Ghim Siong Ow

Targeting glioma stem cells by functional inhibition of a prosurvival oncomiR-138 in malignant gliomas.

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.

Meta-analysis of transcriptome reveals let-7b as an unfavorable prognostic biomarker and predicts molecular and clinical subclasses in high-grade serous ovarian carcinoma

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.

EGF hijacks miR-198/FSTL1 wound-healing switch and steers a two-pronged pathway toward metastasis

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.

Big genomics and clinical data analytics strategies for precision cancer prognosis

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.

Sense-antisense gene-pairs in breast cancer and associated pathological pathways

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.

Identification of two poorly prognosed ovarian carcinoma subtypes associated with CHEK2 germ-line mutation and non-CHEK2 somatic mutation gene signatures.

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.

Expression and clinical role of chemoresponse-associated genes in ovarian serous carcinoma

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<0.001) and 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<0.001) survival, the latter finding retained in multivariate analysis (p=0.035). 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.

Transposon insertional mutagenesis in mice identifies human breast cancer susceptibility genes and signatures for stratification

Significance Despite concerted efforts to identify causal genes that drive breast cancer (BC) initiation and progression, we have yet to establish robust signatures to stratify patient risk. Here we used in vivo transposon-based forward genetic screening to identify potentially relevant BC driver genes. Integrating this approach with survival prediction analysis, we identified six gene pairs that could prognose human BC subtypes into high-, intermediate-, and low-risk groups with high confidence and reproducibility. Furthermore, we identified susceptibility gene sets for basal and claudin-low subtypes (21 and 16 genes, respectively) that stratify patients into three relative risk subgroups. These signatures offer valuable prognostic insight into the genetic basis of BC and allow further exploration of the interconnectedness of BC driver genes during disease progression. 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.

Big data and computational biology strategy for personalized prognosis

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 patients 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.

Genome and transcriptome delineation of two major oncogenic pathways governing invasive ductal breast cancer development

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.