Feeds:
Posts
Comments

Archive for the ‘Exosomes’ Category


Real Time Coverage @BIOConvention #BIO2019: Chat with @FDA Commissioner, & Challenges in Biotech & Gene Therapy June 4 Philadelphia

Reporter: Stephen J. Williams, PhD @StephenJWillia2

 

  • taking patient concerns and voices from anecdotal to data driven system
  • talked about patient accrual hearing patient voice not only in ease of access but reporting toxicities
  • at FDA he wants to remove barriers to trial access and accrual; also talk earlier to co’s on how they should conduct a trial

Digital tech

  • software as medical device
  • regulatory path is mixed like next gen sequencing
  • wearables are concern for FDA (they need to recruit scientists who know this tech

Opioids

  • must address the crisis but in a way that does not harm cancer pain patients
  • smaller pain packs “blister packs” would be good idea

Clinical trial modernization

  • for Alzheimers disease problem is science
  • for diabetes problem is regulatory
  • different diseases calls for different trial design
  • have regulatory problems with rare diseases as can’t form control or placebo group, inhumane. for example ras tumors trials for MEK inhibitors were narrowly focused on certain ras mutants
Realizing the Promise of Gene Therapies for Patients Around the World

103ABC, Level 100

Speakers
Lots of promise, timeline is progressing faster but we need more education on use of the gene therapy
Regulatory issues: Cell and directly delivered gene based therapies have been now approved. Some challenges will be the ultrarare disease trials and how we address manufacturing issues.  Manufacturing is a big issue at CBER and scalability.  If we want to have global impact of these products we need to address the manufacturing issues
 of scalability.
Pfizer – clinical grade and scale is important.
Aventis – he knew manufacturing of biologics however gene therapy manufacturing has its separate issues and is more complicated especially for regulatory purposes for clinical grade as well as scalability.  Strategic decision: focusing on the QC on manufacturing was so important.  Had a major issue in manufacturing had to shut down and redesign the system.
Albert:  Manufacturing is the most important topic even to the investors.  Investors were really conservative especially seeing early problems but when academic centers figured out good efficacy then they investors felt better and market has exploded.  Now you can see investment into preclinical and startups but still want mature companies to focus on manufacturing.  About $10 billion investment in last 4 years.

How Early is Too Early? Valuing and De-Risking Preclinical Opportunities

109AB, Level 100

Speakers
Valuing early-stage opportunities is challenging. Modeling will often provide a false sense of accuracy but relying on comparable transactions is more art than science. With a long lead time to launch, even the most robust estimates can ultimately prove inaccurate. This interactive panel will feature venture capital investors and senior pharma and biotech executives who lead early-stage transactions as they discuss their approaches to valuing opportunities, and offer key learnings from both successful and not-so-successful experiences.
Dr. Schoenbeck, Pfizer:
  • global network of liaisons who are a dedicated team to research potential global startup partners or investments.  Pfizer has a separate team to evaluate academic laboratories.  In Most cases Pfizer does not initiate contact.  It is important to initiate the first discussion with them in order to get noticed.  Could be just a short chat or discussion on what their needs are for their portfolio.

Question: How early is too early?

Luc Marengere, TVM:  His company has early stage focus, on 1st in class molecules.  The sweet spot for their investment is a candidate selected compound, which should be 12-18 months from IND.  They will want to bring to phase II in less than 4 years for $15-17 million.  Their development model is bad for academic labs.  During this process free to talk to other partners.

Dr. Chaudhary, Biogen:  Never too early to initiate a conversation and sometimes that conversation has lasted 3+ years before a decision.  They like build to buy models, will do convertible note deals, candidate compound selection should be entering in GLP/Tox phase (sweet spot)

Merck: have MRL Venture Fund for pre series A funding.  Also reiterated it is never too early to have that initial discussion.  It will not put you in a throw away bin.  They will have suggestions and never like to throw out good ideas.

Michael Hostetler: Set expectations carefully ; data should be validated by a CRO.  If have a platform, they will look at the team first to see if strong then will look at the platform to see how robust it is.

All noted that you should be completely honest at this phase.  Do not overstate your results or data or overhype your compound(s).  Show them everything and don’t have a bias toward compounds you think are the best in your portfolio.  Sometimes the least developed are the ones they are interested in.  Also one firm may reject you however you may fit in others portfolios better so have a broad range of conversations with multiple players.

 

 

Read Full Post »


One blood sample can be tested for a comprehensive array of cancer cell biomarkers: R&D at WPI

Curator: Marzan Khan, B.Sc

 

A team of mechanical engineers at Worcester Polytechnic Institute (WPI) have developed a fascinating technology – a liquid biopsy chip that captures and detects metastatic cancer cells, just from a small blood sample of cancer patients(1). This device is a recent development in the scientific field and holds tremendous potential that will allow doctors to spot signs of metastasis for a variety of cancers at an early stage and initiate an appropriate course of treatment(1).

Metastasis occurs when cancer cells break away from their site of origin and spread to other parts of the body via the lymph or the bloodstream, where they give rise to secondary tumors(2). By this time, the cancer is at an advanced stage and it becomes increasingly difficult to fight the disease. The cells that are shed by primary and metastatic cancers are called circulating tumor cells (CTCs) and their numbers lie in the range of 1–77,200/m(3). The basis of the liquid biopsy chip test is to capture these circulating tumor cells in the patient’s blood and identify the cell type through specific interaction with antibodies(4).

The chip is comprised of individual test units or small elements, about 3 millimeters wide(4). Each small element contains a network of carbon nanotube sensors in a well which are functionalized with antibodies(4). These antibodies will bind cell-surface antigens or protein markers unique for each type of cancer cell. Specific interaction between a cell surface protein and its corresponding antibody is a thermodynamic event that causes a change in free energy which is transduced into electricity(3). This electrical signature is picked up by the semi-conducting carbon nanotubes and can be seen as electrical spikes(4). Specific interactions create an increase in electrical signal, whereas non-specific interactions cause a decrease in signal or no change at all(4). Capture efficiency of cancer cells with the chip has been reported to range between 62-100%(4).

The liquid biopsy chip is also more advanced than microfluidics for several reasons. Firstly, the nanotube-chip arrays can capture as well as detect cancer cells, while microfluidics can only capture(4). Samples do not need to be processed for labeling or fixation, so the cell structures are preserved(4). Unlike microfluidics, these nanotubes will also capture tiny structures called exosomes spanning the nanometer range that are produced from cancer cells and carry the same biomarkers(4).

Pancreatic cancer is the fourth leading cause of cancer-associated deaths in the United states, with a survival window of 5 years in only 6% of the cases with treatment(5). In most patients, the disease has already metastasized at the time of diagnosis due to the lack of early-diagnostic markers, affecting some of the major organs such as liver, lungs and the peritoneum(5,6). Despite surgical resection of the primary tumor, the recurrence of local and metastatic tumors is rampant(5). Metastasis is the major cause of mortality in cancers(5). The liquid biopsy chip, that identifies CTCs can thus become an effective diagnostic tool in early detection of cancer as well as provide information into the efficacy of treatment(3). At present, ongoing experiments with this device involve testing for breast cancers but Dr. Balaji Panchapakesan and his team of engineers at WPI are optimistic about incorporating pancreatic and lung cancers into their research.

REFERENCES

1.Nanophenotype. Researchers build liquid biopsy chip that detects metastatic cancer cells in blood: One blood sample can be tested for a comprehensive array of cancer cell biomarkers. 27 Dec 2016. Genesis Nanotechnology,Inc

https://genesisnanotech.wordpress.com/2016/12/27/researchers-build-liquid-biopsy-chip-that-detects-metastatic-cancer-cells-in-blood-one-blood-sample-can-be-tested-for-a-comprehensive-array-of-cancer-cell-markers/

2.Martin TA, Ye L, Sanders AJ, et al. Cancer Invasion and Metastasis: Molecular and Cellular Perspective. In: Madame Curie Bioscience Database [Internet]. Austin (TX): Landes Bioscience; 2000-2013.

https://www.ncbi.nlm.nih.gov/books/NBK164700/

3.F Khosravi, B King, S Rai, G Kloecker, E Wickstrom, B Panchapakesan. Nanotube devices for digital profiling of cancer biomarkers and circulating tumor cells. 23 Dec 2013. IEEE Nanotechnology Magazine 7 (4), 20-26

Nanotube devices for digital profiling of cancer biomarkers and circulating tumor cells

4.Farhad Khosravi, Patrick J Trainor, Christopher Lambert, Goetz Kloecker, Eric Wickstrom, Shesh N Rai and Balaji Panchapakesan. Static micro-array isolation, dynamic time series classification, capture and enumeration of spiked breast cancer cells in blood: the nanotube–CTC chip. 29 Sept 2016. Nanotechnology. Vol 27, No.44. IOP Publishing Ltd

http://iopscience.iop.org/article/10.1088/0957-4484/27/44/44LT03/meta

5.Seyfried, T. N., & Huysentruyt, L. C. (2013). On the Origin of Cancer Metastasis. Critical Reviews in Oncogenesis18(1-2), 43–73.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3597235/

6.Deeb, A., Haque, S.-U., & Olowoure, O. (2015). Pulmonary metastases in pancreatic cancer, is there a survival influence? Journal of Gastrointestinal Oncology6(3), E48–E51. http://doi.org/10.3978/j.issn.2078-6891.2014.114

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4397254/

Other related articles published in this Open Access Online Scientific Journal include the following:

 

Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood – R&D @Worcester Polytechnic Institute, Micro and Nanotechnology Lab

Reporters: Tilda Barliya, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/12/28/liquid-biopsy-chip-detects-an-array-of-metastatic-cancer-cell-markers-in-blood-rd-worcester-polytechnic-institute-micro-and-nanotechnology-lab/

 

Trovagene’s ctDNA Liquid Biopsy urine and blood tests to be used in Monitoring and Early Detection of Pancreatic Cancer

Reporters: David Orchard-Webb, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/07/06/trovagenes-ctdna-liquide-biopsy-urine-and-blood-tests-to-be-used-in-monitoring-and-early-detection-of-pancreatic-cancer/

 

Liquid Biopsy Assay May Predict Drug Resistance

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/11/06/liquid-biopsy-assay-may-predict-drug-resistance/


New insights in cancer, cancer immunogenesis and circulating cancer cells

Larry H. Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2016/04/15/new-insights-in-cancer-cancer-immunogenesis-and-circulating-cancer-cells/

 

Prognostic biomarker for NSCLC and Cancer Metastasis

Larry H. Bernstein, MD, FCAP, Curato

https://pharmaceuticalintelligence.com/2016/03/24/prognostic-biomarker-for-nsclc-and-cancer-metastasis/

 

Monitoring AML with “cell specific” blood test

Larry H. Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2016/01/23/monitoring-aml-with-cell-specific-blood-test/

 

Diagnostic Revelations

Larry H. Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2015/11/02/diagnostic-revelations/

 

Circulating Biomarkers World Congress, March 23-24, 2015, Boston: Exosomes, Microvesicles, Circulating DNA, Circulating RNA, Circulating Tumor Cells, Sample Preparation

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2015/03/03/circulating-biomarkers-world-congress-march-23-24-2015-boston-exosomes-microvesicles-circulating-dna-circulating-rna-circulating-tumor-cells-sample-preparation/

 

 

 

Read Full Post »


SBI’s Exosome Research Technologies

Reporter: Aviva Lev-Ari, PhD, RN

Welcome to the Fascinating World of Exosomes and Microvesicles

Just learning about exosomes?

The team at SBI has put together this brief overview to help get you started in the growing field of exosome research.

 

Why are exosomes important?

Once thought to be little more than a way for cells to offload waste, the past decade has seen a huge shift in the way we think about exosomes. We’ve begun to recognize that exosomes are deliberately released from the cell, functioning as signal carriers and tissue reshapers through their cargo of RNA, proteins, lipids, and DNA. Involved in a wide range of healthy and pathogenic processes such as cancer, inflammation, immunity, CNS function, cardiac cell function, to name a few – exosomes are being studied for their role in these basic biological processes as well as for their use as biomarkers (see Applications) and even as tools for targeted delivery of biomolecules such as therapeutics (see Engineering).

What are exosomes?

Exosomes are 60 – 180 nm membrane vesicles secreted by most cell types in vivo and in vitro. These extracellular vesicles are endocytic in origin, produced by the inward budding of multivesicular bodies (MVBs). They are released from the cell into the microenvironment following fusion of MVBs with the plasma membrane.

What aren’t exosomes?

Exosomes are not the only small, membrane-bound extracellular vesicle that can be found. They are distinct in origin from apoptotic blebs or apoptotic bodies, which are 50 nm to 5 um in size, carry DNA, RNA, and histones, and display surface markers targeting them for clearance by macrophages. And they are also different from microparticles (also known as microvesicles, ectosomes, shedding vesicles, microparticles, plasma membrane-derived vesicles, and exovesicles), which can range from 50-1000 nm in size and are derived directly from the plasma membrane rather than endocytic bodies within the cell.8 These distinctions and labeling conventions are not always used consistently in the literature and between different groups, leading to some ambiguity in the literature. When isolating exosomes, it’s important to remember that these other types of vesicles may also be present and interpret results accordingly.

What else are exosomes called?

Adding to the confusion, exosomes are sometimes referred to by the source of the sample material. For example, dendritic cell exosomes are also called dexosomes, and cancer cell exosomes may be called oncosomes. Researchers are starting to move towards more standardized nomenclature, but those searching through older literature should be aware of other names for exosomes.

Where are exosomes normally found?

Exosomes have been found in blood, urine, amniotic fluid, breast milk, malignant ascites fluids, and seminal fluid. They contain distinct subsets of molecules depending upon the cell type from which they are secreted, making them useful for biomarker discovery.

How do I study exosomes?

SBI is the only vendor to offer reagents and kits that support all apsects of exosome research-covering isolation, detection and measurement, discovery (characterization and analysis), and even exosome engineering. With a comprehensive set of tools and services to accelerate the study of exosomes and exosome RNA biomarkers, SBI puts the power of exosomes into researchers’ hands.

SBI’s Exosome Research Technologies

ISOLATION

DETECTION

DISCOVERY

ENGINEERING

ExoQuick

Exosome FACS

Purified exosomes

Package miRNAs into exosomes

Exosome FACS and IP

Antibodies and ELISAs

RNA-Seq NGS kit

Transfect exosomes

Exosome depleted FBS

EXOCET assay

Mass Spec library kit

Engineer Exosome Protein Cargo

Label exosome cargo

miRNA qPCR kits

 

SOURCE

https://www.systembio.com/exosome-knowledge

https://www.systembio.com/products

https://www.systembio.com/services

 

Read Full Post »


Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

MicroRNAs (miRNAs) are a group of small non-coding RNA molecules that play a major role in posttranscriptional regulation of gene expression and are expressed in an organ-specific manner. One miRNA can potentially regulate the expression of several genes, depending on cell type and differentiation stage. They control every cellular process and their altered regulation is involved in human diseases. miRNAs are differentially expressed in the male and female gonads and have an organ-specific reproductive function. Exerting their affect through germ cells and gonadal somatic cells, miRNAs regulate key proteins necessary for gonad development. The role of miRNAs in the testes is only starting to emerge though they have been shown to be required for adequate spermatogenesis. In the ovary, miRNAs play a fundamental role in follicles’ assembly, growth, differentiation, and ovulation.

 

Deciphering the underlying causes of idiopathic male infertility is one of the main challenges in reproductive medicine. This is especially relevant in infertile patients displaying normal seminal parameters and no urogenital or genetic abnormalities. In these cases, the search for additional sperm biomarkers is of high interest. This study was aimed to determine the implications of the sperm miRNA expression profiles in the reproductive capacity of normozoospermic infertile individuals. The expression levels of 736 miRNAs were evaluated in spermatozoa from normozoospermic infertile males and normozoospermic fertile males analyzed under the same conditions. 57 miRNAs were differentially expressed between populations; 20 of them was regulated by a host gene promoter that in three cases comprised genes involved in fertility. The predicted targets of the differentially expressed miRNAs unveiled a significant enrichment of biological processes related to embryonic morphogenesis and chromatin modification. Normozoospermic infertile individuals exhibit a specific sperm miRNA expression profile clearly differentiated from normozoospermic fertile individuals. This miRNA cargo has potential implications in the individuals’ reproductive competence.

 

Circulating or “extracellular” miRNAs detected in biological fluids, could be used as potential diagnostic and prognostic biomarkers of several disease, such as cancer, gynecological and pregnancy disorders. However, their contributions in female infertility and in vitro fertilization (IVF) remain unknown. Polycystic ovary syndrome (PCOS) is a frequent endocrine disorder in women. PCOS is associated with altered features of androgen metabolism, increased insulin resistance and impaired fertility. Furthermore, PCOS, being a syndrome diagnosis, is heterogeneous and characterized by polycystic ovaries, chronic anovulation and evidence of hyperandrogenism, as well as being associated with chronic low-grade inflammation and an increased life time risk of type 2 diabetes. Altered miRNA levels have been associated with diabetes, insulin resistance, inflammation and various cancers. Studies have shown that circulating miRNAs are present in whole blood, serum, plasma and the follicular fluid of PCOS patients and that these might serve as potential biomarkers and a new approach for the diagnosis of PCOS. Presence of miRNA in mammalian follicular fluid has been demonstrated to be enclosed within microvesicles and exosomes or they can also be associated to protein complexes. The presence of microvesicles and exosomes carrying microRNAs in follicular fluid could represent an alternative mechanism of autocrine and paracrine communication inside the ovarian follicle. The investigation of the expression profiles of five circulating miRNAs (let-7b, miR-29a, miR-30a, miR-140 and miR-320a) in human follicular fluid from women with normal ovarian reserve and with polycystic ovary syndrome (PCOS) and their ability to predict IVF outcomes showed that these miRNAs could provide new helpful biomarkers to facilitate personalized medical care for oocyte quality in ART (Assisted Reproductive Treatment) and during IVF (In Vitro Fertilization).

 

References:

 

http://link.springer.com/chapter/10.1007%2F978-3-319-31973-5_12

 

http://onlinelibrary.wiley.com/doi/10.1111/andr.12276/abstract;jsessionid=F805A89DCC94BDBD42D6D60C40AD4AB0.f03t03

 

http://www.sciencedirect.com/science/article/pii/S0009279716302241

 

http://link.springer.com/article/10.1007%2Fs10815-016-0657-9

 

http://www.nature.com/articles/srep24976

 

 

Read Full Post »


Postmarketing Safety or Effectiveness Data Needed: The 2013 paper was funded by the firm Sarepta Therapeutics, sellers of eteplirsen, a surge in its shares seen after the approval. Eteplirsen will cost patients around $300,000 a year.

 

Curator: Aviva Lev-Ari, PhD, RN

 

UPDATED on 8/18/2019

Sarepta Duchenne drug rejected by FDA in surprise setback

 

Dive Brief:

  • In an unexpected decision, the Food and Drug Administration rejected Sarepta Therapeutics’ experimental drug for Duchenne muscular dystrophy, issuing on Monday a Complete Response Letter to the rare disease biotech.
  • According to Sarepta, the agency cited in its refusal infection risk tied to the drug’s delivery as well as preclinical signs of kidney toxicity. Called Vyondys 53, the medicine is designed for roughly 8% of Duchenne patients with a specific genetic mutation.
  • Shares in Cambridge, Mass.-based Sarepta fell sharply in post-market trading. Approval of the drug was widely anticipated, making the rejection a setback in Sarepta’s ambitions to treat a wider pool of Duchenne patients.

SOURCE

https://www.biopharmadive.com/news/sarepta-surprise-fda-rejection-duchenne-vyondys-53/561200/

 

On September 19, the FDA okayed eteplirsen to treat Duchenne muscular dystrophy (DMD), a rare genetic disorder that results in muscle degeneration and premature death. Several of its top officials disagreed with the drug’s approval, questioning how beneficial it will be for patients, as ForbesMedPage Today and others reported.

http://retractionwatch.com/2016/09/21/amid-controversial-sarepta-approval-decision-fda-head-calls-for-key-study-retraction/

Factors at play for FDA Approval of eteplirsen

  1. the help of the families of young boys with Duchenne muscular dystrophy, emotional scenes from these families who have campaigned for so long
  2. an executive team from Sarepta who wouldn’t give up,

Ed Kaye, Sarepta, CEO – EK: It’s all about resilience. One of the things we’ve had is a group of people of like minds and anytime one of us gets down, somebody else is there to pick you up. One of the things we’ve always done is: Every time we’ve felt sorry for ourselves, we just need to think about those patients and what they go through. Our struggles in comparison very quickly become meaningless. You end up saying to yourself: What am I complaining about? Quit whining; get up and do your job.

and

3. an emerging new philosophy from some within the FDA, eteplirsen, now Exondys 51, was approved in patients with a confirmed mutation of the dystrophin gene amenable to exon 51 skipping.

http://www.fiercebiotech.com/biotech/sarepta-ceo-ed-kaye-fda-courage-nice-and-resilience?utm_medium=nl&utm_source=internal&mrkid=993697&mkt_tok=eyJpIjoiTXpBeU56aGpNREV3T1RZMiIsInQiOiJIM2poTkVOQ0N6YmxaenVHZDM1RlVvbTFmRkdwZGdxQ0pmYXNVOG5PKzRyenFXTkRMV0dcL3l0bVBPNkJ2NFV3Rnc3bWVFVnUwMCs3YVhWeVhvRkkrUU5FMFJ1RndSQTlHWFRnQmFTbUo3ODg9In0%3D

9/19/2016

FDA grants accelerated approval to first drug for Duchenne muscular dystrophy

The accelerated approval of Exondys 51 is based on the surrogate endpoint of dystrophin increase in skeletal muscle observed in some Exondys 51-treated patients. The FDA has concluded that the data submitted by the applicant demonstrated an increase in dystrophin production that is reasonably likely to predict clinical benefit in some patients with DMD who have a confirmed mutation of the dystrophin gene amenable to exon 51 skipping. A clinical benefit of Exondys 51, including improved motor function, has not been established. In making this decision, the FDA considered the potential risks associated with the drug, the life-threatening and debilitating nature of the disease for these children and the lack of available therapy.

The FDA granted Exondys 51 fast track designation, which is a designation to facilitate the development and expedite the review of drugs that are intended to treat serious conditions and that demonstrate the potential to address an unmet medical need. It was also granted priority review and orphan drug designationPriority review status is granted to applications for drugs that, if approved, would be a significant improvement in safety or effectiveness in the treatment of a serious condition. Orphan drug designation provides incentives such as clinical trial tax credits, user fee waiver and eligibility for orphan drug exclusivity to assist and encourage the development of drugs for rare diseases.

SOURCE

http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm521263.htm

The viability of this drug approval depends  on “to be gathered” Postmarketing safety or effectiveness data, aka follow-up confirmatory trials.

Sarepta CEO Ed Kaye on FDA courage, NICE and resilience

BA: When it comes to flexibility, however, the FDA will likely not be flexible if your drug doesn’t prove the desired efficacy in your longer term postmarketing studies. If at the end of this period your drug doesn’t come through, how easy will it be for you to take this off the market? I don’t think anyone, including the FDA, wants a repeat of what happened in 2011 when Roche saw its breast cancer license for Avastin, which had been approved under an accelerated review, pulled after not being safe or effective enough in the follow-up confirmatory trials. But you face this as a possible scenario.

EK: That’s true, but one of the things we’re trying to do to mitigate that is to obviously, with our ongoing studies, prove the efficacy that the FDA wants to see. And you know, if there is a problem with one study then we’d hope to have other data that are supportive. The other thing we’re doing of course is developing that next-generation chemistry in DMD that could prove more effective, so we could certainly consider using that next-gen chemistry to take our work forward and try and make it better.

We have a lot of shots on goal to make sure we can continue to supply a product for these boys, but there is always a risk. If we can’t show efficacy in the way the FDA wants, then yes they have the option to take it off the market.

http://www.fiercebiotech.com/biotech/sarepta-ceo-ed-kaye-fda-courage-nice-and-resilience?utm_medium=nl&utm_source=internal&mrkid=993697&mkt_tok=eyJpIjoiTXpBeU56aGpNREV3T1RZMiIsInQiOiJIM2poTkVOQ0N6YmxaenVHZDM1RlVvbTFmRkdwZGdxQ0pmYXNVOG5PKzRyenFXTkRMV0dcL3l0bVBPNkJ2NFV3Rnc3bWVFVnUwMCs3YVhWeVhvRkkrUU5FMFJ1RndSQTlHWFRnQmFTbUo3ODg9In0%3D

Need for follow-up confirmatory trials remains outstanding

FDA’s Postmarketing Surveillance Programs

http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/ucm090385.htm

FDA’s Regulations and Policies and Procedures for Postmarketing Surveillance Programs

http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/ucm090394.htm

 

Positions on Sarepta’s eteplirsen Scientific Approach

Gene Editing for Exon 51: Why CRISPR Snipping might be better than Exon Skipping for DMD

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/23/gene-editing-for-exon-51-why-crispr-snipping-might-be-better-than-exon-skipping-for-dmd/

 

QUOTE START

Retraction Watch

Tracking retractions as a window into the scientific process

Amid controversial Sarepta approval decision, FDA head calls for key study retraction

with one comment

FDAThe head of the U.S. Food and Drug Administration (FDA) has called for the retraction of a study about a drug that the agency itself approved earlier this week, despite senior staff opposing the approval.

On September 19, the FDA okayed eteplirsen to treat Duchenne muscular dystrophy (DMD), a rare genetic disorder that results in muscle degeneration and premature death. Several of its top officials disagreed with the drug’s approval, questioning how beneficial it will be for patients, as ForbesMedPage Today and others reported.

In a lengthy report Commissioner Robert Califf sent to senior FDA officials on September 16 — that was made public on September 19 — he called for the retraction of a 2013 study published in Annals of Neurologyfunded by the seller of eteplirsen, which showed beneficial effects of the drug in DMD patients. Califf writes inthe report:

The publication, now known to be misleading, should probably be retracted by its authors.

In a footnote in the report, Califf adds:

In view of the scientific deficiencies identified in this analysis, I believe it would be appropriate to initiate a dialogue that would lead to a formal correction or retraction (as appropriate) of the published report.

The study was not the key factor in the agency’s decision to approve the drug, according to Steve Usdin, Washington editor of the publication BioCentury; still, Usdin told Retraction Watch he is “really surprised” at the call for retraction from top FDA staff, the first he has come across in the last two decades.

The 2013 paper was funded by the firm Sarepta Therapeutics, sellers of eteplirsen, which has seen a surge in its shares after the approval. Eteplirsen will cost patients around $300,000 a year.

DMD affects around 1 in 3,600 boys due to a mutation in the gene that codes for the protein dystrophin, which is important for structural stability of muscles. Eteplirsen is the first drug to treat DMD, and was initially given a green light by Janet Woodcock, director of Center for Drug Evaluation and Research, after a split vote from the FDA’s advisory committee. Despite Califf’s issues with the literature supporting the drug’s use in DMD, he did not overturn Woodcock’s decision, and the agency approved the drug this week.

In 2014, an inspection team visited the Nationwide Children’s Hospital in Columbus, Ohio, where the research was conducted, according to the report. In the report, Ellis Unger, director of the Office of Drug Evaluation I in FDA’s Center for Drug Evaluation, notes:

We found the analytical procedures to be typical of an academic research center, seemingly appropriate for what was simply an exploratory phase 1/2 study, but not suitable for an adequate and well controlled study aimed to serve as the basis for a regulatory action. The procedures and controls that one would expect to see in support of a phase 3 registrational trial were not in evidence.

Specifically, Unger describes concerns about blinding during the experiments, and notes:

The immunohistochemistry images were only faintly stained, and had been read by a single technician using an older liquid crystal display (LCD) computer monitor in a windowed room where lighting was not controlled. (The technician had to suspend reading around mid-day, when brighter light began to fill the room and reading became impossible.)

Unger adds:

Having uncovered numerous technical and operational shortcomings in Columbus, our team worked collaboratively with the applicant to develop improved methods for a reassessment of the stored images…This re-analysis, along with the study published in 2013, provides an instructive example of an investigation with extraordinary results that could not be verified.

Luciana Borio, acting chief scientist at the FDA, is cited in the report saying:

I would be remiss if I did not note that the sponsor has exhibited serious irresponsibility by playing a role in publishing and promoting selective data during the development of this product. Not only was there a misleading published article with respect to the results of Study–which has never been retracted—but Sarepta also issued a press release relying on the misleading article and its findings…As determined by the review team, and as acknowledged by Dr. Woodcock, the article’s scientific findings—with respect to the demonstrated effect of eteplirsen on both surrogate and clinical endpoints—do not withstand proper and objective analyses of the data. Sarepta’s misleading communications led to unrealistic expectations and hope for DMD patients and their families.

Here’s how Sarepta describes the study’s findings in the press release Borio refers to:

Published study results showed that once-weekly treatment with eteplirsen resulted in a statistically significant increase from baseline in novel dystrophin, the protein that is lacking in patients with DMD. In addition, eteplirsen-treated patients evaluable on the 6-minute walk test (6MWT) demonstrated stabilization in walking ability compared to a placebo/delayed-treatment cohort. Eteplirsen was well tolerated in the study with no clinically significant treatment-related adverse events. These data will form the basis of a New Drug Application (NDA) to the U.S. Food and Drug Administration (FDA) for eteplirsen planned for the first half of 2014.

However, Usdin noted that the drug’s approval and the study are two independent events, adding that the 2013 study just “got the ball rolling” for eteplirsen, and the FDA conducted many of its own experiments analyses, as detailed in the newly released report.

Jerry Mendell, the corresponding author of the study (which has so far been cited 118 times, according to Thomson Reuters Web of Science) from Ohio State University in Columbus, told us the allegations were “unfounded” and said the data are “valid.” Therefore, he added, he will not be approaching the journal for a retraction, noting that the FDA asked him hundreds of questions about the paper and audited the trials.

Clifford Saper, the editor-in-chief of Annals of Neurology from the Beth Israel Deaconess Medical Center (which is part of Harvard Medical School), said in an email:

It takes more than a call by a politician for retraction of a paper. It takes actual evidence.

He added:

If the FDA commissioner has, or knows of someone who has, evidence for an error in a paper published in Annals of Neurology, I encourage him to send that evidence to me and a copy to the authors of the article, for their reply. At that point we will engage in a scientific review of the evidence and make appropriate responses.

Linda Lowes, sixth author of the present study, is the last author of a 2016 study in Physical Therapy that was retracted months after publication. Its notice reads:

This article has been retracted by the author due to unintentional deviations in the use of the described modified technique to assess plagiocephaly in the study participants, such that the use of the modified technique cannot be defended for the stated purpose in this population at this time.

Califf was a cardiologist at Duke University during the high-profile scandal of researcher Anil Potti at Duke, which led to more than 10 retractions, settled lawsuits, and medical board reprimands. In 2015, he told TheTriangle Business Journal:

I wish I had gotten myself more involved earlier…There were systems that were not adequate, as we stated. … That was a tough one, I think, for the whole institution.

We’ve contacted the FDA for comment, and will update the post with anything else we learn.

END QUOTE

Correction 9/21/16 10:44 p.m. eastern: When originally published, this post incorrectly reported that Califf was part of an inspection team that visited the Nationwide Children’s Hospital in Ohio, and attributed quotes from Ellis Unger to Califf. We have made appropriate corrections, and apologize for the error.

Like Retraction Watch? Consider making a tax-deductible contribution to support our growth. You can also follow us on Twitter, like us on Facebook, add us to your RSS reader, sign up on our homepage for an email every time there’s a new post, or subscribe to our daily digest. Click here to review our Comments Policy. For a sneak peek at what we’re working on, click here.

SOURCE

http://retractionwatch.com/2016/09/21/amid-controversial-sarepta-approval-decision-fda-head-calls-for-key-study-retraction/

Related Resources on FDA’s Policies on Drugs:

Read Full Post »


Exome Aggregation Consortium (ExAC), generated the largest catalogue so far of variation in human protein-coding regions: Sequence data of 60,000 people, NOW is a publicly accessible database

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 8/22/2016

“The ExAC resource gives us incredible insight when evaluating a patient’s genome sequence in the clinic,” said Heidi Rehm, HMS associate professor of pathology at Brigham and Women’s Hospital, medical clinical director of the Broad’s Clinical Research Sequencing Platform and chief laboratory director of the Laboratory for Molecular Medicine at Partners HealthCare Personalized Medicine.

“In our own research, using the ExAC resource has allowed us to apply novel statistical methods to identify several new severe developmental disorders,” said Matthew Hurles, a researcher at the Wellcome Trust Sanger Institute and frequent user of the ExAC database. “Resources such as ExAC exemplify the benefits that can be achieved for families coping with rare genetic diseases, as a result of the mass altruism of many research participants who allow their data to be aggregated and shared.”

SOURCE

http://hms.harvard.edu/news/going-wide-and-deep?utm_source=Silverpop&utm_medium=email&utm_content=s3&utm_campaign=08.22.16.HMS

 

These variant data already guide diagnoses and treatment

E. V. Minikel et al. Sci. Transl. Med. 8, 322ra9; 2016

Quantifying prion disease penetrance using large population control cohorts

Science Translational Medicine  20 Jan 2016:
Vol. 8, Issue 322, pp. 322ra9
DOI: 10.1126/scitranslmed.aad5169

and

R. Walsh et al. Genet. Med. http://dx.doi.org/10.1038/gim.2016.90; 2016).

Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples

Genetics in Medicine
(2016)
doi:10.1038/gim.2016.90
Published online
17 August 2016

The ExAC project plans to grow over the next year to include 120,000 exome and 20,000 whole-genome sequences. It relies on the willingness of large research consortia to cooperate, and highlights the huge value of sharing, aggregation and harmonization of genomic data. This is also true for patient variants — there is a need for databases that provide greater confidence in variant interpretation, such as the US National Center for Biotechnology Information’s ClinVar database.

SOURCE

Nature536,249(18 August 2016)doi:10.1038/536249a

Read Full Post »


DISCUSSION – Genomics-driven personalized medicine for Pancreatic Cancer

Reporter: Aviva Lev-Ari, PhD, RN

[bold face added, ALA]

Integrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer –>>> Personalized Tumor Models Could Help Identify Combination Therapies for Hard-to-Treat Cancers

 

Original article

Pancreatic Cancer – Genomics-driven personalized medicine

 

PDAC has a particularly poor prognosis, and even with new targeted therapies and chemotherapy, the survival is poor. Here, we show that patient-derived models can be developed and used to investigate therapeutic sensitivities determined by genetic features of the disease and to identify empirical therapeutic vulnerabilities. These data reveal several key points that are of prime relevance to pancreatic cancer and tumor biology in general.

The Challenges of Using Genetic Analysis to Inform Treatment in PDAC

Precision oncology is dependent on the existence of known vulnerabilities encoded by high-potency genetic events and drugs capable of exploiting these vulnerabilities. At present, the repertoire of actionable genetic events in PDAC is limited.

  • Rare BRAF V600E mutations are identified in PDAC and could represent the basis for targeted inhibition, as our group and others have previously published (Collisson et al., 2012; Witkiewicz et al., 2015).

Similarly,

  • germline BRCA deficiency is the basis for ongoing poly(ADP-ribose) polymerase (PARP) inhibitor clinical trials (Lowery et al., 2011).

As shown here, out of 28 cases, only one genetic event was identified that yielded sensitivity to a therapeutic strategy. In this case, existence of the matched model allowed us to confirm the biological relevance of the

  • STAG2 mutation by showing sensitivity of the model to a DNA cross-linking agent.

Therefore, annotated patient-derived models provide a substrate upon which to functionally dissect the significance of novel and potentially actionable genetic events that occur within a tumor.

Another challenge of genomics-driven personalized medicine is

  • assessing the effect of specific molecular aberrations on therapeutic response in the context of complex genetic changes present in individual tumors.
  • KRAS has been proposed to modify therapeutic dependency to EZH2 inhibitors (Kim et al., 2015), and in the models tested, responses to this class of drugs were not uniformly present in cases harboring mutations in chromatin-remodeling genes.

This finding suggests that, although tumors acquire genetic alterations in specific genes, the implicated pathway may not be functionally inactive or therapeutically actionable. Therefore, annotated patient derived models provide a unique test bed for interrogating specific therapeutic dependencies in a genetically tractable system.

Empirical Definition of Therapeutic Sensitivities and Clinical Relevance

Cell lines offer the advantage of the ability to conduct high throughput approaches to interrogate many therapeutic agents. A large number of failed clinical trials have demonstrated the difficulty in treating PDAC. Based on the data herein, the paucity of clinical success is, most probably, due to the diverse therapeutic sensitivity of individual PDAC cases, suggesting that, with an unselected patient population, it will be veritably impossible to demonstrate clinical benefit. Additionally,

  •  very few models exhibited an exceptional response to single agents across the breadth of a library encompassing 305 agents.
  • We could identify only one tumor that was particularly sensitive to MEK inhibition and another model that was sensitive to
  • EGFR and
  • tyrosine kinase inhibitors.

In contrast to the limited activity of single agents, combination screens yielded responses at low-dose concentrations in the majority of models. Specific combinations were effective across several models, indicating that, by potentially screening more models, therapeutic sensitivity clades of PDAC will emerge. In the pharmacological screens performed in this study,

  • MEK inhibition, coupled with MTOR, docetaxel, or tyrosine kinase inhibitors, was effective in _30% of models tested.
  • Resistance to MEK inhibitors occurs through several mechanisms, including
  • Upregulation of oncogenic bypass signaling pathways such as AKT, tyrosine kinase, or MTOR (mammalian target of rapamycin) signaling.

In the clinic, the MEK and MTOR inhibitors (e.g., NCT02583542) are being tested. An intriguing finding from the drug screen was

  • sensitivity of a subset of models to combined MEK and docetaxel inhibition. This combination has been observed to synergistically enhance apoptosis and inhibit tumor growth in human xenograft tumor models (Balko et al., 2012; McDaid et al., 2005) and is currently being tested in a phase III study in patients with KRAS-mutated, advanced non-small-cell lung adenocarcinoma (Ja¨ nne et al., 2016).

Interestingly, in the models tested herein, there was limited sensitivity imparted through

  •  the combination of gemcitabine and MEK inhibition.

This potentially explains why the combination of MEK inhibitor and gemcitabine tested in the clinic did not show improved efficacy over gemcitabine alone (Infante et al., 2014).

Another promising strategy that emerged from this study involves using

  • CHK or BCL2 inhibitors as agents that drive enhanced sensitivity to chemotherapy.

Together, the data suggest that the majority of PDAC tumors have intrinsic therapeutic sensitivities, but the challenge is to prospectively identify effective treatment.

Patient-Derived Model-Based Approach to Precision Medicine

This study supports a path for guiding patient treatment based on the integration of genetic and empirically determined sensitivities of the patient’s tumor (Figure S7). In reference to defined genetic susceptibilities, the models provide a means to interrogate the voracity of specific drug targets. Parallel unbiased screening enables the discovery of sensitivities that could be exploited in the clinic. The model-guided treatment must be optimized, allowing for the generation of data in a time frame compatible with clinical decision making and appropriate validation.

In the present study, the majority of models were developed, cell lines were drug screened, and select hits were validated in PDX models within a 10- to 12-month window (Figure S7). This chronology would allow time to inform frontline therapy for recurrent disease for most patients who were surgically resected and treated with a standard of care where the median time to recurrence is approximately 14 months (Saif, 2013).

Although most models were generated from surgically resected specimens, two of the models (EMC3226 and EMC62) were established from primary tumor biopsies, indicating that this approach could be used with only a limited amount of tumor tissue available.

In the context of inoperable pancreatic cancer, application of data from a cell-line screen without in vivo validation in PDX would permit the generation of sensitivity data in the time frame compatible with treatment.

[We] acknowledge that model-guided treatment is also not without significant logistical hurdles, including the availability of drugs for patient treatment, clinically relevant time frames, patient-performance status, toxicity of combination regiments, and quality metrics related to model development and therapeutic response evaluation.

Additionally, it will be very important to monitor ex vivo genetic and phenotypic divergence with passage and try to understand the features of tumor heterogeneity that could undermine the efficacy of using models to direct treatment. As shown here, drug sensitivities remained stable with passage in cell culture and, importantly, were confirmed in PDX models, suggesting that the dominant genetic drivers and related therapeutic sensitivities are conserved.

In spite of these challenges, progressively more effort is going into the development of patient-derived models for guidance of disease treatment (Aparicio et al., 2015; Boj et al., 2015; Crystal et al., 2014; van de Wetering et al., 2015).

Several ongoing trials use PDX models to direct a limited repertoire of agents (e.g., NCT02312245, NCT02720796, and ERCAVATAR2015). Given the experience here, PDAC cell lines would provide the opportunity to rapidly interrogate a larger portfolio of combinations that could be used to guide patient care and provide a novel approach to precision medicine.

Validation of this approach would require the establishment of challenging multi-arm or N-of-1 clinical trials. However, considering the dire outcome for PDAC patients and the long-lasting difficulty in developing effective treatments, this non-canonical approach might be particularly impactful in pancreatic cancer.

SOURCE

Witkiewicz et al., 2016, Cell Reports 16, 1–15

August 16, 2016 ª 2016 The Author(s).

http://dx.doi.org/10.1016/j.celrep.2016.07.023

Agnieszka K. WitkiewiczPress enter key for correspondence information
Uthra Balaji
Cody Eslinger
Elizabeth McMillan
William Conway
Bruce Posner
Gordon B. Mills
Eileen M. O’Reilly
Erik S. KnudsencorrespondencePress enter key for correspondence information
Publication stage: In Press Corrected Proof
Open Access

Resource Integrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer

Correspondence

awitki@email.arizona.edu (A.K.W.),

eknudsen@email.arizona.edu (E.S.K.)

Accession Numbers: GSE84023

Other related articles on this topic published in this Open Access Online Scientific Journal include the following:

Pancreatic Cancer: Articles of Note @PharmaceuticalIntelligence.com

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/05/26/pancreatic-cancer-articles-of-note-pharmaceuticalintelligence-com/

Read Full Post »


Crowdsourcing Genetic Data Yields Discovery of DNA loci associated with Major Depressive Disorder (MDD) in European Descendants

 

Reporter: Kelly Perlman, Life Sciences Student and Research Assistant, McGill University

 

UPDATED on 11/24/2019

Can AI help diagnose depression? It’s a long shot

At the moment, machine intelligence is just as subjective as human intelligence

Alejandra Canales

https://www.salon.com/2019/11/23/can-ai-help-diagnose-depression-its-a-long-shot_partner/amp?__twitter_impression=true

Researchers from Pfizer Global Research and Development, 23andMe, and the Massachusetts General Hospital have published a study in Nature Genetics, pinpointing 15 genetic loci associated with the risk of developing major depressive disorder (MDD) in individuals of European ancestry. Evidence from previous research suggests that MDD is heritable, but the details of the specific gene correlates are unclear. The identification of loci where single nucleotide polymorphisms (SNPs) related to MDD exist could provide better insight into the neurobiology of depression, and therefore better treatment options.

23andMe, a private biotechnology company situated in California, offers a DNA sequencing service in which consumers send in a saliva swab for testing, and later receive a report listing the findings of the analysis related to ancestry, physical and behavioral traits, along with risk of inheriting certain diseases. The participants of this study had agreed to provide the results of their genetic testing for scientific research.

The results of 75,607 participants with self-reported diagnoses of depression were compared to the results of 231,747 participants reporting having never experienced depression. This data was combined with the results of previously published MDD genome-wide association studies (GWAS). To test the whether these results could be replicated, another set of results from 23andMe was analyzed, in which there were 45,773 MDD subjects, and 106,354 controls.

After the joint analysis, 17 SNPs were identified at 15 different loci. Tissue and gene enrichment assays showed that the genes that were over-expressed in the CNS were related to functions including neurodevelopment, histone methylation, neurogenesis and synaptic modification.

The team then created a weighted genetic risk score (GRS) in which they compared the 17 SNPs with factors including medication use, comorbid diseases and behavioral phenotypes, all of which were correlated with the GRS. Of note, the GRS was very highly correlated with age of onset of MDD.

The crowdsourcing of genetic data proves to be an efficient and powerful tool for large-scale MDD studies. Pooling large subject databases together is essential in order to account for the heterogeneous nature of the disease. Despite not being able to precisely assess each subject’s disease phenotype, scientists can make more rapid headway by collaborating with biotechnology companies in the quest to better understand the biological mechanisms of depression. Ron Perlis, M.D., M.Sc., of the Massachusetts General Hospital and co-author of this paper explained that “finding genes associated with depression should help make clear that this is a brain disease, which we hope will decrease the stigma still associated with these kinds of illnesses”.

 

Details on specific significant genes:

http://www.genecards.org/cgi-bin/carddisp.pl?gene=OLFM4

http://www.genecards.org/cgi-bin/carddisp.pl?gene=TMEM161B

http://www.genecards.org/cgi-bin/carddisp.pl?gene=MEF2C

http://www.genecards.org/cgi-bin/carddisp.pl?gene=MEIS2

http://www.genecards.org/cgi-bin/carddisp.pl?gene=TMCO5A

http://www.genecards.org/cgi-bin/carddisp.pl?gene=NEGR1

 

SOURCES

Hyde, C. L., Nagle, M. W., Tian, C., Chen, X., Paciga, S. A., Wendland, J. R., . . . Winslow, A. R. (2016). Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nature Genetics Nat Genet. doi:10.1038/ng.3623

Major Depressive Disorder Loci Discovered in Large GWAS Enabled by 23andMe Participants’ Data. (2016, August 01). Retrieved August 09, 2016, from https://www.genomeweb.com/microarrays-multiplexing/major-depressive-disorder-loci-discovered-large-gwas-enabled-23andme

 

Read Full Post »


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.

SOURCE

https://www.genomeweb.com/sequencing/reanalysis-clinical-exome-data-over-time-could-yield-new-diagnoses?utm_source=SilverpopMailing&utm_medium=email&utm_campaign=Daily%20News:%20Reanalysis%20of%20Clinical%20Exome%20Data%20Over%20Time%20Could%20Yield%20New%20Diagnoses%20-%2007/22/2016%2011:20:00%20AM

At ACMG, Researchers Report Data Re-Analysis, Matchmaking Boosts Solved Exome Cases

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.

SOURCE

https://www.genomeweb.com/molecular-diagnostics/acmg-researchers-report-data-re-analysis-matchmaking-boosts-solved-exome-cases

 

Related Articles

 

Read Full Post »


mRNA Data Survival Analysis

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

 

 

SURVIV for survival analysis of mRNA isoform variation

Shihao ShenYuanyuan WangChengyang WangYing Nian Wu & Yi Xing
Nature Communications7,Article number:11548
 Feb 2016      doi:10.1038/ncomms11548

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.

TABLE 1 (not shown)

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.

Network of survival-associated alternative splicing events

…see http://www.nature.com/ncomms/2016/160609/ncomms11548/full/ncomms11548.html

Figure 6: Splicing factor regulatory network of survival-associated alternative splicing events in IDC.

(ac) 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.

…..

Alternative splicing predictors of cancer patient survival

see http://www.nature.com/ncomms/2016/160609/ncomms11548/full/ncomms11548.html

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.

 

Read Full Post »

Older Posts »