Purpose: This study aims to develop a robust gene expression classifier that can predict disease relapse in patients with early-stage colorectal cancer (CRC). Patients and Methods: Fresh frozen tumor tissue from 188 patients with stage I to IV CRC undergoing surgery was analyzed using Agilent 44K oligonucleotide arrays. Median follow-up time was 65.1 months, and the majority of patients (83.6%) did not receive adjuvant chemotherapy. A nearest mean classifier was developed using a cross-validation procedure to score all genes for their association with 5-year distant metastasis–free survival. Results: An optimal set of 18 genes was identified and used to construct a prognostic classifier (ColoPrint). The signature was validated on an independent set of 206 samples from patients with stage I, II, and III CRC. The signature classified 60% of patients as low risk and 40% as high risk. Five-year relapse-free survival rates were 87.6% (95% CI, 81.5% to 93.7%) and 67.2% (95% CI, 55.4% to 79.0%) for low- and high-risk patients, respectively, with a hazard ratio (HR) of 2.5 (95% CI, 1.33 to 4.73; P = .005). In multivariate analysis, the signature remained one of the most significant prognostic factors, with an HR of 2.69 (95% CI, 1.41 to 5.14; P = .003). In patients with stage II CRC, the signature had an HR of 3.34 (P = .017) and was superior to American Society of Clinical Oncology criteria in assessing the risk of cancer recurrence without prescreening for microsatellite instability (MSI). Conclusion: ColoPrint significantly improves the prognostic accuracy of pathologic factors and MSI in patients with stage II and III CRC and facilitates the identification of patients with stage II disease who may be safely managed without chemotherapy.

Nonclassical HLAs, HLA-E and HLA-G, are known to affect clinical outcome in various tumor types. We examined the clinical impact of HLA-E and HLA-G expression in early breast cancer patients, and related the results to tumor expression of classical HLA class I. Our study population (n = 677) consisted of all early breast cancer patients primarily treated with surgery in our center between 1985 and 1995. Tissue microarray sections of arrayed tumor and normal control material were immunohistochemically stained for HLA-E and HLA-G. For evaluation of HLA-E and HLA-G and the combined variable, HLA-EG, a binary score was used. Expression of classical HLA class I molecules was determined previously. HLA-E, HLA-G, and HLA-EG on breast tumors were classified as expression in 50, 60, and 23% of patients, respectively. Remarkably, only in patients with loss of classical HLA class I tumor expression, expression of HLA-E (p = 0.027), HLA-G (p = 0.035), or HLA-EG (p = 0.001) resulted in a worse relapse-free period. An interaction was found between classical and nonclassical HLA class I expression (p = 0.002), suggestive for a biological connection. We have demonstrated that, next to expression of classical HLA class I, expression of HLA-E and HLA-G is an important factor in the prediction of outcome of breast cancer patients. These results provide further evidence that breast cancer is immunogenic, but also capable of evading tumor eradication by the host’s immune system, by up- or downregulation of HLA class Ia and class Ib loci.

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.