Archive for the ‘Pharmacogenomics’ Category

Novartis’ Kymriah (tisagenlecleucel), FDA approved genetically engineered immune cells, would charge $475,000 per patient, will use Programs that Payers will pay only for Responding Patients

Curator: Aviva Lev-Ari, PhD, RN


UPDATED on 9/1/2017:

This Pioneering $475,000 Cancer Drug Comes With A Money-Back Guarantee

Novartis defends the eye-popping price of its pioneering gene therapy with arguments about its $1 billion expenditure—and novel “value-based” pricing.


On 8/30/2017 we wrote:

FDA has approved the world’s first CAR-T therapy, Novartis for Kymriah (tisagenlecleucel) and Gilead’s $12 billion buy of KitePharma, no approved drug and Canakinumab for Lung Cancer (may be?)

Curator: Aviva Lev-Ari, PhD, RN


The Price for the Treatment was published on 8/31/2017, a Value-based Pricing Payment Model of a $475,000 per patient charge for the responding patients after ONE month of treatment. Novartis says it takes an average of 22 days to create the therapy, from the time a patient’s cells are removed to when they are infused back into the patient. Kymriah will initially be available at 20 U.S. hospitals within a month, Novartis says. Eventually, 32 total sites will offer the therapy. 

CAR-T gained national attention three years ago when Carl June, a researcher at the University of Pennsylvania, used to put a young girl’s acute lymphoblastic leukemia. Genetically altering the girl’s immune cells had made her deathly ill, but June had used a Roche drug, Actemra, to treat the side effects. She lived, and the results were published in The New England Journal of Medicine. Novartis bought the rights to the Penn treatment for just $20 million up front.

Pharma Buying the right to use from an Academic Institution is a known route to leap frog the R&D lengthy process of Drug discovery.

“I’ve told the team that resources are not an issue. Speed is the issue,” says Novartis’ Chief Executive Joseph Jimenez, told Forbes in a cover story about the work then.

The FDA calls this CAR-T therapy treatment, made by Novartis, the “first gene therapy” in the U.S. The therapy is designed to treat an often-lethal type of blood and bone marrow cancer that affects children and young adults. The FDA defines gene therapy as a medicine that “introduces genetic material into a person’s DNA to replace faulty or missing genetic material” to treat a disease or medical condition. This is the first such therapy to be available in the U.S., according to the FDA.

Two gene therapies for rare, inherited diseases have already been approved in Europe.

To further evaluate the long-term safety, Novartis is also required to conduct a post-marketing observational study involving patients treated with Kymriah.

The FDA granted Kymriah Priority Review and Breakthrough Therapy designations. The Kymriah application was reviewed using a coordinated, cross-agency approach. The clinical review was coordinated by the FDA’s Oncology Center of Excellence, while CBER conducted all other aspects of review and made the final product approval determination.

The FDA granted approval of Kymriah to Novartis Pharmaceuticals Corp. The FDA granted the expanded approval of Actemra to Genentech Inc.

FDA commissioner Scott Gottlieb in a statement.

“We’re entering a new frontier in medical innovation with the ability to reprogram a patient’s own cells to attack a deadly cancer,” 

“Kymriah is a first-of-its-kind treatment approach that fills an important unmet need for children and young adults with this serious disease,” said Peter Marks, M.D., Ph.D., director of the FDA’s Center for Biologics Evaluation and Research (CBER). “Not only does Kymriah provide these patients with a new treatment option where very limited options existed, but a treatment option that has shown promising remission and survival rates in clinical trials.”

The Protocol

A patient’s T cells are extracted and cryogenically frozen so that they can be transported to Novartis’s manufacturing center in New Jersey. There, the cells are genetically altered to have a new gene that codes for a protein—called a chimeric antigen receptor, or CAR. This protein directs the T cells to target and kill leukemia cells with a specific antigen on their surface. The genetically modified cells are then infused back into the patient.

In a clinical trial of 63 children and young adults with a type of acute lymphoblastic leukemia, 83 percent of patients that received the CAR-T therapy had their cancers go into remission within three months. At six months, 89 percent of patients who received the therapy were still living, and at 12 months, 79 percent had survived.

CAR-T Therapies: Product/Molecules/MOA under Development:

  • Similar CAR-T treatments were being developed at other institutions including
  • Memorial Sloan-Kettering Cancer Center,
  • Seattle Children’s Hospital, and
  • The National Cancer Institute.
  • The Memorial and Seattle work was spun off into a startup called Juno Therapeutics, which has fallen behind. Juno Therapeutics ended a CAR-T study earlier this year after patients died from cerebral edema, or swelling in the brain.
  • The NCI work became the basis for the product being developed by Kite Pharma. Kite Pharma, which is awaiting FDA approval for its CAR-T therapy to treat a form of blood cancer in adults, was this week bought out by Gilead in a deal worth $11.9 billion.

On Cambridge Healthtech Institute’s 4th Annual Adoptive T Cell Therapy, Delivering CAR, TCR, and TIL from Research to Reality, August 29 – 30, 2017 | Sheraton Boston | Boston, MA

TUESDAY, AUGUST 29 – I covered in Real Time the talk on Juno Therapeutics: Building Better T Cell Therapies: The Power of Molecular Profiling by Mark Bonyhadi, Ph.D., Head, Research and Academic Affairs, Juno Therapeutics


Precision Medicine is Costly and not a Rapid manufacturing process

All of the CAR-T products are expensive to make, and must be manufactured on an individual basis for each new patient from the patient’s own T-cells, a type of white blood cells, a process that takes weeks.

  • How quickly companies can speed up manufacturing.
  • Kymriah will be manufactured at a facility in Morris Plains, N.J.
  • CAR-T technology, which has so far been used only in patients with blood cancers that have not been cured by other treatments, can be used earlier in the disease or for solid tumors: Breast, Prostate, Melanomas.

Prediction How Patients will Far Well – Researchers use a big-data approach to find links between different genes and patient survival.

A pathology atlas of the human cancer transcriptome

+ See all authors and affiliations

Science  18 Aug 2017:
Vol. 357, Issue 6352, eaan2507
DOI: 10.1126/science.aan2507

Modeling the cancer transcriptome

Recent initiatives such as The Cancer Genome Atlas have mapped the genome-wide effect of individual genes on tumor growth. By unraveling genomic alterations in tumors, molecular subtypes of cancers have been identified, which is improving patient diagnostics and treatment. Uhlen et al. developed a computer-based modeling approach to examine different cancer types in nearly 8000 patients. They provide an open-access resource for exploring how the expression of specific genes influences patient survival in 17 different types of cancer. More than 900,000 patient survival profiles are available, including for tumors of colon, prostate, lung, and breast origin. This interactive data set can also be used to generate personalized patient models to predict how metabolic changes can influence tumor growth.


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FDA has approved the world’s first CAR-T therapy, Novartis for Kymriah (tisagenlecleucel) and Gilead’s $12 billion buy of Kite Pharma, no approved drug and Canakinumab for Lung Cancer (may be?)

Curator: Aviva Lev-Ari, PhD, RN

UPDATED on 9/7/2017

Here’s the inside account of Gilead’s 11-week sprint to its $12B Kite buyout – ENDPOINTS NEWS

UPDATED on 8/31/2017

Gilead-Kite: A New Transformative Deal For Biotech, AUG 30, 2017

Gilead has made a big bet on new technology in Kite’s immunotherapy platforms and has reduced the number of credible large players in the space.

With a reputation for intense diligence and dynamism in its business development efforts, Gilead’s management team will only bolster the immunotherapy field as it prepares to face off with Novartis, its immediate competitor, and enters squarely in the province of Merck and Bristol Myers Squibb, two of the leaders in immuno-oncology.

Gilead has reinvented the transformative transaction for the sector.


I attended this week the Cambridge Healthtech Institute’s 4th Annual

Adoptive T Cell Therapy

Delivering CAR, TCR, and TIL from Research to Reality
August 29 – 30, 2017 | Sheraton Boston | Boston, MA


The following talks on 8/29/2017 presented the frontier of CAR-T Therapies and Technologies from lab to bed side:

  • Building Better T Cell Therapies: The Power of Molecular Profiling

Mark Bonyhadi, Ph.D., Head, Research and Academic Affairs, Juno Therapeutics

  • Tricked-Out Cars, the Next Generation of CAR T Cells

Richard Morgan, Ph.D., Vice President, Immunotherapy, Bluebird Bio

  • The Generation of Lentiviral Vector-Modified CAR-T Cells Using an Automated Process

Boro Dropulic, Ph.D., General Manager and CSO, Lentigen Technology, Inc.

I covered this event in Real Time for the Press

LIVE – 8/29 – CHI’s Oncolytic Virus Immunotherapy and ADOPTIVE CELL THERAPY, August 28-29, 2017 Sheraton Boston Hotel | Boston, MA


One year ago we published the following:

What does this mean for Immunotherapy? FDA put a temporary hold on Juno’s JCAR015, Three Death of Celebral Edema in CAR-T Clinical Trial and Kite Pharma announced Phase II portion of its CAR-T ZUMA-1 trial



Is Canakinumab the Next Viagra?

In this Revolution and Revelation, Milton Packer explains how safety data can sometimes trump a primary endpoint

by Milton PackerAugust 30, 2017

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Breakthroughs: Insights From the Personalized Medicine & Diagnostics Track at the 2017 BIO International Convention

Guest Author: David Davenport, Office Administrator, Personalized Medicine Coalition


“Health care today is reactive and costly … anything but personalized … but we are now entering a new era where health care is becoming proactive, preventive, highly personalized and most importantly predictive,” said J. Craig Venter, Ph.D., Founder, President, CEO, J. Craig Venter Institute, during his opening keynote at the Personalized Medicine and Diagnostics Track at the 2017 BIO International Convention in San Diego from June 21 – 22. The track, co-organized by PMC, brought together thought leaders to discuss breakthroughs in advancing personalized medicine. From those conversations several themes emerged:

Complex genetic data require a “knowledge network” to translate into personalized care.

During the session titled The Next Frontier: Navigating Clinical Adoption of Personalized Medicine, moderated by PMC Vice President for Science Policy Daryl Pritchard, Ph.D., panelists discussed how to accelerate the clinical adoption of innovative personalized therapies. Jennifer Levin Carter, M.D., Founder and Chief Medical Officer of N-of-One, a clinical diagnostic testing interpretation service company, explained that as data grows in complexity, there is a growing need for partnerships to efficiently analyze the data and develop effective targeted treatment plans. India Hook-Barnard, Ph.D., Director of Research Strategy, Associate Director of Precision Medicine, University of California, San Francisco (UCSF), agreed and discussed the need to build a “knowledge network” that can harness data and expertise to inform provider-patient decision-making.

Discussing how personalized medicine can be integrated into community health centers lacking large research budgets, Lynn Dressler, Dr.P.H., Director of Personalized Medicine and Pharmacogenomics at Mission Health Systems, a rural community health care delivery system in Asheville, North Carolina, discussed the need to better educate physicians and patients as well as the role that a knowledge network could play in providing easy and cost-effective access to diagnostic testing services.

Delivering personalized medicine requires innovative partnerships involving industry, IT companies, providers, payers and the government.

During It’s a Converging World: Innovative Partnerships and Precision Medicine, a panel moderated by Kristin Pothier, Global Head of Life Sciences Strategy, Ernst & Young, discussed the need for “open data” where improved patient care is the shared goal, and how public-private partnerships that address education, evidence development and access to care can help foster personalized medicine.

During a session titled Nevada as a New Model for Population Health Study, Nevada-based health system Renown Health outlined a study in which it partnered with genetic testing company 23andMe to examine whether free access to genetic testing changes participants’ practices in managing their own health and facilitates the utilization of personalized medicine.

In the era of personalized medicine, measuring and delivering value requires a paradigm shift from population-based to individual-based evidence.

Following a discussion on regulatory and reimbursement challenges moderated by Bruce Quinn, M.D., Ph.D., Principal, Bruce Quinn Associates, during which panelists called for the simplification of payment structures to be more consistent, more efficient and more connected to the patient market, a panel moderated by Jennifer Snow, Director of Health Policy at Xcenda, discussed how value assessment frameworks must adapt to consider the value of personalized medicine. During The Whole Picture: Consideration of Personalized Medicine in Value Assessment Frameworks, panelist Mitch Higashi, Ph.D., Vice President, Health Economics and Outcomes Research, U.S., Bristol-Myers Squibb, called for patient-centered definitions of value and advocated for the inclusion of predictive biomarkers in all value frameworks. Donna Cryer, J.D., President, CEO, Global Liver Institute, added that the “patient must be the ultimate ‘arbiter of value’” and urged “transparency” in how value assessment frameworks are used.

Noting that different assessment frameworks have different goals, Roger Longman, CEO, Real Endpoints, called for more dynamic frameworks that allow different stakeholders to “use the same criteria but weigh them differently.” The panel concluded that to advance personalized medicine, value frameworks must be meaningful, practical and predictive for patients; reflect evolving evidence needs like real-world evidence; and consider breakthrough payment structures like bundled payments.

From Promise to Practice: The Way Forward for Personalized Medicine

During the concluding session, Creating a Universal Biomarker Program, moderated by Ian Wright, Owner, Strategic Innovations LLC, on behalf of Cedars-Sinai Precision Health, panelists discussed how to make patients the point of reference for their own care, as opposed to being compared to the “normal” range of population averages in treatment decisions using biomarkers. The speakers concluded that moving in that direction requires providers to establish baselines for each patient, along with tools and metrics to facilitate the approach.

In the words of Donna Cryer, “personalized medicine is the definition of value for a patient.” With the ability to detect diseases before they even express themselves, the promise of personalized medicine has never been greater.

However, changing the health care system to improve patient access to valuable personalized medicines requires innovation and collaboration. As PMC President Edward Abrahams, Ph.D., said during his opening remarks for the track, that change

“doesn’t come easily,” but “breakthrough” discussions like these continue to move us forward.

The complete track agenda can be downloaded here.



From: <>

Date: Monday, July 10, 2017 at 10:51 AM

To: Aviva Lev-Ari <>

Subject: Breakthroughs From the 2017 BIO Convention’s PM & Dx Track

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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


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.

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.


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.


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.


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.

Need for follow-up confirmatory trials remains outstanding

FDA’s Postmarketing Surveillance Programs

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


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



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.


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.

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Related Resources on FDA’s Policies on Drugs:

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Milestones in Physiology & Discoveries in Medicine and Genomics: Request for Book Review Writing on


Milestones in Physiology

Discoveries in Medicine, Genomics and Therapeutics

Patient-centric Perspective 




Author, Curator and Editor

Larry H Bernstein, MD, FCAP

Chief Scientific Officer

Leaders in Pharmaceutical Business Intelligence



Chapter 1: Evolution of the Foundation for Diagnostics and Pharmaceuticals Industries

1.1  Outline of Medical Discoveries between 1880 and 1980

1.2 The History of Infectious Diseases and Epidemiology in the late 19th and 20th Century

1.3 The Classification of Microbiota

1.4 Selected Contributions to Chemistry from 1880 to 1980

1.5 The Evolution of Clinical Chemistry in the 20th Century

1.6 Milestones in the Evolution of Diagnostics in the US HealthCare System: 1920s to Pre-Genomics


Chapter 2. The search for the evolution of function of proteins, enzymes and metal catalysts in life processes

2.1 The life and work of Allan Wilson
2.2  The  evolution of myoglobin and hemoglobin
2.3  More complexity in proteins evolution
2.4  Life on earth is traced to oxygen binding
2.5  The colors of life function
2.6  The colors of respiration and electron transport
2.7  Highlights of a green evolution


Chapter 3. Evolution of New Relationships in Neuroendocrine States
3.1 Pituitary endocrine axis
3.2 Thyroid function
3.3 Sex hormones
3.4 Adrenal Cortex
3.5 Pancreatic Islets
3.6 Parathyroids
3.7 Gastointestinal hormones
3.8 Endocrine action on midbrain
3.9 Neural activity regulating endocrine response

3.10 Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression


Chapter 4.  Problems of the Circulation, Altitude, and Immunity

4.1 Innervation of Heart and Heart Rate
4.2 Action of hormones on the circulation
4.3 Allogeneic Transfusion Reactions
4.4 Graft-versus Host reaction
4.5 Unique problems of perinatal period
4.6. High altitude sickness
4.7 Deep water adaptation
4.8 Heart-Lung-and Kidney
4.9 Acute Lung Injury

4.10 Reconstruction of Life Processes requires both Genomics and Metabolomics to explain Phenotypes and Phylogenetics


Chapter 5. Problems of Diets and Lifestyle Changes

5.1 Anorexia nervosa
5.2 Voluntary and Involuntary S-insufficiency
5.3 Diarrheas – bacterial and nonbacterial
5.4 Gluten-free diets
5.5 Diet and cholesterol
5.6 Diet and Type 2 diabetes mellitus
5.7 Diet and exercise
5.8 Anxiety and quality of Life
5.9 Nutritional Supplements


Chapter 6. Advances in Genomics, Therapeutics and Pharmacogenomics

6.1 Natural Products Chemistry

6.2 The Challenge of Antimicrobial Resistance

6.3 Viruses, Vaccines and immunotherapy

6.4 Genomics and Metabolomics Advances in Cancer

6.5 Proteomics – Protein Interaction

6.6 Pharmacogenomics

6.7 Biomarker Guided Therapy

6.8 The Emergence of a Pharmaceutical Industry in the 20th Century: Diagnostics Industry and Drug Development in the Genomics Era: Mid 80s to Present

6.09 The Union of Biomarkers and Drug Development

6.10 Proteomics and Biomarker Discovery

6.11 Epigenomics and Companion Diagnostics


Chapter  7

Integration of Physiology, Genomics and Pharmacotherapy

7.1 Richard Lifton, MD, PhD of Yale University and Howard Hughes Medical Institute: Recipient of 2014 Breakthrough Prizes Awarded in Life Sciences for the Discovery of Genes and Biochemical Mechanisms that cause Hypertension

7.2 Calcium Cycling (ATPase Pump) in Cardiac Gene Therapy: Inhalable Gene Therapy for Pulmonary Arterial Hypertension and Percutaneous Intra-coronary Artery Infusion for Heart Failure: Contributions by Roger J. Hajjar, MD

7.3 Diagnostics and Biomarkers: Novel Genomics Industry Trends vs Present Market Conditions and Historical Scientific Leaders Memoirs

7.4 Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging

7.5 Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

7.6 Imaging Biomarker for Arterial Stiffness: Pathways in Pharmacotherapy for Hypertension and Hypercholesterolemia Management

7.7 Neuroprotective Therapies: Pharmacogenomics vs Psychotropic drugs and Cholinesterase Inhibitors

7.8 Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes

7.9 Preserved vs Reduced Ejection Fraction: Available and Needed Therapies

7.10 Biosimilars: Intellectual Property Creation and Protection by Pioneer and by

7.11 Demonstrate Biosimilarity: New FDA Biosimilar Guidelines


Chapter 7.  Biopharma Today

8.1 A Great University engaged in Drug Discovery: University of Pittsburgh

8.2 Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?

8.3 Predicting Tumor Response, Progression, and Time to Recurrence

8.4 Targeting Untargetable Proto-Oncogenes

8.5 Innovation: Drug Discovery, Medical Devices and Digital Health

8.6 Cardiotoxicity and Cardiomyopathy Related to Drugs Adverse Effects

8.7 Nanotechnology and Ocular Drug Delivery: Part I

8.8 Transdermal drug delivery (TDD) system and nanotechnology: Part II

8.9 The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology

8.10 Natural Drug Target Discovery and Translational Medicine in Human Microbiome

8.11 From Genomics of Microorganisms to Translational Medicine

8.12 Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Homeostasis of Immune Responses for Good and Bad


Chapter 9. BioPharma – Future Trends

9.1 Artificial Intelligence Versus the Scientist: Who Will Win?

9.2 The Vibrant Philly Biotech Scene: Focus on KannaLife Sciences and the Discipline and Potential of Pharmacognosy

9.3 The Vibrant Philly Biotech Scene: Focus on Computer-Aided Drug Design and Gfree Bio, LLC

9.4 Heroes in Medical Research: The Postdoctoral Fellow

9.5 NIH Considers Guidelines for CAR-T therapy: Report from Recombinant DNA Advisory Committee

9.6 1st Pitch Life Science- Philadelphia- What VCs Really Think of your Pitch

9.7 Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer

9.8 Heroes in Medical Research: Green Fluorescent Protein and the Rough Road in Science

9.9 Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

9.10 The SCID Pig II: Researchers Develop Another SCID Pig, And Another Great Model For Cancer Research


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Content Consultant: Larry H Bernstein, MD, FCAP

Genomics Orientations for Personalized Medicine

Volume One

electronic Table of Contents

Chapter 1

1.1 Advances in the Understanding of the Human Genome The Initiation and Growth of Molecular Biology and Genomics – Part I

1.2 CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way – Part IIA

1.3 DNA – The Next-Generation Storage Media for Digital Information

1.4 CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

1.5 Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets

1.6 Genomic Analysis: FLUIDIGM Technology in the Life Science and Agricultural Biotechnology

Chapter 2

2.1 2013 Genomics: The Era Beyond the Sequencing of the Human Genome: Francis Collins, Craig Venter, Eric Lander, et al.

2.2 DNA structure and Oligonucleotides

2.3 Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell 

2.4 Genomics and Evolution

2.5 Protein-folding Simulation: Stanford’s Framework for Testing and Predicting Evolutionary Outcomes in Living Organisms – Work by Marcus Feldman

2.6 The Binding of Oligonucleotides in DNA and 3-D Lattice Structures

2.7 Finding the Genetic Links in Common Disease: Caveats of Whole Genome Sequencing Studies

Chapter 3

3.1 Big Data in Genomic Medicine

3.2 CRACKING THE CODE OF HUMAN LIFE: The Birth of Bioinformatics & Computational Genomics – Part IIB 

3.3 Expanding the Genetic Alphabet and linking the Genome to the Metabolome

3.4 Metabolite Identification Combining Genetic and Metabolic Information: Genetic Association Links Unknown Metabolites to Functionally Related Genes

3.5 MIT Scientists on Proteomics: All the Proteins in the Mitochondrial Matrix identified

3.6 Identification of Biomarkers that are Related to the Actin Cytoskeleton

3.7 Genetic basis of Complex Human Diseases: Dan Koboldt’s Advice to Next-Generation Sequencing Neophytes

3.8 MIT Team Researches Regulatory Motifs and Gene Expression of Erythroleukemia (K562) and Liver Carcinoma (HepG2) Cell Lines

Chapter 4

4.1 ENCODE Findings as Consortium

4.2 ENCODE: The Key to Unlocking the Secrets of Complex Genetic Diseases

4.3 Reveals from ENCODE Project will Invite High Synergistic Collaborations to Discover Specific Targets  

4.4 Human Variome Project: encyclopedic catalog of sequence variants indexed to the human genome sequence

4.5 Human Genome Project – 10th Anniversary: Interview with Kevin Davies, PhD – The $1000 Genome

4.6 Quantum Biology And Computational Medicine

4.7 The Underappreciated EpiGenome

4.8 Unraveling Retrograde Signaling Pathways

4.9  “The SILENCE of the Lambs” Introducing The Power of Uncoded RNA

4.10  DNA: One man’s trash is another man’s treasure, but there is no JUNK after all

Chapter 5

5.1 Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1 

5.2 Computational Genomics Center: New Unification of Computational Technologies at Stanford

5.3 Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3

5.4 Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

5.5 Genome and Genetics: Resources @Stanford, @MIT, @NIH’s NCBCS

5.6 NGS Market: Trends and Development for Genotype-Phenotype Associations Research

5.7 Speeding Up Genome Analysis: MIT Algorithms for Direct Computation on Compressed Genomic Datasets

5.8  Modeling Targeted Therapy

5.9 Transphosphorylation of E-coli Proteins and Kinase Specificity

5.10 Genomics of Bacterial and Archaeal Viruses

Chapter 6

6.1  Directions for Genomics in Personalized Medicine

6.2 Ubiquinin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III

6.3 Mitochondrial Damage and Repair under Oxidative Stress

6.4 Mitochondria: More than just the “Powerhouse of the Cell”

6.5 Mechanism of Variegation in Immutans

6.6 Impact of Evolutionary Selection on Functional Regions: The imprint of Evolutionary Selection on ENCODE Regulatory Elements is Manifested between Species and within Human Populations

6.7 Cardiac Ca2+ Signaling: Transcriptional Control

6.8 Unraveling Retrograde Signaling Pathways

6.9 Reprogramming Cell Fate

6.10 How Genes Function

6.11 TALENs and ZFNs

6.12 Zebrafish—Susceptible to Cancer

6.13 RNA Virus Genome as Bacterial Chromosome

6.14 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome 

6.15 Telling NO to Cardiac Risk- DDAH Says NO to ADMA(1); The DDAH/ADMA/NOS Pathway(2)

6.16  Transphosphorylation of E-coli proteins and kinase specificity

6.17 Genomics of Bacterial and Archaeal Viruses

6.18  Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

Chapter 7

7.1 Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @

7.2 Consumer Market for Personal DNA Sequencing: Part 4

7.3 GSK for Personalized Medicine using Cancer Drugs Needs Alacris Systems Biology Model to Determine the In Silico Effect of the Inhibitor in its “Virtual Clinical Trial”

7.4 Drugging the Epigenome

7.5 Nation’s Biobanks: Academic institutions, Research institutes and Hospitals – vary by Collections Size, Types of Specimens and Applications: Regulations are Needed

7.6 Personalized Medicine: Clinical Aspiration of Microarrays

Chapter 8

8.1 Personalized Medicine as Key Area for Future Pharmaceutical Growth

8.2 Inaugural Genomics in Medicine – The Conference Program, 2/11-12/2013, San Francisco, CA

8.3 The Way With Personalized Medicine: Reporters’ Voice at the 8th Annual Personalized Medicine Conference, 11/28-29, 2012, Harvard Medical School, Boston, MA

8.4 Nanotechnology, Personalized Medicine and DNA Sequencing

8.5 Targeted Nucleases

8.6 Transcript Dynamics of Proinflammatory Genes

8.7 Helping Physicians identify Gene-Drug Interactions for Treatment Decisions: New ‘CLIPMERGE’ program – Personalized Medicine @ The Mount Sinai Medical Center

8.8 Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing[1]

8.9 Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

Chapter 9

9.1 Personal Tale of JL’s Whole Genome Sequencing

9.2 Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics

9.3 Inform Genomics Developing SNP Test to Predict Side Effects, Help MDs Choose among Chemo Regimens

9.4 SNAP: Predict Effect of Non-synonymous Polymorphisms: How Well Genome Interpretation Tools could Translate to the Clinic

9.5  LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2

9.6 The Initiation and Growth of Molecular Biology and Genomics – Part I

9.7 Personalized Medicine-based Cure for Cancer Might Not Be Far Away

9.8 Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)

 Chapter 10

10.1 Pfizer’s Kidney Cancer Drug Sutent Effectively caused REMISSION to Adult Acute Lymphoblastic Leukemia (ALL)

10.2 Imatinib (Gleevec) May Help Treat Aggressive Lymphoma: Chronic Lymphocytic Leukemia (CLL)

10.3 Winning Over Cancer Progression: New Oncology Drugs to Suppress Passengers Mutations vs. Driver Mutations

10.4 Treatment for Metastatic HER2 Breast Cancer

10.5 Personalized Medicine in NSCLC

10.6 Gene Sequencing – to the Bedside

10.7 DNA Sequencing Technology

10.8 Nobel Laureate Jack Szostak Previews his Plenary Keynote for Drug Discovery Chemistry

Chapter 11

11.1 mRNA Interference with Cancer Expression

11.2 Angiogenic Disease Research Utilizing microRNA Technology: UCSD and Regulus Therapeutics

11.3 Sunitinib brings Adult acute lymphoblastic leukemia (ALL) to Remission – RNA Sequencing – FLT3 Receptor Blockade

11.4 A microRNA Prognostic Marker Identified in Acute Leukemia 

11.5 MIT Team: Microfluidic-based approach – A Vectorless delivery of Functional siRNAs into Cells.

11.6 Targeted Tumor-Penetrating siRNA Nanocomplexes for Credentialing the Ovarian Cancer Oncogene ID4

11.7 When Clinical Application of miRNAs?

11.8 How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis,

11.9 Potential Drug Target: Glycolysis Regulation – Oxidative Stress-responsive microRNA-320

11.10  MicroRNA Molecule May Serve as Biomarker

11.11 What about Circular RNAs?

Chapter 12

12.1 The “Cancer Establishments” Examined by James Watson, Co-discoverer of DNA w/Crick, 4/1953

12.2 Otto Warburg, A Giant of Modern Cellular Biology

12.3 Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

12.4 Hypothesis – Following on James Watson

12.5 AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

12.6 AKT signaling variable effects

12.7 Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers

12.8 Phosphatidyl-5-Inositol signaling by Pin1

Chapter 13

13.1 Nanotech Therapy for Breast Cancer

13.2 BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair

13.3 Exome sequencing of serous endometrial tumors shows recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes

13.4 Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors

13.5 Prostate Cancer: Androgen-driven “Pathomechanism” in Early onset Forms of the Disease

13.6 In focus: Melanoma Genetics

13.7 Head and Neck Cancer Studies Suggest Alternative Markers More Prognostically Useful than HPV DNA Testing

13.8 Breast Cancer and Mitochondrial Mutations

13.9  Long noncoding RNA network regulates PTEN transcription

Chapter 14

14.1 HBV and HCV-associated Liver Cancer: Important Insights from the Genome

14.2 Nanotechnology and HIV/AIDS treatment

14.3 IRF-1 Deficiency Skews the Differentiation of Dendritic Cells

14.4 Sepsis, Multi-organ Dysfunction Syndrome, and Septic Shock: A Conundrum of Signaling Pathways Cascading Out of Control

14.5  Five Malaria Genomes Sequenced

14.6 Rheumatoid Arthritis Risk

14.7 Approach to Controlling Pathogenic Inflammation in Arthritis

14.8 RNA Virus Genome as Bacterial Chromosome

14.9 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome

Chapter 15

15.1 Personalized Cardiovascular Genetic Medicine at Partners HealthCare and Harvard Medical School

15.2 Congestive Heart Failure & Personalized Medicine: Two-gene Test predicts response to Beta Blocker Bucindolol

15.3 DDAH Says NO to ADMA(1); The DDAH/ADMA/NOS Pathway(2)

15.4 Peroxisome Proliferator-Activated Receptor (PPAR-gamma) Receptors Activation: PPARγ Transrepression for Angiogenesis in Cardiovascular Disease and PPARγ Transactivation for Treatment of Diabetes

15.5 BARI 2D Trial Outcomes

15.6 Gene Therapy Into Healthy Heart Muscle: Reprogramming Scar Tissue In Damaged Hearts

15.7 Obstructive coronary artery disease diagnosed by RNA levels of 23 genes – CardioDx, a Pioneer in the Field of Cardiovascular Genomic  Diagnostics

15.8 Ca2+ signaling: transcriptional control

15.9 Lp(a) Gene Variant Association

15.9.1 Two Mutations, in the PCSK9 Gene: Eliminates a Protein involved in Controlling LDL Cholesterol

15.9.2. Genomics & Genetics of Cardiovascular Disease Diagnoses: A Literature Survey of AHA’s Circulation Cardiovascular Genetics, 3/2010 – 3/2013

15.9.3 Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging

15.9.4 The Implications of a Newly Discovered CYP2J2 Gene Polymorphism Associated with Coronary Vascular Disease in the Uygur Chinese Population

15.9.5  Gene, Meis1, Regulates the Heart’s Ability to Regenerate after Injuries.

15.10 Genetics of Conduction Disease: Atrioventricular (AV) Conduction Disease (block): Gene Mutations – Transcription, Excitability, and Energy Homeostasis

15.11 How Might Sleep Apnea Lead to Serious Health Concerns like Cardiac and Cancers?

Chapter 16

16.1 Can Resolvins Suppress Acute Lung Injury?

16.2 Lipoxin A4 Regulates Natural Killer Cell in Asthma

16.3 Biological Therapeutics for Asthma

16.4 Genomics of Bronchial Epithelial Dysplasia

16.5 Progression in Bronchial Dysplasia

Chapter 17

17.1 Breakthrough Digestive Disorders Research: Conditions Affecting the Gastrointestinal Tract.

17.2 Liver Endoplasmic Reticulum Stress and Hepatosteatosis

17.3 Biomarkers-identified-for-recurrence-in-hbv-related-hcc-patients-post-surgery

17.4  Usp9x: Promising Therapeutic Target for Pancreatic Cancer

17.5 Battle of Steve Jobs and Ralph Steinman with Pancreatic cancer: How We Lost

Chapter 18

18.1 Ubiquitin Pathway Involved in Neurodegenerative Disease

18.2 Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression

18.3 Neuroprotective Therapies: Pharmacogenomics vs Psychotropic Drugs and Cholinesterase Inhibitors

18.4 Ustekinumab New Drug Therapy for Cognitive Decline Resulting from Neuroinflammatory Cytokine Signaling and Alzheimer’s Disease

18.5 Cell Transplantation in Brain Repair

18.6 Alzheimer’s Disease Conundrum – Are We Near the End of the Puzzle?

Chapter 19

19.1 Genetics and Male Endocrinology

19.2 Genomic Endocrinology and its Future

19.3 Commentary on Dr. Baker’s post “Junk DNA Codes for Valuable miRNAs: Non-coding DNA Controls Diabetes”

19.4 Therapeutic Targets for Diabetes and Related Metabolic Disorders

19.5 Secondary Hypertension caused by Aldosterone-producing Adenomas caused by Somatic Mutations in ATP1A1 and ATP2B3 (adrenal cortical; medullary or Organ of Zuckerkandl is pheochromocytoma)

19.6 Personal Recombination Map from Individual’s Sperm Cell and its Importance

19.7 Gene Trap Mutagenesis in Reproductive Research

19.8 Pregnancy with a Leptin-Receptor Mutation

19.9 Whole-genome Sequencing in Probing the Meiotic Recombination and Aneuploidy of Single Sperm Cells

19.10 Reproductive Genetic Testing

Chapter 20

20.1 Genomics & Ethics: DNA Fragments are Products of Nature or Patentable Genes?

20.2 Understanding the Role of Personalized Medicine

20.3 Attitudes of Patients about Personalized Medicine

20.4  Genome Sequencing of the Healthy

20.5   Genomics in Medicine – Tomorrow’s Promise

20.6  The Promise of Personalized Medicine

20.7 Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk

 20.8 Genomic Liberty of Ownership, Genome Medicine and Patenting the Human Genome

Chapter 21

Recent Advances in Gene Editing Technology Adds New Therapeutic Potential for the Genomic Era:  Medical Interpretation of the Genomics Frontier – CRISPR – Cas9


21.1 Introducing CRISPR/Cas9 Gene Editing Technology – Works by Jennifer A. Doudna

21.1.1 Ribozymes and RNA Machines – Work of Jennifer A. Doudna

21.1.2 Evaluate your Cas9 gene editing vectors: CRISPR/Cas Mediated Genome Engineering – Is your CRISPR gRNA optimized for your cell lines?

21.1.3 2:15 – 2:45, 6/13/2014, Jennifer Doudna “The biology of CRISPRs: from genome defense to genetic engineering”

21.1.4  Prediction of the Winner RNA Technology, the FRONTIER of SCIENCE on RNA Biology, Cancer and Therapeutics  & The Start Up Landscape in BostonGene Editing – New Technology The Missing link for Gene Therapy?

21.2 CRISPR in Other Labs

21.2.1 CRISPR @MIT – Genome Surgery

21.2.2 The CRISPR-Cas9 System: A Powerful Tool for Genome Engineering and Regulation

Yongmin Yan and Department of Gastroenterology, Hepatology & Nutrition, University of Texas M.D. Anderson Cancer, Houston, USADaoyan Wei*

21.2.3 New Frontiers in Gene Editing: Transitioning From the Lab to the Clinic, February 19-20, 2015 | The InterContinental San Francisco | San Francisco, CA

21.2.4 Gene Therapy and the Genetic Study of Disease: @Berkeley and @UCSF – New DNA-editing technology spawns bold UC initiative as Crispr Goes Global

21.2.5 CRISPR & MAGE @ George Church’s Lab @ Harvard

21.3 Patents Awarded and Pending for CRISPR

21.3.1 Litigation on the Way: Broad Institute Gets Patent on Revolutionary Gene-Editing Method

21.3.2 The Patents for CRISPR, the DNA editing technology as the Biggest Biotech Discovery of the Century

2.4 CRISPR/Cas9 Applications

21.4.1  Inactivation of the human papillomavirus E6 or E7 gene in cervical carcinoma cells using a bacterial CRISPR/Cas 

21.4.2 CRISPR: Applications for Autoimmune Diseases @UCSF

21.4.3 In vivo validated mRNAs

21.4.6 Level of Comfort with Making Changes to the DNA of an Organism

21.4.7 Who will be the the First to IPO: Novartis bought in to Intellia (UC, Berkeley) as well as Caribou (UC, Berkeley) vs Editas (MIT)??

21.4.8 CRISPR/Cas9 Finds Its Way As an Important Tool For Drug Discovery & Development


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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


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


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 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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