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

ABSTRACT: Stromal-derived intratumoural heterogeneity (ITH) has been shown to undermine molecular stratification of patients into appropriate prognostic/predictive subgroups. Here, using several clinically relevant colorectal cancer (CRC) gene expression signatures, we assessed the susceptibility of these signatures to the confounding effects of ITH using gene expression microarray data obtained from multiple tumour regions of a cohort of 24 patients, including central tumour, the tumour invasive front and lymph node metastasis. Sample clustering alongside correlative assessment revealed variation in the ability of each signature to cluster samples according to patient-of-origin rather than region-of-origin within the multi-region dataset. Signatures focused on cancer-cell intrinsic gene expression were found to produce more clinically useful, patient-centred classifiers, as exemplified by the CRC intrinsic signature (CRIS), which robustly clustered samples by patient-of-origin rather than region-of-origin. These findings highlight the potential of cancer-cell intrinsic signatures to reliably stratify CRC patients by minimising the confounding effects of stromal-derived ITH.

SUBMITTER: Dunne PD

PROVIDER: S-EPMC5460026 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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

Dunne Philip D PD Alderdice Matthew M O'Reilly Paul G PG Roddy Aideen C AC McCorry Amy M B AMB Richman Susan S Maughan Tim T McDade Simon S SS Johnston Patrick G PG Longley Daniel B DB Kay Elaine E McArt Darragh G DG Lawler Mark M

Nature communications 20170531

Stromal-derived intratumoural heterogeneity (ITH) has been shown to undermine molecular stratification of patients into appropriate prognostic/predictive subgroups. Here, using several clinically relevant colorectal cancer (CRC) gene expression signatures, we assessed the susceptibility of these signatures to the confounding effects of ITH using gene expression microarray data obtained from multiple tumour regions of a cohort of 24 patients, including central tumour, the tumour invasive front an ...[more]

PMID: 28561046

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Project description:BackgroundThe intrinsic immuno-ge7nomic characteristics of colorectal cancer cells that affect tumor biology and shape the tumor immune microenvironment (TIM) are unclear.MethodsWe developed a patient-derived colorectal cancer organoid (CCO) model and performed pairwise analysis of 87 CCOs and their matched primary tumors. The TIM type of the primary tumor was classified as immuno-active, immuno-exhausted, or immuno-desert.ResultsThe gene expression profiles, signaling pathways, major oncogenic mutations, and histology of the CCOs recapitulated those of the primary tumors, but not the TIM of primary tumors. Two distinct intrinsic molecular subgroups of highly proliferative and mesenchymal phenotypes with clinical significance were identified in CCOs with various cancer signaling pathways. CCOs showed variable expression of cancer-specific immune-related genes such as those encoding HLA-I and HLA-II, and molecules involved in immune checkpoint activation/inhibition. Among these genes, the expression of HLA-II in CCOs was associated with favorable patient survival. K-means clustering analysis based on HLA-II expression in CCOs revealed a subgroup of patients, in whom cancer cells exhibited Intrinsically Immunogenic Properties (Ca-IIP), and were characterized by high expression of signatures associated with HLA-I, HLA-II, antigen presentation, and immune stimulation. Patients with the Ca-IIP phenotype had an excellent prognosis, irrespective of age, disease stage, intrinsic molecular type, or TIM status. Ca-IIP was negatively correlated with intrinsic E2F/MYC signaling. Analysis of the correlation between CCO immuno-genotype and TIM phenotype revealed that the TIM phenotype was associated with microsatellite instability, Wnt/β-catenin signaling, APC/KRAS mutations, and the unfolded protein response pathway linked to the FBXW7 mutation in cancer cells. However, Ca-IIP was not associated with the TIM phenotype.ConclusionsWe identified a Ca-IIP phenotype from a large set of CCOs. Our findings may provide an unprecedented opportunity to develop new strategies for optimal patient stratification in this era of immunotherapy.

Project description:The past years have witnessed a rapid increase in the amount of large-scale tumor datasets. The challenge has now become to find a way to obtain useful information from these masses of data that will allow to determine which combination of FDA-approved drugs is best suited to treat the specific tumor. Various statistical analyses are being developed to extract significant signals from cancer datasets. However, tumors are still being assigned to pre-defined categories (breast luminal A, triple negative, etc.), conceptually contradicting the vast heterogeneity that is known to exist among tumors, and likely overlooking unique tumors that must be addressed and treated individually. We present herein an approach based on information theory that, rather than searches for what makes a tumor similar to other tumors, addresses tumors individually and unbiasedly, and impartially decodes the critical patient-specific molecular network reorganization in every tumor. Methods: Using a large dataset obtained from ~3500 tumors of 11 types we decipher the altered protein network structure in each tumor, namely the patient-specific signaling signature. Each signature can harbor several altered protein subnetworks. We suggest that simultaneous targeting of central proteins from every altered subnetwork is essential to efficiently disturb the altered signaling in each tumor. We experimentally validate our ability to dissect sample-specific signaling signatures and to rationally design personalized drug combinations. Results: We unraveled a surprisingly simple order that underlies the extreme apparent complexity of tumor tissues, demonstrating that only 17 altered protein subnetworks characterize ~3500 tumors of 11 types. Each tumor was described by a specific subset of 1-4 subnetworks out of 17, i.e. a tumor-specific altered signaling signature. We show that the majority of tumor-specific signaling signatures are extremely rare, and are shared by only 5 tumors or less, supporting a personalized, comprehensive study of tumors in order to design the optimal combination therapy for every patient. We validate the results by confirming that the processes identified in the 11 original cancer types characterize patients harboring a different cancer type as well. We show experimentally, using different cancer cell lines, that the individualized combination therapies predicted by us achieved higher rates of killing than the clinically prescribed treatments. Conclusions: We present a new strategy to deal with the inter-tumor heterogeneity and to break down the high complexity of cancer systems into simple, easy to crack, patient-specific signaling signatures that guide the rational design of personalized drug therapies.

Project description:Colorectal cancer (CRC) biopsies underpin accurate diagnosis, but are also relevant for patient stratification in molecularly-guided clinical trials. The consensus molecular subtypes (CMSs) and colorectal cancer intrinsic subtypes (CRISs) transcriptional signatures have potential clinical utility for improving prognostic/predictive patient assignment. However, their ability to provide robust classification, particularly in pretreatment biopsies from multiple regions or at different time points, remains untested. In this study, we undertook a comprehensive assessment of the robustness of CRC transcriptional signatures, including CRIS and CMS, using a range of tumour sampling methodologies currently employed in clinical and translational research. These include analyses using (i) laser-capture microdissected CRC tissue, (ii) eight publically available rectal cancer biopsy data sets (n = 543), (iii) serial biopsies (from AXEBeam trial, NCT00828672; n = 10), (iv) multi-regional biopsies from colon tumours (n = 29 biopsies, n = 7 tumours), and (v) pretreatment biopsies from the phase II rectal cancer trial COPERNCIUS (NCT01263171; n = 44). Compared to previous results obtained using CRC resection material, we demonstrate that CMS classification in biopsy tissue is significantly less capable of reliably classifying patient subtype (43% unknown in biopsy versus 13% unknown in resections, p = 0.0001). In contrast, there was no significant difference in classification rate between biopsies and resections when using the CRIS classifier. Additionally, we demonstrated that CRIS provides significantly better spatially- and temporally- robust classification of molecular subtypes in CRC primary tumour tissue compared to CMS (p = 0.003 and p = 0.02, respectively). These findings have potential to inform ongoing biopsy-based patient stratification in CRC, enabling robust and stable assignment of patients into clinically-informative arms of prospective multi-arm, multi-stage clinical trials. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

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

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

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