Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com
Named for ACGT co-founder, Edward Netter, the award recognizes a researcher who has made unparalleled and groundbreaking contributions to the field of cell and gene therapy for cancer. Dr. Mackall is a leader in advancing cell and gene therapies for the treatment of solid tumors, with a major focus on children’s cancers.
In addition to being an ACGT research fellow and a member of ACGT’s Scientific Advisory Council, Dr. Mackall is the Ernest and Amelia Gallo Family professor of Pediatrics and Medicine at Stanford University, the founding director of the Stanford Center for Cancer Cell Therapy, associate director of the Stanford Cancer Institute, leader of the Cancer Immunotherapy Program and director of the Parker Institute for Cancer Immunotherapy. She has led numerous groundbreaking clinical trials to treat children with sarcomas and brain cancers.
“There is exciting progress happening in the field of cancer cell and gene therapy,” said Kevin Honeycutt, CEO and president of ACGT. “We continue to see the FDA approve cell and gene therapy treatments for blood cancers, while research for solid tumors is now progressing to clinical trials. These successes are linked to the funding of ACGT, and Dr. Crystal Mackall is one of the best examples of a researcher who refused to accept the status-quo of standard cancer treatment and committed to developing novel cell and gene therapies for children with difficult-to-treat tumors. ACGT is proud that Dr. Mackall is an ACGT Research Fellow, a member of ACGT’s Scientific Advisory Council, and the newest recipient of the Edward Netter Leadership Award.”
The ACGT Awards Luncheon will celebrate the non-profit organization’s 20th anniversary and usher in a new decade as the only nonprofit dedicated exclusively to funding cancer cell and gene therapy research. ACGT funds innovative scientists and biotechnology companies working to harness the power of cell and gene therapy to transform how cancer is treated and to drive momentum toward a cure.
The Edward Netter Leadership Award will be presented to Dr. Mackall by Carl June, MD, of the University of Pennsylvania, who received the honor at ACGT’s 2019 Awards Gala. ACGT grant funding enabled Dr. June to research and develop cell and gene therapies that led to the first FDA approvals of CAR T-cell therapies for cancer.
For more than 20 years, Alliance for Cancer Gene Therapy has funded research that is bringing innovative treatment options to people living with deadly cancers – treatments that save lives and offer new hope to all cancer patients. Alliance for Cancer Gene Therapy funds researchers who are pioneering the potential of cancer cell and gene therapy – talented visionaries whose scientific advancements are driving the development of groundbreaking treatments for ovarian, prostate, sarcoma, glioblastoma, melanoma and pancreatic cancers. One hundred percent of all public funds raised by Alliance for Cancer Gene Therapy directly support research and programs. For more information, visit acgtfoundation.org, call (203) 358-5055, or join the Alliance for Cancer Gene Therapy community on Facebook, Twitter, LinkedIn, Instagram and YouTube @acgtfoundation.
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Other Related Articles in this Open Access Scientific Journal Include
Use of Systems Biology for Design of inhibitor of Galectins as Cancer Therapeutic – Strategy and Software
Curator:Stephen J. Williams, Ph.D.
Below is a slide representation of the overall mission 4 to produce a PROTAC to inhibit Galectins 1, 3, and 9.
Using A Priori Knowledge of Galectin Receptor Interaction to Create a BioModel of Galectin 3 Binding
Now after collecting literature from PubMed on “galectin-3” AND “binding” to determine literature containing kinetic data we generate a WordCloud on the articles.
This following file contains the articles needed for BioModels generation.
From the WordCloud we can see that these corpus of articles describe galectin binding to the CRD (carbohydrate recognition domain). Interestingly there are many articles which describe van Der Waals interactions as well as electrostatic interactions. Certain carbohydrate modifictions like Lac NAc and Gal 1,4 may be important. Many articles describe the bonding as well as surface interactions. Many studies have been performed with galectin inhibitors like TDGs (thio-digalactosides) like TAZ TDG (3-deoxy-3-(4-[m-fluorophenyl]-1H-1,2,3-triazol-1-yl)-thio-digalactoside). This led to an interesting article
.
Dual thio-digalactoside-binding modes of human galectins as the structural basis for the design of potent and selective inhibitors
Human galectins are promising targets for cancer immunotherapeutic and fibrotic disease-related drugs. We report herein the binding interactions of three thio-digalactosides (TDGs) including TDG itself, TD139 (3,3′-deoxy-3,3′-bis-(4-[m-fluorophenyl]-1H-1,2,3-triazol-1-yl)-thio-digalactoside, recently approved for the treatment of idiopathic pulmonary fibrosis), and TAZTDG (3-deoxy-3-(4-[m-fluorophenyl]-1H-1,2,3-triazol-1-yl)-thio-digalactoside) with human galectins-1, -3 and -7 as assessed by X-ray crystallography, isothermal titration calorimetry and NMR spectroscopy. Five binding subsites (A-E) make up the carbohydrate-recognition domains of these galectins. We identified novel interactions between an arginine within subsite E of the galectins and an arene group in the ligands. In addition to the interactions contributed by the galactosyl sugar residues bound at subsites C and D, the fluorophenyl group of TAZTDG preferentially bound to subsite B in galectin-3, whereas the same group favored binding at subsite E in galectins-1 and -7. The characterised dual binding modes demonstrate how binding potency, reported as decreased Kd values of the TDG inhibitors from μM to nM, is improved and also offer insights to development of selective inhibitors for individual galectins.
Figures
Figure 1. Chemical structures of L3, TDG…
Figure 2. Structural comparison of the carbohydrate…
Accelerating PROTAC drug discovery: Establishing a relationship between ubiquitination and target protein degradation
Curator: Stephen J. Williams, Ph.D.
PROTACs have been explored in multiple disease fields with focus on only few ligases like cereblon (CRBN), Von Hippel-Lindau (VHL), IAP and MDM2. Cancer targets like androgen receptor, estrogen receptor, BTK, BCL2, CDK8 and c-MET [[6], [7], [8], [9], [10], [11]] have been successfully targeted using PROTACs. A variety of BET family (BRD2, BRD3, and BRD4)- PROTACs were designed using multiple ligases; MDM2-based BRD4 PROTAC [12], CRBN based dBET1 [13] and BETd-24-6 [14] for triple-negative breast cancer, enhanced membrane permeable dBET6 [15], and dBET57 PROTAC [16]. PROTACs for Hepatitis c virus (HCV) protease, IRAK4 and Tau [[17], [18], [19]] have been explored for viral, immune and neurodegenerative diseases, respectively. Currently, the PROTAC field expansion to vast undruggable proteome is hindered due to narrow focus on select E3 ligases. Lack of reliable tools to rapidly evaluate PROTACs based on new ligases is hindering the progress. Screening platforms designed must be physiologically relevant and represent true PROTAC cellular function, i.e., PROTAC-mediated target ubiquitination and degradation.
In the current study, we employ TUBEs as affinity capture reagents to monitor PROTAC-induced poly-ubiquitination and degradation as a measure of potency. We established and validated proof-of-concept cell-based assays in a 96-well format using PROTACS for three therapeutic targets BET family proteins, kinases, and KRAS. To our knowledge, the proposed PROTAC assays are first of its kind that can simultaneously 1) detect ubiquitination of endogenous, native protein targets, 2) evaluate the potency of PROTACs, and 3) establish a link between the UPS and protein degradation. Using these TUBE assays, we established rank order potencies between four BET family PROTACs dBET1, dBET6, BETd246 and dBET57 based on peak ubiquitination signals (“UbMax”) of the target protein. TUBE assay was successful in demonstrating promiscuous kinase PROTACs efficiency to degrade Aurora Kinase A at sub-nanomolar concentrations within 1 h. A comparative study to identify changes in the ubiquitination and degradation profile of KRAS G12C PROTACs recruiting two E3 ligases (CRBN and VHL). All of the ubiquitination and degradation profiles obtained from TUBE based assays correlate well with traditional low throughput immunoblotting. Significant correlation between DC50 obtained from protein degradation in western blotting and UbMax values demonstrates our proposed assays can aid in high-throughput screening and drastically eliminate artifacts to overcome bottlenecks in PROTAC drug discovery.
To successfully set up HTS screening with novel PROTACs without pre-existing knowledge, we recommend the following steps. 1. Identify a model PROTAC that can potentially demonstrate activity based on knowledge in PROTAC design or in vitro binding studies. 2. Perform a time course study with 2–3 doses of the model PROTAC based on affinities of the ligands selected. 3. Monitor ubiquitination and degradation profiles using plate-based assay and identify time point that demonstrates UbMax. 4. Perform a dose response at selected time point with a library of PROTACs to establish rank order potency.
INTRODUCTION
Ubiquitination is a major regulatory mechanism to maintain cellular protein homeostasis by marking proteins for proteasomal-mediated degradation [1]. Given ubiquitin’s role in a variety of pathologies, the idea of targeting the Ubiquitin Proteasome System (UPS) is at the forefront of drug discovery [2]. “Event-driven” protein degradation using the cell’s own UPS is a promising technology for addressing the “undruggable” proteome [3]. Targeted protein degradation (TPD) has emerged as a new paradigm and promising therapeutic option to selectively attack previously intractable drug targets using PROteolytic TArgeting Chimeras (PROTACs) [4]. PROTACs are heterobifunctional molecules with a distinct ligand that targets a specific E3 ligase which is tethered to another ligand specific for the target protein using an optimized chemical linker. A functional PROTAC induces a ternary E3-PROTAC-target complex, resulting in poly-ubiquitination and subsequent controlled protein degradation [5]. Ability to function at sub-stoichiometric levels for efficient degradation, a significant advantage over traditional small molecules.
PROTACs have been explored in multiple disease fields with focus on only few ligases like cereblon (CRBN), Von Hippel-Lindau (VHL), IAP and MDM2. Cancer targets like androgen receptor, estrogen receptor, BTK, BCL2, CDK8 and c-MET [[6], [7], [8], [9], [10], [11]] have been successfully targeted using PROTACs. A variety of BET family (BRD2, BRD3, and BRD4)- PROTACs were designed using multiple ligases; MDM2-based BRD4 PROTAC [12], CRBN based dBET1 [13] and BETd-24-6 [14] for triple-negative breast cancer, enhanced membrane permeable dBET6 [15], and dBET57 PROTAC [16]. PROTACs for Hepatitis c virus (HCV) protease, IRAK4 and Tau [[17], [18], [19]] have been explored for viral, immune and neurodegenerative diseases, respectively. Currently, the PROTAC field expansion to vast undruggable proteome is hindered due to narrow focus on select E3 ligases. Lack of reliable tools to rapidly evaluate PROTACs based on new ligases is hindering the progress. Screening platforms designed must be physiologically relevant and represent true PROTAC cellular function, i.e., PROTAC-mediated target ubiquitination and degradation.
Cellular PROTAC screening is traditionally performed using cell lines harboring reporter genes and/or Western blotting. While Western blotting is easy to perform, they are low throughput, semi-quantitative and lack sensitivity. While reporter gene assays address some of the issues, they are challenged by reporter tags having internal lysines leading to artifacts. Currently, no approaches are available that can identify true PROTAC effects such as target ubiquitination and proteasome-mediated degradation simultaneously. High affinity ubiquitin capture reagents like TUBEs [20] (tandem ubiquitin binding entities), are engineered ubiquitin binding domains (UBDs) that allow for detection of ultralow levels of polyubiquitinated proteins under native conditions with affinities as low as 1 nM. The versatility and selectivity of TUBEs makes them superior to antibodies, and they also offer chain-selectivity (-K48, -K63, or linear) [21]. High throughput assays that can report the efficacy of multiple PROTACs simultaneously by monitoring PROTAC mediated ubiquitination can help establish rank order potency and guide chemists in developing meaningful structure activity relationships (SAR) rapidly.
In the current study, we employ TUBEs as affinity capture reagents to monitor PROTAC-induced poly-ubiquitination and degradation as a measure of potency. We established and validated proof-of-concept cell-based assays in a 96-well format using PROTACS for three therapeutic targets BET family proteins, kinases, and KRAS. To our knowledge, the proposed PROTAC assays are first of its kind that can simultaneously 1) detect ubiquitination of endogenous, native protein targets, 2) evaluate the potency of PROTACs, and 3) establish a link between the UPS and protein degradation. Using these TUBE assays, we established rank order potencies between four BET family PROTACs dBET1, dBET6, BETd246 and dBET57 based on peak ubiquitination signals (“UbMax”) of the target protein. TUBE assay was successful in demonstrating promiscuous kinase PROTACs efficiency to degrade Aurora Kinase A at sub-nanomolar concentrations within 1 h. A comparative study to identify changes in the ubiquitination and degradation profile of KRAS G12C PROTACs recruiting two E3 ligases (CRBN and VHL). All of the ubiquitination and degradation profiles obtained from TUBE based assays correlate well with traditional low throughput immunoblotting. Significant correlation between DC50 obtained from protein degradation in western blotting and UbMax values demonstrates our proposed assays can aid in high-throughput screening and drastically eliminate artifacts to overcome bottlenecks in PROTAC drug discovery.
Fig. 1. Schematic representation of TUBE assay to monitor PROTAC mediated cellular ubiquitination of target proteins.Fig. 2. TUBE based assay screening of PROTACs: Jurkat cell lysates were treated with BRD3-specific PROTACs A) dBET1, B) dBET6, C) BETd24-6, and D) dBET57. Polyubiquitination profiles and Ubmax of BRD3 for each PROTAC were represented as relative CL intensity. Relative CL intensities were calculated by dividing raw CL signals from a given PROTAC dose over DMSO treated samples. Error bars represent standard deviations, n = 3.Fig. 3. PROTAC mediated degradation of bromodomain proteins analyzed by anti-BRD3 western blotting. Dose response of PROTACs dBET1, dBET6, Betd-24-6 and dBET57 at 45 min in Jurkat cells demonstrates degradation of BRD3, Acting as loading control.
Fig. 4. PROTAC mediated ubiquitination and degradation of AURKA in K562 cells. (A) Time course study to evaluate intracellular ubiquitination and degradation. (B) Western blot analysis of time course study: degradation kinetics (C) A dose response study to evaluate DC50 of the promiscuous kinase PROTAC in K562 cells. (D) Western blot analysis of dose response study to monitor degradation, GAPDH as loading control. Error bars represent standard deviation, n = 3.
2022 World Medical Innovation Forum, GENE & CELL THERAPY • MAY 2–4, 2022 • BOSTON • IN-PERSON
Reporter: Aviva Lev-Ari, PhD, RN
World Medical Innovation Forum as we bring together global leaders to assess the latest opportunities and challenges, from the investment landscape to key technology developments to manufacturing and regulatory barriers. Gain first-hand insights on medicine’s ultimate game changer.
World Medical Innovation Forum will be held June 12 – 14 in Boston, MA. We hope you’ll join us for #WMIF2023!
From: “Rieck, Lucy (BOS-WSW)” <LRieck@webershandwick.com> Date: Tuesday, April 12, 2022 at 10:25 AM To: Aviva Lev-Ari <avivalev-ari@alum.berkeley.edu> Subject: You’re Invited: Mass General Brigham’s World Medical Innovation Forum
Hi Aviva,
I’m reaching out to extend free registration for you or a colleague to the 8th annual World Medical Innovation Forum (WMIF), taking place May 2-4 at the Westin Copley Place in Boston. This year’s event, co-sponsored with Bank of America, will explore gene and cell therapies (GCT), including the latest opportunities and challenges – from the investment landscape to key technology developments to manufacturing and regulatory barriers.
The event will feature 200 speakers – including CEOs of leading companies in the GCT and biotech fields, investors, entrepreneurs, Harvard clinicians and scientists, government officials and other key influencers – who discover, invest in, and cultivate GCT breakthroughs. Notable speakers include:
Peter Marks: Director, Center for Biologics Evaluation and Research at the FDA
Brian Moynihan: CEO, Bank of America
Anne Klibansky: President & CEO, Mass General Brigham
Senior executives from biopharma and academic institutions of all sizes (including Novartis, BMS, Takeda, Verve, UPenn)
You can view the full list of speakers here and the program agenda here.
WMIF is hosted by the Mass General Brigham health system, which comprises 14 hospitals, including two world-renowned medical centers: Mass General and Brigham & Women’s. Since 2015, the Forum has brought together global leaders to assess medical breakthroughs, the investment landscape and technology developments that have the potential to transform the industry.
In addition to a packed agenda, the 2022 “Disruptive Dozen” – 12 breakthrough technologies most likely to have significant impact on gene and cell therapy in the next 18 months – will also be announced.
Please let me know if you would be interested in attending.
Understanding long-term Gene and Cell Therapy investment complexities requires a keen awareness of where the science and the markets are headed. That’s why “The Doctor is In” in these updates on the latest GCT technologies. Presented by Mass General Brigham clinicians and innovators from the front lines of care, the sessions are co-hosted by expert analysts from Bank of America and include interactive discussion and Q&A.
In this session, Dr. Eichler will discuss the impact of gene defects across the lifespan and how timing and delivery of new genetic therapies is transforming the field of neurogenetics.
In this session, hear from experts in their field as they discuss the need and importance of regenerative medicine for the advancement in the treatment of diseases such as diabetes, kidney disease and blood disorders.
In this session, Dr. Artzi will share how an integrative approach of combining materials science, chemistry, imaging, and biology enables targeted delivery of gene therapy.
In this session, Drs. Robert Green and Adam Shaywitz will discuss how the early detection and prevention of rare diseases is imminent and represents an enormous public health opportunity.
In this session, Dr. Poznansky will share how research and development of vaccines and immunotherapy are safely accelerated from research lab to the patient leveraging novel mechanisms.
This panel features industry leaders who will discuss what the future may hold for gene and cell therapy. Which applications are likely to have the greatest impact? What are the key hurdles to be overcome? What specific platforms and technologies may enable optimal solutions? In what disease areas? Learn more about these and other questions as the panelists discuss the future potential of GCT.
Manufacturing quality and cost are critical for enabling rapid growth in GCT. Panelists will explore a variety of critical questions in this space. For example, are there historic parallels that can be drawn between GCT manufacturing and other groundbreaking technologies? How do key manufacturing concerns in GCT differ from those for more conventional pharmaceutical? What are the long-term opportunities for non-viral vectors? Will manufacturing capacity be a limiting factor in GCT growth over the next 5 to 10 years?
At the end of 2021, roughly 410 novel drugs had been approved in the past decade. On average, there were 40 approvals per year with over 150 of them being between 2018 and 2020. What has changed in the approval process and what is the vision of the future state? What will happen over the next 1–3 years? What does the new iteration of the Prescription Drug User Fees Act (PDUFA) need to do in this area and which fields show the greatest potential for innovation in CGT?
This panel will delve into clinical trials for GCT. How do these trials differ from those for conventional therapeutics? What are the key lessons learned from completed GCT trials? How is the regulatory landscape shifting and what will that mean for the future of GCT?
Dr. Bourla will share what Pfizer has learned from its leadership on mRNA and the development of the Covid vaccine that can be extrapolated to other R&D.
As we enter the third year of the coronavirus pandemic, the world is shifting to a new strategy: living with and managing COVID as a part of our everyday lives. What will the coming year look like? How will mitigation measures differ in this new phase? What about treatment strategies? Should we be bracing for another surge?
This panel will feature a discussion of global biotech clusters with a deep dive into the New England/Boston area. How does the capital availability, scale, and density of New England drive local growth in GCT? Also, the influx of large biopharmaceutical companies into the region has fueled global outcomes. What is the future impact of these investments and when will they peak? How will the biopharmaceutical landscape in New England appear in 2030?
The role of patients and their experiences are critical as the promise of GCT unfolds. This panel will discuss the patient experience and explore the challenges different patient populations face, both in rare diseases and more common conditions. Panelists will also discuss financial considerations, clinical trial access, and the role of advocacy groups in GCT.
As many countries begin to turn the corner on COVID-19, they face a resurgence of chronic illnesses, such as cancer and cardiovascular disease, that were not adequately addressed during the pandemic, and for which new treatments are urgently needed. Population aging – and the resulting increase in chronic diseases associated with aging – has compounded the challenge. There’s never been a greater need for biopharmaceutical innovation – or, fortunately, a greater ability to innovate. Amgen is investing in new discovery research capabilities that portend a revolution in drug design and development.
Understanding long-term Gene and Cell Therapy investment complexities requires a keen awareness of where the science and the markets are headed. That’s why “The Doctor is In” in these updates on the latest GCT technologies. Presented by Mass General Brigham clinicians and innovators from the front lines of care, the sessions are co-hosted by expert analysts from Bank of America and include interactive discussion and Q&A.
In this session, Dr. Vavvas will discuss examples of clinical trials in rare diseases and share insights into how clinical trials should be approached for rare and ultra-rare diseases and how study design is not a one-size fits all.
In this session, hear experts weigh in on the possibilities of cell therapy development and transplantation for the treatment of Parkinson’s Disease. What does the futures hold and how do we get there?
In this session, Dr. Nikiforow will provide insights into the world of gene therapy manufacturing and the complexities of scaling, costs and insurance reimbursement.
In this session, Dr. Marks will discuss the ins and outs of regulatory challenges for biological products and therapies in gene and cell therapy and the responsibility to assure safety and effectiveness.
Dark genome, accounting for ~98.5% of the human genome and containing the non-coding part, offers unprecedented opportunity to look for novel elements that could play a role in human health. This non-coding region consists of repeat elements, enhancers, regulatory sequences and non-coding RNAs. This session will explore this exciting new frontier in biology and how to translate this so called “junk” and previously ignored genome into potential novel therapeutics.
Panelists will discuss the life sciences capital markets environment with particular emphasis on private and public fundraising for GCT companies. What trends do panelists observe that will impact the availability and cost of capital for GCT? Are there novel fundraising structures that will serve GCT in the future?
As one of the foremost researchers of CAR-T cancer treatments, Dr. June will share what he believes is the next wave of cell-and-gene based oncology research and how his work set the stage for breakthrough developments in cancer.
Richard W. Vague Professor in Immunotherapy, Director, Center for Cellular Immunotherapies, Director, Parker Institute for Cancer Immunotherapy, University of Pennsylvania Perelman School of Medicine
This panel will examine the role of academia in driving the promise of GCT. How does academic innovation contribute to the success of GCT? What are the risks and opportunities? Which models have proven most successful and what is the impact on clinical translation? How can these partnerships be accelerated?
Richard W. Vague Professor in Immunotherapy, Director, Center for Cellular Immunotherapies, Director, Parker Institute for Cancer Immunotherapy, University of Pennsylvania Perelman School of Medicine
This panel will bring together gene and cell therapy leaders from across the world to discuss the latest opportunities and challenges in the field, from the investment landscape to key technology developments to manufacturing and regulatory barriers. These global experts will offer first-hand insights on the systemic complexity of this advancing field and its therapeutic promise.
Chronic inflammation in the brain is now recognized as a contributor to many neurodegenerative diseases, ranging from Parkinson’s disease to multiple sclerosis to Alzheimer’s disease. Are solutions to these historically intractable neurological diseases imminent or several years away? Are market-making platforms identifiable for neurological diseases? Are there novel genetic targets that can be explored? What are the prospects for cell therapies?
Cell therapies, ranging from CAR-T cells to stem-cell-based approaches, are emerging as a transformative therapeutic modality. Panelists will examine this emerging landscape and discuss a range of key topics. What drives differentiation in this space given the high number of competing technologies? How will the uptake of autologous cell therapies and allogeneic versions evolve? When will the regenerative medicine market mature?
This panel will explore how GCT technology could lead to disruptions in other areas of medicine, including surgery and medical devices, over the next several years. Could cell replacement therapy in diabetes advance enough to reduce the need for diabetes pumps or insulin? Will stem-cell-based methods for regenerating cartilage advance rapidly enough to disrupt the number of patients seeking hip and knee replacements? How is GCT driving innovations in surgical techniques?
What is the new generation of approaches to gene therapy manufacturing and delivery? What are the lessons learned from Covid and how can it be applied to custom disease response and the ability to custom design biologic organisms?
This panel will feature an in-depth discussion of the safety of gene and cell therapies. What are the unique safety concerns in this field, both acute and potential long-term risks? Which of these concerns are supported by clinical data versus the presumption of theoretical risk? What are the key issues for AAV-based gene therapies? Will redosing become feasible? What are the predominant safety concerns for in vivo versus ex vivo GCT modalities, including base editing?
The label “RNA” encompasses a wide array of biologically active agents spanning therapeutic modalities, vaccines, non-coding controls, and other forms. In this panel we will discuss a number of these forms, discuss examples of recent developments and illustrate why RNA developments represent a promising source of novel therapies and therapeutic approaches.
The Disruptive Dozen identifies and ranks the GCT technologies that Mass General Brigham faculty feel will break through over the next one to five years to significantly improve health care.
#TUBiol5227: Biomarkers & Biotargets: Genetic Testing and Bioethics
Curator: Stephen J. Williams, Ph.D.
The advent of direct to consumer (DTC) genetic testing and the resultant rapid increase in its popularity as well as companies offering such services has created some urgent and unique bioethical challenges surrounding this niche in the marketplace. At first, most DTC companies like 23andMe and Ancestry.com offered non-clinical or non-FDA approved genetic testing as a way for consumers to draw casual inferences from their DNA sequence and existence of known genes that are linked to disease risk, or to get a glimpse of their familial background. However, many issues arose, including legal, privacy, medical, and bioethical issues. Below are some articles which will explain and discuss many of these problems associated with the DTC genetic testing market as well as some alternatives which may exist.
As you can see,this market segment appears to want to expand into the nutritional consulting business as well as targeted biomarkers for specific diseases.
Rising incidence of genetic disorders across the globe will augment the market growth
Increasing prevalence of genetic disorders will propel the demand for direct-to-consumer genetic testing and will augment industry growth over the projected timeline. Increasing cases of genetic diseases such as breast cancer, achondroplasia, colorectal cancer and other diseases have elevated the need for cost-effective and efficient genetic testing avenues in the healthcare market.
For instance, according to the World Cancer Research Fund (WCRF), in 2018, over 2 million new cases of cancer were diagnosed across the globe. Also, breast cancer is stated as the second most commonly occurring cancer. Availability of superior quality and advanced direct-to-consumer genetic testing has drastically reduced the mortality rates in people suffering from cancer by providing vigilant surveillance data even before the onset of the disease. Hence, the aforementioned factors will propel the direct-to-consumer genetic testing market overt the forecast timeline.
Nutrigenomic Testing will provide robust market growth
The nutrigenomic testing segment was valued over USD 220 million market value in 2019 and its market will witness a tremendous growth over 2020-2028. The growth of the market segment is attributed to increasing research activities related to nutritional aspects. Moreover, obesity is another major factor that will boost the demand for direct-to-consumer genetic testing market.
Nutrigenomics testing enables professionals to recommend nutritional guidance and personalized diet to obese people and help them to keep their weight under control while maintaining a healthy lifestyle. Hence, above mentioned factors are anticipated to augment the demand and adoption rate of direct-to-consumer genetic testing through 2028.
Browse key industry insights spread across 161 pages with 126 market data tables & 10 figures & charts from the report, “Direct-To-Consumer Genetic Testing Market Size By Test Type (Carrier Testing, Predictive Testing, Ancestry & Relationship Testing, Nutrigenomics Testing), By Distribution Channel (Online Platforms, Over-the-Counter), By Technology (Targeted Analysis, Single Nucleotide Polymorphism (SNP) Chips, Whole Genome Sequencing (WGS)), Industry Analysis Report, Regional Outlook, Application Potential, Price Trends, Competitive Market Share & Forecast, 2020 – 2028” in detail along with the table of contents: https://www.gminsights.com/industry-analysis/direct-to-consumer-dtc-genetic-testing-market
Targeted analysis techniques will drive the market growth over the foreseeable future
Based on technology, the DTC genetic testing market is segmented into whole genome sequencing (WGS), targeted analysis, and single nucleotide polymorphism (SNP) chips. The targeted analysis market segment is projected to witness around 12% CAGR over the forecast period. The segmental growth is attributed to the recent advancements in genetic testing methods that has revolutionized the detection and characterization of genetic codes.
Targeted analysis is mainly utilized to determine any defects in genes that are responsible for a disorder or a disease. Also, growing demand for personalized medicine amongst the population suffering from genetic diseases will boost the demand for targeted analysis technology. As the technology is relatively cheaper, it is highly preferred method used in direct-to-consumer genetic testing procedures. These advantages of targeted analysis are expected to enhance the market growth over the foreseeable future.
Over-the-counter segment will experience a notable growth over the forecast period
The over-the-counter distribution channel is projected to witness around 11% CAGR through 2028. The segmental growth is attributed to the ease in purchasing a test kit for the consumers living in rural areas of developing countries. Consumers prefer over-the-counter distribution channel as they are directly examined by regulatory agencies making it safer to use, thereby driving the market growth over the forecast timeline.
Favorable regulations provide lucrative growth opportunities for direct-to-consumer genetic testing
Europe direct-to-consumer genetic testing market held around 26% share in 2019 and was valued at around USD 290 million. The regional growth is due to elevated government spending on healthcare to provide easy access to genetic testing avenues. Furthermore, European regulatory bodies are working on improving the regulations set on the direct-to-consumer genetic testing methods. Hence, the above-mentioned factors will play significant role in the market growth.
Focus of market players on introducing innovative direct-to-consumer genetic testing devices will offer several growth opportunities
Few of the eminent players operating in direct-to-consumer genetic testing market share include Ancestry, Color Genomics, Living DNA, Mapmygenome, Easy DNA, FamilytreeDNA (Gene By Gene), Full Genome Corporation, Helix OpCo LLC, Identigene, Karmagenes, MyHeritage, Pathway genomics, Genesis Healthcare, and 23andMe. These market players have undertaken various business strategies to enhance their financial stability and help them evolve as leading companies in the direct-to-consumer genetic testing industry.
For example, in November 2018, Helix launched a new genetic testing product, DNA discovery kit, that allows customer to delve into their ancestry. This development expanded the firm’s product portfolio, thereby propelling industry growth in the market.
The following posts discuss bioethical issues related to genetic testing and personalized medicine from a clinicians and scientisit’s perspective
Question:Each of these articles discusses certain bioethical issues although focuses on personalized medicine and treatment. Given your understanding of the robust process involved in validating clinical biomarkers and the current state of the DTC market, how could DTC testing results misinform patients and create mistrust in the physician-patient relationship?
Question: If you are developing a targeted treatment with a companion diagnostic, what bioethical concerns would you address during the drug development process to ensure fair, equitable and ethical treatment of all patients, in trials as well as post market?
Articles on Genetic Testing, Companion Diagnostics and Regulatory Mechanisms
Question: What type of regulatory concerns should one have during the drug development process in regards to use of biomarker testing?From the last article on Protecting Your IP how important is it, as a drug developer, to involve all payers during the drug development process?
NCCN Shares Latest Expert Recommendations for Prostate Cancer in Spanish and Portuguese
Reporter: Stephen J. Williams, Ph.D.
Currently many biomedical texts and US government agency guidelines are only offered in English or only offered in different languages upon request. However Spanish is spoken in a majority of countries worldwide and medical text in that language would serve as an under-served need. In addition, Portuguese is the main language in the largest country in South America, Brazil.
The LPBI Group and others have noticed this need for medical translation to other languages. Currently LPBI Group is translating their medical e-book offerings into Spanish (for more details see https://pharmaceuticalintelligence.com/vision/)
Below is an article on The National Comprehensive Cancer Network’s decision to offer their cancer treatment guidelines in Spanish and Portuguese.
PLYMOUTH MEETING, PA [8 September, 2021] — The National Comprehensive Cancer Network® (NCCN®)—a nonprofit alliance of leading cancer centers in the United States—announces recently-updated versions of evidence- and expert consensus-based guidelines for treating prostate cancer, translated into Spanish and Portuguese. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) feature frequently updated cancer treatment recommendations from multidisciplinary panels of experts across NCCN Member Institutions. Independent studies have repeatedly found that following these recommendations correlates with better outcomes and longer survival.
“Everyone with prostate cancer should have access to care that is based on current and reliable evidence,” said Robert W. Carlson, MD, Chief Executive Officer, NCCN. “These updated translations—along with all of our other translated and adapted resources—help us to define and advance high-quality, high-value, patient-centered cancer care globally, so patients everywhere can live better lives.”
Prostate cancer is the second most commonly occurring cancer in men, impacting more than a million people worldwide every year.[1] In 2020, the NCCN Guidelines® for Prostate Cancer were downloaded more than 200,000 times by people outside of the United States. Approximately 47 percent of registered users for NCCN.org are located outside the U.S., with Brazil, Spain, and Mexico among the top ten countries represented.
“NCCN Guidelines are incredibly helpful resources in the work we do to ensure cancer care across Latin America meets the highest standards,” said Diogo Bastos, MD, and Andrey Soares, MD, Chair and Scientific Director of the Genitourinary Group of The Latin American Cooperative Oncology Group (LACOG). The organization has worked with NCCN in the past to develop Latin American editions of the NCCN Guidelines for Breast Cancer, Colon Cancer, Non-Small Cell Lung Cancer, Prostate Cancer, Multiple Myeloma, and Rectal Cancer, and co-hosted a webinar on “Management of Prostate Cancer for Latin America” earlier this year. “We appreciate all of NCCN’s efforts to make sure these gold-standard recommendations are accessible to non-English speakers and applicable for varying circumstances.”
NCCN also publishes NCCN Guidelines for Patients®, containing the same treatment information in non-medical terms, intended for patients and caregivers. The NCCN Guidelines for Patients: Prostate Cancer were found to be among the most trustworthy sources of information online according to a recent international study. These patient guidelines have been divided into two books, covering early and advanced prostate cancer; both have been translated into Spanish and Portuguese as well.
NCCN collaborates with organizations across the globe on resources based on the NCCN Guidelines that account for local accessibility, consideration of metabolic differences in populations, and regional regulatory variation. They can be downloaded free-of-charge for non-commercial use at NCCN.org/global or via the Virtual Library of NCCN Guidelines App. Learn more and join the conversation with the hashtag #NCCNGlobal.
[1] Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global Cancer Statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, in press. The online GLOBOCAN 2018 database is accessible at http://gco.iarc.fr/, as part of IARC’s Global Cancer Observatory.
About the National Comprehensive Cancer Network
The National Comprehensive Cancer Network® (NCCN®) is a not-for-profit alliance of leading cancer centers devoted to patient care, research, and education. NCCN is dedicated to improving and facilitating quality, effective, efficient, and accessible cancer care so patients can live better lives. The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) provide transparent, evidence-based, expert consensus recommendations for cancer treatment, prevention, and supportive services; they are the recognized standard for clinical direction and policy in cancer management and the most thorough and frequently-updated clinical practice guidelines available in any area of medicine. The NCCN Guidelines for Patients® provide expert cancer treatment information to inform and empower patients and caregivers, through support from the NCCN Foundation®. NCCN also advances continuing education, global initiatives, policy, and research collaboration and publication in oncology. Visit NCCN.org for more information and follow NCCN on Facebook @NCCNorg, Instagram @NCCNorg, and Twitter @NCCN.
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Reporter: Danielle Smolyar, Research Assistant 3 – Text Analysis for 2.0 LPBI Group’s TNS #1 – 2020/2021 Academic Internship in Medical Text Analysis (MTA)
Recently, researchers at Mount Sinai were able to develop a therapeutic agent that shows high levels of effectiveness in Vitro disrupting a biological pathway that allow cancer to survive. This finding is according to a paper which was published in Cancer Discovery, which is a Journal of the American Association of cancer research in July 2021.
The therapy in which they focus on is a molecule named MS21, which causes the degradation of AKT which is an enzyme that is very active and present in cancers. In this study there was much evidence that pharmacological degradation of AKT is a feasible treatment for cancer’s which have a mutation in certain genes.
AKT is a cancer gene that encodes an enzyme that is abnormally activated in cancer cells to stimulate tumor growth. The degradation of AKT reverses all these processes which ultimately inhibits further tumor growth.
“Our study lays a solid foundation for the clinical development of an AKT degrader for the treatment of human cancers with certain gene mutations,” said Ramon Parsons, MD, Ph.D., Director of The Tisch Cancer Institute and Ward-Coleman Chair in Cancer Research and Chair of Oncological Sciences at the Icahn School of Medicine at Mount Sinai. “Examination of 44,000 human cancers identified that 19 percent of tumors have at least one of these mutations, suggesting that a large population of cancer patients could benefit from therapy with an AKT degrader such as MS21.”
MS21 was tested and human cancer derived cell lines, is used in Laboratories as a model to study the efficacy of different cancer therapies.
At Mount Sinai they were looking to develop MS21 with an industry partner in order to open clinical trials for patients.
“Translating these findings into effective cancer therapies for patients is a high priority because the mutations and the resulting cancer-driving pathways that we lay out in this study are arguably the most commonly activated pathways in human cancer, but this effort has proven to be particularly challenging,” said Jian Jin, Ph.D., Mount Sinai Professor in Therapeutics Discovery and Director of the Mount Sinai Center for Therapeutics Discovery at Icahn Mount Sinai. “We look forward to an opportunity to develop this molecule into a therapy that is ready to be studied in clinical trials.”
Advancing cancer precision medicine by creating a better toolbox for cancer therapy
Jian Jin1,2,3,4,5*, Arvin C. Dar1,2,3,4, Deborah Doroshow1
A
mong approximately 20,000 proteins in the human proteome, 627 have been identified by cancer-dependency studies as priority cancer targets, which are functionally important for various cancers. Of these 600-plus priority targets, 232 are enzymes and 395 are nonenzyme proteins (1). Tremendous progress has been made over the past several decades in targeting enzymes, in particular kinas-es, which have suitable binding pockets that can be occupied by small-molecule inhibitors, leading to U.S. Food and Drug Administration (FDA) approvals of many small-molecule drugs as targeted anticancer thera-
1Tisch Cancer Institute; 2Department of Oncological Sciences; 3Department of Pharmacological Sciences; 4Mount Sinai Center for Therapeutics Discovery; 5Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY
pies. However, most of the 395 nonenzyme protein targets, including transcription factors (TFs), do not have suitable binding pockets that can be effectively targeted by small molecules. These targets have consequently been considered undruggable; however, new cutting-edge approaches and technologies have recently been developed to target some of these “un-druggable” proteins in order to advance precision oncology.
TPD, a promising approach to precision cancer therapeutics
Targeted protein degradation (TPD) refers to the process of chemically eliminating proteins of interest (POIs) by utilizing small molecules, which are broadly divided into two types of modalities: PROteolysis Targeting Chimeras (PROTACs) and molecular glues (2). PROTACs are het-erobifunctional small molecules that contain two moieties: one binding the POI, linked to another binding an ubiquitin E3 ligase. The induced proximity between the POI and ubiquitination machinery leads to selective polyubiquitylation of the POI and its subsequent degradation by the ubiquitin–proteasome system (UPS). Molecular glues are monovalent small molecules, which, when built for TPD, directly induce interactions between the POI and an E3 ligase, also resulting in polyubiquitylation and subsequent degradation of the POI by the UPS. One of the biggest potential advantages of these therapeutic modalities over traditional inhibitors is that PROTACs and molecular glues can target undruggable proteins. Explosive growth has been seen in the TPD field over recent years (2, 3). Here, we highlight several recent advancements.
TF-PROTAC, a novel platform for targeting undruggable
tumorigenic TFs
Many undruggable TFs are tumorigenic. To target them, TF-PROTAC was developed (4), which exploits the fact that TFs bind DNA in a sequence-specific manner. TF-PROTAC was created to selectively bind a TF and E3 ligase simultaneously, by conjugating a DNA oligonucleotide specific for the TF of interest to a selective E3 ligase ligand. As stated earlier, this simultaneous binding and induced proximity leads to selective polyubiquitination of the TF and its subsequent degradation by the UPS. TF-PROTAC is a cutting-edge technology that could potentially provide a universal strategy for targeting most undruggable tumorigenic TFs.
Development of novel PROTAC degraders
WDR5, an important scaffolding protein, not an enzyme, is essential for sustaining tumorigenesis in multiple cancers, including MLL-rearranged (MLL-r) leukemia. However, small-molecule inhibitors that block the pro-tein–protein interaction (PPI) between WDR5 and its binding partners exhibit very modest cancer cell–killing effects, likely due to the confounding fact that these PPI inhibitors target only some—but not all—of WDR5’s on-cogenic functions. To address this shortcoming, a novel WDR5 PROTAC, MS67, was recently created using a powerful approach that effectively eliminates the protein and thereby all WDR5 functions via ternary complex structure-based design (Figure 1) (5). MS67 is a highly effective WDR5 degrader that potently and selectively degrades WDR5 and effectively suppresses the proliferation of tumor cells both in vitro and in vivo. This study provides strong evidence that pharmacological degradation of WDR5 as a novel therapeutic strategy is superior to WDR5 PPI inhibition for treating WDR5-dependent cancers.
EZH2 is an oncogenic methyltransferase that catalyzes histone H3 lysine 27 trimethylation, mediating gene repression. In addition to this canonical function, EZH2 has numerous noncanonical tumorigenic functions. EZH2 enzymatic inhibitors, however, are generally ineffective in
suppressing tumor growth in triple-negative breast cancer (TNBC) and MLL-r leukemia models and fail to phenocopy antitumor effects induced by EZH2 knockdown strategies. To target both canonical and noncanon-ical oncogenic functions of EZH2, several novel EZH2 degraders were recently developed, including MS1943, a hydrophobic tag–based EZH2 degrader (6), and MS177, an EZH2 PROTAC (7). MS1943 and MS177 effectively degrade EZH2 and suppress in vitro and in vivo growth in TNBC and MLL-r leukemia, respectively, suggesting that EZH2 degraders could provide a novel and effective therapeutic strategy for EZH2-dependent tumors.
MS21, a novel AKT PROTAC degrader, was developed to target activated AKT, the central node of the PI3K–AKT–mTOR signaling pathway (8). MS21 effectively suppresses the proliferation of PI3K–PTEN pathway-mutant cancers with wild-type KRAS and BRAF, which represent a large percentage of all human cancers. Another recent technology that expands the bifunctional toolbox for TPD is the demonstration that the E3 ligase KEAP1 can be leveraged for PROTAC development using a selective KEAP1 ligand (9). Overall, tremendous progress has been made in discovering novel degraders, some of which have advanced to clinical development as targeted therapies (2, 3).
Novel approaches to selective TPD in cancer cells
To minimize uncontrolled protein degradation in normal tissues, which may cause potential toxicity, a new technology was developed that incorporates a light-inducible switch, termed “opto-PROTAC” (10). This switch serves as a caging group that renders opto-PROTAC inactive in all cells in the absence of ultraviolet (UV) light. Upon UV irradiation, however, the caging group is removed, resulting in the release of the active degrader and spatiotemporal control of TPD in cancer cells. Another strategy to achieve selective TPD in cancer over normal cells is to cage degraders with a folate group (11, 12). Folate-caged degraders are inert and selectively concentrated within cancer cells, which overexpress folate receptors compared to normal cells. The caging group is subsequently removed inside tumor cells, releasing active degraders and achieving selective TPD in these cells. These novel approaches potentially enable degraders to be precision cancer medicines.
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Frontiers of Medical Research: Cancer
Trametiglue, a novel and atypical molecular glue
The RAS–RAF–MEK–ERK signaling pathway, one of the most frequently mutated pathways in cancer, has been intensively targeted. Several drugs, such as the KRAS G12C inhibitor sotorasib and the MEK inhibitor trametinib, have been approved by the FDA. A significant advancement in this area is the discovery that trametinib unexpectedly binds a pseudokinase scaffold termed “KSR” in addition to MEK through interfacial contacts (13). Based on this structural and mechanistic insight, tra-metiglue, an analog of trametinib, was created as a novel molecular glue to limit adaptive resistance to MEK inhibition by enhancing interfacial binding between MEK, KSR, and the related homolog RAF. This study provides a strong foundation for developing next-generation drugs that target the RAS pathway.
TF-DUBTAC, a novel technology to stabilize undruggable tumor-suppressive TFs
Complementary to degrading tumorigenic TFs, stabilizing tumor-suppressive TFs could provide another effective approach for treating cancer. While most tumor-suppressive TFs are undruggable, TF-DUBTAC was recently developed as a generalizable platform to stabilize tumor-suppressive TFs (14). Deubiquitinase-targeting chimeras (DUBTACs) are heterobifunctional small molecules with a deubiquitinase (DUB) ligand linked to a POI ligand, which stabilize POIs by harnessing the deubiq-uitination machinery (15). Similar to TF-PROTAC, TF-DUBTAC exploits the fact that most TFs bind specific DNA sequences. TF-DUBTAC links a DNA oligonucleotide specific to a tumor-suppressive TF with a selective DUB ligand, resulting in simultaneous binding of the TF and DUB. The induced proximity between the TF and DUB leads to selective deubiquiti-
Putting a bull’s-eye on cancer’s back
Scientists are aiming the immune systems’ “troops” directly at tumors to better treat cancer
Joshua D. Brody, Brian D. Brown
I
mmunotherapy has transformed the treatment of several types of cancers. In particular, immune checkpoint blockade (ICB), which reinvigorates killer T cells, has helped extend the lives of many patients with advanced-stage lung, bladder, kidney, or skin cancers. Unfortunately, ~80% of patients do not respond to current immunotherapies or even-tually relapse. Emerging data indicate that one of the most profound ways cancers resist immunotherapy is by keeping killer T cells out of the tumor and putting other immune cells in a suppressed state (1). This understanding is giving rise to a new frontier in immunotherapy that is using synthetic biology and other approaches to reprogram the tumor from immune “cold” to immune “hot,” so T cells can be recruited to the tumor, and enter, target, and destroy the cancer cells (2) (Figure 1).
Cancers protect themselves by keeping out immune cells
Cancers grow in tissues like foreign invaders. Though they start from healthy cells, mutations turn cells malignant and allow them to grow unchecked. T cells can kill malignant cells that express mutated proteins, but cancers employ strategies to fend off the T cells. One way they do this is
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nation of the TF and its stabilization. As an exciting new technology, TF-DUBTAC provides a potential general strategy to stabilize most undrugga-ble tumor-suppressive TFs for treating cancer.
Future outlook
The breathtaking pace we are seeing in the development of innovative approaches and technologies for advancing cancer therapies is only expected to accelerate. The promising clinical results achieved by PROTACs with established targets are particularly encouraging and pave the way for development of PROTACs for newer and more innovative targets. These groundbreaking discoveries have now put opportunities to fully realize cancer precision medicine within our reach.
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Other related articles published on this Open Access Online Scientific Journal include the following:
Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes
From High-Throughput Assay to Systems Biology: New Tools for Drug Discovery
Curator: Stephen J. Williams, PhD
Marc W. Kirschner*
Department of Systems Biology Harvard Medical School
Boston, Massachusetts 02115
With the new excitement about systems biology, there is understandable interest in a definition. This has proven somewhat difficult. Scientific fields, like species, arise by descent with modification, so in their earliest forms even the founders of great dynasties are only marginally different than their sister fields and species. It is only in retrospect that we can recognize the significant founding events. Before embarking on a definition of systems biology, it may be worth remembering that confusion and controversy surrounded the introduction of the term “molecular biology,” with claims that it hardly differed from biochemistry. Yet in retrospect molecular biology was new and different. It introduced both new subject matter and new technological approaches, in addition to a new style.
As a point of departure for systems biology, consider the quintessential experiment in the founding of molecular biology, the one gene one enzyme hypothesis of Beadle and Tatum. This experiment first connected the genotype directly to the phenotype on a molecular level, although efforts in that direction can certainly be found in the work of Archibald Garrod, Sewell Wright, and others. Here a protein (in this case an enzyme) is seen to be a product of a single gene, and a single function; the completion of a specific step in amino acid biosynthesis is the direct result. It took the next 30 years to fill in the gaps in this process. Yet the one gene one enzyme hypothesis looks very different to us today. What is the function of tubulin, of PI-3 kinase or of rac? Could we accurately predict the phenotype of a nonlethal mutation in these genes in a multicellular organism? Although we can connect structure to the gene, we can no longer infer its larger purpose in the cell or in the organism. There are too many purposes; what the protein does is defined by context. The context also includes a history, either developmental or physiological. Thus the behavior of the Wnt signaling pathway depends on the previous lineage, the “where and when” questions of embryonic development. Similarly the behavior of the immune system depends on previous experience in a variable environment. All of these features stress how inadequate an explanation for function we can achieve solely by trying to identify genes (by annotating them!) and characterizing their transcriptional control circuits.
That we are at a crossroads in how to explore biology is not at all clear to many. Biology is hardly in its dotage; the process of discovery seems to have been perfected, accelerated, and made universally applicable to all fields of biology. With the completion of the human genome and the genomes of other species, we have a glimpse of many more genes than we ever had before to study. We are like naturalists discovering a new continent, enthralled with the diversity itself. But we have also at the same time glimpsed the finiteness of this list of genes, a disturbingly small list. We have seen that the diversity of genes cannot approximate the diversity of functions within an organism. In response, we have argued that combinatorial use of small numbers of components can generate all the diversity that is needed. This has had its recent incarnation in the simplistic view that the rules of cis-regulatory control on DNA can directly lead to an understanding of organisms and their evolution. Yet this assumes that the gene products can be linked together in arbitrary combinations, something that is not assured in chemistry. It also downplays the significant regulatory features that involve interactions between gene products, their localization, binding, posttranslational modification, degradation, etc. The big question to understand in biology is not regulatory linkage but the nature of biological systems that allows them to be linked together in many nonlethal and even useful combinations. More and more we come to realize that understanding the conserved genes and their conserved circuits will require an understanding of their special properties that allow them to function together to generate different phenotypes in different tissues of metazoan organisms. These circuits may have certain robustness, but more important they have adaptability and versatility. The ease of putting conserved processes under regulatory control is an inherent design feature of the processes themselves. Among other things it loads the deck in evolutionary variation and makes it more feasible to generate useful phenotypes upon which selection can act.
Systems biology offers an opportunity to study how the phenotype is generated from the genotype and with it a glimpse of how evolution has crafted the phenotype. One aspect of systems biology is the development of techniques to examine broadly the level of protein, RNA, and DNA on a gene by gene basis and even the posttranslational modification and localization of proteins. In a very short time we have witnessed the development of high-throughput biology, forcing us to consider cellular processes in toto. Even though much of the data is noisy and today partially inconsistent and incomplete, this has been a radical shift in the way we tear apart problems one interaction at a time. When coupled with gene deletions by RNAi and classical methods, and with the use of chemical tools tailored to proteins and protein domains, these high-throughput techniques become still more powerful.
High-throughput biology has opened up another important area of systems biology: it has brought us out into the field again or at least made us aware that there is a world outside our laboratories. Our model systems have been chosen intentionally to be of limited genetic diversity and examined in a highly controlled and reproducible environment. The real world of ecology, evolution, and human disease is a very different place. When genetics separated from the rest of biology in the early part of the 20th century, most geneticists sought to understand heredity and chose to study traits in the organism that could be easily scored and could be used to reveal genetic mechanisms. This was later extended to powerful effect to use genetics to study cell biological and developmental mechanisms. Some geneticists, including a large school in Russia in the early 20th century, continued to study the genetics of natural populations, focusing on traits important for survival. That branch of genetics is coming back strongly with the power of phenotypic assays on the RNA and protein level. As human beings we are most concerned not with using our genetic misfortunes to unravel biology’s complexity (important as that is) but with the role of our genetics in our individual survival. The context for understanding this is still not available, even though the data are now coming in torrents, for many of the genes that will contribute to our survival will have small quantitative effects, partially masked or accentuated by other genetic and environmental conditions. To understand the genetic basis of disease will require not just mapping these genes but an understanding of how the phenotype is created in the first place and the messy interactions between genetic variation and environmental variation.
Extracts and explants are relatively accessible to synthetic manipulation. Next there is the explicit reconstruction of circuits within cells or the deliberate modification of those circuits. This has occurred for a while in biology, but the difference is that now we wish to construct or intervene with the explicit purpose of describing the dynamical features of these synthetic or partially synthetic systems. There are more and more tools to intervene and more and more tools to measure. Although these fall short of total descriptions of cells and organisms, the detailed information will give us a sense of the special life-like processes of circuits, proteins, cells in tissues, and whole organisms in their environment. This meso-scale systems biology will help establish the correspondence between molecules and large-scale physiology.
You are probably running out of patience for some definition of systems biology. In any case, I do not think the explicit definition of systems biology should come from me but should await the words of the first great modern systems biologist. She or he is probably among us now. However, if forced to provide some kind of label for systems biology, I would simply say that systems biology is the study of the behavior of complex biological organization and processes in terms of the molecular constituents. It is built on molecular biology in its special concern for information transfer, on physiology for its special concern with adaptive states of the cell and organism, on developmental biology for the importance of defining a succession of physiological states in that process, and on evolutionary biology and ecology for the appreciation that all aspects of the organism are products of selection, a selection we rarely understand on a molecular level. Systems biology attempts all of this through quantitative measurement, modeling, reconstruction, and theory. Systems biology is not a branch of physics but differs from physics in that the primary task is to understand how biology generates variation. No such imperative to create variation exists in the physical world. It is a new principle that Darwin understood and upon which all of life hinges. That sounds different enough for me to justify a new field and a new name. Furthermore, the success of systems biology is essential if we are to understand life; its success is far from assured—a good field for those seeking risk and adventure.
Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no “one size fits all” approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.
5.1.5. Large-Scale Proteomics
While FITExP is based on protein expression regulation during apoptosis, a study of Ruprecht et al. showed that proteomic changes are induced both by cytotoxic and non-cytotoxic compounds, which can be detected by mass spectrometry to give information on a compound’s mechanism of action. They developed a large-scale proteome-wide mass spectrometry analysis platform for MOA studies, profiling five lung cancer cell lines with over 50 drugs. Aggregation analysis over the different cell lines and the different compounds showed that one-quarter of the drugs changed the abundance of their protein target. This approach allowed target confirmation of molecular degraders such as PROTACs or molecular glues. Finally, this method yielded unexpected off-target mechanisms for the MAP2K1/2 inhibitor PD184352 and the ALK inhibitor ceritinib [97]. While such a mapping approach clearly provides a wealth of information, it might not be easily attainable for groups that are not equipped for high-throughput endeavors.
All-in-all, mass spectrometry methods have gained a lot of traction in recent years and have been successfully applied for target deconvolution and MOA studies of small molecules. As with all high-throughput methods, challenges lie in the accessibility of the instruments (both from a time and cost perspective) and data analysis of complex and extensive data sets.
5.2. Genetic Approaches
Both label-based and mass spectrometry proteomic approaches are based on the physical interaction between a small molecule and a protein target, and focus on the proteome for target deconvolution. It has been long realized that genetics provides an alternative avenue to understand a compound’s action, either through precise modification of protein levels, or by inducing protein mutations. First realized in yeast as a genetically tractable organism over 20 years ago, recent advances in genetic manipulation of mammalian cells have opened up important opportunities for target identification and MOA studies through genetic screening in relevant cell types [98]. Genetic approaches can be roughly divided into two main areas, with the first centering on the identification of mutations that confer compound resistance (Figure 3a), and the second on genome-wide perturbation of gene function and the concomitant changes in sensitivity to the compound (Figure 3b). While both methods can be used to identify or confirm drug targets, the latter category often provides many additional insights in the compound’s mode of action.
Figure 3. Genetic methods for target identification and mode of action studies. Schematic representations of (a) resistance cloning, and (b) chemogenetic interaction screens.
5.2.1. Resistance Cloning
The “gold standard” in drug target confirmation is to identify mutations in the presumed target protein that render it insensitive to drug treatment. Conversely, different groups have sought to use this principle as a target identification method based on the concept that cells grown in the presence of a cytotoxic drug will either die or develop mutations that will make them resistant to the compound. With recent advances in deep sequencing it is now possible to then scan the transcriptome [99] or genome [100] of the cells for resistance-inducing mutations. Genes that are mutated are then hypothesized to encode the protein target. For this approach to be successful, there are two initial requirements: (1) the compound needs to be cytotoxic for resistant clones to arise, and (2) the cell line needs to be genetically unstable for mutations to occur in a reasonable timeframe.
In 2012, the Kapoor group demonstrated in a proof-of-concept study that resistance cloning in mammalian cells, coupled to transcriptome sequencing (RNA-seq), yields the known polo-like kinase 1 (PLK1) target of the small molecule BI 2536. For this, they used the cancer cell line HCT-116, which is deficient in mismatch repair and consequently prone to mutations. They generated and sequenced multiple resistant clones, and clustered the clones based on similarity. PLK1 was the only gene that was mutated in multiple groups. Of note, one of the groups did not contain PLK1 mutations, but rather developed resistance through upregulation of ABCBA1, a drug efflux transporter, which is a general and non-specific resistance mechanism [101]. In a following study, they optimized their pipeline “DrugTargetSeqR”, by counter-screening for these types of multidrug resistance mechanisms so that these clones were excluded from further analysis (Figure 3a). Furthermore, they used CRISPR/Cas9-mediated gene editing to determine which mutations were sufficient to confer drug resistance, and as independent validation of the biochemical relevance of the obtained hits [102].
While HCT-116 cells are a useful model cell line for resistance cloning because of their genomic instability, they may not always be the cell line of choice, depending on the compound and process that is studied. Povedana et al. used CRISPR/Cas9 to engineer mismatch repair deficiencies in Ewing sarcoma cells and small cell lung cancer cells. They found that deletion of MSH2 results in hypermutations in these normally mutationally silent cells, resulting in the formation of resistant clones in the presence of bortezomib, MLN4924, and CD437, which are all cytotoxic compounds [103]. Recently, Neggers et al. reasoned that CRISPR/Cas9-induced non-homologous end-joining repair could be a viable strategy to create a wide variety of functional mutants of essential genes through in-frame mutations. Using a tiled sgRNA library targeting 75 target genes of investigational neoplastic drugs in HAP1 and K562 cells, they generated several KPT-9274 (an anticancer agent with unknown target)-resistant clones, and subsequent deep sequencing showed that the resistant clones were enriched in NAMPT sgRNAs. Direct target engagement was confirmed by co-crystallizing the compound with NAMPT [104]. In addition to these genetic mutation strategies, an alternative method is to grow the cells in the presence of a mutagenic chemical to induce higher mutagenesis rates [105,106].
When there is already a hypothesis on the pathway involved in compound action, the resistance cloning methodology can be extended to non-cytotoxic compounds. Sekine et al. developed a fluorescent reporter model for the integrated stress response, and used this cell line for target deconvolution of a small molecule inhibitor towards this pathway (ISRIB). Reporter cells were chemically mutagenized, and ISRIB-resistant clones were isolated by flow cytometry, yielding clones with various mutations in the delta subunit of guanine nucleotide exchange factor eIF2B [107].
While there are certainly successful examples of resistance cloning yielding a compound’s direct target as discussed above, resistance could also be caused by mutations or copy number alterations in downstream components of a signaling pathway. This is illustrated by clinical examples of acquired resistance to small molecules, nature’s way of “resistance cloning”. For example, resistance mechanisms in Hedgehog pathway-driven cancers towards the Smoothened inhibitor vismodegib include compound-resistant mutations in Smoothened, but also copy number changes in downstream activators SUFU and GLI2 [108]. It is, therefore, essential to conduct follow-up studies to confirm a direct interaction between a compound and the hit protein, as well as a lack of interaction with the mutated protein.
5.2.3. “Chemogenomics”: Examples of Gene-Drug Interaction Screens
When genetic perturbations are combined with small molecule drugs in a chemogenetic interaction screen, the effect of a gene’s perturbation on compound action is studied. Gene perturbation can render the cells resistant to the compound (suppressor interaction), or conversely, result in hypersensitivity and enhanced compound potency (synergistic interaction) [5,117,121]. Typically, cells are treated with the compound at a sublethal dose, to ascertain that both types of interactions can be found in the final dataset, and often it is necessary to use a variety of compound doses (i.e., LD20, LD30, LD50) and timepoints to obtain reliable insights (Figure 3b).
An early example of successful coupling of a phenotypic screen and downstream genetic screening for target identification is the study of Matheny et al. They identified STF-118804 as a compound with antileukemic properties. Treatment of MV411 cells, stably transduced with a high complexity, genome-wide shRNA library, with STF-118804 (4 rounds of increasing concentration) or DMSO control resulted in a marked depletion of cells containing shRNAs against nicotinamide phosphoribosyl transferase (NAMPT) [122].
The Bassik lab subsequently directly compared the performance of shRNA-mediated knockdown versus CRISPR/Cas9-knockout screens for the target elucidation of the antiviral drug GSK983. The data coming out of both screens were complementary, with the shRNA screen resulting in hits leading to the direct compound target and the CRISPR screen giving information on cellular mechanisms of action of the compound. A reason for this is likely the level of protein depletion that is reached by these methods: shRNAs lead to decreased protein levels, which is advantageous when studying essential genes. However, knockdown may not result in a phenotype for non-essential genes, in which case a full CRISPR-mediated knockout is necessary to observe effects [123].
Another NAMPT inhibitor was identified in a CRISPR/Cas9 “haplo-insufficiency (HIP)”-like approach [124]. Haploinsuffiency profiling is a well-established system in yeast which is performed in a ~50% protein background by heterozygous deletions [125]. As there is no control over CRISPR-mediated loss of alleles, compound treatment was performed at several timepoints after addition of the sgRNA library to HCT116 cells stably expressing Cas9, in the hope that editing would be incomplete at early timepoints, resulting in residual protein levels. Indeed, NAMPT was found to be the target of phenotypic hit LB-60-OF61, especially at earlier timepoints, confirming the hypothesis that some level of protein needs to be present to identify a compound’s direct target [124]. This approach was confirmed in another study, thereby showing that direct target identification through CRISPR-knockout screens is indeed possible [126].
An alternative strategy was employed by the Weissman lab, where they combined genome-wide CRISPR-interference and -activation screens to identify the target of the phase 3 drug rigosertib. They focused on hits that had opposite action in both screens, as in sensitizing in one but protective in the other, which were related to microtubule stability. In a next step, they created chemical-genetic profiles of a variety of microtubule destabilizing agents, rationalizing that compounds with the same target will have similar drug-gene interactions. For this, they made a focused library of sgRNAs, based on the most high-ranking hits in the rigosertib genome-wide CRISPRi screen, and compared the focused screen results of the different compounds. The profile for rigosertib clustered well with that of ABT-571, and rigorous target validation studies confirmed rigosertib binding to the colchicine binding site of tubulin—the same site as occupied by ABT-571 [127].
From the above examples, it is clear that genetic screens hold a lot of promise for target identification and MOA studies for small molecules. The CRISPR screening field is rapidly evolving, sgRNA libraries are continuously improving and increasingly commercially available, and new tools for data analysis are being developed [128]. The challenge lies in applying these screens to study compounds that are not cytotoxic, where finding the right dosage regimen will not be trivial.
SYSTEMS BIOLOGY AND CANCER RESEARCH & DRUG DISCOVERY
Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence
The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question
1. Introduction
The development and widespread use of high-throughput technologies founded the era of big data in biology and medicine. In particular, it led to an accumulation of large-scale data sets that opened a vast amount of possible applications for data-driven methodologies. In cancer, these applications range from fundamental research to clinical applications: molecular characteristics of tumors, tumor heterogeneity, drug discovery and potential treatments strategy. Therefore, data-driven bioinformatics research areas have tailored data mining technologies such as systems biology, machine learning, and deep learning, elaborated in this review paper (see Figure 1 and Figure 2). For example, in systems biology, data-driven approaches are applied to identify vital signaling pathways [1]. This pathway-centric analysis is particularly crucial in cancer research to understand the characteristics and heterogeneity of the tumor and tumor subtypes. Consequently, this high-throughput data-based analysis enables us to explore characteristics of cancers with a systems biology and a systems medicine point of view [2].Combining high-throughput techniques, especially next-generation sequencing (NGS), with appropriate analytical tools has allowed researchers to gain a deeper systematic understanding of cancer at various biological levels, most importantly genomics, transcriptomics, and epigenetics [3,4]. Furthermore, more sophisticated analysis tools based on computational modeling are introduced to decipher underlying molecular mechanisms in various cancer types. The increasing size and complexity of the data required the adaptation of bioinformatics processing pipelines for higher efficiency and sophisticated data mining methodologies, particularly for large-scale, NGS datasets [5]. Nowadays, more and more NGS studies integrate a systems biology approach and combine sequencing data with other types of information, for instance, protein family information, pathway, or protein–protein interaction (PPI) networks, in an integrative analysis. Experimentally validated knowledge in systems biology may enhance analysis models and guides them to uncover novel findings. Such integrated analyses have been useful to extract essential information from high-dimensional NGS data [6,7]. In order to deal with the increasing size and complexity, the application of machine learning, and specifically deep learning methodologies, have become state-of-the-art in NGS data analysis.
Figure 1. Next-generation sequencing data can originate from various experimental and technological conditions. Depending on the purpose of the experiment, one or more of the depicted omics types (Genomics, Transcriptomics, Epigenomics, or Single-Cell Omics) are analyzed. These approaches led to an accumulation of large-scale NGS datasets to solve various challenges of cancer research, molecular characterization, tumor heterogeneity, and drug target discovery. For instance, The Cancer Genome Atlas (TCGA) dataset contains multi-omics data from ten-thousands of patients. This dataset facilitates a variety of cancer researches for decades. Additionally, there are also independent tumor datasets, and, frequently, they are analyzed and compared with the TCGA dataset. As the large scale of omics data accumulated, various machine learning techniques are applied, e.g., graph algorithms and deep neural networks, for dimensionality reduction, clustering, or classification. (Created with BioRender.com.)
Figure 2. (a) A multitude of different types of data is produced by next-generation sequencing, for instance, in the fields of genomics, transcriptomics, and epigenomics. (b) Biological networks for biomarker validation: The in vivo or in vitro experiment results are considered ground truth. Statistical analysis on next-generation sequencing data produces candidate genes. Biological networks can validate these candidate genes and highlight the underlying biological mechanisms (Section 2.1). (c) De novo construction of Biological Networks: Machine learning models that aim to reconstruct biological networks can incorporate prior knowledge from different omics data. Subsequently, the model will predict new unknown interactions based on new omics information (Section 2.2). (d) Network-based machine learning: Machine learning models integrating biological networks as prior knowledge to improve predictive performance when applied to different NGS data (Section 2.3). (Created with BioRender.com).
Therefore, a large number of studies integrate NGS data with machine learning and propose a novel data-driven methodology in systems biology [8]. In particular, many network-based machine learning models have been developed to analyze cancer data and help to understand novel mechanisms in cancer development [9,10]. Moreover, deep neural networks (DNN) applied for large-scale data analysis improved the accuracy of computational models for mutation prediction [11,12], molecular subtyping [13,14], and drug repurposing [15,16].
2. Systems Biology in Cancer Research
Genes and their functions have been classified into gene sets based on experimental data. Our understandings of cancer concentrated into cancer hallmarks that define the characteristics of a tumor. This collective knowledge is used for the functional analysis of unseen data.. Furthermore, the regulatory relationships among genes were investigated, and, based on that, a pathway can be composed. In this manner, the accumulation of public high-throughput sequencing data raised many big-data challenges and opened new opportunities and areas of application for computer science. Two of the most vibrantly evolving areas are systems biology and machine learning which tackle different tasks such as understanding the cancer pathways [9], finding crucial genes in pathways [22,53], or predicting functions of unidentified or understudied genes [54]. Essentially, those models include prior knowledge to develop an analysis and enhance interpretability for high-dimensional data [2]. In addition to understanding cancer pathways with in silico analysis, pathway activity analysis incorporating two different types of data, pathways and omics data, is developed to understand heterogeneous characteristics of the tumor and cancer molecular subtyping. Due to its advantage in interpretability, various pathway-oriented methods are introduced and become a useful tool to understand a complex diseases such as cancer [55,56,57].
In this section, we will discuss how two related research fields, namely, systems biology and machine learning, can be integrated with three different approaches (see Figure 2), namely, biological network analysis for biomarker validation, the use of machine learning with systems biology, and network-based models.
2.1. Biological Network Analysis for Biomarker Validation
The detection of potential biomarkers indicative of specific cancer types or subtypes is a frequent goal of NGS data analysis in cancer research. For instance, a variety of bioinformatics tools and machine learning models aim at identify lists of genes that are significantly altered on a genomic, transcriptomic, or epigenomic level in cancer cells. Typically, statistical and machine learning methods are employed to find an optimal set of biomarkers, such as single nucleotide polymorphisms (SNPs), mutations, or differentially expressed genes crucial in cancer progression. Traditionally, resource-intensive in vitro analysis was required to discover or validate those markers. Therefore, systems biology offers in silico solutions to validate such findings using biological pathways or gene ontology information (Figure 2b) [58]. Subsequently, gene set enrichment analysis (GSEA) [50] or gene set analysis (GSA) [59] can be used to evaluate whether these lists of genes are significantly associated with cancer types and their specific characteristics. GSA, for instance, is available via web services like DAVID [60] and g:Profiler [61]. Moreover, other applications use gene ontology directly [62,63]. In addition to gene-set-based analysis, there are other methods that focuse on the topology of biological networks. These approaches evaluate various network structure parameters and analyze the connectivity of two genes or the size and interconnection of their neighbors [64,65]. According to the underlying idea, the mutated gene will show dysfunction and can affect its neighboring genes. Thus, the goal is to find abnormalities in a specific set of genes linked with an edge in a biological network. For instance, KeyPathwayMiner can extract informative network modules in various omics data [66]. In summary, these approaches aim at predicting the effect of dysfunctional genes among neighbors according to their connectivity or distances from specific genes such as hubs [67,68]. During the past few decades, the focus of cancer systems biology extended towards the analysis of cancer-related pathways since those pathways tend to carry more information than a gene set. Such analysis is called Pathway Enrichment Analysis (PEA) [69,70]. The use of PEA incorporates the topology of biological networks. However, simultaneously, the lack of coverage issue in pathway data needs to be considered. Because pathway data does not cover all known genes yet, an integration analysis on omics data can significantly drop in genes when incorporated with pathways. Genes that can not be mapped to any pathway are called ‘pathway orphan.’ In this manner, Rahmati et al. introduced a possible solution to overcome the ‘pathway orphan’ issue [71]. At the bottom line, regardless of whether researchers consider gene-set or pathway-based enrichment analysis, the performance and accuracy of both methods are highly dependent on the quality of the external gene-set and pathway data [72].
2.2. De Novo Construction of Biological Networks
While the known fraction of existing biological networks barely scratches the surface of the whole system of mechanisms occurring in each organism, machine learning models can improve on known network structures and can guide potential new findings [73,74]. This area of research is called de novo network construction (Figure 2c), and its predictive models can accelerate experimental validation by lowering time costs [75,76]. This interplay between in silico biological networks building and mining contributes to expanding our knowledge in a biological system. For instance, a gene co-expression network helps discover gene modules having similar functions [77]. Because gene co-expression networks are based on expressional changes under specific conditions, commonly, inferring a co-expression network requires many samples. The WGCNA package implements a representative model using weighted correlation for network construction that leads the development of the network biology field [78]. Due to NGS developments, the analysis of gene co-expression networks subsequently moved from microarray-based to RNA-seq based experimental data [79]. However, integration of these two types of data remains tricky. Ballouz et al. compared microarray and NGS-based co-expression networks and found the existence of a bias originating from batch effects between the two technologies [80]. Nevertheless, such approaches are suited to find disease-specific co-expressional gene modules. Thus, various studies based on the TCGA cancer co-expression network discovered characteristics of prognostic genes in the network [81]. Accordingly, a gene co-expression network is a condition-specific network rather than a general network for an organism. Gene regulatory networks can be inferred from the gene co-expression network when various data from different conditions in the same organism are available. Additionally, with various NGS applications, we can obtain multi-modal datasets about regulatory elements and their effects, such as epigenomic mechanisms on transcription and chromatin structure. Consequently, a gene regulatory network can consist of solely protein-coding genes or different regulatory node types such as transcription factors, inhibitors, promoter interactions, DNA methylations, and histone modifications affecting the gene expression system [82,83]. More recently, researchers were able to build networks based on a particular experimental setup. For instance, functional genomics or CRISPR technology enables the high-resolution regulatory networks in an organism [84]. Other than gene co-expression or regulatory networks, drug target, and drug repurposing studies are active research areas focusing on the de novo construction of drug-to-target networks to allow the potential repurposing of drugs [76,85].
2.3. Network Based Machine Learning
A network-based machine learning model directly integrates the insights of biological networks within the algorithm (Figure 2d) to ultimately improve predictive performance concerning cancer subtyping or susceptibility to therapy. Following the establishment of high-quality biological networks based on NGS technologies, these biological networks were suited to be integrated into advanced predictive models. In this manner, Zhang et al., categorized network-based machine learning approaches upon their usage into three groups: (i) model-based integration, (ii) pre-processing integration, and (iii) post-analysis integration [7]. Network-based models map the omics data onto a biological network, and proper algorithms travel the network while considering both values of nodes and edges and network topology. In the pre-processing integration, pathway or other network information is commonly processed based on its topological importance. Meanwhile, in the post-analysis integration, omics data is processed solely before integration with a network. Subsequently, omics data and networks are merged and interpreted. The network-based model has advantages in multi-omics integrative analysis. Due to the different sensitivity and coverage of various omics data types, a multi-omics integrative analysis is challenging. However, focusing on gene-level or protein-level information enables a straightforward integration [86,87]. Consequently, when different machine learning approaches tried to integrate two or more different data types to find novel biological insights, one of the solutions is reducing the search space to gene or protein level and integrated heterogeneous datatypes [25,88].
In summary, using network information opens new possibilities for interpretation. However, as mentioned earlier, several challenges remain, such as the coverage issue. Current databases for biological networks do not cover the entire set of genes, transcripts, and interactions. Therefore, the use of networks can lead to loss of information for gene or transcript orphans. The following section will focus on network-based machine learning models and their application in cancer genomics. We will put network-based machine learning into the perspective of the three main areas of application, namely, molecular characterization, tumor heterogeneity analysis, and cancer drug discovery.
3. Network-Based Learning in Cancer Research
As introduced previously, the integration of machine learning with the insights of biological networks (Figure 2d) ultimately aims at improving predictive performance and interpretability concerning cancer subtyping or treatment susceptibility.
3.1. Molecular Characterization with Network Information
Various network-based algorithms are used in genomics and focus on quantifying the impact of genomic alteration. By employing prior knowledge in biological network algorithms, performance compared to non-network models can be improved. A prominent example is HotNet. The algorithm uses a thermodynamics model on a biological network and identifies driver genes, or prognostic genes, in pan-cancer data [89]. Another study introduced a network-based stratification method to integrate somatic alterations and expression signatures with network information [90]. These approaches use network topology and network-propagation-like algorithms. Network propagation presumes that genomic alterations can affect the function of neighboring genes. Two genes will show an exclusive pattern if two genes complement each other, and the function carried by those two genes is essential to an organism [91]. This unique exclusive pattern among genomic alteration is further investigated in cancer-related pathways. Recently, Ku et al. developed network-centric approaches and tackled robustness issues while studying synthetic lethality [92]. Although synthetic lethality was initially discovered in model organisms of genetics, it helps us to understand cancer-specific mutations and their functions in tumor characteristics [91].
Furthermore, in transcriptome research, network information is used to measure pathway activity and its application in cancer subtyping. For instance, when comparing the data of two or more conditions such as cancer types, GSEA as introduced in Section 2 is a useful approach to get an overview of systematic changes [50]. It is typically used at the beginning of a data evaluation [93]. An experimentally validated gene set can provide information about how different conditions affect molecular systems in an organism. In addition to the gene sets, different approaches integrate complex interaction information into GSEA and build network-based models [70]. In contrast to GSEA, pathway activity analysis considers transcriptome data and other omics data and structural information of a biological network. For example, PARADIGM uses pathway topology and integrates various omics in the analysis to infer a patient-specific status of pathways [94]. A benchmark study with pan-cancer data recently reveals that using network structure can show better performance [57]. In conclusion, while the loss of data is due to the incompleteness of biological networks, their integration improved performance and increased interpretability in many cases.
3.2. Tumor Heterogeneity Study with Network Information
The tumor heterogeneity can originate from two directions, clonal heterogeneity and tumor impurity. Clonal heterogeneity covers genomic alterations within the tumor [95]. While de novo mutations accumulate, the tumor obtains genomic alterations with an exclusive pattern. When these genomic alterations are projected on the pathway, it is possible to observe exclusive relationships among disease-related genes. For instance, the CoMEt and MEMo algorithms examine mutual exclusivity on protein–protein interaction networks [96,97]. Moreover, the relationship between genes can be essential for an organism. Therefore, models analyzing such alterations integrate network-based analysis [98].
In contrast, tumor purity is dependent on the tumor microenvironment, including immune-cell infiltration and stromal cells [99]. In tumor microenvironment studies, network-based models are applied, for instance, to find immune-related gene modules. Although the importance of the interaction between tumors and immune cells is well known, detailed mechanisms are still unclear. Thus, many recent NGS studies employ network-based models to investigate the underlying mechanism in tumor and immune reactions. For example, McGrail et al. identified a relationship between the DNA damage response protein and immune cell infiltration in cancer. The analysis is based on curated interaction pairs in a protein–protein interaction network [100]. Most recently, Darzi et al. discovered a prognostic gene module related to immune cell infiltration by using network-centric approaches [101]. Tu et al. presented a network-centric model for mining subnetworks of genes other than immune cell infiltration by considering tumor purity [102].
3.3. Drug Target Identification with Network Information
In drug target studies, network biology is integrated into pharmacology [103]. For instance, Yamanishi et al. developed novel computational methods to investigate the pharmacological space by integrating a drug-target protein network with genomics and chemical information. The proposed approaches investigated such drug-target network information to identify potential novel drug targets [104]. Since then, the field has continued to develop methods to study drug target and drug response integrating networks with chemical and multi-omic datasets. In a recent survey study by Chen et al., the authors compared 13 computational methods for drug response prediction. It turned out that gene expression profiles are crucial information for drug response prediction [105].
Moreover, drug-target studies are often extended to drug-repurposing studies. In cancer research, drug-repurposing studies aim to find novel interactions between non-cancer drugs and molecular features in cancer. Drug-repurposing (or repositioning) studies apply computational approaches and pathway-based models and aim at discovering potential new cancer drugs with a higher probability than de novo drug design [16,106]. Specifically, drug-repurposing studies can consider various areas of cancer research, such as tumor heterogeneity and synthetic lethality. As an example, Lee et al. found clinically relevant synthetic lethality interactions by integrating multiple screening NGS datasets [107]. This synthetic lethality and related-drug datasets can be integrated for an effective combination of anticancer therapeutic strategy with non-cancer drug repurposing.
4. Deep Learning in Cancer Research
DNN models develop rapidly and become more sophisticated. They have been frequently used in all areas of biomedical research. Initially, its development was facilitated by large-scale imaging and video data. While most data sets in the biomedical field would not typically be considered big data, the rapid data accumulation enabled by NGS made it suitable for the application of DNN models requiring a large amount of training data [108]. For instance, in 2019, Samiei et al. used TCGA-based large-scale cancer data as benchmark datasets for bioinformatics machine learning research such as Image-Net in the computer vision field [109]. Subsequently, large-scale public cancer data sets such as TCGA encouraged the wide usage of DNNs in the cancer domain [110]. Over the last decade, these state-of-the-art machine learning methods have been incorporated in many different biological questions [111].
In addition to public cancer databases such as TCGA, the genetic information of normal tissues is stored in well-curated databases such as GTEx [112] and 1000Genomes [113]. These databases are frequently used as control or baseline training data for deep learning [114]. Moreover, other non-curated large-scale data sources such as GEO (https://www.ncbi.nlm.nih.gov/geo/, accessed on 20 May 2021) can be leveraged to tackle critical aspects in cancer research. They store a large-scale of biological data produced under various experimental setups (Figure 1). Therefore, an integration of GEO data and other data requires careful preprocessing. Overall, an increasing amount of datasets facilitate the development of current deep learning in bioinformatics research [115].
4.1. Challenges for Deep Learning in Cancer Research
Many studies in biology and medicine used NGS and produced large amounts of data during the past few decades, moving the field to the big data era. Nevertheless, researchers still face a lack of data in particular when investigating rare diseases or disease states. Researchers have developed a manifold of potential solutions to overcome this lack of data challenges, such as imputation, augmentation, and transfer learning (Figure 3b). Data imputation aims at handling data sets with missing values [116]. It has been studied on various NGS omics data types to recover missing information [117]. It is known that gene expression levels can be altered by different regulatory elements, such as DNA-binding proteins, epigenomic modifications, and post-transcriptional modifications. Therefore, various models integrating such regulatory schemes have been introduced to impute missing omics data [118,119]. Some DNN-based models aim to predict gene expression changes based on genomics or epigenomics alteration. For instance, TDimpute aims at generating missing RNA-seq data by training a DNN on methylation data. They used TCGA and TARGET (https://ocg.cancer.gov/programs/target/data-matrix, accessed on 20 May 2021) data as proof of concept of the applicability of DNN for data imputation in a multi-omics integration study [120]. Because this integrative model can exploit information in different levels of regulatory mechanisms, it can build a more detailed model and achieve better performance than a model build on a single-omics dataset [117,121]. The generative adversarial network (GAN) is a DNN structure for generating simulated data that is different from the original data but shows the same characteristics [122]. GANs can impute missing omics data from other multi-omics sources. Recently, the GAN algorithm is getting more attention in single-cell transcriptomics because it has been recognized as a complementary technique to overcome the limitation of scRNA-seq [123]. In contrast to data imputation and generation, other machine learning approaches aim to cope with a limited dataset in different ways. Transfer learning or few-shot learning, for instance, aims to reduce the search space with similar but unrelated datasets and guide the model to solve a specific set of problems [124]. These approaches train models with data of similar characteristics and types but different data to the problem set. After pre-training the model, it can be fine-tuned with the dataset of interest [125,126]. Thus, researchers are trying to introduce few-shot learning models and meta-learning approaches to omics and translational medicine. For example, Select-ProtoNet applied the ProtoTypical Network [127] model to TCGA transcriptome data and classified patients into two groups according to their clinical status [128]. AffinityNet predicts kidney and uterus cancer subtypes with gene expression profiles [129].
Figure 3. (a) In various studies, NGS data transformed into different forms. The 2-D transformed form is for the convolution layer. Omics data is transformed into pathway level, GO enrichment score, or Functional spectra. (b) DNN application on different ways to handle lack of data. Imputation for missing data in multi-omics datasets. GAN for data imputation and in silico data simulation. Transfer learning pre-trained the model with other datasets and fine-tune. (c) Various types of information in biology. (d) Graph neural network examples. GCN is applied to aggregate neighbor information. (Created with BioRender.com).
4.2. Molecular Charactization with Network and DNN Model
DNNs have been applied in multiple areas of cancer research. For instance, a DNN model trained on TCGA cancer data can aid molecular characterization by identifying cancer driver genes. At the very early stage, Yuan et al. build DeepGene, a cancer-type classifier. They implemented data sparsity reduction methods and trained the DNN model with somatic point mutations [130]. Lyu et al. [131] and DeepGx [132] embedded a 1-D gene expression profile to a 2-D array by chromosome order to implement the convolution layer (Figure 3a). Other algorithms, such as the deepDriver, use k-nearest neighbors for the convolution layer. A predefined number of neighboring gene mutation profiles was the input for the convolution layer. It employed this convolution layer in a DNN by aggregating mutation information of the k-nearest neighboring genes [11]. Instead of embedding to a 2-D image, DeepCC transformed gene expression data into functional spectra. The resulting model was able to capture molecular characteristics by training cancer subtypes [14].
Another DNN model was trained to infer the origin of tissue from single-nucleotide variant (SNV) information of metastatic tumor. The authors built a model by using the TCGA/ICGC data and analyzed SNV patterns and corresponding pathways to predict the origin of cancer. They discovered that metastatic tumors retained their original cancer’s signature mutation pattern. In this context, their DNN model obtained even better accuracy than a random forest model [133] and, even more important, better accuracy than human pathologists [12].
4.3. Tumor Heterogeneity with Network and DNN Model
As described in Section 4.1, there are several issues because of cancer heterogeneity, e.g., tumor microenvironment. Thus, there are only a few applications of DNN in intratumoral heterogeneity research. For instance, Menden et al. developed ’Scaden’ to deconvolve cell types in bulk-cell sequencing data. ’Scaden’ is a DNN model for the investigation of intratumor heterogeneity. To overcome the lack of training datasets, researchers need to generate in silico simulated bulk-cell sequencing data based on single-cell sequencing data [134]. It is presumed that deconvolving cell types can be achieved by knowing all possible expressional profiles of the cell [36]. However, this information is typically not available. Recently, to tackle this problem, single-cell sequencing-based studies were conducted. Because of technical limitations, we need to handle lots of missing data, noises, and batch effects in single-cell sequencing data [135]. Thus, various machine learning methods were developed to process single-cell sequencing data. They aim at mapping single-cell data onto the latent space. For example, scDeepCluster implemented an autoencoder and trained it on gene-expression levels from single-cell sequencing. During the training phase, the encoder and decoder work as denoiser. At the same time, they can embed high-dimensional gene-expression profiles to lower-dimensional vectors [136]. This autoencoder-based method can produce biologically meaningful feature vectors in various contexts, from tissue cell types [137] to different cancer types [138,139].
4.4. Drug Target Identification with Networks and DNN Models
In addition to NGS datasets, large-scale anticancer drug assays enabled the training train of DNNs. Moreover, non-cancer drug response assay datasets can also be incorporated with cancer genomic data. In cancer research, a multidisciplinary approach was widely applied for repurposing non-oncology drugs to cancer treatment. This drug repurposing is faster than de novo drug discovery. Furthermore, combination therapy with a non-oncology drug can be beneficial to overcome the heterogeneous properties of tumors [85]. The deepDR algorithm integrated ten drug-related networks and trained deep autoencoders. It used a random-walk-based algorithm to represent graph information into feature vectors. This approach integrated network analysis with a DNN model validated with an independent drug-disease dataset [15].
The authors of CDRscan did an integrative analysis of cell-line-based assay datasets and other drug and genomics datasets. It shows that DNN models can enhance the computational model for improved drug sensitivity predictions [140]. Additionally, similar to previous network-based models, the multi-omics application of drug-targeted DNN studies can show higher prediction accuracy than the single-omics method. MOLI integrated genomic data and transcriptomic data to predict the drug responses of TCGA patients [141].
4.5. Graph Neural Network Model
In general, the advantage of using a biological network is that it can produce more comprehensive and interpretable results from high-dimensional omics data. Furthermore, in an integrative multi-omics data analysis, network-based integration can improve interpretability over traditional approaches. Instead of pre-/post-integration of a network, recently developed graph neural networks use biological networks as the base structure for the learning network itself. For instance, various pathways or interactome information can be integrated as a learning structure of a DNN and can be aggregated as heterogeneous information. In a GNN study, a convolution process can be done on the provided network structure of data. Therefore, the convolution on a biological network made it possible for the GNN to focus on the relationship among neighbor genes. In the graph convolution layer, the convolution process integrates information of neighbor genes and learns topological information (Figure 3d). Consequently, this model can aggregate information from far-distant neighbors, and thus can outperform other machine learning models [142].
In the context of the inference problem of gene expression, the main question is whether the gene expression level can be explained by aggregating the neighboring genes. A single gene inference study by Dutil et al. showed that the GNN model outperformed other DNN models [143]. Moreover, in cancer research, such GNN models can identify cancer-related genes with better performance than other network-based models, such as HotNet2 and MutSigCV [144]. A recent GNN study with a multi-omics integrative analysis identified 165 new cancer genes as an interactive partner for known cancer genes [145]. Additionally, in the synthetic lethality area, dual-dropout GNN outperformed previous bioinformatics tools for predicting synthetic lethality in tumors [146]. GNNs were also able to classify cancer subtypes based on pathway activity measures with RNA-seq data. Lee et al. implemented a GNN for cancer subtyping and tested five cancer types. Thus, the informative pathway was selected and used for subtype classification [147]. Furthermore, GNNs are also getting more attention in drug repositioning studies. As described in Section 3.3, drug discovery requires integrating various networks in both chemical and genomic spaces (Figure 3d). Chemical structures, protein structures, pathways, and other multi-omics data were used in drug-target identification and repurposing studies (Figure 3c). Each of the proposed applications has a specialty in the different purposes of drug-related tasks. Sun et al. summarized GNN-based drug discovery studies and categorized them into four classes: molecular property and activity prediction, interaction prediction, synthesis prediction, and de novo drug design. The authors also point out four challenges in the GNN-mediated drug discovery. At first, as we described before, there is a lack of drug-related datasets. Secondly, the current GNN models can not fully represent 3-D structures of chemical molecules and protein structures. The third challenge is integrating heterogeneous network information. Drug discovery usually requires a multi-modal integrative analysis with various networks, and GNNs can improve this integrative analysis. Lastly, although GNNs use graphs, stacked layers still make it hard to interpret the model [148].
4.6. Shortcomings in AI and Revisiting Validity of Biological Networks as Prior Knowledge
The previous sections reviewed a variety of DNN-based approaches that present a good performance on numerous applications. However, it is hardly a panacea for all research questions. In the following, we will discuss potential limitations of the DNN models. In general, DNN models with NGS data have two significant issues: (i) data requirements and (ii) interpretability. Usually, deep learning needs a large proportion of training data for reasonable performance which is more difficult to achieve in biomedical omics data compared to, for instance, image data. Today, there are not many NGS datasets that are well-curated and -annotated for deep learning. This can be an answer to the question of why most DNN studies are in cancer research [110,149]. Moreover, the deep learning models are hard to interpret and are typically considered as black-boxes. Highly stacked layers in the deep learning model make it hard to interpret its decision-making rationale. Although the methodology to understand and interpret deep learning models has been improved, the ambiguity in the DNN models’ decision-making hindered the transition between the deep learning model and translational medicine [149,150].
As described before, biological networks are employed in various computational analyses for cancer research. The studies applying DNNs demonstrated many different approaches to use prior knowledge for systematic analyses. Before discussing GNN application, the validity of biological networks in a DNN model needs to be shown. The LINCS program analyzed data of ’The Connectivity Map (CMap) project’ to understand the regulatory mechanism in gene expression by inferring the whole gene expression profiles from a small set of genes (https://lincsproject.org/, accessed on 20 May 2021) [151,152]. This LINCS program found that the gene expression level is inferrable with only nearly 1000 genes. They called this gene list ’landmark genes’. Subsequently, Chen et al. started with these 978 landmark genes and tried to predict other gene expression levels with DNN models. Integrating public large-scale NGS data showed better performance than the linear regression model. The authors conclude that the performance advantage originates from the DNN’s ability to model non-linear relationships between genes [153].
Following this study, Beltin et al. extensively investigated various biological networks in the same context of the inference of gene expression level. They set up a simplified representation of gene expression status and tried to solve a binary classification task. To show the relevance of a biological network, they compared various gene expression levels inferred from a different set of genes, neighboring genes in PPI, random genes, and all genes. However, in the study incorporating TCGA and GTEx datasets, the random network model outperformed the model build on a known biological network, such as StringDB [154]. While network-based approaches can add valuable insights to analysis, this study shows that it cannot be seen as the panacea, and a careful evaluation is required for each data set and task. In particular, this result may not represent biological complexity because of the oversimplified problem setup, which did not consider the relative gene-expressional changes. Additionally, the incorporated biological networks may not be suitable for inferring gene expression profiles because they consist of expression-regulating interactions, non-expression-regulating interactions, and various in vivo and in vitro interactions.
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Use of Systems Biology in Anti-Microbial Drug Development
Genomics, Computational Biology and Drug Discovery for Mycobacterial Infections: Fighting the Emergence of Resistance. Asma Munir, Sundeep Chaitanya Vedithi, Amanda K. Chaplin and Tom L. Blundell. Front. Genet., 04 September 2020 | https://doi.org/10.3389/fgene.2020.00965
In an earlier review article (Waman et al., 2019), we discussed various computational approaches and experimental strategies for drug target identification and structure-guided drug discovery. In this review we discuss the impact of the era of precision medicine, where the genome sequences of pathogens can give clues about the choice of existing drugs, and repurposing of others. Our focus is directed toward combatting antimicrobial drug resistance with emphasis on tuberculosis and leprosy. We describe structure-guided approaches to understanding the impacts of mutations that give rise to antimycobacterial resistance and the use of this information in the design of new medicines.
Genome Sequences and Proteomic Structural Databases
In recent years, there have been many focused efforts to define the amino-acid sequences of the M. tuberculosis pan-genome and then to define the three-dimensional structures and functional interactions of these gene products. This work has led to essential genes of the bacteria being revealed and to a better understanding of the genetic diversity in different strains that might lead to a selective advantage (Coll et al., 2018). This will help with our understanding of the mode of antibiotic resistance within these strains and aid structure-guided drug discovery. However, only ∼10% of the ∼4128 proteins have structures determined experimentally.
Several databases have been developed to integrate the genomic and/or structural information linked to drug resistance in Mycobacteria (Table 1). These invaluable resources can contribute to better understanding of molecular mechanisms involved in drug resistance and improvement in the selection of potential drug targets.
There is a dearth of information related to structural aspects of proteins from M. leprae and their oligomeric and hetero-oligomeric organization, which has limited the understanding of physiological processes of the bacillus. The structures of only 12 proteins have been solved and deposited in the protein data bank (PDB). However, the high sequence similarity in protein coding genes between M. leprae and M. tuberculosis allows computational methods to be used for comparative modeling of the proteins of M. leprae. Mainly monomeric models using single template modeling have been defined and deposited in the Swiss Model repository (Bienert et al., 2017), in Modbase (Pieper et al., 2014), and in a collection with other infectious disease agents (Sosa et al., 2018). There is a need for multi-template modeling and building homo- and hetero-oligomeric complexes to better understand the interfaces, druggability and impacts of mutations.
We are now exploiting Vivace, a multi-template modeling pipeline developed in our lab for modeling the proteomes of M. tuberculosis (CHOPIN, see above) and M. abscessus [Mabellini Database (Skwark et al., 2019)], to model the proteome of M. leprae. We emphasize the need for understanding the protein interfaces that are critical to function. An example of this is that of the RNA-polymerase holoenzyme complex from M. leprae. We first modeled the structure of this hetero-hexamer complex and later deciphered the binding patterns of rifampin (Vedithi et al., 2018; Figures 1A,B). Rifampin is a known drug to treat tuberculosis and leprosy. Owing to high rifampin resistance in tuberculosis and emerging resistance in leprosy, we used an approach known as “Computational Saturation Mutagenesis”, to identify sites on the protein that are less impacted by mutations. In this study, we were able to understand the association between predicted impacts of mutations on the structure and phenotypic rifampin-resistance outcomes in leprosy.
FIGURE 2
Figure 2.(A) Stability changes predicted by mCSM for systematic mutations in the ß-subunit of RNA polymerase in M. leprae. The maximum destabilizing effect from among all 19 possible mutations at each residue position is considered as a weighting factor for the color map that gradients from red (high destabilizing effects) to white (neutral to stabilizing effects) (Vedithi et al., 2020). (B) One of the known mutations in the ß-subunit of RNA polymerase, the S437H substitution which resulted in a maximum destabilizing effect [-1.701 kcal/mol (mCSM)] among all 19 possibilities this position. In the mutant, histidine (residue in green) forms hydrogen bonds with S434 and Q438, aromatic interactions with F431, and other ring-ring and π interactions with the surrounding residues which can impact the shape of the rifampin binding pocket and rifampin affinity to the ß-subunit [-0.826 log(affinity fold change) (mCSM-lig)]. Orange dotted lines represent weak hydrogen bond interactions. Ring-ring and intergroup interactions are depicted in cyan. Aromatic interactions are represented in sky-blue and carbonyl interactions in pink dotted lines. Green dotted lines represent hydrophobic interactions (Vedithi et al., 2020).
Examples of Understanding and Combatting Resistance
The availability of whole genome sequences in the present era has greatly enhanced the understanding of emergence of drug resistance in infectious diseases like tuberculosis. The data generated by the whole genome sequencing of clinical isolates can be screened for the presence of drug-resistant mutations. A preliminary in silico analysis of mutations can then be used to prioritize experimental work to identify the nature of these mutations.
FIGURE 3
Figure 3.(A) Mechanism of isoniazid activation and INH-NAD adduct formation. (B) Mutations mapped (Munir et al., 2019) on the structure of KatG (PDB ID:1SJ2; Bertrand et al., 2004).
Other articles related to Computational Biology, Systems Biology, and Bioinformatics on this online journal include:
Yet another Success Story: Machine Learning to predict immunotherapy response
Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc
Immune-checkpoint blockers(ICBs) immunotherapy appears promising for various cancer types, offering a durable therapeutic advantage. Only a number of cases with cancer respond to this therapy. Biomarkers are required to adequately predict the responses of patients. This article evaluates this issue utilizing a system method to characterize the immune response of the anti-tumor based on the entire tumor environment. Researchers build mechanical biomarkers and cancer-specific response models using interpretable machine learning that predict the response of patients to ICB.
The lymphatic and immunological systems help the body defend itself by combating. The immune system functions as the body’s own personal police force, hunting down and eliminating pathogenic baddies.
According to Federica Eduati, Department of Biomedical Engineering at TU/e, “The immune system of the body is quite adept at detecting abnormally behaving cells. Cells that potentially grow into tumors or cancer in the future are included in this category. Once identified, the immune system attacks and destroys the cells.”
Immunotherapy and machine learning are combining to assist the immune system solve one of its most vexing problems: detecting hidden tumorous cells in the human body.
It is the fundamental responsibility of our immune system to identify and remove alien invaders like bacteria or viruses, but also to identify risks within the body, such as cancer. However, cancer cells have sophisticated ways of escaping death by shutting off immune cells. Immunotherapy can reverse the process, but not for all patients and types of cancer. To unravel the mystery, Eindhoven University of Technology researchers used machine learning. They developed a model to predict whether immunotherapy will be effective for a patient using a simple trick. Even better, the model outperforms conventional clinical approaches.
“Tumor also contains multiple types of immune and fibroblast cells which can play a role in favor of or anti-tumor, and communicates among themselves,” said Oscar Lapuente-Santana, a researcher doctoral student in the computational biology group. “We had to learn how complicated regulatory mechanisms in the micro-environment of the tumor affect the ICB response. We have used RNA sequencing datasets to depict numerous components of the Tumor Microenvironment (TME) in a high-level illustration.”
Using computational algorithms and datasets from previous clinical patient care, the researchers investigated the TME.
Eduati explained
While RNA-sequencing databases are publically available, information on which patients responded to ICB therapy is only available for a limited group of patients and cancer types. So, to tackle the data problem, we used a trick.
All 100 models learned in the randomized cross-validation were included in the EaSIeR tool. For each validation dataset, we used the corresponding cancer-type-specific model: SKCM for the melanoma Gide, Auslander, Riaz, and Liu cohorts; STAD for the gastric cancer Kim cohort; BLCA for the bladder cancer Mariathasan cohort; and GBM for the glioblastoma Cloughesy cohort. To make predictions for each job, the average of the 100 cancer-type-specific models was employed. The predictions of each dataset’s cancer-type-specific models were also compared to models generated for the remaining 17 cancer types.
From the same datasets, the researchers selected several surrogate immunological responses to be used as a measure of ICB effectiveness.
Lapuente-Santana stated
One of the most difficult aspects of our job was properly training the machine learning models. We were able to fix this by looking at alternative immune responses during the training process.
DREAM is an organization that carries out crowd-based tasks with biomedical algorithms. “We were the first to compete in one of the sub-challenges under the name cSysImmunoOnco team,” Eduati remarks.
The researchers noted,
We applied machine learning to seek for connections between the obtained system-based attributes and the immune response, estimated using 14 predictors (proxies) derived from previous publications. We treated these proxies as individual tasks to be predicted by our machine learning models, and we employed multi-task learning algorithms to jointly learn all tasks.
The researchers discovered that their machine learning model surpasses biomarkers that are already utilized in clinical settings to evaluate ICB therapies.
But why are Eduati, Lapuente-Santana, and their colleagues using mathematical models to tackle a medical treatment problem? Is this going to take the place of the doctor?
Eduati explains
Mathematical models can provide an overview of the interconnection between individual molecules and cells and at the same time predicting a particular patient’s tumor behavior. This implies that immunotherapy with ICB can be personalized in a patient’s clinical setting. The models can aid physicians with their decisions about optimum therapy, it is vital to note that they will not replace them.
Furthermore, the model aids in determining which biological mechanisms are relevant for the biological response.
The researchers noted
Another advantage of our concept is that it does not need a dataset with known patient responses to immunotherapy for model training.
Further testing is required before these findings may be implemented in clinical settings.
Main Source:
Lapuente-Santana, Ó., van Genderen, M., Hilbers, P. A., Finotello, F., & Eduati, F. (2021). Interpretable systems biomarkers predict response to immune-checkpoint inhibitors. Patterns, 100293. https://www.cell.com/patterns/pdfExtended/S2666-3899(21)00126-4
Other Related Articles published in this Open Access Online Scientific Journal include the following:
Inhibitory CD161 receptor recognized as a potential immunotherapy target in glioma-infiltrating T cells by single-cell analysis
Deep Learning for In-silico Drug Discovery and Drug Repurposing: Artificial Intelligence to search for molecules boosting response rates in Cancer Immunotherapy: Insilico Medicine @John Hopkins University
Happy 80th Birthday: Radioiodine (RAI) Theranostics: Collaboration between Physics and Medicine, the Utilization of Radionuclides to Diagnose and Treat: Radiation Dosimetry by Discoverer Dr. Saul Hertz, the early history of RAI in diagnosing and treating Thyroid diseases and Theranostics
Both authors contributed to the development, drafting and final editing of this manuscript and are responsible for its content.
Abstract
March 2021 will mark the eightieth anniversary of targeted radionuclide therapy, recognizing the first use of radioactive iodine to treat thyroid disease by Dr. Saul Hertz on March 31, 1941. The breakthrough of Dr. Hertz and collaborator physicist Arthur Roberts was made possible by rapid developments in the fields of physics and medicine in the early twentieth century. Although diseases of the thyroid gland had been described for centuries, the role of iodine in thyroid physiology had been elucidated only in the prior few decades. After the discovery of radioactivity by Henri Becquerel in 1897, rapid advancements in the field, including artificial production of radioactive isotopes, were made in the subsequent decades. Finally, the diagnostic and therapeutic use of radioactive iodine was based on the tracer principal that was developed by George de Hevesy. In the context of these advancements, Hertz was able to conceive the potential of using of radioactive iodine to treat thyroid diseases. Working with Dr. Roberts, he obtained the experimental data and implemented it in the clinical setting. Radioiodine therapy continues to be a mainstay of therapy for hyperthyroidism and thyroid cancer. However, Hertz struggled to gain recognition for his accomplishments and to continue his work and, with his early death in 1950, his contributions have often been overlooked until recently. The work of Hertz and others provided a foundation for the introduction of other radionuclide therapies and for the development of the concept of theranostics.
Dr. Saul Hertz was Director of The Massachusetts General Hospital’s Thyroid Unit, when he heard about the development of artificial radioactivity. He conceived and brought from bench to bedside the successful use of radioiodine (RAI) to diagnose and treat thyroid diseases. Thus was born the science of theragnostics used today for neuroendocrine tumors and prostate cancer. Dr. Hertz’s work set the foundation of targeted precision medicine.
Keywords: Dr. Saul Hertz, nuclear medicine, radioiodine
How to cite this article:
Hertz B. A tribute to Dr. Saul Hertz: The discovery of the medical uses of radioiodine. World J Nucl Med 2019;18:8-12
How to cite this URL:
Hertz B. A tribute to Dr. Saul Hertz: The discovery of the medical uses of radioiodine. World J Nucl Med [serial online] 2019 [cited 2021 Mar 2];18:8-12. Available from: http://www.wjnm.org/text.asp?2019/18/1/8/250309
Dr Saul Hertz (1905-1950) discovers the medical uses of radioiodine
Barbara Hertz, Pushan Bharadwaj, Bennett Greenspan»
Thyroid practitioners and patients are acutely aware of the enormous benefit nuclear medicine has made to mankind. This month we celebrate the 80th anniversary of the early use of radioiodine(RAI).
Dr. Saul Hertz predicted that radionuclides “…would hold the key to the larger problem of cancer in general,” and may just be the best hope for diagnosing and treating cancer successfully. Yes, RAI has been used for decades to diagnose and treat disease. Today’s “theranostics,” a term that is a combination of “therapy” and “diagnosis” is utilized in the treatment of thyroid disease and cancer.
This short note is to celebrate Dr. Saul Hertz who conceived and brought from bench to bedside the medical uses of RAI; then in the form of 25 minute iodine-128.
On March 31st 1941, Massachusetts General Hospital’s Dr. Saul Hertz (1905-1950) administered the first therapeutic use of Massachusetts Institute of Technology (MIT) cyclotron produced RAI. This landmark case was the first in Hertz’s clinical studies conducted with MIT, physicist Arthur Roberts, Ph.D.
[Photo – Courtesy of Dr Saul Hertz Archives ]
Dr Saul Hertz demonstrating RAI Uptake Testing
Dr. Hertz’s research and successful utilization of radionuclides to diagnose and treat diseases and conditions, established the use of radiation dosimetry and the collaboration between physics and medicine and other significant practices. Sadly, Saul Hertz (a WWII veteran) died at a very young age.
About Dr. Saul Hertz
Dr. Saul Hertz (1905 – 1950) discovered the medical uses of radionuclides. His breakthrough work with radioactive iodine (RAI) created a dynamic paradigym change integrating the sciences. Radioactive iodine (RAI) is the first and Gold Standard of targeted cancer therapies. Saul Hertz’s research documents Hertz as the first and foremost person to conceive and develop the experimental data on RAI and apply it in the clinical setting.
Dr. Hertz was born to Orthodox Jewish immigrant parents in Cleveland, Ohio on April 20, 1905. He received his A.B. from the University of Michigan in 1925 with Phi Beta Kappa honors. He graduated from Harvard Medical School in 1929 at a time of quotas for outsiders. He fulfilled his internship and residency at Mt. Sinai Hospital in Cleveland. He came back to Boston in 1931 as a volunteer to join The Massachusetts General Hospital serving as the Chief of the Thyroid Unit from 1931 – 1943.
Two years after the discovery of artifically radioactivity, on November 12, 1936 Dr. Karl Compton, president of the Massachusetts Institute of Technology (MIT), spoke at Harvard Medical School. President Compton’s topic was What Physics can do for Biology and Medicine. After the presentation Dr. Hertz spontaneously asked Dr. Compton this seminal question, “Could iodine be made radioactive artificially?” Dr. Compton responded in writing on December 15, 1936 that in fact “iodine can be made artificially radioactive.”
Shortly thereafter, a collaboration between Dr. Hertz (MGH) and Dr. Arthur Roberts, a physicist of MIT, was established. In late 1937, Hertz and Roberts created and produced animal studies involving 48 rabbits that demonstrated that the normal thyroid gland concentrated Iodine 128 (non cyclotron produced), and the hyperplastic thyroid gland took up even more Iodine. This was a GIANT step for Nuclear Medicine.
In early 1941, Dr. Hertz administer the first therapeutic treatment of MIT Markle Cyclotron produced radioactive iodine (RAI) at the Massachusetts General Hospital. This led to the first series of twenty-nine patients with hyperthyroidism being treated successfully with RAI. ( see “Research” RADIOACTIVE IODINE IN THE STUDY OF THYROID PHYSIOLOGY VII The use of Radioactive Iodine Therapy in Hyperthyroidism, Saul Hertz and Arthur Roberts, JAMA Vol. 31 Number 2).
In 1937, at the time of the rabbit studies Dr Hertz conceived of RAI in therapeutic treatment of thyroid carsonoma. In 1942 Dr Hertz gave clinical trials of RAI to patients with thyroid carcinoma.
After serving in the Navy during World War II, Dr. Hertz wrote to the director of the Mass General Hospital in Boston, Dr. Paxon on March 12, 1946, “it is a coincidence that my new research project is in Cancer of the Thyroid, which I believe holds the key to the larger problem of cancer in general.”
Dr. Hertz established the Radioactive Isotope Research Institute, in September, 1946 with a major focus on the use of fission products for the treatment of thyroid cancer, goiter, and other malignant tumors. Dr Samuel Seidlin was the Associate Director and managed the New York City facilities. Hertz also researched the influence of hormones on cancer.
Dr. Hertz’s use of radioactive iodine as a tracer in the diagnostic process, as a treatment for Graves’ disease and in the treatment of cancer of the thyroid remain preferred practices. Saul Hertz is the Father of Theranostics.
Saul Hertz passed at 45 years old from a sudden death heart attack as documented by an autopsy. He leaves an enduring legacy impacting countless generations of patients, numerous institutions worldwide and setting the cornerstone for the field of Nuclear Medicine. A cancer survivor emailed, The cure delivered on the wings of prayer was Dr Saul Hertz’s discovery, the miracle of radioactive iodine. Few can equal such a powerful and precious gift.
To read and hear more about Dr. Hertz and the early history of RAI in diagnosing and treating thyroid diseases and theranostics see –
Hertz S, Roberts A. Radioactive iodine in the study of thyroid physiology. VII The use of radioactive iodine therapy in hyperthyroidism. J Am Med Assoc 1946;131:81-6.
Hertz S. A plan for analysis of the biologic factors involved in experimental carcinogenesis of the thyroid by means of radioactive isotopes. Bull New Engl Med Cent 1946;8:220-4.
Thrall J. The Story of Saul Hertz, Radioiodine and the Origins of Nuclear Medicine. Available from: http://www.youtube.com/watch?v=34Qhm8CeMuc. [Last accessed on 2018 Dec 01].
Krolicki L, Morgenstern A, Kunikowska J, Koiziar H, Krolicki B, Jackaniski M, et al. Glioma Tumors Grade II/III-Local Alpha Emitters Targeted Therapy with 213 Bi-DOTA-Substance P, Endocrine Abstracts. Vol. 57. Society of Nuclear Medicine and Molecular Imaging; 2016. p. 632.
Baum RP, Kulkarni HP. Duo PRRT of neuroendocrine tumours using concurrent and sequential administration of Y-90- and Lu-177-labeled somatostatin analogues. In: Hubalewska-Dydejczyk A, Signore A, de Jong M, Dierckx RA, Buscombe J, Van de Wiel CJ, editors. Somatostatin Analogues from Research to Clinical Practice. New York: Wiley; 2015.
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