Personalised pathway analysis reveals association between DNA repair pathway dysregulation and chromosomal instability in sporadic breast cancer

Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis

Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in clinics. However, multiple oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. Additionally, the efficacy of single-gene biomarkers is limited by the extensively variable expression levels measured by high-throughput assays. In this study, we hypothesize that in individual tumor samples, the disruption of transcription homeostasis in key pathways or gene sets plays an important role in tumorigenesis and has profound implications for the patient's clinical outcome. We devised a computational method named iPath to identify, at the individual-sample level, which pathways or gene sets significantly deviate from their norms. We conducted a pan-cancer analysis and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor-stage classifications.

CGPE: an integrated online server for Cancer Gene and Pathway Exploration

SUMMARY

Cancer Gene and Pathway Explorer (CGPE) is developed to guide biological and clinical researchers, especially those with limited informatics and programming skills, performing preliminary cancer-related biomedical research using transcriptional data and publications. CGPE enables three user-friendly online analytical and visualization modules without requiring any local deployment. The GenePub HotIndex applies natural language processing, statistics and association discovery to provide analytical results on gene-specific PubMed publications, including gene-specific research trends, cancer types correlations, top-related genes and the WordCloud of publication profiles. The OnlineGSEA enables Gene Set Enrichment Analysis (GSEA) and results visualizations through an easy-to-follow interface for public or in-house transcriptional datasets, integrating the GSEA algorithm and preprocessed public TCGA and GEO datasets. The preprocessed datasets ensure gene sets analysis with appropriate pathway alternation and gene signatures. The CellLine Search presents evidence-based guidance for cell line selections with combined information on cell line dependency, gene expressions and pathway activity maps, which are valuable knowledge to have before conducting gene-related experiments. In a nutshell, the CGPE webserver provides a user-friendly, visual, intuitive and informative bioinformatics tool that allows biomedical researchers to perform efficient analyses and preliminary studies on in-house and publicly available bioinformatics data.

AVAILABILITY AND IMPLEMENTATION

The webserver is freely available online at https://cgpe.soic.iupui.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Improving the Prognosis of Colon Cancer through Knowledge-Based Clinical-Molecular Integrated Analysis

Background

Colon cancer has high morbidity and mortality rates among cancers. Existing clinical staging systems cannot accurately assess the prognostic risk of colon cancer patients. This study was aimed at improving the prognostic performance of the colon cancer clinical staging system through knowledge-based clinical-molecular integrated analysis.

Methods

374 samples from The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset were used as the discovery set. 98 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset were used as the validation set. After converting gene expression data into pathway dysregulation scores (PDSs), the random survival forest and Cox model were used to identify the best prognostic supplementary factors. The corresponding clinical-molecular integrated prognostic model was built, and the improvement of prognostic performance was assessed by comparing with the clinical prognostic model.

Results

The PDS of 14 pathways played important roles in prognostic prediction together with clinical prognostic factors through the random survival forest. Further screening with the Cox model revealed that the PDS of the pathway hsa00532 was the best clinical prognostic supplementary factor. The integrated prognostic model constructed with clinical factors and the identified molecular factor was superior to the clinical prognostic model in discriminative performance. Kaplan-Meier (KM) curves of patients grouped by PDS suggested that patients with a higher PDS had a poorer prognosis, and stage II patients could be distinctly distinguished.

Conclusions

Based on the knowledge-based clinical-molecular integrated analysis, a clinical-molecular integrated prognostic model and corresponding nomogram for colon cancer overall survival prognosis was built, which showed better prognostic performance than the clinical prognostic model. The PDS of the pathway hsa00532 is a considerable clinical prognostic supplementary factor for colon cancer and may represent a potential prognostic marker for stage II colon cancer. The PDS calculation involves only 16 genes, which supports its potential for clinical application.

Inflammatory Breast Cancer: Clinical Implications of Genomic Alterations and Mutational Profiling

Simple Summary

Inflammatory breast cancer (IBC) is an aggressive disease with high mortality rates. Nowadays, there is no targeted treatment for this tumor type. Based on this context, we investigated the molecular profile of this disease by using well-established methodologies (high-resolution microarray platform, targeted next-generation sequencing, and immunohistochemistry) that have proven potential to unveil cancer biomarkers. We found alterations related to IBC aggressiveness and metastasis (gains of MDM4, losses of CHL1, and high homologous recombination deficiency scores), and worse overall survival (variants in HR and mismatch repair genes). We also compared the mutational profiling of our cases with literature data, which includes both non-IBC and IBC cases, validating our findings. Overall, we describe genetic alterations with the potential to be used as prognostic or predictive biomarkers and ultimately improve IBC patients’ care.

Abstract

Inflammatory breast cancer (IBC) is a rare and aggressive type of breast cancer whose molecular basis is poorly understood. We performed a comprehensive molecular analysis of 24 IBC biopsies naïve of treatment, using a high-resolution microarray platform and targeted next-generation sequencing (105 cancer-related genes). The genes more frequently affected by gains were MYC (75%) and MDM4 (71%), while frequent losses encompassed TP53 (71%) and RB1 (58%). Increased MYC and MDM4 protein expression levels were detected in 18 cases. These genes have been related to IBC aggressiveness, and MDM4 is a potential therapeutic target in IBC. Functional enrichment analysis revealed genes associated with inflammatory regulation and immune response. High homologous recombination (HR) deficiency scores were detected in triple-negative and metastatic IBC cases. A high telomeric allelic imbalance score was found in patients having worse overall survival (OS). The mutational profiling was compared with non-IBC (TCGA, n = 250) and IBC (n = 118) from four datasets, validating our findings. Higher frequency of TP53 and BRCA2 variants were detected compared to non-IBC, while PIKC3A showed similar frequency. Variants in mismatch repair and HR genes were associated with worse OS. Our study provided a framework for improved diagnosis and therapeutic alternatives for this aggressive tumor type.

Exploiting Chromosomal Instability of PTEN-Deficient Triple-Negative Breast Cancer Cell Lines for the Sensitization against PARP1 Inhibition in a Replication-Dependent Manner

Simple Summary

The poor prognosis of patients with TNBC have fostered a major effort to identify more patients who would benefit from targeted therapies. Here we recognize PTEN as a potential CIN-causing gene in TNBC and consider PTEN-deficient TNBC for the treatment with PARP1 inhibitors due to the protective role of PTEN during DNA replication.

Abstract

Chromosomal instability (CIN) is an emerging hallmark of cancer and its role in therapeutic responses has been increasingly attracting the attention of the research community. To target the vulnerability of tumors with high CIN, it is important to identify the genes and mechanisms involved in the maintenance of CIN. In our work, we recognize the tumor suppressor gene Phosphatase and Tensin homolog (PTEN) as a potential gene causing CIN in triple-negative breast cancer (TNBC) and show that TNBC with low expression levels of PTEN can be sensitized for the treatment with poly-(ADP-ribose)-polymerase 1 (PARP1) inhibitors, independent of Breast Cancer (BRCA) mutations or a BRCA-like phenotype. In silico analysis of mRNA expression data from 200 TNBC patients revealed low expression of PTEN in tumors with a high CIN70 score. Western blot analysis of TNBC cell lines confirm lower protein expression of PTEN compared to non TNBC cell lines. Further, PTEN-deficient cell lines showed cellular sensitivity towards PARP1 inhibition treatment. DNA fiber assays and examination of chromatin bound protein fractions indicate a protective role of PTEN at stalled replication forks. In this study, we recognize PTEN as a potential CIN-causing gene in TNBC and identify its important role in the replication processes.

Multiple interaction nodes define the postreplication repair response to UV-induced DNA damage that is defective in melanomas and correlated with UV signature mutation load

Ultraviolet radiation‐induced DNA mutations are a primary environmental driver of melanoma. The reason for this very high level of unrepaired DNA lesions leading to these mutations is still poorly understood. The primary DNA repair mechanism for UV‐induced lesions, that is, the nucleotide excision repair pathway, appears intact in most melanomas. We have previously reported a postreplication repair mechanism that is commonly defective in melanoma cell lines. Here we have used a genome‐wide approach to identify the components of this postreplication repair mechanism. We have used differential transcript polysome loading to identify transcripts that are associated with UV response, and then functionally assessed these to identify novel components of this repair and cell cycle checkpoint network. We have identified multiple interaction nodes, including global genomic nucleotide excision repair and homologous recombination repair, and previously unexpected MASTL pathway, as components of the response. Finally, we have used bioinformatics to assess the contribution of dysregulated expression of these pathways to the UV signature mutation load of a large melanoma cohort. We show that dysregulation of the pathway, especially the DNA damage repair components, are significant contributors to UV mutation load, and that dysregulation of the MASTL pathway appears to be a significant contributor to high UV signature mutation load.

Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes

The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile ('personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about 'average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's 'personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive 'personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.

Increased Levels of Genomic Instability and Mutations in Homologous Recombination Genes in Locally Advanced Rectal Carcinomas

Pre-operative 5-fluoracil-based chemoradiotherapy (nCRT) is the standard treatment for patients with locally advanced rectal cancer (LARC). Patients with pathological complete response (pCR–0% of tumor cells in the surgical specimen after nCRT) have better overall survival and lower risk of recurrence in comparison with incomplete responders (pIR). Predictive biomarkers to be used for new therapeutic strategies and capable of stratifying patients to avoid overtreatment are needed. We evaluated the genomic profiles of 33 pre-treatment LARC biopsies using SNP array and targeted-next generation sequencing (tNGS). Based on the large number of identified genomic alterations, we calculated the genomic instability index (GII) and three homologous recombination deficiency (HRD) scores, which have been reported as impaired DNA repair markers. We observed high GII in our LARC cases, which was confirmed in 165 rectal cancer cases from TCGA. Patients with pCR presented higher GII compared with pIR. Moreover, a negative correlation between GII and the fraction of tumor cells remaining after surgery was observed (ρ = –0.382, P = 0.02). High HRD scores were detected in 61% of LARC, of which 70% were incomplete responders. Using tNGS (105 cancer-related genes, 13 involved in HR and 5 in mismatch repair pathways), we identified 23% of cases with mutations in HR genes, mostly in pIR cases (86% of mutated cases). In agreement, the analysis of the TCGA dataset (N = 145) revealed 21% of tumors with mutations in HR genes. The HRD scores were shown to be predictive of better response to PARP-inhibitors and platinum-based chemotherapy in breast and ovarian cancer. Our results suggest that the same strategy could be applied in a set of LARC patients with HRD. In conclusion, we identified high genomic instability in LARC, which was related to alterations in the HR pathway, especially in pIR. These findings suggest that patients with impaired HRD would clinically benefit from PARP-inhibitors and platinum-based therapy.

Genetic variants of genes in the NER pathway associated with risk of breast cancer: A large-scale analysis of 14 published GWAS datasets in the DRIVE study

A recent hypothesis-free pathway-level analysis of genome-wide association study (GWAS) datasets suggested that the overall genetic variation measured by single nucleotide polymorphisms (SNPs) in the nucleotide excision repair (NER) pathway genes was associated with breast cancer (BC) risk, but no detailed SNP information was provided. To substantiate this finding, we performed a larger meta-analysis of 14 previously published GWAS datasets in the Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE) study with 53,107 subjects of European descent. Using a hypothesis-driven approach, we selected 138 candidate genes from the NER pathway using the "Molecular Signatures Database (MsigDB)" and "PathCards". All SNPs were imputed using IMPUTE2 with the 1000 Genomes Project Phase 3. Logistic regression was used to estimate BC risk, and pooled ORs for each SNP were obtained from the meta-analysis using the false discovery rate for multiple test correction. RegulomeDB, HaploReg, SNPinfo and expression quantitative trait loci (eQTL) analysis were used to assess the SNP functionality. We identified four independent SNPs associated with BC risk, BIVM-ERCC5 rs1323697_C (OR = 1.06, 95% CI = 1.03-1.10), GTF2H4 rs1264308_T (OR = 0.93, 95% CI = 0.89-0.97), COPS2 rs141308737_C deletion (OR = 1.06, 95% CI = 1.03-1.09) and ELL rs1469412_C (OR = 0.93, 95% CI = 0.90-0.96). Their combined genetic score was also associated with BC risk (OR = 1.12, 95% CI = 1.08-1.16, p_trend < 0.0001). The eQTL analysis revealed that BIVM-ERCC5 rs1323697 C and ELL rs1469412 C alleles were correlated with increased mRNA expression levels of their genes in 373 lymphoblastoid cell lines (p = 0.022 and 2.67 × 10^-22 , respectively). These SNPs might have roles in the BC etiology, likely through modulating their corresponding gene expression.

Patterns of Genomic Instability in Breast Cancer

Breast cancer is one of the most common cancers affecting women. Despite significant improvements in overall survival, it remains a significant cause of death worldwide. Genomic instability (GI) is a hallmark of cancer and plays a pivotal role in breast cancer development and progression. In the past decade, high-throughput technologies have provided a wealth of information that has facilitated the identification of a diverse repertoire of mutated genes and mutational processes operative across cancers. Here, we review recent findings on genomic alterations and mutational processes in breast cancer pathogenesis. Most importantly, we summarize the clinical challenges and opportunities to utilize omics-based signatures for better management of breast cancer patients and treatment decision-making.

Integrating gene and lncRNA expression to infer subpathway activity for tumor analyses

LncRNAs acting as miRNA sponges to indirectly regulate mRNAs is a novel layer of gene regulation, therefore, it is necessary to integrate lncRNA and gene levels for interpreting tumor biological mechanism. In this study, we developed a lncRNA-gene integrated strategy to infer functional activities for tumor analyses at the subpathway level. In this strategy, we reconstructed subpathway graphs by embedding lncRNA components and considered the expression levels of both genes and lncRNAs to infer subpathway activities for each tumor sample. And the activities were applied to three aspects of tumor analyses; First, the subpathway activities across tumor samples of five tumor types were analyzed, and it was observed that the samples with consistent subpathway activities were derived from the same or similar tumor types. Also, the subpathway activities could stratify samples into several subtypes which has different clinical characterization, e.g. survival status. Second, the subpathway activities between tumor and normal samples were analyzed, and the comparative results showed that subpathway activities displayed more specificities than entire pathway activities. Finally, based on the subpathway activities, we identified prognostic subpathways for lung cancer. Our subpathway-based signatures shared significant overlap with enrichment analysis results and displayed predictive power in the independent testing sets. In conclusion, our integrated strategy provided a framework to infer subpathway activities for tumor analyses and identify subpathway signatures for clinical use.

sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses

MicroRNAs (miRNAs) regulate biological pathways by inhibiting gene expression. However, most current analytical methods fail to consider miRNAs, when inferring functional or pathway activities. In this study, we developed a model called sPAGM to infer subpathway activities by integrating gene and miRNA expressions. In this model, we reconstructed subpathway graphs by embedding miRNA components, and characterized subpathway activity (sPA) scores by simultaneously considering the expression levels of miRNAs and genes. The results showed that the sPA scores could distinguish different samples across tumor types, as well as samples between tumor and normal conditions. Moreover, the sPAGM model displayed more specificities than the entire pathway-based analyses. This model was applied to melanoma tumors to perform a prognosis analysis, which identified a robust 55-subpathway signature. By using The Cancer Genome Atlas and independently verified data sets, the subpathway-based signature significantly predicted the patients’ prognoses, which were independent of clinical variables. In the prognostic performance comparison, the sPAGM model was superior to the gene-only and miRNA-only methods. Finally, we dissected the functional roles and interactions of components within the subpathway signature. Taken together, the sPAGM model provided a framework for inferring subpathway activities and identifying functional signatures for clinical applications.

Integrating Multi-omics Data to Dissect Mechanisms of DNA repair Dysregulation in Breast Cancer

DNA repair genes and pathways that are transcriptionally dysregulated in cancer provide the first line of evidence for the altered DNA repair status in tumours, and hence have been explored intensively as a source for biomarker discovery. The molecular mechanisms underlying DNA repair dysregulation, however, have not been systematically investigated in any cancer type. In this study, we performed a statistical analysis to dissect the roles of DNA copy number alteration (CNA), DNA methylation (DM) at gene promoter regions and the expression changes of transcription factors (TFs) in the differential expression of individual DNA repair genes in normal versus tumour breast samples. These gene-level results were summarised at pathway level to assess whether different DNA repair pathways are affected in distinct manners. Our results suggest that CNA and expression changes of TFs are major causes of DNA repair dysregulation in breast cancer, and that a subset of the identified TFs may exert global impacts on the dysregulation of multiple repair pathways. Our work hence provides novel insights into DNA repair dysregulation in breast cancer. These insights improve our understanding of the molecular basis of the DNA repair biomarkers identified thus far, and have potential to inform future biomarker discovery.

Emerging links among Chromosome Instability (CIN), cancer, and aging

Aneuploidy was predicted to cause cancer. To test the prediction, various Chromosome Instability (CIN) mice models that carry transgenic mutations in mitotic regulators have been created. The availability of these mice has aided researchers in discovering connections between CIN, cancer, and aging. This review will focus on recent interdisciplinary findings regarding how CIN and aneuploidy affect carcinogenesis, immune dysfunction, and aging. High CIN can be generated in vivo by various intrinsic alterations (e.g., gene mutation, epigenetic modification) and extrinsic/environmental challenges (e.g., biological, chemical, biophysical), while immune surveillance, cell death, and natural turnover can remove cells with CIN. CIN itself is mutagenic and may cause further cellular mutations, which can be carcinogenic. Mitotically damaged cells can activate senescence-related tumor suppressors (e.g., p21^WAF1 , p27^KIP1 , p16^INK4A ), which may lead to tissue-level senescence/aging through inflammatory paracrine mechanisms called Senescence-Associated Secretory Phenotype (SASP) and Senescence Inflammatory Response (SIR). Organs with high CIN show altered gene expressions in both organ-specific and non-specific manners. Organ-specific gene expression signatures include activation of oncogenic pathways. Non-organ-specific gene expression signatures include metabolic changes and downregulations in immune functions. Immune surveillance normally targets senescent cells and tetraploid cells, a form of aneuploidy, for elimination. However, with partial immune dysfunction, immune surveillance is weakened with systemic CIN. In this case, more senescent cells and aneuploid cells survive, which further leads to an inflammatory, pro-tumorigenic, and senescent/aging microenvironment. We also discuss how we may intervene in this sequence of events to prevent CIN- or age-related carcinogenesis and/or some aspects of tissue aging. © 2016 Wiley Periodicals, Inc.

Personalised pathway analysis reveals association between DNA repair pathway dysregulation and chromosomal instability in sporadic breast cancer

The Homologous Recombination (HR) pathway is crucial for the repair of DNA double-strand breaks (DSBs) generated during DNA replication. Defects in HR repair have been linked to the initiation and development of a wide variety of human malignancies, and exploited in chemical, radiological and targeted therapies. In this study, we performed a personalised pathway analysis independently for four large sporadic breast cancer cohorts to investigate the status of HR pathway dysregulation in individual sporadic breast tumours, its association with HR repair deficiency and its impact on tumour characteristics. Specifically, we first manually curated a list of HR genes according to our recent review on this pathway (Liu et al., 2014), and then applied a personalised pathway analysis method named Pathifier (Drier et al., 2013) on the expression levels of the curated genes to obtain an HR score quantifying HR pathway dysregulation in individual tumours. Based on the score, we observed a great diversity in HR dysregulation between and within gene expression-based breast cancer subtypes, and by using two published HR-defect signatures, we found HR pathway dysregulation reflects HR repair deficiency. Furthermore, we identified a novel association between HR pathway dysregulation and chromosomal instability (CIN) in sporadic breast cancer. Although CIN has long been considered as a hallmark of most solid tumours, with recent extensive studies highlighting its importance in tumour evolution and drug resistance, the molecular basis of CIN in sporadic cancers remains poorly understood. Our results imply that HR pathway dysregulation might contribute to CIN in sporadic breast cancer.

Power in nursing: a collaborative approach

Breast cancers are very heterogeneous tissues with several cell types and metabolic pathways together sustaining the initiation and progression of disease and contributing to evasion from cancer therapies. Furthermore, breast cancer cells have an impressive metabolic plasticity that is regulated by the heterogeneous tumour microenvironment through bidirectional interactions. The structure and accessibility of nutrients within this unstable microenvironment influence the metabolism of cancer cells that shift between glycolysis and mitochondrial oxidative phosphorylation (OXPHOS) to produce adenosine triphosphate (ATP). In this scenario, the mitochondrial energetic pathways of cancer cells can be reprogrammed to modulate breast cancer’s progression and aggressiveness. Moreover, mitochondrial alterations can lead to crosstalk between the mitochondria and the nucleus, and subsequently affect cancer tissue properties. This article reviewed the metabolic plasticity of breast cancer cells, focussing mainly on breast cancer mitochondrial metabolic reprogramming and the mitochondrial alterations influencing nuclear pathways. Finally, the therapeutic strategies targeting molecules and pathways regulating cancer mitochondrial alterations are highlighted.