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Posts Tagged ‘Cancer immunotherapy’

Alliance for Cancer Gene Therapy to honor Dr. Crystal Mackall with Edward Netter Leadership Award

Posted in Biological Engineering, Biological Networks, Gene Regulation and Evolution, Cancer - General, Cancer and Current Therapeutics, Cell Level, Childhood cancer, Drug Delivery Platform Technology, Gene Therapy & Gene Editing Development, Genetics & Innovations in Treatment, Interviews with Key Opinion Leaders (KOLs), Interviews with Scientific Leaders, Life Sciences Breakthrough Prize, Metabolic Immuno-Oncology, Metastasis Process, Oligonucleotide Therapeutics & Delivery, tagged Alliance for Cancer Gene Therapy, Cancer immunotherapy, CART therapy, Edward Netter Leadership Award, gene therapy, modulation of cell and gene therapies, Stanford, University of Pennsylvania on April 8, 2023| Leave a Comment »

Alliance for Cancer Gene Therapy to honor Dr. Crystal Mackall with Edward Netter Leadership Award

Reporter: Stephen J. Williams, PhD

Past recipient and cancer research pioneer Carl June, MD, to present award to Dr. Mackall

Alliance for Cancer Gene Therapy (ACGT) will award the Edward Netter Leadership Award to Crystal Mackall, MD, of Stanford University, at the ACGT Awards Luncheon on March 30 at Riverpark restaurant at the Alexandria Center for Life Science, located at 450 E. 29th St., New York City.

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 information about purchasing a ticket to the ACGT Awards Luncheon, visit the ACGT Awards Luncheon website (https://acgtfoundation.org/awards/), call Keri Eisenberg at (475) 400-4373, or email keisenberg@acgtfoundation.org. 

Alliance for Cancer Gene Therapy (ACGT) 

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

 

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Yet another Success Story: Machine Learning to predict immunotherapy response

Posted in Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Biomarkers & Medical Diagnostics, Cancer - General, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer-Immune Interactions, Glioblastoma, Immuno-Oncology & Genomics, Immunology, Immunotherapy, Machine Learning, RNA Biology, Cancer and Therapeutics, tumor microenvironment, tagged biomarkers, Cancer immunotherapy, cell-cell interactions, computational algorithms, immune inhibitory checkpoints, immuno-oncology therapeutics, Machine Learning models, mathematical model, PDL1/PD1 pathway, RNA sequencing, transcription factors, tumor microenvironment on July 6, 2021| Leave a Comment »

Yet another Success Story: Machine Learning to predict immunotherapy response

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

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

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

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

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

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

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

The Study

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

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

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

Eduati explained

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

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

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

Lapuente-Santana stated

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

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

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

The researchers noted,

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

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

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

Eduati explains

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

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

The researchers noted

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

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

Main Source:

Lapuente-Santana, Ó., van Genderen, M., Hilbers, P. A., Finotello, F., & Eduati, F. (2021). Interpretable systems biomarkers predict response to immune-checkpoint 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

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

https://pharmaceuticalintelligence.com/2021/02/20/inhibitory-cd161-receptor-identified-in-glioma-infiltrating-t-cells-by-single-cell-analysis-2/

Immunotherapy may help in glioblastoma survival

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

https://pharmaceuticalintelligence.com/2019/03/16/immunotherapy-may-help-in-glioblastoma-survival/

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

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/07/17/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-univer/

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

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

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

AI System Used to Detect Lung Cancer

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/

Cancer detection and therapeutics

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/05/02/cancer-detection-and-therapeutics/

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LIVE: Lectures by The 2017 Award Recipients of Warren Alpert Foundation Prize in Cancer Immunology, October 5, 2017, HMS, 77 Louis Paster, Boston

Posted in Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, CAR-T, Conference Coverage with Social Media, Immune Engineering, Immune Modulatory, Immuno-Oncology & Genomics, Immunodiagnostics, Immunology, Immunotherapy, NK Cell-Based Cancer Immunotherapy, Scientist: Career considerations, Warren Alpert Foundation Prize Recipients, tagged biology of the CTLA-4, Cancer immunotherapy, discovery of the PD-1/PD-L1 and PD-L2 pathway, tumor immune evasion on September 8, 2017| Leave a Comment »

Lectures by The 2017 Award Recipients of Warren Alpert Foundation Prize in Cancer Immunology, October 5, 2017, HMS, 77 Louis Paster, Boston

Reporter: Aviva Lev-Ari, PhD, RN

Top, from left: James Allison and Lieping Chen. Bottom, from left: Gordon Freeman, Tasuku Honjo (NOT ATTENDED), Arlene Sharpe.

Aviva Lev-Ari, PhD, RN was in attendance and covered this event LIVE

 Leaders in Pharmaceutical Business Intelligence (LPBI) Group

The 2017 Warren Alpert Foundation Prize has been awarded to five scientists for transformative discoveries in the field of cancer immunology.

Collectively, their work has elucidated foundational mechanisms in cancer’s ability to evade immune recognition and, in doing so, has profoundly altered the understanding of disease development and treatment. Their discoveries have led to the development of effective immune therapies for several types of cancer.

The 2017 award recipients are:

• James Allison, professor of immunology and chair of the Department of Immunology, The University of Texas MD Anderson Cancer Center – Immune checkpoint blockage in Cancer Therapy strictly Genomics based drug

1. 2017 FDA approved a genomics based drug
2. and co-stimulatory signals
3. CTLA-4 blockade, CD28, AntiCTLA-4 induces regression of Transplantable Murine tumor
4. enhance tumor-specific immune response
5. Fully antibody human immune response in 10,000 patients – FDA approved 2011
6. Metastatic melanoma – 3 years survival, programmed tumor death, PD-1, MHC-A1
7. Ipi/Nivo vs. Ipi – combination – 60% survival vs Ipi alone
8. Anti CTA4 vs Anti-PD-1
9. responsive T cell population – MC38 TILs
10. MC38 Infiltrating T cell populations: T-reg, CD4, Effector, CD8, NKT/gamma-delta
11. Checkpoint blockage modulates infiltrating T cell population frequencies
12. T reg correlated with Tumor growth
13. Combination therapy lead to CURE survival at 80% rate vs CTAL-4 40% positive outcome

Not Attended — Tasuku Honjo, professor of immunology and genomic medicine, Kyoto University – Immune regulation of Cancer Therapy by PD-1 Blockade

 

• Lieping Chen, United Technologies Corporation Professor in Cancer Research and Professor of immunobiology, of dermatology and of medicine, Yale University – Adoptive Resistance: Molecular Pathway t Cancer Therapy – focus on solid tumors

1. Enhancement – Enhance normal immune system – Co-stimulation/Co-inhibition Treg, and Cytokines, adoptive cell therapy, Lymphoid organs stores
2. Normalization – to correct defective immune system – normalizing tumor immunity, diverse tumor escape mechanisms
3. Anti-PD therapy: regression of large solid tumors: normalizing tumor immunity targeting tumor microenvironment: Heterogeneity, functional modulation, cellular and molecular components – classification by LACK of inflamation, adaptive resistance, other inhibitory pathways, intrinsic induction
4. avoid autoimmune toxicity,
5. Resetting immune response (melanoma)
6. Understad Resistance: Target missing resistance or Adaptive resistance Type II= acquired immunity

• Gordon Freeman, professor of medicine, Dana-Farber Cancer Institute, Harvard Medical School – PD-L1/PD-1 Cancer Immunotherapy

1. B7 antibody
2. block pathway – checkpoint blockage, Expand the T cells after recognition of the disease. T cell receptor signal, activation, co -stimulatory: B71 molecule, B72 – survival signals and cytokine production,.Increased T cell proliferation,
3. PDL-1 is a ligand of PD 1. How T cell die? genes – PD1 Gene was highly expressed,
4. Interferon gamma upregulate PD-L1 expression
5. Feedback loop Tumor – stimulating immune response, interferon turn off PD1
6. PD-L1 and PD-L2 Expression: Interferom
7. Trancefuctor MHC, B7-2
8. PD-L! sisgnat inhibit T-cell activation: turn off Proliferation and cytokine production — Decreasing the immune response
9. T cell DNA Content: No S-phase devided cell
10. PD-L1 engagement of PD-1 results in activation : Pd-1 Pathway inhibits T Cell Actiivation – lyposite motility,
11. Pd-L2 is a second ligand for PD-1 and inhibits T cell activation
12. PDl-1 expression: BR CA, Ovarian, Colonol-rectal, tymus, endothelial
13. Blockage of the Pathway – Immune response enhanced
14. Dendritic cells express PD-L1, PD-L2 and combination of Two, Combination was best of all by increase of cytokine production, increasing the immune response.
15. PD-L1 blockade enhanced the immune response , increase killing and increased production of cytokines,
16. anti-tumor efficacy of anti-PD-1/Pd-L1
17. Pancreatic and colono-rector — PD-L, PDL1, PDL2 — does not owrkd.
18. In menaloma: PD-1 works better than CYLA-4
19. Comparison of Targeted Therapy: BRAF TKI vs Chemo high % but short term
20. Immunotherapy – applies several mechanism: pre-existing anti-therapy
21. Immune desert: PD=L does not work for them
22. COMBINATION THERAPY: BLOCK TUMOR INVASION THEN STIMULATE IMMUNE RESPONSE — IT WILL WORK
23. PD blockage + nutrients and probiotic
24. Tumor Genome Therapy
25. Tumore Immuno-evasion Score
26. Antigens for immune response – choose the ones
27. 20PD-1 or PD-L1 drugs in development
28. WHO WILL THE DRUG WORK FOR?

 

• Arlene Sharpe, the George Fabyan Professor of Comparative Pathology, Harvard Medical School; senior scientist, department of pathology, Brigham and Women’s Hospital – Multi-faceted Functionsof the PD-1 Pathway

1. function of the pathway: control T cell activation and function of maintain immune tolerance
2. protect tissues from damage by immune response
3. T cell dysfunction during cancer anf viral infection
4. protection from autoimmunity, inflammation,
5. Mechanism by which PD-1 pathway inhibits anti-tumor immunity
6. regulation of memoryT cell responce of PD-1
7. PD-1 signaling inhibit anti-tumor immunity
8. Compare: Mice lacking CD8-Cre- (0/5) cleared vs PD-1-/-5/5 – PD-1 DELETION: PARTIAL AND TIMED: DELETION OF PD-1 ON HALF OG TILS STARTING AT DAY 7 POSTTUMOR IMPLANTATION OF BOTH PD-1 AND PD-1 TILS: – Tamoxifen days 7-11
9. Transcription profile: analysis of CD8+ TILs reveal altered metabolism: Fatty Acid Metabolism vs Oxidative Phosphorylation
10. DOes metabolic shift: WIld type mouth vs PD-1-/_ P14: analyze Tumor cell killingPD-1-/- enhanced FAO increases CD8+ T cell tocicity
11. Summary: T cell memory development and PD-1: T effectors vs T cell memory: Primary vs Secondary infection: In the absent of PD-1, CD8+ T cels show increase expansion of T cells
12. INFLUENZA INFECTION: PRIMARY more virus in lung in PD-1 is lacking
13. Acute infection: PD-1 controls memory T cell differentiation vs PD-1 increase expansion during effector phase BUT impaired persistence during memory phase: impaired cytokine production post re-challenge
14. PD-1 immunotherapy work for patients with tumor: Recall Response and Primary response
15. TIL density Primary vs Long term survivor – 5 days post tumor implantation – rechallenged long term survival
16. Hot tumor vs Cold tumor – Deletion of PD-1 impairs T memory cell development

Opening Remarks: George Q. Daley, MD, PhD, DEAN, HMS

• Scientific collaboration check point – avoid the body attacking itself, sabotaging the immune system
• 1987 – Vaccine for HepB
• Eight of the awardees got the Nobel Prize

 

Moderated by Joan Brugge, PhD, HMS, Prof. of Cell Biology

• Evolution of concepts of Immunotherapy: William Coley’s Toxin streptoccocus skin infection.
• 20th century: Immuno-surveilence, Immune response – field was dead in 1978 replaced by Immunotherapy
• Rosenberg at NIH, high dose of costimulatory molecule prevented tumor reappearanceantbody induce tumor immunity–>> immune theraphy by check point receptor blockade – incidence of tumor in immune compromised mice – transfer T cell
• T cell defficient, not completely defficient, self recognition of tumor,
• suppress immmune – immune evasion

• Michael Atkins, MD, Detupy Director, Georgetown-Lombardi, Comprehensive Cancer Center Clinical applications of Checkpoint inhibitors: Progress and Promise

1. Overwhelm the Immune system, hide, subvert, Shield, defend-deactivating tumor trgeting T cells that ATTACK the immune system
2. Immune system to TREAT the cancer
3. Monotherapy – anti PD1/PD-L1: Antagonist activity
4. Evading immune response: prostate, colcn
5. MMR deficiency
6. Nivolumab in relaped/Refractory HODGKIN LYMPHOMAS – over expression of PD-L1 and PDL2in Lymphomas
7. 18 month survival better with Duv in Lung cancer stage 3 – anti PD-1- adjuvant therapy with broad effectiveness
8. Biomarkers for pD-L1 Blockage
9. ORR higher in PD-L1
10. Improve Biomarkers: Clonality of T cells in Tumors
11. T-effector Myeloid Inflammation Low – vs Hogh:
12. Biomarker Model: Neoantigen burden vs Gene expression vs CD8+
13. Tissue DIagnostic Labs: Tumor microenveironmenr
14. Microbiome
15. Combination: Nivo vs Nivo+Ipi is superior: DETERMINE WHEN TO STOP TREATMENT
16. 15/16 stopped treatment – Treatment FREE SURVIVAL
17. Sequencing with Standard Therapies
18. Brain metastasis – Immune Oncology Therapy – crosses the BBB
19. Less Toxic regimen, better toxicity management,
20. Use Immuno therapy TFS
21. combination – survival must be justified
22. Goal: to make Cancer a curable disease vs cancer becoming a CHronic disease

Closing Remarks: George Q. Daley, MD, PhD, DEAN, HMS

The honorees will share a $500,000 prize and will be recognized at a day-long symposium on Oct. 5 at Harvard Medical School.

The Warren Alpert Foundation, in association with Harvard Medical School, honors trailblazing scientists whose work has led to the understanding, prevention, treatment or cure of human disease. The award recognizes seminal discoveries that hold the promise to change our understanding of disease or our ability to treat it.

“The discoveries honored by the Warren Alpert Foundation over the years are remarkable in their scope and potential,” said George Q. Daley, dean of Harvard Medical School. “The work of this year’s recipients is nothing short of breathtaking in its profound impact on medicine. These discoveries have reshaped our understanding of the body’s response to cancer and propelled our ability to treat several forms of this recalcitrant disease.”

The Warren Alpert Foundation Prize is given internationally. To date, the foundation has awarded nearly $4 million to 59 scientists. Since the award’s inception, eight honorees have also received a Nobel Prize.

“We commend these five scientists. Allison, Chen, Freeman, Honjoand Sharpe are indisputable standouts in the field of cancer immunology,” said Bevin Kaplan, director of the Warren Alpert Foundation. “Collectively, they are helping to turn the tide in the global fight against cancer. We couldn’t honor more worthy recipients for the Warren Alpert Foundation Prize.”

The 2017 award: Unraveling the mysterious interplay between cancer and immunity

Understanding how tumor cells sabotage the body’s immune defenses stems from the collective work of many scientists over many years and across multiple institutions.

Each of the five honorees identified key pieces of the puzzle.

The notion that cancer and immunity are closely connected and that a person’s immune defenses can be turned against cancer is at least a century old. However, the definitive proof and demonstration of the steps in this process were outlined through findings made by the five 2017 Warren Alpert prize recipients.

Under normal conditions, so-called checkpoint inhibitor molecules rein in the immune system to ensure that it does not attack the body’s own cells, tissues and organs. Building on each other’s work, the five award recipients demonstrated how this normal self-defense mechanism can be hijacked by tumors as a way to evade immune surveillance and dodge an attack. Subverting this mechanism allows cancer cells to survive and thrive.

A foundational discovery made in the 1980s elucidated the role of a molecule on the surface of T cells, the body’s elite assassins trained to seek, spot and destroy invaders.

A protein called CTLA-4 emerged as a key regulator of T cell behavior—one that signals to T cells the need to retreat from an attack. Experiments in mice lacking CTLA-4 and use of CTLA-4 antibodies demonstrated that absence of CTLA-4 or blocking its activity could lead to T cell activation and tumor destruction.

Subsequent work identified a different protein on the surface of T cells—PD-1—as another key regulator of T cell response. Mice lacking this protein developed an autoimmune disease as a result of aberrant T cell activity and over-inflammation.

Later on, scientists identified a molecule, B7-H1, subsequently renamed PD-L1, which binds to PD-1, clicking like a key in a lock. This was followed by the discovery of a second partner for PD-1—the molecule PD-L2—which also appeared to tame T-cell activity by binding to PD-1.

The identification of these molecules led to a set of studies showing that their presence on human and mouse tumors rendered the tumors resistant to immune eradication.

A series of experiments further elucidated just how tumors exploit the interaction between PD-1 and PD-L1 to survive. Specifically, some tumor cells appeared to express PD-L1, essentially “wrapping” themselves in it to avoid immune recognition and destruction.

Additional work demonstrated that using antibodies to block this interaction disarmed the tumors, rendering them vulnerable to immune destruction.

Collectively, the five scientists’ findings laid the foundation for antibody-based therapies that modulate the function of these molecules as a way to unleash the immune system against cancer cells.

Antibody therapy that targets CTLA-4 is currently approved by the FDA for the treatment of melanoma. PD-1/PD-L1 inhibitors have already shown efficacy in a broad range of cancers and have been approved by the FDA for the treatment of melanoma; kidney; lung; head and neck cancer; bladder cancer; some forms of colorectal cancer; Hodgkin lymphoma and Merkel cell carcinoma.

In their own words

“I am humbled to be included among the illustrious scientists who have been honored by the Warren Alpert Foundation for their contributions to the treatment and cure of human disease in its 30+ year history.  It is also recognition of the many investigators who have labored for decades to realize the promise of the immune system in treating cancer.”
        -James Allison

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“The award is a great honor and a wonderful recognition of our work.”
         –Lieping Chen

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“I am thrilled to have made a difference in the lives of cancer patients and to be recognized by fellow scientists for my part in the discovery of the PD-1/PD-L1 and PD-L2 pathway and its role in tumor immune evasion.  I am deeply honored to be a recipient of the Alpert Award and to be recognized for my part in the work that has led to effective cancer immunotherapy. The success of immunotherapy has unleashed the energies of a multitude of scientists to further advance this novel strategy.”
                                        -Gordon Freeman

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“I am extremely honored to receive the Warren Alpert Foundation Prize. I am very happy that our discovery of PD-1 in 1992 and subsequent 10-year basic research on PD-1 led to its clinical application as a novel cancer immunotherapy. I hope this development will encourage many scientists working in the basic biomedical field.”
-Tasuku Honjo

---

“I am truly honored to be a recipient of the Alpert Award. It is especially meaningful to be recognized by my colleagues for discoveries that helped define the biology of the CTLA-4 and PD-1 pathways. The clinical translation of our fundamental understanding of these pathways illustrates the value of basic science research, and I hope this inspires other scientists.”
-Arlene Sharpe

Previous winners

Last year’s award went to five scientists who were instrumental in the discovery and development of the CRISPR bacterial defense mechanism as a tool for gene editing. They were RodolpheBarrangou of North Carolina State University, Philippe Horvath of DuPont in Dangé-Saint-Romain, France, Jennifer Doudna of the University of California, Berkeley, Emmanuelle Charpentier of the Max Planck Institute for Infection Biology in Berlin and Umeå University in Sweden, and Virginijus Siksnys of the Institute of Biotechnology at Vilnius University in Lithuania.

Other past recipients include:

• Tu Youyou of the China Academy of Chinese Medical Science, who went on to receive the 2015 Nobel Prize in Physiology or Medicine with two others, and Ruth and Victor Nussenzweig, of NYU Langone Medical Center, for their pioneering discoveries in chemistry and parasitology of malaria and the translation of their work into the development of drug therapies and an anti-malarial vaccine.
• Oleh Hornykiewicz of the Medical University of Vienna and the University of Toronto; Roger Nicoll of the University of California, San Francisco; and Solomon Snyder of the Johns Hopkins University School of Medicine for research into neurotransmission and neurodegeneration.
• David Botstein of Princeton University and Ronald Davis and David Hogness of Stanford University School of Medicine for contributions to the concepts and methods of creating a human genetic map.
• Alain Carpentier of Hôpital Européen Georges-Pompidou in Paris and Robert Langer of MIT for innovations in bioengineering.
• Harald zur Hausen and Lutz Gissmann of the German Cancer Research Center in Heidelberg for work on the human papillomavirus (HPV) and cancer of the cervix. Zur Hausenand others were honored with the Nobel Prize in Physiology or Medicine in 2008.

The Warren Alpert Foundation

Each year the Warren Alpert Foundation receives between 30 and 50 nominations from scientific leaders worldwide. Prize recipients are selected by the foundation’s scientific advisory board, which is composed of distinguished biomedical scientists and chaired by the dean of Harvard Medical School.

Warren Alpert (1920-2007), a native of Chelsea, Mass., established the prize in 1987 after reading about the development of a vaccine for hepatitis B. Alpert decided on the spot that he would like to reward such breakthroughs, so he picked up the phone and told the vaccine’s creator, Kenneth Murray of the University of Edinburgh, that he had won a prize. Alpert then set about creating the foundation.

To award subsequent prizes, Alpert asked Daniel Tosteson (1925-2009), then dean of Harvard Medical School, to convene a panel of experts to identify scientists from around the world whose research has had a direct impact on the treatment of disease.

SOURCE

https://hms.harvard.edu/news/warren-alpert-foundation-honors-pioneers-cancer-immunology

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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

Posted in Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Informatics, Computational Biology/Systems and Bioinformatics, Gene Regulation and Evolution, Genetics & Pharmaceutical, Genome Biology, Genomic Testing: Methodology for Diagnosis, Immuno-Oncology & Genomics, tagged Cancer immunotherapy, CTLA4, immune dysfunction, immune inhibitory checkpoints, Immunoresistance, immunotherapeutics, method for screening, modulating the immune synapse, PD-1, PD-L1, scoring and personalizing small molecules, signaling pathways, transcriptome-based pathway perturbation analysis on July 17, 2016| Leave a Comment »

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

Reporter: Aviva Lev-Ari, PhD, RN

Insilico Medicine –>>> transcriptome-based pathway perturbation analysis

Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore with R&D resources in Belgium, Russia, and Poland hiring talent through hackathons and competitions. It utilizes advances in genomics, big data analysis and deep learning for in silico drug discovery and drug repurposing for aging and age-related diseases. The company pursues internal drug discovery programs in cancer, Parkinson’s, Alzheimer’s, sarcopenia and geroprotector discovery. Through its Pharmaceutical Artificial Intelligence division the company provides advanced machine learning services to biotechnology, pharmaceutical, and skin care companies.

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Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8.

Insilico Medicine develops a new approach to concomitant cancer immunotherapy

Artificial intelligence to search for molecules boosting response rates in cancer immunotherapy

INSILICO MEDICINE, INC.

Summary:

• Some of the most promising drugs for cancer therapy called checkpoint inhibitors often result in complete remissions, however, a majority of patients fail cancer immunotherapy with antibodies targeting immune checkpoints, such as CTLA-4 or programmed death-1 (PD-1).
• Insilico Medicine developed a set of pathway-based signatures of response to popular checkpoint inhibitors
• Using these markers and a deep learned drug scoring engine Insilico Medicine identified 12 leads that may help increase response to cancer immunotherapy and is seeking industry partnerships to test these leads

Thursday, July 14, 2016, Baltimore, MD — Recent advances in cancer immunotherapy demonstrated complete remission in multiple tumor types including melanoma and lung cancers. Almost every major pharmaceutical company operating in oncology space started multiple programs in immuno-oncology with thousands of clinical trials underway. Immuno-oncology is now a very broad field ranging from treatment of a patient with an engineered antibody to genome editing of patient’s immune cells. Genetic mutations accruing from the inherent genomic instability of tumor cells present neo-antigens that are recognized by the immune system. Cross-presentation of tumor antigens at the immune synapse between antigen-presenting dendritic cells and T lymphocytes can potentially activate an adaptive antitumor immune response, however, tumors continuously evolve to counteract and ultimately defeat such immune surveillance by co-opting and amplifying mechanisms of immune tolerance to evade elimination by the immune system. This prerequisite for tumor progression is enabled by the ability of cancers to produce negative regulators of immune response.

Cancer immunotherapy is currently focused on targeting immune inhibitory checkpoints that control T cell activation, such as CTLA-4 and PD-1. Monoclonal antibodies that block these immune checkpoints (commonly referred to as immune checkpoint inhibitors) can unleash antitumor immunity and produce durable clinical responses in a subset of patients with advanced cancers, such as melanoma and non-small-cell lung cancer. However, these immunotherapeutics are currently constrained by their inability to induce clinical responses in the vast majority of patients and the frequent occurrence of immune-related adverse events. A key limitation of checkpoint inhibitors is that they narrowly focus on modulating the immune synapse but do not address other key molecular determinants that may also be responsible for immune dysfunction.

Immunoresistance often ensues as a result of the concomitant activation of multiple, often overlapping signaling pathways. Therefore, inhibition of multiple, cross-talking pathways involved in survival control with combination therapy is usually more effective in decreasing the likelihood that cancer cells will develop therapeutic resistance than with single agent therapy. While research efforts are now focused on identifying new inhibitory mechanisms that could be targeted to achieve responses in patients with refractory cancers and provide durable and adaptable cancer control, there are outstanding challenges in determining what combination of immunotherapies and conventional therapies will prove beneficial against each tumor type.

“Immunotherapy is the most promising area in oncology resulting in cures, but we need to identify effective combinations of both established methods and new drugs developed specifically to boost response rates. At Insilico Medicine we developed a new method for screening, scoring and personalizing small molecules that may boost response rates to PD-1, PD-L1, CTLA4 and other checkpoint inhibitors. We can identify effective combinations of both established methods and new drugs developed specifically to boost response rates to immunotherapy”, said Artem Artemov, director of computational drug repurposing at Insilico Medicine.

Insilico Medicine, Inc. is one of the leaders in transcriptome-based pathway perturbation analysis. It is also a pioneer in applying cutting edge artificial intelligence techniques to biological and medical data analysis, particularly focused on in silico screening for new compounds against cancer and known drugs which can be repurposed against different cancers. One of the major programs currently ongoing at Insilico Medicine is evaluation of the transcriptional responses to multiple checkpoint inhibitors and analyzing the pathway-level differences in patients who respond and fail to respond to clinically approved checkpoint inhibitors. This novel computational approach is aimed at identifying new drug candidates which can be used in combination with immunotherapy to unleash durable antitumor effect against several types of cancers.

Recently, scientists at Insilico Medicine performed a large in silico screening of compounds that can be administered in combination with anti-PD1 immunotherapy to increase response rates. The researchers collected transcriptomic data from tumors of patients who either responded or failed to respond to standard immunotherapy, using both publically available and internally generated data. Next, they used differential pathway activation analysis and deep learning based approaches to identify transcriptomic signatures predicting the success of immunotherapy in a particular tumor type.

Finally, they analyzed drug-induced transcriptomic effects to screen for the drugs that can robustly drive transcriptomes of tumor cells from non-responsive state to the state responsive to immunotherapy. In other words, researchers developed approach that can predict whether drug of interest would induce a transcriptional signature that characterizes those patients that respond to cancer immunotherapy in non-respondents. This method allows personalizing these drugs to individual patients and specific checkpoint inhibitors. Among the top-scoring drugs, they found several compounds known to increase response rates in combination with cancer immunotherapy. One of the top-scoring compounds included a naturally-occurring substance marketed as a natural product.

The current list of top-scoring leads that may increase response rates to checkpoint inhibitors included 12 small molecules identified using signaling pathway perturbation analysis and annotated using a deeply learned drug scoring system. Insilico Medicine is currently open for partnerships which will allow further testing and validation of the discovered compounds ex vivo on cell cultures established from tumors which respond and failed to respond to immunotherapy, as well as in mice with patient-derived tumor xenografts. This approach may greatly reduce the costs of preclinical trials and significantly shorten the timeframe from a drug prediction to validation and marketing. The compounds, after preclinical and clinical validation, may improve cancer care and dramatically increase the lifespan of cancer patients.

A panel of leads for concomitant immunotherapy is part of a large number of leads developed using DeepPharma™, artificially-intelligent drug discovery engine, which includes a large number of molecules predicted to be effective antineoplastic agents, metabolic regulators, CVD and CNS lead, senolytics and ED drugs. Recently Insilico Medicine published several seminal papers demonstrating proof of concept of utilizing deep learning techniques to predict pharmacological properties of small molecules using transcriptional response data utilizing deep neural networks for biomarker development. “Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data,” a paper published in Molecular Pharmaceuticals received the American Chemical Society Editors’ Choice Award. Another recent collaboration with Biotime, Inc resulted in a launch of Embryonic.AI, deep learned predictor of differentiation state of the sample.

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Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

SOURCE

http://www.eurekalert.org/pub_releases/2016-07/imi-imd071416.php?utm_content=buffer36d53&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer

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