Pan-cancer analysis of whole genomes

Tumor Profiling at the Service of Cancer Therapy

Cancer treatment options have evolved significantly in the past few years. From the initial surgical procedures, to the latest next-generation technologies, we are now in the position to analyze and understand tumors in a one-by-one basis and use that to our advantage to provide with individualized treatment options that may increase patient survival. In this review, we will focus on how tumor profiling has evolved over the past decades to deliver more efficient and personalized treatment options, and how novel technologies can help us envisage the future of precision oncology toward a better management and, ultimately, increased survival.

Next-Generation Analytics for Omics Data

The increasing omics data present a daunting informatics challenge. DrBioRight, a natural language-oriented and artificial intelligence-driven analytics platform, enables the broad research community to perform analysis in an intuitive, efficient, transparent, and collaborative way. The emerging next-generation analytics will maximize the utility of omics data and lead to a new paradigm for biomedical research.

OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, 'Mutation', 'Gene', 'Pathway' and 'Cancer', to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.

Tumor Mutational Burden Is Polygenic and Genetically Associated with Complex Traits and Diseases

Tumor mutational burden (TMB) is an emerging biomarker of response to immunotherapy in solid tumors. However, the extent to which variation in TMB between patients is attributable to germline genetic variation remains elusive. Here, using 7,004 unrelated patients of European descent across 33 cancer types from The Cancer Genome Atlas, we show that pan-cancer TMB is polygenic with approximately 13% of its variation explained by approximately 1.1 million common variants altogether. We identify germline variants that affect TMB in stomach adenocarcinoma through altering the expression levels of BAG5 and KLC1. Further analyses provide evidence that TMB is genetically associated with complex traits and diseases, such as smoking, rheumatoid arthritis, height, and cancers, and some of the associations are likely causal. Overall, these results provide new insights into the genetic basis of somatic mutations in tumors and may inform future efforts to use genetic variants to stratify patients for immunotherapy. SIGNIFICANCE: This study provides evidence for a polygenic architecture of tumor mutational burden and opens an avenue for the use of whole-genome germline genetic variations to stratify patients with cancer for immunotherapy.

Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic

In most disciplines of natural sciences and engineering, mathematical and computational modelling are mainstay methods which are usefulness beyond doubt. These disciplines would not have reached today's level of sophistication without an intensive use of mathematical and computational models together with quantitative data. This approach has not been followed in much of molecular biology and biomedicine, however, where qualitative descriptions are accepted as a satisfactory replacement for mathematical rigor and the use of computational models is seen by many as a fringe practice rather than as a powerful scientific method. This position disregards mathematical thinking as having contributed key discoveries in biology for more than a century, e.g., in the connection between genes, inheritance, and evolution or in the mechanisms of enzymatic catalysis. Here, we discuss the role of computational modelling in the arsenal of modern scientific methods in biomedicine. We list frequent misconceptions about mathematical modelling found among biomedical experimentalists and suggest some good practices that can help bridge the cognitive gap between modelers and experimental researchers in biomedicine. This manuscript was written with two readers in mind. Firstly, it is intended for mathematical modelers with a background in physics, mathematics, or engineering who want to jump into biomedicine. We provide them with ideas to motivate the use of mathematical modelling when discussing with experimental partners. Secondly, this is a text for biomedical researchers intrigued with utilizing mathematical modelling to investigate the pathophysiology of human diseases to improve their diagnostics and treatment.

Emerging paradigms in metastasis research

Historically, therapy of metastatic disease has essentially been limited to using strategies that were identified and established to shrink primary tumors. The limited efficacy of such treatments on overall patient survival stems from diverging intrinsic and extrinsic characteristics of a primary tumor and metastases originating therefrom. To develop better therapeutic strategies to treat metastatic disease, there is an urgent need to shift the paradigm in preclinical metastasis research by conceptualizing metastatic dissemination, colonization, and growth as spatiotemporally dynamic processes and identifying rate-limiting vulnerabilities of the metastatic cascade. Clinically, while metastatic colonization remains the most attractive therapeutic avenue, comprehensive understanding of earlier steps may unravel novel metastasis-restricting therapies for presurgical neoadjuvant application. Moving beyond a primary tumor-centric view, this review adopts a holistic approach to understanding the spatial and temporal progression of metastasis. After reviewing recent developments in metastasis research, we highlight some of the grand challenges and propose a framework to expedite mechanism-based discovery research feeding the translational pipeline.

Molecular Subtyping and Precision Medicine for Pancreatic Cancer

Substantial progress in recent years has dramatically increased our knowledge of the molecular basis of cancer, revealing new potential therapeutic targets and paving the way for effective personalised medicine for the treatment of many tumour types. However, pancreatic cancer has been lagging behind in this success and continues to be one of the most lethal solid malignancies. Its molecular heterogeneity and the unselected design of the majority of clinical trials to date can in part explain the reason for our failure to make a significant change in the survival outcomes for patients with pancreatic cancer. A changing paradigm in drug development is required to validate the new molecular taxonomy and to rapidly translate preclinical discovery into clinical trials. Here, we review the molecular subtyping of pancreatic cancer, the challenges in identifying effective treatment regimens according to defined low-prevalence molecular subgroups and we illustrate a new model of translational therapeutic development that was established in the U.K. (Precision-Panc) as a potentially effective solution to improve outcomes for patients with pancreatic cancer.

Strategies to Study the Functions of Pseudogenes in Mouse Models of Cancer

Aberrant expression of pseudogenes has been observed in many cancer types. Deregulated pseudogenes engage in a multitude of biological processes at the DNA, RNA, and protein levels and eventually facilitate disease progression. To investigate pseudogene functions in cancer, cell lines and cell line transplantation models have been widely used. However, cancer biology is best studied in the context of an intact organism. Here, we present various strategies to investigate pseudogenes in genetically engineered mouse models and discuss advantages and disadvantages of the different approaches.

Cancer Biology, Epidemiology, and Treatment in the 21st Century: Current Status and Future Challenges From a Biomedical Perspective

Since the second half of the 20th century, our knowledge about the biology of cancer has made extraordinary progress. Today, we understand cancer at the genomic and epigenomic levels, and we have identified the cell that starts neoplastic transformation and characterized the mechanisms for the invasion of other tissues. This knowledge has allowed novel drugs to be designed that act on specific molecular targets, the immune system to be trained and manipulated to increase its efficiency, and ever more effective therapeutic strategies to be developed. Nevertheless, we are still far from winning the war against cancer, and thus biomedical research in oncology must continue to be a global priority. Likewise, there is a need to reduce unequal access to medical services and improve prevention programs, especially in countries with a low human development index.

Mutational mechanisms shaping the coding and noncoding genome of germinal center derived B-cell lymphomas

B cells have the unique property to somatically alter their immunoglobulin (IG) genes by V(D)J recombination, somatic hypermutation (SHM) and class-switch recombination (CSR). Aberrant targeting of these mechanisms is implicated in lymphomagenesis, but the mutational processes are poorly understood. By performing whole genome and transcriptome sequencing of 181 germinal center derived B-cell lymphomas (gcBCL) we identified distinct mutational signatures linked to SHM and CSR. We show that not only SHM, but presumably also CSR causes off-target mutations in non-IG genes. Kataegis clusters with high mutational density mainly affected early replicating regions and were enriched for SHM- and CSR-mediated off-target mutations. Moreover, they often co-occurred in loci physically interacting in the nucleus, suggesting that mutation hotspots promote increased mutation targeting of spatially co-localized loci (termed hypermutation by proxy). Only around 1% of somatic small variants were in protein coding sequences, but in about half of the driver genes, a contribution of B-cell specific mutational processes to their mutations was found. The B-cell-specific mutational processes contribute to both lymphoma initiation and intratumoral heterogeneity. Overall, we demonstrate that mutational processes involved in the development of gcBCL are more complex than previously appreciated, and that B cell-specific mutational processes contribute via diverse mechanisms to lymphomagenesis.

Risk factors for pancreas and lung neuroendocrine neoplasms: a case-control study

PURPOSE

Neuroendocrine neoplasia (NEN) has been displaying an incremental trend along the last two decades. This phenomenon is poorly understood, and little information is available on risk factor for neuroendocrine neoplasia development. Aim of this work is to elucidate the role of potentially modifiable risk factors for pancreatic and pulmonary NEN.

METHODS

We conducted a case-control study on 184 patients with NEN (100 pancreas and 84 lung) and 248 controls. The structured questionnaire included 84 queries on socio-demographic, behavioral, dietary and clinical information.

RESULTS

Increased risk was associated with history of cancer ("other tumor", lung OR = 7.18; 95% CI: 2.55-20.20 and pancreas OR = 5.88; 95% CI: 2.43-14.22; "family history of tumor", lung OR = 2.66; 95% CI: 1.53-4.64 and pancreas OR = 1.94; 95% CI: 1.19-3.17; "family history of lung tumor", lung OR = 2.56; 95% CI: 1.05-6.24 and pancreas OR = 2.60; 95% CI: 1.13-5.95). Type 2 diabetes mellitus associated with an increased risk of pancreatic NEN (OR = 3.01; 95% CI: 1.15-7.89).

CONCLUSIONS

Besides site-specific risk factors, there is a significant link between neuroendocrine neoplasia and cancer in general, pointing to a shared cancer predisposition.

Genomic Landscapes of Acral Melanomas in East Asia

BACKGROUND/AIM

Acral melanomas (AM) represent a rare subgroup of melanomas with poor clinical outcomes and are enriched in Asian populations. Recent advances in next generation sequencing have provided opportunities to apply precision medicine to AM.

PATIENTS AND METHODS

Here, we present a series of 13 patients with melanomas from Taiwan and Singapore, including 8 patients with AM profiled using whole exome sequencing and summarize the recent studies on the genomic landscape of AM.

RESULTS

We identified mutually exclusive mutations in BRAF, NRAS, HRAS, NF1 and KIT in 6 AM cases. In addition, recurrent copy number gains in CCND1 and CDK4, as well as recurrent deletions in CDKN2A/CDKN2B, ATM and RAD51 were observed, supporting the potential use of CDK4/6 or PARP inhibitors in the treatment of these patients.

CONCLUSION

The genomic landscape of AM provides an important resource for applying novel targeted therapies in this rare disease.

Transcriptionally active enhancers in human cancer cells

The growth of human cancer cells is driven by aberrant enhancer and gene transcription activity. Here, we use transient transcriptome sequencing (TT-seq) to map thousands of transcriptionally active putative enhancers in fourteen human cancer cell lines covering seven types of cancer. These enhancers were associated with cell type-specific gene expression, enriched for genetic variants that predispose to cancer, and included functionally verified enhancers. Enhancer-promoter (E-P) pairing by correlation of transcription activity revealed ~ 40,000 putative E-P pairs, which were depleted for housekeeping genes and enriched for transcription factors, cancer-associated genes, and 3D conformational proximity. The cell type specificity and transcription activity of target genes increased with the number of paired putative enhancers. Our results represent a rich resource for future studies of gene regulation by enhancers and their role in driving cancerous cell growth.

The Nexus of Endocrine Signaling and Cancer: How Steroid Hormones Influence Genomic Stability

Endocrine-driven malignancies, including breast and prostate cancer, are among the most common human cancers. The relationship between sex steroid hormones (eg, androgen, estrogen, and progesterone), their cognate receptors, and genomic stability lie at the center of endocrine-driven cancer development, progression, and therapeutic resistance. A variety of direct and indirect mechanisms have been described that link steroid hormone signaling to the loss of genomic integrity that drives early carcinogenesis. These effects are often enriched within endocrine receptor cistromes, accounting for the high proportion of mutations and rearrangements in the region of hormone response elements. In other cases, the effects are generalized and rely on a complex array of genetic, epigenetic, and metabolic interactions. Both androgen and estrogen receptors directly modulate the DNA damage response by trans-activating DNA damage response genes and redirecting the cellular repair machinery in the wake of genotoxic stress. Here we review the key mechanistic underpinnings of the relationship between sex steroid hormone receptors and genomic stability. In addition, we summarize emerging research in this area and discuss important implications for cancer prevention and treatment.

svMIL: predicting the pathogenic effect of TAD boundary-disrupting somatic structural variants through multiple instance learning

Motivation

Despite the fact that structural variants (SVs) play an important role in cancer, methods to predict their effect, especially for SVs in non-coding regions, are lacking, leaving them often overlooked in the clinic. Non-coding SVs may disrupt the boundaries of Topologically Associated Domains (TADs), thereby affecting interactions between genes and regulatory elements such as enhancers. However, it is not known when such alterations are pathogenic. Although machine learning techniques are a promising solution to answer this question, representing the large number of interactions that an SV can disrupt in a single feature matrix is not trivial.

Results

We introduce svMIL: a method to predict pathogenic TAD boundary-disrupting SV effects based on multiple instance learning, which circumvents the need for a traditional feature matrix by grouping SVs into bags that can contain any number of disruptions. We demonstrate that svMIL can predict SV pathogenicity, measured through same-sample gene expression aberration, for various cancer types. In addition, our approach reveals that somatic pathogenic SVs alter different regulatory interactions than somatic non-pathogenic SVs and germline SVs.

Availability and implementation

All code for svMIL is publicly available on GitHub: https://github.com/UMCUGenetics/svMIL.

Supplementary information

Supplementary data are available at Bioinformatics online.

Two novel cell culture models of buccal mucosal oral cancer from patients with no risk-habits of tobacco smoking or chewing

OBJECTIVE

Tobacco consumption is one of the major etiological factors for oral cancer, but it also develops in non-tobacco users, with unknown etiologies. Cellular models for tobacco associated oral cancer are available, however; reports of cellular models for studying non-tobacco associated oral cancer are limiting. We report here the establishment and characterization of two novel buccal mucosal cancer cell lines 'GBC02' and 'GBC035' derived from non-tobacco users.

MATERIALS AND METHODS

Short tandem repeats (STR) profiling, Next-generation sequencing for whole-genome, exome and copy number alterations, immunofluorescence, flow-cytometry, proliferation, live-cell chemotaxis, 3D-spheroid formation, chemotherapy response, gene-expression microarray, gene-set enrichment analysis and xenograft development were performed.

RESULTS

Sources of the established cultures were matched to their donors through STR profiling. Genome sequence analysis revealed somatic mutations in TP53, CASP8, CDKN2A for GBC02 with deletions and amplifications encompassing CDKN2A, FAT1 and CCND1, PIK3CA, SOX2, EGFR, MYC genes, respectively. GBC035 harbored mutations in FAT1, NOTCH1, HRAS, CDKN2A, HLA-B, HLA-A genes. While GBC035 cells showed higher E-Cadherin positive cell-cell junctions and collective cell migration in chemotaxis; GBC02 cells were vimentin-positive and demonstrated individual cell migration. Further, exhibiting their relevance to preclinical research, GBC02 3D-spheroids demonstrated enrichment of development-related gene-signatures in microarray transcriptome analysis and were resistant to Cisplatin, but showed sensitivity to cancer stem cells-targeting drug, Salinomycin. Additionally, tumorigenic ability of GBC02 was demonstrated.

CONCLUSIONS

Altogether, we present here comprehensively characterized unique cell lines established from non-tobacco associated tumors, which may serve as models for preclinical investigations of oral cancers caused independent of tobacco usage.

Measuring evolutionary cancer dynamics from genome sequencing, one patient at a time

Cancers progress through the accumulation of somatic mutations which accrue during tumour evolution, allowing some cells to proliferate in an uncontrolled fashion. This growth process is intimately related to latent evolutionary forces moulding the genetic and epigenetic composition of tumour subpopulations. Understanding cancer requires therefore the understanding of these selective pressures. The adoption of widespread next-generation sequencing technologies opens up for the possibility of measuring molecular profiles of cancers at multiple resolutions, across one or multiple patients. In this review we discuss how cancer genome sequencing data from a single tumour can be used to understand these evolutionary forces, overviewing mathematical models and inferential methods adopted in field of Cancer Evolution.

Use of DNA methylation profiling in translational oncology

DNA methylation is a highly regulated process that has a critical role in human development and homeostatic control of the cell. The number of genes affected by anomalous DNA methylation in cancer-associated pathways is swiftly accelerating and with the advancement of molecular technologies, new layers of complexity are opening up and refining our strategies to combat cancer. DNA methylation profiling is an essential facet to understanding malignant transformation and is becoming an increasingly important tool for cancer diagnosis, prognosis and therapy monitoring. In this review, the role of DNA methylation in normal cellular function is discussed, as well as how epigenetic aberrations override normal cellular cues that lead to tumor initiation and propagation. The review also focuses on the latest advancements in DNA methylation profiling as a biomarker for early cancer detection, predicting patient clinical outcomes and responses to treatment and provides new insights into epigenetic-based therapy in clinical oncology.

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet

Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. This article quantifies the stages of drug discovery in which improvements in the time taken, success rate or affordability will have the most profound overall impact on bringing new drugs to market. Changes in clinical success rates will have the most profound impact on improving success in drug discovery; in other words, the quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost. Although current advances in AI focus on how to make a given compound, the question of which compound to make, using clinical efficacy and safety-related end points, has received significantly less attention. As a consequence, current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo. Thus, addressing the questions of which data to generate and which end points to model will be key to improving clinically relevant decision-making in the future.

A unified model for the G1/S cell cycle transition

Efficient S phase entry is essential for development, tissue repair, and immune defences. However, hyperactive or expedited S phase entry causes replication stress, DNA damage and oncogenesis, highlighting the need for strict regulation. Recent paradigm shifts and conflicting reports demonstrate the requirement for a discussion of the G1/S transition literature. Here, we review the recent studies, and propose a unified model for the S phase entry decision. In this model, competition between mitogen and DNA damage signalling over the course of the mother cell cycle constitutes the predominant control mechanism for S phase entry of daughter cells. Mitogens and DNA damage have distinct sensing periods, giving rise to three Commitment Points for S phase entry (CP1-3). S phase entry is mitogen-independent in the daughter G1 phase, but remains sensitive to DNA damage, such as single strand breaks, the most frequently-occurring lesions that uniquely threaten DNA replication. To control CP1-3, dedicated hubs integrate the antagonistic mitogenic and DNA damage signals, regulating the stoichiometric cyclin: CDK inhibitor ratio for ultrasensitive control of CDK4/6 and CDK2. This unified model for the G1/S cell cycle transition combines the findings of decades of study, and provides an updated foundation for cell cycle research.

Fat1 deletion promotes hybrid EMT state, tumour stemness and metastasis

FAT1, which encodes a protocadherin, is one of the most frequently mutated genes in human cancers^1-5. However, the role and the molecular mechanisms by which FAT1 mutations control tumour initiation and progression are poorly understood. Here, using mouse models of skin squamous cell carcinoma and lung tumours, we found that deletion of Fat1 accelerates tumour initiation and malignant progression and promotes a hybrid epithelial-to-mesenchymal transition (EMT) phenotype. We also found this hybrid EMT state in FAT1-mutated human squamous cell carcinomas. Skin squamous cell carcinomas in which Fat1 was deleted presented increased tumour stemness and spontaneous metastasis. We performed transcriptional and chromatin profiling combined with proteomic analyses and mechanistic studies, which revealed that loss of function of FAT1 activates a CAMK2-CD44-SRC axis that promotes YAP1 nuclear translocation and ZEB1 expression that stimulates the mesenchymal state. This loss of function also inactivates EZH2, promoting SOX2 expression, which sustains the epithelial state. Our comprehensive analysis identified drug resistance and vulnerabilities in FAT1-deficient tumours, which have important implications for cancer therapy. Our studies reveal that, in mouse and human squamous cell carcinoma, loss of function of FAT1 promotes tumour initiation, progression, invasiveness, stemness and metastasis through the induction of a hybrid EMT state.

Transcriptome-wide analysis reveals spatial correlation between N6-methyladenosine and binding sites of microRNAs and RNA-binding proteins

N6-methyladenosine (m⁶A), the most prevalent epitranscriptomic modification in eukaryotes, is enriched in 3'-untranslated regions (3'UTRs) of mRNAs. As 3'UTRs are major binding sites of RNA-binding proteins (RBPs) and microRNAs (miRNAs), m⁶A-dependent local RNA structure change may alter the accessibility of RBPs and miRNAs to their target sites and regulate mRNA function. Using a human transcriptome-wide computational analysis to investigate the relation between m⁶A, RBPs and miRNAs, we find a strong positive correlation between number of m⁶A sites, miRNAs and RBPs binding to mRNAs, suggesting m⁶A-modified mRNAs are more targeted by miRNAs and RBPs. Moreover, m⁶A sites are located proximally to miRNA target sites and binding sites of multiple RBPs. Further, miRNA target sites and RBP-binding sites located close to each other are also located proximally to m⁶A. This study indicates three-way interplay between m⁶A, microRNA and RBP binding, suggesting the influence of mRNA modifications on the miRNA and RBP interactomes.

Mechanisms Generating Cancer Genome Complexity: Back to the Future

Understanding the mechanisms underlying cancer genome evolution has been a major goal for decades. A recent study combining live cell imaging and single-cell genome sequencing suggested that interwoven chromosome breakage-fusion-bridge cycles, micronucleation events and chromothripsis episodes drive cancer genome evolution. Here, I discuss the "interphase breakage model," suggested from prior fluorescent in situ hybridization data that led to a similar conclusion. In this model, the rapid genome evolution observed at early stages of gene amplification was proposed to result from the interweaving of an amplification mechanism (breakage-fusion-bridge cycles) and of a deletion mechanism (micronucleation and stitching of DNA fragments retained in the nucleus).

Challenges and Opportunities for Pancreatic Cancer Immunotherapy

Pancreatic ductal adenocarcinoma (PDA) is among the most immune-resistant tumor types. Its unique genomic landscape shaped by oncogenic drivers promotes immune suppression from the earliest stages of tumor inception to subvert adaptive T cell immunity. Single-agent immune modulators have thus far proven clinically ineffective, and multi-modal therapies targeting mechanisms of immunotherapy resistance are likely needed. Here, we review novel immunotherapy strategies currently under investigation to (1) confer antigen specificity, (2) enhance T cell effector function, and (3) neutralize immunosuppressive elements within the tumor microenvironment that may be rationally combined to untangle the web of immune resistance in PDA and other tumors.

Application of Random Forest and data integration identifies three dysregulated genes and enrichment of Central Carbon Metabolism pathway in Oral Cancer

BACKGROUND

Studies of epigenomic alterations associated with diseases primarily focus on methylation profiles of promoter regions of genes, but not of other genomic regions. In our past work (Das et al. 2019) on patients suffering from gingivo-buccal oral cancer - the most prevalent form of cancer among males in India - we have also focused on promoter methylation changes and resultant impact on transcription profiles. Here, we have investigated alterations in non-promoter (gene-body) methylation profiles and have carried out an integrative analysis of gene-body methylation and transcriptomic data of oral cancer patients.

METHODS

Tumor and adjacent normal tissue samples were collected from 40 patients. Data on methylation in the non-promoter (gene-body) regions of genes and transcriptome profiles were generated and analyzed. Because of high dimensionality and highly correlated nature of these data, we have used Random Forest (RF) and other data-analytical methods.

RESULTS

Integrative analysis of non-promoter methylation and transcriptome data revealed significant methylation-driven alterations in some genes that also significantly impact on their transcription levels. These changes result in enrichment of the Central Carbon Metabolism (CCM) pathway, primarily by dysregulation of (a) NTRK3, which plays a dual role as an oncogene and a tumor suppressor; (b) SLC7A5 (LAT1) which is a transporter dedicated to essential amino acids, and is overexpressed in cancer cells to meet the increased demand for nutrients that include glucose and essential amino acids; and, (c) EGFR which has been earlier implicated in progression, recurrence, and stemness of oral cancer, but we provide evidence of epigenetic impact on overexpression of this gene for the first time.

CONCLUSIONS

In rapidly dividing cancer cells, metabolic reprogramming from normal cells takes place to enable enhanced proliferation. Here, we have identified that among oral cancer patients, genes in the CCM pathway - that plays a fundamental role in metabolic reprogramming - are significantly dysregulated because of perturbation of methylation in non-promoter regions of the genome. This result compliments our previous result that perturbation of promoter methylation results in significant changes in key genes that regulate the feedback process of DNA methylation for the maintenance of normal cell division.

The Separation of Benign and Malignant Mesothelial Proliferations: New Markers and How to Use Them

The separation of benign from malignant mesothelial proliferations is an important clinical but often a difficult morphologic problem. Over the last roughly 10 years a variety of new markers that aid in this separation have been published and some older recommended markers reconsidered. Unlike previous, and largely unusable, empiric immunohistochemical (IHC) stains, these new markers, some using IHC and some using fluourescent in situ hybridization (FISH), are largely based on documented genomic abnormalities in malignant mesotheliomas. However, no marker works in all situations; rather, markers need to be chosen by the morphology of the process in question (epithelial vs. spindled) and the body cavity of interest (pleural vs. peritoneal). It is also important to be familiar with the exact pattern, for example nuclear versus cytoplasmic loss, that indicates a positive test. Furthermore, no single marker is 100% sensitive even with the optimal morphology/location, so that combinations of markers are essential. This review covers the various new markers in the literature, highlights their advantages and limitations, and suggests morphology/site specific combinations that can produce sensitivities in the 80% to 90% (and perhaps higher) range. At present only BRCA-1 related protein-1 and methylthioadenosine phosphorylase IHC, and cyclin-dependent kinase inhibitor 2A (p16) FISH have sufficient publications and reproducibility of results to be considered as established markers. 5-Hydroxymethyl cytosine, enhancer of zeste homolog 2, cyclin D1, and programmed death-ligand 1 IHC, and NF2 FISH are all potentially useful but need further study. The newly described entity of malignant mesothelioma in situ sits at the interface of benign and malignant mesothelial process; criteria for this diagnosis are reviewed.

PyClone-VI: scalable inference of clonal population structures using whole genome data

Background

At diagnosis tumours are typically composed of a mixture of genomically distinct malignant cell populations. Bulk sequencing of tumour samples coupled with computational deconvolution can be used to identify these populations and study cancer evolution. Existing computational methods for populations deconvolution are slow and/or potentially inaccurate when applied to large datasets generated by whole genome sequencing data.

Results

We describe PyClone-VI, a computationally efficient Bayesian statistical method for inferring the clonal population structure of cancers. We demonstrate the utility of the method by analyzing data from 1717 patients from PCAWG study and 100 patients from the TRACERx study.

Conclusions

Our proposed method is 10–100× times faster than existing methods, while providing results which are as accurate. Software implementing our method is freely available https://github.com/Roth-Lab/pyclone-vi.

How wide is the application of genetic big data in biomedicine

In the era of big data, massive genetic data, as a new industry, has quickly swept almost all industries, especially the pharmaceutical industry. As countries around the world start to build their own gene banks, scientists study the data to explore the origins and migration of humans. Moreover, big data encourage the development of cancer therapy and bring good news to cancer patients. Big datum has been involved in the study of many diseases, and it has been found that analyzing diseases at the gene level can lead to more beneficial treatment options than ordinary treatments. This review will introduce the development of extensive data in medical research from the perspective of big data and tumor, neurological and psychiatric diseases, cardiovascular diseases, other applications and the development direction of big data in medicine.

Clinical Validation of a Machine-learning-derived Signature Predictive of Outcomes from First-line Oxaliplatin-based Chemotherapy in Advanced Colorectal Cancer

PURPOSE

FOLFOX, FOLFIRI, or FOLFOXIRI chemotherapy with bevacizumab is considered standard first-line treatment option for patients with metastatic colorectal cancer (mCRC). We developed and validated a molecular signature predictive of efficacy of oxaliplatin-based chemotherapy combined with bevacizumab in patients with mCRC.

EXPERIMENTAL DESIGN

A machine-learning approach was applied and tested on clinical and next-generation sequencing data from a real-world evidence (RWE) dataset and samples from the prospective TRIBE2 study resulting in identification of a molecular signature, FOLFOXai. Algorithm training considered time-to-next treatment (TTNT). Validation studies used TTNT, progression-free survival, and overall survival (OS) as the primary endpoints.

RESULTS

A 67-gene signature was cross-validated in a training cohort (N = 105) which demonstrated the ability of FOLFOXai to distinguish FOLFOX-treated patients with mCRC with increased benefit from those with decreased benefit. The signature was predictive of TTNT and OS in an independent RWE dataset of 412 patients who had received FOLFOX/bevacizumab in first line and inversely predictive of survival in RWE data from 55 patients who had received first-line FOLFIRI. Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX HR, 0.629; P = 0.04 and FOLFOXIRI HR, 0.483; P = 0.02). FOLFOXai was also predictive of treatment benefit from oxaliplatin-containing regimens in advanced esophageal/gastro-esophageal junction cancers, as well as pancreatic ductal adenocarcinoma.

CONCLUSIONS

Application of FOLFOXai could lead to improvements of treatment outcomes for patients with mCRC and other cancers because patients predicted to have less benefit from oxaliplatin-containing regimens might benefit from alternative regimens.

Performance-weighted-voting model: An ensemble machine learning method for cancer type classification using whole-exome sequencing mutation

Background

With improvements in next-generation DNA sequencing technology, lower cost is needed to collect genetic data. More machine learning techniques can be used to help with cancer analysis and diagnosis.

Methods

We developed an ensemble machine learning system named performance-weighted-voting model for cancer type classification in 6,249 samples across 14 cancer types. Our ensemble system consists of five weak classifiers (logistic regression, SVM, random forest, XGBoost and neural networks). We first used cross-validation to get the predicted results for the five classifiers. The weights of the five weak classifiers can be obtained based on their predictive performance by solving linear regression functions. The final predicted probability of the performance-weighted-voting model for a cancer type can be determined by the summation of each classifier's weight multiplied by its predicted probability.

Results

Using the somatic mutation count of each gene as the input feature, the overall accuracy of the performance-weighted-voting model reached 71.46%, which was significantly higher than the five weak classifiers and two other ensemble models: the hard-voting model and the soft-voting model. In addition, by analyzing the predictive pattern of the performance-weighted-voting model, we found that in most cancer types, higher tumor mutational burden can improve overall accuracy.

Conclusion

This study has important clinical significance for identifying the origin of cancer, especially for those where the primary cannot be determined. In addition, our model presents a good strategy for using ensemble systems for cancer type classification.

Telomere aberrations, including telomere loss, doublets, and extreme shortening, are increased in patients with infertility

OBJECTIVE

To test the hypothesis that telomere shortening and/or loss are risk factors for infertility.

DESIGN

Retrospective analysis of the telomere status in patients with infertility using conventional cytogenetic data collected prospectively.

SETTING

Academic centers.

PATIENT(S)

Cytogenetic slides with cultured peripheral lymphocytes from 50 patients undergoing fertility treatment and 150 healthy donors, including 100 donors matched for age.

INTERVENTION(S)

Cytogenetic slides were used to detect chromosomal and telomere aberrations.

MAIN OUTCOME MEASURE(S)

Telomere length and telomere aberrations were analyzed after telomere and centromere staining.

RESULT(S)

The mean telomere length of patients consulting for infertility was significantly less than that of healthy donors of similar age. Moreover, patients with infertility showed significantly more extreme telomere loss and telomere doublet formation than healthy controls. Telomere shortening and/or telomere aberrations were more pronounced in patients with structural chromosomal aberrations. Dicentric chromosomes were identified in 6/13 patients, with constitutional chromosomal aberrations leading to chromosomal instability that correlated with chromosomal end-to-end fusions.

CONCLUSION(S)

Our findings demonstrate the feasibility of analyzing telomere aberrations in addition to chromosomal aberrations, using cytogenetic slides. Telomere attrition and/or dysfunction represent the main common cytogenetic characteristic of patients with infertility, leading to potential implications for fertility assessment. Pending further studies, these techniques that correlate the outcome of assisted reproduction and telomere integrity status may represent a novel and useful diagnostic and/or prognostic tool for medical care in this field.

A Tangle of Genomic Aberrations Drives Multiple Myeloma and Correlates with Clinical Aggressiveness of the Disease: A Comprehensive Review from a Biological Perspective to Clinical Trial Results

Multiple myeloma (MM) is a genetically heterogeneous disease, in which the process of tumorigenesis begins and progresses through the appearance and accumulation of a tangle of genomic aberrations. Several are the mechanisms of DNA damage in MM, varying from single nucleotide substitutions to complex genomic events. The timing of appearance of aberrations is well studied due to the natural history of the disease, that usually progress from pre-malignant to malignant phase. Different kinds of aberrations carry different prognostic significance and have been associated with drug resistance in some studies. Certain genetic events are well known to be associated with prognosis and are incorporated in risk evaluation in MM at diagnosis in the revised International Scoring System (R-ISS). The significance of some other aberrations needs to be further explained. Since now, few phase 3 randomized trials included analysis on patient's outcomes according to genetic risk, and further studies are needed to obtain useful data to stratify the choice of initial and subsequent treatment in MM.

First results of the EORTC-SPECTA/Arcagen study exploring the genomics of rare cancers in collaboration with the European reference network EURACAN

PURPOSE

Rare cancers are defined by an incidence of <6 out of 100 000 cases per year. They are under-represented in clinical research including tumour molecular analysis. The aim of Arcagen is to generate a multinational database integrating clinical and molecular information of patients with rare cancers.

PATIENTS AND METHODS

We present the retrospective feasibility cohort of patients with rare cancers, with previously collected tumour samples available from any stage. Molecular analysis was performed using FoundationOne CDx for all histologies except for sarcoma where FoundationOne Heme was used. Clinical data including demographic data, medical history, malignant history, treatment and survival data were collected.

RESULTS

Eighty-seven patients from three centres were screened; molecular data were obtained for 77 patients (41 sarcomas, 9 yolk sac tumours, 14 rare head and neck cancers, 13 thymomas). The median age at the time of diagnosis was 48 (range 28-85). Most patients had reportable genomic alterations (89%). The most common alterations were linked to cell cycle regulation (TP53, RB1, CDKN2A/B deletions and MDM2 amplification). Multiple activating single-nucleotide variants (SNVs) could be detected in the RAS/RAF family. The tumour mutational burden status was globally low across all samples with a median of 3 Muts/MB (range 0-52). Only 4 cases (ie, 4.7% of tumours) had direct actionable mutations for a treatment approved in Europe within the patient's tumour type.

CONCLUSION

The Arcagen project aims to bridge the gap and improve knowledge of the molecular landscape of rare cancers by prospectively recruiting up to 1000 patients.

NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis

Background

Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues.

Results

We developed a negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus). Our approach addresses the over-dispersion of mutation count statistics by (1) using a Gamma–Poisson mixture model to capture the mutation-rate heterogeneity across different individuals and (2) estimating regional background mutation rates by regressing the varying local mutation counts against genomic features extracted from ENCODE. We applied NIMBus to whole-genome cancer sequences from the PanCancer Analysis of Whole Genomes project (PCAWG) and other cohorts. It successfully identified well-known coding and noncoding drivers, such as TP53 and the TERT promoter. To further characterize the burdening of non-coding regions, we used NIMBus to screen transcription factor binding sites in promoter regions that intersect DNase I hypersensitive sites (DHSs). This analysis identified mutational hotspots that potentially disrupt gene regulatory networks in cancer. We also compare this method to other mutation burden analysis methods.

Conclusion

NIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.

The genetics of ductal adenocarcinoma of the pancreas in the year 2020: dramatic progress, but far to go

The publication of the "Pan-Cancer Atlas" by the Pan-Cancer Analysis of Whole Genomes Consortium, a partnership formed by The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC), provides a wonderful opportunity to reflect on where we stand in our understanding of the genetics of pancreatic cancer, as well as on the opportunities to translate this understanding to patient care. From germline variants that predispose to the development of pancreatic cancer, to somatic mutations that are therapeutically targetable, genetics is now providing hope, where there once was no hope, for those diagnosed with pancreatic cancer.

Sudden PSA rise to ≥20 ng/ml and prostate cancer diagnosis in the United States: A population-based study

PURPOSE

While prostate-specific antigen (PSA) screening protocols vary, many clinicians have anecdotes of screened men with low PSA levels that rise significantly and are associated with high-risk prostate cancer (PC). We sought to better understand the frequency of high-risk cases that appear suddenly in a screened population.

METHODS

We utilized data from a Commercial and Medicare advantage claims database to identify all US men ages 50 and above undergoing PSA screening who then had a sudden interval rise in PSA (e.g., PSA ≥ 20) and diagnosis of PC. We determined associations with age, race, screening intensity, and baseline PSA levels.

RESULTS

In all, 526,120 men met entry criteria with an average age of 60.7 and follow-up of 5.6 years. As the baseline PSA increased, the rate of high-risk PC increased from 2/10,000 persons among men with the lowest baseline PSA (<1 ng/ml) to 14/10,000 person-years among men with a baseline PSA < 5 ng/ml. Moreover, as a man's age at baseline PSA increased, the rate of high-risk PC also increased. In contrast, the incidence of high-risk PC did not vary significantly by race/ethnicity. More screening PSAs and shorter intervals between PSA screenings were associated with a lower incidence of high-risk PC.

CONCLUSIONS

The incidence of high-risk PC in a screened population is low (<0.1%). Our findings suggest that systematic screening cannot eliminate all PC deaths and provide an estimate for the risk of the rapid development of high-risk cancers that is comparable to that observed in active surveillance populations.

TERT promoter alterations could provide a solution for Peto's paradox in rodents

Cancer is a genetic disease caused by changes in gene expression resulting from somatic mutations and epigenetic changes. Although the probability of mutations is proportional with cell number and replication cycles, large bodied species do not develop cancer more frequently than smaller ones. This notion is known as Peto's paradox, and assumes stronger tumor suppression in larger animals. One of the possible tumor suppressor mechanisms involved could be replicative senescence caused by telomere shortening in the absence of telomerase activity. We analysed telomerase promoter activity and transcription factor binding in mammals to identify the key element of telomerase gene inactivation. We found that the GABPA transcription factor plays a key role in TERT regulation in somatic cells of small rodents, but its binding site is absent in larger beavers. Protein binding and reporter gene assays verify different use of this site in different species. The presence or absence of the GABPA TF site in TERT promoters of rodents correlates with TERT promoter activity; thus it could determine whether replicative senescence plays a tumor suppressor role in these species, which could be in direct relation with body mass. The GABPA TF binding sites that contribute to TERT activity in somatic cells of rodents are analogous to those mutated in human tumors, which activate telomerase by a non-ALT mechanism.

Tumor evolutionary trajectories during the acquisition of invasiveness in early stage lung adenocarcinoma

The evolutionary trajectories of early lung adenocarcinoma (LUAD) have not been fully elucidated. We hypothesize that genomic analysis between pre-invasive and invasive components will facilitate the description of LUAD evolutionary patterns. We micro-dissect malignant pulmonary nodules (MPNs) into paired pre-invasive and invasive components for panel-genomic sequencing and recognize three evolutionary trajectories. Evolutionary mode 1 (EM1) demonstrates none of the common driver events between paired components, but another two modes, EM2A and EM2B, exhibit critical private alterations restricted to pre-invasive and invasive components, respectively. When ancestral clones harbor EGFR mutations, truncal mutation abundance significantly decrease after the acquisition of invasiveness, which may be associated with the intratumoral accumulation of infiltrated B cells. Harboring EGFR mutations is critical to the selective pressure and further impacts the prognosis. Our findings extend the understanding of evolutionary trajectories during invasiveness acquisition in early LUAD.

Invasive early stage lung adenocarcinoma has a heterogeneous prognosis. Here, the authors microdissect malignant pulmonary nodules to invasive and preinvasive components and study the mutations that are common or private between the lesions, allowing them to understand the evolutionary path of the tumours.

Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer

The expression and regulation of matrisome genes-the ensemble of extracellular matrix, ECM, ECM-associated proteins and regulators as well as cytokines, chemokines and growth factors-is of paramount importance for many biological processes and signals within the tumor microenvironment. The availability of large and diverse multi-omics data enables mapping and understanding of the regulatory circuitry governing the tumor matrisome to an unprecedented level, though such a volume of information requires robust approaches to data analysis and integration. In this study, we show that combining Pan-Cancer expression data from The Cancer Genome Atlas (TCGA) with genomics, epigenomics and microenvironmental features from TCGA and other sources enables the identification of "landmark" matrisome genes and machine learning-based reconstruction of their regulatory networks in 74 clinical and molecular subtypes of human cancers and approx. 6700 patients. These results, enriched for prognostic genes and cross-validated markers at the protein level, unravel the role of genetic and epigenetic programs in governing the tumor matrisome and allow the prioritization of tumor-specific matrisome genes (and their regulators) for the development of novel therapeutic approaches.

Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management

Following the introduction of routine Prostate Specific Antigen (PSA) screening in the early 1990's, Prostate Cancer (PCa) is often detected at an early stage. There are also a growing number of treatment options available and so the associated mortality rate is generally low. However, PCa is an extremely complex and heterogenous disease and many patients suffer disease recurrence following initial therapy. Disease recurrence commonly results in metastasis and metastatic PCa has an average survival rate of just 3-5 years. A significant problem in the clinical management of PCa is being able to differentiate between patients who will respond to standard therapies and those who may benefit from more aggressive intervention at an earlier stage. It is also acknowledged that for many men the disease is not life threatenting. Hence, there is a growing desire to identify patients who can be spared the significant side effects associated with PCa treatment until such time (if ever) their disease progresses to the point where treatment is required. To these important clinical needs, current biomarkers and clinical methods for patient stratification and personlised treatment are insufficient. This review provides a comprehensive overview of the complexities of PCa pathology and disease management. In this context it is possible to review current biomarkers and proteomic technologies that will support development of biomarker-driven decision tools to meet current important clinical needs. With such an in-depth understanding of disease pathology, the development of novel clinical biomarkers can proceed in an efficient and effective manner, such that they have a better chance of improving patient outcomes.

Horizon Scanning for pharmaceuticals and effective health care programming: 2 years' experience at the Italian Medicines Agency

The Italian Medicines Agency (AIFA) developed Horizon Scanning (HS) methodology to address programming and sustainability challenges posed by accelerating pharmaceutical innovation and increasing costs. From July 2018 to June 2020, 563 new potential treatments (new medicines and new indications of existing medicines) with an expected impact in 2020-2022 were identified. In this review, we assess and discuss these HS results, including medicine characteristics related to higher prioritization scores, such as clinical areas of major development. After exclusions because of the selection process, 213 (40.6%) new potential treatments were included in the prioritization tool; 25 (11.7%) were shown to be possibly associated with a major impact on healthcare systems (HCSs) according to overall prioritization criteria. Lessons learned from the process led to HS system improvements.

Germline Variants That Affect Tumor Progression

Germline variants have a rich history of being studied in the context of cancer risk. Emerging studies now suggest that germline variants contribute not only to cancer risk but to tumor progression as well. In this opinion article, we discuss the initial discoveries associating germline variants with patient outcome and the mechanisms by which germline variants affect molecular pathways. Germline variants affect molecular pathways through amino acid changes, alteration of splicing patterns or expression of genes, influencing the selection for somatic mutations, and causing genome-wide mutational enrichment. These molecular alterations can lead to tumor phenotypes that become clinically apparent such as metastasis, alterations to the immune microenvironment, and modulation of therapeutic response. Overall, the growing body of evidence suggests that germline variants play a larger role in tumor progression than has been previously appreciated and that germline variation holds substantial potential for improving personalized medicine and patient outcomes.

XPC deficiency increases risk of hematologic malignancies through mutator phenotype and characteristic mutational signature

Recent studies demonstrated a dramatically increased risk of leukemia in patients with a rare genetic disorder, Xeroderma Pigmentosum group C (XP-C), characterized by constitutive deficiency of global genome nucleotide excision repair (GG-NER). The genetic mechanisms of non-skin cancers in XP-C patients remain unexplored. In this study, we analyze a unique collection of internal XP-C tumor genomes including 6 leukemias and 2 sarcomas. We observe a specific mutational pattern and an average of 25-fold increase of mutation rates in XP-C versus sporadic leukemia which we presume leads to its elevated incidence and early appearance. We describe a strong mutational asymmetry with respect to transcription and the direction of replication in XP-C tumors suggesting association of mutagenesis with bulky purine DNA lesions of probably endogenous origin. These findings suggest existence of a balance between formation and repair of bulky DNA lesions by GG-NER in human body cells which is disrupted in XP-C patients.

Xeroderma Pigmentosum group C (XP-C) is a rare genetic disorder characterised by deficient DNA repair leading to skin and internal cancer, but the latter is not well understood molecularly. Here the authors sequence genomes of non-skin cancers from XP-C patients to unravel its mutational patterns.

Hotspots of Human Mutation

Mutation of the human genome results in three classes of genomic variation: single nucleotide variants; short insertions or deletions; and large structural variants (SVs). Some mutations occur during normal processes, such as meiotic recombination or B cell development, and others result from DNA replication or aberrant repair of breaks in sequence-specific contexts. Regardless of mechanism, mutations are subject to selection, and some hotspots can manifest in disease. Here, we discuss genomic regions prone to mutation, mechanisms contributing to mutation susceptibility, and the processes leading to their accumulation in normal and somatic genomes. With further, more accurate human genome sequencing, additional mutation hotspots, mechanistic details of their formation, and the relevance of hotspots to evolution and disease are likely to be discovered.

Chromosome structural variation in tumorigenesis: mechanisms of formation and carcinogenesis

With the rapid development of next-generation sequencing technology, chromosome structural variation has gradually gained increased clinical significance in tumorigenesis. However, the molecular mechanism(s) underlying this structural variation remain poorly understood. A search of the literature shows that a three-dimensional chromatin state plays a vital role in inducing structural variation and in the gene expression profiles in tumorigenesis. Structural variants may result in changes in copy number or deletions of coding sequences, as well as the perturbation of structural chromatin features, especially topological domains, and disruption of interactions between genes and their regulatory elements. This review focuses recent work aiming at elucidating how structural variations develop and misregulate oncogenes and tumor suppressors, to provide general insights into tumor formation mechanisms and to provide potential targets for future anticancer therapies.

Next-generation diagnostics for precision oncology: Preanalytical considerations, technical challenges, and available technologies

Molecular diagnostics as the centrepiece of precision oncology has gone through revolutionary developments over the last decade, becoming tremendously broad, deep and precise with still ongoing advancements. In the majority of scenarios, treatment selection for cancer patients without any type of molecular characterization is no longer conceivable. Considering the impact of sample quality on the reliability of molecular analyses and the importance of the results for the fate of an individual patient, it is surprising how sparsely preanalytical and analytical requirements are addressed scientifically. Standardization and rigorous quality assessment continue to play only a marginal role in the field. Within this review, we will systematically discuss influencing preanalytic parameters and technology setups affecting molecular test results. We will shed light on the specifics of different analytes, technical modalities, and analysis pipelines. The review will have a certain focus on broad molecular genetic tumour testing with next generation sequencing but will go beyond that including other molecular diagnostic modalities and will give a glimpse into the future of molecular testing.