Notice2024-26464
Government Owned Inventions Available for Licensing or Collaboration: Machine Learning Model for the Prioritization of Cancer Neoepitopes
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Published
November 14, 2024
Issuing agencies
Health and Human Services DepartmentNational Institutes of Health
Abstract
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.
Full Text
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<title>Federal Register, Volume 89 Issue 220 (Thursday, November 14, 2024)</title>
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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 [<a href="http://www.gpo.gov">www.gpo.gov</a>]
[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:
<a href="/cdn-cgi/l/email-protection#6f0e060b0e410c1d0a020a1c1b062f01060741080019"><span class="__cf_email__" data-cfemail="a1c0c8c5c08fc2d3c4ccc4d2d5c8e1cfc8c98fc6ced7">[email protected]</span></a> 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:
<a href="/cdn-cgi/l/email-protection#9bf9eee9f0fefae9dbf6faf2f7b5f5f2f3b5fcf4ed"><span class="__cf_email__" data-cfemail="7b190e09101e1a093b161a121755151213551c140d">[email protected]</span></a> 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
<bullet> Oncology.
<bullet> Prioritization of neoantigens for the development of
effective personalized therapies:
[cir] Cancer vaccines.
[cir] TIL and T-cell receptor therapies.
<bullet> Add-on to current color fundus imaging modalities.
Competitive Advantages
<bullet> Model is trained using a dataset of verified neoantigens
from patient tumor data.
<bullet> Model is unbiased because it does not use prior
assumptions about what features a neoepitope should have.
<bullet> Uses two models (MMP and NMER model) as a more
reproducible approach than a single model.
<bullet> 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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