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The Roles of Graduate Students and Postdocs in the Emergence of Gene Editing: CRISPR Science and Technology
Curator: Aviva Lev-Ari, PhD, RN
2.1.5.13 The Roles of Graduate Students and Postdocs in the Emergence of Gene Editing: CRISPR Science and Technology, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
PLAN TO ATTEND
Understanding CRISPR: Mechanisms and Applications: CHI, September 19-22, 2016, Westin Boston Waterfront, Boston
Announcement from LPBI Group: key code LPBI16 for Exclusive Discount to attend Boston’s Discovery on Target (September 19-22, 2016, CRISPR: Mechanisms to Applications on 9/19/2016)
The emergence of Gene Editing: CRISPR Science and Technology provide evidence that since the NIH effort to sequence the Genome, this endeavor is the second one to follow as an evolving scientific community ecosystem at their best in COMPETITION AND COLLABORATION, as well as in the survival of the fittest struggle that yielded a legal battle on appropriation of the discovery and the rights to its Intellectual Property (IP).
On our Journal we published
70 articles on Gene Editing: CRISPR Science and Technology
See references in
UPDATED – Status “Interference — Initial memorandum” – CRISPR/Cas9 – The Biotech Patent Fight of the Century: UC, Berkeley and Broad Institute @MIT
The soaring popularity of gene editing has made celebrities of the principal investigators who pioneered the field — but their graduate students and postdocs are often overlooked.
Doudna and other principal investigators involved in the seminal work have become scientific celebrities: they are profiled in major newspapers, star in documentaries and are rumoured to be contenders for a Nobel prize. “When I came to the lab, I was the only person studying CRISPR,” Wiedenheft says. “When I left the lab, almost everyone was studying it.”
His work with Doudna yielded a First author place on their 2011 Nature article:
In January 2016, Eric Lander, president of the Broad Institute of MIT and Harvard in Cambridge, Massachusetts, tossed into this minefield a historical portrait called ‘The Heroes of CRISPR
Editor of Cell received letters questioning the decision to publish Eric Lander’s article due to Broad Institute involvement in a legal dispute and presenting an incomplete picture of the evolution of the discovery and using a title that assigns the Heroism on a matter legally unsettled.
Does the Cell, 2016 article present all attributions due to:
1.The quiet revolutionary: How the co-discovery of CRISPR explosively changed Emmanuelle Charpentier’s life
The microbiologist spent years moving labs and relishing solitude. Then her work on gene-editing thrust her into the scientific spotlight.
Prof. Doudna at UC, Berkeley and Prof. Church at Harvard, both support appropriate credit to students involved in the discovery, yet the reality is that the
credit in science goes to the Leader of the lab, as do any prizes that follow.
BioTech Industry Prospect for Student of Powerhouse Academic Labs: Alternative Careers to Academic Positions
Using Online Mendelian Inheritance in Man (OMIM) database and the Human Genome Mutation Database (HGMD) Pro 2015.2 for Quantification of the growth in gene-disease and variant-disease associations, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Using Online Mendelian Inheritance in Man (OMIM) database and the Human Genome Mutation Database (HGMD) Pro 2015.2 for Quantification of the growth in gene-disease and variant-disease associations
Reporter: Aviva Lev-Ari, PhD, RN
Reanalysis of Clinical Exome Data Over Time Could Yield New Diagnoses
NEW YORK (GenomeWeb) – Clinical exomes that are re-evaluated in a systematic way could yield new diagnoses and prove useful to clinicians, according to a study published yesterday in Genetics in Medicine.
A team of researchers from Stanford University set out to examine whether nondiagnostic clinical exomes could provide new information for patients if they were re-examined with current bioinformatics software and knowledge of disease-related variants as presented in the literature.
Clinical exome sequencing yields no diagnosis for about 75 percent of patients evaluated for possible Mendelian disorders, wrote senior author Gill Bejerano and his colleagues. But a reanalysis of exome and phenotypic data from 40 such individuals using current methods identified a definitive diagnosis for four of them — 10 percent — the team said.
In these cases, the causative variant was de novo and found in a relevant autosomal-dominant disease gene. At the time these exomes were first sequenced, the researchers wrote, the existing literature on these causative genes was either “weak, nonexistent, or not readily located.” When the exomes were re-examined by his team, Bejerano noted, the supporting literature was more robust.
In addition to re-analyzing exome data, the researchers have been working on establishing causality for novel candidate disease genes through patient matches. For this, the team has been using the GeneMatcher website, which allows them to find other clinicians and researchers around the world who have patients, or animal models, with mutations in the same genes as their own patients. Through an API developed by the Matchmaker Exchange project, GeneMatcher submitters can also query the PhenomeCentral and Decipher databases. As of March, more than 4,000 genes had been submitted to GeneMatcher from more than 1,300 submitters in 48 countries, and 1,900 matches had been made, Sobreira reported.
Her team has so far submitted data from 104 families, involving 280 genes, and has had 314 matches so far, involving 113 genes. Several cases have been successes, meaning the researchers could establish that a candidate gene is indeed disease causing, and several others are pending, both from Hopkins and from other groups. The total number of solved cases tracing their success to GeneMatcher is currently unknown, Sobreira said, but the organizers are planning to survey submitters about their success rate in the near future.
Lysyl Oxidase (LOX) gene missense mutation causes Thoracic Aortic Aneurysm and Dissection (TAAD) in Humans because of inadequate cross-linking of collagen and elastin in the aortic wall
Mutation carriers may be predisposed to vascular diseases because of weakened vessel walls under stress conditions.
Reporter: Aviva Lev-Ari, PhD, RN
2.1.3.7 Lysyl Oxidase (LOX) gene missense mutation causes Thoracic Aortic Aneurysm and Dissection (TAAD) in Humans because of inadequate cross-linking of collagen and elastin in the aortic wall – Mutation carriers may be predisposed to vascular diseases because of weakened vessel walls under stress conditions, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
Loss of function mutation in LOX causes thoracic aortic aneurysm and dissection in humans
Author contributions: V.S.L., R.P.M., N.Y.F., and N.O.S. designed research; V.S.L., C.M.H., and N.O.S. performed research; E.P.H., N.C., C.G.L., D.V., B.G.M.P., R.P.M., and N.Y.F. contributed new reagents/analytic tools; V.S.L., C.M.
Significance
The mechanical integrity of the arterial wall is dependent on a properly structured ECM. Elastin and collagen are key structural components of the ECM, contributing to the stability and elasticity of normal arteries. Lysyl oxidase (LOX) normally cross-links collagen and elastin molecules in the process of forming proper collagen fibers and elastic lamellae. Here, using whole-genome sequencing in humans and genome engineering in mice, we show that a missense mutation in LOX causes aortic aneurysm and dissection because of insufficient elastin and collagen cross-linking in the aortic wall. These findings confirm mutations in LOX as a cause of aortic disease in humans and identify LOX as a diagnostic and potentially therapeutic target.
Abstract
Thoracic aortic aneurysms and dissections (TAAD) represent a substantial cause of morbidity and mortality worldwide. Many individuals presenting with an inherited form of TAAD do not have causal mutations in the set of genes known to underlie disease. Using whole-genome sequencing in two first cousins with TAAD, we identified a missense mutation in the lysyl oxidase (LOX) gene (c.893T > G encoding p.Met298Arg) that cosegregated with disease in the family. Using clustered regularly interspaced short palindromic repeats (CRISPR)/clustered regularly interspaced short palindromic repeats-associated protein-9 nuclease (Cas9) genome engineering tools, we introduced the human mutation into the homologous position in the mouse genome, creating mice that were heterozygous and homozygous for the human allele. Mutant mice that were heterozygous for the human allele displayed disorganized ultrastructural properties of the aortic wall characterized by fragmented elastic lamellae, whereas mice homozygous for the human allele died shortly after parturition from ascending aortic aneurysm and spontaneous hemorrhage. These data suggest that a missense mutation in LOX is associated with aortic disease in humans, likely through insufficient cross-linking of elastin and collagen in the aortic wall. Mutation carriers may be predisposed to vascular diseases because of weakened vessel walls under stress conditions. LOX sequencing for clinical TAAD may identify additional mutation carriers in the future. Additional studies using our mouse model of LOX-associated TAAD have the potential to clarify the mechanism of disease and identify novel therapeutics specific to this genetic cause.
NEW YORK (GenomeWeb) – Researchers from Washington University School of Medicine have linked a LOX gene variant with aortic rupture and aneurysm.
As they reported in the online early edition of the Proceedings of the National Academy of Sciences yesterday, the researchers sequenced two first cousins from a family with a history of aortic ruptures and aneurysms to uncover a missense mutation in the lysyl oxidase (LOX) gene, which encodes a protein that cross-links elastin and collagen. When they used CRISPR/Cas9 genome engineering to introduce the mutation into a mouse model, mice heterogeneous for the mutation had disorganized aortic walls, while mice homozygous for the mutation died shortly after birth of ascending aneurysm and spontaneous hemorrhage, suggesting that the LOX variant might be causal.
Innovative Gene Families for exploring patterns of Genetic Families applied by Craig Venter’s Team in Deeply Sequencing 10,500 Genomes: an average of 8,579 novel variants found per person –Intolerant sites, might be essential for life or health, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Innovative Gene Families for exploring patterns of Genetic Families applied by Craig Venter’s Team in Deeply Sequencing 10,500 Genomes: an average of 8,579 novel variants found per person – Intolerant sites, might be “essential for life or health.”
This article is a preprint and has not been peer-reviewed [what does this mean?].
Deep Sequencing of 10,000 Human Genomes
Amalio Telentia,b,1, Levi C.T. Piercea,d,1, William H. Biggs a,1, Julia di Iulioa,b, Emily H.M. Wonga, Martin M. Fabania, Ewen F. Kirknessa, Ahmed Moustafaa, Naisha Shaha, Chao Xiec, Suzanne C. Brewertonc, Nadeem Bulsaraa, Chad Garnera, Gary Metzkera, Efren Sandovala, Brad A. Perkinsa, Franz J. Ocha,d, Yaron Turpaz a,c, J. Craig Venter a,b,2
a Human Longevity Inc., San Diego, CA, USA.
b J. Craig Venter Institute, La Jolla, CA, USA.
c Human Longevity Singapore Pte. Ltd. Singapore.
d Human Longevity Inc., Mountain View, CA, USA
1 These authors contributed equally to this work.
2 Corresponding author.
E-mail: jcventer@humanlongevity.com (J.C.V.)
Diving in Deep
Jul 19, 2016
Craig Venter’s team has deeply sequenced the genomes of some 10,500 people, as it reports in a preprint at bioRxiv. Each person was sequenced to a depth of between 30X and 40X coverage, and the team reported that 84 percent of an individual’s genome could be sequenced with confidence — that region, it notes, included more than 95 percent known pathogenic variant loci.
The J. Craig Venter Institute and Human Longevity team also uncovered more than 150 million variants, more than half of which hadn’t been previously seen. This works out to an average of 8,579 novel variants found per person. This and other data collected enabled the researchers to gauge how tolerant different regions of the genome are to genetic change. Intolerant sites, they noted, might be “essential for life or health.”
Hudson Freeze from Sanford Burnham Prebys Medical Discovery Institute tells the San Diego Union-Tribune that this is the first study he knows of to have examined so many genomes of such quality. “This idea of taking more than 10,000 individual genomes and doing this — that takes a lot of guts,” Freeze says. “It’s going to be a guy like Craig Venter, who has the moxie, the finances and the know-how to get this done.”
The University of California, San Francisco’s Atul Butte also calls the work “impressive.” He adds that the researchers’ approach to looking at gene families when exploring patterns of genetic families was “innovative.”
The San Diego Union-Tribune notes that Venter declined to speak about the work as it had not yet been through peer review.
Deep Learning for In-silico Drug Discovery and Drug Repurposing: Artificial Intelligence to search for molecules boosting response rates in Cancer Immunotherapy: Insilico Medicine @John Hopkins University, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Deep Learning for In-silico Drug Discovery and Drug Repurposing: Artificial Intelligence to search for molecules boosting response rates in Cancer Immunotherapy: Insilico Medicine @John Hopkins University
Reporter: Aviva Lev-Ari, PhD, RN
Insilico Medicine –>>> transcriptome-based pathway perturbation analysis
Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore with R&D resources in Belgium, Russia, and Poland hiring talent through hackathons and competitions. It utilizes advances in genomics, big data analysis and deep learning for in silico drug discovery and drug repurposing for aging and age-related diseases. The company pursues internal drug discovery programs in cancer, Parkinson’s, Alzheimer’s, sarcopenia and geroprotector discovery. Through its Pharmaceutical Artificial Intelligence division the company provides advanced machine learning services to biotechnology, pharmaceutical, and skin care companies.
Insilico Medicine develops a new approach to concomitant cancer immunotherapy
Artificial intelligence to search for molecules boosting response rates in cancer immunotherapy
INSILICO MEDICINE, INC.
IMAGE: THIS IS THE INSILICO MEDICINE LOGO.view more
CREDIT: INSILICO MEDICINE
Summary:
Some of the most promising drugs for cancer therapy called checkpoint inhibitors often result in complete remissions, however, a majority of patients fail cancer immunotherapy with antibodies targeting immune checkpoints, such as CTLA-4 or programmed death-1 (PD-1).
Insilico Medicine developed a set of pathway-based signatures of response to popular checkpoint inhibitors
Using these markers and a deep learned drug scoring engine Insilico Medicine identified 12 leads that may help increase response to cancer immunotherapy and is seeking industry partnerships to test these leads
Thursday, July 14, 2016, Baltimore, MD — Recent advances in cancer immunotherapy demonstrated complete remission in multiple tumor types including melanoma and lung cancers. Almost every major pharmaceutical company operating in oncology space started multiple programs in immuno-oncology with thousands of clinical trials underway. Immuno-oncology is now a very broad field ranging from treatment of a patient with an engineered antibody to genome editing of patient’s immune cells. Genetic mutations accruing from the inherent genomic instability of tumor cells present neo-antigens that are recognized by the immune system. Cross-presentation of tumor antigens at the immune synapse between antigen-presenting dendritic cells and T lymphocytes can potentially activate an adaptive antitumor immune response, however, tumors continuously evolve to counteract and ultimately defeat such immune surveillance by co-opting and amplifying mechanisms of immune tolerance to evade elimination by the immune system. This prerequisite for tumor progression is enabled by the ability of cancers to produce negative regulators of immune response.
Cancer immunotherapy is currently focused on targeting immune inhibitory checkpoints that control T cell activation, such as CTLA-4 and PD-1. Monoclonal antibodies that block these immune checkpoints (commonly referred to as immune checkpoint inhibitors) can unleash antitumor immunity and produce durable clinical responses in a subset of patients with advanced cancers, such as melanoma and non-small-cell lung cancer. However, these immunotherapeutics are currently constrained by their inability to induce clinical responses in the vast majority of patients and the frequent occurrence of immune-related adverse events. A key limitation of checkpoint inhibitors is that they narrowly focus on modulating the immune synapse but do not address other key molecular determinants that may also be responsible for immune dysfunction.
Immunoresistance often ensues as a result of the concomitant activation of multiple, often overlapping signaling pathways. Therefore, inhibition of multiple, cross-talking pathways involved in survival control with combination therapy is usually more effective in decreasing the likelihood that cancer cells will develop therapeutic resistance than with single agent therapy. While research efforts are now focused on identifying new inhibitory mechanisms that could be targeted to achieve responses in patients with refractory cancers and provide durable and adaptable cancer control, there are outstanding challenges in determining what combination of immunotherapies and conventional therapies will prove beneficial against each tumor type.
“Immunotherapy is the most promising area in oncology resulting in cures, but we need to identify effective combinations of both established methods and new drugs developed specifically to boost response rates. At Insilico Medicine we developed a new method for screening, scoring and personalizing small molecules that may boost response rates to PD-1, PD-L1, CTLA4 and other checkpoint inhibitors. We can identify effective combinations of both established methods and new drugs developed specifically to boost response rates to immunotherapy”, said Artem Artemov, director of computational drug repurposing at Insilico Medicine.
Insilico Medicine, Inc. is one of the leaders in transcriptome-based pathway perturbation analysis. It is also a pioneer in applying cutting edge artificial intelligence techniques to biological and medical data analysis, particularly focused on in silico screening for new compounds against cancer and known drugs which can be repurposed against different cancers. One of the major programs currently ongoing at Insilico Medicine is evaluation of the transcriptional responses to multiple checkpoint inhibitors and analyzing the pathway-level differences in patients who respond and fail to respond to clinically approved checkpoint inhibitors. This novel computational approach is aimed at identifying new drug candidates which can be used in combination with immunotherapy to unleash durable antitumor effect against several types of cancers.
Recently, scientists at Insilico Medicine performed a large in silico screening of compounds that can be administered in combination with anti-PD1 immunotherapy to increase response rates. The researchers collected transcriptomic data from tumors of patients who either responded or failed to respond to standard immunotherapy, using both publically available and internally generated data. Next, they used differential pathway activation analysis and deep learning based approaches to identify transcriptomic signatures predicting the success of immunotherapy in a particular tumor type.
Finally, they analyzed drug-induced transcriptomic effects to screen for the drugs that can robustly drive transcriptomes of tumor cells from non-responsive state to the state responsive to immunotherapy. In other words, researchers developed approach that can predict whether drug of interest would induce a transcriptional signature that characterizes those patients that respond to cancer immunotherapy in non-respondents. This method allows personalizing these drugs to individual patients and specific checkpoint inhibitors. Among the top-scoring drugs, they found several compounds known to increase response rates in combination with cancer immunotherapy. One of the top-scoring compounds included a naturally-occurring substance marketed as a natural product.
The current list of top-scoring leads that may increase response rates to checkpoint inhibitors included 12 small molecules identified using signaling pathway perturbation analysis and annotated using a deeply learned drug scoring system. Insilico Medicine is currently open for partnerships which will allow further testing and validation of the discovered compounds ex vivo on cell cultures established from tumors which respond and failed to respond to immunotherapy, as well as in mice with patient-derived tumor xenografts. This approach may greatly reduce the costs of preclinical trials and significantly shorten the timeframe from a drug prediction to validation and marketing. The compounds, after preclinical and clinical validation, may improve cancer care and dramatically increase the lifespan of cancer patients.
A panel of leads for concomitant immunotherapy is part of a large number of leads developed using DeepPharma™, artificially-intelligent drug discovery engine, which includes a large number of molecules predicted to be effective antineoplastic agents, metabolic regulators, CVD and CNS lead, senolytics and ED drugs. Recently Insilico Medicine published several seminal papers demonstrating proof of concept of utilizing deep learning techniques to predict pharmacological properties of small molecules using transcriptional response data utilizing deep neural networks for biomarker development. “Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data,” a paper published in Molecular Pharmaceuticals received the American Chemical Society Editors’ Choice Award. Another recent collaboration with Biotime, Inc resulted in a launch of Embryonic.AI, deep learned predictor of differentiation state of the sample.
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First challenge to make use of the new NCI Cloud Pilots – Somatic Mutation Challenge – RNA: Best algorithms for detecting all of the abnormal RNA molecules in a cancer cell, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
First challenge to make use of the new NCI Cloud Pilots – Somatic Mutation Challenge – RNA: Best algorithms for detecting all of the abnormal RNA molecules in a cancer cell
Reporter: Aviva Lev-Ari, PhD, RN
Genomic rearrangements in cancer cells produce fusion transcripts, which may give rise to chimeric protein products not present in normal cells. In addition, cancer cells can express alternate forms of encoded messages that give rise to protein variants different from normal tissue. These chimeras and protein variants can serve as robust diagnostic markers or drug targets. Moreover, ongoing research efforts are beginning to unveil the potential clinical relevance of these variant RNA products. Increasing the “alterome” of tumors by fully characterizing their RNA landscapes will expand our understanding of cancer mechanisms, provide new biomarkers and reveal possible new RNA-based therapeutics, thus improving personalized patient treatment.
“Predicting RNA species in a cancer cell is a particularly challenging task,” says Josh Stuart, Professor at the UC Santa Cruz Genomics Institute and one of the challenge leaders. “RNA expression reflects much of the deranged complexity of the underlying cancer cell DNA and then adds another level of derangement on top of that.”
The goal of the SMC-RNA Challenge is to identify the best methods for detecting rearrangements in RNA sequencing (RNA-seq) data. Sub-challenges are focused on detecting and quantifying mRNA fusions and isoforms. Methods will be evaluated with both in silico and spiked-in data.
Two key questions that will be addressed are:
1) What is the best way to estimate the abundances of a set of known RNA isoforms? and
2) What is the best way to predict the presence of novel gene fusions?
Both of these questions will involve in silico generated and wet lab spiked-in RNA sequencing data.
We are launching the ICGC-TCGA DREAM Somatic Mutation Calling RNA Challenge (SMC-RNA), a community-based collaborative competition of researchers from across the world. We will rigorously assess the accuracy of methods to perform two key tasks in cancer RNA-Seq data analysis: the quantification of known isoforms and detecting novel fusion transcripts. We will generate RNA-Seq data for a synthetic-based challenge. Since synthetic data may not capture the complexity of real human tumours, we will also introduce a phase in which teams make predictions on real human-tumours. Challenge organizers will perform retrospective experimental validation on predictions to create a gold-standard. Validation will employ a combination of long-read sequencing and target-capture approaches. The SMC-RNA Challenge will analyze a couple of dozen samples created to have known alterations representing different tumor types, allowing confidence that the winning methods will be generalizable across the broad range of human cancers.
The ICGC-TCGA DREAM Somatic Mutation Calling – RNA Challenge (SMC-RNA) is an international effort to improve standard methods for identifying cancer-associated rearrangements in RNA sequencing (RNA-seq) data. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].
Why is RNA biology important in cancer?
While only a small fraction of the genome encodes proteins, the majority is either transcribed or has putative regulatory functions, with the consequence that cellular functions are extensively regulated at the RNA level. The regulation of RNA, and its dramatic dysregulation in cancer cells, occurs in multiple ways. RNA abundances may be altered, and these have served as the basis for clinically-important prognostic biomarkers. Genomic rearrangements in cancer cells produce fusion transcripts which may give rise to chimeric protein products not present in normal cells. These can serve as robust diagnostic markers (e.g. TMPRSS2-ERG in prostate cancer) or drug targets (e.g. BCR-ABL in CML). Ongoing research efforts are beginning to unveil the potential clinical relevance of aberrant processing of RNA in cancer, such as defects in alternative-splicing. To fully document the molecular differences in transcription between tumor cells and their normal counterparts an assortment of computational methods are needed. Increasing the “alterome” of tumors by fully characterizing their RNA landscapes will expand our understanding of cancer mechanisms, provide new biomarkers and reveal possible new RNA-based therapeutics, improving personalized patient treatment.
What is RNA Sequencing?
RNA-seq is using next-generation sequencing techniques to sequence RNA. It allows the the transcriptome to be sequenced at high coverage, provides raw read counts that can be used to assess expression levels, and from it elucidate other biologically relevant information. RNA is reverse transcribed into cDNA and then sequenced at high depth by high-throughput technologies, such as Illumina HiSeq, Roche 454, and PacBio [4]. After sequencing, reads can be aligned de novo, to a reference genome, or to a reference transcriptome. Using RNA-seq has some advantages over microarrays, including no prior knowledge of the transcriptome needed and an unbiased expression analysis [5]. In comparison, microarrays, the current gold standard for RNA analysis require probe design and have been found to contain more bias in low-intensity genes [6]. Some key challenges in RNA-seq include biases occurring in RNA fragmentation, cDNA fragmentation, and library preparation, in addition to, potential PCR artifacts that skew expression levels, and possible alignment to multiple locations in a reference genome [5]. Due to these and other influences, methods for detecting and quantifying transcriptional isoforms, as well as fusion genes remains a challenging set of problems and competing methods for interpreting RNA-Seq results continues to be poor.
Fusion Prediction
Scientific Rationale: Gene fusions occur when two genes at the DNA level are joined and may be due to an oncogenic event. A fusion may also occur at the RNA level where a ligation between two transcripts occurs. Gene fusions have an important role in the initial steps of tumorigenesis. Specifically, gene fusions have been found to be the driver mutations in neoplasia and have been linked to various tumour subtypes. An increasing number of gene fusions are being recognized as important diagnostic and prognostic parameters in malignant haematological disorders and childhood sarcomas. Gene fusions occur in all malignancies and account for 20% of human cancer morbidity [7].
Isoform Prediction
Scientific Rationale: Isoforms are alternative expressions of a gene formed from splicing during post-transcriptional processing. Dysregulation of alternative splicing occurs in every category of Hanahan’s and Weinberg’s hallmarks of cancer. Modifications in splicing may occur due to mutations of cis-acting splicing elements, trans-acting regulators, and microRNAs. Moreover, cancer cell lines, regardless of their tissue of origin, can be effectively discriminated from non-cancer cell lines at isoform level, but not at gene level. Existence of an isoform signature, rather than a gene signature, could be used to distinguish cancer cells from normal cells [8].
Challenges
The goal of this Challenge is to use a crowd-based competition to identify the optimal methods for detecting (and quantifying) mRNA fusions and isoforms from RNA-seq data.
Sub-Challenge 1: Quantify Known Isoforms
Can algorithms estimate the levels of a set of provided isoforms?
1a: In silico simulated data challenge
1b: Spike-in data challenge on real data from long-reads and hybrid capture.
Sub-Challenge 2: Detect Gene Fusions
Can algorithms predict the presence of gene fusions?
2a: In silico simulated gene fusions.
2b: Spike-in long-read data.
Reward
All participants will be invited as consortium co-authors on Challenge marker papers
Winners will receive travel awards & speaking invitations at the next DREAM conference or Sage Congress
New methods will be considered for co-publication with the Challenge marker papers by our publishing partner
Other rewards will be announced as they are determined.
Challenge Organizers / Scientific Advisory Board
Kyle Ellrott, Oregon Health Sciences University
Josh Stuart, University of California, Santa Cruz
Paul C. Boutros, Ontario Institute for Cancer Research
Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics, 10(1), 57-63.
Han, Y., Gao, S., Muegge, K., Zhang, W., & Zhou, B. (2015). Advanced Applications of RNA Sequencing and Challenges.Bioinformatics and biology insights, 9(Suppl 1), 29.
Robinson, D., Wang, J., & Storey, J. (2015). A nested parallel experiment demonstrates differences in intensity-dependence between RNA-seq and microarrays. Nucleic Acids Research. 43(20)
Mertens, F., Johansson, B., Fioretos, T., & Mitelman, F. (2015). The emerging complexity of gene fusions in cancer. Nature Reviews Cancer, 15(6), 371-381.
Liu S, Cheng C. Alternative RNA splicing and cancer. Wiley interdisciplinary reviews RNA. 2013,4(5),547-566.
Chest pain patients are often evaluated for acute myocardial infarction through troponin testing, which may prompt downstream services (cascades) of uncertain value.
Objective
Determine the association of high-sensitivity cardiac troponin (hs-cTn) assay implementation with cascade events.
Methods
Using electronic health record and billing data, we examined patient-visits to five emergency departments, April 1, 2017 – April 1, 2019. Difference-in-differences analysis compared patient-visits for chest pain (n=7,564) to patient-visits for other symptoms (n=100,415) (irrespective of troponin testing) before and after hs-cTn assay implementation. Outcomes included presence of any cascade event potentially associated with an initial hs-cTn test (primary), individual cascade events, length of stay, and spending on cardiac services.
Results
Following hs-cTn implementation, patients with chest pain had a 2.8% (95%CI 0.72, 4.9) net increase in experiencing any cascade event. They were more likely to have multiple troponin tests (10.5%, 95%CI 9.0, 12.0) and electrocardiograms (7.1 per 100 patient-visits, 95%CI 1.8, 12.4). However, they received net fewer computed tomography scans (-1.5 per 100 patient-visits, 95%CI -1.8, -1.1), stress tests (-5.9 per 100 patient-visits, 95%CI -6.5, -5.3), and cardiac catheterizations (-0.65 per 100 patient-visits, 95%CI -1.01, -0.30) and were less likely to receive cardiac medications, undergo cardiology evaluation (-3.5%, 95%CI -4.5, 2.6), or be hospitalized (-5.8%, 95%CI -7.7, -3.8). Chest pain patients had lower net mean length of stay (-0.24 days, 95%CI -0.32, -0.16) but no net change in spending.
Conclusions
Hs-cTn assay implementation was associated with more net upfront tests yet fewer net stress tests, catheterizations, cardiology evaluations, and hospital admissions in chest pain patients relative to patients with other symptoms.
Background: We assessed whether plasma troponin I measured by a high-sensitivity assay (hs-TnI) is associated with incident cardiovascular disease (CVD) and mortality in a community-based sample without prior CVD.
Methods: ARIC study (Atherosclerosis Risk in Communities) participants aged 54 to 74 years without baseline CVD were included in this study (n=8121). Cox proportional hazards models were constructed to determine associations between hs-TnI and incident coronary heart disease (CHD; myocardial infarction and fatal CHD), ischemic stroke, atherosclerotic CVD (CHD and stroke), heart failure hospitalization, global CVD (atherosclerotic CVD and heart failure), and all-cause mortality. The comparative association of hs-TnI and high-sensitivity troponin T with incident CVD events was also evaluated. Risk prediction models were constructed to assess prediction improvement when hs-TnI was added to traditional risk factors used in the Pooled Cohort Equation.
Results: The median follow-up period was ≈15 years. Detectable hs-TnI levels were observed in 85% of the study population. In adjusted models, in comparison to low hs-TnI (lowest quintile, hs-TnI ≤1.3 ng/L), elevated hs-TnI (highest quintile, hs-TnI ≥3.8 ng/L) was associated with greater incident CHD (hazard ratio [HR], 2.20; 95% CI, 1.64-2.95), ischemic stroke (HR, 2.99; 95% CI, 2.01-4.46), atherosclerotic CVD (HR, 2.36; 95% CI, 1.86-3.00), heart failure hospitalization (HR, 4.20; 95% CI, 3.28-5.37), global CVD (HR, 3.01; 95% CI, 2.50-3.63), and all-cause mortality (HR, 1.83; 95% CI, 1.56-2.14). hs-TnI was observed to have a stronger association with incident global CVD events in white than in black individuals and a stronger association with incident CHD in women than in men. hs-TnI and high-sensitivity troponin T were only modestly correlated (r=0.47) and were complementary in prediction of incident CVD events, with elevation of both troponins conferring the highest risk in comparison with elevation in either one alone. The addition of hsTnI to the Pooled Cohort Equation model improved risk prediction for atherosclerotic CVD, heart failure, and global CVD.
Conclusions: Elevated hs-TnI is strongly associated with increased global CVD incidence in the general population independent of traditional risk factors. hs-TnI and high-sensitivity troponin T provide complementary rather than redundant information.
Siemens Launches High-sensitivity Troponin Test for Faster Diagnosis of Heart Attacks
The new troponin I assays can detect lower levels of troponin compared to conventional testing
July 25, 2018 — The U.S. Food and Drug Administration (FDA) cleared Siemens Healthineers high-sensitivity troponin I assays (TnIH) for the Atellica IM and ADVIA Centaur XP/XPT in vitro diagnostic analyzers from Siemens Healthineers to aid in the early diagnosis of myocardial infarctions.
The new tests can shorten the time doctors need to diagnose a life-threatening heart attacks. The time to first results is 10 minutes. When a patient experiencing chest pain enters the emergency department, a physician orders a blood test to determine whether troponin is present. As blood flow to the heart is blocked, the heart muscle begins to die in as few as 30 to 60 minutes and releases troponin into the bloodstream.
The company said its high-sensitivity performance of the two new Siemens TnIH assays offers the ability to detect lower levels of troponin at significantly improved precision at the 99th percentile, and detect smaller changes in a patient’s troponin level as repeat testing occurs. This design affords clinicians greater confidence in the results with precision that provides the ability to measure slight, yet critical, changes to begin treatment.[1,2]
Chest pain is the cause of more than 8 million visits annually nationwide to emergency departments, but only 5.5 percent of those visits lead to serious diagnoses such as heart attacks.[3] Armed with data to properly triage patients sooner or to exclude myocardial infarctions, the Siemens Healthineers TnIH assays can help support testing initiatives tied to improving patient experience.
“Our emergency department is overcrowded with patients. If we can do a more efficient job at triaging patients to receive the proper level of care and to discharge the patients who do not need to stay in the emergency department, this will have a tremendous economic advantage for our healthcare system,” said Alan Wu, M.D., chief of clinical chemistry and toxicology at Zuckerberg San Francisco General Hospital and Trauma Center.
Siemens is launching the product at the 70th AACC Annual Scientific Meeting and Clinical Lab Expo taking place July 31 to Aug. 2 in Chicago.
Increases in levels of cardiac troponin T by high-sensitivity assay (hs-cTnT) over time are associated with later risk of death, coronary heart disease (CHD), and especially heart failure in apparently healthy middle-aged people, according to a report published June 8, 2016 in JAMA Cardiology[1].
The novel findings, based on a cohort of >8000 participants from the Atherosclerosis Risk in Communities (ARIC) study followed up to 16 years, are the first to show “an association between temporal hs-cTnT change and incident CHD events” in asymptomatic middle-aged adults,” write the authors, led by Dr John W McEvoy (Johns Hopkins University School of Medicine, Baltimore, MD).
Individuals with the greatest troponin increases over time had the highest risk for poor cardiac outcomes. The strongest association was for risk of heart failure, which reached almost 800% for those with the sharpest hs-cTnT rises.
Intriguingly, those in whom troponin levels fell at least 50% had a reduced mortality risk and may have had a slightly decreased risk of later HF or CHD.
“Serial testing over time with high-sensitivity cardiac troponins provided additional prognostic information over and above the usual clinical risk factors, [natriuretic peptide] levels, and a single troponin measurement. Two measurements appear better than one when it comes to informing risk for future coronary heart disease, heart failure, and death,” McEvoy told heartwirefrom Medscape.
He cautioned, though, that the conclusion is based on observational data and would need to be confirmed in clinical trials. Moreover, high-sensitivity cardiac troponin assays are widely used in Europe but are not approved in the US.
An important next step after this study, according to an accompanying editorial from Dr James Januzzi (Massachusetts General Hospital, Boston, MA), would be to evaluate whether the combination of hs-troponin and natriuretic peptides improves predictive value in this population[2].
“To the extent prevention is ultimately the holy grail for defeating the global pandemic of CHD, stroke, and HF, the main reason to do a biomarker study such as this would be to set the stage for a biomarker-guided strategy to improve the medical care for those patients at highest risk, as has been recently done with [natriuretic peptides],” he wrote.
The ARIC prospective cohort study entered and followed 8838 participants (mean age 56, 59% female, 21.4% black) in North Carolina, Mississippi, Minneapolis, and Maryland from January 1990 to December 2011. At baseline, participants had no clinical signs of CHD or heart failure.
Levels of hs-cTnT, obtained 6 years apart, were categorized as undetectable (<0.005 ng/mL), detectable (≥0.005 ng/mL to <0.014 ng/mL), and elevated (>0.014 ng/mL).
Troponin increases from <0.005 ng/mL to 0.005 ng/mL or higher independently predicted development of CHD (HR 1.41; 95% CI 1.16–1.63), HF (HR 1.96; 95% CI 1.62–2.37), and death (HR 1.50; 95% CI 1.31–1.72), compared with undetectable levels at both measurements.
Hazard ratios were adjusted for age, sex, race, body-mass index, C-reactive protein, smoking status, alcohol-intake history, systolic blood pressure, current antihypertensive therapy, diabetes, serum lipid and cholesterol levels, lipid-modifying therapy, estimated glomerular filtration rate, and left ventricular hypertrophy.
Subjects with >50% increase in hs-cTnT had a significantly increased risk of CHD (HR 1.28; 95% CI 1.09–1.52), HF (HR 1.60; 95% CI 1.35–1.91), and death (HR 1.39; 95% CI 1.22–1.59).
Risks for those end points fell somewhat for those with a >50% decrease in hs-cTnT (CHD: HR 0.47; 95% CI 0.22–1.03; HF: HR 0.49 95% CI 0.23–1.01; death: HR 0.57 95% CI 0.33–0.99).
Among participants with an adjudicated HF hospitalization, the group writes, associations of hs-cTnT changes with outcomes were of similar magnitude for those with HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF).
Few biomarkers have been linked to increased risk for HFpEF, and few effective therapies exist for it. That may be due to problems identifying and enrolling patients with HFpEF in clinical trials, Dr McEvoy pointed out.
“We think the increased troponin over time reflects progressive myocardial injury or progressive myocardial damage,” Dr McEvoy said. “This is a window into future risk, particularly with respect to heart failure but other outcomes as well. It may suggest high-sensitivity troponins as a marker of myocardial health and help guide interventions targeting the myocardium.”
Moreover, he said, “We think that high-sensitivity troponin may also be a useful biomarker along with [natriuretic peptides] for emerging trials of HFpEF therapy.”
But whether hs-troponin has the potential for use as a screening tool is a question for future studies, according to McEvoy.
In his editorial, Januzzi pointed out several implications of the study, including the possibility for lowering cardiac risk in those with measurable hs-troponin, and that HF may be the most obvious outcome to target. Also, optimizing treatment and using cardioprotective therapies may reduce risk linked to increases in hs-troponin. Finally, long-term, large clinical trials on this issue will require a multidisciplinary team effort from various sectors.
“What is needed now are efforts toward developing strategies to upwardly bend the survival curves of those with a biomarker signature of risk, leveraging the knowledge gained from studies such as the report by McEvoy et al to improve public health,” he concluded.
mRNA Data Survival Analysis, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
mRNA Data Survival Analysis
Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
SURVIV for survival analysis of mRNA isoform variation
The rapid accumulation of clinical RNA-seq data sets has provided the opportunity to associate mRNA isoform variations to clinical outcomes. Here we report a statistical method SURVIV (Survival analysis of mRNA Isoform Variation), designed for identifying mRNA isoform variation associated with patient survival time. A unique feature and major strength of SURVIV is that it models the measurement uncertainty of mRNA isoform ratio in RNA-seq data. Simulation studies suggest that SURVIV outperforms the conventional Cox regression survival analysis, especially for data sets with modest sequencing depth. We applied SURVIV to TCGA RNA-seq data of invasive ductal carcinoma as well as five additional cancer types. Alternative splicing-based survival predictors consistently outperform gene expression-based survival predictors, and the integration of clinical, gene expression and alternative splicing profiles leads to the best survival prediction. We anticipate that SURVIV will have broad utilities for analysing diverse types of mRNA isoform variation in large-scale clinical RNA-seq projects.
Eukaryotic cells generate remarkable regulatory and functional complexity from a finite set of genes. Production of mRNA isoforms through alternative processing and modification of RNA is essential for generating this complexity. A prevalent mechanism for producing mRNA isoforms is the alternative splicing of precursor mRNA1. Over 95% of the multi-exon human genes undergo alternative splicing2, 3, resulting in an enormous level of plasticity in the regulation of gene function and protein diversity. In the last decade, extensive genomic and functional studies have firmly established the critical role of alternative splicing in cancer4, 5, 6. Alternative splicing is involved in a full spectrum of oncogenic processes including cell proliferation, apoptosis, hypoxia, angiogenesis, immune escape and metastasis7, 8. These cancer-associated alternative splicing patterns are not merely the consequences of disrupted gene regulation in cancer but in numerous instances actively contribute to cancer development and progression. For example, alternative splicing of genes encoding the Bcl-2 family of apoptosis regulators generates both anti-apoptotic and pro-apoptotic protein isoforms9. Alternative splicing of the pyruvate kinase M (PKM) gene has a significant impact on cancer cell metabolism and tumour growth10. A transcriptome-wide switch of the alternative splicing programme during the epithelial–mesenchymal transition plays an important role in cancer cell invasion and metastasis11, 12.
RNA sequencing (RNA-seq) has become a popular and cost-effective technology to study transcriptome regulation and mRNA isoform variation13, 14. As the cost of RNA-seq continues to decline, it has been widely adopted in large-scale clinical transcriptome projects, especially for profiling transcriptome changes in cancer. For example, as of April 2015 The Cancer Genome Atlas (TCGA) consortium had generated RNA-seq data on over 11,000 cancer patient specimens from 34 different cancer types. Within the TCGA data, breast invasive carcinoma (BRCA) has the largest sample size of RNA-seq data covering over 1,000 patients, and clinical information such as survival times, tumour stages and histological subtypes is available for the majority of the BRCA patients15. Moreover, the median follow-up time of BRCA patients is ~400 days, and 25% of the patients have more than 1,200 days of follow-up. Collectively, the large sample size and long follow-up time of the TCGA BRCA data set allow us to correlate genomic and transcriptomic profiles to clinical outcomes and patient survival times.
To date, systematic analyses have been performed to reveal the association between copy number variation, DNA methylation, gene expression and microRNA expression profiles with cancer patient survival16, 17. By contrast, despite the importance of mRNA isoform variation and alternative splicing, there have been limited efforts in transcriptome-wide survival analysis of alternative splicing in cancer patients. Most RNA-seq studies of alternative splicing in cancer transcriptomes focus on identifying ‘cancer-specific’ alternative splicing events by comparing cancer tissues with normal controls (see refs 18, 19, 20, 21, 22, 23 for examples). A recent analysis of TCGA RNA-seq data identified 163 recurrent differential alternative splicing events between cancer and normal tissues of three cancer types, among which five were found to have suggestive survival signals for breast cancer at a nominal P-value cutoff of 0.05 (ref. 21). Some other studies reported a significant survival difference between cancer patient subgroups after stratifying patients with overall mRNA isoform expression profiles24, 25. However, systematic cancer survival analyses of alternative splicing at the individual exon resolution have been lacking. Two main challenges exist for survival analyses of mRNA isoform variation and alternative splicing using RNA-seq data. The first challenge is to account for the estimation uncertainty of mRNA isoform ratios inferred from RNA-seq read counts. The statistical confidence of mRNA isoform ratio estimation depends on the RNA-seq read coverage for the events of interest, with larger read coverage leading to a more reliable estimation14. Modelling the estimation uncertainty of mRNA isoform ratio is an essential component of RNA-seq analyses of alternative splicing, as shown by various statistical algorithms developed for detecting differential alternative splicing from multi-group RNA-seq data14, 26, 27, 28,29. The second challenge, which is a general issue in survival analysis, is to properly model the association of mRNA isoform ratio with survival time, while accounting for missing data in survival time because of censoring, that is, patients still alive at the end of the survival study, whose precise survival time would be uncertain. To date, no algorithm has been developed for survival analyses of mRNA isoform variation that accounts for these sources of uncertainty simultaneously.
Here we introduce SURVIV (Survival analysis of mRNA Isoform Variation), a statistical model for identifying mRNA isoform ratios associated with patient survival times in large-scale cancer RNA-seq data sets. SURVIV models the estimation uncertainty of mRNA isoform ratios in RNA-seq data and tests the survival effects of isoform variation in both censored and uncensored survival data. In simulation studies, SURVIV consistently outperforms the conventional Cox regression survival analysis that ignores the measurement uncertainty of mRNA isoform ratio. We used SURVIV to identify alternatively spliced exons whose exon-inclusion levels significantly correlated with the survival times of invasive ductal carcinoma (IDC) patients from the TCGA breast cancer cohort. Survival-associated alternative splicing events are identified in gene pathways associated with apoptosis, oxidative stress and DNA damage repair. Importantly, we show that alternative splicing-based survival predictors outperform gene expression-based survival predictors in the TCGA IDC RNA-seq data set, as well as in TCGA data of five additional cancer types. Moreover, the integration of clinical information, gene expression and alternative splicing profiles leads to the best prediction of survival time.
SURVIV statistical model
The statistical model of SURVIV assesses the association between mRNA isoform ratio and patient survival time. While the model is generic for many types of alternative isoform variation, here we use the exon-skipping type of alternative splicing to illustrate the model (Fig. 1a). For each alternative exon involved in exon-skipping, we can use the RNA-seq reads mapping to its exon-inclusion or -skipping isoform to estimate its exon-inclusion level (denoted as ψ, or PSI that is Per cent Spliced In14). A key feature of SURVIV is that it models the RNA-seq estimation uncertainty of exon-inclusion level as influenced by the sequencing coverage for the alternative splicing event of interest. This is a critical issue in accurate quantitative analyses of mRNA isoform ratio in large-scale RNA-seq data sets14, 26, 27, 28, 29. Therefore, SURVIV contains two major components: the first to model the association of mRNA isoform ratio with patient survival time across all patients, and the second to model the estimation uncertainty of mRNA isoform ratio in each individual patient (Fig. 1a).
Figure 1: The statistical framework of the SURVIV model.
(a) For each patient k, the patient’s hazard rate λk(t) is associated with the baseline hazard rate λ0(t) and this patient’s exon-inclusion level ψk. The association of exon-inclusion level with patient survival is estimated by the survival coefficient β. The exon-inclusion level ψk is estimated from the read counts for the exon-inclusion isoform ICk and the exon-skipping isoform SCk. The proportion of the inclusion and skipping reads is adjusted by a normalization function f that considers the lengths of the exon-inclusion and -skipping isoforms (see details in Results and Supplementary Methods). (b) A hypothetical example to illustrate the association of exon-inclusion level with patient survival probability over time Sk(t), with the survival coefficient β=−1 and a constant baseline hazard rate λ0(t)=1. In this example, patients with higher exon-inclusion levels have lower hazard rates and higher survival probabilities. (c) The schematic diagram of an exon-skipping event. The exon-inclusion reads ICk are the reads from the upstream splice junction, the alternative exon itself and the downstream splice junction. The exon-skipping reads SCk are the reads from the skipping splice junction that directly connects the upstream exon to the downstream exon.
Briefly, for any individual exon-skipping event, the first component of SURVIV uses a proportional hazards model to establish the relationship between patient k’s exon-inclusion level ψk and hazard rate λk(t).
For each exon, the association between the exon-inclusion level and patient survival time is reflected by the survival coefficient β. A positive β means increased exon inclusion is associated with higher hazard rate and poorer survival, while a negative β means increased exon inclusion is associated with lower hazard rate and better survival. λ0(t) is the baseline hazard rate estimated from the survival data of all patients (see Supplementary Methods for the detailed estimation procedure). A particular patient’s survival probability over time Sk(t) can be calculated from the patient-specific hazard rate λk(t) as . Figure 1b illustrates a simple example with a negative β=−1 and a constant baseline hazard rate λ0(t)=1, where higher exon-inclusion levels are associated with lower hazard rates and higher survival probabilities.
The second component of SURVIV models the exon-inclusion level and its estimation uncertainty in individual patient samples. As illustrated in Fig. 1c, the exon-inclusion level ψk of a given exon in a particular sample can be estimated by the RNA-seq read count specific to the exon inclusion isoform (ICk) and the exon-skipping isoform (SCk). Other types of alternative splicing and mRNA isoform variation can be similarly modelled by this framework29. Given the effective lengths (that is, the number of unique isoform-specific read positions) of the exon-inclusion isoform (lI) and the exon-skipping isoform (lS), the exon-inclusion level ψk can be estimated as . Assuming that the exon-inclusion read count ICk follows a binomial distribution with the total read count nk=ICk+SCk, we have:
The binomial distribution models the estimation uncertainty of ψk as influenced by the total read count nk, in which the parameter pk represents the proportion of reads from the exon-inclusion isoform, given the exon-inclusion level ψk adjusted by a length normalization function f(ψk) based on the effective lengths of the isoforms. The definitions of effective lengths for all basic types of alternative splicing patterns are described in ref. 29.
Distinct from conventional survival analyses in which predictors do not have estimation uncertainty, the predictors in SURVIV are exon-inclusion levels ψk estimated from RNA-seq count data, and the confidence of ψk estimate for a given exon in a particular sample depends on the RNA-seq read coverage. We use the statistical framework of survival measurement error model30 to incorporate the estimation uncertainty of isoform ratio in the proportional hazards model. Using a likelihood ratio test, we test whether the exon-inclusion levels have a significant association with patient survival over the null hypothesis H0:β=0. The false discovery rate (FDR) is estimated using the Benjamini and Hochberg approach31. Details of the parameter estimation and likelihood ratio test in SURVIV are described in Supplementary Methods.
Figure 2: Simulation studies to assess the performance of SURVIV and the importance of modelling the estimation uncertainty of mRNA isoform ratio.
We compared our SURVIV model with Cox regression using point estimates of exon-inclusion levels, which does not consider the estimation uncertainty of the mRNA isoform ratio. (a) To study the effect of RNA-seq depth, we simulated the mean total splice junction read counts equal to 5, 10, 20, 50, 80 and 100 reads. We generated two sets of simulations with and without data-censoring. For each simulation, the true-positive rate (TPR) at 5% false-positive rate is plotted. The inset figure shows the empirical distribution of the mean total splice junction read counts in the TCGA IDC RNA-seq data (x axis in the log10 scale). (b) To faithfully represent the read count distribution in a real data set, we performed another simulation with read counts directly sampled from the TCGA IDC data. Sampled read counts were then multiplied by different factors ranging from 10 to 300% to simulate data sets with different RNA-seq read depth. Continuous and dashed lines represent the performance of SURVIV and Cox regression, respectively. Red lines represent the area under curve (AUC) of the ROC curve (TPR versus false-positive rate plot). Black lines represent the TPR at 5% false-positive rate.
Using these simulated data, we compared SURVIV with Cox regression in two settings, without or with censoring of the survival time. In the setting without censoring, the death and survival time of each individual is known. In the setting with censoring, certain individuals are still alive at the end of the survival study. Consequently, these patients have unknown death and survival time. Here, in the simulation with censoring, we assumed that 85% of the patients were still alive at the end of the study, similar to the censoring rate of the TCGA IDC data set. In both settings and with different depths of RNA-seq coverage, SURVIV consistently outperformed Cox regression in the true-positive rate at the same false-positive rate of 5% (Fig. 2a). As expected, we observed a more significant improvement in SURVIV over Cox regression when the RNA-seq read coverage was low (Fig. 2a).
To more faithfully recapitulate the read count distribution in a real cancer RNA-seq data set, we performed another simulation study with read counts directly sampled from the TCGA IDC data. To assess the influence of RNA-seq read depth on the performance of SURVIV and Cox regression, sampled read counts were then multiplied by different factors ranging from 10 to 300% to simulate data sets with different RNA-seq read depths (Fig. 2b). The TCGA IDC data set has an average RNA-seq depth of ~60 million paired-end reads per patient. Thus, the read depth of these simulated RNA-seq data sets ranged from ~6 million reads to 180 million reads per patient, representing low-coverage RNA-seq studies designed primarily for gene expression analysis32 up to high-coverage RNA-seq studies designed primarily for alternative isoform analysis29. At all levels of RNA-seq depth, SURVIV consistently outperformed Cox regression, as reflected by the area under curve of the receiver operating characteristic (ROC) curve as well as the true-positive rate at 5% false-positive rate (Fig. 2b). The improvement of SURVIV over Cox regression was particularly prominent when the read depth was low. For example, at 10% read depth, SURVIV had 7% improvement in area under curve (68% versus 61%) and 8% improvement in the true-positive rate at 5% false-positive rate (46% versus 38%). Collectively, these simulation results suggest that SURVIV achieves a higher accuracy by accounting for the estimation uncertainty of mRNA isoform ratio in RNA-seq data.
SURVIV analysis of TCGA IDC breast cancer data
To illustrate the practical utility of SURVIV, we used it to analyse the overall survival time of 682 IDC patients from the TCGA breast cancer (BRCA) RNA-seq data set (see Methods for details of the data source and processing pipeline). We chose to analyse IDC because it is the most frequent type of breast cancer33, comprising ~70% of patients in the TCGA breast cancer data set. To control for the effects of significant clinical parameters such as tumour stage and subtype and identify alternative splicing events associated with patient outcomes across multiple molecular and clinical subtypes, we followed the procedure of Croce and colleagues in analysing mRNA and microRNA prognostic signature of IDC33 and stratified the patients according to their clinical parameters. We then conducted SURVIV analysis in 26 clinical subgroups with at least 50 patients in each subgroup. We identified 229 exon-skipping events associated with patient survival in multiple clinical subgroups that met the criteria of SURVIV P-value≤0.01 in at least two subgroups of the same clinical parameter (cancer subtype, stage, lymph node, metastasis, tumour size, oestrogen receptor status, progesterone receptor status, HER2 status and age as shown in Fig. 3). DAVID (Database for Annotation, Visualization and Integrated Discovery) Gene Ontology analyses34 of the 229 alternative splicing events suggest an enrichment of genes in cancer-related functional categories such as intracellular signalling, apoptosis, oxidative stress and response to DNA damage (Supplementary Fig. 1). Table 1 shows a few selected examples of survival-associated alternative splicing events in cancer-related genes. Using two-means clustering of each individual exon’s inclusion levels, the 682 IDC patients can be segregated into two subgroups with significantly different survival times as illustrated by the Kaplan–Meier survival plot (Fig. 4). We also carried out hierarchical clustering of IDC patients using 176 survival-associated alternative exons (P≤0.01; SURVIV analysis of all IDC patients). Using the exon-inclusion levels of these 176 exons, we clustered IDC patients into three major subgroups, with 95, 194 and 389 patients, respectively. As illustrated by the Kaplan–Meier survival plots, the three subgroups had significantly different survival times (Supplementary Fig. 2).
Figure 3: SURVIV analysis of exon-skipping events in the TCGA IDC RNA-seq data set.
IDC patients are stratified into multiple clinical subgroups based on clinical parameters including cancer subtype, stage, lymph node status, metastasis, tumour size, oestrogen receptor status, progesterone receptor status, HER2 status and age. Only clinical subgroups with at least 50 patients are included in further analyses. Numbers of patients in the subgroups are indicated next to the names of the subgroups. Shown in the heatmap are the log10 SURVIV P-values of the 229 exons associated with patient survival (P≤0.01) in at least two subgroups of the same class of clinical parameters. Turquoise colour indicates positive correlation that higher exon-inclusion levels are associated with higher survival probabilities. Magenta colour indicates negative correlation that lower exon-inclusion levels are associated with higher survival probabilities.
Figure 4: Kaplan–Meier survival plots of IDC patients stratified by two-means clustering of the exon-inclusion levels of four survival-associated alternative splicing events.
Clustering was generated for each of the four exons separately. Black lines represent patients with high exon-inclusion levels. Red lines represent patients with low exon-inclusion levels. The P-values are from SURVIV analysis of the TCGA IDC RNA-seq data. (a) ATRIP. (b) BCL2L11. (c) CD74. (d) PCBP4.
Figure 5: Alternative splicing of STAT5A exon 5 is significantly associated with IDC patient survival.
(a) The gene structure of the STAT5A full-length isoform compared to the ΔEx5 isoform skipping the 5th exon. (b) Kaplan–Meier survival plot of IDC patients stratified by two-means clustering using exon-inclusion levels of STAT5A exon 5. The 420 patients in Group 1 (average exon 5 inclusion level=95%) have significantly higher survival probabilities than the 262 patients in Group 2 (average exon 5 inclusion level=85%) (SURVIV P=6.8e−4). (c) Exon 5 inclusion levels of IDC patients stratified by two-means clustering using exon 5 inclusion levels. Group 1 has 420 patients with average exon-inclusion level at 95%. Group 2 has 262 patients with average exon-inclusion level at 85%. (d) STAT5A exon 5 inclusion levels in normal breast tissues versus breast cancer tumour samples. Exon-inclusion levels are extracted from 86 TCGA breast cancer patients with matched normal and tumour samples. Normal breast tissues have average exon 5 inclusion level at 95%, compared to 91% average exon-inclusion level in tumour samples. Error bars represent 95% confidence interval of the mean.
Figure 6: Splicing factor regulatory network of survival-associated alternative splicing events in IDC.
(a–c) Kaplan–Meier survival plots of IDC patients stratified by the gene expression levels of three splicing factors: TRA2B (a, Cox regression P=1.8e−4), HNRNPH1 (b, P=3.4e−4) and SFRS3 (c, P=2.8e−3). Black lines represent patients with high gene expression levels. Red lines represent patients with low gene expression levels. (d) The exon-inclusion levels of a DHX30 alternative exon are negatively correlated with TRA2B gene expression levels (robust correlation coefficient r=−0.26, correlation P=1.2e−17). (e) The exon-inclusion levels of a MAP3K4 alternative exon are positively correlated withHNRNPH1 gene expression levels (robust correlation coefficient r=0.16, correlation P=2.6e−06). (f) A splicing co-expression network of the three splicing factors and their correlated survival-associated alternative exons. In total, 84 survival-associated alternative exons are significantly correlated with the three splicing factors. The positive/negative correlation between splicing factors and alternative exons is represented by blue/red lines, respectively. Exons whose inclusion levels are positively/negatively correlated with survival times are represented by blue/red dots, respectively. The size of the splicing factor circles is proportional to the number of correlated exons within the network.
Figure 7: Cross-validation of different classes of IDC survival predictors measured by the C-index
A C-index of 1 indicates perfect prediction accuracy and a C-index of 0.5 indicates random guess. The plots indicate the distribution of C-indexes from 100 rounds of cross-validation. The centre value of the box plot is the median C-index from 100 rounds of cross-validation. The notch represents the 95%confidence interval of the median. The box represents the 25 and 75% quantiles. The whiskers extended out from the box represent the 5 and 95% quantiles. Two-sided Wilcoxon test was used to compare different survival predictors. The different classes of predictors are: (a) clinical information (median C-index 0.67). (b) Gene expression (median C-index 0.68). (c) Alternative splicing (median C-index 0.71). (d) Clinical information+gene expression (median C-index 0.69). (e) Clinical information+alternative splicing (median C-index 0.73). (f) Clinical information+gene expression+alternative splicing (median C-index 0.74). Note that ‘Gene’ refers to ‘Gene-level expression’ in these plots.
Next, we carried out the SURVIV analysis in five additional cancer types in TCGA, including GBM (glioblastoma multiforme), KIRC (kidney renal clear cell carcinoma), LGG (lower grade glioma), LUSC (lung squamous cell carcinoma) and OV (ovarian serous cystadenocarcinoma). As expected, the number of significant events at different FDR or P-value significance cutoffs varied across cancer types, with LGG having the strongest survival-associated alternative splicing signals with 660 significant exon-skipping events at FDR≤5% (Supplementary Data 3 and 4). Strikingly, regardless of the number of significant events, alternative splicing-based survival predictors outperformed gene expression-based survival predictors across all cancer types (Supplementary Fig. 3), consistent with our initial observation on the IDC data set.
Alternative processing and modification of mRNA, such as alternative splicing, allow cells to generate a large number of mRNA and protein isoforms with diverse regulatory and functional properties. The plasticity of alternative splicing is often exploited by cancer cells to produce isoform switches that promote cancer cell survival, proliferation and metastasis7, 8. The widespread use of RNA-seq in cancer transcriptome studies15, 47, 48 has provided the opportunity to comprehensively elucidate the landscape of alternative splicing in cancer tissues. While existing studies of alternative splicing in large-scale cancer transcriptome data largely focused on the comparison of splicing patterns between cancer and normal tissues or between different subtypes of cancer18, 21, 49, additional computational tools are needed to characterize the clinical relevance of alternative splicing using massive RNA-seq data sets, including the association of alternative splicing with phenotypes and patient outcomes.
We have developed SURVIV, a novel statistical model for survival analysis of alternative isoform variation using cancer RNA-seq data. SURVIV uses a survival measurement error model to simultaneously model the estimation uncertainty of mRNA isoform ratio in individual patients and the association of mRNA isoform ratio with survival time across patients. Compared with the conventional Cox regression model that uses each patient’s mRNA isoform ratio as a point estimate, SURVIV achieves a higher accuracy as indicated by simulation studies under a variety of settings. Of note, we observed a particularly marked improvement of SURVIV over Cox regression for low- and moderate-depth RNA-seq data (Fig. 2b). This has important practical value because many clinical RNA-seq data sets have large sample size but relatively modest sequencing depth.
Using the TCGA IDC breast cancer RNA-seq data of 682 patients, SURVIV identified 229 alternative splicing events associated with patient survival time, which met the criteria of SURVIVP-values≤0.01 in multiple clinical subgroups. While the statistical threshold seemed loose, several lines of evidence suggest the functional and clinical relevance of these survival-associated alternative splicing events. These alternative splicing events were frequently identified and enriched in the gene functional groups important for cancer development and progression, including apoptosis, DNA damage response and oxidative stress. While some of these events may simply reflect correlation but not causal effect on cancer patient survival, other events may play an active role in regulating cancer cell phenotypes. For example, a survival-associated alternative splicing event involving exon 5 of STAT5A is known to regulate the activity of this transcription factor with important roles in epithelial cell growth and apoptosis37. Using a co-expression network analysis of splicing factor to exon correlation across all patients, we identified three splicing factors (TRA2B, HNRNPH1 and SFRS3) as potential hubs of the survival-associated alternative splicing network of IDC. The expression levels of all three splicing factors were negatively associated with patient survival times (Fig. 6a–c), and both TRA2B and HNRNPH1 were previously reported to have an impact on cancer-related molecular pathways40, 41, 42, 43, 44, 45. Finally, despite the limited power in detecting individual events, we show that the survival-associated alternative splicing events can be used to construct a predictor for patient survival, with an accuracy higher than predictors based on clinical parameters or gene expression profiles (Fig. 7). This further demonstrates the potential biological relevance and clinical utility of the identified alternative splicing events.
We performed cross-validation analyses to evaluate and compare the prognostic value of alternative splicing, gene expression and clinical information for predicting patient survival, either independently or in combination. As expected, the combined use of all three types of information led to the best prediction accuracy. Because we used penalized regression to build the prediction model, combining information from multiple layers of data did not necessarily increase the number of predictors in the model. The perhaps more surprising and intriguing result is that alternative splicing-based predictors appear to outperform gene expression-based predictors when used alone and when either type of data was combined with clinical information (Fig. 7). We observed the same trend in five additional cancer types (Supplementary Fig. 3). We note that this finding was consistent with a previous report that cancer subtype classification based on splicing isoform expression performed better than gene expression-based classification25. While this trend seems counterintuitive because accurate estimation of gene expression requires much lower RNA-seq depth than accurate estimation of alternative splicing29, one possible explanation may be the inherent characteristic of isoform ratio data. By definition, mRNA isoform ratio is estimated as the ratio of multiple mRNA isoforms from a single gene. Therefore, mRNA isoform ratio data have a ‘built-in’ internal control that could be more robust against certain artefacts and confounding issues that influence gene expression estimates across large clinical RNA-seq data sets, such as poor sample quality and RNA degradation12. Regardless of the reasons, our data call for further studies to fully explore the utility of mRNA isoform ratio data for various clinical research applications.
The SURVIV source code is available for download at https://github.com/Xinglab/SURVIV. SURVIV is a general statistical model for survival analysis of mRNA isoform ratio using RNA-seq data. The current statistical framework of SURVIV is applicable to RNA-seq based count data for all basic types of alternative splicing patterns involving two isoform choices from an alternatively spliced region, such as exon-skipping, alternative 5′ splice sites, alternative 3′ splice sites, mutually exclusive exons and retained introns, as well as other forms of alternative isoform variation such as RNA editing. With the rapid accumulation of clinical RNA-seq data sets, SURVIV will be a useful tool for elucidating the clinical relevance and potential functional significance of alternative isoform variation in cancer and other diseases.
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. Assuming that the exon-inclusion read count ICk follows a binomial distribution with the total read count nk
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