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DNA sequence composition determines the topology and stability of G-quadruplexes (G4s). Bulged G-quadruplex structures (G4-Bs) are a subset of G4s characterized by 3D conformations with bulges. Current search algorithms fail to capture stable G4-B, making their genome-wide study infeasible. Here, we introduced a large family of computationally defined and experimentally verified potential G4-B forming sequences (pG4-BS). We found 478 263 pG4-BS regions that do not overlap 'canonical' G4-forming sequences in the human genome and are preferentially localized in transcription regulatory regions including R-loops and open chromatin. Over 90% of protein-coding genes contain pG4-BS in their promoter or gene body. We observed generally higher pG4-BS content in R-loops and their flanks, longer genes that are associated with brain tissue, immune and developmental processes. Also, the presence of pG4-BS on both template and non-template strands in promoters is associated with oncogenesis, cardiovascular disease and stemness. Our G4-BS models predicted G4-forming ability in vitro with 91.5% accuracy. Analysis of G4-seq and CUT&Tag data strongly supports the existence of G4-BS conformations genome-wide. We reconstructed a novel G4-B 3D structure located in the E2F8 promoter. This study defines a large family of G4-like sequences, offering new insights into the essential biological functions and potential future therapeutic uses of G4-B. Agency for Science, Technology and Research (A*STAR) Nanyang Technological University Published version Bioinformatics Institute, Biomedical Institutes/A-STAR, Singapore (in part); V.A.K. was supported by a SUNY EMPIRE innovation program scholar grant; Upstate Medical University Cancer Center grant; Upstate Foundation Turn4ACure Fund; research in A.T.P. lab was supported by Nanyang Technological University Singapore; P.J. was supported by the Office of the Permanent Secretary, Ministry of Higher Education, Science, Research and Innovation (OPS MHESI); Thailand Science Research and Innovation (TSRI) [RGNS 64-161]. Funding for open access charge: SUNY EMPIRE innovation program scholar grant, the Upstate Medical University Cancer Center grant; Upstate Foundation Turn4ACure Fund.

Background Recent characterization of stress-induced promoter-associated antisense lncRNAs (si-paancRNAs) suggests that they modulate transcription and cellular responses to oxidative, metabolic and genotoxic stress and may participate in critical cancer pathways. KDM7A divergent transcript (KDM7A-DT) is one of such stress-induced lncRNAs, whose expression is found deregulated in breast cancer (BC). The mechanisms leading to aberrant KDM7A-DT transcription, biogenesis, and downstream functions in BC types and subtypes have not been studied. Methods Cell lines, biochemical methods, and profiling experiments were used for KDM7A-DT overexpression and knockdown. Integration of experimental models, bioinformatics, and massive BRCA patient metadata analyses were performed to investigate the mechanisms and functions of KDM7A-DT. Results Stable KDM7A-DT overexpression in nonmalignant cells upregulates p53, CDKN1A, and γH2AX signaling, resulting in a prolonged cell growth retardation phenotype. Importantly, KDM7A-DT induction by acute oxidative stress in semi-transformed fibroblasts is p53-dependent. According to BC clinical metadata, KDM7A-DT gene alterations are preferentially associated with TP53 missense mutations and highly aggressive, invasive, basal-like (BL) subtype BC poor outcomes. Overall, KDM7A-DT affects DNA repair via the nonhomologous end-joining pathway, inhibits tumor suppressors involved in epithelial-to-mesenchymal transition, induces oncogenic metabolic changes and G2/M checkpoint arrest, and correlates with histology, aneuploidy, hypoxia and BC-associated proteins. Conclusion KDM7A-DT is a TP53 mutation–associated and copy number–dependent pro-oncogene si-paancRNA that contributes to genome instability and modulates BC initiation, progression, invasiveness and outcomes.

Background: Recent characterisation of stress-induced promoter-associated antisense lncRNAs (si-paancRNAs) suggests that these lncRNAs modulate transcription, cellular responses to oxidative, metabolic and genotoxic stress, and may be part of the pathways critical in cancer. KDM7A divergent transcribed (KDM7A-DT) is among the top overexpressed genes encoded by such lncRNAs. The mechanisms leading to aberrant KDM7A-DT transcription, transcript biogenesis, and downstream functions of KDM7A-DT isoforms in breast cancer (BC) types and subtypes have not been studied. Methods: Cell line models, biochemistry methods and microarrays experiments for overexpression and knockdown of KDM7A-DT were used. Next, integration of experimental models, bioinformatics, and massive BRCA patient metadata analyses were performed to investigate the mechanisms and functions of KDM7A-DT.Results: The stable KDM7A-DT overexpression in non-malignant cells upregulates p53, CDKN1A, and γH2AX signalling, resulting in a prolonged cell growth retardation phenotype and preferential depletion of epithelial-to-mesenchymal transition (EMT) pathway–associated tumour suppressor genes. Importantly, KDM7A-DT induction by acute oxidative stress of semi-transformed fibroblasts is p53-dependent. According to BC clinical metadata, KDM7A-DT is overexpressed in poor survival basal-like BC (BLBC) subtype. The KDM7A-DT overexpression is associated with the overexpression of the TP53-missense mutated gene and KDM7A-DT full-length copy number amplification. KDM7A-DT overexpression affects DNA repair via the non-homologous end-joining (NHEJ) pathway, inhibits tumor suppressors involved in EMT, induces pro-oncogenic metabolic changes, and correlates with histologic grades, aneuploidy, hypoxia, cancer-associated proteins, clinical and molecular markers.Conclusion: KDM7A-DT is the TP53 mutation–associated and copy number–dependent pro-oncogene si-paancRNAs that contributes to genome instability, initiation, progression, invasiveness, and BLBC outcomes.

Exploring the parallels between wound healing and epithelial cancers, Sundaram et al. elucidate the mechanism by which cancer cells hijack the wound healing switch to enhance invasion and metastasis in head and neck squamous cell carcinoma., Epithelial carcinomas are well known to activate a prolonged wound-healing program that promotes malignant transformation. Wound closure requires the activation of keratinocyte migration via a dual-state molecular switch. This switch involves production of either the anti-migratory microRNA miR-198 or the pro-migratory follistatin-like 1 (FSTL1) protein from a single transcript; miR-198 expression in healthy skin is down-regulated in favor of FSTL1 upon wounding, which enhances keratinocyte migration and promotes re-epithelialization. Here, we reveal a defective molecular switch in head and neck squamous cell carcinoma (HNSCC). This defect shuts off miR-198 expression in favor of sustained FSTL1 translation, driving metastasis through dual parallel pathways involving DIAPH1 and FSTL1. DIAPH1, a miR-198 target, enhances directional migration through sequestration of Arpin, a competitive inhibitor of Arp2/3 complex. FSTL1 blocks Wnt7a-mediated repression of extracellular signal–regulated kinase phosphorylation, enabling production of MMP9, which degrades the extracellular matrix and facilitates metastasis. The prognostic significance of the FSTL1-DIAPH1 gene pair makes it an attractive target for therapeutic intervention.

The era of big data and precision medicine has led to accumulation of massive datasets of gene expression data and clinical information of patients. For a new patient, we propose that identification of a highly similar reference patient from an existing patient database via similarity matching of both clinical and expression data could be useful for predicting the prognostic risk or therapeutic efficacy.Here, we propose a novel methodology to predict disease/treatment outcome via analysis of the similarity between any pair of patients who are each characterized by a certain set of pre-defined biological variables (biomarkers or clinical features) represented initially as a prognostic binary variable vector (PBVV) and subsequently transformed to a prognostic signature vector (PSV). Our analyses revealed that Euclidean distance rather correlation distance measure was effective in defining an unbiased similarity measure calculated between two PSVs.We implemented our methods to high-grade serous ovarian cancer (HGSC) based on a 36-mRNA predictor that was previously shown to stratify patients into 3 distinct prognostic subgroups. We studied and revealed that patient's age, when converted into binary variable, was positively correlated with the overall risk of succumbing to the disease. When applied to an independent testing dataset, the inclusion of age into the molecular predictor provided more robust personalized prognosis of overall survival correlated with the therapeutic response of HGSC and provided benefit for treatment targeting of the tumors in HGSC patients.Finally, our method can be generalized and implemented in many other diseases to accurately predict personalized patients' outcomes. ASTAR (Agency for Sci., Tech. and Research, S’pore) Published version

The intestine is key for nutrient absorption and for interactions between the microbiota and its host. Therefore, the intestinal response to caloric restriction (CR) is thought to be more complex than that of any other organ. Submitting mice to 25% CR during 14 days induced a polarization of duodenum mucosa cell gene expression characterised by upregulation, and downregulation of the metabolic and immune/inflammatory pathways, respectively. The HNF, PPAR, STAT, and IRF families of transcription factors, particularly the Pparα and Isgf3 genes, were identified as potentially critical players in these processes. The impact of CR on metabolic genes in intestinal mucosa was mimicked by inhibition of the mTOR pathway. Furthermore, multiple duodenum and faecal metabolites were altered in CR mice. These changes were dependent on microbiota and their magnitude corresponded to microbial density. Further experiments using mice with depleted gut bacteria and CR-specific microbiota transfer showed that the gene expression polarization observed in the mucosa of CR mice is independent of the microbiota and its metabolites. The holistic interdisciplinary approach that we applied allowed us to characterize various regulatory aspects of the host and microbiota response to CR.

More than 30% of human protein-coding genes form hereditary complex genome architectures composed of sense-antisense (SA) gene pairs (SAGPs) transcribing their RNAs from both strands of a given locus. Such architectures represent important novel components of genome complexity contributing to gene expression deregulation in cancer cells. Therefore, the architectures might be involved in cancer pathways and, in turn, be used for novel drug targets discovery. However, the global roles of SAGPs in cancer pathways has not been studied. Here we investigated SAGPs associated with breast cancer (BC)-related pathways using systems biology, prognostic survival and experimental methods. Gene expression analysis identified 73 BC-relevant SAGPs that are highly correlated in BC. Survival modelling and metadata analysis of the 1161 BC patients allowed us to develop a novel patient prognostic grouping method selecting the 12 survival-significant SAGPs. The qRT-PCR-validated 12-SAGP prognostic signature reproducibly stratified BC patients into low- and high-risk prognostic subgroups. The 1381 SAGP-defined differentially expressed genes common across three studied cohorts were identified. The functional enrichment analysis of these genes revealed the GABPA gene network, including BC-relevant SAGPs, specific gene sets involved in cell cycle, spliceosomal and proteasomal pathways. The co-regulatory function of GABPA in BC cells was supported using siRNA knockdown studies. Thus, we demonstrated SAGPs as the synergistically functional genome architectures interconnected with cancer-related pathways and associated with BC patient clinical outcomes. Taken together, SAGPs represent an important component of genome complexity which can be used to identify novel aspects of coordinated pathological gene networks in cancers. ASTAR (Agency for Sci., Tech. and Research, S’pore) Published version

Invasive ductal carcinoma (IDC) is a major histo-morphologic type of breast cancer. Histological grading (HG) of IDC is widely adopted by oncologists as a prognostic factor. However, HG evaluation is highly subjective with only 50%–85% inter-observer agreements. Specifically, the subjectivity in the assignment of the intermediate grade (histologic grade 2, HG2) breast cancers (comprising ~50% of IDC cases) results in uncertain disease outcome prediction and sub-optimal systemic therapy. Despite several attempts to identify the mechanisms underlying the HG classification, their molecular bases are poorly understood. We performed integrative bioinformatics analysis of TCGA and several other cohorts (total 1246 patients). We identified a 22-gene tumor aggressiveness grading classifier (22g-TAG) that reflects global bifurcation in the IDC transcriptomes and reclassified patients with HG2 tumors into two genetically and clinically distinct subclasses: histological grade 1-like (HG1-like) and histological grade 3-like (HG3-like). The expression profiles and clinical outcomes of these subclasses were similar to the HG1 and HG3 tumors, respectively. We further reclassified IDC into low genetic grade (LGG = HG1+HG1-like) and high genetic grade (HGG = HG3-like+HG3) subclasses. For the HG1-like and HG3-like IDCs we found subclass-specific DNA alterations, somatic mutations, oncogenic pathways, cell cycle/mitosis and stem cell-like expression signatures that discriminate between these tumors. We found similar molecular patterns in the LGG and HGG tumor classes respectively. Our results suggest the existence of two genetically-predefined IDC classes, LGG and HGG, driven by distinct oncogenic pathways. They provide novel prognostic and therapeutic biomarkers and could open unique opportunities for personalized systemic therapies of IDC patients. ASTAR (Agency for Sci., Tech. and Research, S’pore) Published version

Objective To validate our earlier observation that 11 chemoresistance-associated mRNAs are molecular markers of poor overall survival in ovarian serous carcinoma. Methods Ovarian serous carcinomas (n=112) and solid metastases (n=63; total=175) were analyzed for mRNA expression of APC , BAG3 , EGFR , S100A10 , ITGAE , MAPK3 , TAP1 , BNIP3 , MMP9 , FASLG and GPX3 using quantitative real-time PCR. mRNA expression was studied for association with clinicopathologic parameters and survival. Tumor heterogeneity was assessed in 20 cases with >1 specimen per patient. APC, BAG3, S100A10 and ERK1 protein expression by immunohistochemistry was analyzed in 58 specimens (38 primary carcinomas, 20 metastases). Results BAG3 (p=0.013), TAP1 (p=0.014), BNIP3 (p MMP9 (p=0.036) were overexpressed in primary tumors, whereas S100A10 (p=0.027) and FASLG (p=0.006) were overexpressed in metastases. Analysis of patient-matched primary carcinomas and metastases showed overexpression of APC (p=0.022), MAPK3 (p=0.002) and BNIP3 (p=0.004) in the former. In primary carcinomas, higher APC (p=0.003) and MAPK3 (p=0.005) levels were related to less favorable chemoresponse. Higher S100A10 (p=0.029) and MAPK3 (p=0.041) levels were related to primary chemoresistance. Higher BAG3 (p=0.026) and APC (p=0.046) levels in primary carcinomas were significantly related to poor overall survival in univariate, though not in multivariate survival analysis. S100A10 protein expression was related to poor chemoresponse (p=0.002) and shorter overall (p=0.005) and progression-free (p Conclusions Our data provide evidence of heterogeneity in ovarian serous carcinoma and identify APC, MAPK3, BAG3 and S100A10 as potential biomarkers of poor chemotherapy response and/or poor outcome in this cancer.

Visual Abstract, Highlights • Targeted profiling of ceramides identified a 12-ceramide plasma signature that predicted 12-month cardiovascular death, MI, and stroke in 2 prospective cohorts of AMI patients. • Among coronary artery bypass grafting patients, plasma ceramides were higher in those with recent AMI compared with those without recent acute MI. • Analysis of rat ischemic myocardium revealed a consistent increase in ceramide levels and overexpression of 3 enzymes in ceramide biosynthesis., Summary We identified a plasma signature of 11 C14 to C26 ceramides and 1 C16 dihydroceramide predictive of major adverse cardiovascular events in patients with acute myocardial infarction (AMI). Among patients undergoing coronary artery bypass surgery, those with recent AMI, compared with those without recent AMI, showed a significant increase in 5 of the signature’s 12 ceramides in plasma but not simultaneously-biopsied aortic tissue. In contrast, a rat AMI model, compared with sham control, showed a significant increase in myocardial concentrations of all 12 ceramides and up-regulation of 3 ceramide-producing enzymes, suggesting ischemic myocardium as a possible source of this ceramide signature.

Robust prognostic gene signatures and therapeutic targets are difficult to derive from expression profiling because of the significant heterogeneity within breast cancer (BC) subtypes. Here, we performed forward genetic screening in mice using Sleeping Beauty transposon mutagenesis to identify candidate BC driver genes in an unbiased manner, using a stabilized N-terminal truncated β-catenin gene as a sensitizer. We identified 134 mouse susceptibility genes from 129 common insertion sites within 34 mammary tumors. Of these, 126 genes were orthologous to protein-coding genes in the human genome (hereafter, human BC susceptibility genes, hBCSGs), 70% of which are previously reported cancer-associated genes, and ∼16% are known BC suppressor genes. Network analysis revealed a gene hub consisting of E1A binding protein P300 (EP300), CD44 molecule (CD44), neurofibromin (NF1) and phosphatase and tensin homolog (PTEN), which are linked to a significant number of mutated hBCSGs. From our survival prediction analysis of the expression of human BC genes in 2,333 BC cases, we isolated a six-gene-pair classifier that stratifies BC patients with high confidence into prognostically distinct low-, moderate-, and high-risk subgroups. Furthermore, we proposed prognostic classifiers identifying three basal and three claudin-low tumor subgroups. Intriguingly, our hBCSGs are mostly unrelated to cell cycle/mitosis genes and are distinct from the prognostic signatures currently used for stratifying BC patients. Our findings illustrate the strength and validity of integrating functional mutagenesis screens in mice with human cancer transcriptomic data to identify highly prognostic BC subtyping biomarkers. ASTAR (Agency for Sci., Tech. and Research, S’pore)

The field of personalized and precise medicine in the era of big data analytics is growing rapidly. Previously, we proposed our model of patient classification termed Prognostic Signature Vector Matching (PSVM) and identified a 37 variable signature comprising 36 let-7b associated prognostic significant mRNAs and the age risk factor that stratified large high-grade serous ovarian cancer patient cohorts into three survival-significant risk groups. Here, we investigated the predictive performance of PSVM via optimization of the prognostic variable weights, which represent the relative importance of one prognostic variable over the others. In addition, we compared several multivariate prognostic models based on PSVM with classical machine learning techniques such as K-nearest-neighbor, support vector machine, random forest, neural networks and logistic regression. Our results revealed that negative log-rank p-values provides more robust weight values as opposed to the use of other quantities such as hazard ratios, fold change, or a combination of those factors. PSVM, together with the classical machine learning classifiers were combined in an ensemble (multi-test) voting system, which collectively provides a more precise and reproducible patient stratification. The use of the multi-test system approach, rather than the search for the ideal classification/prediction method, might help to address limitations of the individual classification algorithm in specific situation.

Summary Malignant gliomas are the most aggressive forms of brain tumors, associated with high rates of morbidity and mortality. Recurrence and tumorigenesis are attributed to a subpopulation of tumor-initiating glioma stem cells (GSCs) that are intrinsically resistant to therapy. Initiation and progression of gliomas have been linked to alterations in microRNA expression. Here, we report the identification of microRNA-138 (miR-138) as a molecular signature of GSCs and demonstrate a vital role for miR-138 in promoting growth and survival of bona fide tumor-initiating cells with self-renewal potential. Sequence-specific functional inhibition of miR-138 prevents tumorsphere formation in vitro and impedes tumorigenesis in vivo. We delineate the components of the miR-138 regulatory network by loss-of-function analysis to identify specific regulators of apoptosis. Finally, the higher expression of miR-138 in GSCs compared to non-neoplastic tissue and association with tumor recurrence and survival highlights the clinical significance of miR-138 as a prognostic biomarker and a therapeutic target for treatment of malignant gliomas.

An iTRAQ-based tandem mass spectrometry approach was employed to relatively quantify proteins in the membrane proteome of eleven gastric cancer cell lines relative to a denominator non-cancer gastric epithelial cell line HFE145. Of the 882 proteins detected, 57 proteins were found to be upregulated with > 1.3-fold change in at least 6 of the 11 cell lines. Bioinformatics analysis revealed that these proteins are significantly associated with cancer, cell growth and proliferation, death, survival and cell movement. The catalogue of membrane proteins presented that are potential regulators/effectors of gastric cancer progression has implications in cancer therapy. DLAT, a subunit of the pyruvate dehydrogenase complex, was selected as a candidate protein for further studies as its function in gastric cancer has yet to be established. SiRNA studies supported a role of DLAT in gastric cancer cell proliferation and carbohydrate metabolism, reprogramming of which is a hallmark of cancer. Our study contributes to recent interest and discussion in cancer energetics and related phenomena such as the Warburg and Reverse Warburg effects. Future mechanistic studies should lead to the elucidation of the mode of action of DLAT in human gastric cancer and establish DLAT as a viable drug target.

Background: The lack of consensus among reported gene signature subsets (GSSs) in multi-gene biomarker discovery studies is often a concern for researchers and clinicians. Subsequently, it discourages larger scale prospective studies, prevents the translation of such knowledge into a practical clinical setting and ultimately hinders the progress of the field of biomarker-based disease classification, prognosis and prediction. Methods: We define all "gene identificators" (gIDs) as constituents of the entire potential disease biomarker space. For each gID in a GSS of interest ("tested GSS"/tGSS), our method counts the empirical frequency of gID co-occurrences/overlaps in other reference GSSs (rGSSs) and compares it with the expected frequency generated via implementation of a randomized sampling procedure. Comparison of the empirical frequency distribution (EFD) with the expected background frequency distribution (BFD) allows dichotomization of statistically novel (SN) and common (SC) gIDs within the tGSS. Results: We identify SN or SC biomarkers for tGSSs obtained from previous studies of high-grade serous ovarian cancer (HG-SOC) and breast cancer (BC). For each tGSS, the EFD of gID co-occurrences/overlaps with other rGSSs is characterized by scale and context-dependent Pareto-like frequency distribution function. Our results indicate that while independently there is little overlap between our tGSS with individual rGSSs, comparison of the EFD with BFD suggests that beyond a confidence threshold, tested gIDs become more common in rGSSs than expected. This validates the use of our tGSS as individual or combined prognostic factors. Our method identifies SN and SC genes of a 36-gene prognostic signature that stratify HG-SOC patients into subgroups with low, intermediate or high-risk of the disease outcome. Using 70 BC rGSSs, the method also predicted SN and SC BC prognostic genes from the tested obesity and IGF1 pathway GSSs. Conclusions: Our method provides a strategy that identify/predict within a tGSS of interest, gID subsets that are either SN or SC when compared to other rGSSs. Practically, our results suggest that there is a stronger association of the IGF1 signature genes with the 70 BC rGSSs, than for the obesity-associated signature. Furthermore, both SC and SN genes, in both signatures could be considered as perspective prognostic biomarkers of BCs that stratify the patients onto low or high risks of cancer development. ASTAR (Agency for Sci., Tech. and Research, S’pore) Published version

High-grade serous ovarian cancer (HG-SOC), a major histologic type of epithelial ovarian cancer (EOC), is a poorly-characterized, heterogeneous and lethal disease where somatic mutations of TP53 are common and inherited loss-of-function mutations in BRCA1/2 predispose to cancer in 9.5–13% of EOC patients. However, the overall burden of disease due to either inherited or sporadic mutations is not known. We performed bioinformatics analyses of mutational and clinical data of 334 HG-SOC tumor samples from The Cancer Genome Atlas to identify novel tumor-driving mutations, survival-significant patient subgroups and tumor subtypes potentially driven by either hereditary or sporadic factors. We identified a sub-cluster of high-frequency mutations in 22 patients and 58 genes associated with DNA damage repair, apoptosis and cell cycle. Mutations of CHEK2, observed with the highest intensity, were associated with poor therapy response and overall survival (OS) of these patients (P = 8.00e-05), possibly due to detrimental effect of mutations at the nuclear localization signal. A 21-gene mutational prognostic signature significantly stratifies patients into relatively low or high-risk subgroups with 5-y OS of 37% or 6%, respectively (P = 7.31e-08). Further analysis of these genes and high-risk subgroup revealed 2 distinct classes of tumors characterized by either germline mutations of genes such as CHEK2, RPS6KA2 and MLL4, or somatic mutations of other genes in the signature. Our results could provide improvement in prediction and clinical management of HG-SOC, facilitate our understanding of this complex disease, guide the design of targeted therapeutics and improve screening efforts to identify women at high-risk of hereditary ovarian cancers distinct from those associated with BRCA1/2 mutations. ASTAR (Agency for Sci., Tech. and Research, S’pore) Published version

High-grade serous ovarian carcinoma (HG-SOC) is a heterogeneous, poorly classified, lethal disease that frequently exhibits altered expressions of microRNAs. Let-7 family members are often reported as tumor suppressors; nonetheless, clinicopathological functions and prognostic values of individual let-7 family members have not been addressed in HG-SOC. In our work, we performed an integrative study to investigate the potential roles, clinicopathological functions and prognostic values of let-7 miRNA family in HG-SOC. Using microarray and clinical data of 1,170 HG-SOC patients, we developed novel survival prediction and system biology methods to analyze prognostic values and functional associations of let-7 miRNAs with global transcriptome and clinicopathological factors. We demonstrated that individual let-7 members exhibit diverse evolutionary history and distinct regulatory characteristics. Statistical tests and network analysis suggest that let-7b could act as a global synergistic interactor and master regulator controlling hundreds of protein-coding genes. The elevated expression of let-7b is associated with poor survival rates, which suggests an unfavorable role of let-7b in treatment response for HG-SOC patients. A novel let-7b-defined 36-gene prognostic survival signature outperforms many clinicopathological parameters, and stratifies HG-SOC patients into three high-confidence, reproducible, clinical subclasses: low-, intermediate- and high-risk, with 5-year overall survival rates of 56–71%, 12–29% and 0–10%, respectively. Furthermore, the high-risk and low-risk subclasses exhibit strong mesenchymal and proliferative tumor phenotypes concordant with resistance and sensitivity to primary chemotherapy. Our results have led to identification of promising prognostic markers of HG-SOC, which could provide a rationale for genetic-based stratification of patients and optimization of treatment regimes. ASTAR (Agency for Sci., Tech. and Research, S’pore)

The lack of consensus among reported molecular (gene, protein, regulatory marker) signatures (MSs) in the literature is often an initial concern for researchers and subsequently it discourages larger scale prospective studies, prevent the translation of such knowledge into a practical clinical setting and ultimately hindering the progress of the field of biomarker-based disease classification, prognosis and prediction. Understanding the high level of clinical and biological heterogeneity in patients' cohort distribution, (e.g. by diseases subtypes and stages, age, treatment methods etc), limitations and misbalances in the number of samples, and uncertainty in the dimensionality of potential biomarker space, are critical for getting the signature consensus and identification of novel potential biomarkers. Differences in use of technological platforms, as well as variations in experimental protocols in different studies are also often contributing factors in the lack of strong consensus among signatures. In view of these differences, it would be inappropriate to compare MSs in entirety. Here, we investigate each variable in the signature of interest, and attempt to generate computationally “a null frequency distribution” of the expected number of co-occurrences in other MSs, i.e. other published MSs, and identify both novel and common biomarker within the given MS. We demonstrated an application of proposed model to identification of clinically essential genes of our somatically mutated genes in breast cancer.

The field of personalized disease prognosis, risk prediction and disease management has always attracted the attention of scientists and clinicians worldwide. Translating current clinical research and emerging genome-scale molecular biomarkers of ovarian and other cancers is yielding promising new insights into predictive signatures and potential clinical targets, moving treatment in favor of more personalized therapeutic approaches. Integrative genomics, big data analysis and the disease prediction modelling have become the major driving factors in understanding how to reproducibly stop a disease for individual patients - a goal of precision medicine. In clinical practice, prognostic and predictive factors could be measured as continuous or discrete variables. Here, we proposed a novel methodology to predict disease/treatment outcome via analysis of the similarities between any pair of patients who are each characterized by a certain set of pre-defined biological variables (biomarkers or clinical features) represented initially as a prognostic binary variable vector (PBVV) and subsequently transformed to a prognostic signature vector (PSV). High-grade serous ovarian carcinoma (HGSC) is the most prevalent ovarian cancer, and is one of the most lethal gynecological diseases in the world today. Many molecular signatures for prognostic and disease prediction of HGSC have been proposed, but no been implemented in clinical practice yet. In our previous study, the highly-confidence prognostic classifier, comprising 36 mRNAs that can stratify the HGSC patients into low, intermediate or high-risk subgroups with significantly distinct overall survival outcomes and sensitivity to post-surgery chemotherapy, has been proposed [1]. In this work, we used this classifier as the discriminative model of prognosis of HGSC patients. We implemented our methods to personalized prognosis of HGSC patients of TCGA database based on our molecular predictor [1]. Our results revealed that patient's age, when converted into discrete binary values, was positively correlated with the overall risk of succumbing to the disease and can be used as independent prognostic factor. The inclusion of age into the molecular survival predictor provided more robust personalized prognosis of overall survival when applied to an independent testing metadata composing 359 HGSC patients. In summary, we propose that our personalized prognosis methods and results after including age as prognostic factor could provide clinical benefits in the context of personalized prognosis and therapeutic assignment of HGSC patients. Our method can be generalized and implemented in many other diseases to accurately predict personalized patients' outcomes. [1]. Tang Z, Ow GS, Thiery JP, Ivshina AV and Kuznetsov VA. Int J Cancer. 2013; 134(2):306-318. Citation Format: Ghim Siong Ow, Zhiqun Tang, Anna Vladimirovna Ivshina, Vladimir Andreevich Kuznetsov. Big data and computational biology method for personalized prognosis of high-grade serous ovarian carcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr A75.

High-grade serous ovarian carcinoma (HG-SOC) is a poorly prognosed and lethal gynecological disease where only 30% of patients survive beyond five years after initial pathologic diagnosis. However, the heterogeneity of HG-SOC tumors meant that patients often respond differently despite similar clinical intervention. Therefore, identification of novel molecular tumor subtypes/classes, tumor-driving mutations and prognostically significant patient subgroups could provide improvement in clinical management as well as facilitate our understanding of this complex disease and subsequent design of targeted personalized therapeutics. In this work, we performed integrative bioinformatics analyses of mutational and clinical data of HG-SOC patients from The Cancer Genome Atlas(TCGA) and aim to identify the driver genes and their mutations with emphasis on their prognostic significance and molecular functions which could be used as molecular targets for personalization of HG-SOC therapeutics. We identified a 27-gene mutagenesis signature from the study of 334 HG-SOC patients, and our results indicated that the use of this signature can significantly stratify a subgroup of 24 patients with very poor overall survival prognosis (p-value=3.8E-09); all these 24 patients did not survive beyond five years after initial diagnosis. Among these 27 genes, physiological functions such as kinase activity or ATP binding were enriched, and suggest that targeting of these specific cellular programs could be employed to modify tumors' response to therapy and patients' overall survival time. Mutations in CHEK2, RPS6KA2 and MLL4 strongly co-occurred in tumor cells of the poor prognosis patient subgroup which in turn, were under-represented with mutations of BRCA1 or BRCA2. CHEK2 mutations were significantly correlated with poor patient prognosis (p-value=0.00012) and poor response to therapy (p-value =0.046). In 22 of the 24 HG-SOC patients, CHEK2 mutations occurred within a nuclear localization signal (NLS). Results from computational modeling, coupled with literature support, suggest that mutations within the NLS locus could potentially inhibit nuclear import of the Chk2 protein and lead to reduction of Chk2 protein levels in the nucleus. Our findings revealed a previously uncharacterized genetic subtype of HG-SOCs hallmarked with the 27 mutated genes which could result in the functional aberrations in interactions and pathways associated with the genes, possibly leading to very poor prognosis and chemotherapeutic response. We identified a cluster of mutations in the final exon of CHEK2 that selectively predict poor prognosis allowing us to stratify patients that might benefit from therapeutic strategies that target aberrant Chk2 oncogenic signaling in HG-SOC progression. Experiments that will validate the impact of Chk2 mutations are currently underway. Citation Format: Ghim Siong Ow, Anna V. Ivshina, Gloria Fuentes, Vladimir A. Kuznetsov. CHEK2 mutation is an adverse prognostic survival factor for patients diagnosed with high-grade serous ovarian carcinoma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3815. doi:10.1158/1538-7445.AM2014-3815