Cancer genomics: from discovery science to personalized medicine

Prediction-Oriented Marker Selection (PROMISE): With Application to High-Dimensional Regression

In personalized medicine, biomarkers are used to select therapies with the highest likelihood of success based on an individual patient's biomarker/genomic profile. Two goals are to choose important biomarkers that accurately predict treatment outcomes and to cull unimportant biomarkers to reduce the cost of biological and clinical verifications. These goals are challenging due to the high dimensionality of genomic data. Variable selection methods based on penalized regression (e.g., the lasso and elastic net) have yielded promising results. However, selecting the right amount of penalization is critical to simultaneously achieving these two goals. Standard approaches based on cross-validation (CV) typically provide high prediction accuracy with high true positive rates but at the cost of too many false positives. Alternatively, stability selection (SS) controls the number of false positives, but at the cost of yielding too few true positives. To circumvent these issues, we propose prediction-oriented marker selection (PROMISE), which combines SS with CV to conflate the advantages of both methods. Our application of PROMISE with the lasso and elastic net in data analysis shows that, compared to CV, PROMISE produces sparse solutions, few false positives, and small type I + type II error, and maintains good prediction accuracy, with a marginal decrease in the true positive rates. Compared to SS, PROMISE offers better prediction accuracy and true positive rates. In summary, PROMISE can be applied in many fields to select regularization parameters when the goals are to minimize false positives and maximize prediction accuracy.

Authentication and characterisation of a new oesophageal adenocarcinoma cell line: MFD-1

New biological tools are required to understand the functional significance of genetic events revealed by whole genome sequencing (WGS) studies in oesophageal adenocarcinoma (OAC). The MFD-1 cell line was isolated from a 55-year-old male with OAC without recombinant-DNA transformation. Somatic genetic variations from MFD-1, tumour, normal oesophagus, and leucocytes were analysed with SNP6. WGS was performed in tumour and leucocytes. RNAseq was performed in MFD-1, and two classic OAC cell lines FLO1 and OE33. Transposase-accessible chromatin sequencing (ATAC-seq) was performed in MFD-1, OE33, and non-neoplastic HET1A cells. Functional studies were performed. MFD-1 had a high SNP genotype concordance with matched germline/tumour. Parental tumour and MFD-1 carried four somatically acquired mutations in three recurrent mutated genes in OAC: TP53, ABCB1 and SEMA5A, not present in FLO-1 or OE33. MFD-1 displayed high expression of epithelial and glandular markers and a unique fingerprint of open chromatin. MFD-1 was tumorigenic in SCID mouse and proliferative and invasive in 3D cultures. The clinical utility of whole genome sequencing projects will be delivered using accurate model systems to develop molecular-phenotype therapeutics. We have described the first such system to arise from the oesophageal International Cancer Genome Consortium project.

Personalized in vitro cancer models to predict therapeutic response: Challenges and a framework for improvement

Personalized cancer therapy focuses on characterizing the relevant phenotypes of the patient, as well as the patient's tumor, to predict the most effective cancer therapy. Historically, these methods have not proven predictive in regards to predicting therapeutic response. Emerging culture platforms are designed to better recapitulate the in vivo environment, thus, there is renewed interest in integrating patient samples into in vitro cancer models to assess therapeutic response. Successful examples of translating in vitro response to clinical relevance are limited due to issues with patient sample acquisition, variability and culture. We will review traditional and emerging in vitro models for personalized medicine, focusing on the technologies, microenvironmental components, and readouts utilized. We will then offer our perspective on how to apply a framework derived from toxicology and ecology towards designing improved personalized in vitro models of cancer. The framework serves as a tool for identifying optimal readouts and culture conditions, thus maximizing the information gained from each patient sample.

ExSurv: A Web Resource for Prognostic Analyses of Exons Across Human Cancers Using Clinical Transcriptomes

Survival analysis in biomedical sciences is generally performed by correlating the levels of cellular components with patients' clinical features as a common practice in prognostic biomarker discovery. While the common and primary focus of such analysis in cancer genomics so far has been to identify the potential prognostic genes, alternative splicing - a posttranscriptional regulatory mechanism that affects the functional form of a protein due to inclusion or exclusion of individual exons giving rise to alternative protein products, has increasingly gained attention due to the prevalence of splicing aberrations in cancer transcriptomes. Hence, uncovering the potential prognostic exons can not only help in rationally designing exon-specific therapeutics but also increase specificity toward more personalized treatment options. To address this gap and to provide a platform for rational identification of prognostic exons from cancer transcriptomes, we developed ExSurv (https://exsurv.soic.iupui.edu), a web-based platform for predicting the survival contribution of all annotated exons in the human genome using RNA sequencing-based expression profiles for cancer samples from four cancer types available from The Cancer Genome Atlas. ExSurv enables users to search for a gene of interest and shows survival probabilities for all the exons associated with a gene and found to be significant at the chosen threshold. ExSurv also includes raw expression values across the cancer cohort as well as the survival plots for prognostic exons. Our analysis of the resulting prognostic exons across four cancer types revealed that most of the survival-associated exons are unique to a cancer type with few processes such as cell adhesion, carboxylic, fatty acid metabolism, and regulation of T-cell signaling common across cancer types, possibly suggesting significant differences in the posttranscriptional regulatory pathways contributing to prognosis.

Transcriptome sequencing uncovers a three-long noncoding RNA signature in predicting breast cancer survival

Long noncoding RNAs (lncRNAs) play a crucial role in tumorigenesis. The aim of this study is to identify lncRNA signature that can predict breast cancer patient survival. RNA expression data from 1064 patients were downloaded from The Cancer Genome Atlas project. Cox regression, Kaplan–Meier, and receiver operating characteristic (ROC) analyses were performed to construct a model for predicting the overall survival (OS) of patients and evaluate it. A model consisting of three lncRNA genes (CAT104, LINC01234, and STXBP5-AS1) was identified. The Kaplan–Meier analysis and ROC curves proved that the model could predict the prognostic survival with good sensitivity and specificity in both the validation set (AUC = 0.752, 95% confidence intervals (CI): 0.651–0.854) and the microarray dataset (AUC = 0.714, 95%CI: 0.615–0.814). Further study showed the three-lncRNA signature was not only pervasive in different breast cancer stages, subtypes and age groups, but also provides more accurate prognostic information than some widely known biomarkers. The results suggested that RNA-seq transcriptome profiling provides that the three-lncRNA signature is an independent prognostic biomarker, and have clinical significance. In addition, lncRNA, miRNA, and mRNA interaction network indicated lncRNAs may intervene in breast cancer pathogenesis by binding to miR-190b, acting as competing endogenous RNAs.

Amplification-Free Detection of Gene Fusions in Prostate Cancer Urinary Samples Using mRNA-Gold Affinity Interactions

A crucial issue in present-day prostate cancer (PCa) detection is the lack of specific biomarkers for accurately distinguishing between benign and malignant cancer forms. This is causing a high degree of overdiagnosis and overtreatment of otherwise clinically insignificant cases. As around half of all malignant PCa cases display a detectable gene fusion mutation between the TMPRSS2 promoter sequence and the ERG coding sequence (TMPRSS2:ERG) in urine, noninvasive screening of TMPRSS2:ERG mRNA in patient urine samples could improve the specificity of current PCa diagnosis. However, current gene fusion detection methodologies are largely dependent on RNA enzymatic amplification, which requires extensive sample manipulation, costly labels for detection, and is prone to bias/artifacts. Herein we introduce the first successful amplification-free electrochemical assay for direct detection of TMPRSS2:ERG mRNA in PCa urinary samples by selectively isolating and adsorbing TMPRSS2:ERG mRNA onto bare gold electrodes without requiring any surface modification. We demonstrated excellent limit-of-detection (10 cells) and specificity using PCa cell line models, and showcased clinical utility by accurately detecting TMPRSS2:ERG in a collection of 17 urinary samples obtained from PCa patients. Furthermore, these results were validated with the current gold standard reverse transcription (RT)-PCR approach with 100% concordance.

Identifying network-based biomarkers of complex diseases from high-throughput data

In this work, we review the main available computational methods of identifying biomarkers of complex diseases from high-throughput data. The emerging omics techniques provide powerful alternatives to measure thousands of molecules in cells in parallel manners. The generated genomic, transcriptomic, proteomic, metabolomic and phenomic data provide comprehensive molecular and cellular information for detecting critical signals served as biomarkers by classifying disease phenotypic states. Networks are often employed to organize these profiles in the identification of biomarkers to deal with complex diseases in diagnosis, prognosis and therapy as well as mechanism deciphering from systematic perspectives. Here, we summarize some representative network-based bioinformatics methods in order to highlight the importance of computational strategies in biomarker discovery.

Genetic heterogeneity in cholangiocarcinoma: a major challenge for targeted therapies

Cholangiocarcinoma (CC) encompasses a group of related but distinct malignancies whose lack of a stereotyped genetic signature makes challenging the identification of genomic landscape and the development of effective targeted therapies.

Accumulated evidences strongly suggest that the remarkable genetic heterogeneity of CC may be the result of a complex interplay among different causative factors, some shared by most human cancers while others typical of this malignancy.

Currently, considerable efforts are ongoing worldwide for the genetic characterization of CC, also using advanced technologies such as next-generation sequencing (NGS). Undoubtedly this technology could offer an unique opportunity to broaden our understanding on CC molecular pathogenesis. Despite this great potential, however, the high complexity in terms of factors potentially contributing to genetic variability in CC calls for a more cautionary application of NGS to this malignancy, in order to avoid possible biases and criticisms in the identification of candidate actionable targets. This approach is further justified by the urgent need to develop effective targeted therapies in this disease.

A multidisciplinary approach integrating genomic, functional and clinical studies is therefore mandatory to translate the results obtained by NGS into effective targeted therapies for this orphan disease.

JAG1 Is Associated with Poor Survival through Inducing Metastasis in Lung Cancer

JAG1 is a Notch ligand that plays a critical role in multiple signaling pathways. However, the functionality of JAG1 in non-small cell lung cancer (NSCLC) has not been investigated thoroughly. By comparison of gene transcripted RNA profiles in the cell line pair with differential invasion ability, we identified JAG1 as a potential metastasis enhancer in lung cancer. Ectopic expression of JAG1 on lung cancer cells enhanced cell migration and invasion as well as metastasis in vitro and in vivo. Conversely, knockdown of JAG1 with siRNA in highly invasive cancer cells led to the reduction of migration and invasion. In clinical analysis, JAG1 mRNA expression was higher in tumors than in adjacent normal tissues in 14 of 20 patients with squamous cell carcinoma (SCC). SCC patients with higher JAG1 transcription had poor overall survival than those with low-transcripted JAG1. Microarray analysis indicated that the enforced JAG1 transcription was associated with an elevated HSPA2 RNA transcription, which played a role in promoting cancer cell migration and invasion. In conclusion, this is the first study that demonstrated that JAG1 might act as a potential prognostic marker and JAG1/HSPA2 axis mediates lung cancer malignancy at least partly.

Microelectrode bioimpedance analysis distinguishes basal and claudin-low subtypes of triple negative breast cancer cells

Triple negative breast cancer (TNBC) is highly aggressive and has a poor prognosis when compared to other molecular subtypes. In particular, the claudin-low subtype of TNBC exhibits tumor-initiating/cancer stem cell like properties. Here, we seek to find new biomarkers to discriminate different forms of TNBC by characterizing their bioimpedance. A customized bioimpedance sensor with four identical branched microelectrodes with branch widths adjusted to accommodate spreading of individual cells was fabricated on silicon and pyrex/glass substrates. Cell analyses were performed on the silicon devices which showed somewhat improved inter-electrode and intra-device reliability. We performed detailed analysis of the bioimpedance spectra of four TNBC cell lines, comparing the peak magnitude, peak frequency and peak phase angle between claudin-low TNBC subtype represented by MDA-MB-231 and Hs578T with that of two basal cells types, the TNBC MDA-MB-468, and an immortalized non-malignant basal breast cell line, MCF-10A. The claudin-low TNBC cell lines showed significantly higher peak frequencies and peak phase angles than the properties might be useful in distinguishing the clinically significant claudin-low subtype of TNBC.

Cancer genomics: the challenge of drug accessibility

PURPOSE OF REVIEW

Although significant strides have been made in genome sequencing technology, target-drug matching remains challenging. This article highlights the difficulties associated with patients accessing targeted drugs based on genomic information, and some proposed solutions.

RECENT FINDINGS

Although cancers are increasingly stratified according to molecular subgroups, challenges remain in improving patient outcome based on drug-target matching. Before a drug-target match is even proposed, significant expertise is required of the clinician to interpret genomic information. Once a potential match is made, barriers remain for patients to access treatment via clinical trials, as approved agents on-label or off-label, or through expanded access programs. Solutions to improve drug accessibility are actively being investigated. Several prospective trials using molecular characterization as an entry to access target-drug matching are underway. For those unable to access target-drug matching on trial, proposals for a facilitated access program and registry have been suggested.

SUMMARY

Although improvements have been made in the drug development and approval timelines, drug accessibility based on molecular characterization remains problematic. However, with the emergence of novel trial designs, and efforts to enhance drug access outside of clinical trial settings, opportunities for drug-target matching are improving.

Mutation pattern is an influential factor on functional mutation rates in cancer

BACKGROUND

Mutation rates are consistently varied in cancer genome and play an important role in tumorigenesis, however, little has been known about their function potential and impact on the distribution of functional mutations. In this study, we investigated genomic features which affect mutation pattern and the function importance of mutation pattern in cancer.

METHODS

Somatic mutations of clear-cell renal cell carcinoma, liver cancer, lung cancer and melanoma and single nucleotide polymorphisms (SNPs) were intersected with 54 distinct genomic features. Somatic mutation and SNP densities were then computed for each feature type. We constructed 2856 1-Mb windows, in which each row (1-Mb window) contains somatic mutation, SNP densities and 54 feature vectors. Correlation analyses were conducted between somatic mutation, SNP densities and each feature vector. We also built two random forest models, namely somatic mutation model (CSM) and SNP model to predict somatic mutation and SNP densities on a 1-Kb scale. The relation of CSM and SNP scores was further analyzed with the distributions of deleterious coding variants predicted by SIFT and Mutation Assessor, non-coding functional variants evaluated with FunSeq 2 and GWAVA and disease-causing variants from HGMD and ClinVar databases.

RESULTS

We observed a wide range of genomic features which affect local mutation rates, such as replication time, transcription levels, histone marks and regulatory elements. Repressive histone marks, replication time and promoter contributed most to the CSM models, while, recombination rate and chromatin organizations were most important for the SNP model. We showed low mutated regions preferentially have higher densities of deleterious coding mutations, higher average scores of non-coding variants, higher fraction of functional regions and higher enrichment of disease-causing variants as compared to high mutated regions.

CONCLUSIONS

Somatic mutation densities vary largely across cancer genome, mutation frequency is a major indication of function and influence on the distribution of functional mutations in cancer.

Extracellular matrix structure

Extracellular matrix (ECM) is a non-cellular three-dimensional macromolecular network composed of collagens, proteoglycans/glycosaminoglycans, elastin, fibronectin, laminins, and several other glycoproteins. Matrix components bind each other as well as cell adhesion receptors forming a complex network into which cells reside in all tissues and organs. Cell surface receptors transduce signals into cells from ECM, which regulate diverse cellular functions, such as survival, growth, migration, and differentiation, and are vital for maintaining normal homeostasis. ECM is a highly dynamic structural network that continuously undergoes remodeling mediated by several matrix-degrading enzymes during normal and pathological conditions. Deregulation of ECM composition and structure is associated with the development and progression of several pathologic conditions. This article emphasizes in the complex ECM structure as to provide a better understanding of its dynamic structural and functional multipotency. Where relevant, the implication of the various families of ECM macromolecules in health and disease is also presented.

Integration of Multi-Modal Biomedical Data to Predict Cancer Grade and Patient Survival

The Big Data era in Biomedical research has resulted in large-cohort data repositories such as The Cancer Genome Atlas (TCGA). These repositories routinely contain hundreds of matched patient samples for genomic, proteomic, imaging, and clinical data modalities, enabling holistic and multi-modal integrative analysis of human disease. Using TCGA renal and ovarian cancer data, we conducted a novel investigation of multi-modal data integration by combining histopathological image and RNA-seq data. We compared the performances of two integrative prediction methods: majority vote and stacked generalization. Results indicate that integration of multiple data modalities improves prediction of cancer grade and outcome. Specifically, stacked generalization, a method that integrates multiple data modalities to produce a single prediction result, outperforms both single-data-modality prediction and majority vote. Moreover, stacked generalization reveals the contribution of each data modality (and specific features within each data modality) to the final prediction result and may provide biological insights to explain prediction performance.

A New Era of Low-Dose Radiation Epidemiology

The last decade has introduced a new era of epidemiologic studies of low-dose radiation facilitated by electronic record linkage and pooling of cohorts that allow for more direct and powerful assessments of cancer and other stochastic effects at doses below 100 mGy. Such studies have provided additional evidence regarding the risks of cancer, particularly leukemia, associated with lower-dose radiation exposures from medical, environmental, and occupational radiation sources, and have questioned the previous findings with regard to possible thresholds for cardiovascular disease and cataracts. Integrated analysis of next generation genomic and epigenetic sequencing of germline and somatic tissues could soon propel our understanding further regarding disease risk thresholds, radiosensitivity of population subgroups and individuals, and the mechanisms of radiation carcinogenesis. These advances in low-dose radiation epidemiology are critical to our understanding of chronic disease risks from the burgeoning use of newer and emerging medical imaging technologies, and the continued potential threat of nuclear power plant accidents or other radiological emergencies.

Serially Ordered Magnetization of Nanoclusters via Control of Various Transition Metal Dopants for the Multifractionation of Cells in Microfluidic Magnetophoresis Devices

A novel method (i.e., continuous magnetic cell separation in a microfluidic channel) is demonstrated to be capable of inducing multifractionation of mixed cell suspensions into multiple outlet fractions. Here, multicomponent cell separation is performed with three different distinguishable magnetic nanoclusters (MnFe2O4, Fe3O4, and CoFe2O4), which are tagged on A431 cells. Because of their mass magnetizations, which can be ideally altered by doping with magnetic atom compositions (Mn, Fe, and Co), the trajectories of cells with each magnetic nanocluster in a flow are shown to be distinct when dragged under the same external magnetic field; the rest of the magnetic characteristics of the nanoclusters are identically fixed. This proof of concept study, which utilizes the magnetization-controlled nanoclusters (NCs), suggests that precise and effective multifractionation is achievable with high-throughput and systematic accuracy for dynamic cell separation.

Identification of potential COPD genes based on multi-omics data at the functional level

A novel systematic approach MMMG (Methylation–MicroRNA–MRNA–GO) to identify potential COPD genes and their classifying performance evaluation.

Proteogenomic Analysis of Single Amino Acid Polymorphisms in Cancer Research

The integration of genomics and proteomics has led to the emergence of proteogenomics, a field of research successfully applied to the characterization of cancer samples. The diagnosis, prognosis and response to therapy of cancer patients will largely benefit from the identification of mutations present in their genome. The current state of the art of high throughput experiments for genome-wide detection of somatic mutations in cancer samples has allowed the development of projects such as the TCGA, in which hundreds of cancer genomes have been sequenced. This huge amount of data can be used to generate protein sequence databases in which each entry corresponds to a mutated peptide associated with certain cancer types. In this chapter, we describe a bioinformatics workflow for creating these databases and detecting mutated peptides in cancer samples from proteomic shotgun experiments. The performance of the proposed method has been evaluated using publicly available datasets from four cancer cell lines.

Microfluidic synthesis of QD-encoded PEGDA microspheres for suspension assay

A simple microfluidic device is designed to generate monodispersed QD-encoded PEGDA microbeads. PEGDA/PDA composite microspheres are prepared to easily couple protein on their surface. A sandwich immunoassay of rabbit IgG is performed to indicate that PDA on the bead surface facilitates efficient attachment of biomacromolecules.

CDK7-dependent transcriptional addiction in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a highly aggressive form of breast cancer that exhibits extremely high levels of genetic complexity and yet a relatively uniform transcriptional program. We postulate that TNBC might be highly dependent on uninterrupted transcription of a key set of genes within this gene expression program and might therefore be exceptionally sensitive to inhibitors of transcription. Utilizing kinase inhibitors and CRISPR/Cas9-mediated gene editing, we show here that triple-negative but not hormone receptor-positive breast cancer cells are exceptionally dependent on CDK7, a transcriptional cyclin-dependent kinase. TNBC cells are unique in their dependence on this transcriptional CDK and suffer apoptotic cell death upon CDK7 inhibition. An "Achilles cluster" of TNBC-specific genes is especially sensitive to CDK7 inhibition and frequently associated with super-enhancers. We conclude that CDK7 mediates transcriptional addiction to a vital cluster of genes in TNBC and CDK7 inhibition may be a useful therapy for this challenging cancer.

Ontogenic growth as the root of fundamental differences between childhood and adult cancer

Cancer, the unregulated proliferation of cells, can occur at any age and may arise from almost all cell types. However, the incidence and types of cancer differ with age. Some cancers are predominantly observed in children, others are mostly restricted to older ages. Treatment strategies of some cancers are very successful and cure is common in childhood, while treatment of the same cancer type is much more challenging in adults. Here, we develop a stochastic model of stem cell proliferation that considers both tissue development and homeostasis and discuss the disturbance of such a system by mutations. Due to changes in population size, mutant fitness becomes context dependent and consequently the effects of mutations on the stem cell population can vary with age. We discuss different mutant phenotypes and show the age dependency of their expected abundances. Most importantly, fitness of particular mutations can change with age and advantageous mutations can become deleterious or vice versa. This perspective can explain unique properties of childhood disorders, for example, the frequently observed phenomenon of a self-limiting leukemia in newborns with trisomy 21, but also explains other puzzling observations such as the increased risk of leukemia in patients with bone marrow failure or chemotherapy induced myelodysplasia.

Preclinical mouse cancer models: a maze of opportunities and challenges

Significant advances have been made in developing novel therapeutics for cancer treatment, and targeted therapies have revolutionized the treatment of some cancers. Despite the promise, only about five percent of new cancer drugs are approved, and most fail due to lack of efficacy. The indication is that current preclinical methods are limited in predicting successful outcomes. Such failure exacts enormous cost, both financial and in the quality of human life. This Primer explores the current status, promise, and challenges of preclinical evaluation in advanced mouse cancer models and briefly addresses emerging models for early-stage preclinical development.

Mechanisms of esophageal adenocarcinoma formation and approaches to chemopreventive intervention

The incidence of esophageal adenocarcinoma (EAC), a debilitating and highly lethal malignancy, has risen dramatically over the past 40 years in the United States and other Western countries. To reverse this trend, EAC prevention and early detection efforts by clinicians, academic researchers and endoscope manufacturers have targeted Barrett's esophagus (BE), the widely accepted EAC precursor lesion. Data from surgical, endoscopic and pre-clinical investigations strongly support the malignant potential of BE. For patients with BE, the risk of developing EAC has been estimated at 11- to 125-fold greater than that of the individual at average risk. Nevertheless, screening for BE in symptomatic patients (ie, with symptoms of reflux) and surveillance in patients diagnosed with BE have not had a substantial impact on the incidence, morbidity or mortality of EAC; the overwhelming majority of EAC patients are diagnosed without a pre-operative diagnosis of BE. This article will discuss the current state of the science of esophageal adenocarcinoma prevention, including ideas about carcinogenesis and its underlying genomic and molecular level mechanisms, and suggest strategies for a systems approach to targeted preventive management.

Targeting apoptosis is the major battle field for killing cancers

Targeting apoptosis is one of the major strategies for cancer therapy. Essentially, most of the conventional cancer therapeutic drugs that are in the clinical use induce apoptosis and in part necrosis of malignant cells and therefore prevent cancer progression and metastasis. Although these cytotoxic anticancer drugs are important weapons for killing cancers, their toxic side effects limited their application. The molecularly targeted therapeutics that are based on the deeper understanding of the defects in the apoptotic signaling in cancers are emerging and have shown promising anticancer activity in selectively killing cancers but not normal cells. The examples of molecular targets that are under exploration for cancer therapy include the cell surface receptors such as TNFR family death receptors, the intrinsic Bcl-2 family members and some other intracellular molecules like p53, MDM2, IAP, and Smac. The advance in the high-throughput bio-technologies has greatly accelerated the progress of cancer drug discovery.

Systematic Prioritization of Druggable Mutations in ∼5000 Genomes Across 16 Cancer Types Using a Structural Genomics-based Approach

A massive amount of somatic mutations has been cataloged in large-scale projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium projects. The majority of the somatic mutations found in tumor genomes are neutral 'passenger' rather than damaging "driver" mutations. Now, understanding their biological consequences and prioritizing them for druggable targets are urgently needed. Thanks to the rapid advances in structural genomics technologies (e.g. X-ray), large-scale protein structural data has now been made available, providing critical information for deciphering functional roles of mutations in cancer and prioritizing those alterations that may mediate drug binding at the atom resolution and, as such, be druggable targets. We hypothesized that mutations at protein-ligand binding-site residues are likely to be druggable targets. Thus, to prioritize druggable mutations, we developed SGDriver, a structural genomics-based method incorporating the somatic missense mutations into protein-ligand binding-site residues using a Bayes inference statistical framework. We applied SGDriver to 746,631 missense mutations observed in 4997 tumor-normal pairs across 16 cancer types from The Cancer Genome Atlas. SGDriver detected 14,471 potential druggable mutations in 2091 proteins (including 1,516 recurrently mutated proteins) across 3558 cancer genomes (71.2%), and further identified 298 proteins harboring mutations that were significantly enriched at protein-ligand binding-site residues (adjusted p value < 0.05). The identified proteins are significantly enriched in both oncoproteins and tumor suppressors. The follow-up drug-target network analysis suggested 98 known and 126 repurposed druggable anticancer targets (e.g. SPOP and NR3C1). Furthermore, our integrative analysis indicated that 13% of patients might benefit from current targeted therapy, and this -proportion would increase to 31% when considering drug repositioning. This study provides a testable strategy for prioritizing druggable mutations in precision cancer medicine.

Reads meet rotamers: structural biology in the age of deep sequencing

Structure has traditionally been interrelated with sequence, usually in the framework of comparing sequences across species sharing a common fold. However, the nature of information within the sequence and structure databases is evolving, changing the type of comparisons possible. In particular, we now have a vast amount of personal genome sequences from human populations and a greater fraction of new structures contain interacting proteins within large complexes. Consequently, we have to recast our conception of sequence conservation and its relation to structure-for example, focusing more on selection within the human population. Moreover, within structural biology there is less emphasis on the discovery of novel folds and more on relating structures to networks of protein interactions. We cover this changing mindset here.

Microfluidics for cell-based high throughput screening platforms - A review

In the last decades, the basic techniques of microfluidics for the study of cells such as cell culture, cell separation, and cell lysis, have been well developed. Based on cell handling techniques, microfluidics has been widely applied in the field of PCR (Polymerase Chain Reaction), immunoassays, organ-on-chip, stem cell research, and analysis and identification of circulating tumor cells. As a major step in drug discovery, high-throughput screening allows rapid analysis of thousands of chemical, biochemical, genetic or pharmacological tests in parallel. In this review, we summarize the application of microfluidics in cell-based high throughput screening. The screening methods mentioned in this paper include approaches using the perfusion flow mode, the droplet mode, and the microarray mode. We also discuss the future development of microfluidic based high throughput screening platform for drug discovery.

Optimal Bayesian adaptive trials when treatment efficacy depends on biomarkers

Clinical biomarkers play an important role in precision medicine and are now extensively used in clinical trials, particularly in cancer. A response adaptive trial design enables researchers to use treatment results about earlier patients to aid in treatment decisions of later patients. Optimal adaptive trial designs have been developed without consideration of biomarkers. In this article, we describe the mathematical steps for computing optimal biomarker-integrated adaptive trial designs. These designs maximize the expected trial utility given any pre-specified utility function, though we focus here on maximizing patient responses within a given patient horizon. We describe the performance of the optimal design in different scenarios. We compare it to Bayesian Adaptive Randomization (BAR), which is emerging as a practical approach to develop adaptive trials. The difference in expected utility between BAR and optimal designs is smallest when the biomarker subgroups are highly imbalanced. We also compare BAR, a frequentist play-the-winner rule with integrated biomarkers and a marker-stratified balanced randomization design (BR). We show that, in contrasting two treatments, BR achieves a nearly optimal expected utility when the patient horizon is relatively large. Our work provides novel theoretical solution, as well as an absolute benchmark for the evaluation of trial designs in personalized medicine.

Perspectives on research activity in the USA on Cancer Precision Medicine

The National Cancer Institute-Molecular Analysis for Therapy Choice trial is a clinical trial that will analyze various genetic statuses of patients' tumors to determine whether they contain abnormalities which can be a target for an available drug. National Cancer Institute-Molecular Analysis for Therapy Choice seeks to determine whether improved outcomes can be achieved when cancer treatments are personalized based on molecular abnormalities found in individual patients. As a master protocol, or basket trial, National Cancer Institute-Molecular Analysis for Therapy Choice can add or remove treatments as indicated over the duration of the study. Each treatment will be used in a unique arm, or sub-study, of the trial. The trial initially has 10 arms, each of which will enroll patients to a specific molecularly targeted treatment. It is ultimately anticipated that 20-25 drugs or combination treatments will be tested. To be eligible for the study, participants must have an advanced solid tumor or lymphoma that is no longer responding or never responded to the standard therapy. National Cancer Institute-Molecular Analysis for Therapy Choice investigators plan to obtain tumor biopsy specimens from as many as 3000 patients initially. To identify multiple genetic abnormalities that may respond to the targeted drugs selected for the trial, next-generation deoxyribonucleic acid and ribonucleic acid sequencing will be done in the genetic testing laboratories, analyzing for >4000 different variants across 143 genes. The drugs included in the trial have all either been approved by the US Food and Drug Administration for another cancer indication or are still being tested in other clinical trials, but have shown some clinical levels of evidence against tumors with a particular genetic alteration.

The La-Related Proteins, a Family with Connections to Cancer

The evolutionarily-conserved La-related protein (LARP) family currently comprises Genuine La, LARP1, LARP1b, LARP4, LARP4b, LARP6 and LARP7. Emerging evidence suggests each LARP has a distinct role in transcription and/or mRNA translation that is attributable to subtle sequence variations within their La modules and specific C-terminal domains. As emerging research uncovers the function of each LARP, it is evident that La, LARP1, LARP6, LARP7 and possibly LARP4a and 4b are dysregulated in cancer. Of these, LARP1 is the first to be demonstrated to drive oncogenesis. Here, we review the role of each LARP and the evidence linking it to malignancy. We discuss a future strategy of targeting members of this protein family as cancer therapy.

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.

Predictive value of ex vivo biodynamic imaging in determining response to chemotherapy in dogs with spontaneous non-Hodgkin's lymphomas: a preliminary study

Biodynamic imaging (BDI) is a novel phenotypic cancer profiling technology which optically characterizes changes in subcellular motion within living tumor tissue samples in response to ex vivo treatment with cancer chemotherapy drugs. The purpose of this preliminary study was to assess the ability of ex vivo BDI to predict in vivo clinical response to chemotherapy in ten dogs with naturally-occurring non-Hodgkin's lymphomas. Pre-treatment tumor biopsy samples were obtained from all dogs and treated ex vivo with doxorubicin (10 μM). BDI measured six dynamic biomarkers of subcellular motion from all biopsy samples at baseline and at regular intervals for 9 h following drug application. All dogs subsequently received doxorubicin to treat their lymphomas. Best overall response to and progression-free survival time following chemotherapy were recorded for all dogs. Receiver operating characteristic (ROC) curves were used to determine accuracy and identify possible cut-off values for the BDI-measured biomarkers which could accurately predict those dogs' cancers that would and would not respond to doxorubicin chemotherapy. One biomarker (designated 'MEM') showed 100% discriminative capability for predicting clinical response to doxorubicin (area under the ROC curve = 1.00, 95% CI 0.692-1.000), while other biomarkers also showed promising predictive capability. These preliminary findings suggest that ex vivo BDI can accurately predict treatment outcome following doxorubicin chemotherapy in a spontaneous animal cancer model, and is worthy of further investigation as a technology for personalized cancer medicine.

Cancer Genetics and Implications for Clinical Management

There is now compelling evidence that the molecular heterogeneity of cancer is associated with disparate phenotypes with variable outcomes and therapeutic responsiveness to therapy in histologically indistinguishable cancers. This diversity may explain why conventional clinical trial designs have mostly failed to show efficacy when patients are enrolled in an unselected fashion. Knowledge of the molecular phenotype has the potential to improve therapeutic selection and hence the early delivery of the optimal therapeutic regimen. Resolution of the challenges associated with a more stratified approach to health care will ensure more precise diagnostics and enhance therapeutic selection, which will improve overall outcomes.

Estrogenic endocrine disruptors: Molecular mechanisms of action

A comprehensive summary of more than 450 estrogenic chemicals including estrogenic endocrine disruptors is provided here to understand the complex and profound impact of estrogen action. First, estrogenic chemicals are categorized by structure as well as their applications, usage and effects. Second, estrogenic signaling is examined by the molecular mechanism based on the receptors, signaling pathways, crosstalk/bypassing and autocrine/paracrine/homeostatic networks involved in the signaling. Third, evaluation of estrogen action is discussed by focusing on the technologies and protocols of the assays for assessing estrogenicity. Understanding the molecular mechanisms of estrogen action is important to assess the action of endocrine disruptors and will be used for risk management based on pathway-based toxicity testing.

Applicability of HIN-1, MGMT and RASSF1A promoter methylation as biomarkers for detecting field cancerization in breast cancer

Introduction

It has been shown in some articles that genetic and epigenetic abnormalities cannot only be found in tumor tissues but also in adjacent regions that appear histologically normal. This phenomenon is metaphorically called field cancerization or field defect. Field cancerization is regarded as clinically significant because it is assumed to be an important factor in local recurrence of cancer. As the field showing these molecular abnormalities may not be removed completely by surgery, these changes might lead to neoplasms and subsequent transformation to a tumor. We aimed to investigate the applicability of the methylation status of six tumor suppressor genes as biomarkers for detecting field cancerization in breast cancer.

Methods

The promoter methylation status of CCND2, DAPK1, GSTP1, HIN-1, MGMT and RASSF1A was determined by methylation-sensitive high-resolution melting (MS-HRM) analysis. MS-HRM methods for CCND2, MGMT and RASSF1A were developed in-house, primer sequences for DAPK1, GSTP1 and HIN-1 have already been published. Biopsy samples were taken from tumor, tumor-adjacent and tumor-distant tissue from 17 breast cancer patients. Normal breast tissues of four healthy women served as controls.

Results

All MS-HRM methods proved to be very sensitive. LODs were in the range from 0.1 to 1.5 %, LOQs ranged from 0.3 to 5.3 %. A total of 94 %, 82 % and 65 % of the tumors showed methylation of RASSF1A, HIN-1 and MGMT promoters, respectively. The methylation status of these promoters was significantly lower in tumor-distant tissues than in tumor tissues. Tumor-adjacent tissues showed higher methylation status of RASSF1A, HIN-1 and MGMT promoters than tumor-distant tissues, indicating field cancerization. The methylation status of the HIN-1 promoter in tumor-adjacent tissues was found to correlate strongly with that in the corresponding tumors (r = 0.785, p < 0.001), but not with that in the corresponding tumor-distant tissues (r = 0.312, p = 0.239).

Conclusions

Among the gene promoters investigated, the methylation status of the HIN-1 promoter can be considered the best suitable biomarker for detecting field cancerization. Further investigation is needed to test whether it can be used for defining surgical margins in order to prevent future recurrence of breast cancer.

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.

Induced Pluripotent Stem Cell Models to Enable In Vitro Models for Screening in the Central Nervous System

There is great need to develop more predictive drug discovery tools to identify new therapies to treat diseases of the central nervous system (CNS). Current nonpluripotent stem cell-based models often utilize non-CNS immortalized cell lines and do not enable the development of personalized models of disease. In this review, we discuss why in vitro models are necessary for translational research and outline the unique advantages of induced pluripotent stem cell (iPSC)-based models over those of current systems. We suggest that iPSC-based models can be patient specific and isogenic lines can be differentiated into many neural cell types for detailed comparisons. iPSC-derived cells can be combined to form small organoids, or large panels of lines can be developed that enable new forms of analysis. iPSC and embryonic stem cell-derived cells can be readily engineered to develop reporters for lineage studies or mechanism of action experiments further extending the utility of iPSC-based systems. We conclude by describing novel technologies that include strategies for the development of diversity panels, novel genomic engineering tools, new three-dimensional organoid systems, and modified high-content screens that may bring toxicology into the 21st century. The strategic integration of these technologies with the advantages of iPSC-derived cell technology, we believe, will be a paradigm shift for toxicology and drug discovery efforts.

Suspension arrays based on nanoparticle-encoded microspheres for high-throughput multiplexed detection

Spectrometrically or optically encoded microsphere based suspension array technology (SAT) is applicable to the high-throughput, simultaneous detection of multiple analytes within a small, single sample volume. Thanks to the rapid development of nanotechnology, tremendous progress has been made in the multiplexed detecting capability, sensitivity, and photostability of suspension arrays. In this review, we first focus on the current stock of nanoparticle-based barcodes as well as the manufacturing technologies required for their production. We then move on to discuss all existing barcode-based bioanalysis patterns, including the various labels used in suspension arrays, label-free platforms, signal amplification methods, and fluorescence resonance energy transfer (FRET)-based platforms. We then introduce automatic platforms for suspension arrays that use superparamagnetic nanoparticle-based microspheres. Finally, we summarize the current challenges and their proposed solutions, which are centered on improving encoding capacities, alternative probe possibilities, nonspecificity suppression, directional immobilization, and "point of care" platforms. Throughout this review, we aim to provide a comprehensive guide for the design of suspension arrays, with the goal of improving their performance in areas such as multiplexing capacity, throughput, sensitivity, and cost effectiveness. We hope that our summary on the state-of-the-art development of these arrays, our commentary on future challenges, and some proposed avenues for further advances will help drive the development of suspension array technology and its related fields.

Machine learning for biomarker identification in cancer research - developments toward its clinical application

The patterns identified from the systematically collected molecular profiles of patient tumor samples, along with clinical metadata, can assist personalized treatments for effective management of cancer patients with similar molecular subtypes. There is an unmet need to develop computational algorithms for cancer diagnosis, prognosis and therapeutics that can identify complex patterns and help in classifications based on plethora of emerging cancer research outcomes in public domain. Machine learning, a branch of artificial intelligence, holds a great potential for pattern recognition in cryptic cancer datasets, as evident from recent literature survey. In this review, we focus on the current status of machine learning applications in cancer research, highlighting trends and analyzing major achievements, roadblocks and challenges toward its implementation in clinics.

The Cancer Genomics Resource List 2014

CONTEXT

Genomic sequencing for cancer is offered by commercial for-profit laboratories, independent laboratory networks, and laboratories in academic medical centers and integrated health networks. The variability among the tests has created a complex, confusing environment.

OBJECTIVE

To address the complexity, the Personalized Health Care (PHC) Committee of the College of American Pathologists proposed the development of a cancer genomics resource list (CGRL). The goal of this resource was to assist the laboratory pathology and clinical oncology communities.

DESIGN

The PHC Committee established a working group in 2012 to address this goal. The group consisted of site-specific experts in cancer genetic sequencing. The group identified current next-generation sequencing (NGS)-based cancer tests and compiled them into a usable resource. The genes were annotated by the working group. The annotation process drew on published knowledge, including public databases and the medical literature.

RESULTS

The compiled list includes NGS panels offered by 19 laboratories or vendors, accompanied by annotations. The list has 611 different genes for which NGS-based mutation testing is offered. Surprisingly, of these 611 genes, 0 genes were listed in every panel, 43 genes were listed in 4 panels, and 54 genes were listed in 3 panels. In addition, tests for 393 genes were offered by only 1 or 2 institutions. Table 1 provides an example of gene mutations offered for breast cancer genomic testing with the annotation as it appears in the CGRL 2014.

CONCLUSIONS

The final product, referred to as the Cancer Genomics Resource List 2014, is available as supplemental digital content.

Association between α-synuclein blood transcripts and early, neuroimaging-supported Parkinson's disease

There are no cures for neurodegenerative diseases and this is partially due to the difficulty of monitoring pathogenic molecules in patients during life. The Parkinson's disease gene α-synuclein (SNCA) is selectively expressed in blood cells and neurons. Here we show that SNCA transcripts in circulating blood cells are paradoxically reduced in early stage, untreated and dopamine transporter neuroimaging-supported Parkinson's disease in three independent regional, national, and international populations representing 500 cases and 363 controls and on three analogue and digital platforms with P < 0.0001 in meta-analysis. Individuals with SNCA transcripts in the lowest quartile of counts had an odds ratio for Parkinson's disease of 2.45 compared to individuals in the highest quartile. Disease-relevant transcript isoforms were low even near disease onset. Importantly, low SNCA transcript abundance predicted cognitive decline in patients with Parkinson's disease during up to 5 years of longitudinal follow-up. This study reveals a consistent association of reduced SNCA transcripts in accessible peripheral blood and early-stage Parkinson's disease in 863 participants and suggests a clinical role as potential predictor of cognitive decline. Moreover, the three independent biobank cohorts provide a generally useful platform for rapidly validating any biological marker of this common disease.

Towards personalized medicine: chemosensitivity assays of patient lung cancer cell spheroids in a perfused microfluidic platform

Cancer is responsible for millions of deaths worldwide and the variability in disease patterns calls for patient-specific treatment. Therefore, personalized treatment is expected to become a daily routine in prospective clinical tests. In addition to genetic mutation analysis, predictive chemosensitive assays using patient's cells will be carried out as a decision making tool. However, prior to their widespread application in clinics, several challenges linked to the establishment of such assays need to be addressed. To best predict the drug response in a patient, the cellular environment needs to resemble that of the tumor. Furthermore, the formation of homogeneous replicates from a scarce amount of patient's cells is essential to compare the responses under various conditions (compound and concentration). Here, we present a microfluidic device for homogeneous spheroid formation in eight replicates in a perfused microenvironment. Spheroid replicates from either a cell line or primary cells from adenocarcinoma patients were successfully created. To further mimic the tumor microenvironment, spheroid co-culture of primary lung cancer epithelial cells and primary pericytes were tested. A higher chemoresistance in primary co-culture spheroids compared to primary monoculture spheroids was found when both were constantly perfused with cisplatin. This result is thought to be due to the barrier created by the pericytes around the tumor spheroids. Thus, this device can be used for additional chemosensitivity assays (e.g. sequential treatment) of patient material to further approach the personalized oncology field.

Big Data: Astronomical or Genomical?

Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a “four-headed beast”—it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the “genomical” challenges of the next decade.

This perspective considers the growth of genomics over the next ten years and assesses the computational needs that we will face relative to other "Big Data" activities such as astronomy, YouTube, and Twitter.

Molecular Technologies in the Clinical Diagnostic Laboratory

BACKGROUND

New technologies for molecular analysis are increasing our ability to diagnose cancer.

METHODS

Several molecular analysis technologies are reviewed and their use in the clinical laboratory is discussed.

RESULTS

Select key technologies, including polymerase chain reaction and next-generation sequencing, are helping transform our ability to analyze cancer specimens. As these technological advances become more and more incorporated into routine diagnostic testing, our classification systems are likely to be impacted and our approach to treatment transformed. The routine use of such technology also brings challenges for analysis and reimbursement.

CONCLUSION

These advances in technology will change the way we diagnose, monitor, and treat patients with cancer.

The promise of methylation on beads for cancer detection and treatment

Despite numerous technical hurdles, the realization of true personalized medicine is becoming a progressive reality for the future of patient care. With the development of new techniques and tools to measure the genetic signature of tumors, biomarkers are increasingly being used to detect occult tumors, determine the choice of treatment and predict outcomes. Methylation of CpG islands at the promoter region of genes is a particularly exciting biomarker as it is cancer-specific. Older methods to detect methylation were cumbersome, operator-dependent and required large amounts of DNA. However, a newer technique called methylation on beads has resulted in a more uniform, streamlined and efficient assay. Furthermore, methylation on beads permits the extraction and processing of miniscule amounts of methylated tumor DNA in the peripheral blood. Such a technique may aid in the clinical detection and treatment of cancers in the future.