Cancer-cell intrinsic gene expression signatures overcome intratumoural heterogeneity bias in colorectal cancer patient classification

Transcriptomic subtyping of gastrointestinal malignancies

Gastrointestinal (GI) cancers are highly heterogeneous at multiple levels. Tumor heterogeneity can be captured by molecular profiling, such as genetic, epigenetic, proteomic, and transcriptomic classification. Transcriptomic subtyping has the advantage of combining genetic and epigenetic information, cancer cell-intrinsic properties, and the tumor microenvironment (TME). Unsupervised transcriptomic subtyping systems of different GI malignancies have gained interest because they reveal shared biological features across cancers and bear prognostic and predictive value. Importantly, transcriptomic subtypes accurately reflect complex phenotypic states varying not only per tumor region, but also throughout disease progression, with consequences for clinical management. Here, we discuss methodologies of transcriptomic subtyping, proposed taxonomies for GI malignancies, and the challenges posed to clinical implementation, highlighting opportunities for future transcriptomic profiling efforts to optimize clinical impact.

XENTURION is a population-level multidimensional resource of xenografts and tumoroids from metastatic colorectal cancer patients

The breadth and depth at which cancer models are interrogated contribute to the successful clinical translation of drug discovery efforts. In colorectal cancer (CRC), model availability is limited by a dearth of large-scale collections of patient-derived xenografts (PDXs) and paired tumoroids from metastatic disease, where experimental therapies are typically tested. Here we introduce XENTURION, an open-science resource offering a platform of 128 PDX models from patients with metastatic CRC, along with matched PDX-derived tumoroids. Multidimensional omics analyses indicate that tumoroids retain extensive molecular fidelity with parental PDXs. A tumoroid-based trial with the anti-EGFR antibody cetuximab reveals variable sensitivities that are consistent with clinical response biomarkers, mirror tumor growth changes in matched PDXs, and recapitulate EGFR genetic deletion outcomes. Inhibition of adaptive signals upregulated by EGFR blockade increases the magnitude of cetuximab response. These findings illustrate the potential of large living biobanks, providing avenues for molecularly informed preclinical research in oncology.

The Glasgow Microenvironment Score: an exemplar of contemporary biomarker evolution in colorectal cancer

Colorectal cancer remains a leading cause of mortality worldwide. Significant variation in response to treatment and survival is evident among patients with similar stage disease. Molecular profiling has highlighted the heterogeneity of colorectal cancer but has had limited impact in daily clinical practice. Biomarkers with robust prognostic and therapeutic relevance are urgently required. Ideally, biomarkers would be derived from H&E sections used for routine pathological staging, have reliable sensitivity and specificity, and require minimal additional training. The biomarker targets would capture key pathological features with proven additive prognostic and clinical utility, such as the local inflammatory response and tumour microenvironment. The Glasgow Microenvironment Score (GMS), first described in 2014, combines assessment of peritumoural inflammation at the invasive margin with quantification of tumour stromal content. Using H&E sections, the Klintrup-Mäkinen (KM) grade is determined by qualitative morphological assessment of the peritumoural lymphocytic infiltrate at the invasive margin and tumour stroma percentage (TSP) calculated in a semi-quantitative manner as a percentage of stroma within the visible field. The resulting three prognostic categories have direct clinical relevance: GMS 0 denotes a tumour with a dense inflammatory infiltrate/high KM grade at the invasive margin and improved survival; GMS 1 represents weak inflammatory response and low TSP associated with intermediate survival; and GMS 2 tumours are typified by a weak inflammatory response, high TSP, and inferior survival. The prognostic capacity of the GMS has been widely validated while its potential to guide chemotherapy has been demonstrated in a large phase 3 trial cohort. Here, we detail its journey from conception through validation to clinical translation and outline the future for this promising and practical biomarker.

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.

Enhancing Chemotherapy Response Prediction via Matched Colorectal Tumor-Organoid Gene Expression Analysis and Network-Based Biomarker Selection

Colorectal cancer (CRC) poses significant challenges in chemotherapy response prediction due to its molecular heterogeneity. This study introduces an innovative methodology that leverages gene expression data generated from matched colorectal tumor and organoid samples to enhance prediction accuracy. By applying Consensus Weighted Gene Co-expression Network Analysis (WGCNA) across multiple datasets, we identify critical gene modules and hub genes that correlate with patient responses, particularly to 5-fluorouracil (5-FU). This integrative approach advances precision medicine by refining chemotherapy regimen selection based on individual tumor profiles. Our predictive model demonstrates superior accuracy over traditional methods on independent datasets, illustrating significant potential in addressing the complexities of high-dimensional genomic data for cancer biomarker research.

Integrated analysis of single-cell and bulk transcriptomics develops a robust neuroendocrine cell-intrinsic signature to predict prostate cancer progression

Neuroendocrine prostate cancer (NEPC) typically implies severe lethality and limited treatment options. The precise identification of NEPC cells holds paramount significance for both research and clinical applications, yet valid NEPC biomarker remains to be defined. Methods: Leveraging 11 published NE-related gene sets, 11 single-cell RNA-sequencing (scRNA-seq) cohorts, 15 bulk transcriptomic cohorts, and 13 experimental models of prostate cancer (PCa), we employed multiple advanced algorithms to construct and validate a robust NEPC risk prediction model. Results: Through the compilation of a comprehensive scRNA-seq reference atlas (comprising a total of 210,879 single cells, including 66 tumor samples) from 9 multicenter datasets of PCa, we observed inconsistent and inefficient performance among the 11 published NE gene sets. Therefore, we developed an integrative analysis pipeline, identifying 762 high-quality NE markers. Subsequently, we derived the NE cell-intrinsic gene signature, and developed an R package named NEPAL, to predict NEPC risk scores. By applying to multiple independent validation datasets, NEPAL consistently and accurately assigned NE feature and delineated PCa progression. Intriguingly, NEPAL demonstrated predictive capabilities for prognosis and therapy responsiveness, as well as the identification of potential epigenetic drivers of NEPC. Conclusion: The present study furnishes a valuable tool for the identification of NEPC and the monitoring of PCa progression through transcriptomic profiles obtained from both bulk and single-cell sources.

Transcriptomic correlates of cell cycle checkpoints with distinct prognosis, molecular characteristics, immunological regulation, and therapeutic response in colorectal adenocarcinoma

Backgrounds

Colorectal adenocarcinoma (COAD), accounting for the most common subtype of colorectal cancer (CRC), is a kind of malignant digestive tumor. Some cell cycle checkpoints (CCCs) have been found to contribute to CRC progression, whereas the functional roles of a lot of CCCs, especially the integrated role of checkpoint mechanism in the cell cycle, remain unclear.

Materials and methods

The Genomic Data Commons (GDC) The Cancer Genome Atlas (TCGA) COAD cohort was retrieved as the training dataset, and GSE24551 and GSE29623 were downloaded from Gene Expression Omnibus (GEO) as the validation datasets. A total of 209 CCC-related genes were derived from the Gene Ontology Consortium and were subsequently enrolled in the univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, finally defining a CCC signature. Cell proliferation and Transwell assay analyses were utilized to evaluate the functional roles of signature-related CCCs. The underlying CCC signature, molecular characteristics, immune-related features, and therapeutic response were finally estimated. The Genomics of Drug Sensitivity in Cancer (GDSC) database was employed for the evaluation of chemotherapeutic responses.

Results

The aberrant gene expression of CCCs greatly contributed to COAD development and progression. Univariate Cox regression analysis identified 27 CCC-related genes significantly affecting the overall survival (OS) of COAD patients; subsequently, LASSO analysis determined a novel CCC signature. Noticeably, CDK5RAP2, MAD1L1, NBN, RGCC, and ZNF207 were first identified to be correlated with the prognosis of COAD, and it was proven that all of them were significantly correlated with the proliferation and invasion of HCT116 and SW480 cells. In TCGA COAD cohort, CCC signature robustly stratified COAD patients into high and low CCC score groups (median OS: 57.24 months vs. unreached, p< 0.0001), simultaneously, with the good AUC values for OS prediction at 1, 2, and 3 years were 0.74, 0.78, and 0.77. Furthermore, the prognostic capacity of the CCC signature was verified in the GSE24551 and GSE29623 datasets, and the CCC signature was independent of clinical features. Moreover, a higher CCC score always indicated worse OS, regardless of clinical features, histological subtypes, or molecular subgroups. Intriguingly, functional enrichment analysis confirmed the CCC score was markedly associated with extracellular, matrix and immune (chemokine)-related signaling, cell cycle-related signaling, and metabolisms. Impressively, a higher CCC score was positively correlated with a majority of chemokines, receptors, immunostimulators, and anticancer immunity, indicating a relatively immune-promoting microenvironment. In addition, GSE173839, GSE25066, GSE41998, and GSE194040 dataset analyses of the underlying CCC signature suggested that durvalumab with olaparib and paclitaxel, taxane-anthracycline chemotherapy, neoadjuvant cyclophosphamide/doxorubicin with ixabepilone or paclitaxel, and immunotherapeutic strategies might be suitable for COAD patients with higher CCC score. Eventually, the GDSC database analysis showed that lower CCC scores were likely to be more sensitive to 5-fluorouracil, bosutinib, gemcitabine, gefitinib, methotrexate, mitomycin C, and temozolomide, while patients with higher CCC score seemed to have a higher level of sensitivity to bortezomib and elesclomol.

Conclusion

The novel CCC signature exhibited a good ability for prognosis prediction for COAD patients, and the CCC score was found to be highly correlated with molecular features, immune-related characteristics, and therapeutic responses, which would greatly promote clinical management and precision medicine for COAD.

Loss of STIM2 in colorectal cancer drives growth and metastasis through metabolic reprogramming and PERK-ATF4 endoplasmic reticulum stress pathway

The endoplasmic reticulum (ER) stores large amounts of calcium (Ca2+), and the controlled release of ER Ca2+ regulates a myriad of cellular functions. Although altered ER Ca2+ homeostasis is known to induce ER stress, the mechanisms by which ER Ca2+ imbalance activate ER stress pathways are poorly understood. Stromal-interacting molecules STIM1 and STIM2 are two structurally homologous ER-resident Ca2+ sensors that synergistically regulate Ca2+ influx into the cytosol through Orai Ca2+ channels for subsequent signaling to transcription and ER Ca2+ refilling. Here, we demonstrate that reduced STIM2, but not STIM1, in colorectal cancer (CRC) is associated with poor patient prognosis. Loss of STIM2 causes SERCA2-dependent increase in ER Ca2+, increased protein translation and transcriptional and metabolic rewiring supporting increased tumor size, invasion, and metastasis. Mechanistically, STIM2 loss activates cMyc and the PERK/ATF4 branch of ER stress in an Orai-independent manner. Therefore, STIM2 and PERK/ATF4 could be exploited for prognosis or in targeted therapies to inhibit CRC tumor growth and metastasis.

Unravelling the Complexity of Colorectal Cancer: Heterogeneity, Clonal Evolution, and Clinical Implications

Colorectal cancer (CRC) is a global health concern and a leading cause of death worldwide. The disease's course and response to treatment are significantly influenced by its heterogeneity, both within a single lesion and between primary and metastatic sites. Biomarkers, such as mutations in KRAS, NRAS, and BRAF, provide valuable guidance for treatment decisions in patients with metastatic CRC. While high concordance exists between mutational status in primary and metastatic lesions, some heterogeneity may be present. Circulating tumor DNA (ctDNA) analysis has proven invaluable in identifying genetic heterogeneity and predicting prognosis in RAS-mutated metastatic CRC patients. Tumor heterogeneity can arise from genetic and non-genetic factors, affecting tumor development and response to therapy. To comprehend and address clonal evolution and intratumoral heterogeneity, comprehensive genomic studies employing techniques such as next-generation sequencing and computational analysis are essential. Liquid biopsy, notably through analysis of ctDNA, enables real-time clonal evolution and treatment response monitoring. However, challenges remain in standardizing procedures and accurately characterizing tumor subpopulations. Various models elucidate the origin of CRC heterogeneity, highlighting the intricate molecular pathways involved. This review focuses on intrapatient cancer heterogeneity and genetic clonal evolution in metastatic CRC, with an emphasis on clinical applications.

MmCMS: mouse models' consensus molecular subtypes of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) primary tumours are molecularly classified into four consensus molecular subtypes (CMS1-4). Genetically engineered mouse models aim to faithfully mimic the complexity of human cancers and, when appropriately aligned, represent ideal pre-clinical systems to test new drug treatments. Despite its importance, dual-species classification has been limited by the lack of a reliable approach. Here we utilise, develop and test a set of options for human-to-mouse CMS classifications of CRC tissue.

METHODS

Using transcriptional data from established collections of CRC tumours, including human (TCGA cohort; n = 577) and mouse (n = 57 across n = 8 genotypes) tumours with combinations of random forest and nearest template prediction algorithms, alongside gene ontology collections, we comprehensively assess the performance of a suite of new dual-species classifiers.

RESULTS

We developed three approaches: MmCMS-A; a gene-level classifier, MmCMS-B; an ontology-level approach and MmCMS-C; a combined pathway system encompassing multiple biological and histological signalling cascades. Although all options could identify tumours associated with stromal-rich CMS4-like biology, MmCMS-A was unable to accurately classify the biology underpinning epithelial-like subtypes (CMS2/3) in mouse tumours.

CONCLUSIONS

When applying human-based transcriptional classifiers to mouse tumour data, a pathway-level classifier, rather than an individual gene-level system, is optimal. Our R package enables researchers to select suitable mouse models of human CRC subtype for their experimental testing.

Global colorectal cancer research, 2007-2021: Outputs and funding

The purpose of this study was to provide an evidence base for colorectal cancer research activity that might influence policy, mainly at the national level. Improvements in healthcare delivery have lengthened life expectancy, but within a situation of increased cancer incidence. The disease burden of CRC has risen significantly, particularly in Africa, Asia and Latin America. Research is key to its control and reduction, but few studies have delineated the volume and funding of global research on CRC. We identified research papers in the Web of Science (WoS) from 2007 to 2021, and determined the contributions of the leading countries, the research domains studied, and their sources of funding. We identified 62 716 papers, representing 5.7% of all cancer papers. This percentage was somewhat disproportionate to the disease burden (7.7% in 2015), especially in Eastern Europe. International collaboration increased over the time period in almost all countries except in China. Genetics, surgery and prognosis were the leading research domains. However, research on palliative care and quality-of-life in CRC was lacking. In Western Europe, the main funding source was the charity sector, particularly in the UK, but in most other countries government played the leading role, especially in China and the USA. There was little support from industry. Several Asian countries provided minimal contestable funding, which may have reduced the impact of their CRC research. Certain countries must perform more CRC research overall, especially in domains such as screening, palliative care and quality-of-life. The private-non-profit sector should be an alternative source of support.

Hidden patterns of gene expression provide prognostic insight for colorectal cancer

Cancer tissue samples contain cancer cells and non-cancer cells with each biopsied site containing distinct proportions of these populations. Consequently, assigning useful tumor subtypes based on gene expression measurements from clinical samples is challenging. We applied a blind source separation approach to extract cancer cell-intrinsic gene expression patterns within clinical tumor samples of colorectal cancer. After a blind source separation, we found that a cancer cell-intrinsic gene expression program unique to each patient exists in the "residual" expression profile remaining after separation of the gene expression data. We performed a consensus clustering analysis of the extracted gene expression profiles to identify novel and robust cancer cell-intrinsic subtypes. We validated the identified subtypes using an independent clinical gene expression dataset. The cancer cell-intrinsic subtypes are independent of biopsy site and provided prognostic information in addition to currently available clinical and molecular variables. After validating this approach in colorectal cancer, we further identified novel tumor subtypes with unique clinical information across multiple types of cancer. These cancer cell-intrinsic molecular subtypes provide novel prognostic value for clinical assessment of cancer.

Multi-region sequencing with spatial information enables accurate heterogeneity estimation and risk stratification in liver cancer

BACKGROUND

Numerous studies have used multi-region sampling approaches to characterize intra-tumor heterogeneity (ITH) in hepatocellular carcinoma (HCC). However, conventional multi-region sampling strategies do not preserve the spatial details of samples, and thus, the potential influences of spatial distribution on patient-wise ITH (represents the overall heterogeneity level of the tumor in a given patient) have long been overlooked. Furthermore, gene-wise transcriptional ITH (represents the expression pattern of genes across different intra-tumor regions) in HCC is also under-explored, highlighting the need for a comprehensive investigation.

METHODS

To address the problem of spatial information loss, we propose a simple and easy-to-implement strategy called spatial localization sampling (SLS). We performed multi-region sampling and sequencing on 14 patients with HCC, collecting a total of 75 tumor samples with spatial information and molecular data. Normalized diversity score and integrated heterogeneity score (IHS) were then developed to measure patient-wise and gene-wise ITH, respectively.

RESULTS

A significant correlation between spatial and molecular heterogeneity was uncovered, implying that spatial distribution of sampling sites did influence ITH estimation in HCC. We demonstrated that the normalized diversity score had the ability to overcome sampling location bias and provide a more accurate estimation of patient-wise ITH. According to this metric, HCC tumors could be divided into two classes (low-ITH and high-ITH tumors) with significant differences in multiple biological properties. Through IHS analysis, we revealed a highly heterogenous immune microenvironment in HCC and identified some low-ITH checkpoint genes with immunotherapeutic potential. We also constructed a low-heterogeneity risk stratification (LHRS) signature based on the IHS results which could accurately predict the survival outcome of patients with HCC on a single tumor biopsy sample.

CONCLUSIONS

This study provides new insights into the complex phenotypes of HCC and may serve as a guide for future studies in this field.

Epithelial TGFβ engages growth-factor signalling to circumvent apoptosis and drive intestinal tumourigenesis with aggressive features

The pro-tumourigenic role of epithelial TGFβ signalling in colorectal cancer (CRC) is controversial. Here, we identify a cohort of born to be bad early-stage (T1) colorectal tumours, with aggressive features and a propensity to disseminate early, that are characterised by high epithelial cell-intrinsic TGFβ signalling. In the presence of concurrent Apc and Kras mutations, activation of epithelial TGFβ signalling rampantly accelerates tumourigenesis and share transcriptional signatures with those of the born to be bad T1 human tumours and predicts recurrence in stage II CRC. Mechanistically, epithelial TGFβ signalling induces a growth-promoting EGFR-signalling module that synergises with mutant APC and KRAS to drive MAPK signalling that re-sensitise tumour cells to MEK and/or EGFR inhibitors. Together, we identify epithelial TGFβ signalling both as a determinant of early dissemination and a potential therapeutic vulnerability of CRC's with born to be bad traits.

Activation of innate-adaptive immune machinery by poly(I:C) exposes a therapeutic vulnerability to prevent relapse in stroma-rich colon cancer

OBJECTIVE

Stroma-rich tumours represent a poor prognostic subtype in stage II/III colon cancer (CC), with high relapse rates and limited response to standard adjuvant chemotherapy.

DESIGN

To address the lack of efficacious therapeutic options for patients with stroma-rich CC, we stratified our human tumour cohorts according to stromal content, enabling identification of the biology underpinning relapse and potential therapeutic vulnerabilities specifically within stroma-rich tumours that could be exploited clinically. Following human tumour-based discovery and independent clinical validation, we use a series of in vitro and stroma-rich in vivo models to test and validate the therapeutic potential of elevating the biology associated with reduced relapse in human tumours.

RESULTS

By performing our analyses specifically within the stroma-rich/high-fibroblast (HiFi) subtype of CC, we identify and validate the clinical value of a HiFi-specific prognostic signature (HPS), which stratifies tumours based on STAT1-related signalling (High-HPS v Low-HPS=HR 0.093, CI 0.019 to 0.466). Using in silico, in vitro and in vivo models, we demonstrate that the HPS is associated with antigen processing and presentation within discrete immune lineages in stroma-rich CC, downstream of double-stranded RNA and viral response signalling. Treatment with the TLR3 agonist poly(I:C) elevated the HPS signalling and antigen processing phenotype across in vitro and in vivo models. In an in vivo model of stroma-rich CC, poly(I:C) treatment significantly increased systemic cytotoxic T cell activity (p<0.05) and reduced liver metastases (p<0.0002).

CONCLUSION

This study reveals new biological insight that offers a novel therapeutic option to reduce relapse rates in patients with the worst prognosis CC.

COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures

Precision medicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of genes as well as the lack of comparisons on the analysis of data, remain a tremendous bottleneck regarding clinical adoption. In this paper, we introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. It offers modularity as concerns metric functions and scalability, while being able to automatically determine the best number of clusters. Our evaluation includes both mathematical and biological criteria to define a quantitative metric. The recovered signature is applied to a variety of biological tasks, including screening of biological pathways and functions, and characterization relevance on tumor types and subtypes. Quantitative comparisons among different distance metrics, commonly used clustering methods and a referential gene signature used in the literature, confirm state of the art performance of our approach. In particular, our signature, based on 27 genes, reports at least 30 times better mathematical significance (average Dunn's Index) and 25% better biological significance (average Enrichment in Protein-Protein Interaction) than those produced by other referential clustering methods. Finally, our signature reports promising results on distinguishing immune inflammatory and immune desert tumors, while reporting a high balanced accuracy of 92% on tumor types classification and averaged balanced accuracy of 68% on tumor subtypes classification, which represents, respectively 7% and 9% higher performance compared to the referential signature.

Phenotypic plasticity and genetic control in colorectal cancer evolution

Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity1. The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather 'plastic'. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis-acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour.

Rerouting the drug response: Overcoming metabolic adaptation in KRAS-mutant cancers

Mutations in guanosine triphosphatase KRAS are common in lung, colorectal, and pancreatic cancers. The constitutive activity of mutant KRAS and its downstream signaling pathways induces metabolic rewiring in tumor cells that can promote resistance to existing therapeutics. In this review, we discuss the metabolic pathways that are altered in response to treatment and those that can, in turn, alter treatment efficacy, as well as the role of metabolism in the tumor microenvironment (TME) in dictating the therapeutic response in KRAS-driven cancers. We highlight metabolic targets that may provide clinical opportunities to overcome therapeutic resistance and improve survival in patients with these aggressive cancers.

Towards precision oncology with patient-derived xenografts

Under the selective pressure of therapy, tumours dynamically evolve multiple adaptive mechanisms that make static interrogation of genomic alterations insufficient to guide treatment decisions. Clinical research does not enable the assessment of how various regulatory circuits in tumours are affected by therapeutic insults over time and space. Likewise, testing different precision oncology approaches informed by composite and ever-changing molecular information is hard to achieve in patients. Therefore, preclinical models that incorporate the biology and genetics of human cancers, facilitate analyses of complex variables and enable adequate population throughput are needed to pinpoint randomly distributed response predictors. Patient-derived xenograft (PDX) models are dynamic entities in which cancer evolution can be monitored through serial propagation in mice. PDX models can also recapitulate interpatient diversity, thus enabling the identification of response biomarkers and therapeutic targets for molecularly defined tumour subgroups. In this Review, we discuss examples from the past decade of the use of PDX models for precision oncology, from translational research to drug discovery. We elaborate on how and to what extent preclinical observations in PDX models have confirmed and/or anticipated findings in patients. Finally, we illustrate emerging methodological efforts that could broaden the application of PDX models by honing their predictive accuracy or improving their versatility.

Biological Misinterpretation of Transcriptional Signatures in Tumor Samples Can Unknowingly Undermine Mechanistic Understanding and Faithful Alignment with Preclinical Data

PURPOSE

Precise mechanism-based gene expression signatures (GES) have been developed in appropriate in vitro and in vivo model systems, to identify important cancer-related signaling processes. However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumor samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumor stroma percentage can directly influence GESs, undermining the intended mechanistic signaling.

EXPERIMENTAL DESIGN

Using colorectal cancer as an exemplar, we deployed numerous orthogonal profiling methodologies, including laser capture microdissection, flow cytometry, bulk and multiregional biopsy clinical samples, single-cell RNA sequencing and finally spatial transcriptomics, to perform a comprehensive assessment of the potential for the most widely used GESs to be influenced, or confounded, by stromal content in tumor tissue. To complement this work, we generated a freely-available resource, ConfoundR; https://confoundr.qub.ac.uk/, that enables users to test the extent of stromal influence on an unlimited number of the genes/signatures simultaneously across colorectal, breast, pancreatic, ovarian and prostate cancer datasets.

RESULTS

Findings presented here demonstrate the clear potential for misinterpretation of the meaning of GESs, due to widespread stromal influences, which in-turn can undermine faithful alignment between clinical samples and preclinical data/models, particularly cell lines and organoids, or tumor models not fully recapitulating the stromal and immune microenvironment.

CONCLUSIONS

Efforts to faithfully align preclinical models of disease using phenotypically-designed GESs must ensure that the signatures themselves remain representative of the same biology when applied to clinical samples.

Modeling Colorectal Cancer Progression Reveals Niche-Dependent Clonal Selection

Colorectal cancer (CRC) is among the deadliest cancers worldwide, with metastasis being the main cause of patient mortality. During CRC progression the complex tumor ecosystem changes in its composition at virtually every stage. However, clonal dynamics and associated niche-dependencies at these stages are unknown. Hence, it is of importance to utilize models that faithfully recapitulate human CRC to define its clonal dynamics. We used an optical barcoding approach in mouse-derived organoids (MDOs) that revealed niche-dependent clonal selection. Our findings highlight that clonal selection is controlled by a site-specific niche, which critically contributes to cancer heterogeneity and has implications for therapeutic intervention.

Development of a miRNA-based classifier for detection of colorectal cancer molecular subtypes

Previously, colorectal cancer (CRC) has been classified into four distinct molecular subtypes based on transcriptome data. These consensus molecular subtypes (CMSs) have implications for our understanding of tumor heterogeneity and the prognosis of patients. So far, this classification has been based on the use of messenger RNAs (mRNAs), although microRNAs (miRNAs) have also been shown to play a role in tumor heterogeneity and biological differences between CMSs. In contrast to mRNAs, miRNAs have a smaller size and increased stability, facilitating their detection. Therefore, we built a miRNA-based CMS classifier by converting the existing mRNA-based CMS classification using machine learning (training dataset of n = 271). The performance of this miRNA-assigned CMS classifier (CMS-miRaCl) was evaluated in several datasets, achieving an overall accuracy of ~ 0.72 (0.6329-0.7987) in the largest dataset (n = 158). To gain insight into the biological relevance of CMS-miRaCl, we evaluated the most important features in the classifier. We found that miRNAs previously reported to be relevant in microsatellite-instable CRCs or Wnt signaling were important features for CMS-miRaCl. Following further studies to validate its robustness, this miRNA-based alternative might simplify the implementation of CMS classification in clinical workflows.

Refining colorectal cancer classification and clinical stratification through a single-cell atlas

BACKGROUND

Colorectal cancer (CRC) consensus molecular subtypes (CMS) have different immunological, stromal cell, and clinicopathological characteristics. Single-cell characterization of CMS subtype tumor microenvironments is required to elucidate mechanisms of tumor and stroma cell contributions to pathogenesis which may advance subtype-specific therapeutic development. We interrogate racially diverse human CRC samples and analyze multiple independent external cohorts for a total of 487,829 single cells enabling high-resolution depiction of the cellular diversity and heterogeneity within the tumor and microenvironmental cells.

RESULTS

Tumor cells recapitulate individual CMS subgroups yet exhibit significant intratumoral CMS heterogeneity. Both CMS1 microsatellite instability (MSI-H) CRCs and microsatellite stable (MSS) CRC demonstrate similar pathway activations at the tumor epithelial level. However, CD8+ cytotoxic T cell phenotype infiltration in MSI-H CRCs may explain why these tumors respond to immune checkpoint inhibitors. Cellular transcriptomic profiles in CRC exist in a tumor immune stromal continuum in contrast to discrete subtypes proposed by studies utilizing bulk transcriptomics. We note a dichotomy in tumor microenvironments across CMS subgroups exists by which patients with high cancer-associated fibroblasts (CAFs) and C1Q+TAM content exhibit poor outcomes, providing a higher level of personalization and precision than would distinct subtypes. Additionally, we discover CAF subtypes known to be associated with immunotherapy resistance.

CONCLUSIONS

Distinct CAFs and C1Q+ TAMs are sufficient to explain CMS predictive ability and a simpler signature based on these cellular phenotypes could stratify CRC patient prognosis with greater precision. Therapeutically targeting specific CAF subtypes and C1Q + TAMs may promote immunotherapy responses in CRC patients.

Identification of TPM2 and CNN1 as Novel Prognostic Markers in Functionally Characterized Human Colon Cancer-Associated Stromal Cells

Simple Summary

Non-transformed cells of tumor microenvironment also impact on cancer outgrowth and progression. In colon cancer, a leading cause of cancer-related death worldwide, a high abundance of a heterogeneous cell population generally referred to as cancer-associated fibroblasts (CAFs) or tumor-associated stromal cells (TASCs) is associated with poor prognosis. The identification of TASC-specific markers could help to select patients for additional treatments and may provide novel targets for innovative therapies. Some markers have been proposed, but their prognostic significance is modest. We successfully expanded TASCs from human colon cancers and demonstrated their capacity to promote tumor growth and metastatic spread in vitro and in in vivo models. By comparing TASC whole protein expression, the so-called “proteome”, with that of stromal cells derived from matched healthy colon tissues, we identified two novel markers highly significantly associated with severe prognosis. Our results might help to identify patients at risk and might suggest new treatment options.

Abstract

Stromal infiltration is associated with poor prognosis in human colon cancers. However, the high heterogeneity of human tumor-associated stromal cells (TASCs) hampers a clear identification of specific markers of prognostic relevance. To address these issues, we established short-term cultures of TASCs and matched healthy mucosa-associated stromal cells (MASCs) from human primary colon cancers and, upon characterization of their phenotypic and functional profiles in vitro and in vivo, we identified differentially expressed markers by proteomic analysis and evaluated their prognostic significance. TASCs were characterized by higher proliferation and differentiation potential, and enhanced expression of mesenchymal stem cell markers, as compared to MASCs. TASC triggered epithelial–mesenchymal transition (EMT) in tumor cells in vitro and promoted their metastatic spread in vivo, as assessed in an orthotopic mouse model. Proteomic analysis of matched TASCs and MASCs identified a panel of markers preferentially expressed in TASCs. The expression of genes encoding two of them, calponin 1 (CNN1) and tropomyosin beta chain isoform 2 (TPM2), was significantly associated with poor outcome in independent databases and outperformed the prognostic significance of currently proposed TASC markers. The newly identified markers may improve prognostication of primary colon cancers and identification of patients at risk.

classifieR a flexible interactive cloud-application for functional annotation of cancer transcriptomes

BACKGROUND

Transcriptionally informed predictions are increasingly important for sub-typing cancer patients, understanding underlying biology and to inform novel treatment strategies. For instance, colorectal cancers (CRCs) can be classified into four CRC consensus molecular subgroups (CMS) or five intrinsic (CRIS) sub-types that have prognostic and predictive value. Breast cancer (BRCA) has five PAM50 molecular subgroups with similar value, and the OncotypeDX test provides transcriptomic based clinically actionable treatment-risk stratification. However, assigning samples to these subtypes and other transcriptionally inferred predictions is time consuming and requires significant bioinformatics experience. There is no "universal" method of using data from diverse assay/sequencing platforms to provide subgroup classification using the established classifier sets of genes (CMS, CRIS, PAM50, OncotypeDX), nor one which in provides additional useful functional annotations such as cellular composition, single-sample Gene Set Enrichment Analysis, or prediction of transcription factor activity.

RESULTS

To address this bottleneck, we developed classifieR, an easy-to-use R-Shiny based web application that supports flexible rapid single sample annotation of transcriptional profiles derived from cancer patient samples form diverse platforms. We demonstrate the utility of the " classifieR" framework to applications focused on the analysis of transcriptional profiles from colorectal (classifieRc) and breast (classifieRb). Samples are annotated with disease relevant transcriptional subgroups (CMS/CRIS sub-types in classifieRc and PAM50/inferred OncotypeDX in classifieRb), estimation of cellular composition using MCP-counter and xCell, single-sample Gene Set Enrichment Analysis (ssGSEA) and transcription factor activity predictions with Discriminant Regulon Expression Analysis (DoRothEA).

CONCLUSIONS

classifieR provides a framework which enables labs without access to a dedicated bioinformation can get information on the molecular makeup of their samples, providing an insight into patient prognosis, druggability and also as a tool for analysis and discovery. Applications are hosted online at https://generatr.qub.ac.uk/app/classifieRc and https://generatr.qub.ac.uk/app/classifieRb after signing up for an account on https://generatr.qub.ac.uk .

Clinical significance of molecular subtypes of gastrointestinal tract adenocarcinoma

Adenocarcinomas of the gastrointestinal tract (esophagus, stomach, and colon) represent a heterogeneous group of diseases with distinct etiology, clinical features, treatment approaches, and prognosis. Studies are ongoing to isolate molecular genetic subtypes, perform complete biological characterization of the tumor, determine prognostic groups, and find predictive markers to the effectiveness of therapy. Separate molecular genetic classifications were created for esophageal adenocarcinoma [The Cancer Genome Atlas (TCGA)], stomach cancer (TCGA, Asian Cancer Research Group), and colon cancer (Colorectal Cancer Subtyping Consortium). In 2018, isolation of TCGA molecular genetic subtypes for adenocarcinomas of the gastrointestinal tract (esophagus, stomach, and colon) highlighted the need for further studies and clinical validation of subtyping of gastrointestinal adenocarcinomas. However, this approach has limitations. The aim of our work was to critically analyze integration of molecular genetic subtyping of gastrointestinal adenocarcinomas in clinical practice.

Immune Landscape Refines the Classification of Colorectal Cancer With Heterogeneous Prognosis, Tumor Microenvironment and Distinct Sensitivity to Frontline Therapies

The immune microenvironment has profound impacts on the initiation and progression of colorectal cancer (CRC). Therefore, the goal of this article is to identify two robust immune subtypes in CRC, further provide novel insights for the underlying mechanisms and clinical management. In this study, two CRC immune subtypes were identified using the consensus clustering of immune-related gene expression profiles in the meta-GEO dataset (n = 1,198), and their reproducibility was further verified in the TCGA-CRC dataset (n = 638). Subsequently, we characterized the immune escape mechanisms, gene alterations, and clinical features of two immune subtypes. Cluster 1 (C1) was defined as the “immune cold subtype” with immune cell depletion and deficiency, while cluster 2 (C2) was designed as the “immune hot subtype”, with abundant immune cell infiltration and matrix activation. We also underlined the potential immune escape mechanisms: lack of MHC molecules and defective tumor antigen presentation capacity in C1, increased immunosuppressive molecules in C2. The prognosis and sensitivity to 5-FU, Cisplatin and immunotherapy differed between two subtypes. According to the two immune subtypes, we developed a prognosis associated risk score (PARS) with the accurate performance for predicting the prognosis. Additionally, two nomograms for overall survival (OS) and disease-free survival (DFS) were further constructed to facilitate clinical management. Overall, our research provides new references and insights for understanding and refining the CRC.

Single patient classifier as a prognostic biomarker in pT1N1 gastric cancer: Results from two large Korean cohorts

Objective

Benefits of adjuvant treatment in pT1N1 gastric cancer (GC) remain controversial. Additionally, an effective biomarker for early GC is the need of the hour. The prognostic and predictive roles of single patient classifier (SPC) were validated in stage II/III GC. In this study, we aimed to elucidate the role of SPC as a biomarker for pT1N1 GC.

Methods

The present retrospective biomarker study (NCT03485105) enrolled patients treated for pT1N1 GC between 1996 and 2012 from two large hospitals (the Y cohort and S cohort). For SPC, mRNA expression of four classifier genes (GZMB, WARS, SFRP4 and CDX1) were evaluated by real-time reverse transcription-polymerase chain reaction assay. The SPC was revised targeting pT1 stages and the prognosis was stratified as high- and low-risk group by the expression of SFRP4, a representative epithelial-mesenchymal transition marker.

Results

SPC was evaluated in 875 patients (n=391 and 484 in the Y and S cohorts, respectively). Among 864 patients whose SPC result was available, 41 (4.7%) patients experience GC recurrence. According to revised SPC, 254 (29.4%) patients were classified as high risk [123 (31.5%) and 131 (27.1%) in the Y and S cohorts, respectively]. The high risk was related to frequent recurrence in both Y and S cohort (log-rank P=0.023, P<0.001, respectively), while there was no difference byGZMB and WARS expression. Multivariable analyses of the overall-cohort confirmed the high risk of revised SPC as a significant prognostic factor [hazard ratio (HR): 4.402 (2.293−8.449), P<0.001] of GC. A significant difference was not detected by SPC in the prognosis of patients in the presence and absence of adjuvant treatment (log-rank P=0.670).

Conclusions

The present study revealed the revised SPC as a prognostic biomarker of pT1N1 GC and suggested the use of the revised SPC for early-stage GC as like stage II/III.

APC and TP53 Mutations Predict Cetuximab Sensitivity across Consensus Molecular Subtypes

Simple Summary

Colorectal cancer (CRC) is a major cause of cancer deaths. Cetuximab is an FDA-approved, underutilized therapeutic targeting the epidermal growth factor receptor (EGFR) in metastatic CRC. To date, despite selection of patients with wild-type RAS, it is still difficult to identify patients who may benefit from EGFR inhibitor (e.g., cetuximab) therapy. Our aim is to molecularly classify CRC patients to better identify subpopulations sensitive to EGFR targeted therapy. APC and TP53 are two major tumor suppressor genes in CRC whose mutations contribute to tumor initiation and progression and may identify cetuximab-sensitive tumors. Recently, it has been suggested that the consensus molecular subtype (CMS) classification may be used to help identify cetuximab-sensitive patients. Here, we report an analysis of multiple CRC tumor/PDX/cell line datasets using combined APC and TP53 mutations to refine the CMS classification to better predict responses to cetuximab to improve patient outcomes.

Abstract

Recently, it was suggested that consensus molecular subtyping (CMS) may aide in predicting response to EGFR inhibitor (cetuximab) therapies. We recently identified that APC and TP53 as two tumor suppressor genes, when mutated, may enhance cetuximab sensitivity and may represent easily measured biomarkers in tumors or blood. Our study aimed to use APC and TP53 mutations (AP) to refine the CMS classification to better predict responses to cetuximab. In total, 433 CRC tumors were classified into CMS1-4 subtypes. The cetuximab sensitivity (CTX-S) signature scores of AP vs. non-AP tumors were determined across each of the CMS classes. Tumors harboring combined AP mutations were predominantly enriched in the CMS2 class, and to a lesser degree, in the CMS4 class. On the other hand, AP mutated CRCs had significantly higher CTX-S scores compared to non-AP CRCs across all CMS classes. Similar results were also obtained in independent TCGA tumor collections (n = 531) and in PDMR PDX/PDO/PDC models (n = 477). In addition, the in vitro cetuximab growth inhibition was preferentially associated with the CMS2 cell lines harboring A/P genotypes. In conclusion, the AP mutation signature represents a convenient biomarker that refines the CMS classification to identify CRC subpopulations predicted to be sensitive to EGFR targeted therapies.

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.

The Emergence of Immune-checkpoint Inhibitors in Colorectal Cancer Therapy

Immunotherapy has revolutionized the treatment landscape in a number of solid tumors. In colorectal cancer, evidence suggests that microsatellite high (MSI-H) tumors are the most responsive to immune checkpoint blockade due to increased neo-antigen load and a favorable tumor microenvironment. Indeed, Pembrolizumab now represents a first-line option in such patients. However, MSI-H tumors represent the minority and a proportion of patients' progress despite initially responding. Trials are investigating different immunotherapy combinatorial strategies to enhance immune response in less immunogenic colorectal tumors. Such strategies include dual immune checkpoint blockade, combining immune checkpoint inhibitors with other treatment modalities such as radiotherapy, chemotherapy or other biological or targeted agents. Moreover, there is an increasing drive to identify biomarkers to better select patients most likely to respond to immunotherapy and understand intrinsic and acquired resistance mechanisms. Apart from MSI-H tumors, there is a strong rationale to suggest that tumors with alterations in DNA polymerase epsilon and DNA polymerase delta are also likely to respond to immunotherapy and trials in this subpopulation are underway. Other strategies such as priming O6-methylguanineDNA methyltransferase silenced tumors with alkylating agents to make them receptive to immune checkpoint blockade are also being investigated. Here we discuss different colorectal subpopulations together with their likelihood of response to immune checkpoint blockade and strategies to overcome barriers to a successful clinical outcome. We summarize evidence from published clinical trials and provide an overview of trials in progress whilst discussing newer immunotherapy strategies such as adoptive cell therapies and cancer vaccines.

Implications for the colorectal surgeon following the 100 000 Genomes Project

AIM

The 100 000 Genomes Project was completed in 2019 with the objective of integrating genomic medicine into routine National Health Service (NHS) clinical pathways. This project and genomic research will revolutionize the way we practice colorectal surgery in the 21st century. This paper aims to provide an overview of genomic medicine and its implications for the colorectal surgeon.

RESULTS

Within NHS England, consolidation has created seven regional Genomic Laboratory Hubs. DNA from solid tumours, including colorectal cancers, will be assessed using 500-gene panels, results will be fed back to Genome Tumour Advisory Boards. Identifying variants from biopsies earlier in the clinical pathway may alter surgical and other treatment options for patients. However, there is an important distinction between somatic variants within a tumour biopsy and germline variants that may suggest a heritable condition such as Lynch syndrome. Novel drugs, for example immunotherapy, will increase treatment options including downstaging cancers and changing the surgical approach. The use of circulating tumour DNA (liquid biopsies) will have applications in diagnosis, treatment and surveillance of cancer. There are many exciting potential future applications of this technology for offering personalized medicine that will require multidisciplinary working and the colorectal community.

CONCLUSION

There are many challenges but also exciting opportunities to embed new 'omic' technologies and innovation into 21st century colorectal surgery. The next phase for the colorectal community is how we engage with this change, with questions around training, identification of genomic multidisciplinary team (MDT) champions and how we collaborate with the core members of the MDT, clinical geneticists and national genomic testing.

Rational Treatment of Metastatic Colorectal Cancer: A Reverse Tale of Men, Mice, and Culture Dishes

Stratification of colorectal cancer into subgroups with different response to therapy was initially guided by descriptive associations between specific biomarkers and treatment outcome. Recently, preclinical models based on propagatable patient-derived tumor samples have yielded an improved understanding of disease biology, which has facilitated the functional validation of correlative information and the discovery of novel response determinants, therapeutic targets, and mechanisms of tumor adaptation and drug resistance. We review the contribution of patient-derived models to advancing colorectal cancer characterization, discuss their influence on clinical decision-making, and highlight emerging challenges in the interpretation and clinical transferability of results obtainable with such approaches. SIGNIFICANCE: Association studies in patients with colorectal cancer have led to the identification of response biomarkers, some of which have been implemented as companion diagnostics for therapeutic decisions. By enabling biological investigation in a clinically relevant experimental context, patient-derived colorectal cancer models have proved useful to examine the causal role of such biomarkers in dictating drug sensitivity and are providing fresh knowledge on new actionable targets, dynamics of tumor evolution and adaptation, and mechanisms of drug resistance.

Prognostic and predictive impact of consensus molecular subtypes and CRCAssigner classifications in metastatic colorectal cancer: a translational analysis of the TRIBE2 study

INTRODUCTION

The consensus molecular subtypes (CMS) demonstrated prognostic value in metastatic colorectal cancer (mCRC). Similarly, a prognostic impact was suggested for the pre-consensus CRCAssigner (CRCA) classifier in early stages. The potential predictive role of these classifiers with regard to the choice of the first-line therapy has not been established. We investigated the prognostic and predictive impact of CMS and CRCA subtypes among mCRC patients treated in the TRIBE2 study.

METHODS

Among 679 randomized patients, 426 and 428 (63%) samples were profiled according to CMS and CRCA classifications, respectively. The prognostic and predictive impact of both CMS and CRCA subtypes was investigated with univariate and multivariate analyses for progression-free survival (PFS), PFS 2 (PFS2), and overall survival (OS).

RESULTS

Significant associations of CMS and CRCA subtypes with PFS, PFS2, and OS were demonstrated; the CMS classifier confirmed its independent prognostic value in the multivariable model (P value for PFS/PFS2/OS = 0.01/0.07/0.08). The effect of treatment intensification was independent of CMS subtypes (P value for interaction for PFS/PFS2/OS = 0.88/0.75/0.55). A significant interaction effect between CRCA subtypes and treatment arm was demonstrated in PFS (P = 0.02), PFS2 (P = 0.01), and OS (P = 0.008). The benefit of FOLFOXIRI seemed more relevant in the stem-like (PFS, hazard ratio = 0.60; P = 0.03) and mixed subtypes (hazard ratio = 0.44; P = 0.002). These findings were confirmed in a subgroup of patients of the previous TRIBE study.

CONCLUSIONS

We confirmed the independent prognostic role of CMS classification in mCRC independently of RAS/BRAF status. CRCA classification may help identifying subgroups of patients who may derive more benefit from FOLFOXIRI/bevacizumab.

AKT3 Expression in Mesenchymal Colorectal Cancer Cells Drives Growth and Is Associated with Epithelial-Mesenchymal Transition

Simple Summary

Colorectal cancer can be subdivided into four distinct subtypes that are characterised by different clinical features and responses to therapies currently used in the clinic to treat this disease. One of those subtypes, called CMS4, is associated with a worse prognosis and poor response to therapies compared to other subtypes. We therefore set out to explore what proteins are differentially expressed and used in CMS4 to find potential new targets for therapy. We found that protein AKT3 is highly expressed in CMS4, and that active AKT3 inhibits a protein that stalls growth of cancer cells (p27^KIP1). We can target AKT3 with inhibitors which leads to strongly reduced growth of cancer cell lines that are categorised as CMS4. Furthermore, our data suggests that high AKT3 expression in tumour cells may be used to identify poor prognosis colorectal cancer patients. Future research should point out if high AKT3 expression can be used to select colorectal cancer patients that have a poor prognosis but that could benefit from AKT3-targeted treatment.

Abstract

Colorectal cancer (CRC) is a heterogeneous disease that can currently be subdivided into four distinct consensus molecular subtypes (CMS) based on gene expression profiling. The CMS4 subtype is marked by high expression of mesenchymal genes and is associated with a worse overall prognosis compared to other CMSs. Importantly, this subtype responds poorly to the standard therapies currently used to treat CRC. We set out to explore what regulatory signalling networks underlie the CMS4 phenotype of cancer cells, specifically, by analysing which kinases were more highly expressed in this subtype compared to others. We found AKT3 to be expressed in the cancer cell epithelium of CRC specimens, patient derived xenograft (PDX) models and in (primary) cell cultures representing CMS4. Importantly, chemical inhibition or knockout of this gene hampers outgrowth of this subtype, as AKT3 controls expression of the cell cycle regulator p27^KIP1. Furthermore, high AKT3 expression was associated with high expression of epithelial-mesenchymal transition (EMT) genes, and this observation could be expanded to cell lines representing other carcinoma types. More importantly, this association allowed for the identification of CRC patients with a high propensity to metastasise and an associated poor prognosis. High AKT3 expression in the tumour epithelial compartment may thus be used as a surrogate marker for EMT and may allow for a selection of CRC patients that could benefit from AKT3-targeted therapy.

Advances in colon cancer research: in vitro and animal models

Modelling human colon cancer has long been the ambition of researchers and oncologists with the aim to better replicate disease progression and treatment response. Advances in our understanding of genetics, stem cell biology, tumour microenvironment and immunology have prepared the groundwork for recent major advances. In the last two years the field has seen the progression of: using patient derived organoids (alone and in co-culture) as predictors of treatment response; molecular stratification of tumours that predict outcome and treatment response; mouse models of metastatic disease; and transplant models that can be used to de-risk clinical trials. We will discuss these advances in this review.

The clinical relevance of gene expression based prognostic signatures in colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent cancers, with more than one million new cases every year. In the last few decades, several advancements in therapeutic and preventative levels have reduced the mortality rate, but new biomarkers are required for improved prognosis. The alterations at the genetic and epigenetic level have been recognized as major players in tumorigenesis. The products of gene expression in the form of mRNA, microRNA, and long-noncoding RNA, have started to emerge as important regulatory molecules, playing an important role in cancer. Gene-expression based prognostic risk scores, which quantify and compare their expression, have emerged as promising biomarkers with enormous clinical value. These composite multi-gene models in which more than one gene is used to predict prognosis have been shown to be significantly effective in identifying patients with multiple clinico-pathological risks like overall mortality, response to chemotherapy, risk of metastasis, etc. The advent of microarray and advanced sequencing technologies have led to the generation of large datasets like TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), which have fueled the search for new biomarkers. Continuous evaluation of these candidate biomarkers in clinical settings is promising to improve the management of CRC. These composite gene signatures provide potential in identifying high-risk patients, which might help clinicians to better manage these patients and design appropriate personalized therapeutic interventions. In this review, we emphasize on composite prognostic scores from diverse resources with clinical utility in CRC.

In-depth Clinical and Biological Exploration of DNA Damage Immune Response as a Biomarker for Oxaliplatin Use in Colorectal Cancer

PURPOSE

The DNA damage immune response (DDIR) assay was developed in breast cancer based on biology associated with deficiencies in homologous recombination and Fanconi anemia pathways. A positive DDIR call identifies patients likely to respond to platinum-based chemotherapies in breast and esophageal cancers. In colorectal cancer, there is currently no biomarker to predict response to oxaliplatin. We tested the ability of the DDIR assay to predict response to oxaliplatin-based chemotherapy in colorectal cancer and characterized the biology in DDIR-positive colorectal cancer.

EXPERIMENTAL DESIGN

Samples and clinical data were assessed according to DDIR status from patients who received either 5-fluorouracil (5-FU) or 5FUFA (bolus and infusion 5-FU with folinic acid) plus oxaliplatin (FOLFOX) within the FOCUS trial (n = 361, stage IV), or neoadjuvant FOLFOX in the FOxTROT trial (n = 97, stage II/III). Whole transcriptome, mutation, and IHC data of these samples were used to interrogate the biology of DDIR in colorectal cancer.

RESULTS

Contrary to our hypothesis, DDIR-negative patients displayed a trend toward improved outcome for oxaliplatin-based chemotherapy compared with DDIR-positive patients. DDIR positivity was associated with microsatellite instability (MSI) and colorectal molecular subtype 1. Refinement of the DDIR signature, based on overlapping IFN-related chemokine signaling associated with DDIR positivity across colorectal cancer and breast cancer cohorts, further confirmed that the DDIR assay did not have predictive value for oxaliplatin-based chemotherapy in colorectal cancer.

CONCLUSIONS

DDIR positivity does not predict improved response following oxaliplatin treatment in colorectal cancer. However, data presented here suggest the potential of the DDIR assay in identifying immune-rich tumors that may benefit from immune checkpoint blockade, beyond current use of MSI status.

Transcriptomic and proteomic intra-tumor heterogeneity of colorectal cancer varies depending on tumor location within the colorectum

Background

Intra-tumor heterogeneity (ITH) of colorectal cancer (CRC) complicates molecular tumor classification, such as transcriptional subtyping. Differences in cellular states, biopsy cell composition, and tumor microenvironment may all lead to ITH. Here we analyze ITH at the transcriptomic and proteomic levels to ascertain whether subtype discordance between multiregional biopsies reflects relevant biological ITH or lack of classifier robustness. Further, we study the impact of tumor location on ITH.

Methods

Multiregional biopsies from stage II and III CRC tumors were analyzed by RNA sequencing (41 biopsies, 14 tumors) and multiplex immune protein analysis (89 biopsies, 29 tumors). CRC subtyping was performed using consensus molecular subtypes (CMS), CRC intrinsic subtypes (CRIS), and TUMOR types. ITH-scores and network maps were defined to determine the origin of heterogeneity. A validation cohort was used with one biopsy per tumor (162 tumors).

Results

Overall, inter-tumor transcriptional variation exceeded ITH, and subtyping calls were frequently concordant between multiregional biopsies. Still, some tumors had high transcriptional ITH and were classified discordantly. Subtyping of proximal MSS tumors were discordant for 50% of the tumors, this ITH was related to differences in the microenvironment. Subtyping of distal MSS tumors were less discordant, here the ITH was more cancer-cell related. The subtype discordancy reflected actual molecular ITH within the tumors. The relevance of the subtypes was reflected at protein level where several inflammation markers were significantly increased in immune related transcriptional subtypes, which was verified in an independent cohort (Wilcoxon rank sum test; p<0.05). Unsupervised hierarchical clustering of the protein data identified large ITH at protein level; as the multiregional biopsies clustered together for only 9 out of 29 tumors.

Conclusion

Our transcriptomic and proteomic analyses show that the tumor location along the colorectum influence the ITH of CRC, which again influence the concordance of subtyping.

Antiangiogenic immunotherapy suppresses desmoplastic and chemoresistant intestinal tumors in mice

Mutations in APC promote colorectal cancer (CRC) progression through uncontrolled WNT signaling. Patients with desmoplastic CRC have a significantly worse prognosis and do not benefit from chemotherapy, but the mechanisms underlying the differential responses of APC-mutant CRCs to chemotherapy are not well understood. We report that expression of the transcription factor prospero homeobox 1 (PROX1) was reduced in desmoplastic APC-mutant human CRCs. In genetic Apc-mutant mouse models, loss of Prox1 promoted the growth of desmoplastic, angiogenic, and immunologically silent tumors through derepression of Mmp14. Although chemotherapy inhibited Prox1-proficient tumors, it promoted further stromal activation, angiogenesis, and invasion in Prox1-deficient tumors. Blockade of vascular endothelial growth factor A (VEGFA) and angiopoietin-2 (ANGPT2) combined with CD40 agonistic antibodies promoted antiangiogenic and immunostimulatory reprogramming of Prox1-deficient tumors, destroyed tumor fibrosis, and unleashed T cell-mediated killing of cancer cells. These results pinpoint the mechanistic basis of chemotherapy-induced hyperprogression and illustrate a therapeutic strategy for chemoresistant and desmoplastic CRCs.

Gut γδ T cells as guardians, disruptors, and instigators of cancer

Colorectal cancer is the third most common cancer worldwide with nearly 2 million cases per year. Immune cells and inflammation are a critical component of colorectal cancer progression, and they are used as reliable prognostic indicators of patient outcome. With the growing appreciation for immunology in colorectal cancer, interest is growing on the role γδ T cells have to play, as they represent one of the most prominent immune cell populations in gut tissue. This group of cells consists of both resident populations-γδ intraepithelial lymphocytes (γδ IELs)-and transient populations that each has unique functions. The homeostatic role of these γδ T cell subsets is to maintain barrier integrity and prevent microorganisms from breaching the mucosal layer, which is accomplished through crosstalk with enterocytes and other immune cells. Recent years have seen a surge in discoveries regarding the regulation of γδ IELs in the intestine and the colon with particular new insights into the butyrophilin family. In this review, we discuss the development, specialities, and functions of γδ T cell subsets during cancer progression. We discuss how these cells may be used to predict patient outcome, as well as how to exploit their behavior for cancer immunotherapy.

Immune status is prognostic for poor survival in colorectal cancer patients and is associated with tumour hypoxia

BACKGROUND

Immunohistochemical quantification of the immune response is prognostic for colorectal cancer (CRC). Here, we evaluate the suitability of alternative immune classifiers on prognosis and assess whether they relate to biological features amenable to targeted therapy.

METHODS

Overall survival by immune (CD3, CD4, CD8, CD20 and FOXP3) and immune-checkpoint (ICOS, IDO-1 and PD-L1) biomarkers in independent CRC cohorts was evaluated. Matched mutational and transcriptomic data were interrogated to identify associated biology.

RESULTS

Determination of immune-cold tumours by combined low-density cell counts of CD3, CD4 and CD8 immunohistochemistry constituted the best prognosticator across stage II-IV CRC, particularly in patients with stage IV disease (HR 1.98 [95% CI: 1.47-2.67]). These immune-cold CRCs were associated with tumour hypoxia, confirmed using CAIX immunohistochemistry (P = 0.0009), which may mediate disease progression through common biology (KRAS mutations, CRIS-B subtype and SPP1 mRNA overexpression).

CONCLUSIONS

Given the significantly poorer survival of immune-cold CRC patients, these data illustrate that assessment of CD4-expressing cells complements low CD3 and CD8 immunohistochemical quantification in the tumour bulk, potentially facilitating immunophenotyping of patient biopsies to predict prognosis. In addition, we found immune-cold CRCs to associate with a difficult-to-treat, poor prognosis hypoxia signature, indicating that these patients may benefit from hypoxia-targeting clinical trials.

Histological phenotypic subtypes predict recurrence risk and response to adjuvant chemotherapy in patients with stage III colorectal cancer

Histological 'phenotypic subtypes' that classify patients into four groups (immune, canonical, latent and stromal) have previously been demonstrated to stratify survival in a stage I-III colorectal cancer (CRC) pilot cohort. However, clinical utility has not yet been validated. Therefore, this study assessed prognostic value of these subtypes in additional patient cohorts along with associations with risk of recurrence and response to chemotherapy. Two independent stage I-III CRC patient cohorts (internal and external cohort) were utilised to investigate phenotypic subtypes. The primary endpoint was disease-free survival (DFS) and the secondary endpoint was recurrence risk (RR). Stage II-III patients, from the SCOT adjuvant chemotherapy trial, were utilised to further validate prognostic value and for exploratory analysis assessing associations with adjuvant chemotherapy. In an 893-patient internal cohort, phenotypic subtype independently associated with DFS (p = 0.025) and this was attenuated in stage III patients (p = 0.020). Phenotypic subtype also independently associated with RR (p < 0.001) in these patients. In a 146-patient external cohort, phenotypic subtype independently stratified patients by DFS (p = 0.028), validating their prognostic value. In 1343 SCOT trial patients, the effect of treatment type significantly depended on phenotypic subtype (pinteraction = 0.011). Phenotypic subtype independently associated with DFS in stage III patients receiving FOLFOX (p = 0.028). Furthermore, the immune subtype significantly associated with better response to FOLFOX compared to CAPOX adjuvant chemotherapy in stage III patients (p = 0.013). In conclusion, histological phenotypic subtypes are an effective prognostic classification in patients with stage III CRC that associates with risk of recurrence and response to FOLFOX adjuvant chemotherapy.

Molecular subtypes and the evolution of treatment management in metastatic colorectal cancer

Colorectal cancer (CRC) is a heterogeneous disease representing a therapeutic challenge, which is further complicated by the common occurrence of several molecular alterations that confer resistance to standard chemotherapy and targeted agents. Mechanisms of resistance have been identified at multiple levels in the epidermal growth factor receptor (EGFR) pathway, including mutations in KRAS, NRAS, and BRAF V600E, and in the HER2 and MET receptors. These alterations represent oncogenic drivers that may co-exist in the same tumor with other primary and acquired alterations via a clonal selection process. Other molecular alterations include DNA damage repair mechanisms and rare kinase fusions, potentially offering a rationale for new therapeutic strategies. In recent years, genomic analysis has been expanded by a more complex study of epigenomic, transcriptomic, and microenvironment features. The Consensus Molecular Subtype (CMS) classification describes four CRC subtypes with distinct biological characteristics that show prognostic and potential predictive value in the clinical setting. Here, we review the panorama of actionable targets in CRC, and the developments in more recent molecular tests, such as liquid biopsy analysis, which are increasingly offering clinicians a means of ensuring optimal tailored treatments for patients with metastatic CRC according to their evolving molecular profile and treatment history.

Exploring and modelling colon cancer inter-tumour heterogeneity: opportunities and challenges

Colon cancer inter-tumour heterogeneity is installed on multiple levels, ranging from (epi)genetic driver events to signalling pathway rewiring reflected by differential gene expression patterns. Although the existence of heterogeneity in colon cancer has been recognised for a longer period of time, it is sparingly incorporated as a determining factor in current clinical practice. Here we describe how unsupervised gene expression-based classification efforts, amongst which the consensus molecular subtypes (CMS), can stratify patients in biological subgroups associated with distinct disease outcome and responses to therapy. We will discuss what is needed to extend these subtyping efforts to the clinic and we will argue that preclinical models recapitulate CMS subtypes and can be of vital use to increase our understanding of treatment response and resistance and to discover novel targets for therapy.

Dichotomous role of the human mitochondrial Na+/Ca2+/Li+ exchanger NCLX in colorectal cancer growth and metastasis

Despite the established role of mitochondria in cancer, the mechanisms by which mitochondrial Ca²⁺ (mtCa²⁺) regulates tumorigenesis remain incompletely understood. The crucial role of mtCa²⁺ in tumorigenesis is highlighted by altered expression of proteins mediating mtCa²⁺ uptake and extrusion in cancer. Here, we demonstrate decreased expression of the mitochondrial Na⁺/Ca²⁺/Li⁺ exchanger NCLX (SLC8B1) in human colorectal tumors and its association with advanced-stage disease in patients. Downregulation of NCLX causes mtCa²⁺ overload, mitochondrial depolarization, decreased expression of cell-cycle genes and reduced tumor size in xenograft and spontaneous colorectal cancer mouse models. Concomitantly, NCLX downregulation drives metastatic spread, chemoresistance, and expression of epithelial-to-mesenchymal, hypoxia, and stem cell pathways. Mechanistically, mtCa²⁺ overload leads to increased mitochondrial reactive oxygen species, which activate HIF1α signaling supporting metastasis of NCLX-null tumor cells. Thus, loss of NCLX is a novel driver of metastasis, indicating that regulation of mtCa²⁺ is a novel therapeutic approach in metastatic colorectal cancer.

Biomarker-guided therapy for colorectal cancer: strength in complexity

The number of molecularly stratified treatment options available to patients with colorectal cancer (CRC) is increasing, with a parallel rise in the use of biomarkers to guide prognostication and treatment decision-making. The increase in both the number of biomarkers and their use has resulted in a progressively complex situation, evident both from the extensive interactions between biomarkers and from their sometimes complex associations with patient prognosis and treatment benefit. Current and emerging biomarkers also reflect the genomic complexity of CRC, and include a wide range of aberrations such as point mutations, amplifications, fusions and hypermutator phenotypes, in addition to global gene expression subtypes. In this Review, we provide an overview of current and emerging clinically relevant biomarkers and their role in the management of patients with CRC, illustrating the intricacies of biomarker interactions and the growing treatment opportunities created by the availability of comprehensive molecular profiling.

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.