Identifying Recurrences Among Non-Metastatic Colorectal Cancer Patients Using National Health Data Registries: Validation and Optimization of a Registry-Based Algorithm in a Modern Danish Cohort.

Purpose: Colorectal cancer (CRC) recurrence is not routinely recorded in Danish health data registries. Here, we aimed to revalidate a registry-based algorithm to identify recurrences in a contemporary cohort and to investigate the accuracy of estimating the time to recurrence (TTR). Patients and Methods: We ascertained data on 1129 patients operated for UICC TNM stage I-III CRC during 2012-2017 registered in the CRC biobank at the Department of Molecular Medicine, Aarhus University Hospital, Denmark. Individual-level data were linked with data from the Danish Colorectal Cancer Group database, Danish Cancer Registry, Danish National Registry of Patients, and Danish Pathology Registry. The algorithm identified recurrence based on diagnosis codes of local recurrence or metastases, the receipt of chemotherapy, or a pathological tissue assessment code of recurrence more than 180 days after CRC surgery. A subgroup was selected for validation of the algorithm using medical record reviews as a reference standard. Results: We found a 3-year cumulative recurrence rate of 20% (95% CI: 17-22%). Manual medical record review identified 80 recurrences in the validation cohort of 522 patients. The algorithm detected recurrence with 94% sensitivity (75/80; 95% CI: 86-98%) and 98% specificity (431/442; 95% CI: 96-99%). The positive and negative predictive values of the algorithm were 87% (95% CI: 78-93%) and 99% (95% CI: 97-100%), respectively. The median difference in TTR (TTRMedical_chart-TTRalgorithm) was -8 days (IQR: -21 to +3 days). Restricting the algorithm to chemotherapy codes from oncology departments increased the positive predictive value from 87% to 94% without changing the negative predictive value (99%). Conclusion: The algorithm detected recurrence and TTR with high precision in this contemporary cohort. Restriction to chemotherapy codes from oncology departments using department classifications improves the algorithm. The algorithm is suitable for use in future observational studies.

Transcriptomic and proteomic intra-tumor heterogeneity of colorectal cancer varies depending on tumor location within the colorectum.

BACKGROUND: Intra-tumor heterogeneity (ITH) of colorectal cancer (CRC) complicates molecular tumor classification, such as transcriptional subtyping. Differences in cellular states, biopsy cell composition, and tumor microenvironment may all lead to ITH. Here we analyze ITH at the transcriptomic and proteomic levels to ascertain whether subtype discordance between multiregional biopsies reflects relevant biological ITH or lack of classifier robustness. Further, we study the impact of tumor location on ITH. METHODS: Multiregional biopsies from stage II and III CRC tumors were analyzed by RNA sequencing (41 biopsies, 14 tumors) and multiplex immune protein analysis (89 biopsies, 29 tumors). CRC subtyping was performed using consensus molecular subtypes (CMS), CRC intrinsic subtypes (CRIS), and TUMOR types. ITH-scores and network maps were defined to determine the origin of heterogeneity. A validation cohort was used with one biopsy per tumor (162 tumors). RESULTS: Overall, inter-tumor transcriptional variation exceeded ITH, and subtyping calls were frequently concordant between multiregional biopsies. Still, some tumors had high transcriptional ITH and were classified discordantly. Subtyping of proximal MSS tumors were discordant for 50% of the tumors, this ITH was related to differences in the microenvironment. Subtyping of distal MSS tumors were less discordant, here the ITH was more cancer-cell related. The subtype discordancy reflected actual molecular ITH within the tumors. The relevance of the subtypes was reflected at protein level where several inflammation markers were significantly increased in immune related transcriptional subtypes, which was verified in an independent cohort (Wilcoxon rank sum test; p<0.05). Unsupervised hierarchical clustering of the protein data identified large ITH at protein level; as the multiregional biopsies clustered together for only 9 out of 29 tumors. CONCLUSION: Our transcriptomic and proteomic analyses show that the tumor location along the colorectum influence the ITH of CRC, which again influence the concordance of subtyping.

Molecular-Subtype-Specific Biomarkers Improve Prediction of Prognosis in Colorectal Cancer.

Colorectal cancer (CRC) is characterized by major inter-tumor diversity that complicates the prediction of disease and treatment outcomes. Recent efforts help resolve this by sub-classification of CRC into natural molecular subtypes; however, this strategy is not yet able to provide clinicians with improved tools for decision making. We here present an extended framework for CRC stratification that specifically aims to improve patient prognostication. Using transcriptional profiles from 1,100 CRCs, including >300 previously unpublished samples, we identify cancer cell and tumor archetypes and suggest the tumor microenvironment as a major prognostic determinant that can be influenced by the microbiome. Notably, our subtyping strategy allowed identification of archetype-specific prognostic biomarkers that provided information beyond and independent of UICC-TNM staging, MSI status, and consensus molecular subtyping. The results illustrate that our extended subtyping framework, combining subtyping and subtype-specific biomarkers, could contribute to improved patient prognostication and may form a strong basis for future studies.