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Principal Investigator: ELLIS L REINHERZ
Organization: DANA-FARBER CANCER INST
Fiscal Year: 2024
Award: $640,540
Funding agency: National Cancer Institute
ABSTRACT
Programming the immune system to detect neoantigens and destroy tumors is critical for effective
immunotherapy. Until now, bioinformatic prediction of neoepitopes on tumors from Next Generation Sequencing
(NGS) information has been used alone or in conjunction with immunological assays to indirectly infer neoepitope
identification. Unfortunately, only a small fraction of predicted epitopes are surface-displayed as HLA-bound
peptides (pMHC), a process required for cytolytic T lymphocyte (CTL) targeting. Moreover, immunologic assays
suffer from both high false positive and false negative rates, confounding correct identification. Conventional
mass spectrometry (MS) approaches to interrogate the pMHC, referred to as the cell's immune peptidome, suffer
from poor HLA recovery, requirement for multiple sample runs to achieve adequate peptide coverage and
necessitate large numbers of tumor cells, all features impractical for routine clinical use. Our Academic-Industrial
Partnership (AIP) advances the creation of a commercial pipeline to deliver personalized tumor neoantigen
identification, integrating NGS-based genomics and transcriptomics, bioinformatics, chemical peptidomics and
a novel, ultrasensitive form of MS. Our interdisciplinary/multi-institutional strategic alliance combines basic
research at Dana Farber Cancer Institute with industrial expertise at Curacloud Corporation and JPT Peptide
Technologies. We propose deployment of an attomole (10-18) Poisson detection liquid chromatography-data
independent acquisition (LC-DIA) MS method for antigen discovery to electronically record and capture the entire
immune peptidome comprising both numerous self-peptides and sparse neoantigens in a single run from small
numbers of tumor cells (106) retrieved by clinical needle biopsy. This approach changes the aforementioned MS
calculus and permits neoantigen search at any point following data collection using existing commercially
marketed MS instrumentation. In Aim 1 neoepitope candidates shall be chemically synthesized in high
throughput pools of up to 6,000 peptides per nanoscale run by JPT for MS fragmentation analysis and elution
mapping reference standards for definitive neoantigen identification using LC-DIAMS on individual tumor
samples based on DFCI technology, optimizing each step. In Aim 2 we shall use NGS data from tumor cells in
conjunction with bioinformatics at Curacloud to predict neoepitopes arising from coding and non-coding regions
capable of interacting with each HLA-A, -B and/or -C allele of a patient. Machine learning-based neoepitope
ranking algorithms incorporating MS data and other results shall be developed for candidate prioritization. An
end user service shall be established involving all aforementioned integrative technologies. From initial tumor
biopsy to identification of neoepitopes, a time scale of approximately one month is anticipated. This generic
neoepitope precision identification pipeline is applicable to multiple immunotherapy protocols as well as immune
monitoring of tumor evolution at the original and any metastatic site, informing therapeutic adjustments as
required.
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