Government Owned Inventions Available for Licensing or Collaboration: Machine Learning Model for the Prioritization of Cancer Neoepitopes, 90023-90024 [2024-26464]
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Federal Register / Vol. 89, No. 220 / Thursday, November 14, 2024 / Notices
Date: December 3, 2024.
Time: 11:00 a.m.–12:00 p.m.
Agenda: The Novel and Exceptional
Technology and Research Advisory
Committee (NExTRAC) will receive an
update from the Working Group on Engaging
the Public as Partners in Clinical Research
and discuss the next steps regarding the
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policies/novel-and-exceptional-technologyand-research-advisory-committeenextrac#tab4/.
Contact Person: Jessica Tucker, Ph.D.,
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SciencePolicy@od.nih.gov.
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(Catalogue of Federal Domestic Assistance
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Training Award; 93.22, Clinical Research
Loan Repayment Program for Individuals
from Disadvantaged Backgrounds; 93.232,
Loan Repayment Program for Research
Generally; 93.39, Academic Research
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Repayment Program; 93.187, Undergraduate
Scholarship Program for Individuals from
Disadvantaged Backgrounds, National
Institutes of Health, HHS)
Dated: November 7, 2024.
David W. Freeman,
Supervisory Program Analyst, Office of
Federal Advisory Committee Policy.
[FR Doc. 2024–26426 Filed 11–13–24; 8:45 am]
BILLING CODE 4140–01–P
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DEPARTMENT OF HEALTH AND
HUMAN SERVICES
National Institutes of Health
National Institute of Arthritis and
Musculoskeletal and Skin Diseases;
Notice of Meeting
Pursuant to section 1009 of the
Federal Advisory Committee Act, as
amended, notice is hereby given of a
meeting of the National Arthritis and
Musculoskeletal and Skin Diseases
Advisory Council.
The meeting will be open to the
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Individuals who plan to attend and
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as amended. The grant applications and
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Date: January 28, 2025.
Open: 9:30 a.m. to 3:00 p.m.
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Address: National Institutes of Health, 31
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Contact Person: Darren D. Sledjeski, Ph.D.,
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(DEA), National Institute of Arthritis and
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451–7766, darren.sledjeski@nih.gov.
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statement should include the name,
address, telephone number and when
applicable, the business or professional
affiliation of the interested person.
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90023
In the interest of security, NIH has
procedures at https://www.nih.gov/
about-nih/visitor-information/campusaccess-security for entrance into oncampus and off-campus facilities. All
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(Catalogue of Federal Domestic Assistance
Program Nos. 93.846, Arthritis,
Musculoskeletal and Skin Diseases Research,
National Institutes of Health, HHS)
Dated: November 8, 2024.
Miguelina Perez,
Program Analyst, Office of Federal Advisory
Committee Policy.
[FR Doc. 2024–26479 Filed 11–13–24; 8:45 am]
BILLING CODE 4140–01–P
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.
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: aida.cremesti@nih.gov or
Phone: 240–276–6641. Inquiries related
SUMMARY:
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90024
Federal Register / Vol. 89, No. 220 / Thursday, November 14, 2024 / Notices
to licensing should be directed to
Andrew Burke, Ph.D., Senior
Technology Transfer Manager, NCI,
Technology Transfer Center, Email:
burkear@mail.nih.gov 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 predetermined 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 nonreactive 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:
Æ Cancer vaccines.
Æ TIL and T-cell receptor therapies.
• Add-on to current color fundus
imaging modalities.
ddrumheller on DSK120RN23PROD with NOTICES1
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.
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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
DEPARTMENT OF HEALTH AND
HUMAN SERVICES
National Institutes of Health
National Cancer Institute; Notice of
Meeting
Pursuant to section 1009 of the
Federal Advisory Committee Act, as
amended, notice is hereby given of a
meeting of the National Cancer
Advisory Board (NCAB) and NCI Board
of Scientific Advisors (BSA).
This will be a hybrid meeting held inperson and virtually and will be open to
the public as indicated below.
Individuals who plan to attend inperson or view the virtual meeting and
need special assistance or other
reasonable accommodations, should
notify the Contact Person listed below
in advance of the meeting. The meeting
can be accessed from the NIH Videocast
at the following link: https://
videocast.nih.gov/.
A portion of the National Cancer
Advisory Board meeting will be closed
to the public in accordance with the
provisions set forth in section
552b(c)(6), Title 5 U.S.C., as amended.
The intramural programs and projects
and the discussions could disclose
confidential trade secrets or commercial
property such as patentable material,
and personal information concerning
individuals associated with the
intramural programs and projects, the
disclosure of which would constitute a
clearly unwarranted invasion of
personal privacy.
Name of Committee: National Cancer
Advisory Board.
Date: December 2, 2024.
Open: 6:00 p.m. to 9:00 p.m.
Agenda: National Cancer Advisory Board
Subcommittee Meetings.
Place: Gaithersburg Marriott
Washingtonian Center, Room—TBD, 9751
Washington Boulevard, Gaithersburg, MD
20878 (In Person Meeting).
Contact Person: Paulette S. Gray, Ph.D.,
Director, Division of Extramural Activities,
National Cancer Institute—Shady Grove,
PO 00000
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National Institutes of Health, 9609 Medical
Center Drive, 7th Floor, Room. 7W444,
Bethesda, MD 20892, 240–276–6340, grayp@
mail.nih.gov.
Name of Committee: National Cancer
Advisory Board.
Date: December 3, 2024.
Closed: 8:00 a.m. to 8:45 a.m.
Agenda: Review of intramural program site
visit outcomes and the discussion of
confidential personnel issues.
Place: National Cancer Institute—Shady
Grove, 9609 Medical Center Drive, Room
TE406 & 408, Rockville, MD 20850 (In Person
and Virtual Meeting).
Contact Person: Paulette S. Gray, Ph.D.,
Director, Division of Extramural Activities,
National Cancer Institute—Shady Grove,
National Institutes of Health, 9609 Medical
Center Drive, 7th Floor, Room. 7W444,
Bethesda, MD 20892 240–276–6340, grayp@
mail.nih.gov.
Name of Committee: National Cancer
Advisory Board and NCI Board of Scientific
Advisors.
Date: December 3, 2024.
Open: 9:00 a.m. to 4:00 p.m.
Agenda: Joint meeting of the National
Cancer Advisory Board and NCI Board of
Scientific Advisors, NCI Director’s report and
presentations, NCI Board of Scientific
Advisors Concepts Review.
Place: National Cancer Institute—Shady
Grove, 9609 Medical Center Drive, Room
TE406 & 408, Rockville, MD 20850 (In Person
and Virtual Meeting).
Contact Person: Paulette S. Gray, Ph.D.,
Director, Division of Extramural Activities,
National Cancer Institute—Shady Grove,
National Institutes of Health, 9609 Medical
Center Drive, 7th Floor, Room 7W444,
Bethesda, MD 20892 240–276–6340 grayp@
mail.nih.gov.
Any interested person may file written
comments with the committee by forwarding
the statement to the Contact Person listed on
this notice. The statement should include the
name, address, telephone number and when
applicable, the business or professional
affiliation of the interested person.
In the interest of security, NIH has
instituted stringent procedures for entrance
onto the NCI-Shady Grove campus. All
visitors will be asked to show one form of
identification (for example, a governmentissued photo ID, driver’s license, or passport)
and to state the purpose of their visit.
Information is also available on the
Institute’s/Center’s home page: NCAB: https://
deainfo.nci.nih.gov/advisory/ncab/
ncab.htm,BSA: https://deainfo.nci.nih.gov/
advisory/bsa/bsa.htm, where an agenda and
any additional information for the meeting
will be posted when available.
(Catalogue of Federal Domestic Assistance
Program Nos. 93.392, Cancer Construction;
93.393, Cancer Cause and Prevention
Research; 93.394, Cancer Detection and
Diagnosis Research; 93.395, Cancer
Treatment Research; 93.396, Cancer Biology
Research; 93.397, Cancer Centers Support;
93.398, Cancer Research Manpower; 93.399,
Cancer Control, National Institutes of Health,
HHS)
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]]>Agencies
[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]
-----------------------------------------------------------------------
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.
-----------------------------------------------------------------------
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
