Intratumor CMS Heterogeneity Impacts Patient Prognosis in Localized Colon Cancer

A consensus molecular subtypes classification strategy for clinical colorectal cancer tissues

Consensus Molecular Subtype (CMS) classification of colorectal cancer (CRC) tissues is complicated by RNA degradation upon formalin-fixed paraffin-embedded (FFPE) preservation. Here, we present an FFPE-curated CMS classifier. The CMSFFPE classifier was developed using genes with a high transcript integrity in FFPE-derived RNA. We evaluated the classification accuracy in two FFPE-RNA datasets with matched fresh-frozen (FF) RNA data, and an FF-derived RNA set. An FFPE-RNA application cohort of metastatic CRC patients was established, partly treated with anti-EGFR therapy. Key characteristics per CMS were assessed. Cross-referenced with matched benchmark FF CMS calls, the CMSFFPE classifier strongly improved classification accuracy in two FFPE datasets compared with the original CMSClassifier (63.6% versus 40.9% and 83.3% versus 66.7%, respectively). We recovered CMS-specific recurrence-free survival patterns (CMS4 versus CMS2: hazard ratio 1.75, 95% CI 1.24-2.46). Key molecular and clinical associations of the CMSs were confirmed. In particular, we demonstrated the predictive value of CMS2 and CMS3 for anti-EGFR therapy response (CMS2&3: odds ratio 5.48, 95% CI 1.10-27.27). The CMSFFPE classifier is an optimized FFPE-curated research tool for CMS classification of clinical CRC samples.

Clinical Challenges of Consensus Molecular Subtype CMS4 Colon Cancer in the Era of Precision Medicine

Over the past decade, our understanding of the diversity of colorectal cancer has expanded significantly, raising hopes of tailoring treatments more precisely for individual patients. A key achievement in this direction was the establishment of the consensus molecular classification, particularly identifying the challenging consensus molecular subtype (CMS) CMS4 associated with poor prognosis. Because of its aggressive nature, extensive research is dedicated to the CMS4 subgroup. Recent years have unveiled molecular and microenvironmental features at the tissue level specific to CMS4 colorectal cancer. This has paved the way for mechanistic studies and the development of preclinical models. Simultaneously, efforts have been made to easily identify patients with CMS4 colorectal cancer. Reassessing clinical trial results through the CMS classification lens has improved our understanding of the therapeutic challenges linked to this subtype. Exploration of the biology of CMS4 colorectal cancer is yielding potential biomarkers and novel treatment approaches. This overview aims to provide insights into the clinico-biological characteristics of the CMS4 subgroup, the molecular pathways driving this subtype, and available diagnostic options. We also emphasize the therapeutic challenges associated with this subtype, offering potential explanations. Finally, we summarize the current tailored treatments for CMS4 colorectal cancer emerging from fundamental and preclinical studies.

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.

Image-based consensus molecular subtyping in rectal cancer biopsies and response to neoadjuvant chemoradiotherapy

The development of deep learning (DL) models to predict the consensus molecular subtypes (CMS) from histopathology images (imCMS) is a promising and cost-effective strategy to support patient stratification. Here, we investigate whether imCMS calls generated from whole slide histopathology images (WSIs) of rectal cancer (RC) pre-treatment biopsies are associated with pathological complete response (pCR) to neoadjuvant long course chemoradiotherapy (LCRT) with single agent fluoropyrimidine. DL models were trained to classify WSIs of colorectal cancers stained with hematoxylin and eosin into one of the four CMS classes using a multi-centric dataset of resection and biopsy specimens (n = 1057 WSIs) with paired transcriptional data. Classifiers were tested on a held out RC biopsy cohort (ARISTOTLE) and correlated with pCR to LCRT in an independent dataset merging two RC cohorts (ARISTOTLE, n = 114 and SALZBURG, n = 55 patients). DL models predicted CMS with high classification performance in multiple comparative analyses. In the independent cohorts (ARISTOTLE, SALZBURG), cases with WSIs classified as imCMS1 had a significantly higher likelihood of achieving pCR (OR = 2.69, 95% CI 1.01-7.17, p = 0.048). Conversely, imCMS4 was associated with lack of pCR (OR = 0.25, 95% CI 0.07-0.88, p = 0.031). Classification maps demonstrated pathologist-interpretable associations with high stromal content in imCMS4 cases, associated with poor outcome. No significant association was found in imCMS2 or imCMS3. imCMS classification of pre-treatment biopsies is a fast and inexpensive solution to identify patient groups that could benefit from neoadjuvant LCRT. The significant associations between imCMS1/imCMS4 with pCR suggest the existence of predictive morphological features that could enhance standard pathological assessment.

Wnt, glucocorticoid and cellular prion protein cooperate to drive a mesenchymal phenotype with poor prognosis in colon cancer

BACKGROUND

The mesenchymal subtype of colorectal cancer (CRC), associated with poor prognosis, is characterized by abundant expression of the cellular prion protein PrPC, which represents a candidate therapeutic target. How PrPC is induced in CRC remains elusive. This study aims to elucidate the signaling pathways governing PrPC expression and to shed light on the gene regulatory networks linked to PrPC.

METHODS

We performed in silico analyses on diverse datasets of in vitro, ex vivo and in vivo models of mouse CRC and patient cohorts. We mined ChIPseq studies and performed promoter analysis. CRC cell lines were manipulated through genetic and pharmacological approaches. We created mice combining conditional inactivation of Apc in intestinal epithelial cells and overexpression of the human prion protein gene PRNP. Bio-informatic analyses were carried out in two randomized control trials totalizing over 3000 CRC patients.

RESULTS

In silico analyses combined with cell-based assays identified the Wnt-β-catenin and glucocorticoid pathways as upstream regulators of PRNP expression, with subtle differences between mouse and human. We uncover multiple feedback loops between PrPC and these two pathways, which translate into an aggravation of CRC pathogenesis in mouse. In stage III CRC patients, the signature defined by PRNP-CTNNB1-NR3C1, encoding PrPC, β-catenin and the glucocorticoid receptor respectively, is overrepresented in the poor-prognosis, mesenchymal subtype and associates with reduced time to recurrence.

CONCLUSIONS

An unleashed PrPC-dependent vicious circle is pathognomonic of poor prognosis, mesenchymal CRC. Patients from this aggressive subtype of CRC may benefit from therapies targeting the PRNP-CTNNB1-NR3C1 axis.

Current advances in targeted therapy for metastatic colorectal cancer - Clinical translation and future directions

The last two decades have witnessed major breakthroughs in the development of targeted therapy for patients with metastatic colorectal cancer (mCRC), an achievement which stems largely from advances in translational research. Precision medicine is now widely practiced in routine oncological care, where systemic therapy is individualized based on clinical factors such as primary tumor sidedness, location and number of metastases, as well as molecular factors such as the RAS and BRAF mutation status, mismatch repair / microsatellite status and presence of other actionable genomic alterations in the tumor. The optimal selection of patients with RAS and BRAF-wild type (WT), left-sided primary tumor for treatment with epidermal growth factor receptor (EGFR) and chemotherapy (chemo) has markedly improved survival in the first-line setting. The pivotal trials of cetuximab in combination with BRAF/ MEK inhibitor for BRAF V600E mutant mCRC, and panitumumab with KRAS G12C inhibitor in KRAS(G12C)-mutant mCRC have been practice-changing. Anti-HER2 small molecular inhibitor, antibodies and antibody-drug conjugates have significantly improved the treatment outcome of patients with HER2 amplified mCRC. Anti-angiogenesis agents are now used across all lines of treatment and novel combinations with immune-checkpoint inhibitors are under active investigation in MSS mCRC. The non-invasive monitoring of molecular resistance to targeted therapies using Next Generation Sequencing analysis of circulating tumor-derived DNA (ctDNA) and captured sequencing of tumors have improved patient selection for targeted therapies. This review will focus on how latest advances, challenges and future directions in the development of targeted therapies in mCRC.

Feature-specific quantile normalization and feature-specific mean-variance normalization deliver robust bi-directional classification and feature selection performance between microarray and RNAseq data

BACKGROUND

Cross-platform normalization seeks to minimize technological bias between microarray and RNAseq whole-transcriptome data. Incorporating multiple gene expression platforms permits external validation of experimental findings, and augments training sets for machine learning models. Here, we compare the performance of Feature Specific Quantile Normalization (FSQN) to a previously used but unvalidated and uncharacterized method we label as Feature Specific Mean Variance Normalization (FSMVN). We evaluate the performance of these methods for bidirectional normalization in the context of nested feature selection.

RESULTS

FSQN and FSMVN provided clinically equivalent bidirectional model performance with and without feature selection for colon CMS and breast PAM50 classification. Using principal component analysis, we determine that these methods eliminate batch effects related to technological platforms. Without feature selection, no statistical difference was identified between the performance of FSQN and FSMVN of cross-platform data compared to within-platform distributions. Under optimal feature selection conditions, balanced accuracy was FSQN and FSMVN were statistically equivalent to the within-platform distribution performance in multivariable linear regression analysis. FSQN and FSMVN also provided similar performance to within-platform distributions as the number of selected genes used to create models decreases.

CONCLUSIONS

In the context of generating supervised machine learning classifiers for molecular subtypes, FSQN and FSMVN are equally effective. Under optimal modeling conditions, FSQN and FSMVN provide equivalent model accuracy performance on cross-platform normalization data compared to within-platform data. Using cross-platform data should still be approached with caution as subtle performance differences may exist depending on the classification problem, training, and testing distributions.

Co-inhibition of TGF-β and PD-L1 pathways in a metastatic colorectal cancer mouse model triggers interferon responses, innate cells and T cells, alongside metabolic changes and tumor resistance

Colorectal cancer (CRC) is the third most prevalent cancer worldwide with a high mortality rate (20-30%), especially due to metastasis to adjacent organs. Clinical responses to chemotherapy, radiation, targeted and immunotherapies are limited to a subset of patients making metastatic CRC (mCRC) difficult to treat. To understand the therapeutic modulation of immune response in mCRC, we have used a genetically engineered mouse model (GEMM), "KPN", which resembles the human 'CMS4'-like subtype. We show here that transforming growth factor (TGF-β1), secreted by KPN organoids, increases cancer cell proliferation, and inhibits splenocyte activation in vitro. TGF-β1 also inhibits activation of naive but not pre-activated T cells, suggesting differential effects on specific immune cells. In vivo, the inhibition of TGF-β inflames the KPN tumors, causing infiltration of T cells, monocytes and monocytic intermediates, while reducing neutrophils and epithelial cells. Co-inhibition of TGF-β and PD-L1 signaling further enhances cytotoxic CD8+T cells and upregulates innate immune response and interferon gene signatures. However, simultaneous upregulation of cancer-related metabolic genes correlated with limited control of tumor burden and/or progression despite combination treatment. Our study illustrates the importance of using GEMMs to predict better immunotherapies for mCRC.

The circadian clock circuitry deconvolutes colorectal cancer and lung adenocarcinoma heterogeneity in a dynamic time-related framework

Increasing evidence imputes cancer progression and resistance to therapy to intra-tumor molecular heterogeneity set off by cancer cell plasticity. Re-activation of developmental programs strictly linked to epithelial-to-mesenchymal transition and gaining of stem cells properties are crucial in this setting. Many biological processes involved in cancer onset and progression show rhythmic fluctuations driven by the circadian clock circuitry. Novel cancer patient stratification tools taking into account the temporal dimension of these biological processes are definitely needed. Lung cancer and colorectal cancer (CRC) are the leading causes of cancer death worldwide. Here, by developing an innovative computational approach we named Phase-Finder, we show that the molecular heterogeneity characterizing the two deadliest cancers, CRC and lung adenocarcinoma (LUAD), rather than a merely stochastic event is the readout of specific cancer molecular states which correlate with time-qualified patterns of gene expression. We performed time-course transcriptome analysis of CRC and LUAD cell lines and upon computing circadian genes expression-based correlation matrices we derived pseudo-time points to infer time-qualified patterns in the transcriptomic analysis of real-world data (RWD) from large cohorts of CRC and LUAD patients. Our temporal classification of CRC and LUAD cohorts was able to effectively render time-specific patterns in cancer phenotype switching determining dynamical distribution of molecular subtypes impacting patient prognosis.

Primary Colorectal Tumor Displays Differential Genomic Expression Profiles Associated with Hepatic and Peritoneal Metastases

BACKGROUND

Despite improvements in characterization of CRC heterogeneity, appropriate risk stratification tools are still lacking in clinical practice. This study aimed to elucidate the primary tumor transcriptomic signatures associated with distinct metastatic routes.

METHODS

Primary tumor specimens obtained from CRC patients with either isolated LM (CRC-Liver) or PM (CRC-Peritoneum) were analyzed by transcriptomic mRNA sequencing, gene set enrichment analyses (GSEA) and immunohistochemistry. We further assessed the clinico-pathological associations and prognostic value of our signature in the COAD-TCGA independent cohort.

RESULTS

We identified a significantly different distribution of Consensus Molecular Subtypes between CRC-Liver and CRC-peritoneum groups. A transcriptomic signature based on 61 genes discriminated between liver and peritoneal metastatic routes. GSEA showed a higher expression of immune response and epithelial invasion pathways in CRC-Peritoneum samples and activation of proliferation and metabolic pathways in CRC-Liver samples. The biological relevance of RNA-Seq results was validated by the immunohistochemical expression of three significantly differentially expressed genes (ACE2, CLDN18 and DUSP4) in our signature. In silico analysis of the COAD-TCGA showed that the CRC-Peritoneum signature was associated with negative prognostic factors and poor overall and disease-free survivals.

CONCLUSIONS

CRC primary tumors spreading to the liver and peritoneum display significantly different transcriptomic profiles. The implementation of this signature in clinical practice could contribute to identify new therapeutic targets for stage IV CRC and to define individualized follow-up programs in stage II-III CRC.

Prediction of Response to Immune Checkpoint Blockade in Patients with Metastatic Colorectal Cancer with Microsatellite Instability

BACKGROUND

Mismatch repair deficient (dMMR) tumors displaying microsatellite instability (MSI) represent a paradigm for the success of immune checkpoint inhibitor (ICI)-based immunotherapy, particularly in patients with metastatic colorectal cancer (mCRC). However, a proportion of patients with dMMR/MSI mCRC exhibit resistance to ICI. Identification of tools predicting MSI mCRC patient response to ICI are required for the design of future strategies further improving this therapy.

PATIENTS AND METHODS

We combined high-throughput DNA and RNA sequencing of tumors from 116 patients with MSI mCRC treated with anti-PD-1 +/- anti-CTLA-4 of the NIPICOL phase II trial (C1, NCT03350126, discovery set) and the IMMUNOMSI prospective cohort (C2, validation set). The DNA/RNA predictors whose status was significantly associated with ICI status of response in C1 were subsequently validated in C2. Primary endpoint was iPFS (progression-free survival by iRECIST).

RESULTS

Analyses showed no impact of previously suggested DNA/RNA indicators of resistance to ICI, e.g., MSISensor score, tumor mutational burden, or specific cellular and molecular tumoral contingents. By contrast, iPFS under ICI was shown in C1 and C2 to depend both on a multiplex MSI signature involving the mutations of 19 microsatellites (HR_C2 = 3.63; 95% CI [1.65-7.99] ; p = 1.4x10^-3) and the expression of a set of 182 RNA markers with a non-epithelial TGFB-related desmoplastic orientation (HR_C2 = 1.75 ; 95% CI [1.03-2.98] ; p = 0.035). Both DNA and RNA signatures were independently predictive of iPFS.

CONCLUSIONS

iPFS in patients with MSI mCRC can be predicted by simply analyzing the mutational status of DNA microsatellite-containing genes in epithelial tumor cells together with nonepithelial TGFB-related desmoplastic RNA markers.

Multi-label transcriptional classification of colorectal cancer reflects tumor cell population heterogeneity

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.

Intratumor heterogeneity and cell secretome promote chemotherapy resistance and progression of colorectal cancer

The major underlying cause for the high mortality rate in colorectal cancer (CRC) relies on its drug resistance, to which intratumor heterogeneity (ITH) contributes substantially. CRC tumors have been reported to comprise heterogeneous populations of cancer cells that can be grouped into 4 consensus molecular subtypes (CMS). However, the impact of inter-cellular interaction between these cellular states on the emergence of drug resistance and CRC progression remains elusive. Here, we explored the interaction between cell lines belonging to the CMS1 (HCT116 and LoVo) and the CMS4 (SW620 and MDST8) in a 3D coculture model, mimicking the ITH of CRC. The spatial distribution of each cell population showed that CMS1 cells had a preference to grow in the center of cocultured spheroids, while CMS4 cells localized at the periphery, in line with observations in tumors from CRC patients. Cocultures of CMS1 and CMS4 cells did not alter cell growth, but significantly sustained the survival of both CMS1 and CMS4 cells in response to the front-line chemotherapeutic agent 5-fluorouracil (5-FU). Mechanistically, the secretome of CMS1 cells exhibited a remarkable protective effect for CMS4 cells against 5-FU treatment, while promoting cellular invasion. Secreted metabolites may be responsible for these effects, as demonstrated by the existence of 5-FU induced metabolomic shifts, as well as by the experimental transfer of the metabolome between CMS1 and CMS4 cells. Overall, our results suggest that the interplay between CMS1 and CMS4 cells stimulates CRC progression and reduces the efficacy of chemotherapy.

Targeting inhibition of prognosis-related lipid metabolism genes including CYP19A1 enhances immunotherapeutic response in colon cancer

BACKGROUND

Lipid metabolic reprogramming in colon cancer shows a potential impact on tumor immune microenvironment and is associated with response to immunotherapy. Therefore, this study aimed to develop a lipid metabolism-related prognostic risk score (LMrisk) to provide new biomarkers and combination therapy strategies for colon cancer immunotherapy.

METHODS

Differentially expressed lipid metabolism-related genes (LMGs) including cytochrome P450 (CYP) 19A1 were screened to construct LMrisk in TCGA colon cancer cohort. The LMrisk was then validated in three GEO datasets. The differences of immune cell infiltration and immunotherapy response between LMrisk subgroups were investigated via bioinformatic analysis. These results were comfirmed by in vitro coculture of colon cancer cells with peripheral blood mononuclear cells, human colon cancer tissue microarray analysis, multiplex immunofluorescence staining and mouse xenograft models of colon cancer.

RESULTS

Six LMGs including CYP19A1, ALOXE3, FABP4, LRP2, SLCO1A2 and PPARGC1A were selected to establish the LMrisk. The LMrisk was positively correlated with the abundance of macrophages, carcinoma-associated fibroblasts (CAFs), endothelial cells and the levels of biomarkers for immunotherapeutic response including programmed cell death ligand 1 (PD-L1) expression, tumor mutation burden and microsatellite instability, but negatively correlated with CD8⁺ T cell infiltration levels. CYP19A1 protein expression was an independent prognostic factor, and positively correlated with PD-L1 expression in human colon cancer tissues. Multiplex immunofluorescence analyses revealed that CYP19A1 protein expression was negatively correlated with CD8⁺ T cell infiltration, but positively correlated with the levels of tumor-associated macrophages, CAFs and endothelial cells. Importantly, CYP19A1 inhibition downregulated PD-L1, IL-6 and TGF-β levels through GPR30-AKT signaling, thereby enhancing CD8⁺ T cell-mediated antitumor immune response in vitro co-culture studies. CYP19A1 inhibition by letrozole or siRNA strengthened the anti-tumor immune response of CD8⁺ T cells, induced normalization of tumor blood vessels, and enhanced the efficacy of anti-PD-1 therapy in orthotopic and subcutaneous mouse colon cancer models.

CONCLUSION

A risk model based on lipid metabolism-related genes may predict prognosis and immunotherapeutic response in colon cancer. CYP19A1-catalyzed estrogen biosynthesis promotes vascular abnormality and inhibits CD8⁺ T cell function through the upregulation of PD-L1, IL-6 and TGF-β via GPR30-AKT signaling. CYP19A1 inhibition combined with PD-1 blockade represents a promising therapeutic strategy for colon cancer immunotherapy.

CINSARC signature outperforms gold-standard TNM staging and consensus molecular subtypes for clinical outcome in stage II-III colorectal carcinoma

The outcome of stage II-III colorectal cancer (CRC) is highly variable and therapeutic choice is currently based on TNM staging with a few additional biomarkers. However, studies show that some stage III patients have a better prognosis than some stage II patients. A promising consensus molecular (CMS) classification with prognostic relevance has been developed, but it is not used in daily practice. Our team developed CINSARC, a 67-gene expression prognostic signature, whose prognostic value has been demonstrated in many cancer types. It is applicable to formalin-fixed, paraffin-embedded (FFPE) blocks using NanoString® technology. We investigated whether it could predict outcome in stage II-III CRC. We established the CINSARC classification on the TCGA retrospective cohort comprising 297 stage II-III CRC patients using RNA sequencing and on a second independent cohort comprising 169 cases using NanoString® technology. We compared its recurrence-free and overall survival prognostic value with TNM staging and CMS classification. In the TCGA cohort, we showed that CINSARC significantly splits the population of stage II-III CRC into two groups with different progression-free interval (P = 1.68 × 10^-2; HR = 1.87 [1.11-3.16]) and overall survival (P = 3.73 × 10^-3; HR = 2.45 [1.31-4.59]) and is a strong prognostic factor in multivariate analysis, outperforming TNM staging and CMS classification. We validated these results in the second cohort by applying CINSARC on FFPE samples with Nanostring® technology. CINSARC is a ready-to-use tool with a robust independent prognostic value in stage II-III CRC.

Predictive biomarkers of colon cancer immunotherapy: Present and future

Immunotherapy has revolutionized colon cancer treatment. Immune checkpoint inhibitors (ICIs) have shown clinical benefits for colon cancer patients, especially those with high microsatellite instability (MSI-H). In 2020, the US Food and Drug Administration (FDA)-approved ICI pembrolizumab as the first-line treatment for metastatic MSI-H colon cancer patients. Additionally, neoadjuvant immunotherapy has presented efficacy in treating early-stage colon cancer patients. Although MSI has been thought of as an effective predictive biomarker for colon cancer immunotherapy, only a small proportion of colon cancer patients were MSI-H, and certain colon cancer patients with MSI-H presented intrinsic or acquired resistance to immunotherapy. Thus, further search for predictive biomarkers to stratify patients is meaningful in colon cancer immunotherapy. Except for MSI, other biomarkers, such as PD-L1 expression level, tumor mutation burden (TMB), tumor-infiltrating lymphocytes (TILs), certain gut microbiota, ctDNA, and circulating immune cells were also proposed to be correlated with patient survival and ICI efficacy in some colon cancer clinical studies. Moreover, developing new diagnostic techniques helps identify accurate predictive biomarkers for colon cancer immunotherapy. In this review, we outline the reported predictive biomarkers in colon cancer immunotherapy and further discuss the prospects of technological changes for biomarker development in colon cancer immunotherapy.

Consensus molecular subtype 4 (CMS4)-targeted therapy in primary colon cancer: A proof-of-concept study

Background

Mesenchymal Consensus Molecular Subtype 4 (CMS4) colon cancer is associated with poor prognosis and therapy resistance. In this proof-of-concept study, we assessed whether a rationally chosen drug could mitigate the distinguishing molecular features of primary CMS4 colon cancer.

Methods

In the ImPACCT trial, informed consent was obtained for molecular subtyping at initial diagnosis of colon cancer using a validated RT-qPCR CMS4-test on three biopsies per tumor (Phase-1, n=69 patients), and for neoadjuvant CMS4-targeting therapy with imatinib (Phase-2, n=5). Pre- and post-treatment tumor biopsies were analyzed by RNA-sequencing and immunohistochemistry. Imatinib-induced gene expression changes were associated with molecular subtypes and survival in an independent cohort of 3232 primary colon cancer.

Results

The CMS4-test classified 52/172 biopsies as CMS4 (30%). Five patients consented to imatinib treatment prior to surgery, yielding 15 pre- and 15 post-treatment samples for molecular analysis. Imatinib treatment caused significant suppression of mesenchymal genes and upregulation of genes encoding epithelial junctions. The gene expression changes induced by imatinib were associated with improved survival and a shift from CMS4 to CMS2.

Conclusion

Imatinib may have value as a CMS-switching drug in primary colon cancer and induces a gene expression program that is associated with improved survival.

Identification of a novel cellular senescence-related signature for the prediction of prognosis and immunotherapy response in colon cancer

The study was conducted to construct a cellular senescence-related risk score signature to predict prognosis and immunotherapy response in colon cancer. Colon cancer data were acquired from the Gene Expression Omnibus and The Cancer Genome Atlas databases. And cellular senescence-related genes were obtained from the CellAge database. The colon cancer data were classified into different clusters based on cellular senescence-related gene expression. Next, prognostic differential genes among clusters were identified with survival analysis. A cellular senescence-related risk score signature was developed by performing the LASSO regression analysis. Finally, PCA analysis, t-SNE analysis, Kaplan-Meier survival analysis, ROC analysis, univariate Cox regression analysis, multivariate Cox regression analysis, C-index analysis, meta-analysis, immune infiltration analysis, and IPS score analysis were used to evaluate the significance of the risk signature for predicting prognosis and immunotherapy response in colon cancer. The colon cancer data were classified into three clusters. The patients in cluster A and cluster B had longer survival. A cellular senescence-related risk score signature was developed. Patients in the low-risk score group showed a better prognosis. The risk score signature could predict colon cancer patients’ prognosis independently of other clinical characteristics. The risk score signature predicted the prognosis of colon cancer patients more accurately than other signatures. Patients in the low-risk score group showed a better response to immunotherapy. The opposite was true for the high-risk score group. In conclusion, the cellular senescence-related risk score signature could be used for the prediction of prognosis and immunotherapy response in colon cancer.

Clinical management of metastatic colorectal cancer in the era of precision medicine

Colorectal cancer (CRC) represents approximately 10% of all cancers and is the second most common cause of cancer deaths. Initial clinical presentation as metastatic CRC (mCRC) occurs in approximately 20% of patients. Moreover, up to 50% of patients with localized disease eventually develop metastases. Appropriate clinical management of these patients is still a challenging medical issue. Major efforts have been made to unveil the molecular landscape of mCRC. This has resulted in the identification of several druggable tumor molecular targets with the aim of developing personalized treatments for each patient. This review summarizes the improvements in the clinical management of patients with mCRC in the emerging era of precision medicine. In fact, molecular stratification, on which the current treatment algorithm for mCRC is based, although it does not completely represent the complexity of this disease, has been the first significant step toward clinically informative genetic profiling for implementing more effective therapeutic approaches. This has resulted in a clinically relevant increase in mCRC disease control and patient survival. The next steps in the clinical management of mCRC will be to integrate the comprehensive knowledge of tumor gene alterations, of tumor and microenvironment gene and protein expression profiling, of host immune competence as well as the application of the resulting dynamic changes to a precision medicine-based continuum of care for each patient. This approach could result in the identification of individual prognostic and predictive parameters, which could help the clinician in choosing the most appropriate therapeutic program(s) throughout the entire disease journey for each patient with mCRC. CA Cancer J Clin. 2022;72:000-000.

Mutant-Allele Tumor Heterogeneity, a Favorable Biomarker to Assess Intra-Tumor Heterogeneity, in Advanced Lung Adenocarcinoma

Background

Intra-tumor heterogeneity (ITH) plays a vital role in drug resistance and recurrence of lung cancer. We used a mutant-allele tumor heterogeneity (MATH) algorithm to assess ITH and investigated its association with clinical and molecular features in advanced lung adenocarcinoma.

Methods

Tissues from 63 patients with advanced lung adenocarcinoma were analyzed by next-generation sequencing (NGS) using a panel targeting 520 cancer-relevant genes. We calculated the MATH values from NGS data and further investigated their correlation with clinical and molecular characteristics.

Results

Among the 63 patients with advanced lung adenocarcinoma, the median value of MATH was 33.06. Patients with EGFR mutation had higher level of MATH score than those with wild-type EGFR status (P = 0.008). Patients with stage IV disease showed a trend to have a higher MATH score than those with stage III (P = 0.052). MATH was higher in patients with disruptive TP53 mutations than in those with non-disruptive mutations (P = 0.036) or wild-type sequence (P = 0.023), but did not differ between tumors with non-disruptive mutations and wild-type TP53 (P = 0.867). High MATH is associated with mutations in mismatch repair (MMR) pathway (P = 0.026) and base excision repair (BER) pathway (P = 0.008). In addition, MATH was found to have a positive correlation with tumor mutational burden (TMB) (Spearman ρ = 0.354; P = 0.004). In 26 patients harboring EGFR mutation treated with first generation EGFR TKI as single-agent therapy, the objective response rate was higher in the Low-MATH group than in the High-MATH group (75% vs. 21%; P = 0.016) and Low-MATH group showed a significantly longer progression-free survival than High-MATH group (median PFS: 13.7 months vs. 10.1 months; P = 0.024).

Conclusions

For patients with advanced lung adenocarcinoma, MATH may serve as a clinically practical biomarker to assess intratumor heterogeneity.

Metabolic pathway-based molecular subtyping of colon cancer reveals clinical immunotherapy potential and prognosis

PURPOSE

Colon cancer presents challenges to clinical diagnosis and management due to its high heterogeneity. For more efficient and convenient diagnosis and treatment of colon cancer, we are committed to characterizing the molecular features of colon cancer by pioneering a classification system based on metabolic pathways.

METHODS

Based on the 113 metabolic pathways and genes collected in the previous stage, we scored and filtered the metabolic pathways of each sample in the training set by ssGSEA, and obtained 16 metabolic pathways related to colon cancer recurrence. In consistent clustering of training set samples with recurrence-related metabolic pathway scores, we identified two robust molecular subtypes of colon cancer (MC1 and MC2). Furthermore, we performed multi-angle analysis on the survival differences of subtypes, metabolic characteristics, clinical characteristics, functional enrichment, immune infiltration, differences with other subtypes, stemness indices, TIDE prediction, and drug sensitivity, and finally constructed colon cancer prognostic model.

RESULTS

The results showed that the MC1 subtype had a poor prognosis based on higher immune activity and immune checkpoint gene expression. The MC2 subtype is associated with high metabolic activity and low expression of immune checkpoint genes and a better prognosis. The MC2 subtype was more responsive to PD-L1 immunotherapy than the MC1 subclass. However, we did not observe significant differences in tumor mutational burden between the two.

CONCLUSION

Two molecular subtypes of colon cancer based on metabolic pathways have distinct immune signatures. Constructing prognostic models based on subtype differential genes provides valuable reference for personalized therapy targeting unique tumor metabolic signatures.

Transcriptomic Profiling of MSI-H/dMMR Gastrointestinal Tumors to Identify Determinants of Responsiveness to Anti-PD-1 Therapy

PURPOSE

Transcriptomic profiling was performed for microsatellite instability-high (MSI-H)/mismatch repair-deficient (dMMR) gastrointestinal tumors to determine the predictors of response to PD-1 blockade.

EXPERIMENTAL DESIGN

Thirty-six patients with MSI-H/dMMR gastrointestinal tumors, including gastric cancer, colorectal cancer, cholangiocarcinoma, small intestine cancer, and pancreatic cancer, being treated with PD-1 blockade were analyzed. We conducted the transcriptomic analysis of gastrointestinal tumors using RNA sequencing data, including the consensus molecular subtypes (CMS) of colorectal cancer.

RESULTS

Gene set enrichment analysis (GSEA) demonstrated that non-responders had upregulations of epithelial-mesenchymal transition, angiogenesis, hypoxia, mTORC1, TNF-α, KRAS, Wnt/β-catenin, TGF-β, and various metabolism-related signaling pathways. Meanwhile, the IFNγ pathway was enriched in responders. On the basis of the leading-edge analysis of GSEA, VEGF-A was significantly correlated with enriched pathways in non-responders. Patients with high VEGF-A expression, compared with those with low expression, had significantly shorter progression-free survival [PFS; median 4.8 months vs. not reached (NR), P = 0.032] and overall survival (median 11.1 months vs. NR, P = 0.045). Among 13 patients with colorectal cancer evaluable for CMS classification, the objective response rate was 100%, 0%, 0%, and 16.7% in CMS1, CMS2, CMS3, and CMS4, respectively. Patients with CMS1 had significantly longer PFS (NR vs. 4.8 months, P = 0.017) than those with CMS2, CMS3, or CMS4.

CONCLUSIONS

Several transcriptomic features, including CMS classification and related genes, were associated with response to PD-1 blockade in MSI-H/dMMR gastrointestinal tumors. These findings can help develop predictive biomarkers or combination immunotherapies.

Fibroblast activation protein identifies Consensus Molecular Subtype 4 in colorectal cancer and allows its detection by 68Ga-FAPI-PET imaging

Background

In colorectal cancer (CRC), the consensus molecular subtype 4 (CMS4) is associated with therapy resistance and poor prognosis. Clinical diagnosis of CMS4 is hampered by locoregional and temporal variables influencing CMS classification. Diagnostic tools that comprehensively detect CMS4 are therefore urgently needed.

Methods

To identify targets for molecular CMS4 imaging, RNA sequencing data of 3232 primary CRC patients were explored. Heterogeneity of marker expression in relation to CMS4 status was assessed by analysing 3–5 tumour regions and 91.103 single-tumour cells (7 and 29 tumours, respectively). Candidate marker expression was validated in CMS4 peritoneal metastases (PM; n = 59). Molecular imaging was performed using the ⁶⁸Ga-DOTA-FAPI-46 PET tracer.

Results

Fibroblast activation protein (FAP) mRNA identified CMS4 with very high sensitivity and specificity (AUROC > 0.91), and was associated with significantly shorter relapse-free survival (P = 0.0038). Heterogeneous expression of FAP among and within tumour lesions correlated with CMS4 heterogeneity (AUROC = 1.00). FAP expression was homogeneously high in PM, a near-homogeneous CMS4 entity. FAPI-PET identified focal and diffuse PM that were missed using conventional imaging. Extra-peritoneal metastases displayed extensive heterogeneity of tracer uptake.

Conclusion

FAP expression identifies CMS4 CRC. FAPI-PET may have value in the comprehensive detection of CMS4 tumours in CRC. This is especially relevant in patients with PM, for whom effective imaging tools are currently lacking.

Cordycepin inhibits colon cancer proliferation by suppressing MYC expression

Background

Cordycepin is a purine nucleoside anti-metabolite and anti-biotic isolated from the fungus Cordyceps militaris, which has potential anti-neoplastic activities. This study aimed to investigate the effect of cordycepin in inhibiting colon cancer development.

Methods

The proliferation of cordycepin-treated HCT116 and Caco-2 colon cancer cell lines was assessed with 3- (4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, and the viability was measured with colony formation assay. At the same time, cordycepin responsive gene and microRNAs (miRNAs, miRs) were screened by qRT-PCR. MYC over-expressing HCT116 and Caco-2 cell lines were constructed, which were further transfected with miR-26a. Inhibitory effect of cordycepin on cell proliferation was evaluated with cell viability assay, cell number count, and colony formation assay. The relative expression of MYC and miR-26a was detected by qRT-PCR and Western blot.

Results

Cordycepin inhibited colon cancer cell proliferation by down-regulating MYC mRNA/protein expression and up-regulating miR-26a in both HCT116 and Caco-2 cells. MYC over-expression could suppress the expression of miR-26a, which could be restored by cordycepin treatment. Additional miR-26a transfection in MYC over-expressing cells could reverse MYC over-expression-promoted proliferation, which could be further potentiated by cordycepin treatment.

Conclusion

Cordycepin is able to suppress colon cancer cell proliferation, likely mediated by the MYC/miR-26a pathway, supporting its potential for the treatment of colon cancer.

Implications of Intratumor Heterogeneity on Consensus Molecular Subtype (CMS) in Colorectal Cancer

The implications of intratumor heterogeneity on the four consensus molecular subtypes (CMS) of colorectal cancer (CRC) are not well known. Here, we use single-cell RNA sequencing (scRNASeq) to build an algorithm to assign CMS classification to individual cells, which we use to explore the distributions of CMSs in tumor and non-tumor cells. A dataset of colorectal tumors with bulk RNAseq (n = 3232) was used to identify CMS specific-marker gene sets. These gene sets were then applied to a discovery dataset of scRNASeq profiles (n = 10) to develop an algorithm for single-cell CMS (scCMS) assignment, which recapitulated the intrinsic biology of all four CMSs. The single-cell CMS assignment algorithm was used to explore the scRNASeq profiles of two prospective CRC tumors with mixed CMS via bulk sequencing. We find that every CRC tumor contains individual cells of each scCMS, as well as many individual cells that have enrichment for features of more than one scCMS (called mixed cells). scCMS4 and scCMS1 cells dominate stroma and immune cell clusters, respectively, but account for less than 3% epithelial cells. These data imply that CMS1 and CMS4 are driven by the transcriptomic contribution of immune and stromal cells, respectively, not tumor cells.