic variations and cellular morphology patterns by evaluating the CRC subtype classification accuracy of the different models in a stratified 5-fold cross-validation experimental setup using the area under the ROC curve (AUROC) and average precision (AP) as the performance metrics. The CNN models that account for potential correlation between genomic variations within CRC subtypes and their cellular morphology pattern achieved superior accuracy compared to the baseline CNN classification model that does not account for genomic variations when using either single-nucleotide-polymorphism (SNP) molecular features (AUROC: 0.824±0.02 vs. 0.761±0.04, p<0.05, AP: 0.652±0.06 vs. 0.58±0.08) or CpG-Island methylation phenotype (CIMP) molecular features (AUROC: 0.834±0.01 vs. 0.787±0.03, p<0.05, AP: 0.687±0.02 vs. 0.64±0.05). Combining the CNN models account for variations in CIMP and SNP further improved classification accuracy (AUROC: 0.847±0.01 vs. 0.787±0.03, p = 0.01, AP: 0.68±0.02 vs. 0.64±0.05). The improved accuracy of CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology as expressed by H&E imaging phenotypes may elucidate the biological cues impacting cancer histopathological imaging phenotypes. Moreover, considering CRC subtypes genomic variations has the potential to improve the accuracy of deep-learning models in discerning cancer subtype from histopathological imaging data., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Hezi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: Diagnosis of Mismatch Repair Deficiency (MMRD) is crucial for tumor management and early detection in patients with the cancer predisposition syndrome constitutional mismatch repair deficiency (CMMRD). Current diagnostic tools are cumbersome and inconsistent both in childhood cancers and in determining germline MMRD., Patients and Methods: We developed and analyzed a functional Low-pass Genomic Instability Characterization (LOGIC) assay to detect MMRD. The diagnostic performance of LOGIC was compared with that of current established assays including tumor mutational burden, immunohistochemistry, and the microsatellite instability panel. LOGIC was then applied to various normal tissues of patients with CMMRD with comprehensive clinical data including age of cancer presentation., Results: Overall, LOGIC was 100% sensitive and specific in detecting MMRD in childhood cancers (N = 376). It was more sensitive than the microsatellite instability panel (14%, P = 4.3 × 10 -12 ), immunohistochemistry (86%, P = 4.6 × 10 -3 ), or tumor mutational burden (80%, P = 9.1 × 10 -4 ). LOGIC was able to distinguish CMMRD from other cancer predisposition syndromes using blood and saliva DNA ( P < .0001, n = 277). In normal cells, MMRDness scores differed between tissues (GI > blood > brain), increased over time in the same individual, and revealed genotype-phenotype associations within the mismatch repair genes. Importantly, increased MMRDness score was associated with younger age of first cancer presentation in individuals with CMMRD ( P = 2.2 × 10 -5 )., Conclusion: LOGIC was a robust tool for the diagnosis of MMRD in multiple cancer types and in normal tissues. LOGIC may inform therapeutic cancer decisions, provide rapid diagnosis of germline MMRD, and support tailored surveillance for individuals with CMMRD.