New initiative identifies key parameters Inherent effective anti-tumor immunity

Overview

• Post By : Kumar Jeetendra


• Source: Parker Institute for Cancer Immunotherapy


• Date: 10 Oct,2020

Neoantigens, tiny markers that arise from cancer mutations, flag cells as cancerous and could be the key to unlocking a new generation of immunotherapies. Targeting the”right” neoantigens – at a cancer vaccine or a cell treatment – has the promise to eliminate a patient’s cancer with minimal side effects. But countless mutations can exist in a tumor, and only some can contribute to neoantigens that can activate an immune response against cancer.

Researchers from an initiative launched by the Parker Institute for Cancer Immunotherapy (PICI) and the Cancer Research Institute known as the Tumor Neoantigen Choice Alliance (TESLA) have discovered parameters to better predict that neoantigens could provoke a cancer-killing effect. TESLA brings together a constellation of 36 leading biotech, pharma, university and scientific nonprofit research teams.

Through advanced computational analysis, the alliance found five characteristics that strongly indicated which cancer markers were most likely to generate an immune reaction. They fell into two important categories: the way in which the neoantigen is introduced on the cancer cell and how the neoantigen is recognized by the immune system.

Our aim is that data produced from TESLA becomes the reference standard when developing a new neoantigen-based treatment. If every method, old and new, used the data to benchmark their predictions, the whole field would be able to collaborate and iterate on new methods much more quickly.”-Daniel Wells, Ph.D., principal data scientist at PICI and the study’s corresponding author

When the data model highlighting these five traits has been put to the test against another set of cancer samples, it correctly predicted 75 percent of successful neoantigen goals and filtered out 98 percent of ineffective ones.

To produce this benchmark, each TESLA team submitted its most promising neoantigen predictions for melanoma and non-small cell lung cancer (NSCLC) tissue to open science nonprofit Sage Bionetworks. PICI then cross-compared and validated which predictions were right and recognizable by a T-cell.

When the five newly-found attributes were reapplied to participating teams’ algorithms, the predictions measurably improved.

“Until today, neoantigen prediction has been a black box. We had hints at what features might be important. The data model out of TESLA is the first to identify these five features as important,” said renowned neoantigen specialist, co-senior author on the paper and professor Robert D. Schreiber, Ph.D., director of the Andrew M. and Jane M. Bursky Center for Human Immunology & Immunotherapy Programs at Washington University School of Medicine in St. Louis.

Findings also demonstrated that no two prediction methodologies were alike, and most were significantly different. No team’s methodology identified every neoantigen, nor a large majority of these cancer markers, indicating a need for a harmonized scientific campaign like TESLA.

Additional study is necessary in other cancer types, but the discoveries are a substantial step forward for neoantigen research.

“This research has the potential to enhance drug manufacturers’ and researchers’ mathematical algorithms. It can prioritize antigens likely to be present on each patient’s cancer and most visible to the immune system when deprioritizing the ones that aren’t. That means better individualized treatments for patients,” said Lisa Butterfield, Ph.D., vice president of research and development in PICI. “We’re eager to see where the area takes these findings.”

The complete TESLA dataset, the biggest of its type, is available freely to the research community. The expectation is that it may lead to rapid personalized therapy development and even enhanced efficacy for cancer patients worldwide.

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