Archive for the ‘News Article’ Category

Ben Gyori named 2018 DARPA Riser

Benjamin M. Gyori, Ph.D., A Research Scientist in Therapeutic Science was selected as one of 50 DARPA Risers: up and coming early career investigators with the potential to perform innovative research at the frontiers of science and technology relevant to the mission of DARPA.

As a DARPA Riser, Ben will attend DARPA’s 60th Anniversary Symposium (D60) at Gaylord National Harbor, Sept 5-7, and present his proposal on a computer system which autonomously monitors events and scientific discoveries, integrates them into actionable models, and proactively reports relevant analysis results.

D60 will bring together 1,500 forward-thinking scientists, engineers, and other innovators interested in sharing ideas and learning how DARPA has shaped and continues to shape breakthrough technologies. D60 will also feature Dr. Peter Sorger, who leads the Harvard Program in Therapeutic Science, as an invited speaker on the Accelerating Science panel where he will present on AI technologies for bridging mathematical models and cellular biology.

Ben’s current work focuses on computational approaches to accelerate scientific discovery. He has been an active performer in the DARPA Big Mechanism, Communicating with Computers, World Modelers, and Automated Scientific Discovery Framework programs.

Gyori is a co-developer of INDRA, a system which automatically assembles information about biochemical mechanisms extracted from the scientific literature into explanatory and predictive models. He is also leading the development a collaborative dialogue system allowing a human user to talk with a machine partner to learn about molecular mechanisms, and formulate, and test model hypotheses.









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Semiconductor synthetic biology promises to exceed limits of current data storage and processing methods

Dr. Eduardo Sontag (Harvard Medical School Laboratory of Systems Pharmacology (LSP) Member, professor of Bioengineering and Electrical and Computer Engineering at Northeastern University) has been awarded, in collaboration with Dr. Chris Voigt from MIT and Dr. Kate Adamala from the University of Minnesota, a $1.5M three-year grant jointly funded from the National Science Foundation and Semiconductor Research Corporation for “Very Large-Scale Genetic Circuit Design Automation”. This grant aims to scale-up synthetic biological circuits for applications in medicine and engineering, by distributing genetic circuit design across multiple communicating cells.

The NSF announcement of awards is at

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Stuart Schreiber, Ph.D.

Broad Institute and Harvard University


Chemical Biology-based Approach to Understanding and Overcoming Resistance of Cancers to Therapies

The ability of cancer cells to resist apoptotic death induced by radiation, chemotherapy, targeted therapies and immunotherapy has been a primary impediment to achieving long-lasting clinical responses. Cancer resistance is often studied through the lens of genetics, and thus on a drug-by-drug basis. I will discuss an alternative approach — studying resistance through the lens of cellular plasticity. Our findings are suggesting that a similar form of plasticity appears recurrently in multiple cancer contexts, thus opening the possibility of a pan-cancer explanation of resistance, wherein cell plasticity provides access to an apoptosis-deficient state and thus resistance, and then subsequent cancer mutations, rather than being the basis of resistance, are needed to optimize the fitness of the resistant state for growth. We found that this myofibroblastic-like cell state has profound changes in lipid biology that result in a novel vulnerability to ferroptotic cell death and a dependency on the activity of a druggable lipid hydroperoxidase.


10:00 am – 11:00 am
Thursday, May 17, 2018

Harvard Medical School
200 Longwood Avenue
Warren Alpert Bldg, 5th Floor, Room 563
Boston, MA 02115

Hosted by Kenichi Shimada, Ph.D.

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Bissan Al-Lazikani, PhD

Head of Data Science, Institute of Cancer Research, UK


Integrative Big Data for Cancer Drug Discovery and Individualised Therapy

Advances in technology are enabling unprecedented systematic mapping of disease mechanisms. To fully exploit this wealth of knowledge in advancing cancer therapy, we must develop integrative ‘Big Knowledge’ approaches to inform drug discovery and adaptive cancer therapy. I will describe with real world use cases how integrative computational methodologies are underpinning target selection and drug discovery, drug combinations and adaptation of therapy.


10:00 am – 11:00 am
Tuesday, March 20, 2018

Harvard Medical School
200 Longwood Avenue
Warren Alpert Bldg, 5th Floor, Room 563
Boston, MA 02115

Hosted by Adam Palmer, Ph.D.

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Neal Rosen, MD, PhD
Memorial Sloan Kettering Cancer Center

Mutant Allele-specific Oncoprotein Function – Basic and Translational Implications

11:00 am – 12:00 pm
Thursday, February 8, 2018

Harvard Medical School
200 Longwood Avenue
Warren Alpert Bldg, 5th Floor, Room 563
Boston, MA 02115

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Benefits of many cancer drug combinations not due to interactions between drugs, but to a form of bet hedging

At a glance:

  • Survival benefits of many cancer drug combinations are not due to drug synergy, but to a form of “bet hedging.”
  • Combination treatment gives each patient multiple chances of responding to at least one drug, increasing overall measures of survival within patient populations.
  • Computational models of combinations in which drugs act independently of each other accurately predict survival.
  • Findings suggest new ways to interpret clinical trial data, identify truly synergistic drug pairings and improve the design of combination therapies.

The efficacy of many FDA-approved cancer drug combinations is not due to synergistic interactions between drugs, but rather to a form of “bet hedging,” according to a new study published by Harvard Medical School researchers in Cell on Dec. 14.

Reanalyzing data from 15 clinical trials, the researchers show that independent action—in which drugs do not enhance each other’s effectiveness—can accurately explain gains in survival for most combination cancer therapies when compared to single-drug treatments.

Genetic variations in cancer from one person to another lead to differences in drug response, the researchers said, and treating populations of patients with multiple drugs boosts the likelihood that a patient will benefit from at least one of them.

The finding differs from current hypotheses about drug interaction, which have commonly attributed benefits to drug synergy. However, this should not be interpreted as diminishing the value of combination therapy for patients, the team cautions. Instead, they argue that exploiting drug independence represents a powerful approach for developing better combinations and treatment strategies in the absence of a complete understanding of disease.

A focus on maximizing the odds of a patient responding to at least one drug, for example, could support treating patients with drugs sequentially instead of simultaneously, thereby reducing compounding side effects, enabling higher dosages when effective and potentially lowering treatment costs.

“Our study provides a conceptual framework for rethinking how and why drugs should be given in combination,” said senior study author Peter Sorger, the Otto Krayer Professor of Systems Pharmacology at HMS and director of the Harvard Program in Therapeutic Science and the Laboratory of Systems Pharmacology.

“Independent action offers a simpler and more satisfactory explanation that can help physicians use existing drugs better, help patients have fewer adverse effects and help drug companies develop better combinations that fully realize the promise of precision medicine,” Sorger added.

These arguments underscore the importance of developing new methods to identify which patients respond best to which drug and to maximize the odds of treatment success.

“Positive results for combination cancer therapies have commonly been interpreted as patients needing two or more drugs to shrink their tumors and for them to get better, but our analysis suggests this is often not the case,” said study author Adam Palmer, research fellow in therapeutic science at the Laboratory of Systems Pharmacology. “Many patients are likely responding to only one of the drugs, and the other may be doing little to nothing but generating toxic side effects.”

Perspective Shift

Combination therapies are a mainstay of modern cancer treatment, supported by numerous clinical trials showing that patients who receive two or more drugs respond better than those who get single-drug therapy.

The design of most combinations is based on a sound biological rationale: Drugs targeting the same or complementary molecular pathways should be able to enhance each other’s efficacy. This additive or synergistic effect is thought to render tumors less resistant to treatment and allow the use of lower doses to lessen toxicity.

Due to the genetic and molecular variability of human cancers, it is difficult to predict whether a treatment will be effective for any individual patient. This unpredictability holds true even for cancer therapies tested on different tumor cell cultures in controlled laboratory experiments. Prompted by this observation, Palmer and Sorger investigated whether this variability contributes to the clinical efficacy of drug combinations.

To do so, they reanalyzed human clinical trial data where combination and single therapies were compared.

For example, a recent phase 3 trial of two FDA-approved immunotherapy drugs for melanoma—ipilimumab and nivolumab—found that combination therapy allowed half of the patients to survive longer than 13 months without their disease getting worse. In comparison, half of the patients treated with either agent alone survived longer than three and seven months, respectively, with their disease kept at bay.

Next, Palmer and Sorger used computational models to simulate how patients would fare if they had received treatment with only the drug that was better matched to their individual tumor. The team predicted that half of the patients in this scenario should survive longer than 14 months without worsening disease, a number that nearly mirrored the actual clinical trial outcomes.

The pattern held true for the majority of trials they analyzed—including ovarian cancer, breast cancer, pancreatic cancer and metastatic melanoma—suggesting that independent drug action can explain the efficacy of many combination therapies. Roughly a third of the trial data did not match their simulations, suggesting that these cases represented truly synergistic drug interactions.

The team also analyzed a database in which dozens of combination and single therapies were tested on hundreds of human-derived tumors implanted in animals. Drug independence explained the superiority of combination therapies across nearly all drugs and tumor types in these experiments, the team found. If drugs were working synergistically, then the best personalized drug combinations should be more effective than the best personalized single therapies. The team’s analysis, however, revealed that survival for the best single treatments was statistically indistinguishable from the best combination therapies.

Future Framework

Within a diverse patient population treated with a two-drug combination, one group of patients will respond to one drug, one group to the other, one group to both and one group to neither. If it exists, drug synergy can only be identified in the small subset that responds to both drugs, which means that the majority of patients are benefiting only from independent action, Palmer and Sorger argue.

“We simulated what effects bet hedging with drugs that act independently would have on patient populations, and our models precisely agreed with the observed data,” Palmer said. “This analysis shifts the perspective for thinking about drug combinations from a molecular rationale to a probabilistic one. They are useful even when we cannot predict which patients need which drugs, a finding that is a strong argument for advancing precision medicine.”

This framework also allows researchers to identify truly synergistic drug combinations and better design clinical trials by estimating the baseline benefit of combinations if they are not synergistic. Drugs that enhance each other’s efficacy should exceed the benefits predicted by independent action.

“The fact that so many drugs conformed to this expectation tells us how much better drug combinations really could be,” Sorger said. “Our findings only emphasize how important it is that we improve our understanding of the mechanisms of drug action at the level of a single patient.”

“What we want when we combine drugs is a greater chance of hitting a home run and a reduction in adverse effects,” he added. “We should be focusing on how to identify which of the drugs a patient is responding to and get them off the other ones.”

During the course of their investigation, Palmer and Sorger found that the idea of independent action was not new but had, in fact, been proposed decades ago. Researchers in the 1950s and 1960s made the case that combination therapies could be used to overcome tumor variation, but the concept was marginalized as scientists focused on genetic and molecular rationales of synergy. The data was not then available to test these ideas however.

“Modern data science helped us rediscover a way of thinking about drugs given in combination, which we believe will help us develop new drugs and treat today’s patients,” Sorger said. “We realized how much we didn’t know about drug combinations when we looked at it more deeply. Scientific progress requires us to continuously reevaluate and improve on our ideas. Basic, fundamental studies can help come up with better practical solutions.”

This study was supported by a National Health and Medical Research Council of Australia Early Career Fellowship and the National Institutes of Health (GM107618).

Release written by Kevin Jiang

HMS Article:

Cell Paper:

About Harvard Medical School
Harvard Medical School ( has more than 11,000 faculty working in 10 academic departments located at the School’s Boston campus or in hospital-based clinical departments at 15 Harvard-affiliated teaching hospitals and research institutes: Beth Israel Deaconess Medical Center, Boston Children’s Hospital, Brigham and Women’s Hospital, Cambridge Health Alliance, Dana-Farber Cancer Institute, Harvard Pilgrim Health Care Institute, Hebrew SeniorLife, Joslin Diabetes Center, Judge Baker Children’s Center, Massachusetts Eye and Ear/Schepens Eye Research Institute, Massachusetts General Hospital, McLean Hospital, Mount Auburn Hospital, Spaulding Rehabilitation Network and VA Boston Healthcare System.

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Juliet Williams, Ph.D. and Hui Gao, Ph.D.
Novartis Institutes of Biomedical Research

10:00 am – 11:00 am
Thursday, November 16, 2017
Harvard Medical School
200 Longwood Avenue
Warren Alpert Bldg, Room 563

Human Clinical Trials in Mice: Modeling Inter-patient Response Heterogeneity in PDXs

Less than 10% of molecules which enter oncology trials become approved drugs. One factor attributing to our inability to predict which molecules may be successful in humans is the poor translatability of our preclinical models. Assessing drug candidates across panels of PDX models which capture inter-tumor heterogeneity could help us understand what therapeutics will work across cancer patient populations and aid identification of factors for patient-selection strategies. We established ∼1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a PDX clinical trial format to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. This experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

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Joshua Apgar, Ph.D.
Co-founder and CSO, Applied BioMath

“Model Aided Drug Invention: Modeling Impact on Pharmaceutical R&D
Applied BioMath Introduction and Case Studies”

10:30 AM
Thursday, September 14, 2017
Harvard Medical School, Warren Alpert Bldg., Room 563

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C. Glenn Begley, MBBS, PhD, FRACP
CEO, BioCurate Pty Ltd, Parkville, Australia

“10% of the Time it Works Every Time” or How to Recognize Sloppy Science

11:00 AM
Tuesday, June 27, 2017
Harvard Medical School, Warren Alpert Bldg., Room 563

As scientists and physicians, we all want to make a difference. We all want to make discoveries that have a real impact on human health. Unfortunately however the rewards that currently apply within the academic system provide a perverse set of incentives that reward flashy science with little regard for the quality, robustness or reliability of the work. That is particularly the case for papers published in the “top tier” journals. In this presentation I will review and ‘dissect’ several high profile, highly cited publications that illustrate the problem. These highly cited publications, from famous investigators and their laboratories typically fail because experiments were not performed by blinded investigators, positive and negative controls were not used, experiments were not repeated, reagents were not validated, only select data was shown, and data analysis was inappropriate. This is a systemic problem, is not limited to a small number of laboratories, and will require a multi-pronged approach to begin to address this issue.

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