Multi-omics landscapes of colorectal cancer subtypes discriminated by an individualized prognostic signature for 5-fluorouracil-based chemotherapy

Deep learning enabled integration of tumor microenvironment microbial profiles and host gene expressions for interpretable survival subtyping in diverse types of cancers

The tumor microbiome, a complex community of microbes found in tumors, has been found to be linked to cancer development, progression, and treatment outcome. However, it remains a bottleneck in distangling the relationship between the tumor microbiome and host gene expressions in tumor microenvironment, as well as their concert effects on patient survival. In this study, we aimed to decode this complex relationship by developing ASD-cancer (autoencoder-based subtypes detector for cancer), a semi-supervised deep learning framework that could extract survival-related features from tumor microbiome and transcriptome data, and identify patients' survival subtypes. By using tissue samples from The Cancer Genome Atlas database, we identified two statistically distinct survival subtypes across all 20 types of cancer Our framework provided improved risk stratification (e.g., for liver hepatocellular carcinoma, [LIHC], log-rank test, P = 8.12E-6) compared to PCA (e.g., for LIHC, log-rank test, P = 0.87), predicted survival subtypes accurately, and identified biomarkers for survival subtypes. Additionally, we identified potential interactions between microbes and host genes that may play roles in survival. For instance, in LIHC, Arcobacter, Methylocella, and Isoptericola may regulate host survival through interactions with host genes enriched in the HIF-1 signaling pathway, indicating these species as potential therapy targets. Further experiments on validation data sets have also supported these patterns. Collectively, ASD-cancer has enabled accurate survival subtyping and biomarker discovery, which could facilitate personalized treatment for broad-spectrum types of cancers.IMPORTANCEUnraveling the intricate relationship between the tumor microbiome, host gene expressions, and their collective impact on cancer outcomes is paramount for advancing personalized treatment strategies. Our study introduces ASD-cancer, a cutting-edge autoencoder-based subtype detector. ASD-cancer decodes the complexities within the tumor microenvironment, successfully identifying distinct survival subtypes across 20 cancer types. Its superior risk stratification, demonstrated by significant improvements over traditional methods like principal component analysis, holds promise for refining patient prognosis. Accurate survival subtype predictions, biomarker discovery, and insights into microbe-host gene interactions elevate ASD-cancer as a powerful tool for advancing precision medicine. These findings not only contribute to a deeper understanding of the tumor microenvironment but also open avenues for personalized interventions across diverse cancer types, underscoring the transformative potential of ASD-cancer in shaping the future of cancer care.

Mitochondrial DNA Copy Number as a Biomarker for Guiding Adjuvant Chemotherapy in Stages II and III Colorectal Cancer Patients with Mismatch Repair Deficiency: Seeking Benefits and Avoiding Harms

BACKGROUND

Colorectal cancer (CRC) patients with mismatch repair-deficient/microsatellite instability-high (dMMR/MSI-H) status are conventionally perceived as unresponsive to adjuvant chemotherapy (ACT). The mitochondrial transcription factor A (TFAM) is required for mitochondrial DNA copy number (mtDNA-CN) expression. In light of previous findings indicating that the frequent truncating-mutation of TFAM affects the chemotherapy resistance of MSI CRC cells, this study aimed to explore the potential of mtDNA-CN as a predictive biomarker for ACT efficacy in dMMR CRC patients.

METHODS

Levels of MtDNA-CN were assessed using quantitative real-time polymerase chain reaction (qRT-PCR) in a cohort of 308 CRC patients with dMMR comprising 180 stage II and 128 stage III patients. Clinicopathologic and therapeutic data were collected. The study examined the association between mtDNA-CN levels and prognosis, as well as the impact of ACT benefit on dMMR CRC patients. Subgroup analyses were performed based mainly on tumor stage and mtDNA-CN level. Kaplan-Meier and Cox regression models were used to evaluate the effect of mtDNA-CN on disease-free survival (DFS) and overall survival (OS).

RESULTS

A substantial reduction in mtDNA-CN expression was observed in tumor tissue, and higher mtDNA-CN levels were correlated with improved DFS (73.4% vs 85.7%; P = 0.0055) and OS (82.5% vs 90.3%; P = 0.0366) in dMMR CRC patients. Cox regression analysis identified high mtDNA-CN as an independent protective factor for DFS (hazard ratio [HR] 0.547; 95% confidence interval [CI] 0.321-0.934; P = 0.0270) and OS (HR 0.520; 95% CI 0.272-0.998; P = 0.0492). Notably, for dMMR CRC patients with elevated mtDNA-CN, ACT significantly improved DFS (74.6% vs 93.4%; P = 0.0015) and OS (81.0% vs 96.7%; P = 0.0017), including those with stage II or III disease.

CONCLUSIONS

The mtDNA-CN levels exhibited a correlation with the prognosis of stage II or III CRC patients with dMMR. Elevated mtDNA-CN emerges as a robust prognostic factor, indicating improved ACT outcomes for stages II and III CRC patients with dMMR. These findings suggest the potential utility of mtDNA-CN as a biomarker for guiding personalized ACT treatment in this population.

Discovery and validation of a 10-gene predictive signature for response to adjuvant chemotherapy in stage II and III colon cancer

Identifying patients with stage II and III colon cancer who will benefit from 5-fluorouracil (5-FU)-based adjuvant chemotherapy is crucial for the advancement of personalized cancer therapy. We employ a semi-supervised machine learning approach to analyze a large dataset with 933 stage II and III colon cancer samples. Our analysis leverages gene regulatory networks to discover an 18-gene prognostic signature and to explore a 10-gene signature that potentially predicts chemotherapy benefits. The 10-gene signature demonstrates strong prognostic power and shows promising potential to predict chemotherapy benefits. We establish a robust clinical assay on the NanoString nCounter platform, validated in a retrospective formalin-fixed paraffin-embedded (FFPE) cohort, which represents an important step toward clinical application. Our study lays the groundwork for improving adjuvant chemotherapy and potentially expanding into immunotherapy decision-making in colon cancer. Future prospective studies are needed to validate and establish the clinical utility of the 10-gene signature in clinical settings.

A new natural killer cell-specific gene signature predicting recurrence in colorectal cancer patients

The protective role of Natural Killer (NK) cell tumour immunosurveillance has long been recognised in colorectal cancer (CRC). However, as most patients show limited intra-tumoral NK cell infiltration, improving our ability to identify those with high NK cell activity might aid in dissecting the molecular features which underlie NK cell sensitivity. Here, a novel CRC-specific NK cell gene signature that infers NK cell load in primary tissue samples was derived and validated in multiple patient CRC cohorts. In contrast with other NK cell gene signatures that have several overlapping genes across different immune cell types, our NK cell signature has been extensively refined to be specific for CRC-infiltrating NK cells. The specificity of the signature is substantiated in tumour-infiltrating NK cells from primary CRC tumours at the single cell level, and the signature includes genes representative of NK cells of different maturation states, activation status and anatomical origin. Our signature also accurately discriminates murine NK cells, demonstrating the applicability of this geneset when mining datasets generated from preclinical studies. Differential gene expression analysis revealed tumour-intrinsic features associated with NK cell inclusion versus exclusion in CRC patients, with those tumours with predicted high NK activity showing strong evidence of enhanced chemotactic and cytotoxic transcriptional programs. Furthermore, survival modelling indicated that NK signature expression is associated with improved survival outcomes in CRC patients. Thus, scoring CRC samples with this refined NK cell signature might aid in identifying patients with high NK cell activity who could be prime candidates for NK cell directed immunotherapies.

Proteomic Profiling and Biomarker Discovery in Colorectal Liver Metastases

Colorectal liver metastases (CRLM) are the leading cause of death among patients with metastatic colorectal cancer (CRC). As part of multimodal therapy, liver resection is the mainstay of curative-intent treatment for select patients with CRLM. However, effective treatment of CRLM remains challenging as recurrence occurs in most patients after liver resection. Proposed clinicopathologic factors for predicting recurrence are inconsistent and lose prognostic significance over time. The rapid development of next-generation sequencing technologies and decreasing DNA sequencing costs have accelerated the genomic profiling of various cancers. The characterisation of genomic alterations in CRC has significantly improved our understanding of its carcinogenesis. However, the functional context at the protein level has not been established for most of this genomic information. Furthermore, genomic alterations do not always result in predicted changes in the corresponding proteins and cancer phenotype, while post-transcriptional and post-translational regulation may alter synthesised protein levels, affecting phenotypes. More recent advancements in mass spectrometry-based technology enable accurate protein quantitation and comprehensive proteomic profiling of cancers. Several studies have explored proteomic biomarkers for predicting CRLM after oncologic resection of primary CRC and recurrence after curative-intent resection of CRLM. The current review aims to rationalise the proteomic complexity of CRC and explore the potential applications of proteomic biomarkers in CRLM.

Multi-omics reveals microbiome, host gene expression, and immune landscape in gastric carcinogenesis

To date, there has been no multi-omic analysis characterizing the intricate relationships between the intragastric microbiome and gastric mucosal gene expression in gastric carcinogenesis. Using multi-omic approaches, we provide a comprehensive view of the connections between the microbiome and host gene expression in distinct stages of gastric carcinogenesis (i.e., healthy, gastritis, cancer). Our integrative analysis uncovers various associations specific to disease states. For example, uniquely in gastritis, Helicobacteraceae is highly correlated with the expression of FAM3D, which has been previously implicated in gastrointestinal inflammation. In addition, in gastric cancer but not in adjacent gastritis, Lachnospiraceae is highly correlated with the expression of UBD, which regulates mitosis and cell cycle time. Furthermore, lower abundances of B cell signatures in gastric cancer compared to gastritis may suggest a previously unidentified immune evasion process in gastric carcinogenesis. Our study provides the most comprehensive description of microbial, host transcriptomic, and immune cell factors of the gastric carcinogenesis pathway.

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Multi-omics finds genetic, microbial, and immunological links in gastric cancer

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Helicobacteraceae was highly associated with the expression of inflammation genes

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Pasteurellaceae and Lachnospiraceae were associated with cancer-related genes

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B cell infiltration was prominent in gastritis tissues but not in gastric cancer

Regulation of signal transduction pathways in colorectal cancer: implications for therapeutic resistance

Resistance to anti-cancer treatments is a critical and widespread health issue that has brought serious impacts on lives, the economy and public policies. Mounting research has suggested that a selected spectrum of patients with advanced colorectal cancer (CRC) tend to respond poorly to both chemotherapeutic and targeted therapeutic regimens. Drug resistance in tumours can occur in an intrinsic or acquired manner, rendering cancer cells insensitive to the treatment of anti-cancer therapies. Multiple factors have been associated with drug resistance. The most well-established factors are the emergence of cancer stem cell-like properties and overexpression of ABC transporters that mediate drug efflux. Besides, there is emerging evidence that signalling pathways that modulate cell survival and drug metabolism play major roles in the maintenance of multidrug resistance in CRC. This article reviews drug resistance in CRC as a result of alterations in the MAPK, PI3K/PKB, Wnt/β-catenin and Notch pathways.

DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data

Multi-omics data are good resources for prognosis and survival prediction; however, these are difficult to integrate computationally. We introduce DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data. It identifies two optimal survival subtypes in most cancers and yields significantly better risk-stratification than other multi-omics integration methods. DeepProg is highly predictive, exemplified by two liver cancer (C-index 0.73-0.80) and five breast cancer datasets (C-index 0.68-0.73). Pan-cancer analysis associates common genomic signatures in poor survival subtypes with extracellular matrix modeling, immune deregulation, and mitosis processes. DeepProg is freely available at https://github.com/lanagarmire/DeepProg.

Characterization of a p53/miR-34a/CSF1R/STAT3 Feedback Loop in Colorectal Cancer

BACKGROUND & AIMS

The miR-34a gene is a direct target of p53 and is commonly silenced in colorectal cancer (CRC). Here we identified the receptor tyrosine kinase CSF1R as a direct miR-34a target and characterized CSF1R as an effector of p53/miR-34a-mediated CRC suppression.

METHODS

Analyses of TCGA-COAD and three other CRC cohorts for association of mRNA expression and signatures with patient survival and molecular subtypes. Bioinformatics identification and experimental validation of miRNA and transcription factor targets. Functional analysis of factors/pathways in the regulation of epithelial-mesenchymal transition (EMT), invasion, migration, acquired chemo-resistance and metastasis. Analyses of protein expression and CpG methylation within primary human colon cancer samples.

RESULTS

In primary CRCs increased CSF1R, CSF1 and IL34 expression was associated with poor patient survival and a mesenchymal-like subtype. CSF1R displayed an inverse correlation with miR-34a expression. This was explained by direct inhibition of CSF1R by miR-34a. Furthermore, p53 repressed CSF1R via inducing miR-34a, whereas SNAIL induced CSF1R both directly and indirectly via repressing miR-34a in a coherent feed-forward loop. Activation of CSF1R induced EMT, migration, invasion and metastasis of CRC cells via STAT3-mediated down-regulation of miR-34a. 5-FU resistance of CRC cells was mediated by CpG-methylation of miR-34a and the resulting elevated expression of CSF1R. In primary CRCs elevated expression of CSF1R was detected at the tumor invasion front and was associated with CpG methylation of the miR-34a promoter as well as distant metastasis.

CONCLUSIONS

The reciprocal inhibition between miR-34a and CSF1R and its loss in tumor cells may be relevant for therapeutic and prognostic approaches towards CRC management.

Predicting the effect of 5-fluorouracil-based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles

It is critical to identify patients with stage II and III colorectal cancer (CRC) who will benefit from adjuvant chemotherapy (ACT) after curative surgery, while the only use of clinical factors is insufficient to predict this beneficial effect. In this study, we performed genetic algorithm (GA) to select ACT candidate genes, and built a predictive model of support vector machine (SVM) using gene expression profiles from the Gene Expression Omnibus database. The model contained four ACT candidate genes (EDEM1, MVD, SEMA5B, and WWP2) and TNM stage (stage II or III). After using Subpopulation Treatment Effect Pattern Plot to determine the optimal cutoff value of predictive scores, the validated patients from The Cancer Genome Atlas database can be divided into the predictive ACT-benefit/-futile groups. Patients in the predictive ACT-benefit group with 5-fluorouracil (5-Fu)-based ACT had significantly longer relapse-free survival (RFS) compared to those without ACT (P = .015); However, the difference in RFS in the predictive ACT-futile group was insignificant (P = .596). The multivariable analysis found that the predictive groups were significantly associated with the effect of ACT (P_interaction  = .011). Consequently, we developed a predictive model based on the SVM and GA algorithm which was further validated to define patients who benefit from ACT on recurrence.

Clinical Role of Epigenetics and Network Analysis in Eye Diseases: A Translational Science Review

Network medicine is a molecular-bioinformatic approach analyzing gene-gene interactions that can perturb the human interactome. This review focuses on epigenetic changes involved in several ocular diseases, such as DNA methylation, histone and nonhistone post-translational modifications, and noncoding RNA regulators. Although changes in aberrant DNA methylation play a major role in the pathogenesis of most ocular diseases, histone modifications are seldom investigated. Hypermethylation in TGM-2 and hypomethylation in MMP-2/CD24 promoter genes may play a crucial role in pterygium development; hypermethylation in regulatory regions of GSTP1 and OGG1 genes appear to be diagnostic biomarkers of cataract; hypomethylation of TGF-β1 promoter may trigger glaucoma onset; hypermethylation of the LOXL1 gene might be associated with pseudoexfoliation syndrome. A large panel of upregulated micro-RNAs (miRNAs), including hsa-hsa-miR-494, hsa-let-7e, hsa-miR-513-1, hsa-miR-513-2, hsa-miR-518c, hsa-miR-129-1, hsa-miR-129-2, hsa-miR-198, hsa-miR-492, hsa-miR-498, hsa-miR-320, hsa-miR-503, and hsa-miR-373, ^∗ may have a putative role in the development of retinoblastoma. Hypermethylation of H3K4 and hypomethylation of H3K27 at the TGFBIp locus are putative pathogenic mechanisms involved in corneal dystrophies. Determining how, where, and when specific epigenetic changes trigger ocular diseases may provide useful clinical biomarkers for their prevention, diagnosis, and management, as well as innovative drug targets. PF-04523655, a 19-nucleotide methylated double-stranded siRNA targeting the RTP80 gene, showed a dose-related improvement in best-corrected visual acuity (BCVA) in patients affected by diabetic macular edema. The observed results support a clinical network-based research program aimed to clarify the role of epigenetic regulators in the development of ocular diseases and personalized therapy.

MiR-106a-5p promotes 5-FU resistance and the metastasis of colorectal cancer by targeting TGFβR2

BACKGROUND

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths. 5-Fluorouracil (5-FU)-based chemotherapy has always been the first-line treatment. However, development of 5-FU resistance seriously affects its curative effect. The aim of this study was to elucidate the molecular mechanisms of 5-FU resistance through miR-106a-5p in CRC.

METHODS

Colorectal cancer tissues were collected to analyze miR-106a-5p and TGFβR2 expressions by qPCR. Functional experiments for evaluating cell survival and metastasis were conducted to observe the biological effects of miR-106a-5p and TGFβR2. The cell survival rate was calculated using an MTT assay; the metastasis was confirmed with a Transwell invasion assay and Western blotting, which we used to measure the expression levels of the epithelial-mesenchymal transition (EMT) markers E-cadherin and vimentin. The combination of miR-106a to TGFβR2 was predicted using Targetscan, and confirmed through the construction of the luciferase reporter plasmid pGL3-basic. The interplay between miR-106a-5p and TGFβR2 was tested with qPCR and Western blotting. A Spearman rank analysis was employed to verify the correlation of miR-106a-5p and TGFβR2 expressions.

RESULTS

MiR-106a-5p was up-regulated and TGFβR2 was down-regulated in 5-FU resistant CRC tissues and HT-29 cells. MiR-106a-5p promoted cell survival and suppressed the apoptosis rate and caspase 3 activity. Additionally, cell invasion was promoted by miR-106a-5p overexpression in the HT-29 cells and was inhibited by miR-106a-5p knockdown in the 5-FU resistant HT-29 cells; miR-106a-5p overexpression contributed to migration by increasing vimentin expression and by decreasing E-cadherin expression in the HT-29 cells; miR-106a-5p functioned by directly binding to TGFβR2. The TGFβR2 knockdown conferred chemoresistance of 5-FU and metastasis in 5-FU resistant HT-29 cells, and TGFβR2 overexpression reduced cell survival, invasion numbers, vimentin expression, and increased the cell apoptosis rate and caspase 3 activity in 5-FU resistant HT-29 cells. Also, miR-106a-5p negatively regulated TGFβR2 in a linear correlation way in the CRC tissues.

CONCLUSION

The up-regulation of miR-106a-5p contributes to the pathomechanism of colorectal cancer by promoting 5-FU resistance and metastasis via inhibiting target TGFβR2. Our findings provide new promising ways for the clinical application of the TGFβR2-miR-106a axis in clinical chemotherapy for 5-FU resistant colorectal cancer.

Transcriptional signatures for coupled predictions of stage II and III colorectal cancer metastasis and fluorouracil-based adjuvant chemotherapy benefit

The current study suggests that the identification of predictive signatures of fluorouracil (5-FU) response for stage II and III colorectal cancer (CRC) could be confounded by chemotherapy-irrelevant low relapse risk. Using the samples of patients with stage II and III CRC who were treated with curative surgery only, we identified a signature with which to predict chemotherapy-irrelevant relapse risk for patients after curative surgery. By applying this signature to the samples of patients with stage II and III CRC who were treated with 5-FU-based adjuvant chemotherapy (ACT) after surgery, we predicted the relapse risk if treated with surgery only. From high-risk samples, we further identified another signature with which to predict therapeutic benefit from 5-FU-based ACT. On the basis of the relative expression orderings of gene pairs, a postsurgery relapse risk signature that consisted of 44 gene pairs was developed and verified in 3 independent data sets. A 5-FU therapeutic benefit signature that consisted of 4 gene pairs was then developed to predict the response of 5-FU-based ACT for those patients with high relapse risk after curative surgery. The signature was verified in 4 independent datasets. For patients with stage II and III CRC, the coupled signatures can first identify patients with high relapse risk after curative surgery, then predict therapeutic benefit from 5-FU-based ACT.-Song, K., Guo, Y., Wang, X., Cai, H., Zheng, W., Li, N., Song, X., Ao, L., Guo, Z., Zhao, W. Transcriptional signatures for coupled predictions of stage II and III colorectal cancer metastasis and fluorouracil-based adjuvant chemotherapy benefit.

Individualized predictive signatures for 5-fluorouracil-based chemotherapy in right- and left-sided colon cancer

5‐Fluorouracil (5‐FU)‐based adjuvant chemotherapy (ACT) is widely used for the treatment of colon cancer. Colon cancers with different primary tumor locations are clinically and molecularly distinct, implied through their response to 5‐FU‐based ACT. In this work, using 69 and 133 samples of patients with stage II‐III right‐sided and left‐sided colon cancer (RCC and LCC) treated with post‐surgery 5‐FU‐based ACT, we preselected gene pairs whose relative expression orderings were significantly correlated with the disease‐free survival of patients by univariate Cox proportional hazards model. Then, from the identified prognostic‐related gene pairs, a forward‐stepwise selection algorithm was formulated to search for an optimal subset of gene pairs that resulted in the highest concordance index, referred to as the gene pair signature (GPS). We identified prognostic signatures, 3‐GPS and 5‐GPS, for predicting response to 5‐FU‐based ACT of patients with RCC and LCC, respectively, which were validated in independent datasets of GSE14333 and GSE72970. With the aid of the signatures, the transcriptional and genomic characteristics between the predicted responders and non‐responders were explored. Notably, both in RCC and LCC, the predicted responders to 5‐FU‐based ACT were characterized by hypermutation, whereas the predicted non‐responders were characterized by frequent copy number alternations. Finally, in comparison with the established relative expression ordering‐based signature, which was developed without considering the differences between RCC and LCC, the newly proposed signatures had a better predictive performance. In conclusion, 3‐GPS or 5‐GPS can robustly predict response to 5‐FU‐based ACT for patients with RCC or LCC, respectively, in an individual level.

Discriminating cancer-related and cancer-unrelated chemoradiation-response genes for locally advanced rectal cancers

For patients with locally advanced rectal cancer (LARC) treated with preoperation chemoradiation (pCRT), identifying differentially expressed (DE) genes between non-responders and responders is a common approach for investigating mechanisms of chemoradiation resistance. However, some of such DE genes might be irrelevant to cancer itself but simply reflect the pharmacokinetic differences of the normal tissues. In this study, we adopted the RankComp algorithm to identify DE genes for each of LARC sample compared with its own normal state. Then, we identified genes with significantly different deregulation frequencies between the non-responders and responders, defined as cancer-related pCRT-response genes. Pathway enrichment and protein-protein interaction analyses showed that these genes specifically and intensively interacted with currently known effective genes of pCRT, involving in DNA replication, cell cycle and DNA repair. In contrast, after excluding the cancer-related pCRT-response genes, the other DE genes between non-responders and responders were enriched in many pathways of drug and protein metabolisms and transports, and interacted with both the known effective genes and pharmacokinetic genes. Hence, these two types of DE genes should be distinguished for investigating mechanisms of pCRT response in LARCs.