Advancing cancer immunotherapy with computer simulations as well as data analysis

A scanning electron micrograph of a human T lymphocyte (also called a T cell) by 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 of which can help the body kill cancer cells. Credit: NIAID

The body incorporates a natural way of fighting cancer – of which’s called the immune system, as well as of which is usually tuned to defend our cells against outside infections as well as internal disorder. although occasionally, of which needs a helping hand.

Immunotherapy fights cancer by supercharging the immune system’s natural defenses or contributing additional immune elements of which can help the body kill cancer cells.

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

although alongside its successes, scientists have discovered of which immunotherapy sometimes has powerful—even fatal—side-effects. Much still needs to be learned about how the immune system fights cancer, as well as in This kind of area, supercomputers play an important role.

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 usually more appropriate than another. Furthermore, tweaking one’s system might heighten the efficacy of certain treatments.

Researchers by Wake Forest School of Medicine as well as Zhejiang University in China developed a novel mathematical design to explore the interactions between prostate tumors as well as common immunotherapy approaches, individually as well as in combination. In a study published in February 2016 in Nature Scientific Reports, they used their design to predict how prostate cancer would likely react to four common immunotherapies:

– Androgen deprivation therapy—used to control prostate cancer cell growth by suppressing or blocking the production as well as action the hormone androgen in men;

– Vaccines—which train the immune system to recognize as well as destroy harmful substances;

– Treg depletion—where the subpopulation of T cells, which modulate the immune system, are reduced to enhance the efficacy of immunotherapy treatments; as well as

– IL-2 neutralization—which disables interleukin, a type of signaling molecule inside the immune system.

To study the systematic effects of these four treatments, the researchers incorporated data by animal studies into their complex mathematical designs as well as simulated tumor responses to the treatments using the Stampede supercomputer at the Texas Advanced Computing Center (TACC).

“We do a lot of modeling which relies on millions of simulations,” said Jing Su, a researcher at the Center for Bioinformatics as well as Systems Biology at Wake Forest School of Medicine as well as assistant professor inside 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 as well as effects as well as different conditions as well as their results.”

The researchers found of which the depletion of T Cells as well as the neutralization of Interleukin 2 can have a stronger effect when combined with androgen deprivation therapy as well as vaccines.

The study highlights a potential therapeutic strategy of which may manage prostate tumor growth more effectively. of which also provides a framework for studying tumor-related immune mechanisms as well as the selection of therapeutic regimens in different types of cancer.

In separate work published in Nature Scientific Reports in April 2017, Zhou as well as collaborators by Wake Forest School of Medicine used TACC’s high performance computing resources to predict how ribonucleic acids (RNA) as well as proteins interact with greater accuracy than previous methods.

RNA-protein interactions are import to the function of RNAs, especially inside the case of long noncoding RNAs (lncRNAs), which play essential roles in a variety of biological processes, including cancer development.

A design construction for predicting treatment outcomes of immunotherapy as well as prostate cancer. Credit: Huiming Peng, Weiling Zhao, Hua Tan, Zhiwei Ji, Jingsong Li, King Li & Xiaobo Zhou, Scientific Reports 6, Article number: 21599 (2016)]

In their study, they first performed an analysis of 1,342 RNA-protein interacting complexes by the Nucleic Acid Database as well as identified diverse interface properties between them, including both binding as well as non-binding sites. They then used a three-step method to predict the interacting regions between them using both the sequences as well as structures of the proteins as well as RNAs. Compared with existing methods, which use only sequences, the design was found to be more accurate as well as outperformed the leading current method by 20 percent.

The computationally-intensive work represents the first approach of which uses local conformations to analyze as well as predict the binding sites of protein, RNA as well as RNA-protein interacting pairs.

“TACC provides an important assistance for discovering clinically meaningful as well as actionable knowledge across highly heterogeneous biomedical big data sets,” Zhou said.

[The research was supported by the National Institutes of Health (U01HL111560 as well as R01LM010185).]

Designing more efficient clinical trials

Biological agents used in immunotherapy—including those of which target a specific tumor pathway, aim for DNA repair, or stimulate the immune system to attack a tumor—function differently by radiation as well as chemotherapy.

Whereas toxicity as well as efficacy typically increase with the dose level for cell-destroying chemicals or x-rays, This kind of relationship may not be true for biological agents. Specifically, toxicity may increase at low dose levels as well as then plateau at higher dose levels when the biological agent has reached a saturation level inside the body. Efficacy may even decrease at higher dose levels.

Because traditional dose-finding designs, which focus on identifying the maximum tolerated dose, are not suitable for trials of biological agents, novel designs of which consider both the toxicity as well as 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 brand-new kinds of dose-finding trials for combinations of immunotherapies.

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

“Our research is usually motivated by a drug combination trial at the MD Anderson Cancer Center for patients diagnosed with relapsed lymphoma,” Cai said. “The trial combined two novel biological agents of which target two different components inside the PI3K/AKT/mTOR signaling pathway.”

Both agents individually demonstrated the ability to partially inhibit the signaling pathway as well as provide therapeutic value. By combining these two agents, the investigators anticipated to obtain a more complete inhibition of the PI3K/AKT/mTOR pathway, as well as thereby achieve better treatment responses.

The trial investigated the combinations of four dose levels of agent A with four dose levels of agent B, resulting in 16 dose combinations. The goal was to find the biologically optimal dose combination among those possibilities.

Cai as well as her colleagues introduced a dose-finding trial design of which explicitly accounted for the unique properties of biological agents.

“Our design is usually conducted in two stages,” she said. “In stage one, we escalate doses along the diagonal of the dose combination matrix as a fast exploration of the dosing space. In stage two, on the basis of the observed toxicity as well as efficacy data by stages one, we continuously update the posterior estimates of toxicity as well as efficacy as well as assign patients to the most appropriate dose combination.”

They investigated six different dose-toxicity as well as dose-efficacy scenarios as well as carried out 2,000 simulated trials for each of the designs using the Lonestar supercomputer at TACC.

The simulations compared the percentage of the biologically optimal dose combination (BODC), the percentage of patients allocated to the BODC, the average efficacy rate, the number of patients assigned to over-toxic doses, as well as the total numbers of patients assigned in stage I as well as stage II of the trial.

VDJServer allows researchers to manage, analyze, archive as well as share data by immune repertoire analyses through a free, scalable web resource. Credit: UT Southwestern Medical Center

The optimal dose-finding design, they discovered, gives higher priority to trying brand-new doses inside the early stage of the trial, as well as toward the end of the trial assigns patients to the most effective dose of which is usually safe.

“Extensive simulation studies show of which the design proposed has desirable operating characteristics in identifying the biologically optimal dose combination under various patterns of dose-toxicity as well as dose-efficacy relationships,” she concluded.

[The research was supported by the National Cancer Institute (Award Number R01 CA154591) as well as the National Institutes of Health’s Clinical as well as Translational Science Award grant (UL1 TR000371).]

Supporting community-wide analyses

Data-driven research as well as clinical dosing studies are essential for understanding how the immune system responds to treatments as well as determining the proper doses of biological agents.

Also, critical, however, are mechanisms of which bring together the research of a whole community—to share, compare as well as integrate disparate research findings.

The VDJServer, which launched last year, serves as such a resource. The server enables researchers to analyze high-throughput immune repertoire sequencing data over the web using the high-performance computing resources available at TACC.

Repertoire sequencing investigates the collection of trans-membrane antigen-receptor proteins located on the surface of T as well as B cells—white blood cells of which play a key role inside the human immune response. A form of next-generation genetic analysis, repertoire sequencing has transformed the field of immunotherapy, enabling quantitative analyses of which help scientists understand the function of immunity in health as well as disease.

VDJServer was developed by bioinformaticians as well as immunologists by UT Southwestern Medical Center, J. Craig Venter Institute as well as Yale University in partnership with computational experts at TACC.

“VDJServer provides access to sophisticated analysis software as well as TACC’s high-performance computing resources through an intuitive interface designed for users who are primarily biologists as well as clinicians,” said project leader Lindsay Cowell, an associate professor of Clinical Sciences at UT Southwestern Medical Center, whose group developed the software at the core of VDJServer.

“In addition, we provide platforms for sharing data, analysis results, as well as analysis pipelines,” she said. “Access to these analyses as well as resource-sharing accelerates research as well as enables insights of which wouldn’t be possible without the opportunity for data integration.”

Researchers can upload B- as well as T-cell-receptor data as well as tap into TACC’s computing power through the site to perform data-driven studies. Immune repertoire analysis is usually relevant in many contexts, including cancer immunology.

One example of This kind of type of research is usually a collaboration between the Cowell group as well as Marco Davila, a cancer researcher at the Moffitt Cancer Center. Together they are developing chimeric antigen receptors – genetically engineered receptors enabling T-cells to express receptors with the antigen specificity of an antibody. These receptors would likely allow the T-cells to recognize as well as kill cancer cells.

“The team is usually using VDJServer to perform bioinformatics analyses to identify appropriate antibodies of which may target specific cancer types,” explained Cowell. “of which’s followed up with experimental validation to determine of which the antibodies are appropriate.”

VDJServer speeds up scientists’ understanding of the immune system as well as help cultivate reproducible findings, according to Matt Vaughn, TACC’s Director of Life Science Computing.

“Immunotherapy is usually a relatively young field as well as the computational tools are emerging alongside with knowledge of the domain,” Vaughn said. “Community-oriented efforts like VDJServer are important because they provide a centralized workbench where best of breed algorithms as well as workflows can be used much more quickly than if they were released just as source code as well as at the end of a long publication cycle. They’re also available democratically: anyone can use software at VDJServer regardless of how computationally experienced they are.”

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

Explore further:
Immunotherapy for glioblastoma well tolerated; survival gains observed

More information:
Jiesi Luo et al, RPI-Bind: a structure-based method for accurate identification of RNA-protein binding sites, Scientific Reports (2017). DOI: 10.1038/s41598-017-00795-4

Huaidong Chen et al. Relational Network for Knowledge Discovery through Heterogeneous Biomedical as well as Clinical Features, Scientific Reports (2016). DOI: 10.1038/srep29915

Advancing cancer immunotherapy with computer simulations as well as data analysis

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