The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.
Although there has been a substantial amount of research conducted on biomarkers for primary colon cancer, the current clinical guidelines in the USA and Europe (including the National Comprehensive Cancer Network and European Society for Medical Oncology guidelines) only rely on the tumor-node-metastasis staging and the detection of DNA mismatch repair (MMR) deficiency or microsatellite instability (MSI), in addition to standard clinicopathological variables, to determine treatment recommendations. MSI is caused by somatic or germline defective of MMR genes and leads to the accumulation of somatic mutations, neoantigens resulting in immune recognition and high density of tumor infiltrating lymphocytes.
The strength of the in situ adaptive immune reaction, as captured for instance by the evaluation of the density and spatial distribution of T cells (Immunoscore), is associated with a reduced risk of relapse and death independently of other clinicopathological variables, including MSI status.
However, despite the overwhelming evidence of the prognostic effect of the Immunoscore and other immune-related parameters in colon cancer, a lack of association between gene-expression-based estimates of immune response and patient survival in The Cancer Genome Atlas (TCGA) colon adenocarcinoma (COAD) cohort has been noted by the research community. TCGA, for its genomic data richness and curation, represents the preeminent dataset for omics analyses; however, the collecting of comprehensive clinical data, including survival outcomes was neither a primary objective of TCGA nor a practical possibility in view of its worldwide scope and time constraints. As such, the limited patient follow-up data associated with TCGA-COAD and other TCGA datasets has hindered statistically rigorous survival analyses. In addition, TCGA did not include dedicated assays for T cell receptor (TCR) repertoire analysis or microbiome characterization, which was later performed using bulk DNA and RNA sequencing (RNA-seq) data and includes only few healthy solid tissue (for example healthy colon) samples. Furthermore, as TCGA focused initially on cataloging genomic and molecular changes that occur in cancer cells, sample inclusion criteria based on stringent tumor purity cutoffs were imposed, potentially biasing the population toward less-immune- or stroma-rich tumor specimens.
In recent years, while quantitative features of primary colon cancer, including those that are cancer cell intrinsic, immunological, stromal or microbial in nature, have been reported to be significantly associated with clinical outcomes, individually, knowledge of how their interactions impact patient outcome is fragmentary.
To dissect this phenotypic complexity with respect to outcomes, we used orthogonal genomic platforms to rigorously profile a large collection of primary colon cancer specimens (unselected for tumor cell purity) and matched healthy colon tissue, complemented with curated clinical and pathological data annotation and appropriate follow-up.
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