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

Abstract

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.

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

“…Transcriptomic subtypes based on cancer cell-intrinsic signals have the presumed advantage of being applicable to both primary and metastatic tumors, without the need to adapt the classification approach. This was supported by PCA based on the ITH-low genes detected in this study and on the iCMS template genes, both showing intermingling of primary tumors and liver metastasis, which is in contrast to results based on unselected genes 41 . In further support of the appropriateness of intrinsic classifications for metastatic tumors, we did not observe any subtype depletions or shift in the distribution of iCMS classes between primary tumors and liver metastases.…”

“…Nonetheless, the profound phenotypic plasticity observed in at least a subset of patients challenges the potential reconciliation of subtyping schemes of primary and metastatic tumors, also of the congruent CMS proposed here. This supports the need for a de novo classification of metastases based on their in situ cellular states 41 .…”

Localization and density of tertiary lymphoid structures associate with molecular subtype and clinical outcome in colorectal cancer liver metastases

“…Next, we investigated the correlation between the TLS enrichment gene signature and a previously proposed liver metastasis subtype (LMS) system recapitulated the main distinction between epithelial-like and mesenchymal-like tumors, with abundant immune and stromal component. 26 We found that TLS enrichment gene signature was significantly associated with LMS ( figure 2I , p<0.001), which showed that samples with LMS5 have a more favorable prognosis than those with LMS1 ( figure 2J ).…”

Bias correction for nonignorable missing counts of areal HIV new diagnosis

“…(2021), it is not well suited for the censored data (e.g., Freeman et al, 2022;Moosavi et al, 2021;Rebouillat et al, 2021). In our data, all observations are at least six while the missing values are smaller or equal to five, directly applying the traditional MF will yield inflated imputations as with other imputation methods.…”

“…When applied to spatiotemporal data, the data can be formed as a matrix according to the spatial dimension and the temporal dimension. However, although MF imputation works well for imputing MAR spatiotemporal data in Huang et al (2013), Ranjbar et al (2015), and Yang et al (2021), it is not well suited for the censored data (e.g., Freeman et al, 2022; Moosavi et al, 2021; Rebouillat et al, 2021). In our data, all observations are at least six while the missing values are smaller or equal to five, directly applying the traditional MF will yield inflated imputations as with other imputation methods.…”