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Purpose: Tumor location and tumor node metastasis (TNM) stage guide treatment decisions in colorectal cancer (CRC) patients. However, patients with the same disease stage do not benefit equally from adjuvant therapy. Hence, there remains an urgent clinical need to identify prognostic and/or predictive biomarker(s) to personalize treatment decisions. In this exploratory study, we investigated whether our previously defined metabolic Warburg-subtypes can predict which CRC patients might derive survival benefit from adjuvant therapy. Methods: Information regarding treatment (surgery only: n = 1451; adjuvant radiotherapy: n = 82; or adjuvant chemotherapy: n = 260) and Warburg-subtype (Warburg-low: n = 485, -moderate: n = 641, or –high: n = 667) was available for 1793 CRC patients from the Netherlands Cohort Study (NLCS). Kaplan–Meier curves and Cox regression models were used to investigate survival benefit from adjuvant therapy compared to surgery-only for the different Warburg-subtypes. Results: Patients with Warburg-moderate CRC (HRCRC-specific 0.64; 95% CI 0.47–0.86, HRoverall 0.61; 95% CI 0.47–0.80), and possibly Warburg-high CRC (HRCRC-specific 0.86; 95% CI 0.65–1.14, HRoverall 0.82; 95% CI 0.64–1.05), had survival benefit from adjuvant therapy. No survival benefit was observed for patients with Warburg-low CRC (HRCRC-specific 1.07; 95% CI 0.76–1.52, HRoverall 0.95; 95% CI 0.70–1.30). There was a significant interaction between Warburg-subtype and adjuvant therapy for CRC-specific survival (p = 0.049) and overall survival (p = 0.035). Conclusion: Our results suggest that Warburg-subtypes may predict survival benefit from adjuvant therapy in CRC patients. A survival benefit from adjuvant therapy was observed for patients with Warburg-moderate and possibly Warburg-high CRC, but not for patients with Warburg-low CRC. Future prospective studies are necessary to validate our findings. [ABSTRACT FROM AUTHOR]

Background: Previous research suggests that Warburg‐subtypes are related to potentially important survival differences in colorectal cancer (CRC) patients. In the present study, we investigated whether mutational subgroups based on somatic mutations in RAS, BRAF, PIK3CA, and MET, which are known to promote the Warburg‐effect, as well as mismatch repair (MMR) status, hold prognostic value in CRC. In addition, we investigated whether Warburg‐subtypes provide additional prognostic information, independent of known prognostic factors like TNM stage. Methods: CRC patients (n = 2344) from the prospective Netherlands Cohort Study (NLCS) were classified into eight mutually exclusive mutational subgroups, based on observed mutations in RAS, BRAF, PIK3CA, and MET, and MMR status: All‐wild‐type + MMRproficient, KRASmut + MMRproficient, KRASmut + PIK3CAmut + MMRproficient, PIK3CAmut + MMRproficient, BRAFmut + MMRproficient, BRAFmut + MMRdeficient, other + MMRproficient, and other + MMRdeficient. Kaplan–Meier curves and Cox regression models were used to investigate associations between mutational subgroups and survival, as well as associations between our previously established Warburg‐subtypes and survival within these mutational subgroups. Results: Compared to patients with all‐wild‐type + MMRproficient CRC, patients with KRASmut + MMRproficient, KRASmut + PIK3CAmut + MMRproficient, BRAFmut + MMRproficient, or other + MMRproficient CRC had a statistically significant worse survival (HRCRC‐specific ranged from 1.29 to 1.88). In contrast, patients with other + MMRdeficient CRC had the most favorable survival (HRCRC‐specific 0.48). No statistically significant survival differences were observed for the Warburg‐subtypes within mutational subgroups. Conclusion: Our results highlight the prognostic potential of mutational subgroups in CRC. Warburg‐subtypes did not provide additional prognostic information within these mutational subgroups. Future larger‐scale prospective studies are necessary to validate our findings and to examine the potential clinical utility of CRC subtyping based on mutational subgroups. [ABSTRACT FROM AUTHOR]

Introduction: KRAS mutations (KRASmut), PIK3CAmut, BRAFmut, and mismatch repair deficiency (dMMR) have been associated with the Warburg-effect. We previously observed differential associations between energy balance-related factors (BMI, clothing-size, physical activity) and colorectal cancer (CRC) subtypes based on the Warburg-effect. We now investigated whether associations between energy balance-related factors and risk of CRC differ between subgroups based on mutation and MMR status. Methods: Information on molecular features was available for 2349 incident CRC cases within the Netherlands Cohort Study (NLCS), with complete covariate data available for 1934 cases and 3911 subcohort members. Multivariable-adjusted Cox-regression was used to estimate associations of energy balance-related factors with risk of CRC based on individual molecular features (KRASmut; PIK3CAmut; BRAFmut; dMMR) and combinations thereof (all-wild-type + MMR-proficient (pMMR); any-mutation/dMMR). Results: In men, BMI and clothing-size were positively associated with risk of colon, but not rectal cancer, regardless of molecular features subgroups; the strongest associations were observed for PIK3CAmut colon cancer. In women, however, BMI and clothing-size were only associated with risk of KRASmut colon cancer (p-heterogeneityKRASmut versus all-wild-type+pMMR = 0.008). Inverse associations of non-occupational physical activity with risk of colon cancer were strongest for any-mutation/dMMR tumors in men and women, and specifically for PIK3CAmut tumors in women. Occupational physical activity was inversely associated with both combination subgroups of colon cancer in men. Conclusion: In men, associations did not vary according to molecular features. In women, a role of KRAS mutations in the etiological pathway between adiposity and colon cancer is suggested, and of PIK3CA mutations between physical activity and colon cancer. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) remains one of the most prevalent and deadly cancers worldwide. The tumour-node-metastasis stage (TNM) is currently the most clinically important tool to predict prognosis for CRC patients. However, patients with the same TNM stage can have different prognoses. The metabolic status of tumour cells (Warburg-subtype) has been proposed as potential prognostic factor in CRC. However, potential biological mechanisms underlying the relationship between Warburg-subtype and prognosis have not been investigated in detail. One potential mechanism could be that the metabolic status of tumour cells affects the tumour microenvironment (TME). Our objective was to investigate the relationship between Warburg-subtypes and the TME. Haematoxylin/Eosin stained tumour tissue microarray cores from 2171 CRC patients from the Netherlands Cohort Study were semi quantitatively assessed for tumour infiltrating lymphocytes (TILs) and relative tumour stroma content. 5745 cores were assessed by putting each core in one of four categories for both TILs and stroma. The relationship between Warburg-subtype, TILs, and tumour stroma content was investigated. The frequency of CRC in the different TIL categories was (n, %): very low (2538, 44.2), low (2463, 42.9), high (722, 12.6), and very high (22, 0.4). The frequency of CRC in the different tumour stroma content categories was: ≤ 25% (2755, 47.9), > 25% ≤ 50% (1553, 27) > 50% ≤ 75% (905, 15.8), and > 75% (532, 9.3). There was neither an association between Warburg-subtype and tumour stroma content (p = 0.229) nor between Warburg-subtype and TILs (p = 0.429). This is the first study to investigate the relationship between Warburg-subtypes and the TME in a large population-based series of CRC patients. Our data suggest that the prognostic value of Warburg-subtypes cannot be directly attributed to differences in TILs or tumour stroma content. Our results require confirmation in an independent series. [ABSTRACT FROM AUTHOR]

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem. [Display omitted] • AI-based prediction of biomarkers (MSI, BRAF , and KRAS) using transformers • MSI prediction reaches clinical-grade performance on biopsies of colorectal cancer • Transformer-based biomarker prediction generalizes better and is more data efficient • Large-scale multi-cohort evaluation on over 13,000 patients from 16 cohorts Wagner et al. show that transformer-based prediction of biomarkers from histology substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. The method significantly outperforms existing approaches for microsatellite instability detection in surgical resections and reaches clinical-grade performance on biopsies of colorectal cancer, solving a long-standing diagnostic problem. [ABSTRACT FROM AUTHOR]