From Science Node: “Computer simulations and big data advance cancer immunotherapy”

Science Node bloc
Science Node

09 Jun, 2017 [Where has this been?]
Aaron Dubrow

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Courtesy National Institute of Allergy and Infectious Diseases.

Supercomputers help classify immune response, design clinical trials, and analyze immune repertoire data.
Scanning electron micrograph of a human T lymphocyte (also called a T cell) from the immune system of a healthy donor. Immunotherapy fights cancer by supercharging the immune system’s natural defenses (include T-cells) or contributing additional immune elements that can help the body kill cancer cells. [Credit: NIAID]

The body has a natural way of fighting cancer – it’s called the immune system, and it is tuned to defend our cells against outside infections and internal disorder. But occasionally, it needs a helping hand.

In recent decades, immunotherapy has become an important tool in treating a wide range of cancers, including breast cancer, melanoma and leukemia.

But alongside its successes, scientists have discovered that immunotherapy sometimes has powerful — even fatal — side-effects.

Identifying patient-specific immune treatments

Not every immune therapy works the same on every patient. Differences in an individual’s immune system may mean one treatment is more appropriate than another. Furthermore, tweaking one’s system might heighten the efficacy of certain treatments.

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Scanning electron micrograph of a human T lymphocyte (also called a T cell) from the immune system of a healthy donor. Immunotherapy fights cancer by supercharging the immune system’s natural defenses (include T-cells) or contributing additional immune elements that can help the body kill cancer cells. [Credit: NIAID]

Researchers from Wake Forest School of Medicine and Zhejiang University in China developed a novel mathematical model to explore the interactions between prostate tumors and common immunotherapy approaches, individually and in combination.

In a study published in Nature Scientific Reports, they used their model to predict how prostate cancer would react to four common immunotherapies.

The researchers incorporated data from animal studies into their complex mathematical models and simulated tumor responses to the treatments using the Stampede supercomputer at the Texas Advanced Computing Center (TACC).

Dell Poweredge U Texas Austin Stampede Supercomputer. Texas Advanced Computer Center 9.6 PF

“We do a lot of modeling which relies on millions of simulations,” says Jing Su, a researcher at the Center for Bioinformatics and Systems Biology at Wake Forest School of Medicine and assistant professor in the Department of Diagnostic Radiology.

“To get a reliable result, we have to repeat each computation at least 100 times. We want to explore the combinations and effects and different conditions and their results.”

TACC’s high performance computing resources allowed the researchers to highlight a potential therapeutic strategy that may manage prostate tumor growth more effectively.

Designing more efficient clinical trials

Biological agents used in immunotherapy — including those that target a specific tumor pathway, aim for DNA repair, or stimulate the immune system to attack a tumor — function differently from radiation and chemotherapy.

Because traditional dose-finding designs are not suitable for trials of biological agents, novel designs that consider both the toxicity and efficacy of these agents are imperative.

Chunyan Cai, assistant professor of biostatistics at UT Health Science Center (UTHSC)’s McGovern Medical School, uses TACC systems to design new kinds of dose-finding trials for combinations of immunotherapies.

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Writing in the Journal of the Royal Statistics Society Series C (Applied Statistics), Cai and her collaborators, Ying Yuan, and Yuan Ji, described efforts to identify biologically optimal dose combinations for agents that target the PI3K/AKT/mTOR signaling pathway, which has been associated with several genetic aberrations related to the promotion of cancer.

After 2,000 simulations on the Lonestar supercomputer for each of six proposed dose-finding designs, they discovered the optimal combination gives higher priority to trying new doses in the early stage of the trial.

TACC Lonestar Cray XC40 supercomputer

The best case also assigns patients to the most effective dose that is safe toward the end of the trial.

“Extensive simulation studies show that the design proposed has desirable operating characteristics in identifying the biologically optimal dose combination under various patterns of dose–toxicity and dose–efficacy relationships,” Cai concludes.

Whether in support of population-level immune response studies, clinical dosing trials, or community-wide efforts, TACC’s advanced computing resources are helping scientists put the immune system to work to better fight cancer.

See the full article here .

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