Translating insights from the cancer genome into clinical practice

Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues

The capability to spatially explore RNA biology in formalin-fixed paraffin-embedded (FFPE) tissues holds transformative potential for histopathology research. Here, we present pathology-compatible deterministic barcoding in tissue (Patho-DBiT) by combining in situ polyadenylation and computational innovation for spatial whole transcriptome sequencing, tailored to probe the diverse RNA species in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for 5 years. Furthermore, genome-wide single-nucleotide RNA variants can be captured to distinguish malignant subclones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis. Single-cell level Patho-DBiT dissects the spatiotemporal cellular dynamics driving tumor clonal architecture and progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to aid in clinical pathology evaluation.

Spatially Exploring RNA Biology in Archival Formalin-Fixed Paraffin-Embedded Tissues

Spatial transcriptomics has emerged as a powerful tool for dissecting spatial cellular heterogeneity but as of today is largely limited to gene expression analysis. Yet, the life of RNA molecules is multifaceted and dynamic, requiring spatial profiling of different RNA species throughout the life cycle to delve into the intricate RNA biology in complex tissues. Human disease-relevant tissues are commonly preserved as formalin-fixed and paraffin-embedded (FFPE) blocks, representing an important resource for human tissue specimens. The capability to spatially explore RNA biology in FFPE tissues holds transformative potential for human biology research and clinical histopathology. Here, we present Patho-DBiT combining in situ polyadenylation and deterministic barcoding for spatial full coverage transcriptome sequencing, tailored for probing the diverse landscape of RNA species even in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for five years. Furthermore, genome-wide single nucleotide RNA variants can be captured to distinguish different malignant clones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA-mRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis trajectory. High resolution Patho-DBiT at the cellular level reveals a spatial neighborhood and traces the spatiotemporal kinetics driving tumor progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to study human tissue biology and aid in clinical pathology evaluation.

Interactive gene identification for cancer subtyping based on multi-omics clustering

Recent advances in multi-omics databases offer the opportunity to explore complex systems of cancers across hierarchical biological levels. Some methods have been proposed to identify the genes that play a vital role in disease development by integrating multi-omics. However, the existing methods identify the related genes separately, neglecting the gene interactions that are related to the multigenic disease. In this study, we develop a learning framework to identify the interactive genes based on multi-omics data including gene expression. Firstly, we integrate different omics based on their similarities and apply spectral clustering for cancer subtype identification. Then, a gene co-expression network is construct for each cancer subtype. Finally, we detect the interactive genes in the co-expression network by learning the dense subgraphs based on the L₁ prosperities of eigenvectors in the modularity matrix. We apply the proposed learning framework on a multi-omics cancer dataset to identify the interactive genes for each cancer subtype. The detected genes are examined by DAVID and KEGG tools for systematic gene ontology enrichment analysis. The analysis results show that the detected genes have relationships to cancer development and the genes in different cancer subtypes are related to different biological processes and pathways, which are expected to yield important references for understanding tumor heterogeneity and improving patient survival.

Role of Nanotechnology in Overcoming the Multidrug Resistance in Cancer Therapy: A Review

Cancer is one of the leading causes of morbidity and mortality around the globe and is likely to become the major cause of global death in the coming years. As per World Health Organization (WHO) report, every year there are over 10 and 9 million new cases and deaths from this disease. Chemotherapy, radiotherapy, and surgery are the three basic approaches to treating cancer. These approaches are aiming at eradicating all cancer cells with minimum off-target effects on other cell types. Most drugs have serious adverse effects due to the lack of target selectivity. On the other hand, resistance to already available drugs has emerged as a major obstacle in cancer chemotherapy, allowing cancer to proliferate irrespective of the chemotherapeutic agent. Consequently, it leads to multidrug resistance (MDR), a growing concern in the scientific community. To overcome this problem, in recent years, nanotechnology-based drug therapies have been explored and have shown great promise in overcoming resistance, with most nano-based drugs being explored at the clinical level. Through this review, we try to explain various mechanisms involved in multidrug resistance in cancer and the role nanotechnology has played in overcoming or reversing this resistance.

MDICC: novel method for multi-omics data integration and cancer subtype identification

Each type of cancer usually has several subtypes with distinct clinical implications, and therefore the discovery of cancer subtypes is an important and urgent task in disease diagnosis and therapy. Using single-omics data to predict cancer subtypes is difficult because genomes are dysregulated and complicated by multiple molecular mechanisms, and therefore linking cancer genomes to cancer phenotypes is not an easy task. Using multi-omics data to effectively predict cancer subtypes is an area of much interest; however, integrating multi-omics data is challenging. Here, we propose a novel method of multi-omics data integration for clustering to identify cancer subtypes (MDICC) that integrates new affinity matrix and network fusion methods. Our experimental results show the effectiveness and generalization of the proposed MDICC model in identifying cancer subtypes, and its performance was better than those of currently available state-of-the-art clustering methods. Furthermore, the survival analysis demonstrates that MDICC delivered comparable or even better results than many typical integrative methods.

Improved outcomes and staging in non-small-cell lung cancer guided by a molecular assay

There remains a critical need for improved staging of non-small-cell lung cancer, as recurrence and mortality due to undetectable metastases at the time of surgery remain high even after complete resection of tumors currently categorized as 'early stage.' A 14-gene quantitative PCR-based expression profile has been extensively validated to better identify patients at high-risk of 5-year mortality after surgical resection than conventional staging - mortality that almost always results from previously undetectable metastases. Furthermore, prospective studies now suggest a predictive benefit in disease-free survival when the assay is used to guide adjuvant chemotherapy decisions in early-stage non-small-cell lung cancer patients.

Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative Study

Lung adenocarcinoma (LUAD) is a common and very lethal cancer. Accurate staging is a prerequisite for its effective diagnosis and treatment. Therefore, improving the accuracy of the stage prediction of LUAD patients is of great clinical relevance. Previous works have mainly focused on single genomic data information or a small number of different omics data types concurrently for generating predictive models. A few of them have considered multi-omics data from genome to proteome. We used a publicly available dataset to illustrate the potential of multi-omics data for stage prediction in LUAD. In particular, we investigated the roles of the specific omics data types in the prediction process. We used a self-developed method, Omics-MKL, for stage prediction that combines an existing feature ranking technique Minimum Redundancy and Maximum Relevance (mRMR), which avoids redundancy among the selected features, and multiple kernel learning (MKL), applying different kernels for different omics data types. Each of the considered omics data types individually provided useful prediction results. Moreover, using multi-omics data delivered notably better results than using single-omics data. Gene expression and methylation information seem to play vital roles in the staging of LUAD. The Omics-MKL method retained 70 features after the selection process. Of these, 21 (30%) were methylation features and 34 (48.57%) were gene expression features. Moreover, 18 (25.71%) of the selected features are known to be related to LUAD, and 29 (41.43%) to lung cancer in general. Using multi-omics data from genome to proteome for predicting the stage of LUAD seems promising because each omics data type may improve the accuracy of the predictions. Here, methylation and gene expression data may play particularly important roles.

Deep Subspace Mutual Learning For Cancer Subtypes Prediction

MOTIVATION

Precise prediction of cancer subtypes is of significant importance in cancer diagnosis and treatment. Disease etiology is complicated existing at different omics levels, hence integrative analysis provides a very effective way to improve our understanding of cancer.

RESULTS

We propose a novel computational framework, named Deep Subspace Mutual Learning (DSML). DSML has the capability to simultaneously learn the subspace structures in each available omics data and in overall multi-omics data by adopting deep neural networks, which thereby facilitates the subtypes prediction via clustering on multi-level, single level, and partial level omics data. Extensive experiments are performed in five different cancers on three levels of omics data from The Cancer Genome Atlas. The experimental analysis demonstrates that DSML delivers comparable or even better results than many state-of-the-art integrative methods.

AVAILABILITY

An implementation and documentation of the DSML is publicly available at https://github.com/polytechnicXTT/Deep-Subspace-Mutual-Learning.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Integrated Deep Network for Cancer Survival Prediction Using Omics Data

As a highly sophisticated disease that humanity faces, cancer is known to be associated with dysregulation of cellular mechanisms in different levels, which demands novel paradigms to capture informative features from different omics modalities in an integrated way. Successful stratification of patients with respect to their molecular profiles is a key step in precision medicine and in tailoring personalized treatment for critically ill patients. In this article, we use an integrated deep belief network to differentiate high-risk cancer patients from the low-risk ones in terms of the overall survival. Our study analyzes RNA, miRNA, and methylation molecular data modalities from both labeled and unlabeled samples to predict cancer survival and subsequently to provide risk stratification. To assess the robustness of our novel integrative analytics, we utilize datasets of three cancer types with 836 patients and show that our approach outperforms the most successful supervised and semi-supervised classification techniques applied to the same cancer prediction problems. In addition, despite the preconception that deep learning techniques require large size datasets for proper training, we have illustrated that our model can achieve better results for moderately sized cancer datasets.

Amplification of RAD54B promotes progression of hepatocellular carcinoma via activating the Wnt/β-catenin signaling

Liver cancer was reported to be the sixth most frequently diagnosed cancer, and hepatocellular carcinoma (HCC) accounts for 75%-85% of primary liver cancer. Nevertheless, the concrete molecular mechanisms of HCC progression remain obscure, which is essential to elucidate. The expression profile of RAD54B in HCC was measured using qPCR and western blotting. Moreover, the levels of RAD54B in paraffin-embedded samples were evaluated using immunohistochemistry (IHC). The effect of RAD54B on HCC progression was testified by in vitro experiments, and in vivo orthotopic xenograft tumor experiments. The mechanisms of RAD54B promoting HCC progression were investigated through molecular and function experiments. Herein, RAD54B are dramatically upregulated in HCC tissues and cell lines both on mRNA and protein levels, and RAD54B can servers as an independent prognostic parameter of 5-year overall survival and 5-year disease-free survival for patients with HCC. Moreover, up-regulation of RAD54B dramatically increases the capacity for in vitro cell viability and motility, and in vivo intrahepatic metastasis of HCC cells. Mechanistically, RAD54B promotes the HCC progression through modulating the wnt/β-catenin signaling. Notably, blocking the wnt/β-catenin signaling axis can counteract the activating effects of RAD54B on motility of HCC cells. Besides, further analysis illustrates that DNA amplification is one of the mechanisms leading to mRNA overexpression of RAD54B in HCC. Our findings indicate that RAD54B might be a promising potential prognostic marker and a candidate therapeutic target to therapy HCC.

Proteomic Interrogation in Cancer Biomarker

Biomarkers factor into the diagnosis and treatment of almost every patient with cancer. The innovation in proteomics follows improvement of mass spectrometry techniques and data processing strategy. Recently, proteomics and typical biological studies have been the answer for clinical applications. The clinical proteomics techniques are now actively adapted to protein identification in large patient cohort, biomarker development for more sensitive and specific screening based on quantitative data. And, it is important for clinical, translational researchers to be acutely aware of the issues surrounding appropriate biomarker development, in order to facilitate entry of clinically useful biomarkers into the clinic. Here, we discuss in detail include the case research for clinical proteomics. Furthermore, we give an overview on the current developments and novel findings in proteomics-based cancer biomarker research.

Integrating Multi-Omic Data With Deep Subspace Fusion Clustering for Cancer Subtype Prediction

One type of cancer usually consists of several subtypes with distinct clinical implications, thus the cancer subtype prediction is an important task in disease diagnosis and therapy. Utilizing one type of data from molecular layers in biological system to predict is difficult to bridge the cancer genome to cancer phenotypes, since the genome is neither simple nor independent but rather complicated and dysregulated from multiple molecular mechanisms. Similarity Network Fusion (SNF) has been recently proposed to integrate diverse omics data for improving the understanding of tumorigenesis. SNF adopts Euclidean distance to measure the similarity between patients, which shows some limitations. In this article, we introduce a novel prediction technique as an extension of SNF, namely Deep Subspace Fusion Clustering (DSFC). DSFC utilizes auto-encoder and data self-expressiveness approaches to guide a deep subspace model, which can achieve effective expression of discriminative similarity between patients. As a result, the dissimilarity between inter-cluster is delivered and enhanced compactness of intra-cluster is achieved at the same time. The validity of DSFC is examined by extensive simulations over six different cancer through three levels omics data. The survival analysis demonstrates that DSFC delivers comparable or even better results than many state-of-the-art integrative methods.

Comprehensive Characterization of Stage IIIA Non-Small Cell Lung Carcinoma

Introduction

Heterogeneity of non-small cell lung carcinoma (NSCLC) among patients is currently not well studied. Pathologic markers and staging systems have not been a precise predictor of the prognosis of an individual patient. Hence, we hypothesize to develop a transcript-based signature to categorize stage IIIA-NSCLC in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), plus identify markers that could indicate the prognosis of the disease.

Methods

Human Transcriptome Array 2.0 (HTA) and NanoString nCounter® platform were used for high-throughput gene-expression profiling. Initially, we profiled stage IIIA-NSCLC through HTA and validated through NanoString. Additionally, two metastatic markers SPP1 and CDH2 were validated in 47 NSCLC stage IIIA samples through real-time PCR.

Results

We observed distinct gene clusters in LUAD and LUSC with down-regulation of six genes and up-regulation of 57 genes through HTA. Ninety-six transcripts were randomly selected after analyzing HTA data and validated on the NanoString platform. We found 40 differentially expressed transcripts that categorized NSCLC into LUAD and LUSC. SPP1 is significantly overexpressed (4.311±1.27 fold in LUAD and 13.41±3.82 fold in LUSC compared to control), and the CDH2 transcript was significantly overexpressed (11.53 ± 4.027-fold compared to control) only in LUSC.

Discussion

These markers enable us to categorize stage IIIA NSCLC into LUAD and LUSC plus these markers may be helpful to understand the pathophysiology of NSCLC. However, more data required to make these findings useful in general clinical practice.

A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy

Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important ‘hub’ genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms.

Surface-enhanced Raman scattering (SERS)-based immunosystem for ultrasensitive detection of the 90K biomarker

The research and the individuation of tumour markers in biological fluids are currently one of the main tools to support diagnosis, prognosis, and monitoring of the therapeutic response in oncology. Although the identification of tumour markers in asymptomatic patients is crucial for early diagnosis, its application is still limited by the relatively low sensitivity and the complexity of existing methods (i.e. ELISA, mass spectrometry). We developed an easy, fast, and ultrasensitive surface-enhanced Raman scattering (SERS)-based system, for the detection and quantitation of the LGALS3BP (90K) biomarker that was used as a model, based on the development of antibody-functionalized nanostructured gold surfaces. The detection system was effective for the ultrasensitive detection and characterization of samples of different biochemical compositions. In conclusion, this work could provide the foundation for the development of a medical diagnostic device with the highest predictive power when compared with the methods currently used in cancer diagnostics.

PertInInt: An Integrative, Analytical Approach to Rapidly Uncover Cancer Driver Genes with Perturbed Interactions and Functionalities

A major challenge in cancer genomics is to identify genes with functional roles in cancer and uncover their mechanisms of action. We introduce an integrative framework that identifies cancer-relevant genes by pinpointing those whose interaction or other functional sites are enriched in somatic mutations across tumors. We derive analytical calculations that enable us to avoid time-prohibitive permutation-based significance tests, making it computationally feasible to simultaneously consider multiple measures of protein site functionality. Our accompanying software, PertInInt, combines knowledge about sites participating in interactions with DNA, RNA, peptides, ions, or small molecules with domain, evolutionary conservation, and gene-level mutation data. When applied to 10,037 tumor samples, PertInInt uncovers both known and newly predicted cancer genes, while additionally revealing what types of interactions or other functionalities are disrupted. PertInInt’s analysis demonstrates that somatic mutations are frequently enriched in interaction sites and domains and implicates interaction perturbation as a pervasive cancer-driving event.

A fast, analytical framework called PertInInt enables efficient integration of multiple measures of protein site functionality—including interaction, domain, and evolutionary conservation—with gene-level mutation data in order to rapidly detect cancer driver genes along with their disrupted functionalities.

The novel TP53 3'-end methylation pattern associated with its expression would be a potential biomarker for breast cancer detection

BACKGROUND

Deficiency or silence of TP53 is an early event in breast tumorigenesis. Aberrant methylation and mutation in regulatory regions were considered as crucial regulators of gene expression.

METHODS

Using multiplex-PCR and next-generation sequencing technology, we analyzed TP53 mutation spectrum in its promoter region. Using PCR target sequence enrichment and next-generation bisulfite sequencing technology, we analyzed the methylation profile of the promoter and 3'-end regions of TP53 gene in paired breast tumor and normal tissues from 120 breast cancer patients. The expression of TP53 and the flanking gene ATP1B2 was explored with qPCR method in the same cohort.

RESULTS

No promoter mutation of TP53 gene was found in the cohort of the 120 breast cancer patients. The 3'-end of TP53 gene was hyper-methylated (average 78.71%) compared with the promoter region (average less than 1%) in breast tumor tissues. TP53 was significantly lower expressed (P = 1.68E-15) and hyper-methylated in 3'-end (P = 1.82E-18) in tumor. Negative cis correlation was found between the TP53 expression and its 3'-end methylation (P = 9.02E-8, R = 0.337). TP53 expression was significantly associated with PR status (P = 0.0128), Ki67 level (P = 0.0091), and breast cancer subtypes (P = 0.0109). TP53 3'-end methylation and expression showed a good performance in discriminating breast cancer and normal tissues with an AUC of 0.930.

CONCLUSIONS

The 3'-end methylation of TP53 might be a crucial regulator for its expression in breast cancer, suggesting that TP53 3'-end hyper-methylation associated with its lower expression could be a potential biomarker for breast cancer diagnosis.

Prospective Decision Analysis Study of Clinical Genomic Testing in Metastatic Breast Cancer: Impact on Outcomes and Patient Perceptions

PURPOSE

To evaluate the impact of targeted DNA sequencing on selection of cancer therapy for patients with metastatic breast cancer (MBC).

PATIENTS AND METHODS

In this prospective, single-center, single-arm trial, patients with MBC were enrolled within 10 weeks of starting a new therapy. At enrollment, tumor samples underwent next-generation sequencing for any of 315 cancer-related genes to high depth (> 500×) using FoundationOne CDx. Sequencing results were released to providers at the time of disease progression, and physician treatment recommendations were assessed via questionnaire. We evaluated three prespecified questions to assess patients' perceptions of genomic testing.

RESULTS

In all, 100 patients underwent genomic testing, with a median of five mutations (range, 0 to 13 mutations) detected per patient. Genomic testing revealed one or more potential therapies in 98% of patients (98 of 100), and 60% of patients (60 of 100) had one or more recommended treatments with level I/II evidence for actionability. Among the 94 genomic text reports that were released, there was physician questionnaire data for 87 patients (response rate, 92.6%) and 31.0% of patients (27 of 87) had treatment change recommended by their physician. Of these, 37.0% (10 of 27) received the treatment supported by genomic testing. We did not detect a statistically significant difference in time-to-treatment failure (log-rank P = .87) or overall survival (P = .71) among patients who had treatment change supported by genomic testing versus those who had no treatment change. For patients who completed surveys before and after genomic testing, there was a significant decrease in confidence of treatment success, specifically among patients who did not have treatment change supported by genomic testing (McNemar's test of agreement P = .001).

CONCLUSION

In this prospective study, genomic profiling of tumors in patients with MBC frequently identified potential treatments and resulted in treatment change in a minority of patients. Patients whose therapy was not changed on the basis of genomic testing seemed to have a decrease in confidence of treatment success.

Indole-2-Carboxamide Derivative LG25 Inhibits Triple-Negative Breast Cancer Growth By Suppressing Akt/mTOR/NF-κB Signalling Pathway

Background

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer which is associated with poor patient outcome and lack of targeted therapy. Our laboratory has synthesized a series of indole-2-carboxamide derivatives. Among this series, compound LG25 showed a favorable pharmacological profile against sepsis and inflammatory diseases. In the present study, we investigated the chemotherapeutic potential of LG25 against TNBC utilizing in vitro and in vivo models.

Methods

Changes in cell viability, cell cycle phases and apoptosis were analyzed using MTT, clonogenic assay, FACS and Western blotting assays. The anti-cancer effects of LG25 were further determined in a xenograft mouse model.

Results

Our findings reveal that LG25 reduced TNBC viability in a dose-dependent manner. Flow cytometric and Western blot analyses showed that LG25 enhances G2/M cell cycle arrest and induced cell apoptosis. In addition, LG25 treatment significantly inhibited Akt/mTOR phosphorylation and nuclear translocation of nuclear factor-κB (NF-κB). These inhibitory activities of LG25 were confirmed in mice implanted MDA-MB-231 TNBC cells.

Conclusion

Our studies provide experimental evidence that indole-2-carboxamide derivative LG25 effectively targeted TNBC in preclinical models by inducing cell cycle arrest and apoptosis, through suppressing Akt/mTOR/NF-κB signaling pathway.

LSCDFS-MKL: A multiple kernel based method for lung squamous cell carcinomas disease-free survival prediction with pathological and genomic data

Lung squamous cell carcinoma (SCC) is a fatal disease in both male and female, for which current treatments are inadequate. Surgical resection is regarded as the cornerstone of treatment for patients with lung SCC, but even for the same stage patients, the wide spectrum of disease-free survival (DFS) times exits. Therefore, how to improve the DFS prediction performance of lung SCC becomes one major research area. In this study, we proposed a novel method called LSCDFS-MKL, which was on the basis of multiple kernel learning to predict DFS of lung SCC. In LSCDFS-MKL, we first efficiently integrated pathological images and genomic data (copy number aberration, gene expression, protein expression) from lung SCC. The results of LSCDFS-MKL between different types of data show that the features extracted from pathological images play an important role in DFS prediction of lung SCC. Then we compared our method LSCDFS-MKL with other existing methods and performance analysis indicates that LSCDFS-MKL has a significantly better performance than other prediction methods. After that, we applied the proposed method on different stage stratums and the performance demonstrates that LSCDFS-MKL remains efficient in DFS prediction of lung SCC patients. Finally, we performed LSCDFS-MKL on an independent validation dataset and the accuracy of DFS prediction achieves 100%, which is promising.

An Integrative "Omics" Approach, for Identification of Bona Fides PLK1 Associated Biomarker in Esophageal Adenocarcinoma

BACKGROUND

The rapid expansion of genome-wide profiling techniques offers the opportunity to utilize various types of information collected in the study of human health and disease. Overexpression of Polo like kinase 1 (PLK1) is associated with esophageal adenocarcinoma (OAC), however biological functions and molecular targets of PLK1 in OAC are still unknown.

OBJECTIVES

Here we performed integrative analysis of two "omics" data sources to reveal high-level interactions of PLK1 associated with OAC.

METHODS

Initially, quantitative gene expression (RPKM) was measured from transcriptomics data set of four OAC patients. In parallel, alteration in phosphorylation levels was evaluated in the proteomics data set (mass spectrometry) in OAC cell line (PLK1 inhibited). Next, two "omics" data sets were integrated and through comprehensive analysis possible true PLK1 targets that may serve as OAC biomarkers were assembled.

RESULTS

Through experimental validation, small ubiquitin-related modifier 1 (SUMO1) and heat shock protein beta-1 (HSPB1) were identified as novel phosphorylation targets of PLK1. Consequently in vivo, in situ and in silico experiments clearly demonstrated the interaction of PLK1 with putative novel targets (SUMO1 and HSPB1).

CONCLUSION

Identification of a PLK1 dependent biosignature in OAC with high confidence in two omics levels proven the robustness and efficacy of our integrative approach.

Clinical significance of APOB inactivation in hepatocellular carcinoma

Recent findings from The Cancer Genome Atlas project have provided a comprehensive map of genomic alterations that occur in hepatocellular carcinoma (HCC), including unexpected mutations in apolipoprotein B (APOB). We aimed to determine the clinical significance of this non-oncogenetic mutation in HCC. An Apob gene signature was derived from genes that differed between control mice and mice treated with siRNA specific for Apob (1.5-fold difference; P < 0.005). Human gene expression data were collected from four independent HCC cohorts (n = 941). A prediction model was constructed using Bayesian compound covariate prediction, and the robustness of the APOB gene signature was validated in HCC cohorts. The correlation of the APOB signature with previously validated gene signatures was performed, and network analysis was conducted using ingenuity pathway analysis. APOB inactivation was associated with poor prognosis when the APOB gene signature was applied in all human HCC cohorts. Poor prognosis with APOB inactivation was consistently observed through cross-validation with previously reported gene signatures (NCIP A, HS, high-recurrence SNUR, and high RS subtypes). Knowledge-based gene network analysis using genes that differed between low-APOB and high-APOB groups in all four cohorts revealed that low-APOB activity was associated with upregulation of oncogenic and metastatic regulators, such as HGF, MTIF, ERBB2, FOXM1, and CD44, and inhibition of tumor suppressors, such as TP53 and PTEN. In conclusion, APOB inactivation is associated with poor outcome in patients with HCC, and APOB may play a role in regulating multiple genes involved in HCC development.

Prediction of early breast cancer patient survival using ensembles of hypoxia signatures

Background

Biomarkers are a key component of precision medicine. However, full clinical integration of biomarkers has been met with challenges, partly attributed to analytical difficulties. It has been shown that biomarker reproducibility is susceptible to data preprocessing approaches. Here, we systematically evaluated machine-learning ensembles of preprocessing methods as a general strategy to improve biomarker performance for prediction of survival from early breast cancer.

Results

We risk stratified breast cancer patients into either low-risk or high-risk groups based on four published hypoxia signatures (Buffa, Winter, Hu, and Sorensen), using 24 different preprocessing approaches for microarray normalization. The 24 binary risk profiles determined for each hypoxia signature were combined using a random forest to evaluate the efficacy of a preprocessing ensemble classifier. We demonstrate that the best way of merging preprocessing methods varies from signature to signature, and that there is likely no ‘best’ preprocessing pipeline that is universal across datasets, highlighting the need to evaluate ensembles of preprocessing algorithms. Further, we developed novel signatures for each preprocessing method and the risk classifications from each were incorporated in a meta-random forest model. Interestingly, the classification of these biomarkers and its ensemble show striking consistency, demonstrating that similar intrinsic biological information are being faithfully represented. As such, these classification patterns further confirm that there is a subset of patients whose prognosis is consistently challenging to predict.

Conclusions

Performance of different prognostic signatures varies with pre-processing method. A simple classifier by unanimous voting of classifications is a reliable way of improving on single preprocessing methods. Future signatures will likely require integration of intrinsic and extrinsic clinico-pathological variables to better predict disease-related outcomes.

Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia

Objective

Computing patients’ similarity is of great interest in precision oncology since it supports clustering and subgroup identification, eventually leading to tailored therapies. The availability of large amounts of biomedical data, characterized by large feature sets and sparse content, motivates the development of new methods to compute patient similarities able to fuse heterogeneous data sources with the available knowledge.

Materials and Methods

In this work, we developed a data integration approach based on matrix trifactorization to compute patient similarities by integrating several sources of data and knowledge. We assess the accuracy of the proposed method: (1) on several synthetic data sets which similarity structures are affected by increasing levels of noise and data sparsity, and (2) on a real data set coming from an acute myeloid leukemia (AML) study. The results obtained are finally compared with the ones of traditional similarity calculation methods.

Results

In the analysis of the synthetic data set, where the ground truth is known, we measured the capability of reconstructing the correct clusters, while in the AML study we evaluated the Kaplan-Meier curves obtained with the different clusters and measured their statistical difference by means of the log-rank test. In presence of noise and sparse data, our data integration method outperform other techniques, both in the synthetic and in the AML data.

Discussion

In case of multiple heterogeneous data sources, a matrix trifactorization technique can successfully fuse all the information in a joint model. We demonstrated how this approach can be efficiently applied to discover meaningful patient similarities and therefore may be considered a reliable data driven strategy for the definition of new research hypothesis for precision oncology.

Conclusion

The better performance of the proposed approach presents an advantage over previous methods to provide accurate patient similarities supporting precision medicine.

RNA Therapeutics in Oncology: Advances, Challenges, and Future Directions

RNA-based therapeutic technologies represent a rapidly expanding class of therapeutic opportunities with the power to modulate cellular biology in ways never before possible. With RNA-targeted therapeutics, inhibitors of previously undruggable proteins, gene expression modulators, and even therapeutic proteins can be rationally designed based on sequence information alone, something that is not possible with other therapeutic modalities. The most advanced RNA therapeutic modalities are antisense oligonucleotides (ASOs) and small interfering RNAs. Particularly with ASOs, recent clinical data have demonstrated proof of mechanism and clinical benefit with these approaches across several nononcology disease areas by multiple routes of administration. In cancer, next-generation ASOs have recently demonstrated single-agent activity in patients with highly refractory cancers. Here we discuss advances in RNA therapeutics for the treatment of cancer and the challenges that remain to solidify these as mainstay therapeutic modalities to bridge the pharmacogenomic divide that remains in cancer drug discovery.

Multifactorial Modes of Action of Arsenic Trioxide in Cancer Cells as Analyzed by Classical and Network Pharmacology

Arsenic trioxide is a traditional remedy in Chinese Medicine since ages. Nowadays, it is clinically used to treat acute promyelocytic leukemia (APL) by targeting PML/RARA. However, the drug's activity is broader and the mechanisms of action in other tumor types remain unclear. In this study, we investigated molecular modes of action by classical and network pharmacological approaches. CEM/ADR5000 resistance leukemic cells were similar sensitive to As2O3 as their wild-type counterpart CCRF-CEM (resistance ratio: 1.88). Drug-resistant U87.MG ΔEGFR glioblastoma cells harboring mutated epidermal growth factor receptor were even more sensitive (collateral sensitive) than wild-type U87.MG cells (resistance ratio: 0.33). HCT-116 colon carcinoma p53-/- knockout cells were 7.16-fold resistant toward As2O3 compared to wild-type cells. Forty genes determining cellular responsiveness to As2O3 were identified by microarray and COMPARE analyses in 58 cell lines of the NCI panel. Hierarchical cluster analysis-based heat mapping revealed significant differences between As2O3 sensitive cell lines and resistant cell lines with p-value: 1.86 × 10-5. The genes were subjected to Galaxy Cistrome gene promoter transcription factor analysis to predict the binding of transcription factors. We have exemplarily chosen NF-kB and AP-1, and indeed As2O3 dose-dependently inhibited the promoter activity of these two transcription factors in reporter cell lines. Furthermore, the genes identified here and those published in the literature were assembled and subjected to Ingenuity Pathway Analysis for comprehensive network pharmacological approaches that included all known factors of resistance of tumor cells to As2O3. In addition to pathways related to the anticancer effects of As2O3, several neurological pathways were identified. As arsenic is well-known to exert neurotoxicity, these pathways might account for neurological side effects. In conclusion, the activity of As2O3 is not restricted to acute promyelocytic leukemia. In addition to PML/RARA, numerous other genes belonging to diverse functional classes may also contribute to its cytotoxicity. Network pharmacology is suited to unravel the multifactorial modes of action of As2O3.

Zerumbone Suppresses Angiogenesis in HepG2 Cells through Inhibition of Matrix Metalloproteinase-9, Vascular Endothelial Growth Factor, and Vascular Endothelial Growth Factor Receptor Expressions

Context

Due to increase in the number of patients with impaired immunity, the incidence of liver cancer has increased considerably.

Aims

The aim of this study is the investigation the in vitro anticancer effect of zerumbone (ZER) on hepatocellular carcinoma (HCC).

Materials and Methods

The anticancer mechanism of ZER was determined by the rat aortic ring, human umbilical vein endothelial cells (HUVECs) proliferation, chorioallantoic membrane, cell migration, and proliferation inhibition assays.

Results

Our results showed that ZER reduced tube formation by HUVECs effectively inhibits new blood vessel and tissue matrix formation. Western blot analysis revealed that ZER significantly (P < 0.05) decreased expression of molecular effectors of angiogenesis, the matrix metalloproteinase-9, vascular endothelial growth factor (VEGF), and VEGF receptor proteins. We found that ZER inhibited the proliferation and suppressed migration of HepG2 cell in dose-dependent manner.

Statistical Analysis Used

Statistical analyses were performed according to the Statistical Package for Social Science (SPSS) version 17.0. The data were expressed as the mean ± standard deviation and analyzed using a one-way analysis of variance. A P < 0.05 was considered statistically significant.

Conclusion

The study for the first time showed that ZER is an inhibitor angiogenesis, tumor growth, and spread, which is suggested to be the mechanisms for its anti-HCC effect.

SUMMARY

Tumor angiogenesis has currently become an important research area for the control of cancer growth and metastasis. The current study determined the effect of zerumbone on factors associated with angiogenesis that occurs in tumor formation. Abbreviations used: ZER: Zerumbone, MMP-9: Matrix metalloproteinase-9, VEGF: Vascular endothelial growth factor, VEGFR: Vascular endothelial growth factor receptor, HUVECs: Human umbilical vein endothelial cells, HCC: Hepatocellular carcinoma, HIFCS: Heat inactivated fetal calf serum, DMSO: Dimethyl sulfoxide, EDTA: Ethyldiaminetetraacetic acid, Ig: Immunoglobulin, CAM: Chorioallantoic membrane, HRP: Horseradish peroxidase, NIH: National Institutes of Health, MTT: Microtetrazolium, SPSS: Statistical Package for Social Science.

Depletion of SIRT6 enzymatic activity increases acute myeloid leukemia cells' vulnerability to DNA-damaging agents

Genomic instability plays a pathological role in various malignancies, including acute myeloid leukemia (AML), and thus represents a potential therapeutic target. Recent studies demonstrate that SIRT6, a NAD⁺-dependent nuclear deacetylase, functions as genome-guardian by preserving DNA integrity in different tumor cells. Here, we demonstrate that also CD34⁺ blasts from AML patients show ongoing DNA damage and SIRT6 overexpression. Indeed, we identified a poor-prognostic subset of patients, with widespread instability, which relies on SIRT6 to compensate for DNA-replication stress. As a result, SIRT6 depletion compromises the ability of leukemia cells to repair DNA double-strand breaks that, in turn, increases their sensitivity to daunorubicin and Ara-C, both in vitro and in vivo In contrast, low SIRT6 levels observed in normal CD34⁺ hematopoietic progenitors explain their weaker sensitivity to genotoxic stress. Intriguingly, we have identified DNA-PKcs and CtIP deacetylation as crucial for SIRT6-mediated DNA repair. Together, our data suggest that inactivation of SIRT6 in leukemia cells leads to disruption of DNA-repair mechanisms, genomic instability and aggressive AML. This synthetic lethal approach, enhancing DNA damage while concomitantly blocking repair responses, provides the rationale for the clinical evaluation of SIRT6 modulators in the treatment of leukemia.

Molecular approaches to the diagnosis and monitoring of production diseases in pigs

Production disease in pigs is caused by a variety of different pathogens, mainly enteric and respiratory and can result in significant economic loss. Other factors such as stress, poor husbandry and nutrition can also contribute to an animal's susceptibility to disease. Molecular biomarkers of production disease could be of immense value by improving diagnosis and risk analysis to determine best practice with an impact on increased economic output and animal welfare. In addition to the use of multiplex PCR or microarrays to detect individual or mixed pathogens during infection, these technologies can also be used to monitor the host response to infection via gene expression. The patterns of gene expression associated with cellular damage or initiation of the early immune response may indicate the type of pathology and, by extension the types of pathogen involved. Molecular methods can therefore be used to monitor both the presence of a pathogen and the host response to it during production disease. The field of biomarker discovery and implementation is expanding as technologies such as microarrays and next generation sequencing become more common. Whilst a large number of studies have been carried out in human medicine, further work is needed to identify molecular biomarkers in veterinary medicine and in particular those associated with production disease in the pig industry. The pig transcriptome is highly complex and still not fully understood. Further gene expression studies are needed to identify molecular biomarkers which may have predictive value in identifying the environmental, nutritional and other risk factors which are associated with production diseases in pigs.

Alternative splicing in cancers: From aberrant regulation to new therapeutics

Alternative splicing is one of the most common mechanisms for gene regulation in humans, and plays a vital role to increase the complexity of functional proteins. In this article, we seek to provide a general review on the relationships between alternative splicing and tumorigenesis. We briefly introduce the basic rules for regulation of alternative splicing, and discuss recent advances on dynamic regulation of alternative splicing in cancers by highlighting the roles of a variety of RNA splicing factors in tumorigenesis. We further discuss several important questions regarding the splicing of long noncoding RNAs and back-splicing of circular RNAs in cancers. Finally, we discuss the current technologies that can be used to manipulate alternative splicing and serve as potential cancer treatment.

Targeting epidermal growth factor receptor co-dependent signaling pathways in glioblastoma

The epidermal growth factor receptor (EGFR) is a transmembrane receptor tyrosine kinase (RTK) that is critical for normal development and function. EGFR is also amplified or mutated in a variety of cancers including in nearly 60% of cases of the highly lethal brain cancer glioblastoma (GBM). EGFR amplification and mutation reprogram cellular metabolism and broadly alter gene transcription to drive tumor formation and progression, rendering EGFR as a compelling drug target. To date, brain tumor patients have yet to benefit from anti-EGFR therapy due in part to an inability to achieve sufficient intratumoral drug levels in the brain, cultivating adaptive mechanisms of resistance. Here, we review an alternative set of strategies for targeting EGFR-amplified GBMs, based on identifying and targeting tumor co-dependencies shaped both by aberrant EGFR signaling and the brain's unique biochemical environment. These approaches may include highly brain-penetrant drugs from non-cancer pipelines, expanding the pharmacopeia and providing promising new treatments. We review the molecular underpinnings of EGFR-activated co-dependencies in the brain and the promising new treatments based on this strategy. WIREs Syst Biol Med 2018, 10:e1398. doi: 10.1002/wsbm.1398 This article is categorized under: Biological Mechanisms > Cell Signaling Laboratory Methods and Technologies > Genetic/Genomic Methods Translational, Genomic, and Systems Medicine > Translational Medicine.

The miR-486-5p plays a causative role in prostate cancer through negative regulation of multiple tumor suppressor pathways

MicroRNAs have been broadly implicated in cancer, but their exact function and mechanism in carcinogenesis remain poorly understood. Aberrant miR-486-5p expression is frequently found in human cancers. Here we showed a significant overexpression of miR-486-5p in prostate cancer compared with that in normal tissue and cells, and we proposed that altered expression of miR-486-5p in the prostate contributed to prostate cancer. Firstly, miR-486-5p inhibition expression reduced prostate cancercell proliferation, migration, and colonization in vitro and prostate tumor development in vivo. Moreover, we integrated RNA sequencing and target genes prediction, and systemically identified miR-486-5p candidate target genes. We conducted an experiment verifying that miR-486-5p drives tumorigenesis by directly targeting multiple negative regulators, which were involved in PTEN/PI3K/Akt, FOXO, and TGF-b/Smad2 signaling. Finally, we demonstrated that hypoxia-inducible factor-1a and TCF-12 are located at the miR-486-5p promoter, which stimulates the transcription of miR-486-5p itself. Collectively, our findings unveil miR-486-5p as a powerful prostate cancer driver that coordinates the activation of multiple oncogenic pathways and demonstrates some stimulators, which mediate the miR-486-5p signaling pathway and may be targeted for therapy.

NetNorM: Capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis

Genome-wide somatic mutation profiles of tumours can now be assessed efficiently and promise to move precision medicine forward. Statistical analysis of mutation profiles is however challenging due to the low frequency of most mutations, the varying mutation rates across tumours, and the presence of a majority of passenger events that hide the contribution of driver events. Here we propose a method, NetNorM, to represent whole-exome somatic mutation data in a form that enhances cancer-relevant information using a gene network as background knowledge. We evaluate its relevance for two tasks: survival prediction and unsupervised patient stratification. Using data from 8 cancer types from The Cancer Genome Atlas (TCGA), we show that it improves over the raw binary mutation data and network diffusion for these two tasks. In doing so, we also provide a thorough assessment of somatic mutations prognostic power which has been overlooked by previous studies because of the sparse and binary nature of mutations.

Molecular Signature for Lymphatic Invasion Associated with Survival of Epithelial Ovarian Cancer

Purpose

We aimed to develop molecular classifier that can predict lymphatic invasion and their clinical significance in epithelial ovarian cancer (EOC) patients.

Materials and Methods

We analyzed gene expression (mRNA, methylated DNA) in data from The Cancer Genome Atlas. To identify molecular signatures for lymphatic invasion, we found differentially expressed genes. The performance of classifier was validated by receiver operating characteristics analysis, logistic regression, linear discriminant analysis (LDA), and support vector machine (SVM). We assessed prognostic role of classifier using random survival forest (RSF) model and pathway deregulation score (PDS). For external validation,we analyzed microarray data from 26 EOC samples of Samsung Medical Center and curatedOvarianData database.

Results

We identified 21 mRNAs, and seven methylated DNAs from primary EOC tissues that predicted lymphatic invasion and created prognostic models. The classifier predicted lymphatic invasion well, which was validated by logistic regression, LDA, and SVM algorithm (C-index of 0.90, 0.71, and 0.74 for mRNA and C-index of 0.64, 0.68, and 0.69 for DNA methylation). Using RSF model, incorporating molecular data with clinical variables improved prediction of progression-free survival compared with using only clinical variables (p < 0.001 and p=0.008). Similarly, PDS enabled us to classify patients into high-risk and low-risk group, which resulted in survival difference in mRNA profiles (log-rank p-value=0.011). In external validation, gene signature was well correlated with prediction of lymphatic invasion and patients’ survival.

Conclusion

Molecular signature model predicting lymphatic invasion was well performed and also associated with survival of EOC patients.

Modulating Effect of Hypnea musciformis (Red Seaweed) on Lipid Peroxidation, Antioxidants and Biotransforming Enzymes in 7,12-Dimethylbenz (a) Anthracene Induced Mammary Carcinogenesis in Experimental Animals

Background:

Breast cancer is the second most widespread diagnosed cancer and second leading cause of cancer death in women.

Objective:

The present work was carried out to evaluate the chemo preventive potential of Hypnea musciformis (ethanol extract) seaweed on oxidative stress markers, bio transforming enzymes, incidence of tumors, and pathological observation in 7,12-dimethylbenzanthracene (DMBA) exposed experimental mammary carcinogenesis.

Materials and Methods:

Female Sprague–Dawley rats were randomly divided into four groups. Rats in the group 1 served as control. Rats in the group 2 and 3 received a single subcutaneous injection of DMBA (25 mg/kg body weight (b.w)) in the mammary gland to develop mammary carcinoma. In addition, group 3 rats were orally administrated with 200 mg/kg between of H. musciformis along with DMBA injection and group 4 rats received ethanolic extract of H. musciformis every day orally (200 mg/kg b.w) throughout the experimental period of 16 weeks.

Results:

Our results revealed that treatment with H. musciformis ethanolic extract to DMBA treated rats significantly reduced the incidence of tumor and tumor volume as compared to DMBA alone treated rats. Moreover, our results showed imbalance in the activities/levels of lipid peroxidation by products, antioxidant enzymes, and bio transforming phase I and II enzymes in the circulation, liver and mammary tissues of DMBA treated rats which were significantly modulated to near normal on treatment with ethanolic extract of H. musciformis. All these alterations were supported by histochemical findings.

Conclusion:

The results obtained from this study suggest that chemo preventive potential of H. musciformis ethanol extract is probably due to their free radicals quenching effect and modulating potential of bio transforming enzymes during DMBA exposed experimental mammary carcinogenesis.

SUMMARY

DMBA is a source of well-established site specific carcinogen

Hypnea musciformis act as a free radical quencher

Hypnea musciformis has a definite chemo preventive efficacy in experimental rats

H. musciformis is a resource of prooxidant/antioxidant balance and also its anti-proliferative effects

H. musciformis has a detoxificant in the mammary carcinoma.

Abbreviations Used: BRCA1: Breast Cancer Gene 1; BRCA2: Breast Cancer Gene 1; CYP: Cytochrome P450; DMBA: 7,12-Dimethylbenzanthracene; DMSO: Dimethyl sulfoxide; H2O2: Hydrogen peroxides; LPO: Lipid peroxidation; PAH: Polycyclic aromatic hydrocarbon; ROS: Reactive oxygen species; TBARS: Thiobarbituric acid reactive substances; GSSG: Oxidized glutathione.

A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival

Cancer survival prediction is an active area of research that can help prevent unnecessary therapies and improve patient's quality of life. Gene expression profiling is being widely used in cancer studies to discover informative biomarkers that aid predict different clinical endpoint prediction. We use multiple modalities of data derived from RNA deep-sequencing (RNA-seq) to predict survival of cancer patients. Despite the wealth of information available in expression profiles of cancer tumors, fulfilling the aforementioned objective remains a big challenge, for the most part, due to the paucity of data samples compared to the high dimension of the expression profiles. As such, analysis of transcriptomic data modalities calls for state-of-the-art big-data analytics techniques that can maximally use all the available data to discover the relevant information hidden within a significant amount of noise. In this paper, we propose a pipeline that predicts cancer patients' survival by exploiting the structure of the input (manifold learning) and by leveraging the unlabeled samples using Laplacian support vector machines, a graph-based semi supervised learning (GSSL) paradigm. We show that under certain circumstances, no single modality per se will result in the best accuracy and by fusing different models together via a stacked generalization strategy, we may boost the accuracy synergistically. We apply our approach to two cancer datasets and present promising results. We maintain that a similar pipeline can be used for predictive tasks where labeled samples are expensive to acquire.

Identifying Cancer Driver Genes Using Replication-Incompetent Retroviral Vectors

Identifying novel genes that drive tumor metastasis and drug resistance has significant potential to improve patient outcomes. High-throughput sequencing approaches have identified cancer genes, but distinguishing driver genes from passengers remains challenging. Insertional mutagenesis screens using replication-incompetent retroviral vectors have emerged as a powerful tool to identify cancer genes. Unlike replicating retroviruses and transposons, replication-incompetent retroviral vectors lack additional mutagenesis events that can complicate the identification of driver mutations from passenger mutations. They can also be used for almost any human cancer due to the broad tropism of the vectors. Replication-incompetent retroviral vectors have the ability to dysregulate nearby cancer genes via several mechanisms including enhancer-mediated activation of gene promoters. The integrated provirus acts as a unique molecular tag for nearby candidate driver genes which can be rapidly identified using well established methods that utilize next generation sequencing and bioinformatics programs. Recently, retroviral vector screens have been used to efficiently identify candidate driver genes in prostate, breast, liver and pancreatic cancers. Validated driver genes can be potential therapeutic targets and biomarkers. In this review, we describe the emergence of retroviral insertional mutagenesis screens using replication-incompetent retroviral vectors as a novel tool to identify cancer driver genes in different cancer types.

Aberrant DNA methylations in chondrosarcoma

Chondrosarcoma (CS) is the second most common primary malignant bone tumor. Unlike other bone tumors, CS is highly resistant to conventional chemotherapy and radiotherapy, thus resulting in poor patient outcomes. There is an urgent need to establish alternative therapies for CS. However, the etiology and pathogenesis of CS still remain elusive. Recently, DNA methylation-associated epigenetic changes have been found to play a pivotal role in the initiation and development of human cancers, including CS, by regulating target gene expression in different cellular pathways. Elucidating the mechanisms of DNA methylation alteration may provide biomarkers for diagnosis and prognosis, as well as novel treatment options for CS. We have conducted a critical review to summarize the evidence regarding aberrant DNA methylation patterns as diagnostic biomarkers, predictors of progression and potential treatment strategies in CS.

AZD9150, a next-generation antisense oligonucleotide inhibitor of STAT3 with early evidence of clinical activity in lymphoma and lung cancer

Next-generation sequencing technologies have greatly expanded our understanding of cancer genetics. Antisense technology is an attractive platform with the potential to translate these advances into improved cancer therapeutics, because antisense oligonucleotide (ASO) inhibitors can be designed on the basis of gene sequence information alone. Recent human clinical data have demonstrated the potent activity of systemically administered ASOs targeted to genes expressed in the liver. We describe the preclinical activity and initial clinical evaluation of a class of ASOs containing constrained ethyl modifications for targeting the gene encoding the transcription factor STAT3, a notoriously difficult protein to inhibit therapeutically. Systemic delivery of the unformulated ASO, AZD9150, decreased STAT3 expression in a broad range of preclinical cancer models and showed antitumor activity in lymphoma and lung cancer models. AZD9150 preclinical activity translated into single-agent antitumor activity in patients with highly treatment-refractory lymphoma and non-small cell lung cancer in a phase 1 dose-escalation study.

Omics Profiling in Precision Oncology

Cancer causes significant morbidity and mortality worldwide, and is the area most targeted in precision medicine. Recent development of high-throughput methods enables detailed omics analysis of the molecular mechanisms underpinning tumor biology. These studies have identified clinically actionable mutations, gene and protein expression patterns associated with prognosis, and provided further insights into the molecular mechanisms indicative of cancer biology and new therapeutics strategies such as immunotherapy. In this review, we summarize the techniques used for tumor omics analysis, recapitulate the key findings in cancer omics studies, and point to areas requiring further research on precision oncology.

X-linked FHL1 as a novel therapeutic target for head and neck squamous cell carcinoma

To identify X-linked novel tumor suppressors could provide novel insights to improve prognostic prediction and therapeutic strategy for some cancers. Using bioinformatics and Venn analysis of gene transcriptional profiling, we identified downregulation of X-linked four-and-a-half LIM domains protein 1 (FHL1) gene in head and neck squamous cell carcinoma (HNSCC). FHL1 functions were investigated and confirmed in vitro and in vivo. FHL1 downregulated mechanisms were analyzed in HNSCCs by using methylation specific PCR, bisulfate-based sequencing, 5-Aza-dC treatment and chromatin immunoprecipitation assays. Two independent HNSCC cohorts (the training cohort n = 105 and the validation cohort n = 101) were enrolled to evaluate clinical implications of FHL1 expression by using real-time PCR or immunohistochemistry. FHL1 mRNA and protein expressions were frequently decreased in HNSCCs. FHL1 overexpression or depletion gave rise to suppress or promote cell growth through Cyclin D1, Cyclin E and p27 dysregulations. Abundant occupy of EZH2 or H3K27Me3 was observed in FHL1 promoter except for DNA hypermethylation. Reduced FHL1 mRNA expression was notably associated with poor differentiation (p = 0.020). Multivariate analysis demonstrated FHL1 mRNA expression was identified as independent prognostic predictors of overall survival (OS) (p = 0.036; HR 0.520; Cl, 0.283-0.958) and disease-free survival (DFS) (p = 0.041; HR 0.527; Cl, 0.284-0.975), which was validated by another independent cohort (p = 0.021; HR 0.404; Cl, 0.187-0.871 for OS; p = 0.011; HR 0.407; Cl, 0.203-0.815 for DFS). These results suggest epigenetic silencing of X-linked FHL1 may have an important role in adjuvant therapeutic intervention of HNSCCs and is an independent prognostic factor in patients with HNSCCs.

Registered report: Diverse somatic mutation patterns and pathway alterations in human cancers

The Reproducibility Project: Cancer Biology seeks to address growing concerns about reproducibility in scientific research by conducting replications of selected experiments from a number of high-profile papers in the field of cancer biology. The papers, which were published between 2010 and 2012, were selected on the basis of citations and Altmetric scores (Errington et al., 2014). This Registered Report describes the proposed replication plan of key experiments from "Diverse somatic mutation patterns and pathway alterations in human cancers" by Kan and colleagues published in Nature in 2010 (Kan et al., 2010). The experiments to be replicated are those reported in Figures 3D-F and 4C-F. Kan and colleagues utilized mismatch repair detection (MRD) technology to identify somatic mutations in primary human tumor samples and identified a previously uncharacterized arginine 243 to histidine (R243H) mutation in the G-protein α subunit GNAO1 in breast carcinoma tissue. In Figures 3D-F, Kan and colleagues demonstrated that stable expression of mutant GNAO1(R243D) conferred a significant growth advantage in human mammary epithelial cells, confirming the oncogenic potential of this mutation. Similarly, expression of variants with somatic mutations in MAP2K4, a JNK pathway kinase (shown in Figures 4C-E) resulted in a significant increase in anchorage-independent growth. Interestingly, these mutants exhibited reduced kinase activity compared to wild type MAP2K4, indicating these mutations impose a dominant-negative influence to promote growth (Figure 4F). The Reproducibility Project: Cancer Biology is a collaboration between the Center for Open Science and Science Exchange and the results of the replications will be published in eLife.

Capture-based next-generation sequencing reveals multiple actionable mutations in cancer patients failed in traditional testing

BACKGROUND

Targeted therapies including monoclonal antibodies and small molecule inhibitors have dramatically changed the treatment of cancer over past 10 years. Their therapeutic advantages are more tumor specific and with less side effects. For precisely tailoring available targeted therapies to each individual or a subset of cancer patients, next-generation sequencing (NGS) has been utilized as a promising diagnosis tool with its advantages of accuracy, sensitivity, and high throughput.

METHODS

We developed and validated a NGS-based cancer genomic diagnosis targeting 115 prognosis and therapeutics relevant genes on multiple specimen including blood, tumor tissue, and body fluid from 10 patients with different cancer types. The sequencing data was then analyzed by the clinical-applicable analytical pipelines developed in house.

RESULTS

We have assessed analytical sensitivity, specificity, and accuracy of the NGS-based molecular diagnosis. Also, our developed analytical pipelines were capable of detecting base substitutions, indels, and gene copy number variations (CNVs). For instance, several actionable mutations of EGFR,PIK3CA,TP53, and KRAS have been detected for indicating drug susceptibility and resistance in the cases of lung cancer.

CONCLUSION

Our study has shown that NGS-based molecular diagnosis is more sensitive and comprehensive to detect genomic alterations in cancer, and supports a direct clinical use for guiding targeted therapy.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies.

Patient-centric trials for therapeutic development in precision oncology

An enhanced understanding of the molecular pathology of disease gained from genomic studies is facilitating the development of treatments that target discrete molecular subclasses of tumours. Considerable associated challenges include how to advance and implement targeted drug-development strategies. Precision medicine centres on delivering the most appropriate therapy to a patient on the basis of clinical and molecular features of their disease. The development of therapeutic agents that target molecular mechanisms is driving innovation in clinical-trial strategies. Although progress has been made, modifications to existing core paradigms in oncology drug development will be required to realize fully the promise of precision medicine.

Organocatalytic removal of formaldehyde adducts from RNA and DNA bases

Formaldehyde is universally employed to fix tissue specimens, where it forms hemiaminal and aminal adducts with biomolecules, hindering the ability to retrieve molecular information. Common methods for removing these adducts involve extended heating, which can cause extensive degradation of nucleic acids, particularly RNA. Here we show that water-soluble bifunctional catalysts (anthranilates and phosphanilates) speed the reversal of formaldehyde adducts of mononucleotides over standard buffers. Studies with formaldehyde-treated RNA oligonucleotides show that the catalysts enhance adduct removal, restoring unmodified RNA at 37 °C even when extensively modified, and avoiding high temperatures that promote RNA degradation. Experiments with formalin-fixed, paraffin-embedded cell samples show that the catalysis is compatible with common RNA extraction protocols, with detectable RNA yields increased by 1.5–2.4 fold using a catalyst under optimized conditions, and by 7–25 fold compared to a commercial kit. Such catalytic strategies show promise for general use in reversing formaldehyde adducts in clinical specimens.

Role of miRNA in cancer diagnosis, prognosis, therapy and regulation of its expression by Epstein-Barr virus and human papillomaviruses: With special reference to oral cancer

MicroRNAs (miRNAs) belong to class of small non-coding RNAs that regulate numerous biological processes by targeting broad set of messenger RNAs. Research on miRNA-based biomarkers has witnessed phenomenal growth, owing to non-invasive nature of miRNA based screening assays and their sensitivity and specificity in detecting cancers. Their discovery in humans in 2000 has led to an explosion in research in terms of their role as biomarker, therapeutic target and trying to elucidate their function. This review aims to summarize the function of microRNAs as well as to examine how dysregulation at any step in their biogenesis or functional pathway can play a role in development of cancer, together with its possible involvement in oral cancer. Overexpression of oncogenic miRNA may reduce protein products of tumor-suppressor genes but loss of tumor-suppressor miRNA expression may cause elevated levels of oncogenic protein. One or both of these alterations could represent new targets for cancer diagnosis and treatment in future. Many researchers have focused on genetic and epigenetic alterations in OSCC cells. The genetic susceptibility, endemic environment factors, and Epstein-Barr virus (EBV) infection are believed to be the major etiologic factors of OSCC. Once metastasis occurs, prognosis is very poor. It is urgently needed to develop biomarkers for early clinical diagnosis/prognosis, and novel effective therapies for oral carcinoma. High-risk HPV infection leads to aberrant expression of cellular oncogenic and tumor suppressive miRNAs. The emergence of miRNA knowledge, and its potential interactive action with such alterations, therefore creates new understanding of cell transformation.