Correlation of somatic mutation and expression identifies genes important in human glioblastoma progression and survival

A longer time to relapse is associated with a larger increase in differences between paired primary and recurrent IDH wild-type glioblastomas at both the transcriptomic and genomic levels

Glioblastoma (GBM) is the most common malignant brain tumor in adults, which remains incurable and often recurs rapidly after initial therapy. While large efforts have been dedicated to uncover genomic/transcriptomic alternations associated with the recurrence of GBMs, the evolutionary trajectories of matched pairs of primary and recurrent (P-R) GBMs remain largely elusive. It remains challenging to identify genes associated with time to relapse (TTR) and construct a stable and effective prognostic model for predicting TTR of primary GBM patients. By integrating RNA-sequencing and genomic data from multiple datasets of patient-matched longitudinal GBMs of isocitrate dehydrogenase wild-type (IDH-wt), here we examined the associations of TTR with heterogeneities between paired P-R GBMs in gene expression profiles, tumor mutation burden (TMB), and microenvironment. Our results revealed a positive correlation between TTR and transcriptomic/genomic differences between paired P-R GBMs, higher percentages of non-mesenchymal-to-mesenchymal transition and mesenchymal subtype for patients with a short TTR than for those with a long TTR, a high correlation between paired P-R GBMs in gene expression profiles and TMB, and a negative correlation between the fitting level of such a paired P-R GBM correlation and TTR. According to these observations, we identified 55 TTR-associated genes and thereby constructed a seven-gene (ZSCAN10, SIGLEC14, GHRHR, TBX15, TAS2R1, CDKL1, and CD101) prognostic model for predicting TTR of primary IDH-wt GBM patients using univariate/multivariate Cox regression analyses. The risk scores estimated by the model were significantly negatively correlated with TTR in the training set and two independent testing sets. The model also segregated IDH-wt GBM patients into two groups with significantly divergent progression-free survival outcomes and showed promising performance for predicting 1-, 2-, and 3-year progression-free survival rates in all training and testing sets. Our findings provide new insights into the molecular understanding of GBM progression at recurrence and potential targets for therapeutic treatments.

Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

Chromosome 7 to the rescue: overcoming chromosome 10 loss in gliomas

The co-occurrence of chromosome 10 loss and chromosome 7 gain in gliomas is the most frequent loss-gain co-aneuploidy pair in human cancers, a phenomenon that has been investigated without resolution since the late 1980s. Expanding beyond previous gene-centric studies, we investigate the co-occurrence in a genome-wide manner taking an evolutionary perspective. First, by mining large tumor aneuploidy data, we predict that the more likely order is 10 loss followed by 7 gain. Second, by analyzing extensive genomic and transcriptomic data from both patients and cell lines, we find that this co-occurrence can be explained by functional rescue interactions that are highly enriched on 7, which can possibly compensate for any detrimental consequences arising from the loss of 10. Finally, by analyzing transcriptomic data from normal, non-cancerous, human brain tissues, we provide a plausible reason why this co-occurrence happens preferentially in cancers originating in certain regions of the brain.

Profiling the molecular and clinical landscape of glioblastoma utilizing the Oncology Research Information Exchange Network brain cancer database

Background

Glioblastoma exhibits aggressive growth and poor outcomes despite treatment, and its marked variability renders therapeutic design and prognostication challenging. The Oncology Research Information Exchange Network (ORIEN) database contains complementary clinical, genomic, and transcriptomic profiling of 206 glioblastoma patients, providing opportunities to identify novel associations between molecular features and clinical outcomes.

Methods

Survival analyses were performed using the Logrank test, and clinical features were evaluated using Wilcoxon and chi-squared tests with q-values derived via Benjamini-Hochberg correction. Mutational analyses utilized sample-level enrichments from whole exome sequencing data, and statistical tests were performed using the one-sided Fisher Exact test with Benjamini-Hochberg correction. Transcriptomic analyses utilized a student's t-test with Benjamini-Hochberg correction. Expression fold changes were processed with Ingenuity Pathway Analysis to determine pathway-level alterations between groups.

Results

Key findings include an association of MUC17, SYNE1, and TENM1 mutations with prolonged overall survival (OS); decreased OS associated with higher epithelial growth factor receptor (EGFR) mRNA expression, but not with EGFR amplification or mutation; a 14-transcript signature associated with OS > 2 years; and 2 transcripts associated with OS < 1 year.

Conclusions

Herein, we report the first clinical, genomic, and transcriptomic analysis of ORIEN glioblastoma cases, incorporating sample reclassification under updated 2021 diagnostic criteria. These findings create multiple avenues for further investigation and reinforce the value of multi-institutional consortia such as ORIEN in deepening our knowledge of intractable diseases such as glioblastoma.

Predator: Predicting the Impact of Cancer Somatic Mutations on Protein-Protein Interactions

Since many biological processes are governed by protein-protein interactions, understanding which mutations lead to a disruption in these interactions is profoundly important for cancer research. Most of the existing methods focus on the stability of the protein without considering the specific effects of a mutation on its interactions with other proteins. Here, we focus on somatic mutations that appear on the interface regions of the protein and predict the interactions that would be affected by a mutation of interest. We build an ensemble model, Predator, that classifies the interface mutations as disruptive or nondisruptive based on the predicted effects of mutations on specific protein-protein interactions. We show that Predator outperforms existing approaches in literature in terms of prediction accuracy. We then apply Predator on various TCGA cancer cohorts and perform comprehensive analysis at cohort level, patient level, and gene level in determining the genes whose interface mutations tend to yield a disruption in its interactions. The predictions obtained by Predator shed light on interesting patterns on several genes for each cohort regarding their potential as cancer drivers. Our analyses further reveal that the identified genes and their frequently disrupted partners exhibit patterns of mutually exclusivity across cancer cohorts under study.

Mutational landscape of primary spinal cord astrocytoma

Primary spinal cord astrocytoma (SCA) is a rare disease. Knowledge about the molecular profiles of SCAs mostly comes from intracranial glioma; the pattern of genetic alterations of SCAs is not well understood. Herein, we describe genome-sequencing analyses of primary SCAs, aiming to characterize the mutational landscape of primary SCAs. We utilized whole exome sequencing (WES) to analyze somatic nucleotide variants (SNVs) and copy number variants (CNVs) among 51 primary SCAs. Driver genes were searched using four algorithms. GISTIC2 was used to detect significant CNVs. Additionally, recurrently mutated pathways were also summarized. A total of 12 driver genes were identified. Of those, H3F3A (47.1%), TP53 (29.4%), NF1 (19.6%), ATRX (17.6%), and PPM1D (17.6%) were the most frequently mutated genes. Furthermore, three novel driver genes seldom reported in glioma were identified: HNRNPC, SYNE1, and RBM10. Several germline mutations, including three variants (SLC16A8 rs2235573, LMF1 rs3751667, FAM20C rs774848096) that were associated with risk of brain glioma, were frequently observed in SCAs. Moreover, 12q14.1 (13.7%) encompassing the oncogene CDK4 was recurrently amplified and negatively affected patient prognosis. Besides frequently mutated RTK/RAS pathway and PI3K pathway, the cell cycle pathway controlling the phosphorylation of retinoblastoma protein (RB) was mutated in 39.2% of patients. Overall, a considerable degree of the somatic mutation landscape is shared between SCAs and brainstem glioma. Our work provides a key insight into the molecular profiling of primary SCAs, which might represent candidate drug targets and complement the molecular atlas of glioma. © 2023 The Pathological Society of Great Britain and Ireland.

Tumor-secreted lactate contributes to an immunosuppressive microenvironment and affects CD8 T-cell infiltration in glioblastoma

Introduction

Glioblastoma is a malignant brain tumor with poor prognosis. Lactate is the main product of tumor cells, and its secretion may relate to immunocytes' activation. However, its role in glioblastoma is poorly understood.

Methods

This work performed bulk RNA-seq analysis and single cell RNA-seq analysis to explore the role of lactate in glioblastoma progression. Over 1400 glioblastoma samples were grouped into different clusters according to their expression and the results were validated with our own data, the xiangya cohort. Immunocytes infiltration analysis, immunogram and the map of immune checkpoint genes' expression were applied to analyze the potential connection between the lactate level with tumor immune microenvironment. Furthermore, machine learning algorithms and cell-cell interaction algorithm were introduced to reveal the connection of tumor cells with immunocytes. By co-culturing CD8 T cells with tumor cells, and performing immunohistochemistry on Xiangya cohort samples further validated results from previous analysis.

Discussion

In this work, lactate is proved that contributes to glioblastoma immune suppressive microenvironment. High level of lactate in tumor microenvironment can affect CD8 T cells' migration and infiltration ratio in glioblastoma. To step further, potential compounds that targets to samples from different groups were also predicted for future exploration.

Modeling the relationship between gene expression and mutational signature

Background

Mutational signatures computed from somatic mutations, allow an in-depth understanding of tumorigenesis and may illuminate early prevention strategies. Many studies have shown the regulation effects between somatic mutation and gene expression dysregulation.

Methods

We hypothesized that there are potential associations between mutational signature and gene expression. We capitalized upon RNA-seq data to model 49 established mutational signatures in 33 cancer types. Both accuracy and area under the curve were used as performance measures in five-fold cross-validation.

Results

A total of 475 models using unconstrained genes, and 112 models using protein-coding genes were selected for future inference purposes. An independent gene expression dataset on lung cancer smoking status was used for validation which achieved over 80% for both accuracy and area under the curve.

Conclusion

These results demonstrate that the associations between gene expression and somatic mutations can translate into the associations between gene expression and mutational signatures.

Genotype-Phenotype Correlations in Human Diseases Caused by Mutations of LINC Complex-Associated Genes: A Systematic Review and Meta-Summary

Mutations in genes encoding proteins associated with the linker of nucleoskeleton and cytoskeleton (LINC) complex within the nuclear envelope cause different diseases with varying phenotypes including skeletal muscle, cardiac, metabolic, or nervous system pathologies. There is some understanding of the structure of LINC complex-associated proteins and how they interact, but it is unclear how mutations in genes encoding them can cause the same disease, and different diseases with different phenotypes. Here, published mutations in LINC complex-associated proteins were systematically reviewed and analyzed to ascertain whether patterns exist between the genetic sequence variants and clinical phenotypes. This revealed LMNA is the only LINC complex-associated gene in which mutations commonly cause distinct conditions, and there are no clear genotype-phenotype correlations. Clusters of LMNA variants causing striated muscle disease are located in exons 1 and 6, and metabolic disease-associated LMNA variants are frequently found in the tail of lamin A/C. Additionally, exon 6 of the emerin gene, EMD, may be a mutation "hot-spot", and diseases related to SYNE1, encoding nesprin-1, are most often caused by nonsense type mutations. These results provide insight into the diverse roles of LINC-complex proteins in human disease and provide direction for future gene-targeted therapy development.

Glioblastoma CD105+ cells define a SOX2- cancer stem cell-like subpopulation in the pre-invasive niche

Glioblastoma (GBM) is the most common and most aggressive primary brain tumor in adults. Glioma stem like cells (GSC) represent the highest cellular hierarchy in GBM and have a determining role in tumor growth, recurrence and patient prognosis. However, a better definition of GSC subpopulations, especially at the surgical resection margin, is warranted for improved oncological treatment options. The present study interrogated cells expressing CD105 (CD105⁺) specifically within the tumor front and the pre-invasive niche as a potential GSC subpopulation. GBM primary cell lines were generated from patients (n = 18) and CD105⁺ cells were isolated and assessed for stem-like characteristics. In vitro, CD105⁺ cells proliferated and enriched in serum-containing medium but not in serum-free conditions. CD105⁺ cells were characterized by Nestin⁺, Vimentin⁺ and SOX2^-, clearly distinguishing them from SOX2⁺ GCS. GBM CD105⁺ cells differentiated into osteocytes and adipocytes but not chondrocytes. Exome sequencing revealed that GBM CD105⁺ cells matched 83% of somatic mutations in the Cancer cell line encyclopedia, indicating a malignant phenotype and in vivo xenotransplantation assays verified their tumorigenic potential. Cytokine assays showed that immunosuppressive and protumorigenic cytokines such as IL6, IL8, CCL2, CXCL-1 were produced by CD105⁺ cells. Finally, screening for 88 clinical drugs revealed that GBM CD105⁺ cells are resistant to most chemotherapeutics except Doxorubicin, Idarubicin, Fludarabine and ABT-751. Our study provides a rationale for targeting tumoral CD105⁺ cells in order to reshape the tumor microenvironment and block GBM progression.

Extracellular vesicles derived from glioblastoma promote proliferation and migration of neural progenitor cells via PI3K-Akt pathway

BACKGROUND

Glioblastomas are lethal brain tumors under the current combinatorial therapeutic strategy that includes surgery, chemo- and radio-therapies. Extensive changes in the tumor microenvironment is a key reason for resistance to chemo- or radio-therapy and frequent tumor recurrences. Understanding the tumor-nontumor cell interaction in TME is critical for developing new therapy. Glioblastomas are known to recruit normal cells in their environs to sustain growth and encroachment into other regions. Neural progenitor cells (NPCs) have been noted to migrate towards the site of glioblastomas, however, the detailed mechanisms underlying glioblastoma-mediated NPCs' alteration remain unkown.

METHODS

We collected EVs in the culture medium of three classic glioblastoma cell lines, U87 and A172 (male cell lines), and LN229 (female cell line). U87, A172, and LN229 were co-cultured with their corresponding EVs, respectively. Mouse NPCs (mNPCs) were co-cultured with glioblastoma-derived EVs. The proliferation and migration of tumor cells and mNPCs after EVs treatment were examined. Proteomic analysis and western blotting were utilized to identify the underlying mechanisms of glioblastoma-derived EVs-induced alterations in mNPCs.

RESULTS

We first show that glioblastoma cell lines U87-, A172-, and LN229-derived EVs were essential for glioblastoma cell prolifeartion and migration. We then demonstrated that glioblastoma-derived EVs dramatically promoted NPC proliferation and migration. Mechanistic studies identify that glioblastoma-derived EVs achieve their functions via activating PI3K-Akt-mTOR pathway in mNPCs. Inhibiting PI3K-Akt pathway reversed the elevated prolfieration and migration of glioblastoma-derived EVs-treated mNPCs.

CONCLUSION

Our findings demonstrate that EVs play a key role in intercellular communication in tumor microenvironment. Inhibition of the tumorgenic EVs-mediated PI3K-Akt-mTOR pathway activation might be a novel strategy to shed light on glioblastoma therapy. Video Abstract.

SYNE1 Exonic Variant rs9479297 Contributes to Concurrent Hepatocellular and Transitional Cell Carcinoma Double Primary Cancer

Unexpected high risk of synchronous/metachronous hepatocellular carcinoma (HCC) and transitional cell carcinoma (TCC) co-occurrence has been discovered previously. Here, we searched for genetic variation contributing to the co-occurrence of this double primary cancer (DPC). Using targeted exome sequencing, a panel of variants associated with concurrent DPC was identified. However, only a nonsynonymous variant within the Spectrin Repeat Containing Nuclear Envelope Protein 1 (SYNE1) gene was associated with DPC occurrence (p = 0.002), compared with that in the healthy population. Further independent cohort verification analysis revealed that the SYNE1-rs9479297-TT genotype (versus TC + CC genotypes) was enriched in patients with DPC, compared with that in those with TCC alone (p = 0.039), those with HCC alone (p = 0.006), those with non-HCC/non-TCC (p < 0.001), and healthy population (p < 0.001). SYNE1 mRNA expression reduced in both patients with HCC and TCC, and its lower expression in HCC was associated with shorter recurrence-free (p = 0.0314) and metastasis-free (p = 0.0479) survival. SYNE1-rs9479297 genotypes were correlated with tissue SYNE1 levels and clinical outcomes in HCC patients. Finally, SYNE1 silencing enhanced the cell proliferation and migration of HCC/TCC cells. In conclusion, SYNE1-rs9479297 genotypes were associated with HCC/TCC DPC co-occurrence and correlated with SYNE1 expression, which in turn contributed to HCC/TCC cell proliferation and migration, thereby affecting clinical outcomes.

Whole-exome sequencing, EGFR amplification and infiltration patterns in human glioblastoma

Glioblastoma (GBM) is the most common malignant primary brain tumor in adults. This cancer shows rapid, highly infiltrative growth, that invades individually or in small groups the surrounding tissue. The aggressive tumor biology of GBM has devastating consequences with a median survival of 15 months. GBM often has Epidermal Growth Factor Receptor (EGFR) abnormalities. Despite recent advances in the study of GBM tumor biology, it is unclear whether mutations in GBM are related to EGFR amplification and relevant phenotypes like tumor infiltration. This study aimed to perform whole-exome sequencing analysis in 30 human GBM samples for identifying mutational portraits associated with EGFR amplification and infiltrative patterns. Our results show that EGFR-amplified tumors have overall higher mutation rates than EGFR-no-amplified. Six genes out of 2029 candidate genes show mutations associated with EGFR amplification status. Mutations in these genes for GBM are novel, not previously reported in GBM, and with little presence in the TCGA database. GPR179, USP48, and BLK show mutation only in EGFR-amplified cases, and all the affected cases exhibit diffuse infiltrative patterns. On the other hand, mutations in ADGB, EHHADH, and PTPN13, were present only in the EGFR-no-amplified group with a more diverse infiltrative phenotype. Overall, our work identified different mutational portraits of GBM related to well-established features like EGFR amplification and tumor infiltration.

Surfaceome Proteomic of Glioblastoma Revealed Potential Targets for Immunotherapy

Glioblastoma (GBM) is the most common and devastating malignant brain tumor in adults. The mortality rate is very high despite different treatments. New therapeutic targets are therefore highly needed. Cell-surface proteins represent attractive targets due to their accessibility, their involvement in essential signaling pathways, and their dysregulated expression in cancer. Moreover, they are potential targets for CAR-based immunotherapy or mRNA vaccine strategies. In this context, we investigated the GBM-associated surfaceome by comparing it to astrocytes cell line surfaceome to identify new specific targets for GBM. For this purpose, biotinylation of cell surface proteins has been carried out in GBM and astrocytes cell lines. Biotinylated proteins were purified on streptavidin beads and analyzed by shotgun proteomics. Cell surface proteins were identified with Cell Surface Proteins Atlas (CSPA) and Gene Ontology enrichment. Among all the surface proteins identified in the different cell lines we have confirmed the expression of 66 of these in patient’s glioblastoma using spatial proteomic guided by MALDI-mass spectrometry. Moreover, 87 surface proteins overexpressed or exclusive in GBM cell lines have been identified. Among these, we found 11 specific potential targets for GBM including 5 mutated proteins such as RELL1, CYBA, EGFR, and MHC I proteins. Matching with drugs and clinical trials databases revealed that 7 proteins were druggable and under evaluation, 3 proteins have no known drug interaction yet and none of them are the mutated form of the identified proteins. Taken together, we discovered potential targets for immune therapy strategies in GBM.

Review of PP2A Tumor Biology and Antitumor Effects of PP2A Inhibitor LB100 in the Nervous System

Simple Summary

Central and peripheral nervous system tumors represent a heterogenous group of neoplasms which often demonstrate resistance to treatment. Given that these tumors are often refractory to conventional therapy, novel pharmaceutical regimens are needed for successfully treating this pathology. One such therapeutic is the serine/threonine phosphatase inhibitor, LB100. LB100 is a water-soluble competitive protein phosphtase inhibitor that has demonstrated antitumor effects in preclinical and clinical trials. In this review, we aim to summarize current evidence demonstrating the efficacy of LB100 as an inhibitor of nervous system tumors. Furthermore, we review the involvement of the well-studied phosphatase, protein phosphatase 2A, in oncogenic cell signaling pathways, neurophysiology, and neurodevelopment.

Abstract

Protein phosphatase 2A (PP2A) is a ubiquitous serine/threonine phosphatase implicated in a wide variety of regulatory cellular functions. PP2A is abundant in the mammalian nervous system, and dysregulation of its cellular functions is associated with myriad neurodegenerative disorders. Additionally, PP2A has oncologic implications, recently garnering attention and emerging as a therapeutic target because of the antitumor effects of a potent PP2A inhibitor, LB100. LB100 abrogation of PP2A is believed to exert its inhibitory effects on tumor progression through cellular chemo- and radiosensitization to adjuvant agents. An updated and unifying review of PP2A biology and inhibition with LB100 as a therapeutic strategy for targeting cancers of the nervous system is needed, as other reviews have mainly covered broader applications of LB100. In this review, we discuss the role of PP2A in normal cells and tumor cells of the nervous system. Furthermore, we summarize current evidence regarding the therapeutic potential of LB100 for treating solid tumors of the nervous system.

Low KRT15 expression is associated with poor prognosis in patients with breast invasive carcinoma

Although keratin 15 (KRT15) has been indicated to be overexpressed in several types of tumor, its role in breast invasive carcinoma (BRCA) has so far remained elusive. The aim of the present study was to explore KRT15 expression in BRCA based on data obtained from The Cancer Genome Atlas and The Genotype-Tissue Expression. KRT15 expression was compared using a Wilcoxon rank-sum test. Functional enrichment analysis was performed to reveal the biological roles and pathways of KRT15. The association between KRT15 expression and immune-cell infiltration was evaluated via single-sample gene set enrichment analysis (ssGSEA). To investigate the relationship between clinicopathological features and KRT15 expression, the prognostic value of KRT15 and other clinical factors was evaluated using Cox regression analysis and Kaplan-Meier (KM) plots. Subgroup prognostic analysis was also performed using forest plots and KM curves. Finally, a tissue microarray was used to assess KRT15 expression in BRCA tissues. KRT15 expression was significantly lower in BRCA tissues compared with that in normal tissues. Functional enrichment analysis suggested that KRT15-related genes were primarily enriched in the transmembrane transporter complex, cornification and ligand-receptor interactions. Increased KRT15 was associated with several tumor-suppressive pathways. ssGSEA revealed that high KRT15 expression was significantly associated with natural killer-cell, B-cell and mast-cell infiltration. Significant associations were observed between low KRT15 expression and advanced stage clinicopathological factors, as well as unfavorable overall survival (OS) and disease-specific survival. Multivariate Cox regression analysis suggested that KRT15 was an independent prognostic factor for OS (P=0.039; hazard ratio, 0.590; 95% CI, 0.358-0.974). Subgroup prognostic analysis demonstrated that low KRT15 was a reliable predictor of poor OS. Immunohistochemistry of a tissue microarray indicated that positive KRT15 expression rates were significantly higher in normal tissues compared with those in the BRCA tissues. In conclusion, low KRT15 expression was significantly associated with poor prognosis in patients with BRCA. Thus, KRT15 may serve an important role in BRCA progression and may be used as a promising prognostic marker for diagnostic and prognostic analyses in patients with BRCA.

Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression

Exploring the molecular mechanisms of breast cancer is essential for the early prediction, diagnosis, and treatment of cancer patients. The large scale of data obtained from the high-throughput sequencing technology makes it difficult to identify the driver mutations and a minimal optimal set of genes that are critical to the classification of cancer. In this study, we propose a novel method without any prior information to identify mutated genes associated with breast cancer. For the somatic mutation data, it is processed to a mutated matrix, from which the mutation frequency of each gene can be obtained. By setting a reasonable threshold for the mutation frequency, a mutated gene set is filtered from the mutated matrix. For the gene expression data, it is used to generate the gene expression matrix, while the mutated gene set is mapped onto the matrix to construct a co-expression profile. In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. In the stage of evaluation, we propose a weighted metric to modify the traditional accuracy to solve the sample imbalance problem. We apply the proposed method to The Cancer Genome Atlas breast cancer data and identify a mutated gene set, among which the implicated genes are oncogenes or tumor suppressors previously reported to be associated with carcinogenesis. As a comparison with the integrative network, we also perform the optimal model on the individual gene expression and the gold standard PMA50. The results show that the integrative network outperforms the gene expression and PMA50 in the average of most metrics, which indicate the effectiveness of our proposed method by integrating multiple data sources, and can discover the associated mutated genes in breast cancer.

Ranking cancer drivers via betweenness-based outlier detection and random walks

Background

Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes.

Results

We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets.

Conclusions

Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis

While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation. We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations. Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.

Molecular and Immunologic Signatures are Related to Clinical Benefit from Treatment with Vocimagene Amiretrorepvec (Toca 511) and 5-Fluorocytosine (Toca FC) in Patients with Glioma

PURPOSE

High-grade gliomas (HGGs) are central nervous system tumors with poor prognoses and limited treatment options. Vocimagene amiretrorepvec (Toca 511) is a retroviral replicating vector encoding cytosine deaminase, which converts extended release 5-fluorocytosine (Toca FC) into the anticancer agent, 5-fluorouracil. According to preclinical studies, this therapy kills cancer cells and immunosuppressive myeloid cells in the tumor microenvironment, leading to T-cell-mediated antitumor immune activity. Therefore, we sought to elucidate this immune-related mechanism of action in humans, and to investigate potential molecular and immunologic indicators of clinical benefit from therapy.

PATIENTS AND METHODS

In a phase I clinical trial (NCT01470794), patients with recurrent HGG treated with Toca 511 and Toca FC showed improved survival relative to historical controls, and some had durable complete responses to therapy. As a part of this trial, we performed whole-exome DNA sequencing, RNA-sequencing, and multiplex digital ELISA measurements on tumor and blood samples.

RESULTS

Genetic analyses suggest mutations, copy-number variations, and neoantigens are linked to survival. Quantities of tumor immune infiltrates estimated by transcript abundance may potentially predict clinical outcomes. Peak values of cytokines in peripheral blood samples collected during and after therapy could indicate response.

CONCLUSIONS

These results support an immune-related mechanism of action for Toca 511 and Toca FC, and suggest that molecular and immunologic signatures are related to clinical benefit from treatment.

A multimodality test to guide the management of patients with a pancreatic cyst

Pancreatic cysts are common and often pose a management dilemma, because some cysts are precancerous, whereas others have little risk of developing into invasive cancers. We used supervised machine learning techniques to develop a comprehensive test, CompCyst, to guide the management of patients with pancreatic cysts. The test is based on selected clinical features, imaging characteristics, and cyst fluid genetic and biochemical markers. Using data from 436 patients with pancreatic cysts, we trained CompCyst to classify patients as those who required surgery, those who should be routinely monitored, and those who did not require further surveillance. We then tested CompCyst in an independent cohort of 426 patients, with histopathology used as the gold standard. We found that clinical management informed by the CompCyst test was more accurate than the management dictated by conventional clinical and imaging criteria alone. Application of the CompCyst test would have spared surgery in more than half of the patients who underwent unnecessary resection of their cysts. CompCyst therefore has the potential to reduce the patient morbidity and economic costs associated with current standard-of-care pancreatic cyst management practices.

The Expressions and Mechanisms of Sarcomeric Proteins in Cancers

The sarcomeric proteins control the movement of cells in diverse species, whereas the deregulation can induce tumours in model organisms and occurs in human carcinomas. Sarcomeric proteins are recognized as oncogene and related to tumor cell metastasis. Recent insights into their expressions and functions have led to new cancer therapeutic opportunities. In this review, we appraise the evidence for the sarcomeric proteins as cancer genes and discuss cancer-relevant biological functions, potential mechanisms by which sarcomeric proteins activity is altered in cancer.

Stratifying Intraductal Papillary Mucinous Neoplasms by Cyst Fluid Analysis: Present and Future

A significant proportion of patients with intraductal papillary mucinous neoplasms (IPMNs) undergo surgical resection in order to prevent or treat pancreatic cancer at the risk of significant perioperative morbidity. Efforts have been made to stratify the potential risk of malignancy based on the clinical and radiographic features of IPMN to delineate which cysts warrant resection versus observation. An analysis of the cyst fluid obtained by preoperative endoscopic examination appears to be correlative of cyst type and risk, whereas serum markers and radiographic findings have not yet reached a level of sensitivity or specificity that proves they are clinically meaningful. In this review, we investigate the current cyst fluid analysis studies and present those that have shown promise in effectively stratifying high-risk versus low-risk lesions. While new cyst fluid markers continue to be identified, additional efforts in testing panels and marker composites in conjunction with clinical algorithms have also shown promise in distinguishing dysplasia and the risk of malignancy. These should be tested prospectively in order to determine their role in guiding the surveillance of low-risk lesions and to evaluate the new markers detected by proteomics and genetic sequencing.

Elevated expression of cellular SYNE1, MMP10, and GTPase1 and their regulatory role in hepatocellular carcinoma progression

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy resulting in high mortality. HCC progression is associated with abnormal signal transduction that changes cell signaling pathways and ultimately leads to dysregulation of cell functions and uncontrolled cell proliferation. Present study was undertaken with the objective to identify differentially expressed proteins and quantify their transcript expression in the liver of HCC-bearing rats vis-à-vis controls and to decipher the network involving interaction of genes coding for the characterized proteins to an insight into mechanism of HCC tumorigenesis. 2D-Electrophoresis and MALDI-TOF-MS/MS were used to characterize differentially expressed proteins in DEN (diethylnitrosamine)-induced HCC tissue using the protocol reported by us earlier. Real-time PCR was performed to quantify the expression of transcripts for the identified proteins. GENEMANIA, an interacting network of genes coding for selected proteins, was deciphered that provided the functional role of these proteins in HCC progression. Upregulation of proteins SYNE1, MMP10, and MTG1 was observed. The mRNA quantification revealed elevated expression of their transcripts at HCC initiation, progression, and tumor stages. Network analysis showed the involvement of the genes coding for these proteins in dysregulation of signaling pathways during HCC development. The elevated expression of SYNE1, MMP10, and MTG1 suggests the role of these proteins as potential players in HCC progression and tumorigenesis.

Next generation DNA sequencing of atypical choroid plexus papilloma of brain: Identification of novel mutations in a female patient by Ion Proton

Choroid plexus papilloma (CPP) is a rare benign tumor of the central nervous system that is usually confined to the cerebral ventricles. According to the World Health Organization, CPP corresponds to a grade I atypical CPP (a-CPP); however, it can become more aggressive and reach grade II, which can rarely undergo malignant transformation into a choroid plexus carcinoma (grade III). To the best of our knowledge, identification of these tumors mutations by next generation DNA sequencing (NGS) has not been yet reported. In the present study, NGS analysis of an a-CPP case was performed. Data were analyzed using Advaita Bioinformatics i-VariantGuide and Ion Reporter 5.6 programs. The results from NGS identified 12 novel missense mutations in the following genes: NOTCH1, ATM, STK36, MAGI1, DST, RECQL4, NUMA1, THBS1, MYH11, MALT1, SMARCA4 and CDH20. The PolyPhen score of six variants viz., DST, RECQL4, NUMA1, THBS1, MYHI1 and SMARCA4 were high, which suggested these variants represents pathogenic variants. Two novel insertions that caused frameshift were also found. Furthermore, two novel nonsense mutations and 14 novel intronic variants were identified in this tumor. The novel missense mutation detected in ATM gene was situated in c.5808A>T; p. (Leu1936Phe) in exon 39, and a known ATM mutation was in c.5948A>G; p. (Asn1983Ser). These novel mutations had not been reported in previous database. Subsequently, the quality statistics of these variants, including allele coverage, allele ratio, P-value, Phred quality score, sequencing coverage, PolyPhen score and alleles frequency was performed. For all variants, P-value was highly significant and the Phred quality score was high. In addition, the results from sequencing coverage demonstrated that 97.02% reads were on target and that 97.88% amplicons had at least 500 reads. These findings may serve at determining new strategies to distinguish the types of choroid plexus tumor, and at developing novel targeted therapies. Development of NGS technologies in the Kingdom of Saudi Arabia may be used in molecular pathology laboratories.

Comprehensive Characterization of Somatic Mutations Impacting lncRNA Expression for Pan-Cancer

Somatic mutations have long been recognized as an important feature of cancer. However, analysis of somatic mutations, to date, has focused almost entirely on the protein coding regions of the genome. The potential roles of somatic mutations in human long noncoding RNAs (lncRNAs) are therefore largely unknown, particularly their functional significance across different cancer types. In this study, we characterized some lncRNAs whose expression was affected by somatic mutations (defined as MutLncs) and constructed global MutLnc landscapes across 17 cancer types by systematically integrating multiple levels of data. MutLncs were commonly downregulated and carried low mutation frequencies and non-silent mutations in most cancer types. Co-occurrence analysis in pan-cancer highlighted combined patterns of specific MutLncs, suggesting that a number of MutLncs influence diverse cancer types through combination effects. Several conserved and cancer-specific functions of MutLncs were determined. We further explored the somatic mutations affecting lncRNA expression via mixed and unmixed effects, which led to specific functions in pan-cancer. Survival analysis indicated that MutLncs and co-occurrence pairs can potentially serve as cancer biomarkers. Clarification of the specific roles of MutLncs in human cancers could be beneficial for understanding the molecular pathogenesis of different cancer types and developing the appropriate treatments.

Activation of WEE1 confers resistance to PI3K inhibition in glioblastoma

Background

Oncogenic activation of phosphatidylinositol-3 kinase (PI3K) signaling plays a pivotal role in the development of glioblastoma (GBM). However, pharmacological inhibition of PI3K has so far not been therapeutically successful due to adaptive resistance through a rapid rewiring of cancer cell signaling. Here we identified that WEE1 is activated after transient exposure to PI3K inhibition and confers resistance to PI3K inhibition in GBM.

Methods

Patient-derived glioma-initiating cells and established GBM cells were treated with PI3K inhibitor or WEE1 inhibitor alone or in combination, and cell proliferation was evaluated by CellTiter-Blue assay. Cell apoptosis was analyzed by TUNEL, annexin V staining, and blotting of cleaved caspase-3 and cleaved poly(ADP-ribose) polymerase. Both subcutaneous xenograft and orthotropic xenograft studies were conducted to evaluate the effects of the combination on tumorigenesis; the tumor growth was monitored by bioluminescence imaging, and tumor tissue was analyzed by immunohistochemistry to validate signaling changes.

Results

PI3K inhibition activates WEE1 kinase, which in turn phosphorylates cell division control protein 2 homolog (Cdc2) at Tyr15 and inhibits Cdc2 activity, leading to G2/M arrest in a p53-independent manner. WEE1 inhibition abrogated the G2/M arrest and propelled cells to prematurely enter into mitosis and consequent cell death through mitotic catastrophe and apoptosis. Additionally, combination treatment significantly suppressed tumor growth in a subcutaneous model but not in an intracranial model due to limited blood-brain barrier penetration.

Conclusions

Our findings highlight WEE1 as an adaptive resistant gene activated after PI3K inhibition, and inhibition of WEE1 potentiated the effectiveness of PI3K targeted inhibition, suggesting that a combinational inhibition of WEE1 and PI3K might allow successful targeted therapy in GBM.

The Oncogene Addiction Switch from NOTCH to PI3K Requires Simultaneous Targeting of NOTCH and PI3K Pathway Inhibition in Glioblastoma

The NOTCH pathway regulates neural stem cells and glioma initiating cells (GICs). However, blocking NOTCH activity with γ-secretase inhibitors (GSIs) fails to alter the growth of GICs, as GSIs seem to be active in only a fraction of GICs lines with constitutive NOTCH activity. Here we report loss of PTEN function as a critical event leading to resistance to NOTCH inhibition, which causes the transfer of oncogene addiction from the NOTCH pathway to the PI3K pathway. Drug cytotoxicity testing of eight GICs showed a differential growth response to GSI, and the GICs were thus stratified into two groups: sensitive and resistant. In the sensitive group, GICs with loss of PTEN function appeared less sensitive to GSI treatment. Here we show that NOTCH regulates PTEN expression and the activity of the PI3K pathway in GICs, as treatment with GSI attenuated the NOTCH pathway and increased PTEN expression. NOTCH regulates PTEN expression via Hes-1, as knockdown of Notch or Hes1 increased expression of PTEN. This novel observation suggests that both pathways must be simultaneously inhibited in order to improve therapeutic efficacy in human glioblastomas (GBMs).

A multi-omics approach for identifying important pathways and genes in human cancer

Background

Cancer develops when pathways controlling cell survival, cell fate or genome maintenance are disrupted by the somatic alteration of key driver genes. Understanding how pathway disruption is driven by somatic alterations is thus essential for an accurate characterization of cancer biology and identification of therapeutic targets. Unfortunately, current cancer pathway analysis methods fail to fully model the relationship between somatic alterations and pathway activity.

Results

To address these limitations, we developed a multi-omics method for identifying biologically important pathways and genes in human cancer. Our approach combines single-sample pathway analysis with multi-stage, lasso-penalized regression to find pathways whose gene expression can be explained largely in terms of gene-level somatic alterations in the tumor. Importantly, this method can analyze case-only data sets, does not require information regarding pathway topology and supports personalized pathway analysis using just somatic alteration data for a limited number of cancer-associated genes. The practical effectiveness of this technique is illustrated through an analysis of data from The Cancer Genome Atlas using gene sets from the Molecular Signatures Database.

Conclusions

Novel insights into the pathophysiology of human cancer can be obtained from statistical models that predict expression-based pathway activity in terms of non-silent somatic mutations and copy number variation. These models enable the identification of biologically important pathways and genes and support personalized pathway analysis in cases where gene expression data is unavailable.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2476-8) contains supplementary material, which is available to authorized users.

Association analysis using somatic mutations

Somatic mutations drive the growth of tumor cells and are pivotal biomarkers for many cancer treatments. Genetic association analysis using somatic mutations is an effective approach to study the functional impact of somatic mutations. However, standard regression methods are not appropriate for somatic mutation association studies because somatic mutation calls often have non-ignorable false positive rate and/or false negative rate. While large scale association analysis using somatic mutations becomes feasible recently—thanks for the improvement of sequencing techniques and the reduction of sequencing cost—there is an urgent need for a new statistical method designed for somatic mutation association analysis. We propose such a method with computationally efficient software implementation: Somatic mutation Association test with Measurement Errors (SAME). SAME accounts for somatic mutation calling uncertainty using a likelihood based approach. It can be used to assess the associations between continuous/dichotomous outcomes and individual mutations or gene-level mutations. Through simulation studies across a wide range of realistic scenarios, we show that SAME can significantly improve statistical power than the naive generalized linear model that ignores mutation calling uncertainty. Finally, using the data collected from The Cancer Genome Atlas (TCGA) project, we apply SAME to study the associations between somatic mutations and gene expression in 12 cancer types, as well as the associations between somatic mutations and colon cancer subtype defined by DNA methylation data. SAME recovered some interesting findings that were missed by the generalized linear model. In addition, we demonstrated that mutation-level and gene-level analyses are often more appropriate for oncogene and tumor-suppressor gene, respectively.

Cancer is a genetic disease that is driven by the accumulation of somatic mutations. Association studies using somatic mutations is a powerful approach to identify the potential impact of somatic mutations on molecular or clinical features. One challenge for such tasks is the non-ignorable somatic mutation calling errors. We have developed a statistical method to address this challenge and applied our method to study the gene expression traits associated with somatic mutations in 12 cancer types. Our results show that some somatic mutations affect gene expression in several cancer types. In particular, we show that the associations between gene expression traits and TP53 gene level mutation reveal some similarities across a few cancer types.

Whole Exome Sequencing Uncovers Germline Variants of Cancer-Related Genes in Sporadic Pheochromocytoma

Background

Pheochromocytomas (PCCs) show the highest degree of heritability in human neoplasms. However, despite the wide number of alterations until now reported in PCCs, it is likely that other susceptibility genes remain still unknown, especially for those PCCs not clearly syndromic.

Methods

Whole exome sequencing of tumor DNA was performed on a set of twelve PCCs clinically defined as sporadic.

Results

About 50% of PCCs examined had somatic mutations on the known susceptibility VHL, NF1, and RET genes. In addition to these driver events, mutations on SYNE1, ABCC10, and RAD54B genes were also detected. Moreover, extremely rare germline variants were present in half of the sporadic PCC samples analyzed, in particular variants of MAX and SAMD9L were detected in the germline of cases wild-type for mutations in the known susceptibility genes.

Conclusions

Additional somatic passenger mutations can be associated with known susceptibility VHL, NF1, and RET genes in PCCs, and a wide number of germline variants with still unknown clinical significance can be detected in these patients. Therefore, many efforts should be aimed to better define the pathogenetic role of all these germline variants for discovering novel potential therapeutic targets for this disease still orphan of effective treatments.

Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network

BACKGROUND

Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions.

RESULTS

To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods.

CONCLUSIONS

In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework.

Identification of different mutational profiles in cancers arising in specific colon segments by next generation sequencing

The objective of this study was to investigate the mutational profiles of cancers arising in different colon segments. To this aim, we have analyzed 37 colon cancer samples by use of the Ion AmpliSeq™ Comprehensive Cancer Panel. Overall, we have found 307 mutated genes, most of which already implicated in the development of colon cancer. Among these, 15 genes were mutated in tumors originating in all six colon segments and were defined "common genes" (i.e. APC, PIK3CA, TP53) whereas 13 genes were preferentially mutated in tumors originating only in specific colon segments and were defined "site-associated genes" (i.e. BLNK, PTPRD). In addition, the presence of mutations in 10 of the 307 identified mutated genes (NBN, SMUG1, ERBB2, PTPRT, EPHB1, ALK, PTPRD, AURKB, KDR and GPR124) were found to be of clinical relevance. Among clinically relevant genes, NBN and SMUG1 were identified as independent prognostic factors that predicted poor survival in colon cancer patients. In conclusion, the findings reported here indicate that tumors arising in different colon segments present differences in the type and/or frequency of genetic variants, with two of them being independent prognostic factors that predict poor survival in colon cancer patients.

Cancer driver mutation prediction through Bayesian integration of multi-omic data

Identification of cancer driver mutations is critical for advancing cancer research and personalized medicine. Due to inter-tumor genetic heterogeneity, many driver mutations occur at low frequencies, which make it challenging to distinguish them from passenger mutations. Here, we show that a novel Bayesian hierarchical modeling approach, named rDriver can achieve enhanced prediction accuracy by identifying mutations that not only have high functional impact scores but also are associated with systemic variation in gene expression levels. In examining 3,080 tumor samples from 8 cancer types in The Cancer Genome Atlas, rDriver predicted 1,389 driver mutations. Compared with existing tools, rDriver identified more low frequency mutations associated with lineage specific functional properties, timing of occurrence and patient survival. Evaluation of rDriver predictions using engineered cell-line models resulted in a positive predictive value of 0.94 in PIK3CA genes. Our study highlights the importance of integrating multi-omic data in predicting cancer driver mutations and provides a statistically rigorous solution for cancer target discovery and development.

The Discovery of Mutated Driver Pathways in Cancer: Models and Algorithms

The pathogenesis of cancer in human is still poorly understood. With the rapid development of high-throughput sequencing technologies, huge volumes of cancer genomics data have been generated. Deciphering that data poses great opportunities and challenges to computational biologists. One of such key challenges is to distinguish driver mutations, genes as well as pathways from passenger ones. Mutual exclusivity of gene mutations (each patient has no more than one mutation in the gene set) has been observed in various cancer types and thus has been used as an important property of a driver gene set or pathway. In this article, we aim to review the recent development of computational models and algorithms for discovering driver pathways or modules in cancer with the focus on mutual exclusivity-based ones.

An efficient strategy for identifying cancer-related key genes based on graph entropy

Gene networks are beneficial to identify functional genes that are highly relevant to clinical outcomes. Most of the current methods require information about the interaction of genes or proteins to construct genetic network connection. However, the conclusion of these methods may be bias because of the current incompleteness of human interactome. In this paper, we propose an efficient strategy to use gene expression data and gene mutation data for identifying cancer-related key genes based on graph entropy (iKGGE). Firstly, we construct a gene network using only gene expression data based on the sparse inverse covariance matrix, then, cluster genes use the algorithm of parallel maximal cliques for quickly obtaining a series of subgraphs, and at last, we introduce a novel metric that combine graph entropy and the influence of upstream gene mutations information to measure the impact factors of genes. Testing of the three available cancer datasets shows that our strategy can effectively extract key genes that may play distinct roles in tumorigenesis, and the cancer patient risk groups are well predicted based on key genes.

Applying Expression Profile Similarity for Discovery of Patient-Specific Functional Mutations

The progress of cancer genome sequencing projects yields unprecedented information of mutations for numerous patients. However, the complexity of mutation profiles of cancer patients hinders the further understanding to mechanisms of oncogenesis. One basic question is how to find mutations with functional impacts. In this work, we introduce a computational method to predict functional somatic mutations of each patient by integrating mutation recurrence with expression profile similarity. With this method, the functional mutations are determined by checking the mutation enrichment among a group of patients with similar expression profiles. We applied this method to three cancer types and identified the functional mutations. Comparison of the predictions for three cancer types suggested that most of the functional mutations were cancer-type-specific with one exception to p53. By checking predicted results, we found that our method effectively filtered non-functional mutations resulting from large protein sizes. In addition, this method can also perform functional annotation to each patient to describe their association with signalling pathways or biological processes. In breast cancer, we predicted "cell adhesion" and other terms to be significantly associated with oncogenesis.

Cancer transcriptome profiling at the juncture of clinical translation

Methodological breakthroughs over the past four decades have repeatedly revolutionized transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples. These transcriptomes provide a link between cellular phenotypes and their molecular underpinnings, such as mutations. In the context of cancer, this link represents an opportunity to dissect the complexity and heterogeneity of tumours and to discover new biomarkers or therapeutic strategies. Here, we review the rationale, methodology and translational impact of transcriptome profiling in cancer.

The Role of Class IA Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunits in Glioblastoma

Phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) plays a critical role in the pathogenesis of cancer including glioblastoma, the most common and aggressive form of brain cancer. Targeting the PI3K pathway to treat glioblastoma has been tested in the clinic with modest effect. In light of the recent finding that PI3K catalytic subunits (PIK3CA/p110α, PIK3CB/p110β, PIK3CD/p110δ, and PIK3CG/p110γ) are not functionally redundant, it is imperative to determine whether these subunits play divergent roles in glioblastoma and whether selectively targeting PI3K catalytic subunits represents a novel and effective strategy to tackle PI3K signaling. This article summarizes recent advances in understanding the role of PI3K catalytic subunits in glioblastoma and discusses the possibility of selective blockade of one PI3K catalytic subunit as a treatment option for glioblastoma.

Identification of cancer genes that are independent of dominant proliferation and lineage programs

Large, multidimensional “landscaping” projects have provided datasets that can be mined to identify potential targets for subgroups of tumors. Here, we analyzed genomic and transcriptomic data from human breast tumors to identify genes whose expression is enriched in tumors harboring specific genetic alterations. However, this analysis revealed that two other factors, proliferation rate and tumor lineage, are more dominant factors in shaping tumor transcriptional programs than genetic alterations. This discovery shifted our attention to identifying genes that are independent of the dominant proliferation and lineage programs. A small subset of these genes represents candidate targets for combination cancer therapies because they are druggable, maintained after treatment with chemotherapy, essential for cell line survival, and elevated in drug-resistant stem-like cancer cells.

Large, multidimensional cancer datasets provide a resource that can be mined to identify candidate therapeutic targets for specific subgroups of tumors. Here, we analyzed human breast cancer data to identify transcriptional programs associated with tumors bearing specific genetic driver alterations. Using an unbiased approach, we identified thousands of genes whose expression was enriched in tumors with specific genetic alterations. However, expression of the vast majority of these genes was not enriched if associations were analyzed within individual breast tumor molecular subtypes, across multiple tumor types, or after gene expression was normalized to account for differences in proliferation or tumor lineage. Together with linear modeling results, these findings suggest that most transcriptional programs associated with specific genetic alterations in oncogenes and tumor suppressors are highly context-dependent and are predominantly linked to differences in proliferation programs between distinct breast cancer subtypes. We demonstrate that such proliferation-dependent gene expression dominates tumor transcriptional programs relative to matched normal tissues. However, we also identified a relatively small group of cancer-associated genes that are both proliferation- and lineage-independent. A subset of these genes are attractive candidate targets for combination therapy because they are essential in breast cancer cell lines, druggable, enriched in stem-like breast cancer cells, and resistant to chemotherapy-induced down-regulation.

Clinical significance of YAP1 activation in head and neck squamous cell carcinoma

By analyzing the genomic data of head and neck squamous cell cancer (HNSCC), we investigated clinical significance of YAP1 activation. Copy number and mRNA expression of YAP1 were analyzed together to assess clinical relevance of YAP1 activation in HNSCC. The clinical significance of YAP1 activation was further validated in four independent test cohorts. We also assessed the correlation of YAP1 activation with genomic alterations such as copy number alteration, somatic mutation, and miRNA expression. The YAP1-activated (YA) subgroup showed worse prognosis for HNSCC as tested and validated in five cohorts. In a multivariate risk analysis, the YAP1 signature was the most significant predictor of overall survival. The YAP1-inactivated (YI) subgroup was associated with HPV-positive status. In multiplatform analysis, YA tumors had gain of EGFR and SNAI2; loss of tumor-suppressor genes such as CSMD1, CDKN2A, NOTCH1, and SMAD4; and high mutation rates of TP53 and CDKN2A. YI tumors were characterized by gain of PIK3CA, SOX2, and TP63; deletion of 11q23.1; and high mutation rates of NFE2L2, PTEN, SYNE1, and NSD1. YA tumors also showed weaker immune activity as reflected in low IFNG composite scores and YAP1 activity is negatively associated with potential response to treatment of pembrolizumab. In conclusion, activation of YAP1 is associated with worse prognosis of patients with HNSCC and potential resistance to immunotherapy.

Computational drug repositioning for rare diseases in the era of precision medicine

There are tremendous unmet needs in drug development for rare diseases. Computational drug repositioning is a promising approach and has been successfully applied to the development of treatments for diseases. However, how to utilize this knowledge and effectively conduct and implement computational drug repositioning approaches for rare disease therapies is still an open issue. Here, we focus on the means of utilizing accumulated genomic data for accelerating and facilitating drug repositioning for rare diseases. First, we summarize the current genome landscape of rare diseases. Second, we propose several promising bioinformatics approaches and pipelines for computational drug repositioning for rare diseases. Finally, we discuss recent regulatory incentives and other enablers in rare disease drug development and outline the remaining challenges.

Preclinical therapeutic efficacy of a novel blood-brain barrier-penetrant dual PI3K/mTOR inhibitor with preferential response in PI3K/PTEN mutant glioma

Glioblastoma (GBM) is an ideal candidate disease for signal transduction targeted therapy because the majority of these tumors harbor genetic alterations that result in aberrant activation of growth factor signaling pathways. Loss of heterozygosity of chromosome 10, mutations in the tumor suppressor gene PTEN, and PI3K mutations are molecular hallmarks of GBM and indicate poor prognostic outcomes in many cancers. Consequently, inhibiting the PI3K pathway may provide therapeutic benefit in these cancers. PI3K inhibitors generally block proliferation rather than induce apoptosis. To restore the sensitivity of GBM to apoptosis induction, targeted agents have been combined with conventional therapy. However, the molecular heterogeneity and infiltrative nature of GBM make it resistant to traditional single agent therapy. Our objectives were to test a dual PI3K/mTOR inhibitor that may cross the blood–brain barrier (BBB) and provide the rationale for using this inhibitor in combination regimens to chemotherapy-induced synergism in GBM. Here we report the preclinical potential of a novel, orally bioavailable PI3K/mTOR dual inhibitor, DS7423 (hereafter DS), in in-vitro and in-vivo studies. DS was tested in mice, and DS plasma and brain concentrations were determined. DS crossed the BBB and led to potent suppression of PI3K pathway biomarkers in the brain. The physiologically relevant concentration of DS was tested in 9 glioma cell lines and 22 glioma-initiating cell (GIC) lines. DS inhibited the growth of glioma tumor cell lines and GICs at mean 50% inhibitory concentration values of less than 250 nmol/L. We found that PI3K mutations and PTEN alterations were associated with cellular response to DS treatment; with preferential inhibition of cell growth in PI3KCA-mutant and PTEN altered cell lines. DS showed efficacy and survival benefit in the U87 and GSC11 orthotopic models of GBM. Furthermore, administration of DS enhanced the antitumor efficacy of temozolomide against GBM in U87 glioma models, which shows that PI3K/mTOR inhibitors may enhance alkylating agent-mediated cytotoxicity, providing a novel regimen for the treatment of GBM. Our present findings establish that DS can specifically be used in patients who have PI3K pathway activation and/or loss of PTEN function. Further studies are warranted to determine the potential of DS for glioma treatment.

A NOVEL AND EFFICIENT ALGORITHM FOR DE NOVO DISCOVERY OF MUTATED DRIVER PATHWAYS IN CANCER

Next-generation sequencing studies on cancer somatic mutations have discovered that driver mutations tend to appear in most tumor samples, but they barely overlap in any single tumor sample, presumably because a single driver mutation can perturb the whole pathway. Based on the corresponding new concepts of coverage and mutual exclusivity, new methods can be designed for de novo discovery of mutated driver pathways in cancer. Since the computational problem is a combinatorial optimization with an objective function involving a discontinuous indicator function in high dimension, many existing optimization algorithms, such as a brute force enumeration, gradient descent and Newton's methods, are practically infeasible or directly inapplicable. We develop a new algorithm based on a novel formulation of the problem as non-convex programming and non-convex regularization. The method is computationally more efficient, effective and scalable than existing Monte Carlo searching and several other algorithms, which have been applied to The Cancer Genome Atlas (TCGA) project. We also extend the new method for integrative analysis of both mutation and gene expression data. We demonstrate the promising performance of the new methods with applications to three cancer datasets to discover de novo mutated driver pathways.

Identification of driver copy number alterations in diverse cancer types and application in drug repositioning

Results from numerous studies suggest an important role for somatic copy number alterations (SCNAs) in cancer progression. Our work aimed to identify the drivers (oncogenes or tumor suppressor genes) that reside in recurrently aberrant genomic regions, including a large number of genes or non-coding genes, which remain a challenge for decoding the SCNAs involved in carcinogenesis. Here, we propose a new approach to comprehensively identify drivers, using 8740 cancer samples involving 18 cancer types from The Cancer Genome Atlas (TCGA). On average, 84 drivers were revealed for each cancer type, including protein-coding genes, long non-coding RNAs (lncRNA) and microRNAs (miRNAs). We demonstrated that the drivers showed significant attributes of cancer genes, and significantly overlapped with known cancer genes, including MYC, CCND1 and ERBB2 in breast cancer, and the lncRNA PVT1 in multiple cancer types. Pan-cancer analyses of drivers revealed specificity and commonality across cancer types, and the non-coding drivers showed a higher cancer-type specificity than that of coding drivers. Some cancer types from different tissue origins were found to converge to a high similarity because of the significant overlap of drivers, such as head and neck squamous cell carcinoma (HNSC) and lung squamous cell carcinoma (LUSC). The lncRNA SOX2-OT, a common driver of HNSC and LUSC, showed significant expression correlation with the oncogene SOX2. In addition, because some drivers are common in multiple cancer types and have been targeted by known drugs, we found that some drugs could be successfully repositioned, as validated by the datasets of drug response assays in cell lines. Our work reported a new method to comprehensively identify drivers in SCNAs across diverse cancer types, providing a feasible strategy for cancer drug repositioning as well as novel findings regarding cancer-associated non-coding RNA discovery.

Molecular Characterization of Somatic Alterations in Dukes' B and C Colorectal Cancers by Targeted Sequencing

Despite global progress in research, improved screening and refined treatment strategies, colorectal cancer (CRC) remains as the third most common malignancy. As each type of cancer is different and exhibits unique alteration patterns, identifying and characterizing gene alterations in CRC that may serve as biomarkers might help to improve diagnosis, prognosis and predict potential response to therapy. With the emergence of next generation sequencing technologies (NGS), it is now possible to extensively and rapidly identify the gene profile of individual tumors. In this study, we aimed to identify actionable somatic alterations in Dukes’ B and C in CRC via NGS. Targeted sequencing of 409 cancer-related genes using the Ion Ampliseq^TM Comprehensive Cancer Panel was performed on genomic DNA obtained from paired fresh frozen tissues, cancer and normal, of Dukes’ B (n = 10) and Dukes’ C (n = 9) CRC. The sequencing results were analyzed using Torrent Suite, annotated using ANNOVAR and validated using Sanger sequencing. A total of 141 somatic non-synonymous sequence variations were identified in 86 genes. Among these, 64 variants (45%) were predicted to be deleterious, 38 variants (27%) possibly deleterious while the other 39 variants (28%) have low or neutral protein impact. Seventeen genes have alterations with frequencies of ≥10% in the patient cohort and with 14 overlapped genes in both Dukes’ B and C. The adenomatous polyposis coli gene (APC) was the most frequently altered gene in both groups (n = 6 in Dukes’ B and C). In addition, TP53 was more frequently altered in Dukes’ C (n = 7) compared to Dukes’ B (n = 4). Ten variants in APC, namely p.R283^∗, p.N778fs, p.R805^∗, p.Y935fs, p.E941fs, p.E1057^∗, p.I1401fs, p.Q1378^∗, p.E1379^∗, and p.A1485fs were predicted to be driver variants. APC remains as the most frequently altered gene in the intermediate stages of CRC. Wnt signaling pathway is the major affected pathway followed by P53, RAS, TGF-β, and PI3K signaling. We reported the alteration profiles in each of the patient which has the potential to affect the clinical decision. We believe that this study will add further to the understanding of CRC molecular landscape.

HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology

Prioritizing molecular alterations that act as drivers of cancer remains a crucial bottleneck in therapeutic development. Here we introduce HIT'nDRIVE, a computational method that integrates genomic and transcriptomic data to identify a set of patient-specific, sequence-altered genes, with sufficient collective influence over dysregulated transcripts. HIT'nDRIVE aims to solve the "random walk facility location" (RWFL) problem in a gene (or protein) interaction network, which differs from the standard facility location problem by its use of an alternative distance measure: "multihitting time," the expected length of the shortest random walk from any one of the set of sequence-altered genes to an expression-altered target gene. When applied to 2200 tumors from four major cancer types, HIT'nDRIVE revealed many potentially clinically actionable driver genes. We also demonstrated that it is possible to perform accurate phenotype prediction for tumor samples by only using HIT'nDRIVE-seeded driver gene modules from gene interaction networks. In addition, we identified a number of breast cancer subtype-specific driver modules that are associated with patients' survival outcome. Furthermore, HIT'nDRIVE, when applied to a large panel of pan-cancer cell lines, accurately predicted drug efficacy using the driver genes and their seeded gene modules. Overall, HIT'nDRIVE may help clinicians contextualize massive multiomics data in therapeutic decision making, enabling widespread implementation of precision oncology.