SEARCH · Search bioRxiv

Results for “Cancer Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,315 records · Page 73Linked to original sources

bioRxiv2018

An integrative systems biology and experimental approach identifies convergence of epithelial plasticity, metabolism, and autophagy to promote chemoresistance

The evolution of therapeutic resistance is a major cause of death for patients with solid tumors. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure induced by the host immune system. These ecological and selective forces often lead to evolutionary convergence on one or more pathways or hallmarks that drive progression. These hallmarks are, in turn, intimately linked to each other through gene expression networks. Thus, a deeper understanding of the evolutionary convergences that occur at the gene expression level could reveal vulnerabilities that could be targeted to treat therapy-resistant cancer. To this end, we used a combination of phylogenetic clustering, systems biology analyses, and wet-bench molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during TGF-{beta}-mediated epithelial-mesenchymal transition in a lung cancer model system. Phylogenetics analyses of gene expression data from TGF-{beta} treated cells revealed evolutionary convergence of cells toward amine-metabolic pathways and autophagy during TGF-{beta} treatment. Using high-throughput drug screens, we found that knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-{beta}-induced resistance, implicating autophagy as a TGF-{beta}-mediated chemoresistance mechanism. Analysis of publicly-available clinical data sets validated the adverse prognostic importance of ATG16L expression in multiple cancer types including kidney, lung, and colon cancer patients. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities.

cancer biology↗

bioRxiv2018

Single-domain antibodies represent novel alternatives to monoclonal antibodies as targeting agents against the human papillomavirus 16 E6 protein

Approximately one-fifth of all malignancies worldwide are etiologically-associated with a persistent viral or bacterial infection. Thus, there is particular interest in therapeutic molecules which utilize components of a natural immune response to specifically inhibit oncogenic microbial proteins, as it is anticipated they will elicit fewer off-target effects than conventional treatments. This concept has been explored in the context of human papillomavirus type 16 (HPV16)-related cancers, through the development of monoclonal antibodies and fragments thereof against the viral E6 oncoprotein. However, challenges related to the biology of E6 as well as the functional properties of the antibodies themselves appear to have precluded their clinical translation. In this study, we attempted to address these issues by exploring the utility of the variable domains of camelid heavy-chain-only antibodies (denoted as VHHs). Through the construction and panning of two llama immune VHH phage display libraries, a pool of potential VHHs was isolated. The interactions of these VHHs with recombinant E6 protein were further characterized using ELISA, Western blotting under both denaturing and native conditions, as well as surface plasmon resonance, and three antibodies were identified that bound recombinant E6 with affinities in the nanomolar range. Our results now lead the way for subsequent studies into the ability of these novel molecules to inhibit HPV16-infected cells in vitro and in vivo.

cancer biology↗

bioRxiv2017

Cancer subtype identification using somatic mutation data

BACKGROUNDWith the onset of next generation sequencing technologies, we have made great progress in identifying recurrent mutational drivers of cancer. As cancer tissues are now frequently screened for specific sets of mutations, a large amount of samples has become available for analysis. Classification of patients with similar mutation profiles may help identifying subgroups of patients who might benefit from specific types of treatment. However, classification based on somatic mutations is challenging due to the sparseness and heterogeneity of the data.\n\nMETHODSHere, we describe a new method to de-sparsify somatic mutation data using biological pathways. We applied this method to 23 cancer types from The Cancer Genome Atlas, including samples from 5, 805 primary tumors.\n\nRESULTSWe show that, for most cancer types, de-sparsified mutation data associates with phenotypic data. We identify poor prognostic subtypes in three cancer types, which are associated with mutations in signal transduction pathways for which targeted treatment options are available. We identify subtype-drug associations for 14 additional subtypes. Finally, we perform a pan-cancer subtyping analysis and identify nine pan-cancer subtypes, which associate with mutations in four overarching sets of biological pathways.\n\nCONCLUSIONSThis study is an important step towards understanding mutational patterns in cancer.

genomics↗

bioRxiv2018

Oxygen diffusion in ellipsoidal tumor spheroids

Oxygen plays a central role in cellular metabolism, in both healthy and tumour tissue. The presence and concentration of molecular oxygen in tumours has a substantial effect on both radiotherapy response and tumour evolution, and as a result the oxygen micro-environment is an area of intense research interest. Multicellular tumour spheroids closely mimic real avascular tumours, and in particular they exhibit physiologically relevant heterogeneous oxygen distribution. This property has made them a vital part of in vitro experimentation. For ideal spheroids, their heterogeneous oxygen distributions can be predicted from theory, allowing determination of cellular oxygen consumption rate (OCR) and anoxic extent. However, experimental tumour spheroids often depart markedly from perfect sphericity. There has been little consideration of this reality. To date, the question of how far an ellipsoid can diverge from perfect sphericity before spherical assumptions breakdown remains unanswered. In this work we derive equations governing oxygen distribution (and more generally, nutrient and drug distribution) in both prolate and oblate tumour ellipsoids, and quantify the theoretical limits of the assumption that the spheroid is a perfect sphere. Results of this analysis yield new methods for quantifying OCR in ellipsoidal spheroids, and how this can be applied to markedly increase experimental throughput and quality.\n\nAuthor summaryMulticellular tumour spheroids (MCTS) are an increasingly important tool in cancer research, exhibiting non-homogeneous oxygen distributions and central necrosis. These are more similar to in situ avascular tumours than conventional 2D biology, rendering them exceptionally useful experimental models. Analysis of spheroids can yield vital information about cellular oxygen consumption rates, and the heterogeneous oxygen contribution. However, such analysis pivots on the assumption of perfect sphericity, when in reality spheroids often depart from such an ideal. In this work, we construct a theoretical oxygen diffusion model for ellipsoidal tumour spheroids in both prolate and oblate geometries. With these models established, we quantify the limits of the spherical assumption, and illustrate the effect of this assumption breaking down. Methods of circumventing this breakdown are also presented, and the analysis here suggests new methods for expanding experimental throughput to also include ellipsoidal data.

cancer biology↗

bioRxiv2017

Driver Pattern Identification Over The Gene Co-Expression Of Drug Response In Ovarian Cancer By Integrating High Throughput Genomics Data

The multiple types of high throughput genomics data create a potential opportunity to identify driver pattern in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. However, it is a great challenging work to integrate omics data, including somatic mutations, Copy Number Variations (CNVs) and gene expression profiles, to distinguish interactions and regulations which are hidden in drug response dataset of ovarian cancer. To distinguish the candidate driver genes and the corresponding driving pattern for resistant and sensitive tumor from the heterogeneous data, we combined gene co-expression modules and mutation modulators and proposed the identification driver patterns method. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles via weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNVs and somatic mutations, and a mutation network is constructed from this mutation matrix. The candidate modulators are selected from the significant genes by clustering the vertex of the mutation network. At last, regression tree model is utilized for module networks learning in which the achieved gene modules and candidate modulators are trained for the driving pattern identification and modulator regulatory exploring. Many of the candidate modulators identified are known to be involved in biological meaningful processes associated with ovarian cancer, which can be regard as potential driver genes, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11 and so on, which can help to facilitate the discovery of biomarkers, molecular diagnostics, and drug discovery.

bioinformatics↗

bioRxiv2018

Identification of an immune gene expression signature associated with favorable clinical features in Treg-enriched patient tumor samples

Immune heterogeneity within the tumor microenvironment undoubtedly adds several layers of complexity to our understanding of drug sensitivity and patient prognosis across various cancer types. Within the tumor microenvironment, immunogenicity is a favorable clinical feature in part driven by the antitumor activity of CD8+ T cells. However, tumors often inhibit this antitumor activity by exploiting the suppressive function of Regulatory T cells (Tregs), thus suppressing the adaptive immune response. Despite the seemingly intuitive immunosuppressive biology of Tregs, prognostic studies have produced contradictory results regarding the relationship between Treg enrichment and survival. We therefore analyzed RNA-seq data of Treg-enriched tumor samples to derive a pan-cancer gene signature able to help reconcile the inconsistent results of Treg studies, by better understanding the variable clinical association of Tregs across alternative tumor contexts. We show that increased expression of a 32-gene signature in Treg-enriched tumor samples (n=135) is able to distinguish a cohort of patients associated with chemosensitivity and overall survival This cohort is also enriched for CD8+ T cell abundance, as well as the antitumor M1 macrophage subtype. With a subsequent validation in a larger TCGA pool of Treg-enriched patients (n = 626), our results reveal a gene signature able to produce unsupervised clusters of Treg-enriched patients, with one cluster of patients uniquely representative of an immunogenic tumor microenvironment. Ultimately, these results support the proposed gene signature as a putative biomarker to identify certain Treg-enriched patients with immunogenic tumors that are more likely to be associated with features of favorable clinical outcome.

cancer biology↗

bioRxiv2018

Intraductal patient derived xenografts of estrogen receptor positive breast cancer recapitulate the histopathological spectrum and metastatic potential of human lesions

Estrogen receptor positive (ER+) or \"luminal\" breast cancers were notoriously difficult to establish as patient-derived xenografts (PDXs). We and others recently demonstrated that the microenvironment is critical for ER+ tumor cells; by grafting them into milk ducts >90% take rates are achieved and many features of the human disease are recapitulated. This intra-ductal (ID) approach holds promise for personalized medicine, yet human and murine stroma are organized differently and this and other species specificities may limit the value of this model. Here, we analyzed 21 ER+ ID-PDXs histopathologically. We find that ID-PDXs vary in extent and define four histopathological patterns: flat, lobular, in situ, and invasive, which occur in pure and combined forms. The ID-PDXs replicate earlier stages of tumor development than their clinical counterparts. Micrometastases are already detected when lesions appear in situ. Tumor extent, histopathological patterns, and metastatic load correlate with biological properties of their tumors of origin. Our findings add evidence to the validity of the intraductal model for in vivo studies of ER+ breast cancer and raise the intriguing possibility that tumor cell dissemination may occur earlier than currently thought.\n\nConflict of interest statementThe authors declare no conflict of interest.

cancer biology↗

bioRxiv2017

Unique genomic features and deeply-conserved functions of long non-coding RNAs in the Cancer LncRNA Census (CLC)

Long non-coding RNAs (lncRNAs) that drive tumorigenesis are a growing focus of cancer genomics studies. To facilitate further discovery, we have created the \"Cancer LncRNA Census\" (CLC), a manually-curated and strictly-defined compilation of lncRNAs with causative roles in cancer. CLC has two principle applications: first, as a resource for training and benchmarking de novo identification methods; and second, as a dataset for studying the fundamental properties of these genes.\n\nCLC Version 1 comprises 122 lncRNAs implicated in 29 distinct cancers. LncRNAs are included based on functional or genetic evidence for causative roles in cancer progression. All belong to the GENCODE reference annotation, to enable integration across projects and datasets. For each entry, the evidence type, biological activity (oncogene or tumour suppressor), source reference and cancer type are recorded. Supporting its usefulness, CLC genes are significantly enriched amongst de novo predicted driver genes from PCAWG. CLC genes are distinguished from other lncRNAs by a series of features consistent with biological function, including gene length, high expression and sequence conservation of both exons and promoters. We identify a trend for CLC genes to be co-localised with known protein-coding cancer genes along the human genome. Finally, by integrating data from transposon-mutagenesis functional screens, we show that mouse orthologues of CLC genes tend also to be cancer genes.\n\nThus CLC represents a valuable resource for research into long non-coding RNAs in cancer. Their evolutionary and genomic properties have implications for understanding disease mechanisms and point to conserved functions across ~80 million years of evolution.

bioinformatics↗

bioRxiv2018

LOTUS: a Single- and Multitask Machine Learning Algorithm for the Prediction of Cancer Driver Genes

Cancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selective growth advantage, allowing uncontrolled proliferation and avoiding apoptosis. It is therefore important to identify these driver genes, both for the fundamental understanding of cancer and to help finding new therapeutic targets. Although the most frequently mutated driver genes have been identified, it is believed that many more remain to be discovered, particularly for driver genes specific to some cancer types.\n\nIn this paper we propose a new computational method called LOTUS to predict new driver genes. LOTUS is a machine-learning based approach which allows to integrate various types of data in a versatile manner, including informations about gene mutations and protein-protein interactions. In addition, LOTUS can predict cancer driver genes in a pan-cancer setting as well as for specific cancer types, using a multitask learning strategy to share information across cancer types.\n\nWe empirically show that LOTUS outperforms three other state-of-the-art driver gene prediction methods, both in terms of intrinsic consistency and prediction accuracy, and provide predictions of new cancer genes across many cancer types.\n\nAuthor summaryCancer development is driven by mutations and dysfunction of important, so-called cancer driver genes, that could be targeted by targeted therapies. While a number of such cancer genes have already been identified, it is believed that many more remain to be discovered. To help prioritize experimental investigations of candidate genes, several computational methods have been proposed to rank promising candidates based on their mutations in large cohorts of cancer cases, or on their interactions with known driver genes in biological networks. We propose LOTUS, a new computational approach to identify genes with high oncogenic potential. LOTUS implements a machine learning approach to learn an oncogenic potential score from known driver genes, and brings two novelties compared to existing methods. First, it allows to easily combine heterogeneous informations into the scoring function, which we illustrate by learning a scoring function from both known mutations in large cancer cohorts and interactions in biological networks. Second, using a multitask learning strategy, it can predict different driver genes for different cancer types, while sharing information between them to improve the prediction for every type. We provide experimental results showing that LOTUS significantly outperforms several state-of-the-art cancer gene prediction softwares.

bioinformatics↗

bioRxiv2017

Combining radiomics and mathematical modeling to elucidate mechanisms of resistance to immune checkpoint blockade in non-small cell lung cancer

Immune therapies have shown promise in a number of cancers, and clinical trials using the anti-PD-L1/PD-1 checkpoint inhibitor in lung cancer have been successful for a number of patients. However, some patients either do not respond to the treatment or have cancer recurrence after an initial response. It is not clear which patients might fall into these categories or what mechanisms are responsible for treatment failure. To explore the different underlying biological mechanisms of resistance, we created a spatially explicit mathematical model with a modular framework. This construction enables different potential mechanisms to be turned on and off in order to adjust specific tumor and tissue interactions to match a specific patient's disease. In parallel, we developed a software suite to identify significant computed tomography (CT) imaging features correlated with outcome using data from an anti-PDL-1 checkpoint inhibitor clinical trial for lung cancer and a tool that extracts these features from both patient CT images and \"virtual CT\" images created from the cellular density profile of the model. The combination of our two toolkits provides a framework that feeds patient data through an iterative pipeline to identify predictive imaging features associated with outcome, whilst at the same time proposing hypotheses about the underlying resistance mechanisms.

cancer biology↗

bioRxiv2018

Using neural networks to bridge scales in cancer: Mapping signaling pathways to phenotypes

Cancer is an evolving system subject to mutation and selection. Selection is driven by the microenvironment that the cancer cells are growing in and acts on the cell phenotype, which is in turn modulated by intracellular signaling pathways regulated by the cell genotype. Integrating all of these processes requires bridging different biological scales. We present a mathematical model that uses a neural network as a means to connecting these scales. In particular, we consider the mapping from intracellular pathway activity to phenotype under different microenvironmental conditions.

cancer biology↗

bioRxiv2017

Transcriptional Changes of DNA Replication and Repair Factors Over Uveal Melanoma Subtypes

BackgroundUncontrolled replication is a process common to all cancers facilitated by the summation of changes accumulated as tumors progress. The aim of this study was to examine small groups of genes with known biology in replication and repair at the transcriptional and genomic levels, correlating alterations with survival in Uveal Melanoma tumor progression. Selected components of Pre-Replication, Pre-Initiation, and Replisome Complexes, DNA Damage Response and Mismatch Repair have been observed.\n\nMethodsWe have generated two groups for each gene examined above and below the average alteration level, and compared relative expression and survival across TCGA UVM subtypes based on somatic copy number alteration supported by DNA methylation and mRNA/miRNA/lncRNA expression. Significance between subtypes monosomic or disomic for chromosome 3 was determined by Fishers exact test. Kaplan Meier survival distribution based on disease specific survival was compared by log-rank test.\n\nResultsSpecific genes with significant alteration include MCM2 MCM4 and MCM5 of the Minichromosome Maintenance helicase complex, CDC45, MCM10, CIZ1, PCNA, FEN1, LIG1, POLD1, POLE, HUS1, CHECK1, ATRIP, MLH3, and MSH6. We found evidence of Exon 4 skipping in CIZ1 previously identified as a cancer variant and reportedly used as an early serum biomarker in lung cancer, accompanied by evidence of instability of a mononucleotide repeat in Intron 3. Mismatch Repair protein MLH3 was found to have splicing variations with deletions to both Exon 5 and Exon 7 simultaneously. PCNA, FEN1, and LIG1 had increased relative expression levels not due to their mutation or to copy number variation.\n\nConclusionWe have observed differences in relative and differential expression that support the concept that selected replication and repair genes and their products are causally involved in the origin and progression of uveal melanoma, suggesting specific avenues for early biomarker identification and also therapeutic approach.

cancer biology↗

bioRxiv2017

Prediction of Molecular Mechanisms of Breast Cancer Metastasis

Metastasis -the spread of cancer to other parts of the body- causes 90% of cancer deaths, underlies major health complications in cancer patients and renders most cancers incurable. Unfortunately, the molecular mechanisms underlying the process are poorly understood and therapeutics to block it remain elusive. Here, we present a computational technique for scanning genome-scale regulatory networks for potential genes associated with metastasis. First, we demonstrate that in the breast cancer cell line MCF7, the commonly dysregulated cancer biomarkers TP53, ERBB2, ESR1 and PGR are closely connected to known metastasis genes with a significant proportion being 2nd degree neighbors of a given biomarker. Next, we identify genes whose 2nd degree neighbors are connected in a similar manner to these biomarkers. Consequently, these are referred to as metastasis associated genes or MAGs. We identify 190 genes that are TP53-MAGs, 22 ERBB2-MAGs, 240 ESR1-MAGs and 84 PGR-MAGs (FDR adjusted P <0.001). Analysis of the MAGs reveals statistically significant enrichment with biological functions previously associated with metastasis including the extracellular matrix (ECM) receptor interaction, focal adhesion, cytokine-cytokine receptor interaction and chemokine signaling. The biological significance of MAGs is further supported by their enrichment with experimentally validated binding sites for transcription factors that regulate metastasis, for example BACH1- a master regulator of breast cancer metastasis to bone. The predicted MAGs are also clinically relevant as therapeutic targets for metastasis blocking agents. Specifically, genes that are perturbed by drugs and miRNAs that influence metastasis are enriched with MAGs. Furthermore, some MAGs are associated with patient survival and provide insights into the proclivity for breast cancer subtypes to preferentially spread to specific organs. The results of this study imply that aberrations in primary tumors may constrict metastasis trajectories. This could enable the prediction of organ specific metastases based on aberrations in the primary tumor and lay a foundation for future studies on individualized or personalized models of metastasis. The approach is potentially scalable across other cancers and has clinical implications.

cancer biology↗

bioRxiv2018

Programmed mutation of liver fluke granulin using CRISPR/Cas9 attenuates virulence of infection-induced hepatobiliary morbidity

Infections with several flatworm parasites represent group 1 biological carcinogens, i.e. definite causes of cancer. Infection with the food-borne liver fluke Opisthorchis viverrini causes cholangiocarcinoma (CCA). Whereas the causative agent for most cancers, including CCA in the West, remains obscure, the principal risk factor for CCA in Thailand is opisthorchiasis. We exploited this established link to explore the role of the secreted parasite growth factor termed liver fluke granulin (Ov-GRN-1) in pre-malignant lesions of the biliary tract. We targeted the Ov-grn-1 gene for programmed knockout and investigated gene-edited parasites in vitro and in experimentally infected hamsters. Both adult and juvenile stages of the liver fluke were transfected with a plasmid encoding a guide RNA sequence specific for exon 1 of Ov-grn-1 and the Cas9 nuclease. Deep sequencing of amplicon libraries from genomic DNA from gene-edited parasites exhibited programmed, Cas9-catalyzed mutations within the Ov-grn-1 locus, and tandem analyses by RT-PCR and western blot revealed rapid depletion of Ov-grn-1 transcripts and protein. Newly excysted juvenile flukes that had undergone editing of Ov-grn-1 colonized the biliary tract, grew and developed over a period of 60 days, were active and motile, and induced a clinically relevant pathophysiological tissue phenotype of attenuated biliary hyperplasia and fibrosis in comparison to infection with wild type flukes. This is the first report of gene knock-out using CRISPR/Cas9 in a parasitic flatworm, demonstrating the activity and utility of the process for functional genomics in these pathogens. The striking clinical phenotype highlights the role in virulence that liver fluke growth factors play in biliary tract morbidity during chronic opisthorchiasis.

molecular biology↗

bioRxiv2018

Modelling the nuclear envelope of HeLa cells

This paper describes a framework for the automatic segmentation of the nuclear envelope of cancerous HeLa cells and the modelling of the volumetric shape against an ellipsoid. The framework is automatic and unsupervised and reported a Jaccard Similarity Index of 0.968 against a manual segmentation. The modelling of the surface provides a visual display of the variations, both smooth and rugged over the surface, and measurements can be extracted with the expectation that they can correlate with the biological characteristics of the cells.

cancer biology↗

bioRxiv2018

DCARS: Differential correlation across ranked samples

Genes act as a system and not in isolation. Thus, it is important to consider coordinated changes of gene expression rather than single genes when investigating biological phenomena such as the aetiology of cancer. We have developed an approach for quantifying how changes in the association between pairs of genes may inform patient prognosis called Differential Correlation across Ranked Samples (DCARS). Modelling gene correlation across a continuous sample ranking does not require the classification of patients into good or poor prognosis groups and can identify differences in gene correlation across early, mid or late stages of survival outcome. When we evaluated DCARS against the typical Fisher Z-transformation test for differential correlation, as well as a typical approach testing for interaction within a linear model, on real TCGA data, DCARS significantly ranked gene pairs containing known cancer genes more highly across a number of cancers. Similar results are found with our simulation study. DCARS was applied to 13 cancers datasets in TCGA, revealing a number of distinct relationships for which survival ranking was found to be associated with a change in correlation between genes. Furthermore, we demonstrated that DCARS can be used in conjunction with network analysis techniques to extract biological meaning from multilayered and complex data.\n\nAvailability: https://github.com/shazanfar/DCARS.

bioinformatics↗

bioRxiv2018

The prokaryotic SPHINX 1.8 REP protein is tissue-specific and expressed in human germline cells

Small circular DNAs of 1.8 and 2.4kb were initially discovered in highly infectious CJD and scrapie particles from mammalian brain and cultured cells. Surprisingly, these protected cytoplasmic \"SPHINX\" DNAs contained replication (REP) initiation sequences resembling those of Acinetobacter phage viruses. An antibody was generated against a REP peptide encoded by the SPHINX 1.8 ORF that was not present in mammals. It bound to a 41kd \"spx1\" protein on Western blots. Cytologically, spx1 concentrated in spinal cord synapses and pancreatic islet, but not exocrine cells. We hypothesized that circular SPHINX DNAs are ancient symbiotic elements that can participate in functional differentiation and neurodegeneration. Cell and tissue specific patterns of spx1 expression shown below implicate somatic cell-to-cell communication and differentiation functions that would favor conservation of SPHINX 1.8 in evolution. Remarkably, primary human oocytes and spermatogonia, but not mature sperm, displayed intense cytoplasmic spx1 signals that underscore the maternal inheritance of SPHINX 1.8. These findings should encourage investigations of unexplored networks of incorporated environmental infectious agents that can be key actors in progressive neurodegeneration, immunity and cancer.

cell biology↗

bioRxiv2018

Distinct Microbes, Metabolites, and Ecologies Define the Microbiome in Deficient and Proficient Mismatch Repair Colorectal Cancers

Background\n\nThe link between colorectal cancer (CRC) and the gut microbiome has been established, but the specific microbial species and their role in carcinogenesis remain controversial. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology.\n\nMethods\n\nWe collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject sample correlation, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks.\n\nResults\n\nWe demonstrate distinct roles for microbes in dMMR and pMMR CRC. Sulfidogenic Fusobacterium nucleatum and hydrogen sulfide production were significantly enriched in dMMR CRC, but not pMMR CRC. We also surveyed the butyrate-producing microbial species, but did not find a significant difference in predicted or actual butyrate production between dMMR and pMMR microbial communities. Finally, we observed that dMMR microbial communities were predicted to be less stable than pMMR microbial communities. Community stability may play an important role in CRC development, progression, or immune activation within the respective MMR subtypes.\n\nConclusions\n\nIntegrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

cancer biology↗