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The Vibrant Philly Biotech Scene: Focus on Computer-Aided Drug Design and Gfree Bio, LLC
Curator: Stephen J. Williams, Ph.D.
Article ID #166: The Vibrant Philly Biotech Scene: Focus on Computer-Aided Drug Design and Gfree Bio, LLC. Published on 2/10/2015
WordCloud Image Produced by Adam Tubman
This post is the second in a series of posts highlighting interviews with Philadelphia area biotech startup CEO’s and show how a vibrant biotech startup scene is evolving in the city as well as the Delaware Valley area. Philadelphia has been home to some of the nation’s oldest biotechs including Cephalon, Centocor, hundreds of spinouts from a multitude of universities as well as home of the first cloned animal (a frog), the first transgenic mouse, and Nobel laureates in the field of molecular biology and genetics. Although some recent disheartening news about the fall in rankings of Philadelphia as a biotech hub and recent remarks by CEO’s of former area companies has dominated the news, biotech incubators like the University City Science Center and Bucks County Biotechnology Center as well as a reinvigorated investment community (like PCCI and MABA) are bringing Philadelphia back. And although much work is needed to bring the Philadelphia area back to its former glory days (including political will at the state level) there are many bright spots such as the innovative young companies as outlined in these posts.
In today’s post, I had the opportunity to talk with molecular modeler Charles H. Reynolds, Ph.D., founder and CEO of Gfree Bio LLC, a computational structure-based design and modeling company based in the Pennsylvania Biotech Center of Bucks County. Gfree is actually one of a few molecular modeling companies at the Bucks County Biotech Center (I highlighted another company RabD Biotech which structural computational methods to design antibody therapeutics).
Below is the interview with Dr. Reynolds of Gfree Bio LLC and Leaders in Pharmaceutical Business Intelligence (LPBI):
LPBI: Could you briefly explain, for non-molecular modelers, your business and the advantages you offer over other molecular modeling programs (either academic programs or other biotech companies)? As big pharma outsources more are you finding that your company is filling a needed niche market?
GfreeBio: Gfree develops and deploys innovative computational solutions to accelerate drug discovery. We can offer academic labs a proven partner for developing SBIR/STTR proposals that include a computational or structure-based design component. This can be very helpful in developing a successful proposal. We also provide the same modeling and structure-based design input for small biotechs that do not have these capabilities internally. Working with Gfree is much more cost-effective than trying to develop these capabilities internally. We have helped several small biotechs in the Philadelphia region assess their modeling needs and apply computational tools to advance their discovery programs. (see publication and collaboration list here).
LPBI: Could you offer more information on the nature of your 2014 STTR award?
GfreeBio: Gfree has been involved in three successful SBIR/STTR awards in 2014. I am the PI for an STTR with Professor Burgess of Texas A&M that is focused on new computational and synthetic approaches to designing inhibitors for protein-protein interactions. Gfree is also collaborating with the Wistar Institute and Phelix Therapeutics on two other Phase II proposals in the areas of oncology and infectious disease.
LPBI: Why did you choose the Bucks County Pennsylvania Biotechnology Center?
GfreeBio: I chose to locate my company at the Biotech Center because it is a regional hub for small biotech companies and it provides a range of shared resources that are very useful to the company. Many of my most valuable collaborations have resulted from contacts at the center.
LPBI: The Blumberg Institute and Natural Products Discovery Institute has acquired a massive phytochemical library. How does this resource benefit the present and future plans for GfreeBio?
GfreeBio: To date Gfree Bio has not been an active collaborator with the Natural Products Insititute, but I have a good relationship with the Director and that could change at any time.
LPBI: Was the state of Pennsylvania and local industry groups support GfreeBio’s move into the Doylestown incubator? Has the partnership with Ben Franklin Partners and the Center provided you with investment and partnership opportunities?
GfreeBio: Gfree Bio has not been actively seeking outside investors, at least to date. We have been focused on growing the company through collaborations and consulting relationships. However, we have benefitted from being part of the Keystone Innovation Zone, a state program that provides incentives for small technology-based businesses in Pennsylvania.
LPBI: You will be speaking at a conference in the UK on reinventing the drug discovery process through tighter collaborations between biotech, academia, and non-profit organizations. How do you feel the Philadelphia area can increase this type of collaboration to enhance not only the goals and missions of nonprofits, invigorate the Pennsylvania biotech industry, but add much needed funding to the local academic organizations?
GfreeBio: I think this type of collaboration across sectors appears to be one of the most important emerging models for drug discovery. The Philadelphia region has been in many ways hard hit by the shift of drug discovery from large vertically integrated pharmaceutical companies to smaller biotechs, since this area was at the very center of “Big Pharma.” But I think the region is bouncing back as it shifts more to being a center for biotech. The three ingredients for success in the new pharma model are great universities, a sizeable talent pool, and access to capital. The last item may be the biggest challenge locally. The KIZ program (Keystone Innovation Zone) is a good start, but the region and state could do more to help promote innovation and company creation. Some other states are being much more aggressive.
LPBI: In addition, the Pennsylvania Biotechnology Center in Bucks County appears to have this ecosystem: nonprofit organizations, biotechs, and academic researchers. Does this diversity of researchers/companies under one roof foster the type of collaboration needed, as will be discussed at the UK conference? Do you feel collaborations which are in close physical proximity are more effective and productive than a “virtual-style” (online) collaboration model? Could you comment on some of the collaborations GfreeBio is doing with other area biotechs and academics?
GfreeBio: I do think the “ecosystem” at the Pennsylvania Biotechnology Center is important in fostering new innovative companies. It promotes collaborations that might not happen otherwise, and I think close proximity is always a big plus. As I mentioned before, many of the current efforts of Gfree have come from contacts at the center. This includes SBIR/STTR collaborations and contract work for local small biotech companies.
LPBI: Thompson Reuters just reported that China’s IQ (Innovation Quotient) has risen dramatically with the greatest patents for pharmaceuticals and compounds from natural products. Have you or your colleagues noticed more competition or business from Chinese pharmaceutical companies?
GfreeBio: The rise of Asia, particularly China, has been one of the most significant recent trends in the pharmaceutical industry. Initially, this was almost exclusively in the CRO space, but now China is aggressively building a fully integrated domestic pharmaceutical industry.
LPBI: How can the Philadelphia ecosystem work closer together to support greater innovation?
GfreeBio: A lot has happened in recent years to promote innovation and company creation in the region. There could always be more opportunities for networking and collaboration within the Philadelphia community. Of course the biggest obstacle in this business is often financing. Philadelphia needs more public and private sources for investment in startups.
LPBI: Thank you Dr. Reynolds.
Please look for future posts in this series on the Philly Biotech Scene on this site
A recent Science article (Who are the science stars of Twitter?; Sept. 19, 2014) reported the top 50 scientists followed on Twitter. However, the article tended to focus on the use of Twitter as a means to develop popularity, a sort of “Science Kardashian” as they coined it. So the writers at Science developed a “Kardashian Index (K-Index) to determine scientists following and popularity on Twitter.
Now as much buzz Kim Kardashian or a Perez Hilton get on social media, their purpose is solely for entertainment and publicity purposes, the Science sort of fell flat in that it focused mainly on the use of Twitter as a metric for either promotional or public outreach purposes. A notable scientist was mentioned in the article, using Twitter feed to gauge the receptiveness of his presentation. In addition, relying on Twitter for effective public discourse of science is problematic as:
Twitter feeds are rapidly updated and older feeds quickly get buried within the “Twittersphere” =LIMITED EXPOSURE TIMEFRAME
Short feeds may not provide the access to appropriate and understandable scientific information (The Science Communication Trap) which is explained in The Art of Communicating Science: traps, tips and tasks for the modern-day scientist. “The challenge of clearly communicating the intended scientific message to the public is not insurmountable but requires an understanding of what works and what does not work.” – from Heidi Roop, G.-Martinez-Mendez and K. Mills
However, as highlighted below, Twitter, and other social media platforms are being used in creative ways to enhance the research, medical, and bio investment collaborative, beyond a simple news-feed. And the power of Twitter can be attributed to two simple features
Ability to organize – through use of the hashtag (#) and handle (@), Twitter assists in the very important task of organizing, indexing, and ANNOTATING content and conversations. A very great article on Why the Hashtag in Probably the Most Powerful Tool on Twitter by Vanessa Doctor explains how hashtags and # search may be as popular as standard web-based browser search. Thorough annotation is crucial for any curation process, which are usually in the form of database tags or keywords. The use of # and @ allows curators to quickly find, index and relate disparate databases to link annotated information together. The discipline of scientific curation requires annotation to assist in the digital preservation, organization, indexing, and access of data and scientific & medical literature. For a description of scientific curation methodologies please see the following links:
Multiple analytic software packages have been made available to analyze information surrounding Twitter feeds, including Twitter feeds from #chat channels one can set up to cover a meeting, product launch etc.. Some of these tools include:
In a paper entitled Twitter Use at a Family Medicine Conference: Analyzing #STFM13 authors Ranit Mishori, MD, Frendan Levy, MD, and Benjamin Donvan analyzed the public tweets from the 2013 Society of Teachers of Family Medicine (STFM) conference bearing the meeting-specific hashtag #STFM13. Thirteen percent of conference attendees (181 users) used the #STFM13 to share their thoughts on the meeting (1,818 total tweets) showing a desire for social media interaction at conferences but suggesting growth potential in this area. As we have also seen, the heaviest volume of conference-tweets originated from a small number of Twitter users however most tweets were related to session content.
However, as the authors note, although it is easy to measure common metrics such as number of tweets and retweets, determining quality of engagement from tweets would be important for gauging the value of Twitter-based social-media coverage of medical conferences.
Thea authors compared their results with similar analytics generated by the HealthCare Hashtag Project, a project and database of medically-related hashtag use, coordinated and maintained by the company Symplur. Symplur’s database includes medical and scientific conference Twitter coverage but also Twitter usuage related to patient care. In this case the database was used to compare meeting tweets and hashtag use with the 2012 STFM conference.
These are some of the published journal articles that have employed Symplur (www.symplur.com) data in their research of Twitter usage in medical conferences.
Here, at Leaders in Pharmaceutical Business Intelligence (LBPI) we have integrated our web site, Twitter handle (@pharma_BI), and meeting specific hashtags, with a unique methodology, to monitor and measure meeting participant engagement for various international meetings (please see our Press Coverage section of our site for more information). These meetings included the 2nd Annual Sachs Associates Cancer BioInvestment & Partnering Forum and the 14th Annual Sachs Associates Global Forum.
B. Twitter Usage for Patient Care and Engagement
Although the desire of patients to use and interact with their physicians over social media is increasing, along with increasing health-related social media platforms and applications, there are certain obstacles to patient-health provider social media interaction, including lack of regulatory framework as well as database and security issues. Some of the successes and issues of social media and healthcare are discussed in the post Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.
Networking opportunities for learning disability nurses.
Abdulla, S., Marsden, D., Wilson, S., & Parker, M. (2013). Networking opportunities for learning disability nurses: Samuel Abdulla and colleagues explain why social media offer professionals new opportunities for information sharing, discussion and peer support. Learning Disability Practice, 16(5), 30-32.
Using Twitter for professional knowledge.
Kraft, M. A. (2013). Using Twitter for professional knowledge. Journal of the European Association for Health Information and Libraries, 9(4), 10.
There have been concerns with using Twitter and social media to monitor for adverse events. For example FDA funded a study where a team of researchers from Harvard Medical School and other academic centers examined more than 60,000 tweets, of which 4,401 were manually categorized as resembling adverse events and compared with the FDA pharmacovigilance databases. Problems associated with such social media strategy were inability to obtain extra, needed information from patients and difficulty in separating the relevant Tweets from irrelevant chatter. The UK has launched a similar program called WEB-RADR to determine if monitoring #drug_reaction could be useful for monitoring adverse events. Many researchers have found the adverse-event related tweets “noisy” due to varied language but had noticed many people do understand some principles of causation including when adverse event subsides after discontinuing the drug.
However Dr. Clark Freifeld, Ph.D., from Boston University and founder of the startup Epidemico, feels his company has the algorithms that can separate out the true adverse events from the junk. According to their web site, their algorithm has high accuracy when compared to the FDA database. Dr. Freifeld admits that Twitter use for pharmacovigilance purposes is probably a starting point for further follow-up, as each patient needs to fill out the four-page forms required for data entry into the FDA database.
Course: Information 290. Analyzing Big Data with Twitter
School of Information
UC Berkeley
Lecture 1: August 23, 2012
Course description:
How to store, process, analyze and make sense of Big Data is of increasing interest and importance to technology companies, a wide range of industries, and academic institutions. In this course, UC Berkeley professors and Twitter engineers will lecture on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter microblog data. Topics will include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing. Social science perspectives on analyzing social media will also be covered.
This is a hands-on project course in which students are expected to form teams to complete intensive programming and analytics projects using the real-world example of Twitter data and code bases. Engineers from Twitter will help advise student projects, and students will have the option of presenting their final project presentations to an audience of engineers at the headquarters of Twitter in San Francisco (in addition to on campus). Project topics include building on existing infrastructure tools, building Twitter apps, and analyzing Twitter data. Access to data will be provided.
Other posts on this site on USE OF SOCIAL MEDIA AND TWITTER IN HEALTHCARE and Conference Coverage include:
Heroes in Medical Research: The Postdoctoral Fellow
Writer: Stephen J. Williams, Ph.D
The National Postdoctoral Association (NPA) had its Fifth Annual Celebration Of National Postdoc Appreciation Week (NPAW) in September and I wanted to focus a posting on curating stories from postdoctoral fellows as well as private investigators (PIs) and mentors on the impacts that postdoctoral fellows had in research and to recognize the critical and tremendous contributions which postdocs make to science.
During our postdoctoral years, we develop deep friendships which last a lifetime, a close bonding to our kindred scientists different in nature than our bonding with our mentors. Nothing can replace a great mentor but our fellow postdocs make a huge difference in our complete scientific training.
It’s always the little things that stand out in our fondest memories
Unfortunately I have a plethora of fond, little memories; too many for this posting but just want to ad in a few things:
Thank you! – To all those postdocs who worked tirelessly to make a memorable PostDoc Day!
Thank you! – To all my postdoc colleagues who stayed late n the lab with me giving each other moral and scientific support
Thank you! – All my postdoc friends who would give up their time to show me how to make and use a text box correctly in Word
Thank you! =- for your friendship and understanding in those rough times we had experienced
To enliven the discussion, I ask that postdocs, past, present, and future, as well as PI’s and postdoc mentors comment on their postdoc experience. I also would like PI’s to share the stories how their postdocs made an impact to their labs.
A few interesting links and articles from the web on the importance and struggles of postdocs are included below:
Keith Micoli, from New York University Langone Medical Center states in an Elsevier article on The Academic Executive Brief
“
Consequently, it’s very difficult to come up with accurate numbers. Current estimates on number of postdocs come between 40,000 and 90,000 — a range that is unacceptable. A solid bet is that there are 60,000 postdocs and that more than half, if not two thirds or higher, are international.
Since Science started conducting annual surveys seven years ago, alternating between polling postdocs and postdoc advisors, the attributes that survey respondents select as being most important to a successful postdoc have not varied much.This year’s survey was launched on March 15, 2011, with e-mail invitations sent out to about 40,000 current and former postdoc advisors worldwide. Of the 798 completed surveys that were collected, 71 percent came from Europe (39 percent) and North America (32 percent). The remaining respondents were located in Asia/Australia/Pacific Rim (20 percent) or other areas of the world (9 percent). Most were males (72 percent) 40 years of age and older (76 percent) and worked in academic institutions (70 percent) and government organizations (13 percent). The primary area represented was the life sciences (57 percent).
However only a handful of institutions were featured.
An open letter to AAAS journal “Science”: Postdocs need to address the “The Future of Research”
This letter, posted on the Winnower.com, was a response to Callier’s article “Ailing academia needs culture change”1 and discussed how postdoctoral fellows have to lead in effecting change if the US research enterprise is to flourish in the future. In addition, the authors have been organizing Boston area postdoctoral associations and are sponsoring a symposium to be held at Boston University October 2-3 2014, focusing on the challenges facing graduate students and postdoctoral fellows: the “Future of Research” symposium (futureofresearch.org, @FORsymp).
and see if you could be a star in their movie about Ph.D.’s: The PhD Movie and the sequel.
Don’t Underestimate Your Postdoc
Dr. Thomas C. Sudhof, MD is an example of a postdoctoral fellow making great contributions to a lab. A summary of his work is seen below and obtained from the site thebestschools.org on the “50 Most Influential Scientists”.
Thomas C. Südhof is a biochemist and professor in the School of Medicine in the Department of Molecular and Cellular Physiology at Stanford University. He is best known for his work in the area of synaptic transmission, which is the process by which signaling chemicals known as neurotransmitters are released by one neuron and bind to and activate the receptors of another neuron.
Südhof won the 1985 Nobel Prize in Physiology or Medicine, along with Randy Schekman and James Rothman.
Südhof, a native of Germany, obtained his MD from the University of Göttingen and conducted his postdoctoral training in the department of molecular genetics at the University of Texas’s Health Science Center. During his postdoctoral training, he worked on describing the role of the LDL receptor in cholesterol metabolism, for which Michael S. Brown and Joseph L. Goldstein were awarded the Nobel Prize in Physiology or Medicine in 1985.
Another example from the site includes Dr. Craig Mello (Craig C. Mello’s Home Page.) who, along with Dr., Andrew Fire discovered RNAi when both at Carnegie Institute. Both received a Nobel for their work.
So again would love to hear and curate personal stories highlighting how postdocs make a great contribution to US science.
More articles in this “Heroes in Medical Research” series and posts on Scientific Careers from this site include:
It is estimated that the medical imaging market will exceed $30 billion in 2014 (FierceMedicalImaging). To put this amount in perspective; the global pharmaceutical market size for the same year is expected to be ~$1 trillion (IMS) while the global health care spending as a percentage of Gross Domestic Product (GDP) will average 10.5% globally in 2014 (Deloitte); it will reach ~$3 trillion in the USA.
Recent technology-advances, mainly miniaturization and improvement in electronic-processing components is driving increased introduction of innovative medical-imaging devices into critical nodes of major-diseases’ management pathways. Consequently, in contrast to it’s very small contribution to global health costs, medical imaging bears outstanding potential to reduce the future growth in spending on major segments in this market mainly: Drugs development and regulation (e.g. companion diagnostics and imaging surrogate markers); Disease management (e.g. non-invasive diagnosis, guided treatment and non-invasive follow-ups); and Monitoring aging-population (e.g. Imaging-based domestic sensors).
In; The Role of Medical Imaging in Personalized Medicine I discussed in length the role medical imaging assumes in drugs development. Integrating imaging into drug development processes, specifically at the early stages of drug discovery, as well as for monitoring drug delivery and the response of targeted processes to the therapy is a growing trend. A nice (and short) review highlighting the processes, opportunities, and challenges of medical imaging in new drug development is: Medical imaging in new drug clinical development.
The following is dedicated to the role of imaging in guiding treatment.
Precise treatment is a major pillar of modern medicine. An important aspect to enable accurate administration of treatment is complementing the accurate identification of the organ location that needs to be treated with a system and methods that ensure application of treatment only, or mainly to, that location. Imaging is off-course, a major component in such composite systems. Amongst the available solution, functional-imaging modalities are gaining traction. Specifically, molecular imaging (e.g. PET, MRS) allows the visual representation, characterization, and quantification of biological processes at the cellular and subcellular levels within intact living organisms. In oncology, it can be used to depict the abnormal molecules as well as the aberrant interactions of altered molecules on which cancers depend. Being able to detect such fundamental finger-prints of cancer is key to improved matching between drugs-based treatment and disease. Moreover, imaging-based quantified monitoring of changes in tumor metabolism and its microenvironment could provide real-time non-invasive tool to predict the evolution and progression of primary tumors, as well as the development of tumor metastases.
A recent review-paper: Image-guided interventional therapy for cancer with radiotherapeutic nanoparticles nicely illustrates the role of imaging in treatment guidance through a comprehensive discussion of; Image-guided radiotherapeutic using intravenous nanoparticles for the delivery of localized radiation to solid cancer tumors.
Abstract
One of the major limitations of current cancer therapy is the inability to deliver tumoricidal agents throughout the entire tumor mass using traditional intravenous administration. Nanoparticles carrying beta-emitting therapeutic radionuclides[DN: radioactive isotops that emits electrons as part of the decay process a list of β-emitting radionuclides used in radiotherapeutic nanoparticle preparation is given in table1 of this paper.) that are delivered using advanced image-guidance have significant potential to improve solid tumor therapy. The use of image-guidance in combination with nanoparticle carriers can improve the delivery of localized radiation to tumors. Nanoparticles labeled with certain beta-emitting radionuclides are intrinsically theranostic agents that can provide information regarding distribution and regional dosimetry within the tumor and the body. Image-guided thermal therapy results in increased uptake of intravenous nanoparticles within tumors, improving therapy. In addition, nanoparticles are ideal carriers for direct intratumoral infusion of beta-emitting radionuclides by convection enhanced delivery, permitting the delivery of localized therapeutic radiation without the requirement of the radionuclide exiting from the nanoparticle. With this approach, very high doses of radiation can bedelivered to solid tumors while sparing normal organs. Recent technological developments in image-guidance, convection enhanced delivery and newly developed nanoparticles carrying beta-emitting radionuclides will be reviewed. Examples will be shown describing how this new approach has promise for the treatment of brain, head and neck, and other types of solid tumors.
The challenges this review discusses
intravenously administered drugs are inhibited in their intratumoral penetration by high interstitial pressures which prevent diffusion of drugs from the blood circulation into the tumor tissue [1–5].
relatively rapid clearance of intravenously administered drugs from the blood circulation by kidneys and liver.
drugs that do reach the solid tumor by diffusion are inhomogeneously distributed at the micro-scale – This cannot be overcome by simply administering larger systemic doses as toxicity to normal organs is generally the dose limiting factor.
even nanoparticulate drugs have poor penetration from the vascular compartment into the tumor and the nanoparticles that do penetrate are most often heterogeneously distributed
How imaging could mitigate the above mentioned challenges
The inclusion of an imaging probe during drug development can aid in determining the clearance kinetics and tissue distribution of the drug non-invasively. Such probe can also be used to determine the likelihood of the drug reaching the tumor and to what extent.
Note:Drugs that have increased accumulation within the targeted site are likely to be more effective as compared with others. In that respect, Nanoparticle-based drugs have an additional advantage over free drugs with their potential to be multifunctional carriers capable of carrying both therapeutic and diagnostic imaging probes (theranostic) in the same nanocarrier. These multifunctional nanoparticles can serve as theranostic agents and facilitate personalized treatment planning.
Imaging can also be used for localization of the tumor to improve the placement of a catheter or external device within tumors to cause cell death through thermal ablation or oxidative stress secondary to reactive oxygen species.
Note: Image guided thermal ablation methods include radiofrequency (RF) ablation, microwave ablation or high intensity focused ultrasound (HIFU). Photodynamic therapy methods using external light devices to activate photosensitizing agents can also be used to treat superficial tumors or deeper tumors when used with endoscopic catheters.
Quality control during and post treatment
For example: The use of high intensity focused ultrasound (HIFU) combined with nanoparticle therapeutics: HIFU is applied to improve drug delivery and to trigger drug release from nanoparticles. Gas-bubbles are playing the role of the drug’s nano-carrier. These are used both to increase the drug transport into the cell and as ultrasound-imaging contrast material. The ultrasound is also used for processes of drug-release and ablation.
Additional example; Multifunctional nanoparticles for tracking CED (convection enhanced delivery) distribution within tumors: Nanoparticle that could serve as a carrier not only for the therapeutic radionuclides but simultaneously also for a therapeutic drug and 4 different types of imaging contrast agents including an MRI contrast agent, PET and SPECT nuclear diagnostic imaging agents and optical contrast agents as shown below. The ability to perform multiple types of imaging on the same nanoparticles will allow studies investigating the distribution and retention of nanoparticles initially in vivo using non-invasive imaging and later at the histological level using optical imaging.
Conclusions
Image-guided radiotherapeutic nanoparticles have significant potential for solid tumor cancer therapy. The current success of this therapy in animals is most likely due to the improved accumulation, retention and dispersion of nanoparticles within solid tumor following image-guided therapies as well as the micro-field of the β-particle which reduces the requirement of perfectly homogeneous tumor coverage. It is also possible that the intratumoral distribution of nanoparticles may benefit from their uptake by intratumoral macrophages although more research is required to determine the importance of this aspect of intratumoral radionuclide nanoparticle therapy. This new approach to cancer therapy is a fertile ground for many new technological developments as well as for new understandings in the basic biology of cancer therapy. The clinical success of this approach will depend on progress in many areas of interdisciplinary research including imaging technology, nanoparticle technology, computer and robot assisted image-guided application of therapies, radiation physics and oncology. Close collaboration of a wide variety of scientists and physicians including chemists, nanotechnologists, drug delivery experts, radiation physicists, robotics and software experts, toxicologists, surgeons, imaging physicians, and oncologists will best facilitate the implementation of this novel approach to the treatment of cancer in the clinical environment. Image-guided nanoparticle therapies including those with β-emission radionuclide nanoparticles have excellent promise to significantly impact clinical cancer therapy and advance the field of drug delivery.
NIH Considers Guidelines for CAR-T therapy: Report from Recombinant DNA Advisory Committee
Reporter: Stephen J. Williams, Ph.D.
UPDATED 5/27/2024
The practice of pharmacovigilence, both premarketing and postmarketing, has very well defined best practices concerning most small molecule drugs and even medical devices. However, for many cell based therapies and many gene based therapies, often still administered within the university, academic setting, pharmacovigilence reporting and adherence may be a not as efficient and thorough as conducted by large big pharmaceutical firms. Big pharma will devote massive resources for the conduct of pharmacovigilence data collecting and analysis. For many cell based therapies, like CAR-T therapies and some gene therapies are almost conducted as clinical trials within university medical centers, which may not have the resources for a large pharmacovigilence program.
In a report by IQVIA, oncologists were asked about their concerns with cell based therapies. A recurring concern involved the lack of information on the adverse events related to these therapies, especially after an oncologist’s patient would return from administration of their CAR-T therapy and then both patient and oncologist felt ‘on their own’.
Note: the IQVIA will be submitted as an abstract at the current ASCO meeting
UPDATED 5/10/2022
In the mid to late 1970’s a public debate (and related hysteria) had emerged surrounding two emerging advances in recombinant DNA technology;
the development of vectors useful for cloning pieces of DNA (the first vector named pBR322) and
the discovery of bacterial strains useful in propagating such vectors
As discussed by D. S, Fredrickson of NIH’s Dept. of Education and Welfare in his historical review” A HISTORY OF THE RECOMBINANT DNA GUIDELINES IN THE UNITED STATES” this international concern of the biological safety issues of this new molecular biology tool led the National Institute of Health to coordinate a committee (the NIH Recombinant DNA Advisory Committee) to develop guidelines for the ethical use, safe development, and safe handling of such vectors and host bacterium. The first conversations started in 1974 and, by 1978, initial guidelines had been developed. In fact, as Dr. Fredrickson notes, public relief was voiced even by religious organizations (who had the greatest ethical concerns)
On December 16, 1978, a telegram purporting to be from the Vatican was hand delivered to the office of Joseph A. Califano, Jr., Secretary of Health, Education,
and Welfare. “Habemus regimen recombinatum,” it proclaimed, in celebration of the
The overall Committee resulted in guidelines (2013 version) which assured the worldwide community that
organisms used in such procedures would have limited pathogenicity in humans
vectors would be developed in a manner which would eliminate their ability to replicate in humans and have defined antibiotic sensitivity
So great was the success and acceptance of this committee and guidelines, the NIH felt the Recombinant DNA Advisory Committee should meet regularly to discuss and develop ethical guidelines and clinical regulations concerning DNA-based therapeutics and technologies.
A PowerPoint Slideshow: Introduction toNIH OBA and the History of Recombinant DNA Oversight can be viewed at the following link:
Please see the following link for a video discussion between Dr. Paul Berg, who pioneered DNA recombinant technology, and Dr. James Watson (Commemorating 50 Years of DNA Science):
The Recombinant DNA Advisory Committee has met numerous times to discuss new DNA-based technologies and their biosafety and clinical implication including:
A recent Symposium was held in the summer of 2010 to discuss ethical and safety concerns and discuss potential clinical guidelines for use of an emerging immunotherapy technology, the Chimeric Antigen Receptor T-Cells (CART), which at that time had just been started to be used in clinical trials.
Considerations for the Clinical Application of Chimeric Antigen Receptor T Cells: Observations from a Recombinant DNA Advisory Committee Symposium Held June 15, 2010[1]
Contributors to the Symposium discussing opinions regarding CAR-T protocol design included some of the prominent members in the field including:
Drs. Hildegund C.J. Ertl, John Zaia, Steven A. Rosenberg, Carl H. June, Gianpietro Dotti, Jeffrey Kahn, Laurence J. N. Cooper, Jacqueline Corrigan-Curay, And Scott E. Strome.
The discussions from the Symposium, reported in Cancer Research[1]. were presented in three parts:
Summary of the Evolution of the CAR therapy
Points for Future Consideration including adverse event reporting
Considerations for Design and Implementation of Trials including mitigating toxicities and risks
1. Evolution of Chimeric Antigen Receptors
Early evidence had suggested that adoptive transfer of tumor-infiltrating lymphocytes, after depletion of circulating lymphocytes, could result in a clinical response in some tumor patients however developments showed autologous T-cells (obtained from same patient) could be engineered to express tumor-associated antigens (TAA) and replace the TILS in the clinical setting.
However there were some problems noticed.
Problem: HLA restriction of T-cells. Solution: genetically engineer T-cells to redirect T-cell specificity to surface TAAs
Problem: 1st generation vectors designed to engineer T-cells to recognize surface epitopes but engineered cells had limited survival in patients. Solution: development of 2nd generation vectors with co-stimulatory molecules such as CD28, CD19 to improve survival and proliferation in patients
A summary table of limitations of the two types of genetically-modified T-cell therapies were given and given (in modified form) below
Type of Gene-modified T-Cell
Limitations
aβ TCR
CAR
Affected by loss or decrease of HLA on tumor cells
yes
no
Affected by altered tumor cell antigen processing?
yes
no
Need to have defined tumor target antigen?
no
yes
Vector recombination with endogenous TCR
yes
no
A brief history of construction of 2nd and 3rd generation CAR-T cells given by cancer.gov:
Differences between second- and third-generation chimeric antigen receptor T cells. (Adapted by permission from the American Association for Cancer Research: Lee, DW et al. The Future Is Now: Chimeric Antigen Receptors as New Targeted Therapies for Childhood Cancer. Clin Cancer Res; 2012;18(10); 2780–90. doi:10.1158/1078-0432.CCR-11-1920)
The first efforts to engineer T cells to be used as a cancer treatment began in the early 1990s. Since then, researchers have learned how to produce T cells that express chimeric antigen receptors (CARs) that recognize specific targets on cancer cells.
The T cells are genetically modified to produce these receptors. To do this, researchers use viral vectors that are stripped of their ability to cause illness but that retain the capacity to integrate into cells’ DNA to deliver the genetic material needed to produce the T-cell receptors.
The second- and third-generation CARs typically consist of a piece of monoclonal antibody, called a single-chain variable fragment (scFv), that resides on the outside of the T-cell membrane and is linked to stimulatory molecules (Co-stim 1 and Co-stim 2) inside the T cell. The scFv portion guides the cell to its target antigen. Once the T cell binds to its target antigen, the stimulatory molecules provide the necessary signals for the T cell to become fully active. In this fully active state, the T cells can more effectively proliferate and attack cancer cells.
2. Adverse Event Reporting and Protocol Considerations
The symposium had been organized mainly in response to two reported deaths of patients enrolled in a CART trial, so that clinical investigators could discuss and formulate best practices for the proper conduct and analysis of such trials. One issue raised was lack of pharmacovigilence procedures (adverse event reporting). Although no pharmacovigilence procedures (either intra or inter-institutional) were devised from meeting proceedings, it was stressed that each institution should address this issue as well as better clinical outcome reporting.
Analysis of non T and T-cell subsets, e.g. natural killer cells and CD*8 cells
3. Consideration for Design of Trials and Mitigating Toxicities
Early Toxic effects– Cytokine Release Syndrome– The effectiveness of CART therapy has been manifested by release of high levels of cytokines resulting in fever and inflammatory sequelae. One such cytokine, interleukin 6, has been attributed to this side effect and investigators have successfully used an IL6 receptor antagonist, tocilizumab (Acterma™), to alleviate symptoms of cytokine release syndrome (see review Adoptive T-cell therapy: adverse events and safety switches by Siok-Keen Tey).
Below is a video form Dr. Renier Brentjens, M.D., Ph.D. for Memorial Sloan Kettering concerning the finding he made that the adverse event from cytokine release syndrome may be a function of the tumor cell load, and if they treat the patient with CAR-T right after salvage chemotherapy the adverse events are alleviated..
Early Toxic effects – Over-activation of CAR T-cells; mitigation by dose escalation strategy (as authors in reference [3] proposed). Most trials give billions of genetically modified cells to a patient.
Late Toxic Effects – long-term depletion of B-cells . For example CART directing against CD19 or CD20 on B cells may deplete the normal population of CD19 or CD20 B-cells over time; possibly managed by IgG supplementation
Below is a curation of various examples of the need for developing a Pharmacovigilence Framework for Engineered T-Cell Therapies
As shown above the first reported side effects from engineered T-cell or CAR-T therapies stemmed from the first human trial occuring at University of Pennsylvania, the developers of the first CAR-T therapy. The clinical investigators however anticipated the issue of a potential cytokine storm and had developed ideas in the pre-trial phase of how to ameliorate such toxicity using anti-cytokine antibodies. However, until the trial was underway they were unsure of which cytokines would be prominent in causing a cytokine storm effect from the CAR-T therapy. Fortunately, the investigators were able to save patient 1 (described here in other posts) using anti-IL1 and 10 antibodies.
Over the years, however, multiple trials had to be discontinued as shown below in the following posts:
The NIH has put a crimp in the clinical trial work of Steven Rosenberg, Kite Pharma’s star collaborator at the National Cancer Institute. The feds slammed the brakes on the production of experimental drugs at two of its facilities–including cell therapies that Rosenberg works with–after an internal inspection found they weren’t in compliance with safety and quality regulations.
In this instance Kite was being cited for manufacturing issues, apparantly fungal contamination in their cell therapy manufacturing facility. However shortly after other CAR-T developers were having tragic deaths in their initial phase 1 safety studies.
Juno Halts Cancer Trial Using Gene-Altered Cells After 3 Deaths
Juno halts its immunotherapy trial for cancer after three patient deaths
Juno Therapeutics said Tuesday it will restart one of its most prominent clinical trials after the Food and Drug Administration lifted a hold that had been placed last week on the trial.
The FDA halted Juno’s “Rocket” clinical trial after the company reported thattwo patients undergoing treatment had died. Juno determined the deaths resulted from swelling in the brain caused by a new drug that had been added to the treatment.
he trial seeks to treat patients with relapsed acute lymphoblastic leukemia by using engineered T-cells to attack cancer cells.
Juno added the chemotherapy drug fludarabine to the treatment plan as part of an early step that gets the patient’s body ready for the T-cell injection. Previously, Juno was using only cyclophosphamide in that part of the treatment.
Under an agreement with the FDA, Juno will continue the trial without fludarabine, using only cyclophosphamide instead.
So What Did this all mean for the CAR-T world? Is it end for CAR-T Therapies?
NO!
This, as I have posted before is a matter of pharmacovigilence, the part of drug development and premarketing trials and postmarketing analysis that deals with adverse events and safety
The JUNO trial called for co-treatment with the drug fludarabine, and antimetabolite known, in some cases to promote a cytotoxic lysis syndrome and central nervous system complications. GRANTED these two side effects were deemed RARE however it appears that the addition of fludarabine pre CART therapy aggravated either the side effect of fludarabine pretreatment or a cytoxic lysis syndrome from CAR-T (an adverse event from CAR-T therapy as I had posted here INCLUDING REPORTS OF TWO DEATHS DURING THE MSK CAR-t TRIAL
Certainly with so many issues there would seem to be more rigorous work to either establish a pharmacovigilence framework or to develop alternative engineered T cells with a safer profile
However here we went again
New paper sheds fresh light on Tmunity’s high-profile CAR-T deaths
Jason Mast
Editor
The industry-wide effort to push CAR-T therapies — wildly effective in several blood cancers — into solid tumors took a hit last year when Tmunity, a biotech founded by CAR-T pioneer Carl June and backed by several blue-chip VCs, announced it shut down its lead program for prostate cancer after two patients died.
On a personal note this trial was announced in a Bio International meeting here in Philadelphia a few years ago in 2019
and the indication was for prostate cancer, in particular hormone resistant castration resistant. Another one was planned for pancreatic cancer from the same group and the early indications were favorable.
Tmunity Therapeutics, a clinical-stage biotherapeutics company, has halted the development of its lead CAR T-cell product following the deaths of 2 patients who were enrolled to a trial investigating its use in solid tumors.1
The patients reportedly died from immune effector cell-associated neurotoxicity syndrome (ICANS), which is a known adverse effect associated with CAR T-cell therapies.
“What we are discovering is that the cytokine profiles we see in solid tumors are completely different from hematologic cancers,” Oz Azam, co-founder of Tmunity said in an interview with Endpoints News. “We observed ICANS. And we had 2 patient deaths as a result of that. We navigated the first event and obviously saw the second event, and as a result of that we have shut down the version one of that program and pivoted quickly to our second generation.”
Previously, with first-generation CAR T-cell therapies in patients with blood cancers, investigators were presented with the challenge of overcoming cytokine release syndrome. Now ICANS, or macrophage activation, is proving to have deadly effects in the realm of solid tumors. Carl June, the other co-founder of Tmunity, noted that investigators will now need to dedicate their efforts to engineering around this, as had been done with tocilizumab (Actemra) in 2012.
The company is dedicated to the development of novel approaches that produce best-in-class control over T-cell activation and direction in the body.2 The product examined in the trial was developed to utilize engineered patient cells to target prostate-specific membrane antigen; it was also designed to use a dominant TGFβ receptor to block an important checkpoint involved in cancer.
Twenty-four patients were recruited for the dose-escalating study and the company plans to release data from high-dose cohorts later in 2021.
“We are going to present all of this in a peer-reviewed publication because we want to share this with the field,” Azam said. “Because everything we’ve encountered, no matter what…people are going to encounter this when they get into the clinic, and I don’t think they’ve really understood yet because so many are preclinical companies that are not in the clinic with solid tumors. And the rubber meets the road when you get in the clinic, because the ultimate in vivo model is the human model.”
Azam added that the company plans to develop a new investigational new drug for version 2, which they hope will result in a safer product.
References
Carroll J. Exclusive: Carl June’s Tmunity encounters a lethal roadblock as 2 patient deaths derail lead trial, raise red flag forcing rethink of CAR-T for solid tumors. Endpoints News. June 2, 2021. Accessed June 3, 2021. https://bit.ly/3wPYWm0
Research and Development. Tmunity Therapeutics website. Accessed June 3, 2021. https://bit.ly/3fOH3OR
Forward to 2022
Reprogramming a new type of T cell to go after cancers with less side effects, longer impact
A Sloan Kettering Institute research team thinks new, killer, innate-like T cells could make promising candidates to treat cancers that so far haven’t responded to immunotherapy treatments. (koto_feja)
Immunotherapy is one of the more appealing and effective kinds of cancer treatment when it works, but the relatively new approach is still fairly limited in the kinds of cancer it can be used for. Researchers at the Sloan Kettering Institute have discovered a new kind of immune cell and how it could be used to expand the reach of immunotherapy treatments to a much wider pool of patients.
The cells in question are called killer innate-like T cells, a threatening name for a potentially lifesaving innovation. Unlike normal killer T cells, killer innate-like T cells stay active much longer and can burrow further into potentially cancerous tissue to attack tumors. The research team first reported these cells in 2016, but it’s only recently that they were able to properly understand and identify them.
“We think these killer innate-like T cells could be targeted or genetically engineered for cancer therapy,” said the study’s lead author, Ming Li, Ph.D., in a press release. “They may be better at reaching and killing solid tumors than conventional T cells.”
Below is the referenced paper from Pubmed:
Evaluation of the safety and efficacy of humanized anti-CD19 chimeric antigen receptor T-cell therapy in older patients with relapsed/refractory diffuse large B-cell lymphoma based on the comprehensive geriatric assessment system
Anti-CD19 chimeric antigen receptor (CAR) T-cell therapy has led to unprecedented results to date in relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL), yet its clinical application in elderly patients with R/R DLBCL remains somewhat limited. In this study, a total of 31 R/R DLBCL patients older than 65 years of age were enrolled and received humanized anti-CD19 CAR T-cell therapy. Patients were stratified into a fit, unfit, or frail group according to the comprehensive geriatric assessment (CGA). The fit group had a higher objective response (OR) rate (ORR) and complete response (CR) rate than that of the unfit/frail group, but there was no difference in the part response (PR) rate between the groups. The unfit/frail group was more likely to experience AEs than the fit group. The peak proportion of anti-CD19 CAR T-cells in the fit group was significantly higher than that of the unfit/frail group. The CGA can be used to effectively predict the treatment response, adverse events, and long-term survival.
Introduction
Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL), accounting for 30–40% of cases, with the median age of onset being older than 65 years [1]. Although the five-year survival rate for patients with DLBCL has risen to more than 60% with the application of standardized treatments and hematopoietic stem cell transplantation, nearly half of patients progress to relapsed/refractory (R/R) DLBCL. Patients with R/R DLBCL, especially elderly individuals, have a poor prognosis [2,3], so new treatments are needed to prolong survival and improve the prognosis of this population.
As a revolutionary immunotherapy therapy, anti-CD19 chimeric antigen receptor (CAR) T-cell therapy has achieved unprecedented results in hematological tumors [4]. As CD19 is expressed on the surface of most B-cell malignant tumors but not on pluripotent bone marrow stem cells, CD19 has been used as a target for B-cell malignancies, including B-cell acute lymphoblastic leukemia, NHL, multiple myeloma, and chronic lymphocytic leukemia [5]. Despite the wide application and high efficacy of anti-CD19 CAR T-cell therapy, reports of adverse events (AEs) such as cytokine release syndrome (CRS) and immune effector cell-associated neurotoxic syndrome (ICANS) have influenced its use [6]. Especially in elderly patients, AEs associated with anti-CD19 CAR T-cell therapy might be more obvious.
Although anti-CD19 CAR T-cell therapy has been reported in the treatment of NHL, including R/R DLBCL, few studies to date have assessed the safety of anti-CD19 CAR T-cell therapy in elderly R/R DLBCL patients, and its clinical application in the elderly R/R DLBCL population is limited. In ZUMA-1 [7] to R/R DLBCL patients who received CAR T-cell therapy, the CR rate in patients ≥65 years was higher than that of in patients <65 years (75% vs. 53%). Lin et al. [8] reported 49 R/R DLBCL patients (24 patients >65 years, 25 patients <65 years) who received CAR T-cell therapy with a median follow-up of 179 days. The CR rate at 100 days was 51%, while the 6-month progression-free survival (PFS) and overall survival (OS) were 48% and 71%, respectively. Neither of the two studies carried out a comprehensive geriatric assessment (CGA) of fit, unfit, and frail groups of R/R DLBCL patients over 65 years of age and further analyzed the differences in efficacy and side effects in the three groups. The CGA is an effective system designed to evaluate the prognosis and improve the survival of elderly patients with cancer. The CGA system includes age, activities of daily living (ADL), instrumental ADL (IADL), and the Cumulative Illness Rating Score for Geriatrics (CIRS-G) [9].
In this study, elderly R/R DLBCL patients were grouped according to their CGA results (fit vs. unfit/frail) before receiving humanized anti-CD19 CAR T-cell therapy. We then analyzed the efficacy and AEs of anti-CD19 CAR T-cell therapy and compared findings between these groups.
Well it appears that the discriminator was only fitness going into the trial a bit odd that the whole field appears to be lacking in development of Safety Biomarkers.
However Genentech (subsidiary of Roche) may now be using some data to develop therapies which may combat resistance to CART therapies which may provide at least, for now, a toxicokinetic approach to reducing AEs by lowering the amount of CARTs needed to be administered.
Roche’s Genentech is exploring inhibiting ESCRT as an anticancer strategy, said Ira Mellman, Ph.D., Genentech’s vice president of cancer immunology. (Roche)
Cancer cells deploy various tactics to avoid being targeted and killed by the immune system. A research team led by Roche’s Genentech has now identified one such method that cancer cells use to resist T-cell assault by repairing damage.
To destroy their targets, cancer-killing T cells known as cytotoxic T lymphocytes (CTLs) secrete the toxin perforin to form little pores in the target cells’ surface. Another type of toxin called granzymes are delivered directly into the cells through those portals to induce cell death.
By using high-res imaging in live cells, the Genentech-led team found that the membrane damage caused by perforin could trigger a repair response. The tumor cells could recruit endosomal sorting complexes required for transport (ESCRT) proteins to remove the lesions, thereby preventing granzymes from entering, the team showed in a new study published in Science.
Killer T cells destroy virus-infected and cancer cells by secreting two protein toxins that act as a powerful one-two punch. Pore-forming toxins, perforins, form holes in the plasma membrane of the target cell. Cytotoxic proteins released by T cells then pass through these portals, inducing target cell death. Ritter et al. combined high-resolution imaging data with functional analysis to demonstrate that tumor-derived cells fight back (see the Perspective by Andrews). Protein complexes of the ESCRT family were able to repair perforin holes in target cells, thereby delaying or preventing T cell–induced killing. ESCRT-mediated membrane repair may thus provide a mechanism of resistance to immune attack. —SMH
Abstract
Cytotoxic T lymphocytes (CTLs) and natural killer cells kill virus-infected and tumor cells through the polarized release of perforin and granzymes. Perforin is a pore-forming toxin that creates a lesion in the plasma membrane of the target cell through which granzymes enter the cytosol and initiate apoptosis. Endosomal sorting complexes required for transport (ESCRT) proteins are involved in the repair of small membrane wounds. We found that ESCRT proteins were precisely recruited in target cells to sites of CTL engagement immediately after perforin release. Inhibition of ESCRT machinery in cancer-derived cells enhanced their susceptibility to CTL-mediated killing. Thus, repair of perforin pores by ESCRT machinery limits granzyme entry into the cytosol, potentially enabling target cells to resist cytolytic attack.
Cytotoxic lymphocytes, including cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells, are responsible for identifying and destroying virus-infected or tumorigenic cells. To kill their targets, CTLs and NK cells secrete a pore-forming toxin called perforin through which apoptosis-inducing serine proteases (granzymes) are delivered directly into the cytosol. Successful killing of target cells often requires multiple hits from single or multiple T cells (1). This has led to the idea that cytotoxicity is additive, often requiring multiple rounds of sublethal lytic granule secretion events before a sufficient threshold of cytosolic granzyme activity is reached to initiate apoptosis in the target (2).
Loss of plasma membrane integrity induced by cytolytic proteins or mechanical damage leads to a membrane repair response. Damage results in an influx of extracellular Ca2+, which has been proposed to lead to the removal of the membrane lesion by endocytosis, resealing of the lesions by lysosomal secretion, or budding into extracellular vesicles (3). Perforin pore formation was initially reported to enhance endocytosis of perforin (4), but subsequent work has challenged this claim (5). Endosomal sorting complexes required for transport (ESCRT) proteins can repair small wounds and pores in the plasma membrane caused by bacterial pore-forming toxins, mechanical wounding, and laser ablation (6, 7). ESCRT proteins are transiently recruited to sites of membrane damage in a Ca2+-dependent fashion, where they assemble budding structures that shed to eliminate the wound and restore plasma membrane integrity. ESCRT-dependent membrane repair has been implicated in the resealing of endogenous pore-mediated plasma membrane damage during necroptosis (8) and pyroptosis (9).
Localization of target-derived ESCRT proteins to the cytolytic synapse
To investigate whether ESCRT-mediated membrane repair might be involved in the removal of perforin pores during T cell killing, we first determined whether ESCRT proteins in cancer-derived cells were recruited to sites of CTL engagement after perforin secretion. We used CTLs from OT-I mice that express a high-affinity T cell receptor (TCR) that recognizes the ovalbumin peptide SIINFEKL (OVA257-264) bound to the major histocompatibility complex (MHC) allele H-2Kb (10). We performed live-cell microscopy of OT-I CTLs engaging SIINFEKL-pulsed target cells that express enhanced green fluorescent protein (EGFP)–tagged versions of Tsg101 or Chmp4b, two ESCRT proteins implicated in membrane repair (6). To correlate recruitment of ESCRT proteins with perforin exposure in time, we monitored CTL-target interaction in media with a high concentration of propidium iodide (PI), a cell-impermeable fluorogenic dye that can rapidly diffuse through perforin pores to bind and illuminate nucleic acids in the cytosol and nucleus of the target (5). EGFP-tagged ESCRT proteins were consistently recruited to the site of CTL engagement within 30 to 60 s after PI influx (Fig. 1, A and B). EGFP-Tsg101 and EGFP-Chmp4b in target cells accumulated at the cytolytic synapse after PI influx in 25 of 27 (92.6%) and 31 of 33 (93.9%) of conjugates monitored, respectively, compared with a cytosolic EGFP control, which was not recruited (Fig. 1C and movies S1 to S3). Notably, ESCRT-laden material, presumably membrane fragments, frequently detached from the target cell and adhered to the surface of the CTL (Fig. 1, D and E, and movie S2). We observed this phenomenon in ~60% of conjugates imaged in which targets expressed EGFP-Tsg101 or EGFP-Chmp4b (17 of 27 and 20 of 33 conjugates, respectively; Fig. 1D). Shedding of ESCRT-positive membrane from the cell after repair occurs after laser-induced plasma membrane wounding (6, 7). Plasma membrane fragments shed from the target cell into the synaptic cleft likely contain ligands for CTL-resident receptors. Target cell death would separate the CTL and target, revealing target-derived material on the CTL surface.
FIG. 1. Fluorescently tagged ESCRT proteins in targets localize to site of CTL killing after perforin secretion.
(A) Live-cell spinning disk confocal imaging of a fluorescently labeled OT-I CTL (magenta) engaging an MC38 cancer cell expressing EGFP-Tsg101 (green) in media containing 100 μM PI (red). Yellow arrowheads highlight ESCRT recruitment. T-0:00 is the first frame of PI influx into the target cell (time in minutes:seconds). Scale bar, 10 μm. (B) Graph of EGFP-Tsg101 and PI fluorescence intensity at the IS within the target over time, from example in (A). AU, arbitrary units. (C and D) Quantification of CTL-target conjugates exhibiting accumulation of EGFP at the synapse after PI influx (C) or detectable EGFP-labeled material associated with CTL after target interaction (D) (EGFP condition: N = 22 conjugates in seven independent experiments; EGFP-Tsg101 condition: N = 27 conjugates in nine independent experiments; EGFP-Chmp4b condition: N = 33 conjugates in 24 independent experiments). (E) Live-cell spinning disk confocal imaging of OT-I CTL (magenta) killing MC38 expressing EGFP-Chmp4b (green), demonstrating the presence of target-derived EGFP-Chmp4b material (yellow arrowheads) associated with CTL membrane after a productive target encounter. T-0:00 is the first frame of PI influx into the target cell. Scale bar, 10 μm.
3D cryo-SIM and FIB-SEM imaging of CTLs caught in the act of killing target cells
Although live-cell imaging indicated that ESCRT complexes were rapidly recruited at sites of T cell–target cell contact, light microscopy alone is of insufficient resolution to establish that this event occurred at the immunological synapse (IS). We thus sought to capture a comprehensive view of the IS in the moments immediately after secretion of lytic granules. We used cryo–fluorescence imaging followed by correlative focused ion beam–scanning electron microscopy (FIB-SEM), which can achieve isotropic three-dimensional (3D) imaging of whole cells at 8-nm resolution or better (11–13). To capture the immediate response of target cells after perforin exposure, we developed a strategy whereby cryo-fixed CTL-target conjugates were selected shortly after perforation, indicated by the presence of a PI gradient in the target (fig. S1A). In live-cell imaging experiments, PI fluorescence across the nucleus of SIINFEKL-pulsed ID8 target cells began as a gradient and became homogeneous 158 ± 64 s, on average, after initial PI influx (N = 31 conjugates; fig. S1, B and C, and movie S4). Thus, fixed CTL-target conjugates that exhibited a gradient of PI across the nucleus would have been captured within ~3 min of perforin exposure.
Coverslips of CTL-target conjugates underwent high-pressure freezing and were subsequently imaged with wide-field cryogenic fluorescence microscopy followed by 3D cryo–structured illumination microscopy (3D cryo-SIM) performed in a customized optical cryostat (14). We selected candidate conjugates for FIB-SEM imaging on the basis of whether a gradient of PI fluorescence was observed across the nucleus of the target emanating from an attached CTL (movie S5). FIB-SEM imaging of the CTL-target conjugate at 8-nm isotropic voxels resulted in a stack of >10,000 individual electron microscopy (EM) images. The image stack was then annotated using a human-assisted machine learning–computer vision platform to segment the plasma membranes of each cell along with cell nuclei and various organelles (https://ariadne.ai/).
We captured four isotropic 3D 8-nm-resolution EM datasets of CTLs killing cancer cells moments after the secretion of lytic granule contents (Fig. 2A and movie S6). Semiautomated segmentation of the cell membranes, nuclei, lytic granules, Golgi apparatus, mitochondria, and centrosomes of the T cells allow for easier visualization and analysis of the 3D EM data. All FIB-SEM datasets and segmentations can be explored online at https://openorganelle.janelia.org (see links in the supplementary materials). Reconstructed views of the segmented data clearly demonstrate the polarization of the centrosome, Golgi apparatus, and lytic granules to the IS—all of which are hallmarks of CTL killing [Fig. 2A, i to iii, and movie S6, time stamp (TS) 1:33] (15, 16). On the target cell side, we noted cytoplasmic alterations consistent with cell damage including enhanced electron density of mitochondria adjacent to the IS (fig. S2A). Close visual scanning of the postsynaptic target cell membrane in the raw EM data failed to reveal obvious perforin pores, which have diameters (16 to 22 nm) close to the limit of resolution for this technique (17).
FIG. 2. Eight-nm-resolution 3D FIB-SEM imaging of whole CTL-target conjugate.
(A) 3D rendering of segmented plasma membrane predictions derived from isotropic 8-nm-resolution FIB-SEM imaging of a high-pressure frozen OT-I CTL (red) captured moments after secretion of lytic granules toward a peptide-pulsed ID8 ovarian cancer cell (blue). (i) Side-on sliced view corresponding to the gray horizontal line within the inset box in (A). Seen here are 3D renderings of the segmented plasma membrane of the cancer cell (blue) as well as the CTL plasma membrane (red), centrosome (gold), Golgi apparatus (cyan), lytic granules (purple), mitochondria (green), and nucleus (gray). (ii and iii) A zoomed-in view from the dashed white box in (i) shows the details of the IS (ii) and a single corresponding FIB-SEM slice docked onto the segmented data (iii). (B) Single top-down FIB-SEM slice showing overlaid target cell (blue) and CTL (red) segmentation. (i) Zoomed-in view from dashed white box in (B) details the intercellular material (IM) (gray) between the CTL and target at the IS. (C) Zoomed-in image of a 3D rendering of the surface of the target cell plasma membrane (white) opposite the intercellular material (IM) at the IS. Yellow arrowheads mark plasma membrane buds protruding into the synaptic cleft. (i and ii) Accompanying images demonstrate the orientation of the view in (C) with the rendering of the CTL (red) present (i) and removed (ii), and the dashed yellow box in (ii) indicates the area of detail shown in (C).
The segmentation of the two cells illustrates the detailed topography of the plasma membrane of the CTL and target at the IS (fig. S2B). The raw EM and segmentation data reveal a dense accumulation of particles, vesicles, and multilamellar membranous materials, which crowd the synaptic cleft between the CTL and the target (Fig. 2B and movie S6, TS 0:40 to 0:50). The source of this intercellular material (IM) was likely in part the lytic granules because close inspection revealed similar particles and dense vesicles located within as-yet-unreleased granules (fig. S2C). To determine whether some of the membranous material within the intercellular space might also have been derived from the target cell, we examined the surface topology of the postsynaptic target cell. We noted multiple tubular and bud-like protrusions of the target cell membrane that extended into the synaptic space; thus, at least some of the membrane structures observed were still in continuity with the target cell (Fig. 2C and movie S6, TS 0:58 to 1:11). ESCRT proteins have been shown to generate budding structures in the context of plasma membrane repair (6), which led us to next assess where target-derived ESCRT proteins are distributed in the context of the postsecretion IS.
To map the localization of target-derived ESCRT proteins onto a high-resolution landscape of the IS, we captured three FIB-SEM datasets that have associated 3D cryo-SIM fluorescence data for mEmerald-Chmp4b localization (Fig. 3A, fig. S3, and movie S7). This correlative light and electron microscopy (CLEM) revealed that mEmerald-Chmp4b expressed in the target cell was specifically recruited to the target plasma membrane opposite the secreted IM (Fig. 3, B and C). The topography of the plasma membrane at the site of ESCRT recruitment was markedly convoluted, exhibiting many bud-like projections (movie S7, TS 0:37 to 0:40). mEmerald-Chmp4b fluorescence also overlapped with some vesicular structures in the intercellular synaptic space (Fig. 3C). Together, the live-cell imaging and the 3D cryo-SIM and FIB-SEM CLEM demonstrate the localization of ESCRT proteins at the synapse that was the definitive site of CTL killing and was thus spatially and temporally correlated to perforin secretion. These data implicate the ESCRT complex in the repair of perforin pores.
FIG. 3. Correlative 3D cryo-SIM and FIB-SEM reveal localization of target-derived ESCRT within the cytolytic IS.
(A) Three example datasets showing correlative 3D cryo-SIM and FIB-SEM imaging of OT-I CTLs (red) captured moments after secretion of lytic granules toward peptide-pulsed ID8 cancer cells (blue) expressing mEmerald-Chmp4b (green fluorescence). (B and C) Single FIB-SEM slices corresponding to the orange boxes in (A), overlaid with CTL and cancer cell segmentation (B) or correlative cryo-SIM fluorescence of mEmerald-Chmp4b derived from the target cell (C).
Function of ESCRT proteins in repair of perforin pores
We next investigated whether ESCRT inhibition could enhance the susceptibility of target cells to CTL-mediated killing. Prolonged inactivation of the ESCRT pathway is itself cytotoxic (9). We thus developed strategies to ablate ESCRT function that would allow us a window of time to assess CTL killing (fig. S4). We used two approaches to block ESCRT function: CRISPR knockout of the Chmp4b gene or overexpression of VPS4aE228Q (E228Q, Glu228 → Gln), a dominant-negative kinase allele that impairs ESCRT function (fig. S4, A to C) (10). We took care to complete our assessment of target killing well in advance of spontaneous target cell death (fig. S4D).
We tested the capacity of OT-I CTLs to kill targets presenting one of four previously characterized peptides that demonstrate a range of potencies at stimulating the OT-I TCR: SIINFEKL (N4), the cognate peptide, and three separate variants (in order of highest to lowest affinity), SIITFEKL (T4), SIIQFEHL (Q4H7), and SIIGFEKL (G4) (18, 19). Target cells were pulsed with peptide, washed, transferred to 96-well plates, and allowed to adhere before the addition of OT-I CTLs. Killing was assessed by monitoring the uptake of a fluorogenic caspase 3/7 indicator (Fig. 4, A to D, and fig. S5A). Killing was significantly more efficient in ESCRT-inhibited target cells for both CRISPR depletion of Chmp4b (Fig. 4, A to C) and expression of the dominant-negative VPS4aE228Q (Fig. 4D). The difference in killing between the ESCRT-inhibited and control cells was greater when the lower-potency T4, Q4H7, and G4 peptides were used. Nevertheless, ESCRT inhibition moderately improved killing efficiency even in the case of the high-potency SIINFEKL peptide. ESCRT inhibition had no effect on MHC class I expression on the surface of target cells (fig. S5B). Thus, ESCRT inhibition could sensitize target cells to perforin- and granzyme-mediated killing, especially at physiologically relevant TCR-peptide MHC affinities.
FIG. 4. ESCRT inhibition enhances susceptibility of cancer cells to CTL killing and recombinant lytic proteins.
(A) Representative time-lapse data of killing of peptide-pulsed Chmp4b knockout (KO) or control B16-F10 cells by OT-I CTLs. Affinity of the pulsed peptide to OT-I TCR decreases from left to right. Error bars indicate SDs. (B) Images extracted from T4 medium-affinity peptide condition show software-detected caspase 3/7+ events in control and Chmp4b KO conditions. (C and D) Data representing the 4-hour time point of assays measuring OT-I T cells killing either Chmp4b KO (C) or VPS4 dominant-negative (D) target cells with matched controls. Error bars indicate SDs of data. Data are representative of at least three independent experimental replicates. pMHC, peptide-MHC; HA, hemagglutinin. (E and F) Determination of sublytic dose of Prf. B16-F10 cells expressing VPS4a (WT or E228Q) were exposed to increasing concentrations of Prf. Cell viability was determined by morphological gating (E). FSC, forward scatter; SSC, side scatter. (G and H) B16-F10 cells expressing VPS4a (WT or E228Q) were exposed to a sublytic dose of Prf in combination with increasing concentrations of recombinant GZMB (rGZMB). Cell death was determined by Annexin V–allophycocyanin (APC) staining (G). Controls include a condition with no perforin and 5000 ng/ml rGZMB and sublytic perforin with no rGZMB. Graphs in (F) and (H) represent the means of three experiments, and error bars indicate SDs. Statistical significance was determined by multiple unpaired t tests with alpha = 0.05. ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001.
We next directly tested the effects of ESCRT inhibition when target cells were exposed to both recombinant perforin (Prf) and granzyme B (GZMB), the most potently proapoptotic granzyme in humans and mice (20). Prf alone at high concentrations can lyse cells (4), so we first determined a sublytic Prf concentration that would temporarily permeabilize the plasma membrane but permit the cells to recover. B16-F10 cells expressing either VPS4aWT (WT, wild-type) or VPS4aE228Q were exposed to a range of Prf concentrations in the presence of PI, and cell viability and PI uptake were assessed using flow cytometry. Cells that expressed dominant-negative VPS4aE228Q were more sensitive to Prf alone than ESCRT-competent cells (Fig. 4, E and F). At 160 ng/ml Prf, there was no significant difference in cell viability for either condition. Cells in the live gate that were PI+ had been permeabilized by Prf but recovered. Although the percentage of PI+ live cells was similar under both sets of conditions, the mean fluorescence intensity of PI was higher in live ESCRT-inhibited cells (fig. S6). A delay in plasma membrane resealing could account for this difference.
We reasoned that delaying perforin pore repair might also enhance GZMB uptake into the target. ESCRT-inhibited cells were more sensitive to combined perforin-GZMB when cell death was measured by Annexin V staining (Fig. 4, G and H). Similar results were observed when these experiments were repeated with a murine lymphoma cancer cell line (fig. S7). The observation that ESCRT-inhibited target cells are more sensitive to both CTL-secreted and Prf-GZMB supports the hypothesis that the ESCRT pathway contributes to membrane repair after Prf exposure.
Escaping cell death is one of the hallmarks of cancer. Our findings suggest that ESCRT-mediated membrane repair of perforin pores may restrict accessibility of the target cytosol to CTL-secreted granzyme, thus promoting survival of cancer-derived cells under cytolytic attack. Although other factors may contribute to setting the threshold for target susceptibility to killing, the role of active repair of perforin pores must now be considered as a clear contributing factor.
Acknowledgments
We thank members of the Mellman laboratory for advice, discussion, and reagents; B. Haley for assistance with plasmid construct design; the Genentech FACS Core Facility for technical assistance; S. Van Engelenburg of Denver University for invaluable discussions and guidance; A. Wanner, S. Spaar, and the Ariande AI AG (https://ariadne.ai/) for assistance with FIB-SEM segmentation, CLEM coregistration, data presentation, and rendering; D. Bennett of the Janelia Research Campus for assisting with data upload to https://openorganelle.janelia.org; and the Genentech Postdoctoral Program for support.
Funding: A.T.R. and I.M. are funded by Genentech/Roche. C.S.X., G.S., A.W., D.A., N.I., and H.F.H. are funded by the Howard Hughes Medical Institute (HHMI).
Please look for a Followup Post concerning “Developing a Pharmacovigilence Framework for Engineered T-Cell Therapies”
References
Ertl HC, Zaia J, Rosenberg SA, June CH, Dotti G, Kahn J, Cooper LJ, Corrigan-Curay J, Strome SE: Considerations for the clinical application of chimeric antigen receptor T cells: observations from a recombinant DNA Advisory Committee Symposium held June 15, 2010. Cancer research 2011, 71(9):3175-3181.
Kandalaft LE, Powell DJ, Jr., Coukos G: A phase I clinical trial of adoptive transfer of folate receptor-alpha redirected autologous T cells for recurrent ovarian cancer. Journal of translational medicine 2012, 10:157.
Other posts on this site on Immunotherapy and Cancer include
The meeting, as described by meeting organizer and Steering Committee member of Mid Atlantic Bio Angels Lorraine Marchand, as a “benevolent sharktank”, where presenters get open and honest feedback from experienced venture capitalists on how to improve their pitch and business. The meeting here in Philadelphia was well attended with” over 70 attendants compareable to the 130 we get in New york”, according to MABA Founder Yaniv Sneor.
A few key points were discussed to improve the presenters future pitches to VC.
Define your technology/product, its purpose, how it fills an unmet need, and how you are unique.
Timelines and Milestones VERY IMPORTANT to have specific dates on when and what you will accomplish.
If your EXIT Strategy involves OUT-LICENSING, it is important to keep this in mind when framing your patent
VC’s want to see a STRONG MANAGEMENT TEAM, preferably a CEO from big pharma if you need to deal with them later
if PITCH sounds too much like a science project VC’s would NOT be interested. Show also the BUSINESS not just science
know the REGULATORY RISK – talk with the FDA
if market is small, son’t fret, show PROOF OF CONCEPT then show how relates to other markets
show your TANGIBLE ASSETS in your pitch – if you use a new equipment show it,
Other posts related to this meeting are included below
This update was performed by the following methods:
A. GPT 5 Text analysis and Reasoning
B. Insertion of Knowledge Graph on topic Curation of Genomic Analysis from Non Small Cell Lung Cancer Studies from Nodus Labs using InfraNodus software
C. Domain Knowledge Expert evaluation of the Update outcomes
This article has the following Structure:
Part A: Introduction to LLM, Knowledge Graph software InfraNodus, ChatGPT5 and Background Information on curated material for Test Case
Part B: InfraNodus Analysis of manual curation and Knowledge Graph Creation
Part C: Chat GPT 5 Analysis of Manually Curated Material
Part D: Curation entitled Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer originally published on 09/05/2014
Results of Article Update with GPT 5
1. GPT5 alone was not able to understand the goal of the article, namely to determine knowledge gaps in a particular research area involving 5 genomic studies on lung cancer patients
2. GPT5 alone was not able to group concepts or comonalities between biological pathways unless supplied with a manually curated list of KEGG pathways from a list of mutated genes. However this precluded any effect that fusion proteins had on the analysis and so GPT5 would only concentrate on mutated genes commonly found in literature
3. GPT was not able to access some of the open Access databases like NCBI Gene Ontology database
Results of Article Update with KnowledgeGraph presentation to GPT 5
4. As the Knowledge Graph understood the importance of fusion proteins and transversions, the knowledgegraph augmented the GPT analysis and so enriched the known pathways as well as could correctly identify the less represented pathways in the knowledge graph
5. This led to the identification of many novel signaling pathways not identified in the original analysis, and was able to perform this task with ease and speed
6. GPT with InfraNodus Analysis was able to propose pertinent questions for future research (the goal of the original curation) such as:
How does the interaction between [[EGFR]] mutations and sex-specific gene alterations, including [[RBM10]], influence treatment outcomes in lung adenocarcinoma?
How does the intersection of mutational patterns from smoking influence pathway activation in NSCLC, and can identifying these interactions improve targeted therapy development?
Novelty in comparison to Original article published on 09/05/2014
7. it appears that manual curation is necessary to assist in the building of relevant knowledge graphs in the biomedical fields to augment generative AI analysis
8. by itself, generative AI is not optimized for inference of higher concepts from biomedical text, and therefore, at this point, requires the input from human curators developing domain-specific knowledge graphs
9. The combination of ChatGPT5 and Knowledge graphs of this manually curated biomedical text added a further layer of complexity of gaps of knowledge not seen in the original curations including the need to study noncanonical signaling pathways like WNT and Hedgehog in smoker versus nonsmoker cohorts of lung cancer patients
A Comparison of Manual Expert-Curative and an LLM-based analysis of Knowledge Gaps in Non Small Lung Cancer Whole Exome Sequencing Studies and a Use Case Example of Chat GPT 5
Part A: Introduction to LLM, Knowledge Graph software InfraNodus, ChatGPT5 and Background Information on curated material for Test Case
The development of Large Language Models (LLMs), together with development of knowledge graphs, have facilitated the ability to analyze text and determine the relationships among the various concepts contained within series of texts. These concepts and relationships can be visualized, and new insights inferred from these visualizations. As a result, this type of analysis suggests new directions and lines of research.
Alternatively, these types of visualizations can also reveal gaps in knowledge which should be addressed. A new type of LLM and visualization tools have been developed to understand the gaps in knowledge in biomedical text.
Nodus Labs InfrNodus AI Knowledge Graph Software Tools Allow Text Relationship Visualization and Integrated AI Functionality
Infranodus makes knowlegde graphs from text and then is able to visualize the relationships between concepts (or nodes). In doing so, the tool also highlights the various knowledge gaps (or large differences between nodes) which can be used to investigate new hypotheses and research directions of previously univestigated relationships between concepts. This generates new research questions, in which these gaps can be used as prompts in the software’s integrated AI tool. The AI tool, much like a GPT, returns recommendations for research to be conducted in the area.
In addition, the InfraNodus software can detect if text is too biased on a particular concept or conclusion, and using a GPT3 or GPT4, can determine if the nodes are too dispersed and will recommend which gaps should be focused on.
The software can upload any biomedical text in various formats
A full demonstration is on their website but a good summary is found on their Youtube site at
Previously we had manually curated and analyzed the knowledge gaps from a series of publications on whole exome sequencing of biopsied tumors from cohorts of non small lung cancer patients. This curation (from 2016) is seen in the lower half of this updated link below and I separated with a bar and highlighted in Yellow as Text for AI Analysis.
Govindan R, Ding L, Griffith M, Subramanian J, Dees ND, Kanchi KL, Maher CA, Fulton R, Fulton L, Wallis J et al: Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 2012, 150(6):1121-1134.
Imielinski M, Berger AH, Hammerman PS, Hernandez B, Pugh TJ, Hodis E, Cho J, Suh J, Capelletti M, Sivachenko A et al: Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 2012, 150(6):1107-1120.
Peifer M, Fernandez-Cuesta L, Sos ML, George J, Seidel D, Kasper LH, Plenker D, Leenders F, Sun R, Zander T et al: Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nature genetics 2012, 44(10):1104-1110.
were performed.
The purpose of this analysis was to uncover biological functions related to the sets of mutated genes with limited research publications in the area of non small cell lung cancer. The identification of such biological functions would represent a gap in knowledge in this disease. In addition, this analysis attempted to find new lines of research or potential new biotargets to investigate for lung cancer therapy.
However this manual method is time consuming and may miss relationships not defined in a GO ontology or gene knowledgebases.
Therefore we turned to an AI-driven approach:
Using InfraNodus ability to develop a knowledge graph based on our curation and determine if the AI platform could infer knowledge gaps
Utilize Chat GPT5 to analyze the same curated set to determine if OpenAI analysis would lead to the similar analysis from curated material
Determine if combining a knowledge graph within GPT would lead to a higher level of analysis
See below (Part D) of this update for the curated studies which were included in this analysis and the text which was entered into both InfraNodus and Chat GPT5.
As a summary, it seems that manual curation is necessary to assist in the building of relevant knowledge graphs in the biomedical fields to augment generative AI analysis. In addition, it appears that , by itself, generative AI is not optimized for inference of higher concepts from biomedical text, and therefore, at this point, requires the input from human curators developing domain-specific knowledge graphs.
Part B. InfraNodus Analysis of manual curation and Knowledge Graph Creation
Methods:
Text of the curation was copied and directly pasted into the text analysis module of InfraNodus. There was no editing of words however genes in the curation were linked to their GeneCard entry. GeneCards is a database run by the Weizmann Institute. InfraNodus utilizes a combination of LLMs and its own GraphRAG system to provide insights from text analysis. While it leverages various models, including those from OpenAI and Anthropic, it’s not limited to a single LLM. Instead, InfraNodus integrates these models within its GraphRAG framework, which enhances their capabilities by adding a relational understanding of the context through a knowledge graph.
InfraNodus then autogenerates a knowledge graph and returns entities and relationships between entities. InfraNodus offers the opportunity to modify the knowledge graph however for this analysis we used the first graph InfraNodus generated. Inspection of this graph (as shown below) was deemed reasonable.
Results
The knowledge graph of the input text is shown below:
InfraNodus generated Knowledge Graph of 5 WES Non Smal Cell Lung Cancer studies involving smokers and non smokers
Four main concepts were returned: tumors, genes, literature, and mutations.
A snapshot of the Analysis window is given below. It should be noted that InfraNodus felt there needed to be more connections between Pathway and Mutational Patterns.
An InfraNodus reposrt with Knowlege Graph on Whole Exome Sequencing studies in NSCLC to determine mutational spectrum in smokers versus non smokers
alk clinical [[egfr]] mutational pathway [[paper]] found key literature study [[genomic]] reveal [[transversion]]
Top relations / ngrams:
1) [[lung]] [[tumors]]
2) alk fusion
3) link function
4) eml alk
5) function [[gene_ontology]]
Modulary: 0.47
Relations:
InfraNodus identified 744 relations between entities (nodes)
A list of some of the more frequent are given here:
source
target
occurrences
weight
betweenness
[[lung]]
[[tumors]]
8
24
0.4676
analysis
pathway
5
12
0.2291
significantly
[[genes]]
5
9
0.1074
significantly
[[mutated]]
4
12
0.0281
[[mutated]]
[[genes]]
4
12
0.0847
[[transversion]]
high
3
12
0.0329
[[smoking]]
history
3
10
0.0352
study
identify
3
9
0.2051
mutational
pattern
3
9
0.0921
[[rbm10]]
[[mutations]]
3
8
0.1776
literature
analysis
3
7
0.2218
[[egfr]]
[[mutations]]
3
7
0.2139
[[transversion]]
group
3
7
0.0259
enriched
cohort
3
6
0.0219
[[whole_exome_sequencing]]
[[tumors]]
3
6
0.3485
identify
[[genes]]
3
6
0.2268
including
analysis
3
5
0.1985
alteration
[[genes]]
3
4
0.1298
[[tumors]]
analysis
3
4
0.5192
alk
fusion
2
15
0.0671
link
function
2
14
0.0269
function
[[gene_ontology]]
2
13
0.0054
Notice how the betweenness or importance of connection of disparate concepts vary but are high between concepts like tumors and analysis, or lung and tumor, however many important linked concepts like alk and fusion may have low betweenness but are mentioned frequently and have a much higher weight or closeness to each other. Gene-mutations-transversions-smoking seem to have a high correspondence to each other
Genetic Alterations: identify, [[genes]], study:The recent comprehensive studies on lung adenocarcinoma have significantly advanced our understanding of the genetic landscape by identifying key mutations and their intricate interactions. Notably, EGFR and RBM10 exhibit distinct mutational patterns, with RBM10 inactivations being notably enriched in male cohorts. This gender-linked enrichment underscores a potential differential oncogenic pathway involving ERBB2 and RB1 alterations.Moreover, these projects emphasize the quest to map significant gene alterations within lung adenocarcinoma. The identification of such genes not only corroborates prior reports but also expands upon them by highlighting new connections between mutation signatures and clinical factors like smoking history. These findings are crucial as they can inform future therapeutic targeting strategies, ensuring that personalized treatment approaches consider both gender-specific genomic enrichments and mutation-driven tumorigenesis pathways elucidated through rigorous analyses.elaborate
questions generated using AI to help you explore “alk, clinical, [[egfr]], mutational, pathway, [[paper]], found, key, literature, study, [[genomic]], reveal, [[transversion]]…”:How do mutational patterns, specifically EGFR mutations and transversions related to smoking history, influence the effectiveness of targeted therapies in NSCLC patients?elaborate
ideas generated using AI to help you explore “alk, clinical, [[egfr]], mutational, pathway, [[paper]], found, key, literature, study, [[genomic]], reveal, [[transversion]]…”:Develop a predictive model that utilizes genomic data and smoking history to forecast patient response to targeted therapies. This model would identify key mutational signatures linked to EGFR and other genes, highlighting the impact of smoking-induced transversions on drug efficacy.elaborate
Project Notes
”
The recent comprehensive studies on lung adenocarcinoma have significantly advanced our understanding of the genetic landscape by identifying key mutations and their intricate interactions. Notably, EGFR and RBM10 exhibit distinct mutational patterns, with RBM10 inactivations being notably enriched in male cohorts. This gender-linked enrichment underscores a potential differential oncogenic pathway involving ERBB2 and RB1 alterations.
Moreover, these projects emphasize the quest to map significant gene alterations within lung adenocarcinoma. The identification of such genes not only corroborates prior reports but also expands upon them by highlighting new connections between mutation signatures and clinical factors like smoking history. These findings are crucial as they can inform future therapeutic targeting strategies, ensuring that personalized treatment approaches consider both gender-specific genomic enrichments and mutation-driven tumorigenesis pathways elucidated through rigorous analyses.”
<ConceptualGateways>
alk
clinical
[[egfr]]
mutational
pathway
[[paper]]
found
key
literature
study
[[genomic]]
reveal
[[transversion]]
</ConceptualGateways>
How do mutational patterns, specifically EGFR mutations and transversions related to smoking history, influence the effectiveness of targeted therapies in NSCLC patients?
The report from the NCI Bulletin outlines significant advancements in understanding lung cancer through genome sequencing projects. These studies have revealed a plethora of genetic and epigenetic alterations across various forms of lung tumors, including adenocarcinomas, squamous cell carcinomas, and small cell lung cancers. Notably, some identified alterations could be targeted by existing therapies, providing potential new avenues for treatment.Dr. Meyerson emphasizes the complexity of these genetic changes, highlighting that distinct mechanisms inactivating genes can vary between tumors. The report also notes gaps in knowledge regarding non-coding DNA alterations, which comprise a major part of the human genome.Key findings include:1. Comprehensive genomic analyses revealing unique driver mutations in lung adenocarcinoma, such as those affecting MET and ERBB2, alongside significant mutations in known cancer drivers like TP53 and KRAS.2. A classification system based on genomic data enabling more accurate patient stratification—achieving a 75% classification rate of lung cancer subtypes.3. Smoking history is shown to influence mutational patterns significantly, with smokers exhibiting a higher incidence of point mutations compared to never-smokers.Moreover, the integration of genomic data and pathway analysis highlighted recurrent mutations across various pathways related to tumorigenesis, suggesting new therapeutic targets and underscoring the importance of personalized medicine approaches that factor in gender-specific mutation distributions.This synthesis of findings not only corroborates earlier studies but also extends our understanding of the interplay between genomic alterations, smoking habits, and clinical outcomes in lung cancer. Future research is needed to explore the implications of these findings further and to develop targeted therapies that leverage the identified genetic alterations.
Tumor Genomics
Genetic Alterations
Pathway Analysis
Mutation Patterns
Main Ideas Summary from Infranodus:
The report from the NCI Bulletin highlights significant developments in lung cancer genomics through five genome sequencing projects. These studies have uncovered various genetic and epigenetic alterations across multiple forms of lung cancer, including adenocarcinomas, squamous cell cancers, and small cell lung cancers. Notably, these alterations may correspond to existing drugs, offering new avenues for targeted therapy.
Key findings include the identification of unique mutations in genes like NF1, MET, and ERBB2, with particular attention to smoking’s role in mutational patterns. The analysis shows that smokers exhibit a higher incidence of specific point mutations, marking a distinct divergence in mutation profiles compared to never-smokers.
The research also emphasizes the need for continued exploration of the data to link mutation signatures with clinical outcomes, particularly regarding gender-specific differences in mutation prevalence. Emerging insights suggest that pathways involving MYC and alterations in splicing factors could be vital targets for future therapeutic strategies.
Overall, the studies collectively underscore an urgent need for integrating genomic data into clinical practice, which can lead to more personalized treatment approaches for lung cancer patients by considering both the genetic landscape of tumors and their histories, such as smoking exposure. The ongoing efforts to map gene alterations within lung adenocarcinoma not only reinforce existing knowledge but also reveal new potential therapeutic targets that can significantly impact patient outcomes.
Questions to Explore from InfraNodus:
How does the interaction between [[EGFR]] mutations and sex-specific gene alterations, including [[RBM10]], influence treatment outcomes in lung adenocarcinoma?
How does the intersection of mutational patterns from smoking influence pathway activation in NSCLC, and can identifying these interactions improve targeted therapy development?
Part C: Chat GPT 5 Analysis of Manually Curated Material
Now let us see how Chat GPT5 analyzed the same material.
Methods
Nice — I’ll work from the KEGG-pathway list shown in your text (the table of pathways with PubMed counts) and call out major KEGG pathways that are not represented there.
Important caveat: “Not represented in the table” below means not listed in the KEGG-pathway hits shown in your pasted literature analysis, not that these pathways never play a role in lung cancer. Absence from that table can reflect lack of reporting in the specific WES/WGS studies or the PubMed keyword search used — it’s not proof the pathway is uninvolved biologically.
The above Important caveat was very important because it showed that ChatGPT5 could understand what my experimental goals were, or at least understood the problem I was presenting to ChatGPT5!
Major KEGG pathways missing from the provided KEGG list
(However Note that GPT did NOT take into account the mutational spectrum of the studies or the gene lists supplied in the curation)
These are commonly cited cancer-related KEGG pathways that do not appear in the table you gave:
Wnt signaling pathway
Notch signaling pathway
Hedgehog signaling pathway
TGF-beta signaling pathway
Hippo signaling pathway
p53 signaling pathway — (note: TP53 is mutated in many tumors, so p53 biology is implicated in the text, but “p53 signaling” does not appear in that KEGG hits table)
Autocrine/paracrine inflammation/chemokine signaling beyond the one low-count entry (general cytokine networks other than the single “cytokine–cytokine receptor interaction” hit)
Signal transduction modules sometimes separate in KEGG:g. Calcium signaling was listed in your table, but other modules like cGMP–PKG signaling or PKA/PKC broader signaling networks are not.
Quick summary
The KEGG table in your text highlights many cancer and receptor-tyrosine/MAPK/PI3K related pathways (cell cycle, MAPK, ErbB, JAK-STAT, focal adhesion, etc.). What’s not listed are numerous developmental morphogen pathways (Wnt/Notch/Hedgehog/TGF-β/Hippo), many DNA-repair and core metabolic pathways, canonical apoptosis/autophagy/ubiquitin pathways, and broad adaptive-immune signaling pathways.
If you want, I can:
(A) cross-check this against the full KEGG pathway list and produce a definitive “KEGG universe → present vs absent in your table” spreadsheet, or
(B) prioritize which of the missing pathways you’d most want investigated in lung tumor sequencing data and suggest genes to look for.
Which follow-up would you prefer?
Now taking out the KEGG list I gave GPT in order to force GPT to do a similar but unbiased analysis
From the studies you’ve shared, we can identify which KEGG pathways are under-represented (i.e., appear less frequently altered than expected) in the altered genes in lung cancer.
To do this, we note that the researchers used KEGG and other pathway analysis tools (PathScan, literature curation, proteomics) to see which biological pathways the mutated genes mapped to.
While many well-known cancer pathways were found to be activated (MAPK, PI3K-AKT, mTOR, cell cycle, oxidative stress, etc.), a few key pathways were reported as unexpectedly low in mutation frequency despite their known role in cancer biology.
KEGG Pathways Under-Represented in Altered Genes in Lung Cancer
From the descriptions in Papers 1–5, these are the main pathways that appear under-represented:
MAPK signaling pathway
Normally, MAPK is a major cancer driver pathway. In this dataset, MAPK activity was high in proteomics, but many tumors lacked mutations in MAPK pathway genes.
This means MAPK activation is often non-mutational (possibly via upstream signaling, epigenetic changes, or ligand activation), so in mutation-based KEGG enrichment it is under-represented.
PI3K-Akt signaling pathway
Like MAPK, the PI3K-Akt pathway was highly activated in proteomics, but often without mutations in PI3K pathway genes.
Under-represented in gene mutation lists, despite strong functional activation.
mTOR signaling pathway
Functional activation was observed (especially when STK11 was mutated), but most tumors had no mTOR-pathway gene mutations.
Under-represented when looking only at altered genes.
JAK-STAT signaling pathway
Mentioned as novel pathway alterations in some tumors, but in KEGG enrichment analysis, JAK-STAT genes did not appear as a dominant mutated group—implying low mutation frequency compared to other cancer types.
Oxidative phosphorylation & metabolic pathways
While oxidative stress response genes (e.g., KEAP1) were sometimes mutated, the oxidative phosphorylation KEGG pathway itself did not emerge as mutation-enriched, suggesting functional changes occur via other mechanisms (e.g., hypoxia signaling).
While some epigenetic regulators (ARID1A, CREBBP, EP300, MLL) were mutated, KEGG’s “Chromatin remodeling” and “Histone modification” groupings were less represented than expected given the role of epigenetics in lung cancer, indicating epigenetic dysregulation is often non-mutational.
Why They Are Under-Represented
Proteomics revealed activation without DNA mutation (e.g., ligand-driven activation, phosphorylation cascades).
Epigenetic or post-translational modifications can activate pathways without altering the coding sequence.
Tumor heterogeneity: some pathways are only altered in subsets of tumors and thus don’t reach statistical enrichment.
Limitations of mutation-based KEGG analysis: pathways that are activated downstream or by non-coding alterations are missed.
✅ Summary Table
Pathway (KEGG ID)
Representation in Mutated Genes
Reason for Under-Representation
hsa04010 MAPK signaling
Low
Activated via non-mutational mechanisms
hsa04151 PI3K-Akt signaling
Low
Functional activation w/o gene mutations
hsa04150 mTOR signaling
Low
Activation often via upstream STK11 loss or growth factor signaling
hsa04630 JAK-STAT signaling
Low
Rarely mutated, altered mostly via cytokine/growth factor changes
hsa00190 Oxidative phosphorylation
Low
Functional metabolic shift via hypoxia & ROS stress, not mutations
Chromatin remodeling / histone modification
Low
Mostly altered via epigenetic changes, not coding mutations
If you want, I can cross-reference the exact KEGG IDs and run a quick gene-to-pathway mapping using the genes listed in the reports so we can get a precise statistical under-representation list. That way it’s backed by an enrichment p-value rather than just qualitative observation.
I tried to re-run the KEGG-backed analysis you requested, but the KEGG REST endpoints/web pages aren’t reachable from my browsing tool right now (the web.run calls to KEGG returned an internal/safety error). I can’t produce a properly-sourced, KEGG-ID–backed enrichment report unless I can successfully query KEGG (or a comparable pathway service) for the exact pathway membership.
Two clear, immediate options — tell me which you prefer and I’ll proceed right away:
Option 1 — Proceed with a KEGG-backed run using an alternate route (recommended)
I’ll attempt a small number of web.run search queries (to public pathway tools that mirror KEGG membership, e.g., g:Profiler / Reactome / Enrichr / DAVID) and then follow links to KEGG pathway pages. This often works even when direct KEGG REST calls fail. It will take a few web.run calls and I’ll cite the pages used. (If you want me to prefer a particular service — e.g., g:Profiler, Enrichr, DAVID — say so; otherwise I’ll pick g:Profiler as it’s robust.)
Option 2 — Run an immediate local (best-effort) analysis without web lookups
I’ll map the genes you listed to likely KEGG pathways from internal knowledge, run hypergeometric enrichment using a standard human background (20,000 genes), and give a ranked list of under-represented KEGG pathways with p-values — but I must be clear this is approximate (I cannot provide verified KEGG IDs or exact pathway sizes).
If you pick Option 1, I’ll start the web lookups now and produce the KEGG-ID mapped, statistically precise under-representation list with citations.
If you pick Option 2, I’ll run the approximation immediately and give results plus a confidence caveat.
Which do you want?
Sources
Ask ChatGPT
You said:
Use Option 1 to Proceed with a KEGG-backed run using an alternate route
ChatGPT said:
Thought for 37s
Thanks — I followed Option 1 trying to do a KEGG-backed run, but hit the same problem: my web tool (web.run) can reach g:Profiler/KEGG reliably in some contexts but it failed for the programmatic queries I attempted (internal / non-retryable errors). Because you explicitly requested a KEGG-backed run, I tried an alternate route (g:Profiler web query) and the site blocked the automated call.
Rather than leave you waiting, I made a transparent, reproducible best-effort analysis right now so you have immediate, concrete results — but please read the very important limitations below before using these numbers: (GPT having problems accessing material behind firewalls and paywalls)
What I did (short)
I extracted a conservative set of 48 unique genes from the papers you supplied (recurrent drivers, fusion partners, and the druggable gene list). Gene list used (48 genes):
TP53, KRAS, EGFR, STK11, KEAP1, SMARCA4, RBM10, NF1, MET, ERBB2, RIT1, BRAF, PIK3CA, PTEN, ARID1A, U2AF1, CREBBP, EP300, MLL, ALK, ROS1, DACH1, CFTR, RELN, ABCB5, HGF, JAK2, JAK3, HDAC2, HDAC6, HDAC9, BIRC6, ITGB1, ITGB3, MMP2, PRKCB, PIK3CG, TERT, MMP14, AURKC, ERBB4, FGFR1, EML4, SIK2, ZFHX4, EPHA3, EPHA7, SLIT2.
Because direct KEGG lookups failed, I mapped those genes to a short list of common KEGG pathways by manual curation (based on canonical pathway membership knowledge) and used conservative, literature-style approximate pathway sizes (number of genes in that KEGG pathway) to compute exact hypergeometric lower-tail p-values (i.e., probability of observing as few or fewer genes in the pathway than we did — an under-representation test).
I computed exact hypergeometric probabilities (sum of combinations) with a standard human gene background of N = 20,000. All calculations were executed programmatically (so no hand-approximate combinatorics).
Important limitations (please read)
This is NOT a true KEGG-backed run. I could not fetch KEGG pathway membership lists or KEGG IDs because the web tool could not access the KEGG/g:Profiler APIs in this session. (more GPT issues with access to data)
I used manually mapped genes → pathways (conservative, but not exhaustive) and approximate pathway sizes (estimates). Because both gene membership and exact pathway sizes matter a lot for enrichment statistics, the p-values below are illustrative only — they are useful to prioritize where to retry a proper KEGG query, but should not be taken as authoritative.
The correct, defensible way to produce what you asked is to re-run this on g:Profiler/Enrichr/DAVID (or download KEGG GMTs) so pathway membership and KEGG IDs are exact. I can do that as soon as the web tool can access those services (or you can paste exact KEGG gene sets if you prefer).
Results — immediate best-effort under-representation test
For each pathway I show: pathway size (K, approximate), number of genes from your list found in that pathway (k), and the hypergeometric lower-tail p-value P(X ≤ k) (probability of seeing k or fewer pathway genes by chance). Lower p-values (e.g., < 0.05) would suggest significant under-representation.
Interpretation: none of the pathways above reach statistical significance for under-representation under the assumptions I used. The closest to a (moderate) signal is oxidative phosphorylation where we observed 0 genes from the list (p ≈ 0.73) — but that p-value is still far from being significant (p < 0.05 would be needed). In short: no pathway is robustly under-represented in this small gene set using the conservative approximations above.
What I recommend (next steps to get the exact KEGG-ID–backed answer you originally asked for)
Allow me to retry the web-backed run (g:Profiler / Enrichr / DAVID) — I’ll re-run the enrichment and return:
gene → exact KEGG IDs mapping,
KEGG pathway sizes (exact K),
over- and under-representation p-values (Fisher/hypergeometric), and
multiple-testing corrected q-values (Benjamini–Hochberg). (This requires the web tool to be able to reach g:Profiler/KEGG; I already tried and the call failed. If you want, I’ll attempt again immediately.)
If you prefer not to rely on the web tool, you can paste your final canonical gene list (e.g., a CSV of the exact genes you want analyzed) and I will run a conservative local analysis like the above but I will be explicit that KEGG IDs and pathway sizes are approximations.
If you’d like, I can also send you the exact code/notebook I used for the above best-effort run so you (or your bioinformatics person) can re-run it locally against an up-to-date KEGG GMT file.
If you want me to retry the web-backed run now, say “Retry web run now” and I’ll immediately attempt g:Profiler/Enrichr again and produce the precise KEGG-ID–backed enrichment with citations. If you prefer I proceed without web access (i.e., run a local approximation on a larger set of genes or with different pathway groupings), tell me what gene list to use and I’ll compute immediately.
Top of Form
Bottom of Form
Done — I created a bar chart showing estimated PubMed article counts (prior to 2018) for “lung cancer” mentions of the pathways we discussed: MAPK, PI3K-Akt, mTOR, JAK-STAT, and Oxidative phosphorylation.
Important caveats (please read)
I could not access PubMed programmatically from this session (the web tool failed on PubMed/NCBI requests), so the numbers used are illustrative estimates, not exact counts.
If you need exact PubMed counts (filtered to publications before 2018), I can re-run the web-backed PubMed queries and regenerate the graph as soon as web access to NCBI is available — just say “Retry PubMed queries” and I’ll attempt that immediately.
Re-run the exact PubMed queries and update the chart with real counts, or
Expand the chart to include more pathways (e.g., chromatin modifiers, spliceosome, cell cycle), or
Provide the code/notebook used so you can run it locally against PubMed.
Here’s the expanded bar chart with additional pathways — Spliceosome, Chromatin remodeling / histone modification, ECM / focal adhesion, and Cell cycle — alongside the original set.
Would you like me to now replace these illustrative numbers with exact PubMed counts (filtered to before 2018) by running targeted PubMed searches?
Yes — even though the dataset and pathway lists we discussed focused mostly on signaling, metabolic, and genomic alterations, there is a clear immune component to lung cancer supported by both your provided text and the broader literature. (Now note that GPT noticed a clear literature coverage of cell cycle in lung cancer however it is claiming there was evidence of an immune component in the literature before 2018).
Evidence from your provided studies (Note GPT is using recent evidence not just restricting to periods before 2018)
Pathways with immune relevance
JAK-STAT signaling (detected in the analysis)
Central to cytokine signaling, immune cell activation, and inflammatory responses in the tumor microenvironment.
Altered JAK-STAT can drive immune evasion or chronic inflammation that promotes tumor growth.
RTK/RAS/RAF pathway indirectly affects immune signaling by altering cytokine/chemokine secretion from tumor cells.
ROS1-ALK fusions
These can alter the tumor’s immunogenic profile; your text notes they may create epitopes that make tumors amenable to immunotherapy.
Shown in cited literature to be less chemosensitive but potentially sensitive to immune-based approaches when certain epitopes are exposed.
Also exhibit HSP90 chaperone dependence, which links to immune stress response pathways.
Tumor heterogeneity and immune escape
The genomic heterogeneity reported (monoclonal vs biclonal tumors) suggests different subclones may evade immune surveillance differently — a hallmark of tumor–immune system interaction.
From broader lung cancer research
Even when not highlighted in mutation frequency lists, immune-related processes in lung cancer are driven by:
Tumor-infiltrating lymphocytes (TILs) and their exhaustion profiles.
Immune checkpoints (PD-1/PD-L1, CTLA-4) — a major therapeutic target in NSCLC.
Inflammatory cytokine networks (many driven via JAK-STAT, NF-κB, MAPK cross-talk).
Neoantigen load (especially in smokers, due to high mutation rates) that can trigger immune recognition — unless suppressed by tumor-mediated immunosuppression.
Conclusion
Yes — there is an immune component to lung cancer. It may not appear as strongly in mutation-based KEGG under-representation analysis (because immune evasion is often mediated by gene expression, epigenetics, and tumor–immune interactions rather than recurrent coding mutations), but immune signaling pathways like JAK-STAT and cytokine-related signaling are still altered and clinically relevant.
(Remember our initial test was to see if GPT could do our manual curative analysis without relying on current information!)
That’s why immune checkpoint inhibitors (anti–PD-1, anti–PD-L1, anti–CTLA-4) are now standard-of-care in many lung cancer treatment regimens (The evidence that led to immune checkpoint inhibitors was not based just on JAK-STAT or cytokine signaling).
Now Let Us See What Happens When a Knowledge Graph from InfraNodus is Imported into Chat GPT5
The import of a knowledge graph as a CSV from InfraNodus into ChatGPT5 is rather straightforward as can directly upload into the GPT interface.
ChatGPT was prompted with: “based on the imported knowledge graph tell me what Gene ontology biologic functions are missing?”
ChatGPT said:
Your imported graph isn’t labeled with standard Gene Ontology (GO) IDs or names, so I can’t directly map it to “biological process” terms yet.
The next step is to align your node name column with the official GO Biological Process ontology and then report which expected functions are missing.
I can:
Pull the latest GO Biological Process list.
Match your nodes against it.
Output the GO BP terms absent from your network.
Do you want me to fetch the current GO BP ontology from the Gene Ontology database and run that comparison?
You said:
based on the imported knowledge graph tell me what KEGG processes are missing?
ChatGPT said:
To identify which KEGG processes are missing from your imported knowledge graph, the first step is to retrieve an up-to-date list of KEGG human pathways and then compare that against the nodes present in your graph. Here’s how I can help:
Fetch the current list of KEGG human pathways
I can pull the latest catalog of KEGG pathways for Homo sapiens, which includes major metabolic routes (e.g., purine metabolism, glycolysis) as well as signaling pathways (e.g., JAK-STAT, Wnt, PI3K-Akt) KEGG+1.
Compare with your knowledge graph nodes
Once I have that KEGG list, I’ll match it against your graph’s node name entries. Any pathway names or identifiers (like hsa04110 for “Cell cycle”) not present in your graph would be flagged as missing.
Deliver the findings
I’ll provide you with:
A list of KEGG processes present in your graph
A list of those missing, enabling you to fill in gaps or trigger further investigation
Does that sound good? If you’d like, please share a sample or the full list of your graph’s node name entries, especially if they include KEGG-style labels or pathway names—it’ll help speed up the matching process.
Sources
You said:
Fetch the current list of KEGG human pathways and compare with my knowledge graph nodes and provide me with a list of KEGG processes present and a list of KEGG processes missing.
ChatGPT said:
I’ve fetched the current list of KEGG human pathways (hsa) and compared it to the nodes in your imported knowledge graph. Here’s what we can do next—and what I need from you to complete the analysis:
KEGG Human Pathway List
Below are just a few highlights from the complete KEGG human pathway list (retrieved via KEGG REST /list/pathway/hsa) rest.kegg.jp:
hsa00010 – Glycolysis / Gluconeogenesis
hsa00020 – Citrate cycle (TCA cycle)
hsa01100 – Metabolic pathways
hsa04010 – MAPK signaling pathway
hsa04151 – PI3K-Akt signaling pathway
hsa04910 – Insulin signaling pathway
hsa05200 – Pathways in cancer
…plus hundreds more spanning metabolism, genetic information processing, organismal systems, disease, and more.
You can view the full list in the KEGG API or in your local copy if you’ve grabbed it via /list/pathway/hsa.
What I Need From You
To identify which KEGG pathways are present or missing in your graph, please provide the list of node names (or IDs) from your knowledge graph that correspond to KEGG pathways. For example, this might include:
KEGG identifiers like hsa04110
Pathway names like “Cell cycle” or “Glycolysis / Gluconeogenesis”—ideally matching exact KEGG naming
With those node names in hand, I can:
Match them against the official KEGG list.
Generate two clear lists:
Present in your graph
Missing from your graph (i.e., not represented)
If you can share your node set (even a sample if too large), we can proceed immediately. Let me know when you’re ready!
Sources
You said:
based on my knowledge graph I uploaded, is there any relation between lung cancer and the immune system?
ChatGPT said:
In the knowledge graph you uploaded, there aren’t any nodes explicitly labeled with immune-related terms (like “immune,” “T cell,” “macrophage,” “cytokine,” etc.) that directly link to lung cancer concepts.
That means your graph doesn’t appear to contain an obvious connection between lung cancer and the immune system—at least not through explicitly named immune processes or cell types.
If you want, I can run a broader search for indirect immune-related concepts (e.g., inflammation, checkpoint pathways like PD-1/PD-L1, MHC) to see if they’re present but labeled differently.
You said:
Based on my knowledge graph I uploaded is there any relation between lung cancer and inflammation or genes related to inflammation?
ChatGPT said:
Your uploaded knowledge graph doesn’t contain any nodes that explicitly mention inflammation or common inflammation-related genes (e.g., TNF, interleukins, NF-κB, COX-2, JAK/STAT, chemokines).
So based on the current data, there’s no recorded link between lung cancer and inflammation in your network—either directly or via inflammation-related genes.
If you want, I can try mapping your existing nodes to KEGG’s inflammation and immune-related pathways to catch indirect associations that aren’t obvious from node names alone.
You said:
Are there any nodes in my graph that maps to KEGG inflammation and immune-related pathways?
ChatGPT said:
Here’s what I discovered regarding KEGG immune- and inflammation-related pathways, and how they relate (or don’t) to the nodes in your uploaded knowledge graph:
Key KEGG Immune-Related Pathways
KEGG organizes numerous human pathways under the “Immune System” category, including:
From your graph’s node name entries, I didn’t see any nodes that correspond to the KEGG immune or inflammation pathways listed above. There are no direct matches like:
“Toll-like receptor signaling”
“Chemokine signaling”
“Antigen processing and presentation”
“Th1 cell differentiation,” etc.
So, based solely on node names, none of the canonical KEGG immune or inflammation pathways appear to be represented in your graph.
Next Steps to Explore Potential Indirect Links
Even if those pathways aren’t explicitly named, your graph might still include immune- or inflammation-related genes under alternate labels. If you’d like, I can help:
Search for common immune-related genes in your graph (e.g., TLR4, NLRP3, interleukins, NF-κB) to spot indirect associations;
Perform pathway mapping, using tools like KEGG Mapper or Reactome, to see if the gene set from your graph corresponds to any immune-related pathways—even if they’re not explicitly named.
Would you like me to proceed with one of those approaches to dig deeper?
Sources
You said:
Search for common immune-related genes in my graph and pathways using KEGG Mapper
ChatGPT said:
Your uploaded knowledge graph doesn’t contain any nodes that match common immune-related genes from the KEGG immune and inflammation pathways list—at least not under standard gene symbols like IL6, TNF, TLR4, STAT3, etc.
This means KEGG Mapper wouldn’t find direct immune pathway hits from your current node set unless those genes are present under alternate labels or embedded inside other entity names.
If you want, I can:
Expand the search to look for partial matches (e.g., “IL-6” or “interleukin 6” instead of strictly “IL6”)
Try mapping all your nodes to KEGG to see if indirect immune pathway associations appear
You said:
Based on the above analysis and my knowledge graph would you say investigating the role of the immune system in lung cancer would uncover interesting therapeutic targets?
ChatGPT said:
Thought for 9s
Short answer: Yes — very likely.
Even though your uploaded graph does not currently show immune or inflammation nodes, independent evidence strongly supports that the immune system and inflammatory programs are central to lung-cancer biology and have already yielded major therapeutic targets (and will likely yield more). PMC+1
Why I say that (big points, with sources):
Immune checkpoint therapy (anti-PD-1/PD-L1, anti-CTLA-4) is an established, practice-changing class of treatments in non-small cell lung cancer (NSCLC) — i.e., the immune axis is clinically actionable in lung cancer. PMCAACR Journals
The tumor immune microenvironment (TIME) — infiltrating T cells, myeloid cells, cytokines and stromal factors — strongly influences lung cancer progression and patient outcome, and is a major focus for biomarker/target discovery. PMC+1
Chronic inflammation (IL-1, IL-6, TNF, NF-κB, STAT3 and related signaling) promotes tumor initiation/progression and shapes the microenvironment in lung cancer; those pathways are plausible therapeutic targets or combinatorial partners with immunotherapy. FrontiersPMC
Beyond PD-1/PD-L1, many immune/TME components are under active translational study (co-inhibitory/activating receptors, cytokines, myeloid suppressor cells, TME remodeling) — so there’s real opportunity for new targets or combination strategies. NatureBioMed Central
What this means for your graph / next steps I recommend (practical, doable):
Expand node matching to include gene name variants and immune terms (e.g., IL6, IL-6, interleukin 6, PDCD1, PD-1, CD274, PD-L1, TLR4, NFKB1, STAT3) — you may have immune hits under alternate labels. I can do this mapping for you and return matches.
Map your gene set (all nodes that are genes/keywords) to pathway databases (KEGG, Reactome) and to immune gene lists — that will reveal indirect associations even if pathway names aren’t nodes. I can run KEGG Mapper / Reactome mapping and give a present/missing report.
If you have expression / mutation / clinical metadata, do: (a) differential expression / mutation enrichment for immune genes, (b) deconvolution to estimate immune cell infiltration, (c) network-neighborhood expansion to find immune neighbors — each can point to actionable targets.
Prioritize candidate targets by combining evidence: presence in your graph, pathway membership, druggability, and clinical relevance (e.g., known checkpoint ligands, cytokines, myeloid regulators).
Would you like me to (A) run a relaxed search of your graph for immune/inflammation gene name variants now, and then (B) map any hits to KEGG/Reactome so we can produce a ranked list of immune-related targets and pathways? If yes I’ll start immediately and return the matched nodes + pathway hits.
Sources
Short answer: Yes — very likely.
Even though your uploaded graph does not currently show immune or inflammation nodes, independent evidence strongly supports that the immune system and inflammatory programs are central to lung-cancer biology and have already yielded major therapeutic targets (and will likely yield more). (PMC)
Why I say that (big points, with sources):
Immune checkpoint therapy (anti-PD-1/PD-L1, anti-CTLA-4) is an established, practice-changing class of treatments in non-small cell lung cancer (NSCLC) — i.e., the immune axis is clinically actionable in lung cancer. (PMC, AACR Journals)
The tumor immune microenvironment (TIME) — infiltrating T cells, myeloid cells, cytokines and stromal factors — strongly influences lung cancer progression and patient outcome, and is a major focus for biomarker/target discovery. (PMC)
Chronic inflammation (IL-1, IL-6, TNF, NF-κB, STAT3 and related signaling) promotes tumor initiation/progression and shapes the microenvironment in lung cancer; those pathways are plausible therapeutic targets or combinatorial partners with immunotherapy. (Frontiers, PMC)
Beyond PD-1/PD-L1, many immune/TME components are under active translational study (co-inhibitory/activating receptors, cytokines, myeloid suppressor cells, TME remodeling) — so there’s real opportunity for new targets or combination strategies. (Nature, BioMed Central)
What this means for your graph / next steps I recommend (practical, doable):
Expand node matching to include gene name variants and immune terms (e.g., IL6, IL-6, interleukin 6, PDCD1, PD-1, CD274, PD-L1, TLR4, NFKB1, STAT3) — you may have immune hits under alternate labels. I can do this mapping for you and return matches.
Map your gene set (all nodes that are genes/keywords) to pathway databases (KEGG, Reactome) and to immune gene lists — that will reveal indirect associations even if pathway names aren’t nodes. I can run KEGG Mapper / Reactome mapping and give a present/missing report.
If you have expression / mutation / clinical metadata, do: (a) differential expression / mutation enrichment for immune genes, (b) deconvolution to estimate immune cell infiltration, (c) network-neighborhood expansion to find immune neighbors — each can point to actionable targets.
Prioritize candidate targets by combining evidence: presence in your graph, pathway membership, druggability, and clinical relevance (e.g., known checkpoint ligands, cytokines, myeloid regulators).
Would you like me to (A) run a relaxed search of your graph for immune/inflammation gene name variants now, and then (B) map any hits to KEGG/Reactome so we can produce a ranked list of immune-related targets and pathways? If yes I’ll start immediately and return the matched nodes + pathway hits.
Based on the uploaded knowledge graph would there be any plausible reason to develop PD-L1 inhibitors for treatment of lung cancer?
ChatGPT said:
Thought for a few seconds
Short answer: Not from the uploaded graph alone.
Your current knowledge graph contains no node matching PD-L1 (CD274) or close synonyms (PD-L1, PDL1, PDCD1LG1/2) and—more broadly—no explicit immune/inflammation gene nodes were found. So, based solely on the graph you uploaded there’s no direct evidence in that dataset that would, by itself, justify developing PD-L1 inhibitors.
That said — important context and why the answer is still “probably yes” in real biology:
Clinically, PD-1/PD-L1 blockade is already an established, effective therapy in many lung cancers (especially NSCLC). The absence of PD-L1 in your graph likely reflects a gap in the data you uploaded (labels, scope, or data sources), not a biological counterargument.
Rationale for PD-L1 targeting typically depends on features that your graph currently lacks: PD-L1 (CD274) expression on tumor or immune cells, presence of PD-1 on T cells, tumor mutational burden/neoantigens, and evidence of T cell infiltration or an immunosuppressive microenvironment. None of those features appear in your nodes.
If you want to pursue PD-L1 as a target for this dataset, here are concrete next steps I recommend (I can do these for you):
Search the graph for PD-L1 synonyms and related immune genes (CD274, PDCD1, PDCD1LG2, PD1, PDL1, PD-L2, CD8A, CTLA4, etc.) — I’ll run a relaxed search (IL-6 vs IL6, hyphens, full names) to catch alternate labels.
Map all gene nodes to standard gene symbols (HGNC) and then run pathway mapping (KEGG/Reactome) to reveal immune pathway hits even if pathway names aren’t nodes.
If you have expression or mutation metadata (linked to nodes), check: PD-L1 expression levels, TMB/neoantigen load, and immune-deconvolution signatures — these are strong predictors of benefit from PD-1/PD-L1 therapies.
Network neighborhood expansion: find genes directly connected to known immune regulators — that can reveal indirect immune associations missed by exact-name matching.
If no immune content is present, consider integrating orthogonal data (TCGA expression for lung cancer, published PD-L1 expression datasets) to augment the graph and inform target prioritization.
Summary and Discussions: The goal of our original curation was to determine if gaps of knowledge exists between five whole exome studies on mutational spectrum in non small cell lung cancer (NSCLC) and if differences in mutational spectrum in smokers versus non-smokers. Our manual curation involved a process in which individual genes and mutations were analayzed for literature coverage in Pubmed and if the potential affected pathways were also being investigated in lung cancer research (what we called a literomics approach). Our manual analysis (as of 2016) revealed while many mutated genes were involved in the well researched fields of Cell Cycle, there were substantial gaps in knowledge of the role of the immune system in lung cancer, especially given the mutational spectrum seen in these studies. We had also noticed a number of fusion proteins which may be interesting for further (post 2016) investigation. This involved some inference into the use of ALK inhibitors and a suggestion of noncanonical pathways of EGFR to smoker versus nonsmoker patients, based on differences in mutational spectrum and KEGG analysis.
Using both an AI tool to generate knowledge graphs and gain insights into knowledge gaps (InfraNodus) and a generative AI new tool (Chat GPT5) we attempted to determine if our inital analysis in 2016 using more labor intensive manual curation methods could be similar to results that both AI tools could infer. It is interesting to note that InfraNodus generated knowledge graphs could generate concepts and relationships pertinent to lung cancer, mutational spectrum and gave some interesting insights into the importance of transversions, especially relating to fusion proteins. InfraNodus did not see much relations to immune functions however to further probe this we asked the same question to GPT5 in two different formats: with text alone and text with uploaded knowledge graph. Surprisingly Chat GPT had some issues retrieving data from certain online open access databases such as NCBI GO but better luck with the KEGG database. However GPT, being trained on the most recent data inferred there must be an immune component of lung cancer, although it admitted this was from recent studies; not the studies we supplied to it. When we narrowed down GPT to look at studies before 2018 there was similarities in the relations and lack of relations we had found in our previous manual method. We then supplied GPT with our knowledge graph and forced GPT to focus on our knowledge graph from older studies. Under these constraints GPT correctly admitted there were no links between the immune system and lung cancer mutational specrum although it did give some interesting insights into the role of fusion proteins and reactive oxygen signaling. After our intial curation, one of our experts Dr. Larry Bernstein had noticed that KEAP1 and 2 showed genetic alterations in the studies, as he suggested there were differences in redox signaling between smokers and nonsmokers. KEAP1 and 2 are intracellular redox sensors.
Therefore it is possible that GPT alone, including the new 5 version, may not be as effective in complex inference into biomedical literature analysis, and a human expert curated knowledge graph incorporated into GPT analysis returns better inference and more novel insights than either modality alone.
For further reading on Artificial Intelligence, Machine Learning and Immunotherapy on this Open Access Scientific Journal please read these articles:
Part D: Curation entitled Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer originally published on 09/05/2014
Note the text below this point was used for all AI-based text analsysis
summarizes the clinical importance of five new lung cancer genome sequencing projects. These studies have identified genetic and epigenetic alterations in hundreds of lung tumors, of which some alterations could be taken advantage of using currently approved medications.
The reports, all published this month, included genomic information on more than 400 lung tumors. In addition to confirming genetic alterations previously tied to lung cancer, the studies identified other changes that may play a role in the disease.
“All of these studies say that lung cancers are genomically complex and genomically diverse,” said Dr. Matthew Meyerson of Harvard Medical School and the Dana-Farber Cancer Institute, who co-led several of the studies, including a large-scale analysis of squamous cell lung cancer by The Cancer Genome Atlas (TCGA) Research Network.
Some genes, Dr. Meyerson noted, were inactivated through different mechanisms in different tumors. He cautioned that little is known about alterations in DNA sequences that do not encode genes, which is most of the human genome.
Four of the papers are summarized below, with the first described in detail, as the Nature paper used a multi-‘omics strategy to evaluate expression, mutation, and signaling pathway activation in a large cohort of lung tumors. A literature informatics analysis is given for one of the papers. Please note that links on GENE names usually refer to the GeneCard entry.
Paper 1. Comprehensive genomic characterization of squamous cell lung cancers[1]
The Cancer Genome Atlas Research Network Project just reported, in the journal Nature, the results of their comprehensive profiling of 230 resected lung adenocarcinomas. The multi-center teams employed analyses of
microRNA
Whole Exome Sequencing including
Exome mutation analysis
Gene copy number
Splicing alteration
Methylation
Proteomic analysis
Summary:
Some very interesting overall findings came out of this analysis including:
High rates of somatic mutations including activating mutations in common oncogenes
Newly described loss of function MGA mutations
Sex differences in EGFR and RBM10 mutations
driver roles for NF1, MET, ERBB2 and RITI identified in certain tumors
differential mutational pattern based on smoking history
splicing alterations driven by somatic genomic changes
MAPK and PI3K pathway activation identified by proteomics not explained by mutational analysis = UNEXPLAINED MECHANISM of PATHWAY ACTIVATION
however, given the plethora of data, and in light of a similar study results recently released, there appears to be a great need for additional mining of this CGAP dataset. Therefore I attempted to curate some of the findings along with some other recent news relevant to the surprising findings with relation to biomarker analysis.
Makeup of tumor samples
230 lung adenocarcinomas specimens were categorized by:
Subtype
33% acinar
25% solid
14% micro-papillary
9% papillary
8% unclassified
5% lepidic
4% invasive mucinous
Gender
Smoking status
81% of patients reported past of present smoking
The authors note that TCGA samples were combined with previous data for analysis purpose.
A detailed description of Methodology and the location of deposited data are given at the following addresses:
Gender and Smoking Habits Show different mutational patterns
WES mutational analysis
a) smoking status
– there was a strong correlations of cytosine to adenine nucleotide transversions with past or present smoking. In fact smoking history separated into transversion high (past and previous smokers) and transversion low (never smokers) groups, corroborating previous results.
→ mutations in groups Transversion High Transversion Low
TP53, KRAS, STK11, EGFR, RB1, PI3CA
KEAP1, SMARCA4 RBM10
b) Gender
Although gender differences in mutational profiles have been reported, the study found minimal number of significantly mutated genes correlated with gender. Notably:
EGFR mutations enriched in female cohort
RBM10 loss of function mutations enriched in male cohort
Although the study did not analyze the gender differences with smoking patterns, it was noted that RBM10 mutations among males were more prevalent in the transversion high group.
Whole exome Sequencing and copy number analysis reveal Unique, Candidate Driver Genes
Whole exome sequencing revealed that 62% of tumors contained mutations (either point or indel) in known cancer driver genes such as:
KRAS, EGFR, BRMF, ERBB2
However, authors looked at the WES data from the oncogene-negative tumors and found unique mutations not seen in the tumors containing canonical oncogenic mutations.
Unique potential driver mutations were found in
TP53, KEAP1, NF1, and RIT1
The genomics and expression data were backed up by a proteomics analysis of three pathways:
MAPK pathway
mTOR
PI3K pathway
…. showing significant activation of all three pathways HOWEVER the analysis suggested that activation of signaling pathways COULD NOT be deduced from DNA sequencing alone. Phospho-proteomic analysis was required to determine the full extent of pathway modification.
For example, many tumors lacked an obvious mutation which could explain mTOR or MAPK activation.
Altered cell signaling pathways included:
Increased MAPK signaling due to activating KRAS
Higher mTOR due to inactivating STK11 leading to increased proliferation, translation
Pathway analysis of mutations revealed alterations in multiple cellular pathways including:
Reduced oxidative stress response
Nucleosome remodeling
RNA splicing
Cell cycle progression
Histone methylation
Summary:
Authors noted some interesting conclusions including:
MET and ERBB2 amplification and mutations in NF1 and RIT1 may be unique driver events in lung adenocarcinoma
Possible new drug development could be targeted to the RTK/RAS/RAF pathway
MYC pathway as another important target
Cluster analysis using multimodal omics approach identifies tumors based on single-gene driver events while other tumor have multiple driver mutational events (TUMOR HETEROGENEITY)
Paper 2. A Genomics-Based Classification of Human Lung Tumors[2]
3,726 point mutations and more than 90 indels in the coding sequence
Smokers with lung cancer show 10× the number of point mutations than never-smokers
Novel lung cancer genes, including DACH1, CFTR, RELN, ABCB5, and HGF were identified
Tumor samples from males showed high frequency of MYCBP2 MYCBP2 involved in transcriptional regulation of MYC.
Variant allele frequency analysis revealed 10/17 tumors were at least biclonal while 7/17 tumors were monoclonal revealing majority of tumors displayed tumor heterogeneity
Novel pathway alterations in lung cancer include cell-cycle and JAK-STAT pathways
14 fusion proteins found, including ROS1-ALK fusion. ROS1-ALK fusions have been frequently found in lung cancer and is indicative of poor prognosis[4].
Novel metabolic enzyme fusions
Alterations were identified in 54 genes for which targeted drugs are available. Drug-gable mutant targets include: AURKC, BRAF, HGF, EGFR, ERBB4, FGFR1, MET, JAK2, JAK3, HDAC2, HDAC6, HDAC9, BIRC6, ITGB1, ITGB3, MMP2, PRKCB, PIK3CG, TERT, KRAS, MMP14
Table. Validated Gene-Fusions Obtained from Ref-Seq Data
Note: Gene columns contain links for GeneCard while Gene function links are to the gene’s GO (Gene Ontology) function.
There has been a recent literature on the importance of the EML4-ALK fusion protein in lung cancer. EML4-ALK positive lung tumors were found to be les chemo sensitive to cytotoxic therapy[5] and these tumor cells may exhibit an epitope rendering these tumors amenable to immunotherapy[6]. In addition, inhibition of the PI3K pathway has sensitized EMl4-ALK fusion positive tumors to ALK-targeted therapy[7]. EML4-ALK fusion positive tumors show dependence on the HSP90 chaperone, suggesting this cohort of patients might benefit from the new HSP90 inhibitors recently being developed[8].
Table. Significantly mutated genes (point mutations, insertions/deletions) with associated function.
Table. Literature Analysis of pathways containing significantly altered genes in NSCLC reveal putative targets and risk factors, linkage between other tumor types, and research areas for further investigation.
Note: Significantly mutated genes, obtained from WES, were subjected to pathway analysis (KEGG Pathway Analysis) in order to see which pathways contained signicantly altered gene networks. This pathway term was then used for PubMed literature search together with terms “lung cancer”, “gene”, and “NOT review” to determine frequency of literature coverage for each pathway in lung cancer. Links are to the PubMEd search results.
KEGG pathway Name
# of PUBMed entries containing Pathway Name, Gene ANDLung Cancer
A few interesting genetic risk factors and possible additional targets for NSCLC were deduced from analysis of the above table of literature including HIF1-α, mIR-31, UBQLN1, ACE, mIR-193a, SRSF1. In addition, glioma, melanoma, colorectal, and prostate and lung cancer share many validated mutations, and possibly similar tumor driver mutations.
please click on graph for larger view
Paper 4. Mapping the Hallmarks of Lung Adenocarcinoma with Massively Parallel Sequencing[9]
Exome and genome characterization of somatic alterations in 183 lung adenocarcinomas
12 somatic mutations/megabase
U2AF1, RBM10, and ARID1A are among newly identified recurrently mutated genes
Structural variants include activating in-frame fusion of EGFR
Epigenetic and RNA deregulation proposed as a potential lung adenocarcinoma hallmark
Summary
Lung adenocarcinoma, the most common subtype of non-small cell lung cancer, is responsible for more than 500,000 deaths per year worldwide. Here, we report exome and genome sequences of 183 lung adenocarcinoma tumor/normal DNA pairs. These analyses revealed a mean exonic somatic mutation rate of 12.0 events/megabase and identified the majority of genes previously reported as significantly mutated in lung adenocarcinoma. In addition, we identified statistically recurrent somatic mutations in the splicing factor gene U2AF1 and truncating mutations affecting RBM10 and ARID1A. Analysis of nucleotide context-specific mutation signatures grouped the sample set into distinct clusters that correlated with smoking history and alterations of reported lung adenocarcinoma genes. Whole-genome sequence analysis revealed frequent structural rearrangements, including in-frame exonic alterations within EGFR and SIK2 kinases. The candidate genes identified in this study are attractive targets for biological characterization and therapeutic targeting of lung adenocarcinoma.
Paper 5. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer[10]
Highlights
Whole exome and transcriptome (RNASeq) sequencing 29 small-cell lung carcinomas
High mutation rate 7.4 protein-changing mutations/million base pairs
Inactivating mutations in TP53 and RB1
Functional mutations in CREBBP, EP300, MLL, PTEN, SLIT2, EPHA7, FGFR1 (determined by literature and database mining)
The mutational spectrum seen in human data also present in a Tp53-/- Rb1-/- mouse lung tumor model
Curator Graphical Summary of Interesting Findings From the Above Studies
The above figure (please click on figure) represents themes and findings resulting from the aforementioned studies including
questions which will be addressed in Future Postson this site.
UPDATED 10/10/2021
The following article uses RNASeq to screen lung adenocarcinomas for fusion proteins in patients with either low or high tumor mutational burden. Findings included presence of MET fusion proteins in addition to other fusion proteins irrespective if tumors were driver negative by DNASeq screening.
High Yield of RNA Sequencing for Targetable Kinase Fusions in Lung Adenocarcinomas with No Mitogenic Driver Alteration Detected by DNA Sequencing and Low Tumor Mutation Burden
Source:
High Yield of RNA Sequencing for Targetable Kinase Fusions in Lung Adenocarcinomas with No Mitogenic Driver Alteration Detected by DNA Sequencing and Low Tumor Mutation Burden
RymaBenayed, MichaelOffin, KerryMullaney, PurvilSukhadia, KellyRios, PatriceDesmeules, RyanPtashkin, HelenWon, JasonChang, DarraghHalpenny, Alison M.Schram, Charles M.Rudin, David M.Hyman, Maria E.Arcila, Michael F.Berger, AhmetZehir, Mark G.Kris, AlexanderDrilon and MarcLadanyi
Purpose: Targeted next-generation sequencing of DNA has become more widely used in the management of patients with lung adenocarcinoma; however, no clear mitogenic driver alteration is found in some cases. We evaluated the incremental benefit of targeted RNA sequencing (RNAseq) in the identification of gene fusions and MET exon 14 (METex14) alterations in DNA sequencing (DNAseq) driver–negative lung cancers.
Experimental Design: Lung cancers driver negative by MSK-IMPACT underwent further analysis using a custom RNAseq panel (MSK-Fusion). Tumor mutation burden (TMB) was assessed as a potential prioritization criterion for targeted RNAseq.
Results: As part of prospective clinical genomic testing, we profiled 2,522 lung adenocarcinomas using MSK-IMPACT, which identified 195 (7.7%) fusions and 119 (4.7%) METex14 alterations. Among 275 driver-negative cases with available tissue, 254 (92%) had sufficient material for RNAseq. A previously undetected alteration was identified in 14% (36/254) of cases, 33 of which were actionable (27 in-frame fusions, 6 METex14). Of these 33 patients, 10 then received matched targeted therapy, which achieved clinical benefit in 8 (80%). In the 32% (81/254) of DNAseq driver–negative cases with low TMB [0–5 mutations/Megabase (mut/Mb)], 25 (31%) were positive for previously undetected gene fusions on RNAseq, whereas, in 151 cases with TMB >5 mut/Mb, only 7% were positive for fusions (P < 0.0001).
Conclusions: Targeted RNAseq assays should be used in all cases that appear driver negative by DNAseq assays to ensure comprehensive detection of actionable gene rearrangements. Furthermore, we observed a significant enrichment for fusions in DNAseq driver–negative samples with low TMB, supporting the prioritization of such cases for additional RNAseq.
Translational Relevance
Inhibitors targeting kinase fusions have shown dramatic and durable responses in lung cancer patients, making their comprehensive detection critical. Here, we evaluated the incremental benefit of targeted RNA sequencing (RNAseq) in the identification of gene fusions in patients where no clear mitogenic driver alteration is found by DNA sequencing (DNAseq)–based panel testing. We found actionable alterations (kinase fusions or MET exon 14 skipping) in 13% of cases apparently driver negative by previous DNAseq testing. Among the driver-negative samples tested by RNAseq, those with low tumor mutation burden (TMB) were significantly enriched for gene fusions when compared with the ones with higher TMB. In a clinical setting, such patients should be prioritized for RNAseq. Thus, a rational, algorithmic approach to the use of targeted RNA-based next-generation sequencing (NGS) to complement large panel DNA-based NGS testing can be highly effective in comprehensively uncovering targetable gene fusions or oncogenic isoforms not just in lung cancer but also more generally across different tumor types.
Wake Up and Smell the Fusions: Single-Modality Molecular Testing Misses Drivers
by Kurtis D.Davies and Dara L.Aisner
Abstract
Multitarget assays have become common in clinical molecular diagnostic laboratories. However, all assays, no matter how well designed, have inherent gaps due to technical and biological limitations. In some clinical cases, testing by multiple methodologies is needed to address these gaps and ensure the most accurate molecular diagnoses.
In this issue of Clinical Cancer Research, Benayed and colleagues illustrate the growing need to consider multiple molecular testing methodologies for certain clinical specimens (1). The rapidly expanding list of actionable molecular alterations across cancer types has resulted in the wide adoption of multitarget testing approaches, particularly those based on next-generation sequencing (NGS). NGS-based assays are commonly viewed as “one-stop shops” to detect a vast array of molecular variants. However, as Benayed and colleagues discuss, even well-designed and highly vetted NGS assays have inherent gaps that, under certain circumstances, are ideally addressed by analyzing the sample using an alternative approach.
In the article, the authors examined a cohort of lung adenocarcinoma patient samples that had been deemed “driver- negative” via MSK-IMPACT, an FDA-cleared test that is widely considered by experts in the field to be one of the best examples of a DNA-based large gene panel NGS assay (2). Of 589 driver-negative cases, 254 had additional material amenable for a different approach: RNA-based NGS designed specifically for gene fusion and oncogenic gene isoform detection. After accounting for quality control failures, 232 samples were successfully sequenced, and, among these, 36 samples (representing an astonishing 15.5% of tested cases) were found to be positive for a driver gene fusion or oncogenic isoform that had not been detected by DNA-based NGS. The real-world value derived from this orthogonal testing schema was more than theoretical, with 8 of 10 (80%) patients demonstrating clinical benefit when treated according to the alteration identified via the RNA-based approach.
To detect gene rearrangements that lead to oncogenic gene fusions (and to detect mutations and insertions/deletions that lead to MET exon 14 skipping), MSK-IMPACT employs hybrid capture-based enrichment of selected intronic regions from genomic DNA. While this approach has proven to be successful in a variety of settings, there are associated limitations that were determined in this study to underlie the discrepancies between MSK-IMPACT and the RNA-based assay. First, some introns that are involved in clinically actionable rearrangement events are very large, thus requiring substantial sequencing capital that can represent a disproportionate fraction of the assay. Despite the ability via NGS to perform sequencing at a large scale, this sequencing capacity is still finite, and thus decisions must be made to sacrifice coverage of certain large genomic regions to ensure sufficient sequencing depth for other desired genomic targets. In the case of MSK-IMPACT (and most other DNA-based NGS assays), certain important introns in NTRK3 and NRG1 are not included in covered content, simply because they are too large (>90 Kb each). The second primary problem with DNA-based analysis of introns is that they often contain highly repetitive elements that are extremely difficult to assess via NGS due to their recurring presence across the genome. Attempts to sequence these regions are largely unfruitful because any sequencing data obtained cannot be specifically aligned/mapped to the desired targeted region of the genome (3). This is particularly true for intron 31 of ROS1, because it contains two repetitive long interspersed nuclear elements, and many DNA-based assays, including MSK-IMPACT, poorly cover this intron (4). In this study by Benayed and colleagues, the most common discrepant alteration was fusion involving ROS1, which accounted for 10 of 36 (28%) cases. At least six of these, those that demonstrated fusion to ROS1 exon 32, were likely directly explained by incomplete intron 31 sequencing. RNA-based analysis is able to overcome the above described limitations owing to the simple fact that sequencing is focused on exons post-splicing and the need to sequence introns is entirely avoided (Fig. 1).
Schematic representation of underlying genomic complexities that can lead to false-negative gene fusion results in DNA-based NGS analysis. In some cases, RNA-based approaches may overcome the limitations of DNA-based testing.
Lack of sufficient intronic coverage could not account for all of the discrepancies between DNA-based and RNA-based analysis however. Six samples in the cohort were found to be positive for MET exon 14 skipping based on RNA. In five of these, genomic alterations in MET introns 13 or 14 were observed, however they did not conform to canonical splice site alterations and thus were not initially called (although this was addressed by bioinformatics updates). In RNA-based testing, however, determination of exon skipping is simplified such that, regardless of the specific genomic alteration that interferes with splicing, absence of the exon in the transcript is directly observed (5). In another two of the discrepant cases, tumor purity was observed to be low in the sample, meaning that the expected variant allele frequency (VAF) for a genomic event would also likely be low, potentially below detectable levels. However, overexpression of the fusions at the transcript level was theorized to compensate for low VAF (Fig. 1). Additional explanations for discordant findings between the assays included sample-specific poor sequencing in selected introns and complex rearrangements that hindered proper capture (Fig. 1).
The take home message from Benayed and colleagues is simply this: there is no perfect assay that will detect 100% of the potential actionable alterations in patient samples. Even an extremely well designed, thoroughly vetted, and FDA-cleared assay such as MSK-IMPACT will have inherent and unavoidable “holes” due to intrinsic limitations. The solution to this dilemma, as adeptly described by Benayed and colleagues, is additional testing using a different approach. While in an ideal world every clinical tumor sample would be tested by multiple modalities to ensure the most comprehensive clinical assessment, the reality is that these samples are often scant and testing is fiscally burdensome (and often not reimbursed). Therefore, algorithms to determine which samples should be reflexed to secondary assays after testing with a primary assay are critical for maximizing benefit. In this study, the first algorithmic step was lack of an identified driver (because activated oncogenic drivers tend to exist exclusively of each other), which amounted to 23% of samples tested with the primary assay. In addition, the authors found a significantly higher rate of actionable gene fusions in samples with a low (<5 mut/Mb) tumor mutational burden, meaning that this metric, which was derived from the primary assay, could also be used to help inform decision making regarding additional testing. While this scenario is somewhat specific to lung cancer, similar approaches could be prescribed on a cancer type–specific basis.
These findings should be considered a “wake-up call” for oncologists in regard to the ordering and interpretation of molecular testing. It is clear from these and other published findings that advanced molecular analysis has limitations that require nuanced technical understanding. As this arena evolves, it is critical for oncologists (and trainees) to gain an increased comprehension of how to identify when the “gaps” in a test might be most clinically relevant. This requires a level of technical cognizance that has been previously unexpected of clinical practitioners, yet is underscored by the reality that opportunities for effective targeted therapy can and will be missed if the treating oncologist is unaware of how to best identify patients for whom additional testing is warranted. This study also highlights the mantra of “no test is perfect” regardless of prestige of the testing institution, number of past tests performed, or regulatory status. NGS, despite its benefits, does not mean all-encompassing. It is only through the adaptability of laboratories to utilize knowledge such as is provided by Benayed and colleagues that advances in laboratory medicine can be quickly deployed to maximize benefits for oncology patients.
Govindan R, Ding L, Griffith M, Subramanian J, Dees ND, Kanchi KL, Maher CA, Fulton R, Fulton L, Wallis J et al: Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 2012, 150(6):1121-1134.
Takeuchi K, Soda M, Togashi Y, Suzuki R, Sakata S, Hatano S, Asaka R, Hamanaka W, Ninomiya H, Uehara H et al: RET, ROS1 and ALK fusions in lung cancer. Nature medicine 2012, 18(3):378-381.
Morodomi Y, Takenoyama M, Inamasu E, Toyozawa R, Kojo M, Toyokawa G, Shiraishi Y, Takenaka T, Hirai F, Yamaguchi M et al: Non-small cell lung cancer patients with EML4-ALK fusion gene are insensitive to cytotoxic chemotherapy. Anticancer research 2014, 34(7):3825-3830.
Yoshimura M, Tada Y, Ofuzi K, Yamamoto M, Nakatsura T: Identification of a novel HLA-A 02:01-restricted cytotoxic T lymphocyte epitope derived from the EML4-ALK fusion gene. Oncology reports 2014, 32(1):33-39.
Workman P, van Montfort R: EML4-ALK fusions: propelling cancer but creating exploitable chaperone dependence. Cancer discovery 2014, 4(6):642-645.
Imielinski M, Berger AH, Hammerman PS, Hernandez B, Pugh TJ, Hodis E, Cho J, Suh J, Capelletti M, Sivachenko A et al: Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 2012, 150(6):1107-1120.
Peifer M, Fernandez-Cuesta L, Sos ML, George J, Seidel D, Kasper LH, Plenker D, Leenders F, Sun R, Zander T et al: Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nature genetics 2012, 44(10):1104-1110.
Other posts on this site which refer to Lung Cancer and Cancer Genome Sequencing include:
The Society for Immunotherapy of Cancer (SITC) is a 501 (c)(3) non-profit society of medical professionals. Recent advances in immunology and biology have opened up new horizons in the field of cancer therapy, with an upsurge in the integration of new biologic agents into clinical practice. With several high-caliber scientific meetings with a focus on clinical and translational aspects of biologic approaches to cancer treatment and numerous networking opportunities unique to this organization, the Society for Immunotherapy of Cancer (SITC) has developed into the premier destination for interaction and innovation in the cancer biologics community.
Portland, OR – Friday, October 3, 2014 Charlotte, NC – Friday, October 3, 2014
Tampa, FL – Friday, December 5, 2014
September 2014
Hematologic Malignancies: Translating Discoveries to Novel Therapies September 20-23, 2014 • Sheraton Philadelphia Downtown • Philadelphia, PA
The AACR is proud to announce our conference focused on the blood-based cancers and associated disorders categorized as hematologic malignancies. Sessions will include presentations on leukemia, lymphoma, myeloma, myelodysplastic syndrome, and myeloproliferative neoplasms.
Advances in Melanoma: From Biology to Therapy
Loews Philadelphia • Philadelphia, PA • September 20-23, 2014
With so many recent advances in treating metastatic melanoma, including approaches like immunotherapies, targeted therapies, and combination therapies, melanoma research is at a critical point where it is extremely important for the field to have a continuous exchange of information. Despite the success of various “targeted” inhibitors, therapeutic responses in melanoma patients are often short-lived due to rapidly acquired drug resistance. Therefore, it is essential that melanoma researchers translate the novel understanding of melanoma biology to decipher the mechanisms of innate and acquired drug resistance for the development of improved therapeutic options. To bridge the gap between scientists and clinician-scientists’ professional practice, this conference will provide a platform for discussion and potential collaborations for the discovery of new therapeutic targets.
Join leaders in the immunogenicity field for a two day conference to learn what constitutes a successful strategy for managing immunogenicity risk, and explore the business case for introducing immunogenicity assessment into your program.
Learn about the latest strategies and exciting new technologies
Discuss current and developing challenges and exchange new ideas
Improve the outcome of your R&D programs
Our 4th Mastering Immunogenicity Conference will continue to have a strong focus on immunogenicity sciences, particularly on what basic research needs to be carried out to improve our understanding of immune regulation to biotherapeutics. We will review progress made in correlating data from pre-clinical predictive tools to clinical outcomes, as well as continuing our discussions surrounding the benefits that Quality by Design has on reduced immunogenicity, considering subsequent patient benefits as well as competitive advantage. Presentations by experts will provide an overview of the wide range of technologies currently used for immunogenicity risk management and how they can be incorporated for a ‘quality by design’ approach.
HudsonAlpha Biotechnology Campus
Huntsville, Alabama, USA
The HudsonAlpha-Science Conference on Immunogenomics will bring together preeminent leaders and thinkers at the intersection of genomics and immunology.
October 2014
Cancer Immunotherapy: Out of the Gate
October 06, 2014 Grand Hyatt New York Hotel at Grand Central, New York, NY
The Cancer Research Institute (CRI) will host its 22nd Annual International Cancer Immunotherapy Symposium October 6-8, 2014 at The Grand Hyatt in New York City. Attracting clinicians, laboratory scientists, postdoctoral fellows, and graduate students, the symposium will feature plenary presentations from leaders in immunology and cancer immunotherapy, a poster session, and numerous networking opportunities.
This year’s CRI symposium, entitled Cancer Immunotherapy: Out of the Gate, will harness the excitement and enthusiasm generated by recent clinical successes to explore new and emerging areas of basic, translational, and clinical research. Topics such as the use of genomic methods to catalogue cancer heterogeneity, mechanistic studies of checkpoint blockage antibodies, new views on immunosurveillance and immunoregulation, and emerging therapies that are altering the landscape of cancer treatment will be discussed.
Heroes in Medical Research: Green Fluorescent Protein and the Rough Road in Science
Curator: Stephen J. Williams, Ph.D.
Article ID #147: Heroes in Medical Research: Green Fluorescent Protein and the Rough Road in Science. Published on 7/27/2014
WordCloud Image Produced by Adam Tubman
In this series, “Heroes in Medical Research”, I like to discuss the people who made some important contributions to science and medicine which underlie the great transformative changes but don’t usually get the notoriety given to Nobel Laureates or who seem to fly under the radar of popular news. Their work may be the development of research tools which allowed a great discovery leading to a line of transformative research, a moment of serendipity leading to discovery of a cure, or just contributions to the development of a new field or the mentoring of a new generation of scientists and clinicians. One such discovery, which has probably been pivotal in many of our research, is the discovery of the green fluorescent protein (GFP), commonly used as an invaluable tool to monitor protein for cellular expression and localization studies. Although the development of research tools, whether imaging tools, vectors, animal models, cell lines, and such are not heralded, they always assist in the pivotal discoveries of our time. The following is a heartwarming story by Discover Magazine’s Yudhijit Bhattacharjee behind Dr. Douglas Prasher’s discovery of the green fluorescent protein, his successful efforts to sequence the gene and subsequent struggles in science and finally scientific recognition for his work. In addition the story describes Dr. Prather’s perseverance, a trait necessary for every scientist.
In summary, Dr. Prather had been working at Wood’s Hole in Massachusetts trying to discover, isolate, then clone the protein which allowed a species of jellyfish living in the cold waters of the North Pacific, Aequorea victoria, to emit a green glow. Eventually he cloned the GFP gene, but gave up on work to express the gene in mammalian cells. Before leaving Wood’s Hole he gave the gene to Dr. Roger Tsien, who with Dr. Martin Chalfie and Osamu Shimomura showed the utility of GFP as an intracellular tracer to visualize, in real time, the expression and localization of GFP-tagged proteins (all three shared the 2008 Nobel Prize for this work). Dr. Tsien however realized the importance of Douglas’s cloning work as pivotal for their research, contacted Douglas (who now due to the bad economy was working at a Toyota dealership in Alabama) and invited him to the Nobel Prize Award Ceremony in Sweden as his guest. Although Dr. Prasher had “left academic science” he never really stopped his quest for a scientific career, using his spare time to review manuscripts.
Other researchers have invited their colleagues who made important contributions to the ultimate Nobel work. One such guest was one of my colleagues Dr. Leonard Cohen, who worked with Dr. Irwin Rose and Avram Hershko at the Institute for Cancer Research in Philadelphia a cell-free system from clams to discover the mechanism how cyclin B is degraded during the exit from the cell cycle (from A. Hershko’s Nobel speech). Dr. Hershko had acknowledged a slew of colleagues and highlighted their contributions to the ultimate work. It shows how even small discoveries can contribute to the sphere of scientific knowledge and breakthrough.
1. Chalfie, M., Tu, Y., Euskirchen, G., Ward, W.W., Prasher, D.C., Green fluorescent protein as a marker for gene expression. Science, 263(5148), 802-805 (1994).
In today’s post, I had the opportunity to talk with molecular modeler Charles H. Reynolds, Ph.D., founder and CEO of Gfree Bio LLC, a computational structure-based design and modeling company based in the Pennsylvania Biotech Center of Bucks County. Gfree is actually one of a few molecular modeling companies at the Bucks County Biotech Center (I highlighted another company RabD Biotech which structural computational methods to design antibody therapeutics).
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