ABSTRACT
BACKGROUND: Transcriptional classification has been used to stratify colorectal cancer (CRC) into molecular subtypes with distinct biological and clinical features. However, it is not clear whether such subtypes represent discrete, mutually exclusive entities or molecular/phenotypic states with potential overlap. Therefore, we focused on the CRC Intrinsic Subtype (CRIS) classifier and evaluated whether assigning multiple CRIS subtypes to the same sample provides additional clinically and biologically relevant information. METHODS: A multi-label version of the CRIS classifier (multiCRIS) was applied to newly generated RNA-seq profiles from 606 CRC patient-derived xenografts (PDXs), together with human CRC bulk and single-cell RNA-seq datasets. Biological and clinical associations of single- and multi-label CRIS were compared. Finally, a machine learning-based multi-label CRIS predictor (ML2CRIS) was developed for single-sample classification. RESULTS: Surprisingly, about half of the CRC cases could be significantly assigned to more than one CRIS subtype. Single-cell RNA-seq analysis revealed that multiple CRIS membership can be a consequence of the concomitant presence of cells of different CRIS class or, less frequently, of cells with hybrid phenotype. Multi-label assignments were found to improve prediction of CRC prognosis and response to treatment. Finally, the ML2CRIS classifier was validated for retaining the same biological and clinical associations also in the context of single-sample classification. CONCLUSIONS: These results show that CRIS subtypes retain their biological and clinical features even when concomitantly assigned to the same CRC sample. This approach could be potentially extended to other cancer types and classification systems.
Subject(s)
Colorectal Neoplasms , Animals , Humans , Colorectal Neoplasms/pathology , Prognosis , Disease Models, Animal , Biomarkers, Tumor/geneticsABSTRACT
In recent years, cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) have substantially improved the clinical outcome of pseudomyxoma peritonei (PMP) originating from mucinous appendiceal cancer. However, current histopathological grading of appendiceal PMP frequently fails in predicting disease outcome. We recently observed that the integration of cancer cell transcriptional traits with those of cancer-associated fibroblasts (CAFs) improves prognostic prediction for tumors of the large intestine. We therefore generated global expression profiles on a consecutive series of 24 PMP patients treated with CRS plus HIPEC. Multiple lesions were profiled for nine patients. We then used expression data to stratify the samples by a previously published "high-risk appendiceal cancer" (HRAC) signature and by a CAF signature that we previously developed for colorectal cancer, or by a combination of both. The prognostic value of the HRAC signature was confirmed in our cohort and further improved by integration of the CAF signature. Classification of cases profiled for multiple lesions revealed the existence of outlier samples and highlighted the need of profiling multiple PMP lesions to select representative samples for optimal performances. The integrated predictor was subsequently validated in an independent PMP cohort. These results provide new insights into PMP biology, revealing a previously unrecognized prognostic role of the stromal component and supporting integration of standard pathological grade with the HRAC and CAF transcriptional signatures to better predict disease outcome.
