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Archive for the ‘Cancer and Current Therapeutics’ Category

2022 World Medical Innovation Forum, GENE & CELL THERAPY • MAY 2–4, 2022 • BOSTON • IN-PERSON

Reporter: Aviva Lev-Ari, PhD, RN

World Medical Innovation Forum as we bring together global leaders to assess the latest opportunities and challenges, from the investment landscape to key technology developments to manufacturing and regulatory barriers. Gain first-hand insights on medicine’s ultimate game changer.

https://worldmedicalinnovation.org/

View all videos on youtube.com

https://www.youtube.com/channel/UCauKpbsS_hUqQaPp8EVGYOg

View all tweets on Twitter.com

#WMIF2022

@MGBInnovation

@MassGenBrigham

@pharma_BI

@AVIVA1950

Mass General Brigham Innovation Discovery Grants Program

https://innovation.massgeneralbrigham.org/about/special-programs/partners-innovation-development-grants-program

World Medical Innovation Forum Videos

https://www.youtube.com/channel/UCauKpbsS_hUqQaPp8EVGYOg

World Medical Innovation Forum will be held June 12 – 14 in Boston, MA. We hope you’ll join us for #WMIF2023!

From: “Rieck, Lucy (BOS-WSW)” <LRieck@webershandwick.com>
Date: Tuesday, April 12, 2022 at 10:25 AM
To: Aviva Lev-Ari <avivalev-ari@alum.berkeley.edu>
Subject: You’re Invited: Mass General Brigham’s World Medical Innovation Forum

Hi Aviva,

I’m reaching out to extend free registration for you or a colleague to the 8th annual World Medical Innovation Forum (WMIF), taking place May 2-4 at the Westin Copley Place in Boston. This year’s event, co-sponsored with Bank of America, will explore gene and cell therapies (GCT), including the latest opportunities and challenges – from the investment landscape to key technology developments to manufacturing and regulatory barriers.

The event will feature 200 speakers – including CEOs of leading companies in the GCT and biotech fields, investors, entrepreneurs, Harvard clinicians and scientists, government officials and other key influencers – who discover, invest in, and cultivate GCT breakthroughs. Notable speakers include:

  • Peter Marks: Director, Center for Biologics Evaluation and Research at the FDA
  • Brian Moynihan: CEO, Bank of America
  • Anne Klibansky: President & CEO, Mass General Brigham
  • Senior executives from biopharma and academic institutions of all sizes (including Novartis, BMS, Takeda, Verve, UPenn)

 

You can view the full list of speakers here and the program agenda here.

WMIF is hosted by the Mass General Brigham health system, which comprises 14 hospitals, including two world-renowned medical centers: Mass General and Brigham & Women’s. Since 2015, the Forum has brought together global leaders to assess medical breakthroughs, the investment landscape and technology developments that have the potential to transform the industry.

In addition to a packed agenda, the 2022 “Disruptive Dozen” – 12 breakthrough technologies most likely to have significant impact on gene and cell therapy in the next 18 months – will also be announced.

Please let me know if you would be interested in attending.

Best,

Lucy 

Lucy Rieck

Senior Associate, Healthcare

C: +1 203-331-7894

33 Arch Street

Boston, MA, 02109

webershandwick.com

Ad Age Agency A-List (2020)

Ad Age Best Place to Work (2019)

PRovoke Global Agency of the Decade (2020)

PRWeek Purpose Agency of the Year (2020)

PRWeek US Large Agency of the Year (2020)

www.linkedin.com/in/lucyrieck

AGENDA

7:00 AM – 5:00 PMAmerica Foyer
7:00 AM – 8:00 AMAmerica Foyer
8:00 AM – 9:30 AMAmerica Ballroom

FIRST LOOK

First Look: 8 rapid fire presentations on Mass General Brigham’s new GCT technologies

New Gene and Cell Therapy technologies

Moderators:
Meredith Fisher, PhD
  • Partner, Mass General Brigham Ventures
Roger Kitterman
  • VP, Mass General Brigham Ventures
Presenters:
Bakhos Tannous, PhD
  • Director, Experimental Therapeutics Unit, Director, Viral Vector Core, MGH
  • Professor of Neurology, HMS
Vijaya Ramesh, PhD
  • Co-Director of Neuroscience, Associate Geneticist in Neurology, MGH
  • Professor of Neurology, HMS
Anna Krichevsky, PhD
  • Associate Professor of Neurology, BWH, HMS
Nerea Zabaleta, PhD
  • Principal Investigator, Grousbeck Gene Therapy Center, Mass Eye and Ear
  • Instructor in Ophthalmology, HMS
Francisco Quintana, PhD
  • Professor, Neurology, Ann Romney Center for Neurologic Diseases, BWH
  • Kuchroo Weiner Distinguished Professor of Neuroimmunology, BWH
Stephen Haggarty, PhD
  • Director, Chemical Neurobiology Laboratory, Center for Genomic Medicine, MGH
  • Associate Professor of Neurology, HMS
Michael Young, PhD
  • Director, Minda de Gunzburg Center for Retinal Regeneration, Associate Scientist, Schepens Eye Research Institute, Mass Eye and Ear
  • Associate Professor of Ophthalmology, Co-Director, Ocular Regenerative Medicine Institute, HMS
Max Jan, MD, PhD
  • Principal Investigator, Center for Cancer Research, MGH
  • Assistant Professor of Pathology, HMS
9:30 AM – 9:45 AM
9:45 AM – 11:15 AMAmerica Ballroom

FIRST LOOK

First Look: 8 rapid fire presentations on Mass General Brigham’s new GCT technologies

New Gene and Cell Therapy technologies

Moderators:
Meredith Fisher, PhD
  • Partner, Mass General Brigham Ventures
Roger Kitterman
  • VP, Mass General Brigham Ventures
Presenters:
Choi-Fong Cho, PhD
  • Assistant Professor of Neurosurgery, BWH, HMS
Yulia Grishchuk, PhD
  • Assistant Investigator, Center for Genomic Medicine, MGH
  • Assistant Professor of Neurology, HMS
Lynn Bry, MD, PhD
  • Director, Massachusetts Host-Microbiome Center, BWH
  • Associate Professor of Pathology, HMS
David Corey, PhD
  • Bertarelli Professor of Translational Medical Science, Neurobiology, HMS
Anil Chandraker, MD
  • Medical Director of Kidney and Pancreas Transplantation, BWH
  • Associate Professor of Medicine, HMS
Ole Isacson, MD, PhD
  • Director, Neuroregeneration Research Institute, McLean
  • Professor of Neurology & Neuroscience, HMS
Marco Mineo, PhD
  • Instructor in Neurosurgery, BWH, HMS
Susan Cotman, PhD
  • Assistant in Neuroscience, Center for Genomic Medicine, MGH
  • Assistant Professor of Neurology, HMS
11:15 AM – 11:45 AM
11:45 AM – 12:45 PM3rd Floor and 7th Floor

DR. IS IN

Dr. Is In Sessions

Understanding long-term Gene and Cell Therapy investment complexities requires a keen awareness of where the science and the markets are headed. That’s why “The Doctor is In” in these updates on the latest GCT technologies. Presented by Mass General Brigham clinicians and innovators from the front lines of care, the sessions are co-hosted by expert analysts from Bank of America and include interactive discussion and Q&A.

1:00 PM – 1:30 PMAmerica Ballroom

Opening Remarks

Introducer:
Scott Sperling
  • Co-Chief Executive Officer, Thomas H. Lee Partners
  • Chairman of the Board of Directors, Mass General Brigham
Panelists:
Anne Klibanski, MD
  • President & CEO, Mass General Brigham
  • Laurie Carrol Guthart Professor of Medicine, HMS
Brian Moynihan
  • Chair & CEO, Bank of America
1:30 PM – 2:00 PMAmerica Ballroom

Co-Chair Kick Off

Moderator:
Susan Hockfield, PhD
  • President Emerita, MIT
Panelists:
Miceal Chamberlain
  • President of Massachusetts, Northeast Region Executive, Bank of America
Marcela Maus, MD, PhD
  • Director, Cellular Immunotherapy Program, Cancer Center, MGH
  • Associate Professor, Medicine, HMS
Geoff Meacham, PhD
  • Managing Director, Global Research, BofA Securities
Ravi Thadhani, MD
  • Chief Academic Officer, Mass General Brigham
2:00 PM – 2:40 PMAmerica Ballroom

GCT’s Historic Potential | Priorities and Trade Offs

This panel features industry leaders who will discuss what the future may hold for gene and cell therapy. Which applications are likely to have the greatest impact? What are the key hurdles to be overcome? What specific platforms and technologies may enable optimal solutions? In what disease areas? Learn more about these and other questions as the panelists discuss the future potential of GCT.

Moderator:
Jean-François Formela, MD
  • Partner, Atlas Venture
Panelists:
Pablo Cagnoni, MD
  • CEO, Rubius Therapeutics
Kristen Hege, MD
  • Senior Vice President, Early Clinical Development, Hematology/Oncology & Cell Therapy, Bristol Myers Squibb
Andrew Plump, MD, PhD
  • President, R&D, Takeda
Catherine Stehman-Breen, MD
  • CEO, Chroma Medicine
2:40 PM – 3:20 PMAmerica Ballroom

Manufacturing | Process Control

Manufacturing quality and cost are critical for enabling rapid growth in GCT. Panelists will explore a variety of critical questions in this space. For example, are there historic parallels that can be drawn between GCT manufacturing and other groundbreaking technologies? How do key manufacturing concerns in GCT differ from those for more conventional pharmaceutical? What are the long-term opportunities for non-viral vectors? Will manufacturing capacity be a limiting factor in GCT growth over the next 5 to 10 years?

Moderator:
John Bishai, PhD
  • Managing Director, Global Investment Banking, BofA Securities
Panelists:
Christopher Murphy
  • Vice President Viral Vector Services, Thermo Fisher
Michael Paglia
  • COO, ElevateBio BaseCamp, ElevateBio
Rahul Singhvi, ScD
  • CEO, National Resilience, Inc.
Ran Zheng
  • CEO, Landmark Bio
3:20 PM – 3:40 PM
3:40 PM – 4:05 PMAmerica Ballroom

FIRESIDE

Regulatory Perspectives on Gene and Cell Therapy: Past Lessons, Current Challenges, Future Directions

At the end of 2021, roughly 410 novel drugs had been approved in the past decade. On average, there were 40 approvals per year with over 150 of them being between 2018 and 2020. What has changed in the approval process and what is the vision of the future state? What will happen over the next 1–3 years? What does the new iteration of the Prescription Drug User Fees Act (PDUFA) need to do in this area and which fields show the greatest potential for innovation in CGT?

Moderator:
Luk Vandenberghe, PhD
  • Grousbeck Associate Professor in Gene Therapy, Mass General Brigham (on leave)
Panelist:
Peter Marks, MD, PhD
  • Director, Center for Biologics Evaluation and Research, FDA
4:10 PM – 4:50 PMAmerica Ballroom

Clinical GCT Trial Design | Regulatory | Strategy, Innovation and Future Direction | Risk vs Hype

This panel will delve into clinical trials for GCT. How do these trials differ from those for conventional therapeutics? What are the key lessons learned from completed GCT trials? How is the regulatory landscape shifting and what will that mean for the future of GCT?

Moderator:
Angela Shen, MD
  • Vice President, Strategic Innovation Leaders, Mass General Brigham Innovation
Panelists:
Laura Aguilar, MD, PhD
  • Co-Founder, Candel Therapeutics
Matthew Frigault, MD
  • Clinical Director, Cellular Immunotherapy Program, MGH
  • Assistant Professor of Medicine, HMS
Arati Rao, MD
  • Senior Vice President, Clinical Development, PACT Pharma
John Rossi
  • VP Head of Translational Medicine, Syncopation Life Sciences
4:50 PM – 5:15 PMAmerica Ballroom

FIRESIDE

mRNA Opportunities: Lessons Learned, Priorities, and the Future of GCT

Dr. Bourla will share what Pfizer has learned from its leadership on mRNA and the development of the Covid vaccine that can be extrapolated to other R&D.

Moderator:
Geoff Meacham, PhD
  • Managing Director, Global Research, BofA Securities
Panelist:
Albert Bourla, PhD
  • CEO, Pfizer Inc.
5:15 PM – 6:15 PMAmerica Foyer

#WMIF2022

@MGBInnovation

@MassGenBrigham

@pharma_BI

@AVIVA1950

7:00 AM – 5:00 PMAmerica Foyer
7:00 AM – 8:00 AMAmerica Foyer

Breakfast

Sponsored by Bayer

7:45 AM – 8:00 AMAmerica Ballroom

Opening Remarks

Introducer:
Chris Coburn
  • Chief Innovation Officer, Mass General Brigham
8:00 AM – 8:25 AMAmerica Ballroom

FIRESIDE

1:1 Fireside Chat: Robert Califf, MD, Commissioner Food and Drugs, FDA

Moderators:
Tazeen Ahmad
  • Managing Director, Global Research, BofA Securities
J. Keith Joung, MD, PhD
  • Robert B. Colvin, M.D. Endowed Chair in Pathology & Pathologist, MGH
  • Professor of Pathology, HMS
Panelist:
Robert Califf, MD
  • Commissioner of Food and Drugs, US Food and Drug Administration
8:25 AM – 9:05 AMAmerica Ballroom

Living with COVID | Lessons Learned and Looking Ahead

As we enter the third year of the coronavirus pandemic, the world is shifting to a new strategy: living with and managing COVID as a part of our everyday lives. What will the coming year look like? How will mitigation measures differ in this new phase? What about treatment strategies? Should we be bracing for another surge?

Introducer:
Jonathan Kraft
  • President, The Kraft Group
  • Chairman of the Board of Trustees, MGH
Moderator:
David Brown, MD
  • President, Massachusetts General Hospital
  • Executive Vice President, Mass General Brigham
Panelists:
Paul Biddinger, MD
  • Chief Preparedness and Continuity Officer, Mass General Brigham
  • Associate Professor of Emergency Medicine, HMS
Helen Branswell
  • Senior Writer, STAT
Daniel Kuritzkes, MD
  • Chief, Division of Infectious Diseases, BWH
  • Harriet Ryan Albee Professor of Medicine, HMS
Erica Shenoy, MD, PhD
  • Associate Chief, Infection Control Unit, MGH
  • Associate Professor of Medicine, HMS
9:05 AM – 9:45 AMAmerica Ballroom

The Global Biotech Epicenter | New England Now and in 2030

This panel will feature a discussion of global biotech clusters with a deep dive into the New England/Boston area. How does the capital availability, scale, and density of New England drive local growth in GCT? Also, the influx of large biopharmaceutical companies into the region has fueled global outcomes. What is the future impact of these investments and when will they peak? How will the biopharmaceutical landscape in New England appear in 2030?

Moderator:
Anne Finucane
  • Chairman of the Board, Bank of America Europe
Panelists:
Seth Ettenberg, PhD
  • President & CEO, BlueRock Therapeutics
Joel Marcus
  • Executive Chairman & Founder, Alexandria Real Estate Equities, Inc.
Terry McGuire
  • Founding Partner, Polaris Partners
Vicki Sato, PhD
  • Chairman of the Board, Vir Biotechnology
  • Chairman, Denali Therapeutics
Phillip Sharp, PhD
  • Institute Professor and Professor of Biology, Koch Institute for Integrative Cancer Research at MIT
  • Co-Founder, Alnylam Pharmaceuticals, Inc.
9:45 AM – 10:05 AM
10:10 AM – 10:50 AMAmerica Ballroom

The Patient Experience

The role of patients and their experiences are critical as the promise of GCT unfolds. This panel will discuss the patient experience and explore the challenges different patient populations face, both in rare diseases and more common conditions. Panelists will also discuss financial considerations, clinical trial access, and the role of advocacy groups in GCT.

Moderator:
Merit Cudkowicz, MD
  • Chair, Dept of Neurology, MGH
  • Julieanne Dorn Professor of Neurology, HMS
Panelist:
James Beck, PhD
  • CSO, Parkinson’s Foundation
Monica Coenraads
  • CEO, Rett Syndrome Research Trust
Annie Ganot
  • VP, Head of Patient Advocacy, Solid Biosciences
Staci Kallish, DO
  • President, Board of Directors, National Tay Sachs and Allied Diseases
  • Medical Geneticist, Associate Professor of Clinical Medicine, Penn Medicine
Rebecca Oberman, PhD
  • Executive Director, Mucolipidosis Type IV (ML4) Foundation
10:50 AM – 11:15 AMAmerica Ballroom

FIRESIDE

Meeting the Moment: The Next Wave of Innovation in Cancer and Cardiology

As many countries begin to turn the corner on COVID-19, they face a resurgence of chronic illnesses, such as cancer and cardiovascular disease, that were not adequately addressed during the pandemic, and for which new treatments are urgently needed. Population aging – and the resulting increase in chronic diseases associated with aging – has compounded the challenge. There’s never been a greater need for biopharmaceutical innovation – or, fortunately, a greater ability to innovate. Amgen is investing in new discovery research capabilities that portend a revolution in drug design and development.

Moderator:
Geoff Meacham, PhD
  • Managing Director, Global Research, BofA Securities
Panelist:
Robert Bradway
  • CEO, Amgen
11:15 AM – 11:20 AMAmerica Ballroom

First Look Award Presentation

Presenters:
Miceal Chamberlain
  • President of Massachusetts, Northeast Region Executive, Bank of America
Nino Chiocca, MD, PhD
  • Neurosurgeon-in-Chief and Chairman, Neurosurgery, BWH
  • Harvey W. Cushing Professor of Neurosurgery, HMS
11:20 AM – 11:30 AMAmerica Ballroom
11:30 AM – 11:45 AM
11:45 AM – 12:45 PM3rd Floor and 7th Floor

DR. IS IN

Dr. Is In Sessions

Lunch Sponsored by Astellas

Understanding long-term Gene and Cell Therapy investment complexities requires a keen awareness of where the science and the markets are headed. That’s why “The Doctor is In” in these updates on the latest GCT technologies. Presented by Mass General Brigham clinicians and innovators from the front lines of care, the sessions are co-hosted by expert analysts from Bank of America and include interactive discussion and Q&A.

  • Personalizing Cancer Care through RNA Therapies

    11:45 AM – 12:45 PM

    In this session, Dr. Peruzzi will discuss how RNA for cancer therapy is a versatile of a tool for a protean problem.

    Moderator:
    Jason Gerberry
    • Managing Director, Global Research, BofA Securities
    Panelist:
    Pierpaolo Peruzzi, MD, PhD
    • Neurosurgeon and Principal Investigator, BWH
    • Assistant Professor of Neurosurgery, HMS
  • Designing for Success: Clinical Trial Approaches for Rare and Ultra-Rare Diseases

    11:45 AM – 12:45 PM

    In this session, Dr. Vavvas will discuss examples of clinical trials in rare diseases and share insights into how clinical trials should be approached for rare and ultra-rare diseases and how study design is not a one-size fits all.

    Moderator:
    Tazeen Ahmad
    • Managing Director, Global Research, BofA Securities
    Panelist:
    Demetrios Vavvas, MD, PhD
    • Associate Director of the Retina Service, Mass Eye and Ear
    • Solman and Libe Friedman Professor of Ophthalmology, Co-Director Ocular Regenerative Medical Institute, HMS
  • A New Hope: Cell Therapy and Transplantation for Parkinson’s Disease

    11:45 AM – 12:45 PM

    In this session, hear experts weigh in on the possibilities of cell therapy development and transplantation for the treatment of Parkinson’s Disease. What does the futures hold and how do we get there?

    Moderator:
    Greg Harrison
    • Vice President, Global Research, BofA Securities
    Panelist:
    Bob Carter, MD, PhD
    • Chairman, Department of Neurosurgery, MGH
    • William and Elizabeth Sweet Professor of Neurosurgery, HMS
    Todd Herrington, MD, PhD
    • Director, Deep Brain Stimulation Program, MGH
    • Assistant Professor of Neurology, HMS
    Kwang-Soo Kim, PhD
    • Director, Molecular Neurobiology Laboratory, McLean
    • Professor of Neuroscience and Psychiatry, HMS
    Jeffrey Schweitzer, MD, PhD
    • Neurosurgeon, MGH
    • Assistant Professor of Neurosurgery, HMS
  • The Inner Workings of Gene Therapy Manufacturing

    11:45 AM – 12:45 PM

    In this session, Dr. Nikiforow will provide insights into the world of gene therapy manufacturing and the complexities of scaling, costs and insurance reimbursement.

    Moderator:
    Michael Ryskin
    • Director, Global Research, BofA Securities
    Panelist:
    Sarah Nikiforow, MD, PhD
    • Medical Director, Cell Manipulation Core Facility, Technical Director, Immune Effector Cell Therapy Program, DFCI
    • Assistant Professor, HMS
  • The Road Ahead: Regulatory Challenges for Gene and Cell Therapy

    11:45 AM – 12:45 PM

    In this session, Dr. Marks will discuss the ins and outs of regulatory challenges for biological products and therapies in gene and cell therapy and the responsibility to assure safety and effectiveness.

    Moderator:
    Geoff Meacham, PhD
    • Managing Director, Global Research, BofA Securities
    Panelist:
    Peter Marks, MD, PhD
    • Director, Center for Biologics Evaluation and Research, FDA
  • The Mysterious Dark Genome

    11:45 AM – 12:45 PM

    Dark genome, accounting for ~98.5% of the human genome and containing the non-coding part, offers unprecedented opportunity to look for novel elements that could play a role in human health. This non-coding region consists of repeat elements, enhancers, regulatory sequences and non-coding RNAs. This session will explore this exciting new frontier in biology and how to translate this so called “junk” and previously ignored genome into potential novel therapeutics.

    Moderators:
    Angela Shen, MD
    • Vice President, Strategic Innovation Leaders, Mass General Brigham Innovation
    Richard Young, PhD
    • Professor, Whitehead Institute, MIT
    Panelists:
    Rosana Kapeller, MD, PhD
    • Co-Founder, President & CEO, ROME Therapeutics
    Josh Mandel-Brehm
    • President & CEO, CAMP4 Therapeutics
    Amir Nashat, PhD
    • Managing Partner, Polaris Ventures
    Issi Rozen
    • Venture Partner, GV
1:00 PM – 1:40 PMAmerica Ballroom

Capital Formation | Shaping Innovation

Panelists will discuss the life sciences capital markets environment with particular emphasis on private and public fundraising for GCT companies. What trends do panelists observe that will impact the availability and cost of capital for GCT? Are there novel fundraising structures that will serve GCT in the future?

Moderator:
Greg Butz
  • Managing Director, Head of Life Sciences Investment Banking, BofA Securities
Sumit Mukherjee
  • Managing Director & Head of Healthcare in Equity Capital Markets, BofA Securities
Panelists:
Shelley Chu, MD, PhD
  • Partner, Lightspeed
Stephen Knight, MD
  • President & Managing Partner, F-Prime Capital
Adam Koppel, MD, PhD
  • Managing Director, Bain Capital Life Sciences
Daniel Krizek
  • Portfolio Manager, Citadel
1:40 PM – 2:05 PMAmerica Ballroom

FIRESIDE

Ending Cancer as We Know It: The Game Changing Potential of GCT

50 years after the nation’s War on Cancer was launched, do new treatment innovations have us at a turning point to end cancer “as we know it”.

Moderator:
Erin Harris
  • Chief Editor, Cell & Gene
Panelists:
David Scadden, MD
  • Director, Center for Regenerative Medicine, MGH
  • Gerald and Darlene Jordan Professor of Medicine, HMS
Norman Sharpless, MD
  • Former Director, National Cancer Institute
2:05 PM – 2:30 PMAmerica Ballroom

FIRESIDE

Vision and Execution: Curing Disease with Cell Therapies

As one of the foremost researchers of CAR-T cancer treatments, Dr. June will share what he believes is the next wave of cell-and-gene based oncology research and how his work set the stage for breakthrough developments in cancer.

Moderators:
Marcela Maus, MD, PhD
  • Director, Cellular Immunotherapy Program, Cancer Center, MGH
  • Associate Professor, Medicine, HMS
Ravi Thadhani, MD
  • Chief Academic Officer, Mass General Brigham
Panelist:
Carl June, MD
  • Richard W. Vague Professor in Immunotherapy, Director, Center for Cellular Immunotherapies, Director, Parker Institute for Cancer Immunotherapy, University of Pennsylvania Perelman School of Medicine
2:30 PM – 3:10 PMAmerica Ballroom

GCT Development Centers | Academia’s Unique Contribution

This panel will examine the role of academia in driving the promise of GCT. How does academic innovation contribute to the success of GCT? What are the risks and opportunities? Which models have proven most successful and what is the impact on clinical translation? How can these partnerships be accelerated?

Moderator:
Ravi Thadhani, MD
  • Chief Academic Officer, Mass General Brigham
Panelists:
Carl June, MD
  • Richard W. Vague Professor in Immunotherapy, Director, Center for Cellular Immunotherapies, Director, Parker Institute for Cancer Immunotherapy, University of Pennsylvania Perelman School of Medicine
Maria Millan, MD
  • President & CEO, California Institute for Regenerative Medicine
Richard Mulligan, PhD
  • Mallinckrodt Professor of Genetics, Emeritus, HMS
  • Executive Vice Chairman, Sana Biotechnology, Inc
Norman Sharpless, MD
  • Former Director, National Cancer Institute
3:10 PM – 3:30 PM
3:30 PM – 3:55 PMAmerica Ballroom

FIRESIDE

1:1 Fireside Chat: Marc Casper

Moderator:
Derik de Bruin, PhD
  • Managing Director, Global Research, BofA Securities
Panelist:
Marc Casper
  • CEO, ThermoFisher
3:55 PM – 4:35 PMAmerica Ballroom

Gene and Cell Therapy | The World Speaks

This panel will bring together gene and cell therapy leaders from across the world to discuss the latest opportunities and challenges in the field, from the investment landscape to key technology developments to manufacturing and regulatory barriers. These global experts will offer first-hand insights on the systemic complexity of this advancing field and its therapeutic promise.

Moderator:
Christine Fox
  • President, Novartis Gene Therapies
Panelists:
Christopher Baum, MD
  • Chairman of the Board of Directors, Berlin Institute of Health
Nicholas Galakatos, PhD
  • Global Head of Life Sciences, Blackstone
Luigi Naldini, MD, PhD
  • Director, San Raffaele Telethon Institute for Gene Therapy
Kendra Rose, PhD
  • VP, Head of New Platforms, Ophthalmology and Hemophilia, Bayer
4:35 PM – 5:15 PMAmerica Ballroom

Control or Mitigation of the Effects of Chronic Neuroinflammation

Chronic inflammation in the brain is now recognized as a contributor to many neurodegenerative diseases, ranging from Parkinson’s disease to multiple sclerosis to Alzheimer’s disease. Are solutions to these historically intractable neurological diseases imminent or several years away? Are market-making platforms identifiable for neurological diseases? Are there novel genetic targets that can be explored? What are the prospects for cell therapies?

Moderator:
Ole Isacson, MD, PhD
  • Director, Neuroregeneration Research Institute, McLean
  • Professor of Neurology & Neuroscience, HMS
Panelists:
Colin Hill
  • CEO, GNS Healthcare
Spyros Papapetropoulos, MD, PhD
  • CMO, Vigil Neuroscience
Richard Ransohoff, MD
  • CMO, Abata Therapeutics
  • Venture Partner, Third Rock Ventures
Beth Stevens, PhD
  • HHMI Investigator, F.M. Kirby Neurobiology Research Program, Boston Children’s Hospital
  • Associate Professor of Neurology, HMS
Rudolph Tanzi, PhD
  • Vice-Chair, Neurology, Director, Genetics and Aging Research Unit, MGH
  • Joseph P. and Rose F. Kennedy Professor of Neurology, HMS
5:15 PM – 6:15 PMAmerica Foyer

Attendee Networking Reception

Sponsored by Novartis

#WMIF2022

@MGBInnovation

@MassGenBrigham

@pharma_BI

@AVIVA1950

7:00 AM – 12:00 PMAmerica Foyer
7:00 AM – 8:00 AMAmerica Foyer
8:05 AM – 8:45 AMAmerica Ballroom

The Cell Therapy Landscape | CAR-T to Stem Cells

Cell therapies, ranging from CAR-T cells to stem-cell-based approaches, are emerging as a transformative therapeutic modality. Panelists will examine this emerging landscape and discuss a range of key topics. What drives differentiation in this space given the high number of competing technologies? How will the uptake of autologous cell therapies and allogeneic versions evolve? When will the regenerative medicine market mature?

Moderator:
Marcela Maus, MD, PhD
  • Director, Cellular Immunotherapy Program, Cancer Center, MGH
  • Associate Professor, Medicine, HMS
Panelists:
Christina Coughlin, MD, PhD
  • CEO, Cytoimmune
Rachel Haurwitz, PhD
  • President & CEO, Caribou Biosciences
Nick Leschly
  • CEO, 2seventy bio
Dhvanit Shah, PhD
  • President & CEO, Garuda Therapeutics
Rusty Williams, MD, PhD
  • Chairman & CEO, Walking Fish Therapeutics
8:50 AM – 9:30 AMAmerica Ballroom

Disrupting Interventions

This panel will explore how GCT technology could lead to disruptions in other areas of medicine, including surgery and medical devices, over the next several years. Could cell replacement therapy in diabetes advance enough to reduce the need for diabetes pumps or insulin? Will stem-cell-based methods for regenerating cartilage advance rapidly enough to disrupt the number of patients seeking hip and knee replacements? How is GCT driving innovations in surgical techniques?

Introducer:
John Fish
  • Chairman & CEO, Suffolk
  • Chair, Brigham and Women’s Hospital
Moderator:
Robert Higgins, MD
  • President, Brigham and Women’s Hospital
  • Executive Vice President, Mass General Brigham
Panelists:
Irina Antonijevic, MD, PhD
  • CMO and Head of R&D, Triplet Therapeutics, Inc.
Rachel McMinn, PhD
  • Founder & CEO, Neurogene
Harith Rajagopalan, MD, PhD
  • CEO & Co-Founder, Fractyl Health
Bastiano Sanna, PhD
  • EVP, Chief of Cell & Gene Therapies and VCGT Site Head, Vertex Pharmaceuticals
Jeffrey Schweitzer, MD, PhD
  • Neurosurgeon, MGH
  • Assistant Professor of Neurosurgery, HMS
9:30 AM – 9:55 AMAmerica Ballroom

FIRESIDE

1:1 Fireside Chat: Dan Skovronsky

Moderator:
Geoff Meacham, PhD
  • Managing Director, Global Research, BofA Securities
Panelist:
Daniel Skovronsky, MD, PhD
  • Chief Scientific and Medical Officer, Eli Lilly and Company
9:55 AM – 10:20 AMAmerica Ballroom

FIRESIDE

Reimagining GCT Production

What is the new generation of approaches to gene therapy manufacturing and delivery? What are the lessons learned from Covid and how can it be applied to custom disease response and the ability to custom design biologic organisms?

Moderator:
Derik de Bruin, PhD
  • Managing Director, Global Research, BofA Securities
Panelist:
Jason Kelly, PhD
  • Co-Founder & CEO, Ginkgo Bioworks
10:20 AM – 11:00 AMAmerica Ballroom

Gene and Cell Therapy Safety | Enduring Framework Required

This panel will feature an in-depth discussion of the safety of gene and cell therapies. What are the unique safety concerns in this field, both acute and potential long-term risks? Which of these concerns are supported by clinical data versus the presumption of theoretical risk? What are the key issues for AAV-based gene therapies? Will redosing become feasible? What are the predominant safety concerns for in vivo versus ex vivo GCT modalities, including base editing?

Moderator:
Christine Seidman, MD
  • Director, Cardiovascular Genetics Center, BWH
  • Smith Professor of Medicine & Genetics, HMS
Panelists:
Rick Fair
  • President & CEO, Bellicum
Alexandria Forbes, PhD
  • President & CEO, MeiraGTx
Sekar Kathiresan, MD
  • CEO, Verve Therapeutics
Rick Modi
  • CEO, Affinia Therapeutics
11:00 AM – 11:40 AMAmerica Ballroom

RNA Therapeutics | Lessons Learned

The label “RNA” encompasses a wide array of biologically active agents spanning therapeutic modalities, vaccines, non-coding controls, and other forms. In this panel we will discuss a number of these forms, discuss examples of recent developments and illustrate why RNA developments represent a promising source of novel therapies and therapeutic approaches.

Moderator:
Janet Wu
  • Anchor/Reporter, Bloomberg
Panelists:
Sarah Boyce
  • President & CEO, Avidity Biosciences, Inc.
Jim Burns, PhD
  • CEO, Locanabio
Jeannie Lee, MD, PhD
  • Molecular Biologist, MGH
  • Professor of Genetics, HMS
Laura Sepp-Lorenzino, PhD
  • Chief Scientific Officer, Executive Vice President, Intellia Therapeutics
11:40 AM – 12:40 PMAmerica Ballroom

Disruptive Dozen: 12 Technologies That Will Reinvent GCT in the Next Five Years

The Disruptive Dozen identifies and ranks the GCT technologies that Mass General Brigham faculty feel will break through over the next one to five years to significantly improve health care.

Moderators:
Nino Chiocca, MD, PhD
  • Neurosurgeon-in-Chief and Chairman, Neurosurgery, BWH
  • Harvey W. Cushing Professor of Neurosurgery, HMS
Susan Slaugenhaupt, PhD
  • Scientific Director and Elizabeth G. Riley and Daniel E. Smith Jr. Endowed Chair, Mass General Research Institute
  • Professor, Neurology, HMS
Ravi Thadhani, MD
  • Chief Academic Officer, Mass General Brigham
Panelists:
Galit Alter, PhD
  • Principal Investigator, Ragon Institute, MGH
  • Professor of Medicine, HMS
Natalie Artzi, PhD
  • Assistant Professor of Medicine, HMS
Fengfeng Bei, PhD
  • Principal Investigator, Department of Neurosurgery, BWH
  • Assistant Professor of Neurosurgery, HMS
Zheng-Yi Chen, DPhil
  • Associate Scientist, Eaton-Peabody Laboratories, Mass Eye and Ear
  • Associate Professor of Otolaryngology Head and Neck Surgery, HMS
Matthew Frigault, MD
  • Clinical Director, Cellular Immunotherapy Program, MGH
  • Assistant Professor of Medicine, HMS
Michael Gilmore, PhD
  • Chief Scientific Officer, Mass Eye and Ear
  • Sir William Osler Professor of Ophthalmology, HMS
Allan Goldstein, MD
  • Chief of Pediatric Surgery, MGH
  • Surgeon-in-Chief, MassGeneral for Children
Anna Krichevsky, PhD
  • Associate Professor of Neurology, BWH, HMS
Jeannie Lee, MD, PhD
  • Molecular Biologist, MGH
  • Professor of Genetics, HMS
James Markmann, MD, PhD
  • Chief, Division of Transplant Surgery, MGH
  • Claude E. Welch Professor of Surgery, HMS
Khalid Shah, PhD
  • Vice Chairman of Research, Department of Neurosurgery, BWH
  • Professor, HMS
Demetrios Vavvas, MD, PhD
  • Associate Director of the Retina Service, Mass Eye and Ear
  • Solman and Libe Friedman Professor of Ophthalmology, Co-Director Ocular Regenerative Medical Institute, HMS

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#TUBiol5227: Biomarkers & Biotargets: Genetic Testing and Bioethics

Curator: Stephen J. Williams, Ph.D.

The advent of direct to consumer (DTC) genetic testing and the resultant rapid increase in its popularity as well as companies offering such services has created some urgent and unique bioethical challenges surrounding this niche in the marketplace. At first, most DTC companies like 23andMe and Ancestry.com offered non-clinical or non-FDA approved genetic testing as a way for consumers to draw casual inferences from their DNA sequence and existence of known genes that are linked to disease risk, or to get a glimpse of their familial background. However, many issues arose, including legal, privacy, medical, and bioethical issues. Below are some articles which will explain and discuss many of these problems associated with the DTC genetic testing market as well as some alternatives which may exist.

‘Direct-to-Consumer (DTC) Genetic Testing Market to hit USD 2.5 Bn by 2024’ by Global Market Insights

This post has the following link to the market analysis of the DTC market (https://www.gminsights.com/pressrelease/direct-to-consumer-dtc-genetic-testing-market). Below is the highlights of the report.

As you can see,this market segment appears to want to expand into the nutritional consulting business as well as targeted biomarkers for specific diseases.

Rising incidence of genetic disorders across the globe will augment the market growth

Increasing prevalence of genetic disorders will propel the demand for direct-to-consumer genetic testing and will augment industry growth over the projected timeline. Increasing cases of genetic diseases such as breast cancer, achondroplasia, colorectal cancer and other diseases have elevated the need for cost-effective and efficient genetic testing avenues in the healthcare market.
 

For instance, according to the World Cancer Research Fund (WCRF), in 2018, over 2 million new cases of cancer were diagnosed across the globe. Also, breast cancer is stated as the second most commonly occurring cancer. Availability of superior quality and advanced direct-to-consumer genetic testing has drastically reduced the mortality rates in people suffering from cancer by providing vigilant surveillance data even before the onset of the disease. Hence, the aforementioned factors will propel the direct-to-consumer genetic testing market overt the forecast timeline.
 

DTC Genetic Testing Market By Technology

Get more details on this report – Request Free Sample PDF
 

Nutrigenomic Testing will provide robust market growth

The nutrigenomic testing segment was valued over USD 220 million market value in 2019 and its market will witness a tremendous growth over 2020-2028. The growth of the market segment is attributed to increasing research activities related to nutritional aspects. Moreover, obesity is another major factor that will boost the demand for direct-to-consumer genetic testing market.
 

Nutrigenomics testing enables professionals to recommend nutritional guidance and personalized diet to obese people and help them to keep their weight under control while maintaining a healthy lifestyle. Hence, above mentioned factors are anticipated to augment the demand and adoption rate of direct-to-consumer genetic testing through 2028.
 

Browse key industry insights spread across 161 pages with 126 market data tables & 10 figures & charts from the report, “Direct-To-Consumer Genetic Testing Market Size By Test Type (Carrier Testing, Predictive Testing, Ancestry & Relationship Testing, Nutrigenomics Testing), By Distribution Channel (Online Platforms, Over-the-Counter), By Technology (Targeted Analysis, Single Nucleotide Polymorphism (SNP) Chips, Whole Genome Sequencing (WGS)), Industry Analysis Report, Regional Outlook, Application Potential, Price Trends, Competitive Market Share & Forecast, 2020 – 2028” in detail along with the table of contents:
https://www.gminsights.com/industry-analysis/direct-to-consumer-dtc-genetic-testing-market
 

Targeted analysis techniques will drive the market growth over the foreseeable future

Based on technology, the DTC genetic testing market is segmented into whole genome sequencing (WGS), targeted analysis, and single nucleotide polymorphism (SNP) chips. The targeted analysis market segment is projected to witness around 12% CAGR over the forecast period. The segmental growth is attributed to the recent advancements in genetic testing methods that has revolutionized the detection and characterization of genetic codes.
 

Targeted analysis is mainly utilized to determine any defects in genes that are responsible for a disorder or a disease. Also, growing demand for personalized medicine amongst the population suffering from genetic diseases will boost the demand for targeted analysis technology. As the technology is relatively cheaper, it is highly preferred method used in direct-to-consumer genetic testing procedures. These advantages of targeted analysis are expected to enhance the market growth over the foreseeable future.
 

Over-the-counter segment will experience a notable growth over the forecast period

The over-the-counter distribution channel is projected to witness around 11% CAGR through 2028. The segmental growth is attributed to the ease in purchasing a test kit for the consumers living in rural areas of developing countries. Consumers prefer over-the-counter distribution channel as they are directly examined by regulatory agencies making it safer to use, thereby driving the market growth over the forecast timeline.
 

Favorable regulations provide lucrative growth opportunities for direct-to-consumer genetic testing

Europe direct-to-consumer genetic testing market held around 26% share in 2019 and was valued at around USD 290 million. The regional growth is due to elevated government spending on healthcare to provide easy access to genetic testing avenues. Furthermore, European regulatory bodies are working on improving the regulations set on the direct-to-consumer genetic testing methods. Hence, the above-mentioned factors will play significant role in the market growth.
 

Focus of market players on introducing innovative direct-to-consumer genetic testing devices will offer several growth opportunities

Few of the eminent players operating in direct-to-consumer genetic testing market share include Ancestry, Color Genomics, Living DNA, Mapmygenome, Easy DNA, FamilytreeDNA (Gene By Gene), Full Genome Corporation, Helix OpCo LLC, Identigene, Karmagenes, MyHeritage, Pathway genomics, Genesis Healthcare, and 23andMe. These market players have undertaken various business strategies to enhance their financial stability and help them evolve as leading companies in the direct-to-consumer genetic testing industry.
 

For example, in November 2018, Helix launched a new genetic testing product, DNA discovery kit, that allows customer to delve into their ancestry. This development expanded the firm’s product portfolio, thereby propelling industry growth in the market.

The following posts discuss bioethical issues related to genetic testing and personalized medicine from a clinicians and scientisit’s perspective

Question: Each of these articles discusses certain bioethical issues although focuses on personalized medicine and treatment. Given your understanding of the robust process involved in validating clinical biomarkers and the current state of the DTC market, how could DTC testing results misinform patients and create mistrust in the physician-patient relationship?

Personalized Medicine, Omics, and Health Disparities in Cancer:  Can Personalized Medicine Help Reduce the Disparity Problem?

Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies

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

The following posts discuss the bioethical concerns of genetic testing from a patient’s perspective:

Ethics Behind Genetic Testing in Breast Cancer: A Webinar by Laura Carfang of survivingbreastcancer.org

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

23andMe Product can be obtained for Free from a new app called Genes for Good: UMich’s Facebook-based Genomics Project

Question: If you are developing a targeted treatment with a companion diagnostic, what bioethical concerns would you address during the drug development process to ensure fair, equitable and ethical treatment of all patients, in trials as well as post market?

Articles on Genetic Testing, Companion Diagnostics and Regulatory Mechanisms

Centers for Medicare & Medicaid Services announced that the federal healthcare program will cover the costs of cancer gene tests that have been approved by the Food and Drug Administration

Real Time Coverage @BIOConvention #BIO2019: Genome Editing and Regulatory Harmonization: Progress and Challenges

New York Times vs. Personalized Medicine? PMC President: Times’ Critique of Streamlined Regulatory Approval for Personalized Treatments ‘Ignores Promising Implications’ of Field

Live Conference Coverage @Medcitynews Converge 2018 Philadelphia: Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Protecting Your Biotech IP and Market Strategy: Notes from Life Sciences Collaborative 2015 Meeting

Question: What type of regulatory concerns should one have during the drug development process in regards to use of biomarker testing? From the last article on Protecting Your IP how important is it, as a drug developer, to involve all payers during the drug development process?

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NCCN Shares Latest Expert Recommendations for Prostate Cancer in Spanish and Portuguese

Reporter: Stephen J. Williams, Ph.D.

Currently many biomedical texts and US government agency guidelines are only offered in English or only offered in different languages upon request. However Spanish is spoken in a majority of countries worldwide and medical text in that language would serve as an under-served need. In addition, Portuguese is the main language in the largest country in South America, Brazil.

The LPBI Group and others have noticed this need for medical translation to other languages. Currently LPBI Group is translating their medical e-book offerings into Spanish (for more details see https://pharmaceuticalintelligence.com/vision/)

Below is an article on The National Comprehensive Cancer Network’s decision to offer their cancer treatment guidelines in Spanish and Portuguese.

Source: https://www.nccn.org/home/news/newsdetails?NewsId=2871

PLYMOUTH MEETING, PA [8 September, 2021] — The National Comprehensive Cancer Network® (NCCN®)—a nonprofit alliance of leading cancer centers in the United States—announces recently-updated versions of evidence- and expert consensus-based guidelines for treating prostate cancer, translated into Spanish and Portuguese. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) feature frequently updated cancer treatment recommendations from multidisciplinary panels of experts across NCCN Member Institutions. Independent studies have repeatedly found that following these recommendations correlates with better outcomes and longer survival.

“Everyone with prostate cancer should have access to care that is based on current and reliable evidence,” said Robert W. Carlson, MD, Chief Executive Officer, NCCN. “These updated translations—along with all of our other translated and adapted resources—help us to define and advance high-quality, high-value, patient-centered cancer care globally, so patients everywhere can live better lives.”

Prostate cancer is the second most commonly occurring cancer in men, impacting more than a million people worldwide every year.[1] In 2020, the NCCN Guidelines® for Prostate Cancer were downloaded more than 200,000 times by people outside of the United States. Approximately 47 percent of registered users for NCCN.org are located outside the U.S., with Brazil, Spain, and Mexico among the top ten countries represented.

“NCCN Guidelines are incredibly helpful resources in the work we do to ensure cancer care across Latin America meets the highest standards,” said Diogo Bastos, MD, and Andrey Soares, MD, Chair and Scientific Director of the Genitourinary Group of The Latin American Cooperative Oncology Group (LACOG). The organization has worked with NCCN in the past to develop Latin American editions of the NCCN Guidelines for Breast Cancer, Colon Cancer, Non-Small Cell Lung Cancer, Prostate Cancer, Multiple Myeloma, and Rectal Cancer, and co-hosted a webinar on “Management of Prostate Cancer for Latin America” earlier this year. “We appreciate all of NCCN’s efforts to make sure these gold-standard recommendations are accessible to non-English speakers and applicable for varying circumstances.”

NCCN also publishes NCCN Guidelines for Patients®, containing the same treatment information in non-medical terms, intended for patients and caregivers. The NCCN Guidelines for Patients: Prostate Cancer were found to be among the most trustworthy sources of information online according to a recent international study. These patient guidelines have been divided into two books, covering early and advanced prostate cancer; both have been translated into Spanish and Portuguese as well.

NCCN collaborates with organizations across the globe on resources based on the NCCN Guidelines that account for local accessibility, consideration of metabolic differences in populations, and regional regulatory variation. They can be downloaded free-of-charge for non-commercial use at NCCN.org/global or via the Virtual Library of NCCN Guidelines App. Learn more and join the conversation with the hashtag #NCCNGlobal.


[1] Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global Cancer Statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, in press. The online GLOBOCAN 2018 database is accessible at http://gco.iarc.fr/, as part of IARC’s Global Cancer Observatory.

About the National Comprehensive Cancer Network

The National Comprehensive Cancer Network® (NCCN®) is a not-for-profit alliance of leading cancer centers devoted to patient care, research, and education. NCCN is dedicated to improving and facilitating quality, effective, efficient, and accessible cancer care so patients can live better lives. The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) provide transparent, evidence-based, expert consensus recommendations for cancer treatment, prevention, and supportive services; they are the recognized standard for clinical direction and policy in cancer management and the most thorough and frequently-updated clinical practice guidelines available in any area of medicine. The NCCN Guidelines for Patients® provide expert cancer treatment information to inform and empower patients and caregivers, through support from the NCCN Foundation®. NCCN also advances continuing educationglobal initiativespolicy, and research collaboration and publication in oncology. Visit NCCN.org for more information and follow NCCN on Facebook @NCCNorg, Instagram @NCCNorg, and Twitter @NCCN.

Please see LPBI Group’s efforts in medical text translation and Natural Language Processing of Medical Text at

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Reporter: Danielle Smolyar, Research Assistant 3 – Text Analysis for 2.0 LPBI Group’s TNS #1 – 2020/2021 Academic Internship in Medical Text Analysis (MTA)

Image source by https://medicalxpress.com/news/2021-07-therapy-effective-cancers.html
 
Credit: Pixabay/CC0 Public Domain 

Recently, researchers at Mount Sinai were able to develop a therapeutic agent that shows high levels of effectiveness in Vitro disrupting a biological pathway that allow cancer to survive. This finding is according to a paper which was published in Cancer Discovery, which is a Journal of the American Association of cancer research in July 2021.

The therapy in which they focus on is a molecule named MS21, which causes the degradation of AKT which is an enzyme that is very active and present in cancers. In this study there was much evidence that pharmacological degradation of AKT is a feasible treatment for cancer’s which have a mutation in certain genes. 

AKT is a cancer gene that encodes an enzyme that is abnormally activated in cancer cells to stimulate tumor growth. The degradation of AKT reverses all these processes which ultimately inhibits further tumor growth.

“Our study lays a solid foundation for the clinical development of an AKT degrader for the treatment of human cancers with certain gene mutations,” said Ramon Parsons, MD, Ph.D., Director of The Tisch Cancer Institute and Ward-Coleman Chair in Cancer Research and Chair of Oncological Sciences at the Icahn School of Medicine at Mount Sinai. “Examination of 44,000 human cancers identified that 19 percent of tumors have at least one of these mutations, suggesting that a large population of cancer patients could benefit from therapy with an AKT degrader such as MS21.”

https://medicalxpress.com/news/2021-07-therapy-effective-cancers.html.

MS21 was tested and human cancer derived cell lines, is used in Laboratories as a model to study the efficacy of different cancer therapies.

At Mount Sinai they were looking to develop MS21 with an industry partner in order to open clinical trials for patients. 

“Translating these findings into effective cancer therapies for patients is a high priority because the mutations and the resulting cancer-driving pathways that we lay out in this study are arguably the most commonly activated pathways in human cancer, but this effort has proven to be particularly challenging,” said Jian Jin, Ph.D., Mount Sinai Professor in Therapeutics Discovery and Director of the Mount Sinai Center for Therapeutics Discovery at Icahn Mount Sinai. “We look forward to an opportunity to develop this molecule into a therapy that is ready to be studied in clinical trials.”

https://medicalxpress.com/news/2021-07-therapy-effective-cancers.html.

Image credit: National Cancer Institute

Original article: 

Researchers develop novel therapy that could be effective in many cancers

staff, S. X. (2021, July 23). R. Medical Xpress – by The Mount Sinai Hospital

https://medicalxpress.com/news/2021-07-therapy-effective-cancers.html. 

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

Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Reporter and Curator: Dr. Pati

https://pharmaceuticalintelligence.com/2021/05/04/machine-learning-ml-in-cancer-prognosis-prediction-helps-the-researcher-to-identify-multiple-known-as-well-as-candidate-cancer-diver-genes/

New approaches to cancer therapy using mathematics

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2021/02/12/new-approaches-to-cancer-therapy-using-mathematics/

Cancer treatment using CRISPR-based Genome Editing System

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2021/01/09/59906/

Novel biomarkers for targeting cancer immunotherapy

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/10/10/novel-biomarkers-for-targeting-cancer-immunotherapy/

Novel Approaches to Cancer Therapy [11.1]


Writer and Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/04/11/novel-approaches-to-cancer-therapy-7-12/

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From High-Throughput Assay to Systems Biology: New Tools for Drug Discovery

Curator: Stephen J. Williams, PhD

Marc W. Kirschner*

Department of Systems Biology
Harvard Medical School

Boston, Massachusetts 02115

With the new excitement about systems biology, there is understandable interest in a definition. This has proven somewhat difficult. Scientific fields, like spe­cies, arise by descent with modification, so in their ear­liest forms even the founders of great dynasties are only marginally different than their sister fields and spe­cies. It is only in retrospect that we can recognize the significant founding events. Before embarking on a def­inition of systems biology, it may be worth remember­ing that confusion and controversy surrounded the in­troduction of the term “molecular biology,” with claims that it hardly differed from biochemistry. Yet in retro­spect molecular biology was new and different. It intro­duced both new subject matter and new technological approaches, in addition to a new style.

As a point of departure for systems biology, consider the quintessential experiment in the founding of molec­ular biology, the one gene one enzyme hypothesis of Beadle and Tatum. This experiment first connected the genotype directly to the phenotype on a molecular level, although efforts in that direction can certainly be found in the work of Archibald Garrod, Sewell Wright, and others. Here a protein (in this case an enzyme) is seen to be a product of a single gene, and a single function; the completion of a specific step in amino acid biosynthesis is the direct result. It took the next 30 years to fill in the gaps in this process. Yet the one gene one enzyme hypothesis looks very different to us today. What is the function of tubulin, of PI-3 kinase or of rac? Could we accurately predict the phenotype of a nonle­thal mutation in these genes in a multicellular organ­ism? Although we can connect structure to the gene, we can no longer infer its larger purpose in the cell or in the organism. There are too many purposes; what the protein does is defined by context. The context also includes a history, either developmental or physiologi­cal. Thus the behavior of the Wnt signaling pathway depends on the previous lineage, the “where and when” questions of embryonic development. Similarly the behavior of the immune system depends on previ­ous experience in a variable environment. All of these features stress how inadequate an explanation for function we can achieve solely by trying to identify genes (by annotating them!) and characterizing their transcriptional control circuits.

That we are at a crossroads in how to explore biology is not at all clear to many. Biology is hardly in its dotage; the process of discovery seems to have been per­fected, accelerated, and made universally applicable to all fields of biology. With the completion of the human genome and the genomes of other species, we have a glimpse of many more genes than we ever had before to study. We are like naturalists discovering a new con­tinent, enthralled with the diversity itself. But we have also at the same time glimpsed the finiteness of this list of genes, a disturbingly small list. We have seen that the diversity of genes cannot approximate the diversity of functions within an organism. In response, we have argued that combinatorial use of small numbers of components can generate all the diversity that is needed. This has had its recent incarnation in the sim­plistic view that the rules of cis-regulatory control on DNA can directly lead to an understanding of organ­isms and their evolution. Yet this assumes that the gene products can be linked together in arbitrary combina­tions, something that is not assured in chemistry. It also downplays the significant regulatory features that in­volve interactions between gene products, their local­ization, binding, posttranslational modification, degra­dation, etc. The big question to understand in biology is not regulatory linkage but the nature of biological systems that allows them to be linked together in many nonlethal and even useful combinations. More and more we come to realize that understanding the con­served genes and their conserved circuits will require an understanding of their special properties that allow them to function together to generate different pheno­types in different tissues of metazoan organisms. These circuits may have certain robustness, but more impor­tant they have adaptability and versatility. The ease of putting conserved processes under regulatory control is an inherent design feature of the processes them­selves. Among other things it loads the deck in evolu­tionary variation and makes it more feasible to generate useful phenotypes upon which selection can act.

Systems biology offers an opportunity to study how the phenotype is generated from the genotype and with it a glimpse of how evolution has crafted the pheno­type. One aspect of systems biology is the develop­ment of techniques to examine broadly the level of pro­tein, RNA, and DNA on a gene by gene basis and even the posttranslational modification and localization of proteins. In a very short time we have witnessed the development of high-throughput biology, forcing us to consider cellular processes in toto. Even though much of the data is noisy and today partially inconsistent and incomplete, this has been a radical shift in the way we tear apart problems one interaction at a time. When coupled with gene deletions by RNAi and classical methods, and with the use of chemical tools tailored to proteins and protein domains, these high-throughput techniques become still more powerful.

High-throughput biology has opened up another im­portant area of systems biology: it has brought us out into the field again or at least made us aware that there is a world outside our laboratories. Our model systems have been chosen intentionally to be of limited genetic diversity and examined in a highly controlled and repro­ducible environment. The real world of ecology, evolu­tion, and human disease is a very different place. When genetics separated from the rest of biology in the early part of the 20th century, most geneticists sought to understand heredity and chose to study traits in the organism that could be easily scored and could be used to reveal genetic mechanisms. This was later ex­tended to powerful effect to use genetics to study cell biological and developmental mechanisms. Some ge­neticists, including a large school in Russia in the early 20th century, continued to study the genetics of natural populations, focusing on traits important for survival. That branch of genetics is coming back strongly with the power of phenotypic assays on the RNA and pro­tein level. As human beings we are most concerned not with using our genetic misfortunes to unravel biology’s complexity (important as that is) but with the role of our genetics in our individual survival. The context for understanding this is still not available, even though the data are now coming in torrents, for many of the genes that will contribute to our survival will have small quan­titative effects, partially masked or accentuated by other genetic and environmental conditions. To under­stand the genetic basis of disease will require not just mapping these genes but an understanding of how the phenotype is created in the first place and the messy interactions between genetic variation and environ­mental variation.

Extracts and explants are relatively accessible to syn­thetic manipulation. Next there is the explicit recon­struction of circuits within cells or the deliberate modifi­cation of those circuits. This has occurred for a while in biology, but the difference is that now we wish to construct or intervene with the explicit purpose of de­scribing the dynamical features of these synthetic or partially synthetic systems. There are more and more tools to intervene and more and more tools to measure. Although these fall short of total descriptions of cells and organisms, the detailed information will give us a sense of the special life-like processes of circuits, pro­teins, cells in tissues, and whole organisms in their en­vironment. This meso-scale systems biology will help establish the correspondence between molecules and large-scale physiology.

You are probably running out of patience for some definition of systems biology. In any case, I do not think the explicit definition of systems biology should come from me but should await the words of the first great modern systems biologist. She or he is probably among us now. However, if forced to provide some kind of label for systems biology, I would simply say that systems biology is the study of the behavior of complex biologi­cal organization and processes in terms of the molecu­lar constituents. It is built on molecular biology in its special concern for information transfer, on physiology for its special concern with adaptive states of the cell and organism, on developmental biology for the impor­tance of defining a succession of physiological states in that process, and on evolutionary biology and ecol­ogy for the appreciation that all aspects of the organ­ism are products of selection, a selection we rarely understand on a molecular level. Systems biology attempts all of this through quantitative measurement, modeling, reconstruction, and theory. Systems biology is not a branch of physics but differs from physics in that the primary task is to understand how biology gen­erates variation. No such imperative to create variation exists in the physical world. It is a new principle that Darwin understood and upon which all of life hinges. That sounds different enough for me to justify a new field and a new name. Furthermore, the success of sys­tems biology is essential if we are to understand life; its success is far from assured—a good field for those seeking risk and adventure.

Source: “Meaning of Systems Biology” Cell, Vol. 121, 503–504, May 20, 2005, DOI 10.1016/j.cell.2005.05.005

Old High-throughput Screening, Once the Gold Standard in Drug Development, Gets a Systems Biology Facelift

From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems

Quentin T. L. Pasquer, Ioannis A. Tsakoumagkos and Sascha Hoogendoorn 

Molecules 202025(23), 5702; https://doi.org/10.3390/molecules25235702

Abstract

Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no “one size fits all” approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.

5.1.5. Large-Scale Proteomics

While FITExP is based on protein expression regulation during apoptosis, a study of Ruprecht et al. showed that proteomic changes are induced both by cytotoxic and non-cytotoxic compounds, which can be detected by mass spectrometry to give information on a compound’s mechanism of action. They developed a large-scale proteome-wide mass spectrometry analysis platform for MOA studies, profiling five lung cancer cell lines with over 50 drugs. Aggregation analysis over the different cell lines and the different compounds showed that one-quarter of the drugs changed the abundance of their protein target. This approach allowed target confirmation of molecular degraders such as PROTACs or molecular glues. Finally, this method yielded unexpected off-target mechanisms for the MAP2K1/2 inhibitor PD184352 and the ALK inhibitor ceritinib [97]. While such a mapping approach clearly provides a wealth of information, it might not be easily attainable for groups that are not equipped for high-throughput endeavors.

All-in-all, mass spectrometry methods have gained a lot of traction in recent years and have been successfully applied for target deconvolution and MOA studies of small molecules. As with all high-throughput methods, challenges lie in the accessibility of the instruments (both from a time and cost perspective) and data analysis of complex and extensive data sets.

5.2. Genetic Approaches

Both label-based and mass spectrometry proteomic approaches are based on the physical interaction between a small molecule and a protein target, and focus on the proteome for target deconvolution. It has been long realized that genetics provides an alternative avenue to understand a compound’s action, either through precise modification of protein levels, or by inducing protein mutations. First realized in yeast as a genetically tractable organism over 20 years ago, recent advances in genetic manipulation of mammalian cells have opened up important opportunities for target identification and MOA studies through genetic screening in relevant cell types [98]. Genetic approaches can be roughly divided into two main areas, with the first centering on the identification of mutations that confer compound resistance (Figure 3a), and the second on genome-wide perturbation of gene function and the concomitant changes in sensitivity to the compound (Figure 3b). While both methods can be used to identify or confirm drug targets, the latter category often provides many additional insights in the compound’s mode of action.

Figure 3. Genetic methods for target identification and mode of action studies. Schematic representations of (a) resistance cloning, and (b) chemogenetic interaction screens.

5.2.1. Resistance Cloning

The “gold standard” in drug target confirmation is to identify mutations in the presumed target protein that render it insensitive to drug treatment. Conversely, different groups have sought to use this principle as a target identification method based on the concept that cells grown in the presence of a cytotoxic drug will either die or develop mutations that will make them resistant to the compound. With recent advances in deep sequencing it is now possible to then scan the transcriptome [99] or genome [100] of the cells for resistance-inducing mutations. Genes that are mutated are then hypothesized to encode the protein target. For this approach to be successful, there are two initial requirements: (1) the compound needs to be cytotoxic for resistant clones to arise, and (2) the cell line needs to be genetically unstable for mutations to occur in a reasonable timeframe.

In 2012, the Kapoor group demonstrated in a proof-of-concept study that resistance cloning in mammalian cells, coupled to transcriptome sequencing (RNA-seq), yields the known polo-like kinase 1 (PLK1) target of the small molecule BI 2536. For this, they used the cancer cell line HCT-116, which is deficient in mismatch repair and consequently prone to mutations. They generated and sequenced multiple resistant clones, and clustered the clones based on similarity. PLK1 was the only gene that was mutated in multiple groups. Of note, one of the groups did not contain PLK1 mutations, but rather developed resistance through upregulation of ABCBA1, a drug efflux transporter, which is a general and non-specific resistance mechanism [101]. In a following study, they optimized their pipeline “DrugTargetSeqR”, by counter-screening for these types of multidrug resistance mechanisms so that these clones were excluded from further analysis (Figure 3a). Furthermore, they used CRISPR/Cas9-mediated gene editing to determine which mutations were sufficient to confer drug resistance, and as independent validation of the biochemical relevance of the obtained hits [102].

While HCT-116 cells are a useful model cell line for resistance cloning because of their genomic instability, they may not always be the cell line of choice, depending on the compound and process that is studied. Povedana et al. used CRISPR/Cas9 to engineer mismatch repair deficiencies in Ewing sarcoma cells and small cell lung cancer cells. They found that deletion of MSH2 results in hypermutations in these normally mutationally silent cells, resulting in the formation of resistant clones in the presence of bortezomib, MLN4924, and CD437, which are all cytotoxic compounds [103]. Recently, Neggers et al. reasoned that CRISPR/Cas9-induced non-homologous end-joining repair could be a viable strategy to create a wide variety of functional mutants of essential genes through in-frame mutations. Using a tiled sgRNA library targeting 75 target genes of investigational neoplastic drugs in HAP1 and K562 cells, they generated several KPT-9274 (an anticancer agent with unknown target)-resistant clones, and subsequent deep sequencing showed that the resistant clones were enriched in NAMPT sgRNAs. Direct target engagement was confirmed by co-crystallizing the compound with NAMPT [104]. In addition to these genetic mutation strategies, an alternative method is to grow the cells in the presence of a mutagenic chemical to induce higher mutagenesis rates [105,106].

When there is already a hypothesis on the pathway involved in compound action, the resistance cloning methodology can be extended to non-cytotoxic compounds. Sekine et al. developed a fluorescent reporter model for the integrated stress response, and used this cell line for target deconvolution of a small molecule inhibitor towards this pathway (ISRIB). Reporter cells were chemically mutagenized, and ISRIB-resistant clones were isolated by flow cytometry, yielding clones with various mutations in the delta subunit of guanine nucleotide exchange factor eIF2B [107].

While there are certainly successful examples of resistance cloning yielding a compound’s direct target as discussed above, resistance could also be caused by mutations or copy number alterations in downstream components of a signaling pathway. This is illustrated by clinical examples of acquired resistance to small molecules, nature’s way of “resistance cloning”. For example, resistance mechanisms in Hedgehog pathway-driven cancers towards the Smoothened inhibitor vismodegib include compound-resistant mutations in Smoothened, but also copy number changes in downstream activators SUFU and GLI2 [108]. It is, therefore, essential to conduct follow-up studies to confirm a direct interaction between a compound and the hit protein, as well as a lack of interaction with the mutated protein.

5.2.3. “Chemogenomics”: Examples of Gene-Drug Interaction Screens

When genetic perturbations are combined with small molecule drugs in a chemogenetic interaction screen, the effect of a gene’s perturbation on compound action is studied. Gene perturbation can render the cells resistant to the compound (suppressor interaction), or conversely, result in hypersensitivity and enhanced compound potency (synergistic interaction) [5,117,121]. Typically, cells are treated with the compound at a sublethal dose, to ascertain that both types of interactions can be found in the final dataset, and often it is necessary to use a variety of compound doses (i.e., LD20, LD30, LD50) and timepoints to obtain reliable insights (Figure 3b).

An early example of successful coupling of a phenotypic screen and downstream genetic screening for target identification is the study of Matheny et al. They identified STF-118804 as a compound with antileukemic properties. Treatment of MV411 cells, stably transduced with a high complexity, genome-wide shRNA library, with STF-118804 (4 rounds of increasing concentration) or DMSO control resulted in a marked depletion of cells containing shRNAs against nicotinamide phosphoribosyl transferase (NAMPT) [122].

The Bassik lab subsequently directly compared the performance of shRNA-mediated knockdown versus CRISPR/Cas9-knockout screens for the target elucidation of the antiviral drug GSK983. The data coming out of both screens were complementary, with the shRNA screen resulting in hits leading to the direct compound target and the CRISPR screen giving information on cellular mechanisms of action of the compound. A reason for this is likely the level of protein depletion that is reached by these methods: shRNAs lead to decreased protein levels, which is advantageous when studying essential genes. However, knockdown may not result in a phenotype for non-essential genes, in which case a full CRISPR-mediated knockout is necessary to observe effects [123].

Another NAMPT inhibitor was identified in a CRISPR/Cas9 “haplo-insufficiency (HIP)”-like approach [124]. Haploinsuffiency profiling is a well-established system in yeast which is performed in a ~50% protein background by heterozygous deletions [125]. As there is no control over CRISPR-mediated loss of alleles, compound treatment was performed at several timepoints after addition of the sgRNA library to HCT116 cells stably expressing Cas9, in the hope that editing would be incomplete at early timepoints, resulting in residual protein levels. Indeed, NAMPT was found to be the target of phenotypic hit LB-60-OF61, especially at earlier timepoints, confirming the hypothesis that some level of protein needs to be present to identify a compound’s direct target [124]. This approach was confirmed in another study, thereby showing that direct target identification through CRISPR-knockout screens is indeed possible [126].

An alternative strategy was employed by the Weissman lab, where they combined genome-wide CRISPR-interference and -activation screens to identify the target of the phase 3 drug rigosertib. They focused on hits that had opposite action in both screens, as in sensitizing in one but protective in the other, which were related to microtubule stability. In a next step, they created chemical-genetic profiles of a variety of microtubule destabilizing agents, rationalizing that compounds with the same target will have similar drug-gene interactions. For this, they made a focused library of sgRNAs, based on the most high-ranking hits in the rigosertib genome-wide CRISPRi screen, and compared the focused screen results of the different compounds. The profile for rigosertib clustered well with that of ABT-571, and rigorous target validation studies confirmed rigosertib binding to the colchicine binding site of tubulin—the same site as occupied by ABT-571 [127].

From the above examples, it is clear that genetic screens hold a lot of promise for target identification and MOA studies for small molecules. The CRISPR screening field is rapidly evolving, sgRNA libraries are continuously improving and increasingly commercially available, and new tools for data analysis are being developed [128]. The challenge lies in applying these screens to study compounds that are not cytotoxic, where finding the right dosage regimen will not be trivial.

SYSTEMS BIOLOGY AND CANCER RESEARCH & DRUG DISCOVERY

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

Youngjun Park, Dominik Heider and Anne-Christin Hauschild. Cancers 202113(13), 3148; https://doi.org/10.3390/cancers13133148

Abstract

The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question

1. Introduction

The development and widespread use of high-throughput technologies founded the era of big data in biology and medicine. In particular, it led to an accumulation of large-scale data sets that opened a vast amount of possible applications for data-driven methodologies. In cancer, these applications range from fundamental research to clinical applications: molecular characteristics of tumors, tumor heterogeneity, drug discovery and potential treatments strategy. Therefore, data-driven bioinformatics research areas have tailored data mining technologies such as systems biology, machine learning, and deep learning, elaborated in this review paper (see Figure 1 and Figure 2). For example, in systems biology, data-driven approaches are applied to identify vital signaling pathways [1]. This pathway-centric analysis is particularly crucial in cancer research to understand the characteristics and heterogeneity of the tumor and tumor subtypes. Consequently, this high-throughput data-based analysis enables us to explore characteristics of cancers with a systems biology and a systems medicine point of view [2].Combining high-throughput techniques, especially next-generation sequencing (NGS), with appropriate analytical tools has allowed researchers to gain a deeper systematic understanding of cancer at various biological levels, most importantly genomics, transcriptomics, and epigenetics [3,4]. Furthermore, more sophisticated analysis tools based on computational modeling are introduced to decipher underlying molecular mechanisms in various cancer types. The increasing size and complexity of the data required the adaptation of bioinformatics processing pipelines for higher efficiency and sophisticated data mining methodologies, particularly for large-scale, NGS datasets [5]. Nowadays, more and more NGS studies integrate a systems biology approach and combine sequencing data with other types of information, for instance, protein family information, pathway, or protein–protein interaction (PPI) networks, in an integrative analysis. Experimentally validated knowledge in systems biology may enhance analysis models and guides them to uncover novel findings. Such integrated analyses have been useful to extract essential information from high-dimensional NGS data [6,7]. In order to deal with the increasing size and complexity, the application of machine learning, and specifically deep learning methodologies, have become state-of-the-art in NGS data analysis.

Figure 1. Next-generation sequencing data can originate from various experimental and technological conditions. Depending on the purpose of the experiment, one or more of the depicted omics types (Genomics, Transcriptomics, Epigenomics, or Single-Cell Omics) are analyzed. These approaches led to an accumulation of large-scale NGS datasets to solve various challenges of cancer research, molecular characterization, tumor heterogeneity, and drug target discovery. For instance, The Cancer Genome Atlas (TCGA) dataset contains multi-omics data from ten-thousands of patients. This dataset facilitates a variety of cancer researches for decades. Additionally, there are also independent tumor datasets, and, frequently, they are analyzed and compared with the TCGA dataset. As the large scale of omics data accumulated, various machine learning techniques are applied, e.g., graph algorithms and deep neural networks, for dimensionality reduction, clustering, or classification. (Created with BioRender.com.)

Figure 2. (a) A multitude of different types of data is produced by next-generation sequencing, for instance, in the fields of genomics, transcriptomics, and epigenomics. (b) Biological networks for biomarker validation: The in vivo or in vitro experiment results are considered ground truth. Statistical analysis on next-generation sequencing data produces candidate genes. Biological networks can validate these candidate genes and highlight the underlying biological mechanisms (Section 2.1). (c) De novo construction of Biological Networks: Machine learning models that aim to reconstruct biological networks can incorporate prior knowledge from different omics data. Subsequently, the model will predict new unknown interactions based on new omics information (Section 2.2). (d) Network-based machine learning: Machine learning models integrating biological networks as prior knowledge to improve predictive performance when applied to different NGS data (Section 2.3). (Created with BioRender.com).

Therefore, a large number of studies integrate NGS data with machine learning and propose a novel data-driven methodology in systems biology [8]. In particular, many network-based machine learning models have been developed to analyze cancer data and help to understand novel mechanisms in cancer development [9,10]. Moreover, deep neural networks (DNN) applied for large-scale data analysis improved the accuracy of computational models for mutation prediction [11,12], molecular subtyping [13,14], and drug repurposing [15,16]. 

2. Systems Biology in Cancer Research

Genes and their functions have been classified into gene sets based on experimental data. Our understandings of cancer concentrated into cancer hallmarks that define the characteristics of a tumor. This collective knowledge is used for the functional analysis of unseen data.. Furthermore, the regulatory relationships among genes were investigated, and, based on that, a pathway can be composed. In this manner, the accumulation of public high-throughput sequencing data raised many big-data challenges and opened new opportunities and areas of application for computer science. Two of the most vibrantly evolving areas are systems biology and machine learning which tackle different tasks such as understanding the cancer pathways [9], finding crucial genes in pathways [22,53], or predicting functions of unidentified or understudied genes [54]. Essentially, those models include prior knowledge to develop an analysis and enhance interpretability for high-dimensional data [2]. In addition to understanding cancer pathways with in silico analysis, pathway activity analysis incorporating two different types of data, pathways and omics data, is developed to understand heterogeneous characteristics of the tumor and cancer molecular subtyping. Due to its advantage in interpretability, various pathway-oriented methods are introduced and become a useful tool to understand a complex diseases such as cancer [55,56,57].

In this section, we will discuss how two related research fields, namely, systems biology and machine learning, can be integrated with three different approaches (see Figure 2), namely, biological network analysis for biomarker validation, the use of machine learning with systems biology, and network-based models.

2.1. Biological Network Analysis for Biomarker Validation

The detection of potential biomarkers indicative of specific cancer types or subtypes is a frequent goal of NGS data analysis in cancer research. For instance, a variety of bioinformatics tools and machine learning models aim at identify lists of genes that are significantly altered on a genomic, transcriptomic, or epigenomic level in cancer cells. Typically, statistical and machine learning methods are employed to find an optimal set of biomarkers, such as single nucleotide polymorphisms (SNPs), mutations, or differentially expressed genes crucial in cancer progression. Traditionally, resource-intensive in vitro analysis was required to discover or validate those markers. Therefore, systems biology offers in silico solutions to validate such findings using biological pathways or gene ontology information (Figure 2b) [58]. Subsequently, gene set enrichment analysis (GSEA) [50] or gene set analysis (GSA) [59] can be used to evaluate whether these lists of genes are significantly associated with cancer types and their specific characteristics. GSA, for instance, is available via web services like DAVID [60] and g:Profiler [61]. Moreover, other applications use gene ontology directly [62,63]. In addition to gene-set-based analysis, there are other methods that focuse on the topology of biological networks. These approaches evaluate various network structure parameters and analyze the connectivity of two genes or the size and interconnection of their neighbors [64,65]. According to the underlying idea, the mutated gene will show dysfunction and can affect its neighboring genes. Thus, the goal is to find abnormalities in a specific set of genes linked with an edge in a biological network. For instance, KeyPathwayMiner can extract informative network modules in various omics data [66]. In summary, these approaches aim at predicting the effect of dysfunctional genes among neighbors according to their connectivity or distances from specific genes such as hubs [67,68]. During the past few decades, the focus of cancer systems biology extended towards the analysis of cancer-related pathways since those pathways tend to carry more information than a gene set. Such analysis is called Pathway Enrichment Analysis (PEA) [69,70]. The use of PEA incorporates the topology of biological networks. However, simultaneously, the lack of coverage issue in pathway data needs to be considered. Because pathway data does not cover all known genes yet, an integration analysis on omics data can significantly drop in genes when incorporated with pathways. Genes that can not be mapped to any pathway are called ‘pathway orphan.’ In this manner, Rahmati et al. introduced a possible solution to overcome the ‘pathway orphan’ issue [71]. At the bottom line, regardless of whether researchers consider gene-set or pathway-based enrichment analysis, the performance and accuracy of both methods are highly dependent on the quality of the external gene-set and pathway data [72].

2.2. De Novo Construction of Biological Networks

While the known fraction of existing biological networks barely scratches the surface of the whole system of mechanisms occurring in each organism, machine learning models can improve on known network structures and can guide potential new findings [73,74]. This area of research is called de novo network construction (Figure 2c), and its predictive models can accelerate experimental validation by lowering time costs [75,76]. This interplay between in silico biological networks building and mining contributes to expanding our knowledge in a biological system. For instance, a gene co-expression network helps discover gene modules having similar functions [77]. Because gene co-expression networks are based on expressional changes under specific conditions, commonly, inferring a co-expression network requires many samples. The WGCNA package implements a representative model using weighted correlation for network construction that leads the development of the network biology field [78]. Due to NGS developments, the analysis of gene co-expression networks subsequently moved from microarray-based to RNA-seq based experimental data [79]. However, integration of these two types of data remains tricky. Ballouz et al. compared microarray and NGS-based co-expression networks and found the existence of a bias originating from batch effects between the two technologies [80]. Nevertheless, such approaches are suited to find disease-specific co-expressional gene modules. Thus, various studies based on the TCGA cancer co-expression network discovered characteristics of prognostic genes in the network [81]. Accordingly, a gene co-expression network is a condition-specific network rather than a general network for an organism. Gene regulatory networks can be inferred from the gene co-expression network when various data from different conditions in the same organism are available. Additionally, with various NGS applications, we can obtain multi-modal datasets about regulatory elements and their effects, such as epigenomic mechanisms on transcription and chromatin structure. Consequently, a gene regulatory network can consist of solely protein-coding genes or different regulatory node types such as transcription factors, inhibitors, promoter interactions, DNA methylations, and histone modifications affecting the gene expression system [82,83]. More recently, researchers were able to build networks based on a particular experimental setup. For instance, functional genomics or CRISPR technology enables the high-resolution regulatory networks in an organism [84]. Other than gene co-expression or regulatory networks, drug target, and drug repurposing studies are active research areas focusing on the de novo construction of drug-to-target networks to allow the potential repurposing of drugs [76,85].

2.3. Network Based Machine Learning

A network-based machine learning model directly integrates the insights of biological networks within the algorithm (Figure 2d) to ultimately improve predictive performance concerning cancer subtyping or susceptibility to therapy. Following the establishment of high-quality biological networks based on NGS technologies, these biological networks were suited to be integrated into advanced predictive models. In this manner, Zhang et al., categorized network-based machine learning approaches upon their usage into three groups: (i) model-based integration, (ii) pre-processing integration, and (iii) post-analysis integration [7]. Network-based models map the omics data onto a biological network, and proper algorithms travel the network while considering both values of nodes and edges and network topology. In the pre-processing integration, pathway or other network information is commonly processed based on its topological importance. Meanwhile, in the post-analysis integration, omics data is processed solely before integration with a network. Subsequently, omics data and networks are merged and interpreted. The network-based model has advantages in multi-omics integrative analysis. Due to the different sensitivity and coverage of various omics data types, a multi-omics integrative analysis is challenging. However, focusing on gene-level or protein-level information enables a straightforward integration [86,87]. Consequently, when different machine learning approaches tried to integrate two or more different data types to find novel biological insights, one of the solutions is reducing the search space to gene or protein level and integrated heterogeneous datatypes [25,88].

In summary, using network information opens new possibilities for interpretation. However, as mentioned earlier, several challenges remain, such as the coverage issue. Current databases for biological networks do not cover the entire set of genes, transcripts, and interactions. Therefore, the use of networks can lead to loss of information for gene or transcript orphans. The following section will focus on network-based machine learning models and their application in cancer genomics. We will put network-based machine learning into the perspective of the three main areas of application, namely, molecular characterization, tumor heterogeneity analysis, and cancer drug discovery.

3. Network-Based Learning in Cancer Research

As introduced previously, the integration of machine learning with the insights of biological networks (Figure 2d) ultimately aims at improving predictive performance and interpretability concerning cancer subtyping or treatment susceptibility.

3.1. Molecular Characterization with Network Information

Various network-based algorithms are used in genomics and focus on quantifying the impact of genomic alteration. By employing prior knowledge in biological network algorithms, performance compared to non-network models can be improved. A prominent example is HotNet. The algorithm uses a thermodynamics model on a biological network and identifies driver genes, or prognostic genes, in pan-cancer data [89]. Another study introduced a network-based stratification method to integrate somatic alterations and expression signatures with network information [90]. These approaches use network topology and network-propagation-like algorithms. Network propagation presumes that genomic alterations can affect the function of neighboring genes. Two genes will show an exclusive pattern if two genes complement each other, and the function carried by those two genes is essential to an organism [91]. This unique exclusive pattern among genomic alteration is further investigated in cancer-related pathways. Recently, Ku et al. developed network-centric approaches and tackled robustness issues while studying synthetic lethality [92]. Although synthetic lethality was initially discovered in model organisms of genetics, it helps us to understand cancer-specific mutations and their functions in tumor characteristics [91].

Furthermore, in transcriptome research, network information is used to measure pathway activity and its application in cancer subtyping. For instance, when comparing the data of two or more conditions such as cancer types, GSEA as introduced in Section 2 is a useful approach to get an overview of systematic changes [50]. It is typically used at the beginning of a data evaluation [93]. An experimentally validated gene set can provide information about how different conditions affect molecular systems in an organism. In addition to the gene sets, different approaches integrate complex interaction information into GSEA and build network-based models [70]. In contrast to GSEA, pathway activity analysis considers transcriptome data and other omics data and structural information of a biological network. For example, PARADIGM uses pathway topology and integrates various omics in the analysis to infer a patient-specific status of pathways [94]. A benchmark study with pan-cancer data recently reveals that using network structure can show better performance [57]. In conclusion, while the loss of data is due to the incompleteness of biological networks, their integration improved performance and increased interpretability in many cases.

3.2. Tumor Heterogeneity Study with Network Information

The tumor heterogeneity can originate from two directions, clonal heterogeneity and tumor impurity. Clonal heterogeneity covers genomic alterations within the tumor [95]. While de novo mutations accumulate, the tumor obtains genomic alterations with an exclusive pattern. When these genomic alterations are projected on the pathway, it is possible to observe exclusive relationships among disease-related genes. For instance, the CoMEt and MEMo algorithms examine mutual exclusivity on protein–protein interaction networks [96,97]. Moreover, the relationship between genes can be essential for an organism. Therefore, models analyzing such alterations integrate network-based analysis [98].

In contrast, tumor purity is dependent on the tumor microenvironment, including immune-cell infiltration and stromal cells [99]. In tumor microenvironment studies, network-based models are applied, for instance, to find immune-related gene modules. Although the importance of the interaction between tumors and immune cells is well known, detailed mechanisms are still unclear. Thus, many recent NGS studies employ network-based models to investigate the underlying mechanism in tumor and immune reactions. For example, McGrail et al. identified a relationship between the DNA damage response protein and immune cell infiltration in cancer. The analysis is based on curated interaction pairs in a protein–protein interaction network [100]. Most recently, Darzi et al. discovered a prognostic gene module related to immune cell infiltration by using network-centric approaches [101]. Tu et al. presented a network-centric model for mining subnetworks of genes other than immune cell infiltration by considering tumor purity [102].

3.3. Drug Target Identification with Network Information

In drug target studies, network biology is integrated into pharmacology [103]. For instance, Yamanishi et al. developed novel computational methods to investigate the pharmacological space by integrating a drug-target protein network with genomics and chemical information. The proposed approaches investigated such drug-target network information to identify potential novel drug targets [104]. Since then, the field has continued to develop methods to study drug target and drug response integrating networks with chemical and multi-omic datasets. In a recent survey study by Chen et al., the authors compared 13 computational methods for drug response prediction. It turned out that gene expression profiles are crucial information for drug response prediction [105].

Moreover, drug-target studies are often extended to drug-repurposing studies. In cancer research, drug-repurposing studies aim to find novel interactions between non-cancer drugs and molecular features in cancer. Drug-repurposing (or repositioning) studies apply computational approaches and pathway-based models and aim at discovering potential new cancer drugs with a higher probability than de novo drug design [16,106]. Specifically, drug-repurposing studies can consider various areas of cancer research, such as tumor heterogeneity and synthetic lethality. As an example, Lee et al. found clinically relevant synthetic lethality interactions by integrating multiple screening NGS datasets [107]. This synthetic lethality and related-drug datasets can be integrated for an effective combination of anticancer therapeutic strategy with non-cancer drug repurposing.

4. Deep Learning in Cancer Research

DNN models develop rapidly and become more sophisticated. They have been frequently used in all areas of biomedical research. Initially, its development was facilitated by large-scale imaging and video data. While most data sets in the biomedical field would not typically be considered big data, the rapid data accumulation enabled by NGS made it suitable for the application of DNN models requiring a large amount of training data [108]. For instance, in 2019, Samiei et al. used TCGA-based large-scale cancer data as benchmark datasets for bioinformatics machine learning research such as Image-Net in the computer vision field [109]. Subsequently, large-scale public cancer data sets such as TCGA encouraged the wide usage of DNNs in the cancer domain [110]. Over the last decade, these state-of-the-art machine learning methods have been incorporated in many different biological questions [111].

In addition to public cancer databases such as TCGA, the genetic information of normal tissues is stored in well-curated databases such as GTEx [112] and 1000Genomes [113]. These databases are frequently used as control or baseline training data for deep learning [114]. Moreover, other non-curated large-scale data sources such as GEO (https://www.ncbi.nlm.nih.gov/geo/, accessed on 20 May 2021) can be leveraged to tackle critical aspects in cancer research. They store a large-scale of biological data produced under various experimental setups (Figure 1). Therefore, an integration of GEO data and other data requires careful preprocessing. Overall, an increasing amount of datasets facilitate the development of current deep learning in bioinformatics research [115].

4.1. Challenges for Deep Learning in Cancer Research

Many studies in biology and medicine used NGS and produced large amounts of data during the past few decades, moving the field to the big data era. Nevertheless, researchers still face a lack of data in particular when investigating rare diseases or disease states. Researchers have developed a manifold of potential solutions to overcome this lack of data challenges, such as imputation, augmentation, and transfer learning (Figure 3b). Data imputation aims at handling data sets with missing values [116]. It has been studied on various NGS omics data types to recover missing information [117]. It is known that gene expression levels can be altered by different regulatory elements, such as DNA-binding proteins, epigenomic modifications, and post-transcriptional modifications. Therefore, various models integrating such regulatory schemes have been introduced to impute missing omics data [118,119]. Some DNN-based models aim to predict gene expression changes based on genomics or epigenomics alteration. For instance, TDimpute aims at generating missing RNA-seq data by training a DNN on methylation data. They used TCGA and TARGET (https://ocg.cancer.gov/programs/target/data-matrix, accessed on 20 May 2021) data as proof of concept of the applicability of DNN for data imputation in a multi-omics integration study [120]. Because this integrative model can exploit information in different levels of regulatory mechanisms, it can build a more detailed model and achieve better performance than a model build on a single-omics dataset [117,121]. The generative adversarial network (GAN) is a DNN structure for generating simulated data that is different from the original data but shows the same characteristics [122]. GANs can impute missing omics data from other multi-omics sources. Recently, the GAN algorithm is getting more attention in single-cell transcriptomics because it has been recognized as a complementary technique to overcome the limitation of scRNA-seq [123]. In contrast to data imputation and generation, other machine learning approaches aim to cope with a limited dataset in different ways. Transfer learning or few-shot learning, for instance, aims to reduce the search space with similar but unrelated datasets and guide the model to solve a specific set of problems [124]. These approaches train models with data of similar characteristics and types but different data to the problem set. After pre-training the model, it can be fine-tuned with the dataset of interest [125,126]. Thus, researchers are trying to introduce few-shot learning models and meta-learning approaches to omics and translational medicine. For example, Select-ProtoNet applied the ProtoTypical Network [127] model to TCGA transcriptome data and classified patients into two groups according to their clinical status [128]. AffinityNet predicts kidney and uterus cancer subtypes with gene expression profiles [129].

Figure 3. (a) In various studies, NGS data transformed into different forms. The 2-D transformed form is for the convolution layer. Omics data is transformed into pathway level, GO enrichment score, or Functional spectra. (b) DNN application on different ways to handle lack of data. Imputation for missing data in multi-omics datasets. GAN for data imputation and in silico data simulation. Transfer learning pre-trained the model with other datasets and fine-tune. (c) Various types of information in biology. (d) Graph neural network examples. GCN is applied to aggregate neighbor information. (Created with BioRender.com).

4.2. Molecular Charactization with Network and DNN Model

DNNs have been applied in multiple areas of cancer research. For instance, a DNN model trained on TCGA cancer data can aid molecular characterization by identifying cancer driver genes. At the very early stage, Yuan et al. build DeepGene, a cancer-type classifier. They implemented data sparsity reduction methods and trained the DNN model with somatic point mutations [130]. Lyu et al. [131] and DeepGx [132] embedded a 1-D gene expression profile to a 2-D array by chromosome order to implement the convolution layer (Figure 3a). Other algorithms, such as the deepDriver, use k-nearest neighbors for the convolution layer. A predefined number of neighboring gene mutation profiles was the input for the convolution layer. It employed this convolution layer in a DNN by aggregating mutation information of the k-nearest neighboring genes [11]. Instead of embedding to a 2-D image, DeepCC transformed gene expression data into functional spectra. The resulting model was able to capture molecular characteristics by training cancer subtypes [14].

Another DNN model was trained to infer the origin of tissue from single-nucleotide variant (SNV) information of metastatic tumor. The authors built a model by using the TCGA/ICGC data and analyzed SNV patterns and corresponding pathways to predict the origin of cancer. They discovered that metastatic tumors retained their original cancer’s signature mutation pattern. In this context, their DNN model obtained even better accuracy than a random forest model [133] and, even more important, better accuracy than human pathologists [12].

4.3. Tumor Heterogeneity with Network and DNN Model

As described in Section 4.1, there are several issues because of cancer heterogeneity, e.g., tumor microenvironment. Thus, there are only a few applications of DNN in intratumoral heterogeneity research. For instance, Menden et al. developed ’Scaden’ to deconvolve cell types in bulk-cell sequencing data. ’Scaden’ is a DNN model for the investigation of intratumor heterogeneity. To overcome the lack of training datasets, researchers need to generate in silico simulated bulk-cell sequencing data based on single-cell sequencing data [134]. It is presumed that deconvolving cell types can be achieved by knowing all possible expressional profiles of the cell [36]. However, this information is typically not available. Recently, to tackle this problem, single-cell sequencing-based studies were conducted. Because of technical limitations, we need to handle lots of missing data, noises, and batch effects in single-cell sequencing data [135]. Thus, various machine learning methods were developed to process single-cell sequencing data. They aim at mapping single-cell data onto the latent space. For example, scDeepCluster implemented an autoencoder and trained it on gene-expression levels from single-cell sequencing. During the training phase, the encoder and decoder work as denoiser. At the same time, they can embed high-dimensional gene-expression profiles to lower-dimensional vectors [136]. This autoencoder-based method can produce biologically meaningful feature vectors in various contexts, from tissue cell types [137] to different cancer types [138,139].

4.4. Drug Target Identification with Networks and DNN Models

In addition to NGS datasets, large-scale anticancer drug assays enabled the training train of DNNs. Moreover, non-cancer drug response assay datasets can also be incorporated with cancer genomic data. In cancer research, a multidisciplinary approach was widely applied for repurposing non-oncology drugs to cancer treatment. This drug repurposing is faster than de novo drug discovery. Furthermore, combination therapy with a non-oncology drug can be beneficial to overcome the heterogeneous properties of tumors [85]. The deepDR algorithm integrated ten drug-related networks and trained deep autoencoders. It used a random-walk-based algorithm to represent graph information into feature vectors. This approach integrated network analysis with a DNN model validated with an independent drug-disease dataset [15].

The authors of CDRscan did an integrative analysis of cell-line-based assay datasets and other drug and genomics datasets. It shows that DNN models can enhance the computational model for improved drug sensitivity predictions [140]. Additionally, similar to previous network-based models, the multi-omics application of drug-targeted DNN studies can show higher prediction accuracy than the single-omics method. MOLI integrated genomic data and transcriptomic data to predict the drug responses of TCGA patients [141].

4.5. Graph Neural Network Model

In general, the advantage of using a biological network is that it can produce more comprehensive and interpretable results from high-dimensional omics data. Furthermore, in an integrative multi-omics data analysis, network-based integration can improve interpretability over traditional approaches. Instead of pre-/post-integration of a network, recently developed graph neural networks use biological networks as the base structure for the learning network itself. For instance, various pathways or interactome information can be integrated as a learning structure of a DNN and can be aggregated as heterogeneous information. In a GNN study, a convolution process can be done on the provided network structure of data. Therefore, the convolution on a biological network made it possible for the GNN to focus on the relationship among neighbor genes. In the graph convolution layer, the convolution process integrates information of neighbor genes and learns topological information (Figure 3d). Consequently, this model can aggregate information from far-distant neighbors, and thus can outperform other machine learning models [142].

In the context of the inference problem of gene expression, the main question is whether the gene expression level can be explained by aggregating the neighboring genes. A single gene inference study by Dutil et al. showed that the GNN model outperformed other DNN models [143]. Moreover, in cancer research, such GNN models can identify cancer-related genes with better performance than other network-based models, such as HotNet2 and MutSigCV [144]. A recent GNN study with a multi-omics integrative analysis identified 165 new cancer genes as an interactive partner for known cancer genes [145]. Additionally, in the synthetic lethality area, dual-dropout GNN outperformed previous bioinformatics tools for predicting synthetic lethality in tumors [146]. GNNs were also able to classify cancer subtypes based on pathway activity measures with RNA-seq data. Lee et al. implemented a GNN for cancer subtyping and tested five cancer types. Thus, the informative pathway was selected and used for subtype classification [147]. Furthermore, GNNs are also getting more attention in drug repositioning studies. As described in Section 3.3, drug discovery requires integrating various networks in both chemical and genomic spaces (Figure 3d). Chemical structures, protein structures, pathways, and other multi-omics data were used in drug-target identification and repurposing studies (Figure 3c). Each of the proposed applications has a specialty in the different purposes of drug-related tasks. Sun et al. summarized GNN-based drug discovery studies and categorized them into four classes: molecular property and activity prediction, interaction prediction, synthesis prediction, and de novo drug design. The authors also point out four challenges in the GNN-mediated drug discovery. At first, as we described before, there is a lack of drug-related datasets. Secondly, the current GNN models can not fully represent 3-D structures of chemical molecules and protein structures. The third challenge is integrating heterogeneous network information. Drug discovery usually requires a multi-modal integrative analysis with various networks, and GNNs can improve this integrative analysis. Lastly, although GNNs use graphs, stacked layers still make it hard to interpret the model [148].

4.6. Shortcomings in AI and Revisiting Validity of Biological Networks as Prior Knowledge

The previous sections reviewed a variety of DNN-based approaches that present a good performance on numerous applications. However, it is hardly a panacea for all research questions. In the following, we will discuss potential limitations of the DNN models. In general, DNN models with NGS data have two significant issues: (i) data requirements and (ii) interpretability. Usually, deep learning needs a large proportion of training data for reasonable performance which is more difficult to achieve in biomedical omics data compared to, for instance, image data. Today, there are not many NGS datasets that are well-curated and -annotated for deep learning. This can be an answer to the question of why most DNN studies are in cancer research [110,149]. Moreover, the deep learning models are hard to interpret and are typically considered as black-boxes. Highly stacked layers in the deep learning model make it hard to interpret its decision-making rationale. Although the methodology to understand and interpret deep learning models has been improved, the ambiguity in the DNN models’ decision-making hindered the transition between the deep learning model and translational medicine [149,150].

As described before, biological networks are employed in various computational analyses for cancer research. The studies applying DNNs demonstrated many different approaches to use prior knowledge for systematic analyses. Before discussing GNN application, the validity of biological networks in a DNN model needs to be shown. The LINCS program analyzed data of ’The Connectivity Map (CMap) project’ to understand the regulatory mechanism in gene expression by inferring the whole gene expression profiles from a small set of genes (https://lincsproject.org/, accessed on 20 May 2021) [151,152]. This LINCS program found that the gene expression level is inferrable with only nearly 1000 genes. They called this gene list ’landmark genes’. Subsequently, Chen et al. started with these 978 landmark genes and tried to predict other gene expression levels with DNN models. Integrating public large-scale NGS data showed better performance than the linear regression model. The authors conclude that the performance advantage originates from the DNN’s ability to model non-linear relationships between genes [153].

Following this study, Beltin et al. extensively investigated various biological networks in the same context of the inference of gene expression level. They set up a simplified representation of gene expression status and tried to solve a binary classification task. To show the relevance of a biological network, they compared various gene expression levels inferred from a different set of genes, neighboring genes in PPI, random genes, and all genes. However, in the study incorporating TCGA and GTEx datasets, the random network model outperformed the model build on a known biological network, such as StringDB [154]. While network-based approaches can add valuable insights to analysis, this study shows that it cannot be seen as the panacea, and a careful evaluation is required for each data set and task. In particular, this result may not represent biological complexity because of the oversimplified problem setup, which did not consider the relative gene-expressional changes. Additionally, the incorporated biological networks may not be suitable for inferring gene expression profiles because they consist of expression-regulating interactions, non-expression-regulating interactions, and various in vivo and in vitro interactions.

“ However, although recently sophisticated applications of deep learning showed improved accuracy, it does not reflect a general advancement. Depending on the type of NGS data, the experimental design, and the question to be answered, a proper approach and specific deep learning algorithms need to be considered. Deep learning is not a panacea. In general, to employ machine learning and systems biology methodology for a specific type of NGS data, a certain experimental design, a particular research question, the technology, and network data have to be chosen carefully.”

References

  1. Janes, K.A.; Yaffe, M.B. Data-driven modelling of signal-transduction networks. Nat. Rev. Mol. Cell Biol. 20067, 820–828. [Google Scholar] [CrossRef] [PubMed]
  2. Kreeger, P.K.; Lauffenburger, D.A. Cancer systems biology: A network modeling perspective. Carcinogenesis 201031, 2–8. [Google Scholar] [CrossRef] [PubMed]
  3. Vucic, E.A.; Thu, K.L.; Robison, K.; Rybaczyk, L.A.; Chari, R.; Alvarez, C.E.; Lam, W.L. Translating cancer ‘omics’ to improved outcomes. Genome Res. 201222, 188–195. [Google Scholar] [CrossRef]
  4. Hoadley, K.A.; Yau, C.; Wolf, D.M.; Cherniack, A.D.; Tamborero, D.; Ng, S.; Leiserson, M.D.; Niu, B.; McLellan, M.D.; Uzunangelov, V.; et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 2014158, 929–944. [Google Scholar] [CrossRef] [PubMed]
  5. Hutter, C.; Zenklusen, J.C. The cancer genome atlas: Creating lasting value beyond its data. Cell 2018173, 283–285. [Google Scholar] [CrossRef]
  6. Chuang, H.Y.; Lee, E.; Liu, Y.T.; Lee, D.; Ideker, T. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 20073, 140. [Google Scholar] [CrossRef]
  7. Zhang, W.; Chien, J.; Yong, J.; Kuang, R. Network-based machine learning and graph theory algorithms for precision oncology. NPJ Precis. Oncol. 20171, 25. [Google Scholar] [CrossRef] [PubMed]
  8. Ngiam, K.Y.; Khor, W. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 201920, e262–e273. [Google Scholar] [CrossRef]
  9. Creixell, P.; Reimand, J.; Haider, S.; Wu, G.; Shibata, T.; Vazquez, M.; Mustonen, V.; Gonzalez-Perez, A.; Pearson, J.; Sander, C.; et al. Pathway and network analysis of cancer genomes. Nat. Methods 201512, 615. [Google Scholar]
  10. Reyna, M.A.; Haan, D.; Paczkowska, M.; Verbeke, L.P.; Vazquez, M.; Kahraman, A.; Pulido-Tamayo, S.; Barenboim, J.; Wadi, L.; Dhingra, P.; et al. Pathway and network analysis of more than 2500 whole cancer genomes. Nat. Commun. 202011, 729. [Google Scholar] [CrossRef]
  11. Luo, P.; Ding, Y.; Lei, X.; Wu, F.X. deepDriver: Predicting cancer driver genes based on somatic mutations using deep convolutional neural networks. Front. Genet. 201910, 13. [Google Scholar] [CrossRef]
  12. Jiao, W.; Atwal, G.; Polak, P.; Karlic, R.; Cuppen, E.; Danyi, A.; De Ridder, J.; van Herpen, C.; Lolkema, M.P.; Steeghs, N.; et al. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nat. Commun. 202011, 728. [Google Scholar] [CrossRef]
  13. Chaudhary, K.; Poirion, O.B.; Lu, L.; Garmire, L.X. Deep learning–based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res. 201824, 1248–1259. [Google Scholar] [CrossRef]
  14. Gao, F.; Wang, W.; Tan, M.; Zhu, L.; Zhang, Y.; Fessler, E.; Vermeulen, L.; Wang, X. DeepCC: A novel deep learning-based framework for cancer molecular subtype classification. Oncogenesis 20198, 44. [Google Scholar] [CrossRef]
  15. Zeng, X.; Zhu, S.; Liu, X.; Zhou, Y.; Nussinov, R.; Cheng, F. deepDR: A network-based deep learning approach to in silico drug repositioning. Bioinformatics 201935, 5191–5198. [Google Scholar] [CrossRef]
  16. Issa, N.T.; Stathias, V.; Schürer, S.; Dakshanamurthy, S. Machine and deep learning approaches for cancer drug repurposing. In Seminars in Cancer Biology; Elsevier: Amsterdam, The Netherlands, 2020. [Google Scholar]
  17. Weinstein, J.N.; Collisson, E.A.; Mills, G.B.; Shaw, K.R.M.; Ozenberger, B.A.; Ellrott, K.; Shmulevich, I.; Sander, C.; Stuart, J.M.; Network, C.G.A.R.; et al. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 201345, 1113. [Google Scholar] [CrossRef]
  18. The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 2020578, 82. [Google Scholar] [CrossRef] [PubMed]
  19. King, M.C.; Marks, J.H.; Mandell, J.B. Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2. Science 2003302, 643–646. [Google Scholar] [CrossRef] [PubMed]
  20. Courtney, K.D.; Corcoran, R.B.; Engelman, J.A. The PI3K pathway as drug target in human cancer. J. Clin. Oncol. 201028, 1075. [Google Scholar] [CrossRef] [PubMed]
  21. Parker, J.S.; Mullins, M.; Cheang, M.C.; Leung, S.; Voduc, D.; Vickery, T.; Davies, S.; Fauron, C.; He, X.; Hu, Z.; et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 200927, 1160. [Google Scholar] [CrossRef]
  22. Yersal, O.; Barutca, S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J. Clin. Oncol. 20145, 412. [Google Scholar] [CrossRef] [PubMed]
  23. Zhao, L.; Lee, V.H.; Ng, M.K.; Yan, H.; Bijlsma, M.F. Molecular subtyping of cancer: Current status and moving toward clinical applications. Brief. Bioinform. 201920, 572–584. [Google Scholar] [CrossRef] [PubMed]
  24. Jones, P.A.; Issa, J.P.J.; Baylin, S. Targeting the cancer epigenome for therapy. Nat. Rev. Genet. 201617, 630. [Google Scholar] [CrossRef] [PubMed]
  25. Huang, S.; Chaudhary, K.; Garmire, L.X. More is better: Recent progress in multi-omics data integration methods. Front. Genet. 20178, 84. [Google Scholar] [CrossRef]
  26. Chin, L.; Andersen, J.N.; Futreal, P.A. Cancer genomics: From discovery science to personalized medicine. Nat. Med. 201117, 297. [Google Scholar] [CrossRef] [PubMed]

Use of Systems Biology in Anti-Microbial Drug Development

Genomics, Computational Biology and Drug Discovery for Mycobacterial Infections: Fighting the Emergence of Resistance. Asma Munir, Sundeep Chaitanya Vedithi, Amanda K. Chaplin and Tom L. Blundell. Front. Genet., 04 September 2020 | https://doi.org/10.3389/fgene.2020.00965

In an earlier review article (Waman et al., 2019), we discussed various computational approaches and experimental strategies for drug target identification and structure-guided drug discovery. In this review we discuss the impact of the era of precision medicine, where the genome sequences of pathogens can give clues about the choice of existing drugs, and repurposing of others. Our focus is directed toward combatting antimicrobial drug resistance with emphasis on tuberculosis and leprosy. We describe structure-guided approaches to understanding the impacts of mutations that give rise to antimycobacterial resistance and the use of this information in the design of new medicines.

Genome Sequences and Proteomic Structural Databases

In recent years, there have been many focused efforts to define the amino-acid sequences of the M. tuberculosis pan-genome and then to define the three-dimensional structures and functional interactions of these gene products. This work has led to essential genes of the bacteria being revealed and to a better understanding of the genetic diversity in different strains that might lead to a selective advantage (Coll et al., 2018). This will help with our understanding of the mode of antibiotic resistance within these strains and aid structure-guided drug discovery. However, only ∼10% of the ∼4128 proteins have structures determined experimentally.

Several databases have been developed to integrate the genomic and/or structural information linked to drug resistance in Mycobacteria (Table 1). These invaluable resources can contribute to better understanding of molecular mechanisms involved in drug resistance and improvement in the selection of potential drug targets.

There is a dearth of information related to structural aspects of proteins from M. leprae and their oligomeric and hetero-oligomeric organization, which has limited the understanding of physiological processes of the bacillus. The structures of only 12 proteins have been solved and deposited in the protein data bank (PDB). However, the high sequence similarity in protein coding genes between M. leprae and M. tuberculosis allows computational methods to be used for comparative modeling of the proteins of M. leprae. Mainly monomeric models using single template modeling have been defined and deposited in the Swiss Model repository (Bienert et al., 2017), in Modbase (Pieper et al., 2014), and in a collection with other infectious disease agents (Sosa et al., 2018). There is a need for multi-template modeling and building homo- and hetero-oligomeric complexes to better understand the interfaces, druggability and impacts of mutations.

We are now exploiting Vivace, a multi-template modeling pipeline developed in our lab for modeling the proteomes of M. tuberculosis (CHOPIN, see above) and M. abscessus [Mabellini Database (Skwark et al., 2019)], to model the proteome of M. leprae. We emphasize the need for understanding the protein interfaces that are critical to function. An example of this is that of the RNA-polymerase holoenzyme complex from M. leprae. We first modeled the structure of this hetero-hexamer complex and later deciphered the binding patterns of rifampin (Vedithi et al., 2018Figures 1A,B). Rifampin is a known drug to treat tuberculosis and leprosy. Owing to high rifampin resistance in tuberculosis and emerging resistance in leprosy, we used an approach known as “Computational Saturation Mutagenesis”, to identify sites on the protein that are less impacted by mutations. In this study, we were able to understand the association between predicted impacts of mutations on the structure and phenotypic rifampin-resistance outcomes in leprosy.

FIGURE 2

Figure 2. (A) Stability changes predicted by mCSM for systematic mutations in the ß-subunit of RNA polymerase in M. leprae. The maximum destabilizing effect from among all 19 possible mutations at each residue position is considered as a weighting factor for the color map that gradients from red (high destabilizing effects) to white (neutral to stabilizing effects) (Vedithi et al., 2020). (B) One of the known mutations in the ß-subunit of RNA polymerase, the S437H substitution which resulted in a maximum destabilizing effect [-1.701 kcal/mol (mCSM)] among all 19 possibilities this position. In the mutant, histidine (residue in green) forms hydrogen bonds with S434 and Q438, aromatic interactions with F431, and other ring-ring and π interactions with the surrounding residues which can impact the shape of the rifampin binding pocket and rifampin affinity to the ß-subunit [-0.826 log(affinity fold change) (mCSM-lig)]. Orange dotted lines represent weak hydrogen bond interactions. Ring-ring and intergroup interactions are depicted in cyan. Aromatic interactions are represented in sky-blue and carbonyl interactions in pink dotted lines. Green dotted lines represent hydrophobic interactions (Vedithi et al., 2020).

Examples of Understanding and Combatting Resistance

The availability of whole genome sequences in the present era has greatly enhanced the understanding of emergence of drug resistance in infectious diseases like tuberculosis. The data generated by the whole genome sequencing of clinical isolates can be screened for the presence of drug-resistant mutations. A preliminary in silico analysis of mutations can then be used to prioritize experimental work to identify the nature of these mutations.

FIGURE 3

Figure 3. (A) Mechanism of isoniazid activation and INH-NAD adduct formation. (B) Mutations mapped (Munir et al., 2019) on the structure of KatG (PDB ID:1SJ2; Bertrand et al., 2004).

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Yet another Success Story: Machine Learning to predict immunotherapy response

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

Immune-checkpoint blockers (ICBs) immunotherapy appears promising for various cancer types, offering a durable therapeutic advantage. Only a number of cases with cancer respond to this therapy. Biomarkers are required to adequately predict the responses of patients. This article evaluates this issue utilizing a system method to characterize the immune response of the anti-tumor based on the entire tumor environment. Researchers build mechanical biomarkers and cancer-specific response models using interpretable machine learning that predict the response of patients to ICB.

The lymphatic and immunological systems help the body defend itself by combating. The immune system functions as the body’s own personal police force, hunting down and eliminating pathogenic baddies.

According to Federica Eduati, Department of Biomedical Engineering at TU/e, “The immune system of the body is quite adept at detecting abnormally behaving cells. Cells that potentially grow into tumors or cancer in the future are included in this category. Once identified, the immune system attacks and destroys the cells.”

Immunotherapy and machine learning are combining to assist the immune system solve one of its most vexing problems: detecting hidden tumorous cells in the human body.

It is the fundamental responsibility of our immune system to identify and remove alien invaders like bacteria or viruses, but also to identify risks within the body, such as cancer. However, cancer cells have sophisticated ways of escaping death by shutting off immune cells. Immunotherapy can reverse the process, but not for all patients and types of cancer. To unravel the mystery, Eindhoven University of Technology researchers used machine learning. They developed a model to predict whether immunotherapy will be effective for a patient using a simple trick. Even better, the model outperforms conventional clinical approaches.

The outcomes of this research are published on 30th June, 2021 in the journal Patterns in an article entitled “Interpretable systems biomarkers predict response to immune-checkpoint inhibitors”.

The Study

  • Characterization of the tumor microenvironment from RNAseq and prior knowledge
  • Multi-task machine-learning models for predicting antitumor immune responses
  • Identification of cancer-type-specific, interpretable biomarkers of immune responses
  • EaSIeR is a tool to predict biomarker-based immunotherapy response from RNA-seq

“Tumor also contains multiple types of immune and fibroblast cells which can play a role in favor of or anti-tumor, and communicates among themselves,” said Oscar Lapuente-Santana, a researcher doctoral student in the computational biology group. “We had to learn how complicated regulatory mechanisms in the micro-environment of the tumor affect the ICB response. We have used RNA sequencing datasets to depict numerous components of the Tumor Microenvironment (TME) in a high-level illustration.”

Using computational algorithms and datasets from previous clinical patient care, the researchers investigated the TME.

Eduati explained

While RNA-sequencing databases are publically available, information on which patients responded to ICB therapy is only available for a limited group of patients and cancer types. So, to tackle the data problem, we used a trick.

All 100 models learned in the randomized cross-validation were included in the EaSIeR tool. For each validation dataset, we used the corresponding cancer-type-specific model: SKCM for the melanoma Gide, Auslander, Riaz, and Liu cohorts; STAD for the gastric cancer Kim cohort; BLCA for the bladder cancer Mariathasan cohort; and GBM for the glioblastoma Cloughesy cohort. To make predictions for each job, the average of the 100 cancer-type-specific models was employed. The predictions of each dataset’s cancer-type-specific models were also compared to models generated for the remaining 17 cancer types.

From the same datasets, the researchers selected several surrogate immunological responses to be used as a measure of ICB effectiveness.

Lapuente-Santana stated

One of the most difficult aspects of our job was properly training the machine learning models. We were able to fix this by looking at alternative immune responses during the training process.

Some of the researchers employed the machine learning approach given in the paper to participate in the “Anti-PD1 Response Prediction DREAM Challenge.”

DREAM is an organization that carries out crowd-based tasks with biomedical algorithms. “We were the first to compete in one of the sub-challenges under the name cSysImmunoOnco team,” Eduati remarks.

The researchers noted,

We applied machine learning to seek for connections between the obtained system-based attributes and the immune response, estimated using 14 predictors (proxies) derived from previous publications. We treated these proxies as individual tasks to be predicted by our machine learning models, and we employed multi-task learning algorithms to jointly learn all tasks.

The researchers discovered that their machine learning model surpasses biomarkers that are already utilized in clinical settings to evaluate ICB therapies.

But why are Eduati, Lapuente-Santana, and their colleagues using mathematical models to tackle a medical treatment problem? Is this going to take the place of the doctor?

Eduati explains

Mathematical models can provide an overview of the interconnection between individual molecules and cells and at the same time predicting a particular patient’s tumor behavior. This implies that immunotherapy with ICB can be personalized in a patient’s clinical setting. The models can aid physicians with their decisions about optimum therapy, it is vital to note that they will not replace them.

Furthermore, the model aids in determining which biological mechanisms are relevant for the biological response.

The researchers noted

Another advantage of our concept is that it does not need a dataset with known patient responses to immunotherapy for model training.

Further testing is required before these findings may be implemented in clinical settings.

Main Source:

Lapuente-Santana, Ó., van Genderen, M., Hilbers, P. A., Finotello, F., & Eduati, F. (2021). Interpretable systems biomarkers predict response to immune-checkpoint inhibitorsPatterns, 100293. https://www.cell.com/patterns/pdfExtended/S2666-3899(21)00126-4

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Happy 80th Birthday: Radioiodine (RAI) Theranostics: Collaboration between Physics and Medicine, the Utilization of Radionuclides to Diagnose and Treat: Radiation Dosimetry by Discoverer Dr. Saul Hertz, the early history of RAI in diagnosing and treating Thyroid diseases and Theranostics

 

Guest Author: Barbara Hertz

 203-661-0777

htziev@aol.com

Celebrating eighty years of radionuclide therapy and the work of Saul Hertz

First published: 03 February 2021

Both authors contributed to the development, drafting and final editing of this manuscript and are responsible for its content.

Abstract

March 2021 will mark the eightieth anniversary of targeted radionuclide therapy, recognizing the first use of radioactive iodine to treat thyroid disease by Dr. Saul Hertz on March 31, 1941. The breakthrough of Dr. Hertz and collaborator physicist Arthur Roberts was made possible by rapid developments in the fields of physics and medicine in the early twentieth century. Although diseases of the thyroid gland had been described for centuries, the role of iodine in thyroid physiology had been elucidated only in the prior few decades. After the discovery of radioactivity by Henri Becquerel in 1897, rapid advancements in the field, including artificial production of radioactive isotopes, were made in the subsequent decades. Finally, the diagnostic and therapeutic use of radioactive iodine was based on the tracer principal that was developed by George de Hevesy. In the context of these advancements, Hertz was able to conceive the potential of using of radioactive iodine to treat thyroid diseases. Working with Dr. Roberts, he obtained the experimental data and implemented it in the clinical setting. Radioiodine therapy continues to be a mainstay of therapy for hyperthyroidism and thyroid cancer. However, Hertz struggled to gain recognition for his accomplishments and to continue his work and, with his early death in 1950, his contributions have often been overlooked until recently. The work of Hertz and others provided a foundation for the introduction of other radionuclide therapies and for the development of the concept of theranostics.

SOURCE

https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/acm2.13175

 

 

SOURCE

https://www.youtube.com/watch?v=34Qhm8CeMuc

 

http://www.wjnm.org/article.asp?issn=1450-1147;year=&#8230;

http://www.wjnm.org/text.asp?2019/18/1/8/250309

Abstract

Dr. Saul Hertz was Director of The Massachusetts General Hospital’s Thyroid Unit, when he heard about the development of artificial radioactivity. He conceived and brought from bench to bedside the successful use of radioiodine (RAI) to diagnose and treat thyroid diseases. Thus was born the science of theragnostics used today for neuroendocrine tumors and prostate cancer. Dr. Hertz’s work set the foundation of targeted precision medicine.

Keywords: Dr. Saul Hertz, nuclear medicine, radioiodine

 

How to cite this article:
Hertz B. A tribute to Dr. Saul Hertz: The discovery of the medical uses of radioiodine. World J Nucl Med 2019;18:8-12

 

How to cite this URL:
Hertz B. A tribute to Dr. Saul Hertz: The discovery of the medical uses of radioiodine. World J Nucl Med [serial online] 2019 [cited 2021 Mar 2];18:8-12. Available from: http://www.wjnm.org/text.asp?2019/18/1/8/250309

 

 

  • Dr Saul Hertz (1905-1950) discovers the medical uses of radioiodine

Barbara Hertz, Pushan Bharadwaj, Bennett Greenspan»

Abstract » PDF» doi: 10.24911/PJNMed.175-1582813482

 

SOURCE

http://saulhertzmd.com/home

 

  • Happy 80th Birthday: Radioiodine (RAI) Theranostics

Thyroid practitioners and patients are acutely aware of the enormous benefit nuclear medicine has made to mankind. This month we celebrate the 80th anniversary of the early use of radioiodine(RAI).

Dr. Saul Hertz predicted that radionuclides “…would hold the key to the larger problem of cancer in general,” and may just be the best hope for diagnosing and treating cancer successfully.  Yes, RAI has been used for decades to diagnose and treat disease.  Today’s “theranostics,” a term that is a combination of “therapy” and “diagnosis” is utilized in the treatment of thyroid disease and cancer. 

            This short note is to celebrate Dr. Saul Hertz who conceived and brought from bench to bedside the medical uses of RAI; then in the form of 25 minute iodine-128.  

On March 31st 1941, Massachusetts General Hospital’s Dr. Saul Hertz (1905-1950) administered the first therapeutic use of Massachusetts Institute of Technology (MIT) cyclotron produced RAI.  This landmark case was the first in Hertz’s clinical studies conducted with MIT, physicist Arthur Roberts, Ph.D.

[Photo – Courtesy of Dr Saul Hertz Archives ]

Dr Saul Hertz demonstrating RAI Uptake Testing

            Dr. Hertz’s research and successful utilization of radionuclides to diagnose and treat diseases and conditions, established the use of radiation dosimetry and the collaboration between physics and medicine and other significant practices.   Sadly, Saul Hertz (a WWII veteran) died at a very young age.  

 

About Dr. Saul Hertz

Dr. Saul Hertz (1905 – 1950) discovered the medical uses of radionuclides.  His breakthrough work with radioactive iodine (RAI) created a dynamic paradigym change integrating the sciences.  Radioactive iodine (RAI) is the first and Gold Standard of targeted cancer therapies.  Saul Hertz’s research documents Hertz as the first and foremost person to conceive and develop the experimental data on RAI and apply it in the clinical setting.

Dr. Hertz was born to Orthodox Jewish immigrant parents in Cleveland, Ohio on April 20, 1905. He received his A.B. from the University of Michigan in 1925 with Phi Beta Kappa honors. He graduated from Harvard Medical School in 1929 at a time of quotas for outsiders. He fulfilled his internship and residency at Mt. Sinai Hospital in Cleveland. He came back to Boston in 1931 as a volunteer to join The Massachusetts General Hospital serving as the Chief of the Thyroid Unit from 1931 – 1943.

Two years after the discovery of artifically radioactivity, on November 12, 1936 Dr. Karl Compton, president of the Massachusetts Institute of Technology (MIT), spoke at Harvard Medical School.  President Compton’s topic was What Physics can do for Biology and Medicine. After the presentation Dr. Hertz spontaneously asked Dr. Compton this seminal question, “Could iodine be made radioactive artificially?” Dr. Compton responded in writing on December 15, 1936 that in fact “iodine can be made artificially radioactive.”

Shortly thereafter, a collaboration between Dr. Hertz (MGH) and Dr. Arthur Roberts, a physicist of MIT, was established. In late 1937, Hertz and Roberts created and produced animal studies  involving 48 rabbits that demonstrated that the normal thyroid gland concentrated Iodine 128 (non cyclotron produced), and the hyperplastic thyroid gland took up even more Iodine.  This was a GIANT step for Nuclear Medicine.

In early 1941, Dr. Hertz administer the first therapeutic treatment of MIT Markle Cyclotron produced radioactive iodine (RAI) at the Massachusetts General Hospital.  This  led to the first series of twenty-nine patients with hyperthyroidism being treated successfully with RAI. ( see “Research” RADIOACTIVE IODINE IN THE STUDY OF THYROID PHYSIOLOGY VII The use of Radioactive Iodine Therapy in Hyperthyroidism, Saul Hertz and Arthur Roberts, JAMA Vol. 31 Number 2).

In 1937, at the time of the rabbit studies Dr Hertz conceived of RAI in therapeutic treatment of thyroid carsonoma.  In 1942 Dr Hertz gave clinical trials of RAI to patients with thyroid carcinoma.

After serving in the Navy during World War II, Dr. Hertz wrote to the director of the Mass General Hospital in Boston, Dr. Paxon on March 12, 1946, “it is a coincidence that my new research project is in Cancer of the Thyroid, which I believe holds the key to the larger problem of cancer in general.”

Dr. Hertz established the Radioactive Isotope Research Institute, in September, 1946 with a major focus on the use of fission products for the treatment of thyroid cancer, goiter, and other malignant tumors. Dr Samuel Seidlin was the Associate Director and managed the New York City facilities. Hertz also researched the influence of hormones on cancer.

Dr. Hertz’s use of radioactive iodine as a tracer in the diagnostic process, as a treatment for Graves’ disease and in the treatment of cancer of the thyroid remain preferred practices. Saul Hertz is the Father of Theranostics.

Saul Hertz passed at 45 years old from a sudden death heart attack as documented by an autopsy. He leaves an enduring legacy impacting countless generations of patients, numerous institutions worldwide and setting the cornerstone for the field of Nuclear Medicine. A cancer survivor emailed, The cure delivered on the wings of prayer was Dr Saul Hertz’s discovery, the miracle of radioactive iodine. Few can equal such a powerful and precious gift. 

To read and hear more about Dr. Hertz and the early history of RAI in diagnosing and treating thyroid diseases and theranostics see –

http://saulhertzmd.com/home

 

   References in https://www.wjnm.org/article.asp?issn=1450-1147;year=2019;volume=18;issue=1;spage=8;epage=12;aulast=Hertz

 

Top

 

1.
Hertz S, Roberts A. Radioactive iodine in the study of thyroid physiology. VII The use of radioactive iodine therapy in hyperthyroidism. J Am Med Assoc 1946;131:81-6.  Back to cited text no. 1
2.
Hertz S. A plan for analysis of the biologic factors involved in experimental carcinogenesis of the thyroid by means of radioactive isotopes. Bull New Engl Med Cent 1946;8:220-4.  Back to cited text no. 2
3.
Thrall J. The Story of Saul Hertz, Radioiodine and the Origins of Nuclear Medicine. Available from: http://www.youtube.com/watch?v=34Qhm8CeMuc. [Last accessed on 2018 Dec 01].  Back to cited text no. 3
4.
Braverman L. 131 Iodine Therapy: A Brief History. Available from: http://www.am2016.aace.com/presentations/friday/F12/hertz_braverman.pdf. [Last accessed on 2018 Dec 01].  Back to cited text no. 4
5.
Hofman MS, Violet J, Hicks RJ, Ferdinandus J, Thang SP, Akhurst T, et al. [177Lu]-PSMA-617 radionuclide treatment in patients with metastatic castration-resistant prostate cancer (LuPSMA trial): A single-centre, single-arm, phase 2 study. Lancet Oncol 2018;19:825-33.  Back to cited text no. 5
6.
Krolicki L, Morgenstern A, Kunikowska J, Koiziar H, Krolicki B, Jackaniski M, et al. Glioma Tumors Grade II/III-Local Alpha Emitters Targeted Therapy with 213 Bi-DOTA-Substance P, Endocrine Abstracts. Vol. 57. Society of Nuclear Medicine and Molecular Imaging; 2016. p. 632.  Back to cited text no. 6
7.
Baum RP, Kulkarni HP. Duo PRRT of neuroendocrine tumours using concurrent and sequential administration of Y-90- and Lu-177-labeled somatostatin analogues. In: Hubalewska-Dydejczyk A, Signore A, de Jong M, Dierckx RA, Buscombe J, Van de Wiel CJ, editors. Somatostatin Analogues from Research to Clinical Practice. New York: Wiley; 2015.  Back to cited text no. 7

 

SOURCE

From: htziev@aol.com” <htziev@aol.com>

Reply-To: htziev@aol.com” <htziev@aol.com>

Date: Tuesday, March 2, 2021 at 11:04 AM

To: “Aviva Lev-Ari, PhD, RN” <AvivaLev-Ari@alum.berkeley.edu>

Subject: Dr Saul Hertz : Discovery for the Medical Uses of RADIOIODINE (RAI) MARCH 31ST: 80 Years

 

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Inhibitory CD161 receptor recognized as a potential immunotherapy target in glioma-infiltrating T cells by single-cell analysis

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Brain tumors, especially the diffused Gliomas are of the most devastating forms of cancer and have so-far been resistant to immunotherapy. It is comprehended that T cells can penetrate the glioma cells, but it still remains unknown why infiltrating cells miscarry to mount a resistant reaction or stop the tumor development.

Gliomas are brain tumors that begin from neuroglial begetter cells. The conventional therapeutic methods including, surgery, chemotherapy, and radiotherapy, have accomplished restricted changes inside glioma patients. Immunotherapy, a compliance in cancer treatment, has introduced a promising strategy with the capacity to penetrate the blood-brain barrier. This has been recognized since the spearheading revelation of lymphatics within the central nervous system. Glioma is not generally carcinogenic. As observed in a number of cases, the tumor cells viably reproduce and assault the adjoining tissues, by and large, gliomas are malignant in nature and tend to metastasize. There are four grades in glioma, and each grade has distinctive cell features and different treatment strategies. Glioblastoma is a grade IV glioma, which is the crucial aggravated form. This infers that all glioblastomas are gliomas, however, not all gliomas are glioblastomas.

Decades of investigations on infiltrating gliomas still take off vital questions with respect to the etiology, cellular lineage, and function of various cell types inside glial malignancies. In spite of the available treatment options such as surgical resection, radiotherapy, and chemotherapy, the average survival rate for high-grade glioma patients remains 1–3 years (1).

A recent in vitro study performed by the researchers of Dana-Farber Cancer Institute, Massachusetts General Hospital, and the Broad Institute of MIT and Harvard, USA, has recognized that CD161 is identified as a potential new target for immunotherapy of malignant brain tumors. The scientific team depicted their work in the Cell Journal, in a paper entitled, “Inhibitory CD161 receptor recognized in glioma-infiltrating T cells by single-cell analysis.” on 15th February 2021.

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Dr. Wucherpfennig has praised the other authors of the report Mario Suva, MD, PhD, of Massachusetts Common Clinic; Aviv Regev, PhD, of the Klarman Cell Observatory at Broad Institute of MIT and Harvard, and David Reardon, MD, clinical executive of the Center for Neuro-Oncology at Dana-Farber.

Hence, this new study elaborates the effectiveness of the potential effectors of anti-tumor immunity in subsets of T cells that co-express cytotoxic programs and several natural killer (NK) cell genes.

The Study-

IMAGE SOURCE: Experimental Strategy (Mathewson et al., 2021)

 

The group utilized single-cell RNA sequencing (RNA-seq) to mull over gene expression and the clonal picture of tumor-infiltrating T cells. It involved the participation of 31 patients suffering from diffused gliomas and glioblastoma. Their work illustrated that the ligand molecule CLEC2D activates CD161, which is an immune cell surface receptor that restrains the development of cancer combating activity of immune T cells and tumor cells in the brain. The study reveals that the activation of CD161 weakens the T cell response against tumor cells.

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  • genetic inactivation of KLRB1 or
  • antibody-mediated CD161 blockade

enhances T cell-mediated killing of glioma cells in vitro and their anti-tumor function in vivo. KLRB1 and its associated transcriptional program are also expressed by substantial T cell populations in other forms of human cancers. The work provides an atlas of T cells in gliomas and highlights CD161 and other NK cell receptors as immune checkpoint targets.

Further, it has been identified that many cancer patients are being treated with immunotherapy drugs that disable their “immune checkpoints” and their molecular brakes are exploited by the cancer cells to suppress the body’s defensive response induced by T cells against tumors. Disabling these checkpoints lead the immune system to attack the cancer cells. One of the most frequently targeted checkpoints is PD-1. However, recent trials of drugs that target PD-1 in glioblastomas have failed to benefit the patients.

In the current study, the researchers found that fewer T cells from gliomas contained PD-1 than CD161. As a result, they said, “CD161 may represent an attractive target, as it is a cell surface molecule expressed by both CD8 and CD4 T cell subsets [the two types of T cells engaged in response against tumor cells] and a larger fraction of T cells express CD161 than the PD-1 protein.”

However, potential side effects of antibody-mediated blockade of the CLEC2D-CD161 pathway remain unknown and will need to be examined in a non-human primate model. The group hopes to use this finding in their future work by

utilizing their outline by expression of KLRB1 gene in tumor-infiltrating T cells in diffuse gliomas to make a remarkable contribution in therapeutics related to immunosuppression in brain tumors along with four other common human cancers ( Viz. melanoma, non-small cell lung cancer (NSCLC), hepatocellular carcinoma, and colorectal cancer) and how this may be manipulated for prevalent survival of the patients.

References

(1) Anders I. Persson, QiWen Fan, Joanna J. Phillips, William A. Weiss, 39 – Glioma, Editor(s): Sid Gilman, Neurobiology of Disease, Academic Press, 2007, Pages 433-444, ISBN 9780120885923, https://doi.org/10.1016/B978-012088592-3/50041-4.

Main Source

Mathewson ND, Ashenberg O, Tirosh I, Gritsch S, Perez EM, Marx S, et al. 2021. Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis. Cell.https://www.cell.com/cell/fulltext/S0092-8674(21)00065-9?elqTrackId=c3dd8ff1d51f4aea87edd0153b4f2dc7

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CAR T-CELL THERAPY MARKET: 2020 – 2027

G L O B A L  M A R K E T  A N A L Y S I S  A N D

I N D U S T R Y  F O R E C A S T

 

DISCLAIMER

LPBI Group’s decision to publish the Table of Contents of this Report does not imply endorsement of the Report

Aviva Lev-Ari, PhD, RN, Founder 1.0 & 2.0 LPBI Group

Guest Reporter: MIKE WOOD

Marketing Executive
BIOTECH FORECASTS

 

ABOUT BIOTECH FORECASTS

BIOTECH FORECASTS is a full-service market research and business- consulting firm primarily focusing on healthcare, pharmaceutical, and biotechnology industries. BIOTECH FORECASTS provides global as well as medium and small Pharmaceutical and Biotechnology businesses with unmatched quality of “Market Research Reports” and “Business Intelligence Solutions”. BIOTECH FORECASTS has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions, and achieve sustainable growth in their respective market domain.

UPDATED on 10/13/2020

CAR T-CELL THERAPY MARKET

Mike Wood

Mike Wood

Marketing Executive at Biotech Forecasts

CAR T-cell therapy as a part of adoptive cell therapy (ACT), has become one of the most rapidly growing and promising fields in the Immuno-oncology. As compared to the conventional cancer therapies, CAR T-cell therapy is the single-dose solution for the treatment of various cancers, significantly for some lethal forms of hematological malignancies.

CAR T-cell therapy mainly involves the use of engineered T-cells, the process starts with the extraction of T-cells through leukapheresis, either from the patient (autologous) or a healthy donor (allogeneic). After the expression of a synthetic receptor (Chimeric Antigen Receptor) in the lab, the altered T-cells are expanded to the right dose and administered into the patient’s body. where they target and attach to a specific antigen on the tumor surface, to kill the cancerous cells by igniting the apoptosis.

The global CAR T-cell therapy market was valued at $734 million in 2019 and is estimated to reach $4,078 million by 2027, registering a CAGR of 23.91% from 2020 to 2027.

Factors that drive the market growth involve, (1) Increased in funding for R&D activities pertaining to cell and gene therapy. By H1 2020 cell and gene therapy companies set new records in the fundraising despite the pandemic crisis. For Instance, by June 2020 totaled $1,452 Million raised in Five IPOs including, Legend Biotech ($487M), Passage Bio ($284M), Akouos ($244M), Generation Bio ($230M), and Beam Therapeutics ($207M), which is 2.5 times the total IPO of 2019.

Moreover, in 2019 cell therapy companies specifically have raised $560 million of venture capital, including Century Therapeutics ($250M), Achilles Therapeutics Ltd. ($121M in series B), NKarta Therapeutics Inc. ($114M), and Tmunity Therapeutics ($75M in Series B).

(2) Increased in No. of Approved Products, By July 2020, there are a total of 03 approved CAR T-cell therapy products, including KYMRIAH®, YESCARTA®, and the most recently approved TECARTUS™ (formerly KTE-X19). Furthermore, two CAR T-cell therapies BB2121, and JCAR017 are expected to get the market approval by the end of 2020 or in early 2021.

Other factors that boost the market growth involves; (3) increase in government support, (4) ethical acceptance of Cell and Gene therapy for cancer treatment, (5) rise in the prevalence of cancer, and (6) an increase in awareness regarding the CAR T-cell therapy.

However, high costs associated with the treatment (KYMRIAH® cost around $475,000, and YESCARTA® costs $373,000 per infusion), long production hours, obstacles in treating solid tumors, and unwanted immune responses & potential side effects might hamper the market growth.

The report also presents a detailed quantitative analysis of the current market trends and future estimations from 2020 to 2027.

The forecasts cover 2 Approach Types, 5 Antigen Types, 5 Application Types, Regions, and 14 Countries.

The report comes with an associated file covering quantitative data from all numeric forecasts presented in the report, as well as with a Clinical Trials Data File.

KEY FINDINGS

The report has the following key findings:

  • The global CAR T-cell therapy market accounted for $734 million in 2019 and is estimated to reach $4,078 million by 2027, registering a CAGR of 23.91% from 2020 to 2027.
  • By approach type the autologous segment was valued at $655.26 million in 2019 and is estimated to reach $ 3,324.52 million by 2027, registering a CAGR of 22.51% from 2020 to 2027.
  • By approach type, the allogeneic segment exhibits the highest CAGR of 32.63%.
  • Based on the Antigen segment CD19 was the largest contributor among the other segments in 2019.
  • The Acute lymphocytic leukemia (ALL) segment generated the highest revenue and is expected to continue its dominance in the future, followed by the Diffuse large B-cell lymphoma (DLBCL) segment.
  • North America dominated the global CAR T-cell therapy market in 2019 and is projected to continue its dominance in the future.
  • China is expected to grow the highest in the Asia-Pacific region during the forecast period.

TOPICS COVERED

The report covers the following topics:

  • Market Drivers, Restraints, and Opportunities
  • Porters Five Forces Analysis
  • CAR T-Cell Structure, Generations, Manufacturing, and Pricing Models
  • Top Winning Strategies, Top Investment Pockets
  • Analysis of by Approach Type, Antigen Type, Application, and Region
  • 51 Company Profiles, Product Portfolio, and Key Strategies
  • Approved Products Profiles, and list of Expected Approvals
  • COVID-19 Impact on the Cell and Gene Therapy Industry
  • CAR T-cell therapy clinical trials analysis from 1997 to 2019
  • Market analysis and forecasts from 2020 to 2027

FORECAST SEGMENTATION

By Approach Type

  • Autologous
  • Allogeneic

By Antigen Type

  • CD19
  • CD20
  • BCMA
  • MSLN
  • Others

By Application

  • Acute lymphoblastic leukemia (ALL)
  • Diffuse large B-Cell lymphoma (DLBCL)
  • Multiple Myeloma (MM)
  • Acute Myeloid Leukemia (AML)
  • Other Cancer Indications

By Region

  • North America: USA, Canada, Mexico
  • Europe: UK, Germany, France, Spain, Italy, Rest of Europe
  • Asia-Pacific: China, Japan, India, South Korea, Rest of Asia-Pacific
  • LAMEA: Brazil, South Africa, Rest of LAMEA

Contact at info@biotechforecasts.com for any Queries or Free Report Sample

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Marketing Executive at Biotech Forecasts
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The global CAR T-cell therapy market was valued at $734 million in 2019 and is estimated to reach $4,078 million by 2027, registering a CAGR of 23.91% from 2020 to 2027. hashtagcelltherapy hashtaggenetherapy hashtagimmunotherapy hashtagcancertreatment hashtagcartcell hashtagregenerativemedicine hashtagbiotech hashtagcancer

 

Table of Contents

 

CHAPTER 1: INTRODUCTION

1.1 REPORT DESCRIPTION 17
1.2 TOPICS COVERED 19
1.3 KEY MARKET SEGMENTS 20
1.4 KEY BENEFITS 21
1.5 RESEARCH METHODOLOGY 21
1.6 TARGET AUDIENCE 22
1.7 COMPANIES MENTIONED 23

CHAPTER 2: EXECUTIVE SUMMARY

2.1 EXECUTIVE SUMMARY 26
2.2 CXO PROSPECTIVE 29

CHAPTER 3: MARKET OVERVIEW

3.1 MARKET DEFINITION AND SCOPE 30
3.2 KEY FINDINGS 31
3.3 TOP INVESTMENT POCKETS 32
3.4 TOP WINNING STRATEGIES 33
3.4.1.Top winning strategies, by year, 2017-2019* 34
3.4.2.Top winning strategies, by development, 2017-2019*(%) 34
3.4.3.Top winning strategies, by company, 2017-2019* 35
3.5 TOP PLAYER POSITIONING, BY PIPELINE VOLUME, 2019 38
3.6 PORTERS FIVE FORCES ANALYSIS 39
3.7 COVID19 IMPACT ON CELL AND GENE THERAPY (CGT) INDUSTRY 41
3.8 MARKET DYNAMICS 46
3.8.1    Drivers 46
3.8.1.1   Increase in funding for R&D activities of CAR T-cell therapy 46
3.8.1.2   The rise in the prevalence of cancer 47
3.8.1.3   Increase in awareness regarding CAR T-cell therapy 47

 

3.8.2    Restrains 48
3.8.2.1   The high cost of CAR T-cell therapy treatment 48
3.8.2.2   Unwanted immune responses and side effects 48
3.8.2.3   Long production time 48
3.8.2.4   Obstacles in treating solid tumors 49
3.8.3    Opportunities 49
3.8.3.1   Untapped potential for emerging markets 49

CHAPTER 4: CAR T-CELL THERAPY, A BRIEF INTRODUCTION

4.1 OVERVIEW 50
4.2 SIXTY YEARS HISTORY OF CAR T-CELL THERAPY 51
4.3 CAR T-CELL STRUCTURE AND GENERATIONS 53
4.4 CAR T-CELL MANUFACTURING PROCESSES 56
4.5 PRICING AND PAYMENT MODELS FOR CAR T-CELL THERAPIES 59

CHAPTER 5: CAR T-CELL THERAPY MARKET, BY APPROACH TYPE

5.1 OVERVIEW 61
5.1.1    Market size and forecast 62
5.2 AUTOLOGOUS 63
5.2.1    Key market trends 63
5.2.2    Key growth factors and opportunities 64
5.2.3    Market size and forecast 64
5.2.4    Market size and forecast by country 65
5.3 ALLOGENEIC 66
5.3.1    Key market trends 67
5.3.2    Key growth factors and opportunities 68
5.3.3    Market size and forecast 68
5.3.4    Market size and forecast by country 69

CHAPTER 6: CAR T-CELL THERAPY MARKET, BY ANTIGEN TYPE

6.1 OVERVIEW 70
6.1.1         Market size and forecast 71
6.2 CD19 72
6.2.1         Market size and forecast 73
6.2.2         Market size and forecast by country 74

 

6.3 CD20 75
6.3.1 Market size and forecast 76
6.3.2 Market size and forecast by country 77
6.4 BCMA 78
6.4.1 Market size and forecast 79
6.4.2 Market size and forecast by country 80
6.5 MSLN 81
6.5.1 Market size and forecast 82
6.5.2 Market size and forecast by country 83
6.6 OTHERS 84
6.6.1 Market size and forecast 85
6.6.2 Market size and forecast by country 86

CHAPTER 7: CAR T-CELL THERAPY MARKET, BY APPLICATION

7.1 OVERVIEW 87
7.1.1       Market size and forecast 88
7.2 ACUTE LYMPHOBLASTIC LEUKEMIA (ALL) 89
7.2.1       Market size and forecast 90
7.2.2       Market size and forecast by country 91
7.3 DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) 92
7.3.1       Market size and forecast 93
7.3.2       Market size and forecast by country 94
7.4 MULTIPLE MYELOMA (MM) 95
7.4.1       Market size and forecast 96
7.4.2       Market size and forecast by country 97
7.5 ACUTE MYELOID LEUKEMIA (AML) 98
7.5.1       Market size and forecast 99
7.5.2       Market size and forecast by country 100
7.6 OTHERS 101
7.6.1       Market size and forecast 102
7.6.2       Market size and forecast by country 103

CHAPTER 8: CAR T-CELL THERAPY MARKET, BY REGION

8.1 OVERVIEW 104
8.1.1       Market size and forecast 104
8.2 NORTH AMERICA 105
8.2.1       Key market trends 105
8.2.2       Key growth factors and opportunities 105

 

8.2.3       Market size and forecast, by country 106
8.2.4       Market size and forecast, by approach type 106
8.2.5       Market size and forecast, by antigen type 107
8.2.6 Market size and forecast, by application 107
8.2.6.1 U.S. market size and forecast, by approach type 108
8.2.6.2 U.S. market size and forecast, by antigen type 108
8.2.6.3 U.S. market size and forecast, by application 109
8.2.6.4 Canada market size and forecast, by approach type 110
8.2.6.5 Canada market size and forecast, by antigen type 110
8.2.6.6 Canada market size and forecast, by application 111
8.2.6.7 Mexico market size and forecast, by approach type 112
8.2.6.8 Mexico market size and forecast, by antigen type 112
8.2.6.9 Mexico market size and forecast, by application 113
8.3 EUROPE 114
8.4.1 Key market trends 114
8.4.2 Key growth factors and opportunities 114
8.4.3 Market size and forecast, by country 115
8.4.4 Market size and forecast, by approach type 115
8.4.5 Market size and forecast, by antigen type 116
8.4.6 Market size and forecast, by application 116
8.3.6.1 UK market size and forecast, by approach type 117
8.3.6.2 UK market size and forecast, by antigen type 117
8.3.6.3 UK market size and forecast, by application 118
8.3.6.4 Germany market size and forecast, by approach type 119
8.3.6.5 Germany market size and forecast, by antigen type 119
8.3.6.6 Germany market size and forecast, by application 120
8.3.6.7 France market size and forecast, by approach type 121
8.3.6.8 France market size and forecast, by antigen type 121
8.3.6.9 France market size and forecast, by application 122
8.3.6.10 Spain market size and forecast, by approach type 123
8.3.6.11 Spain market size and forecast, by antigen type 123
8.3.6.12 Spain market size and forecast, by application 124
8.3.6.13 Italy market size and forecast, by approach type 125
8.3.6.14 Italy market size and forecast, by antigen type 125
8.3.6.15 Italy market size and forecast, by application 126
8.3.6.16 Rest of Europe market size and forecast, by approach type 127
8.3.6.17 Rest of Europe market size and forecast, by antigen type 127
8.3.6.18 Rest of Europe market size and forecast, by application 128
8.4 ASIA-PACIFIC 129
8.4.1 Key market trends 129
8.4.2 Key growth factors and opportunities 129
8.4.3 Market size and forecast, by country 130
8.4.4 Market size and forecast, by approach type 130

 

8.4.5       Market size and forecast, by antigen type 131
8.4.6 Market size and forecast, by application 131
8.4.6.1 China market size and forecast, by approach type 132
8.4.6.2 China market size and forecast, by antigen type 132
8.4.6.3 China market size and forecast, by application 133
8.4.6.4 Japan market size and forecast, by approach type 134
8.4.6.5 Japan market size and forecast by antigen type 134
8.4.6.6 Japan market size and forecast, by application 135
8.4.6.7 India market size and forecast, by approach type 136
8.4.6.8 India market size and forecast, by antigen type 136
8.4.6.9 India market size and forecast, by application 137
8.4.6.10 South Korea market size and forecast, by approach type 138
8.4.6.11 South Korea market size and forecast, by antigen type 138
8.4.6.12 South Korea market size and forecast, by application 139
8.4.6.13 Rest of Asia-Pacific market size and forecast, by approach type 140
8.4.6.14 Rest of Asia-Pacific market size and forecast, by antigen type 140
8.4.6.15 Rest of Asia-Pacific market size and forecast, by application 141
8.5 LAMEA 142
8.5.1 Key market trends 142
8.5.2 Key growth factors and opportunities 142
8.5.3 Market size and forecast, by country 143
8.5.4 Market size and forecast, by approach type 143
8.5.5 Market size and forecast, by antigen type 144
8.5.6 Market size and forecast, by application 144
8.5.6.1 Brazil market size and forecast by approach type 145
8.5.6.2 Brazil market size and forecast, by antigen type 145
8.5.6.3 Brazil market size and forecast, by application 146
8.5.6.4 South Africa market size and forecast, by approach type 147
8.5.6.5 South Africa market size and forecast, by antigen type 147
8.5.6.6 South Africa market size and forecast, by application 148
8.5.6.7 Rest of LAMEA market size and forecast by approach type 149
8.5.6.8 Rest of LAMEA market size and forecast, by antigen type 149
8.5.6.9 Rest of LAMEA market size and forecast, by application 150

CHAPTER 9: CLINICAL TRIALS ANALYSIS & PRODUCT PROFILES

9.1 OVERVIEW 151
9.1.1      No. of Clinical Trials from 1997 to 2019 151
9.1.2      Clinical Trials from 1997 to 2019: Based on Approach Type 152
9.1.3      Clinical Trials from 1997 to 2019: Based on Antigen Type 153
9.1.4      Clinical Trials from 1997 to 2019: Based on Application 154
9.1.5      Clinical Trials from 1997 to 2019: Based on Region 155

 

9.2 EXPECTED APPROVALS 156
9.3 APPROVED PRODUCTS PROFILES 157
9.3.1      KYMRIAH® 157
9.3.2      YESCARTA® 159
9.3.3      TECARTUS™ 161

CHAPTER 10: COMPANY PROFILES

10.1       Abbvie Inc. 162
10.2       Adaptimmune Therapeutics Plc 164
10.3 Allogene Therapeutics, Inc. 166
10.4 Amgen, Inc 168
10.5 Anixa Biosciences, Inc. 170
10.6 Arcellx, Inc. 172
10.7 Atara Biotherapeutics, Inc. 173
10.8 Autolus Therapeutics Plc. 175
10.9 Beam Therapeutics, Inc. 177
10.10 Bellicum Pharmaceuticals, Inc. 179
10.11 BioNtech SE 181
10.12 Bluebird Bio, Inc. 183
10.13 Carsgen Therapeutics, Ltd 185
10.14 Cartesian Therapeutics, Inc. 187
10.15 Cartherics Pty Ltd. 188
10.16 Celgene Corporation 189
10.17 Cellectis SA 191
10.18 Cellular Biomedicine Group, Inc. 193
10.19 Celularity, Inc. 195
10.20 Celyad SA 196
10.21 CRISPR Therapeutics AG 198
10.22 Eureka Therapeutics, Inc. 200
10.23 Fate Therapeutics, Inc. 201
10.24 Fortress Biotech, Inc 203
10.25 Gilead Sciences, Inc. 205
10.26 Gracell Biotechnology Ltd 207
10.27 icell Gene Therapeutics 208
10.28 Johnson & Johnson 209
10.29 Juventas Cell Therapy Ltd. 211
10.30 Kuur Therapeutics 212
10.31 Legend Biotech Corp. 213
10.32 Leucid Bio Ltd. 214
10.33 Minerva Biotechnologies Corp. 215

 

10.34     Molecular Medicine SPA (Molmed) 216
10.35     Nanjing Bioheng Biotech Co., Ltd. 218
10.36     Noile-Immune Biotech Inc. 219
10.37     Novartis AG 220
10.38     Oxford Biomedica PLC 222
10.39     Persongen Biotherapeutics (Suzhou) Co., Ltd. 224
10.40     Poseida Therapeutics, Inc. 226
10.41     Precigen, Inc. 227
10.42     Precision Biosciences, Inc. 229
10.43     Sorrento Therapeutics, Inc. 231
10.44     Takara Bio Inc. 233
10.45     Takeda Pharmaceutical Company Ltd. 235
10.46     TC Biopharm Ltd. 237
10.47     Tessa Therapeutics Pte Ltd. 238
10.48     Tmunity Therapeutics, Inc. 239
10.49     Unum Therapeutics Inc. 240
10.50     Xyphos Inc. 242
10.51     Ziopharm Oncology, Inc. 243

CHAPTER 11: CONCLUSION & STRATEGIC RECOMMENTATIONS

11.1     STRATEGIC RECOMMENDATIONS 245
11.2     CONCLUSION 247

 

CONTACT

info@biotechforecasts.com

MIKE WOOD

Marketing Executive

BIOTECH FORECASTS

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Noninvasive blood test can detect cancer 4 years before conventional diagnosis

Reporter : Irina Robu, PhD

Several international researchers at  Fudan University and at Singlera Genomics have developed a noninvasive blood test, PanSeer that can detect whether a patient with five common type of cancers such as stomach, esophageal, colorectal, lung and liver cancer; four years before the condition can be diagnosed by the current methods. Early detection is significant for the reason that the survival of cancer patients increases when the disease is identified at early stages, as the tumor can be surgically removed or treated with suitable drugs. Yet, only a partial number of early screening tests exist for a few cancer types.

The blood test detected cancer in 91 percent of samples from individuals who have been asymptomatic when the samples were collected, but only diagnosed with cancer one to four years later. It was found that the test can accurately detect cancer in 88 percent from samples of 113 patience who were diagnosed. The blood test also detects cancer free samples 95 percent of the time.

What is clear is that the study is unique, in that the scientists had access to blood samples from patients who were asymptomatic but not diagnosed yet. This permitted the researchers to design a test that can find a cancer marker much earlier than conventional diagnosis. The sample were collected as part of 10-year longitudinal study started in 2007 by Fudan University in China.

The researchers highlight that the PanSeer assay is improbable to predict which patients will later go on to develop cancer. As a substitute, it is most possible identifying patients who already have cancerous growths, but continue  to be asymptomatic for current detection methods. The team decided that further large-scale longitudinal studies are needed to confirm the potential of the test for the early detection of cancer in pre-diagnosis individuals.

SOURCE

https://www.universityofcalifornia.edu/news/non-invasive-blood-test-can-detect-cancer-4-years-conventional-diagnosis-methods?utm_source=fiat-lux

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