Multiregional transcriptomics identifies congruent consensus subtypes with prognostic value beyond tumor heterogeneity of colorectal cancer.

Intra-tumor heterogeneity compromises the clinical value of transcriptomic classifications of colorectal cancer. We investigated the prognostic effect of transcriptomic heterogeneity and the potential for classifications less vulnerable to heterogeneity in a single-hospital series of 1093 tumor samples from 692 patients, including multiregional samples from 98 primary tumors and 35 primary-metastasis sets. We show that intra-tumor heterogeneity of the consensus molecular subtypes (CMS) is frequent and has poor-prognostic associations independently of tumor microenvironment markers. Multiregional transcriptomics uncover cancer cell-intrinsic and low-heterogeneity signals that recapitulate the intrinsic CMSs proposed by single-cell sequencing. Further subclassification identifies congruent CMSs that explain a larger proportion of variation in patient survival than intra-tumor heterogeneity. Plasticity is indicated by discordant intrinsic phenotypes of matched primary and metastatic tumors. We conclude that multiregional sampling reconciles the prognostic power of tumor classifications from single-cell and bulk transcriptomics in the context of intra-tumor heterogeneity, and phenotypic plasticity challenges the reconciliation of primary and metastatic subtypes.

Spatial analysis and CD25-expression identify regulatory T cells as predictors of a poor prognosis in colorectal cancer.

Regulatory T cells (Tregs) are a heterogeneous cell population that can either suppress or stimulate immune responses. Tumor-infiltrating Tregs are associated with an adverse outcome from most cancer types, but have generally been found to be associated with a good prognosis in colorectal cancer (CRC). We investigated the prognostic heterogeneity of Tregs in CRC by co-expression patterns and spatial analyses with diverse T cell markers, using multiplex fluorescence immunohistochemistry and digital image analysis in two consecutive series of primary CRCs (total n = 1720). Treg infiltration in tumors, scored as FOXP3+ or CD4+/CD25+/FOXP3+ (triple-positive) cells, was strongly correlated to the overall amount of CD3+ and CD8+ T cells, and consequently associated with a favorable 5-year relapse-free survival rate among patients with stage I-III CRC who underwent complete tumor resection. However, high relative expression of the activation marker CD25 in triple-positive Tregs was independently associated with an adverse outcome in a multivariable model incorporating clinicopathological and known molecular prognostic markers (hazard ratio = 1.35, p = 0.028). Furthermore, spatial marker analysis based on Voronoi diagrams and permutation testing of cellular neighborhoods revealed a statistically significant proximity between Tregs and CD8+-cells in 18% of patients, and this was independently associated with a poor survival (multivariable hazard ratio = 1.36, p = 0.017). These results show prognostic heterogeneity of different Treg populations in primary CRC, and highlight the importance of multi-marker and spatial analyses for accurate immunophenotyping of tumors in relation to patient outcome.

Increased sensitivity to SMAC mimetic LCL161 identified by longitudinal ex vivo pharmacogenomics of recurrent, KRAS mutated rectal cancer liver metastases.

Tumor heterogeneity is a primary cause of treatment failure. However, changes in drug sensitivity over time are not well mapped in cancer. Patient-derived organoids (PDOs) may predict clinical drug responses ex vivo and offer an opportunity to evaluate novel treatment strategies in a personalized fashion. Here we have evaluated spatio-temporal functional and molecular dynamics of five PDO models established after hepatic re-resections and neoadjuvant combination chemotherapies in a patient with microsatellite stable and KRAS mutated metastatic rectal cancer. Histopathological differentiation phenotypes of the PDOs corresponded with the liver metastases, and ex vivo drug sensitivities generally reflected clinical responses and selection pressure, assessed in comparison to a reference data set of PDOs from metastatic colorectal cancers. PDOs from the initial versus the two recurrent metastatic settings showed heterogeneous cell morphologies, protein marker expression, and drug sensitivities. Exploratory analyses of a drug screen library of 33 investigational anticancer agents showed the strongest ex vivo sensitivity to the SMAC mimetic LCL161 in PDOs of recurrent disease compared to those of the initial metastasis. Functional analyses confirmed target inhibition and apoptosis induction in the LCL161 sensitive PDOs from the recurrent metastases. Gene expression analyses indicated an association between LCL161 sensitivity and tumor necrosis factor alpha signaling and RIPK1 gene expression. In conclusion, LCL161 was identified as a possible experimental therapy of a metastatic rectal cancer that relapsed after hepatic resection and standard systemic treatment.

De novo transcriptomic subtyping of colorectal cancer liver metastases in the context of tumor heterogeneity.

BACKGROUND: Gene expression-based subtyping has the potential to form a new paradigm for stratified treatment of colorectal cancer. However, current frameworks are based on the transcriptomic profiles of primary tumors, and metastatic heterogeneity is a challenge. Here we aimed to develop a de novo metastasis-oriented framework. METHODS: In total, 829 transcriptomic profiles from patients with colorectal cancer were analyzed, including primary tumors, liver metastases, and non-malignant liver samples. High-resolution microarray gene expression profiling was performed of 283 liver metastases from 171 patients treated by hepatic resection, including multiregional and/or multi-metastatic samples from each of 47 patients. A single randomly selected liver metastasis sample from each patient was used for unsupervised subtype discovery by nonnegative matrix factorization, and a random forest prediction model was trained to classify multi-metastatic samples, as well as liver metastases from two independent series of 308 additional patients. RESULTS: Initial comparisons with non-malignant liver samples and primary colorectal tumors showed a highly variable degree of influence from the liver microenvironment in metastases, which contributed to inter-metastatic transcriptomic heterogeneity, but did not define subtype distinctions. The de novo liver metastasis subtype (LMS) framework recapitulated the main distinction between epithelial-like and mesenchymal-like tumors, with a strong immune and stromal component only in the latter. We also identified biologically distinct epithelial-like subtypes originating from different progenitor cell types. LMS1 metastases had several transcriptomic features of cancer aggressiveness, including secretory progenitor cell origin, oncogenic addictions, and microsatellite instability in a microsatellite stable background, as well as frequent RAS/TP53 co-mutations. The poor-prognostic association of LMS1 metastases was independent of mutation status, clinicopathological variables, and current subtyping frameworks (consensus molecular subtypes and colorectal cancer intrinsic subtypes). LMS1 was also the least heterogeneous subtype in comparisons of multiple metastases per patient, and tumor heterogeneity did not confound the prognostic value of LMS1. CONCLUSIONS: We report the first large study of multi-metastatic gene expression profiling of colorectal cancer. The new metastasis-oriented subtyping framework showed potential for clinically relevant transcriptomic classification in the context of metastatic heterogeneity, and an LMS1 mini-classifier was constructed to facilitate prognostic stratification and further clinical testing.

Metastatic heterogeneity of the consensus molecular subtypes of colorectal cancer.

Gene expression-based subtypes of colorectal cancer have clinical relevance, but the representativeness of primary tumors and the consensus molecular subtypes (CMS) for metastatic cancers is not well known. We investigated the metastatic heterogeneity of CMS. The best approach to subtype translation was delineated by comparisons of transcriptomic profiles from 317 primary tumors and 295 liver metastases, including multi-metastatic samples from 45 patients and 14 primary-metastasis sets. Associations were validated in an external data set (n = 618). Projection of metastases onto principal components of primary tumors showed that metastases were depleted of CMS1-immune/CMS3-metabolic signals, enriched for CMS4-mesenchymal/stromal signals, and heavily influenced by the microenvironment. The tailored CMS classifier (available in an updated version of the R package CMScaller) therefore implemented an approach to regress out the liver tissue background. The majority of classified metastases were either CMS2 or CMS4. Nonetheless, subtype switching and inter-metastatic CMS heterogeneity were frequent and increased with sampling intensity. Poor-prognostic value of CMS1/3 metastases was consistent in the context of intra-patient tumor heterogeneity.

Patient-Derived Organoids from Multiple Colorectal Cancer Liver Metastases Reveal Moderate Intra-patient Pharmacotranscriptomic Heterogeneity.

PURPOSE: Molecular tumor heterogeneity may have important implications for the efficacy of targeted therapies in metastatic cancers. Inter-metastatic heterogeneity of sensitivity to anticancer agents has not been well explored in colorectal cancer. EXPERIMENTAL DESIGN: We established a platform for ex vivo pharmacogenomic profiling of patient-derived organoids (PDO) from resected colorectal cancer liver metastases. Drug sensitivity testing (n = 40 clinically relevant agents) and gene expression profiling were performed on 39 metastases from 22 patients. RESULTS: Three drug-response clusters were identified among the colorectal cancer metastases, based primarily on sensitivities to EGFR and/or MDM2 inhibition, and corresponding with RAS mutations and TP53 activity. Potentially effective therapies, including off-label use of drugs approved for other cancer types, could be nominated for eighteen patients (82%). Antimetabolites and targeted agents lacking a decisive genomic marker had stronger differential activity than most approved chemotherapies. We found limited intra-patient drug sensitivity heterogeneity between PDOs from multiple (2-5) liver metastases from each of ten patients. This was recapitulated at the gene expression level, with a highly proportional degree of transcriptomic and pharmacological variation. One PDO with a multi-drug resistance profile, including resistance to EGFR inhibition in a RAS-mutant background, showed sensitivity to MEK plus mTOR/AKT inhibition, corresponding with low-level PTEN expression. CONCLUSIONS: Intra-patient inter-metastatic pharmacological heterogeneity was not pronounced and ex vivo drug screening may identify novel treatment options for metastatic colorectal cancer. Variation in drug sensitivities was reflected at the transcriptomic level, suggesting potential to develop gene expression-based predictive signatures to guide experimental therapies.

Technical differences between sequencing and microarray platforms impact transcriptomic subtyping of colorectal cancer.

Gene expression profiling has increasing relevance in the molecular screening of patients with colorectal cancer (CRC). We investigated potential platform-specific effects on transcriptomic subtyping according to established frameworks by comparisons of expression profiles from RNA sequencing and exon-resolution microarrays in 126 primary microsatellite stable CRCs. There was a strong platform correspondence in global gene expression levels, albeit with systematic technical bias likely attributed to few sequencing reads covering short (<2000 nucleotides) and/or lowly expressed genes (<1 FPKM), as well as over-saturation of highly expressed genes on microarrays. Classification concordances according to both the consensus molecular subtypes and CRC intrinsic subtypes (CRIS) were also strong, but with disproportionate subtype distributions between platforms caused by frequent disagreements in adherence to sample classification thresholds. Subtypes defined largely by genes expressed at low levels, including the CRIS-D subtype and the estimated level of tumor-infiltrating cytotoxic lymphocytes, had a weaker correspondence in classification metrics between platforms. In conclusion, even subtle differences between platforms suggest that clinical translation of transcriptomic CRC subtyping frameworks is dependent on assay standardization, and systematic technical biases reinforce the need for careful selection of classifier genes.