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[Federal Register Volume 89, Number 220 (Thursday, November 14, 2024)]
[Notices]
[Pages 90023-90024]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2024-26464]

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DEPARTMENT OF HEALTH AND HUMAN SERVICES

National Institutes of Health

Government Owned Inventions Available for Licensing or 
Collaboration: Machine Learning Model for the Prioritization of Cancer 
Neoepitopes

AGENCY: National Institutes of Health, HHS.

ACTION: Notice.

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SUMMARY: The National Cancer Institute (NCI), an institute of the 
National Institutes of Health (NIH), Department of Health and Human 
Services (HHS), is giving notice of licensing and collaboration 
opportunities for the inventions listed below, which are owned by an 
agency of the U.S. Government and are available for license and 
collaboration in the U.S. to achieve expeditious commercialization of 
results of federally-funded research and development.

FOR FURTHER INFORMATION CONTACT: Inquiries related to a collaboration 
opportunity should be directed to: Aida Cremesti, Senior Technology 
Transfer Manager, NCI, Technology Transfer Center, Email: 
[email protected] or Phone: 240-276-6641. Inquiries related

[[Page 90024]]

to licensing should be directed to Andrew Burke, Ph.D., Senior 
Technology Transfer Manager, NCI, Technology Transfer Center, Email: 
[email protected] or Phone: 240-276-5484.

SUPPLEMENTARY INFORMATION: Success in immunotherapy is often 
attributable to the reactivity of patient T-cells to specific mutated 
peptide(s) found in the patient's tumor known as neoepitopes. In the 
development of patient-specific immunotherapies, there is no consistent 
standard for prioritizing such neoepitopes. Current models arrive at a 
ranked list of potential candidates by removing epitopes based on pre-
determined criteria which might lead to the elimination of known 
reactive neoepitopes. Identification, prioritization and targeting of 
patient neoepitopes are crucial for developing effective, personalized 
treatments. Ranking or prioritizing neoepitopes is especially important 
when trying to construct a cancer vaccine that will elicit a 
therapeutically beneficial immune response. Accordingly, scientists at 
the NCI created a novel approach to identify and prioritize patient 
neoantigens. This model uses a training dataset of known neoantigens 
from patient screening and determines features of importance to epitope 
recognition using both reactive and non-reactive epitopes. The machine 
learning algorithm scores epitopes for their likelihood of reactivity 
and provides a stable, reproducible method to prioritize epitopes that 
can be used anywhere.
    This Notice is in accordance with 35 U.S.C. 209 and 37 CFR part 
404.
    NIH Reference Number: E-022-2024-0.

Potential Commercial Applications

     Oncology.
     Prioritization of neoantigens for the development of 
effective personalized therapies:
    [cir] Cancer vaccines.
    [cir] TIL and T-cell receptor therapies.
     Add-on to current color fundus imaging modalities.

Competitive Advantages

     Model is trained using a dataset of verified neoantigens 
from patient tumor data.
     Model is unbiased because it does not use prior 
assumptions about what features a neoepitope should have.
     Uses two models (MMP and NMER model) as a more 
reproducible approach than a single model.
     Particularly useful for prioritizing epitopes for patients 
with large numbers of mutations.
    Publication: A machine learning model for ranking candidate HLA 
class I neoantigens based on known neoepitopes from multiple human 
tumor types. (PMID: 34927080).
    Product Type: Research Tool.
    Development Stage: Prototype.
    Therapeutic Area(s): Cancer.

    Dated: November 8, 2024.
Richard U. Rodriguez,
Associate Director, Technology Transfer Center, National Cancer 
Institute.
[FR Doc. 2024-26464 Filed 11-13-24; 8:45 am]
BILLING CODE 4140-01-P
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