Neoantigens and uses thereof for treating cancer
Inventors
Luksza, Marta • Balachandran, Vinod P. • Levine, Arnold J. • Wolchok, Jedd D. • Merghoub, Taha • Leach, Steven D. • Chan, Timothy A. • Greenbaum, Benjamin D. • Laessig, Michael
Assignees
Icahn School of Medicine at Mount Sinai • Institute for Systems Biology • Memorial Sloan Kettering Cancer Center
Publication Number
US-12331359-B2
Publication Date
2025-06-17
Expiration Date
2038-01-18
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Abstract
Systems and methods for determining the likely responsiveness of a human cancer subject to a checkpoint blockade immunotherapy regimen are provided. Sequencing reads are obtained from samples from the subject representative of the cancer. A human leukocyte antigen type and a plurality of clones is determined from the sequencing reads. For each clone, an initial frequency Xα in the one or more samples is determined and a corresponding clone fitness score of the clone is computed, thereby computing clone fitness scores. Each such fitness score is computed by identifying neoantigens in the respective clone, computing a recognition potential for each neoantigen, and determining the corresponding clone fitness score of the respective clone as an aggregate of these recognition potentials. A total fitness, quantifying the likely responsiveness of the subject to the regimen, is computed by summing the clone fitness scores across the plurality of clones.
Core Innovation
The invention provides systems and methods for determining the likelihood that a human subject afflicted with a cancer will be responsive to a treatment regimen comprising administering a checkpoint blockade immunotherapy directed to the cancer. This is achieved by obtaining sequencing reads from samples representative of the cancer, determining the human leukocyte antigen (HLA) type and a plurality of tumor clones, and computing clone fitness scores. Each clone fitness score is based on identifying neoantigens within the clone, computing a recognition potential for each neoantigen, and aggregating these recognition potentials. The total fitness, which quantifies the likely responsiveness of the subject to the immunotherapy regimen, is computed by summing the clone fitness scores weighted by their respective frequencies.
The problem addressed arises from the limited impact of immune checkpoint inhibitors in certain cancers, such as pancreatic ductal adenocarcinoma (PDAC), which has a relatively low mutational load and presents multiple immunosuppressive mechanisms that inhibit effective antitumor immunity. Existing biomarkers such as neoantigen burden are coarse proxies for immunotherapy response, and heterogeneous tumors with subclonal neoantigens complicate treatment. There is a need for systems and methods that consider tumor heterogeneity and immune interactions to more accurately predict patient responsiveness to checkpoint blockade immunotherapy, and also for identifying novel therapeutic targets to improve survival among PDAC patients.
Claims Coverage
The patent contains two independent claims relating to methods for selecting human cancer subjects for treatment with checkpoint blockade immunotherapy and for selecting an immunotherapy for treating cancer based on neoantigen fitness models.
Method for selecting cancer patients for checkpoint blockade immunotherapy based on neoantigen recognition potential fitness model
Obtaining sequencing reads representative of the cancer from the human cancer subject; determining the HLA type of the subject; determining a plurality of tumor clones and initial frequencies for each clone; computing clone fitness scores based on a first procedure involving identification of neoantigens within each clone, computation of each neoantigen's recognition potential by calculating an amplitude as a function of relative MHC affinity between the neoantigen and its wildtype counterpart given the subject's HLA type, and the probability of T-cell receptor recognition based on probable binding between each neoantigen and known epitopes (comprising up to 1×106 epitopes) after class I MHC presentation; aggregating neoantigen recognition potentials within clones to obtain clone fitness scores; and computing total fitness as a sum of clone fitness scores weighted by clone frequencies to quantify the likelihood of responsiveness to the immunotherapy.
Method for selecting an immunotherapy for treating cancer using neoantigen fitness scoring
Obtaining sequencing reads from samples representative of the cancer in a human subject; determining the HLA type from the sequencing reads; determining a plurality of clones and initial frequencies for each; computing clone fitness scores by identifying neoantigens per clone and computing each neoantigen's recognition potential via amplitude based on relative MHC affinities and T-cell receptor recognition probability as a function of binding between neoantigens and known epitopes after class I MHC presentation; aggregating these recognition potentials into clone fitness scores; and selecting an immunotherapy based on the neoantigen with the highest recognition potential across clones and potentially across subjects and HLA types.
The independent claims cover methods that compute neoantigen recognition potentials by integrating MHC presentation and T-cell receptor recognition probabilities to determine clone fitness scores and total fitness for the cancer. These methods enable selection of cancer patients for checkpoint blockade immunotherapy and identification of immunotherapy approaches based on neoantigen fitness modeling.
Stated Advantages
The mathematical model using genomic data allows broad consideration of the neoantigen landscape and describes evolutionary dynamics of cancer cell populations under checkpoint blockade immunotherapy.
The recognition potential fitness model improves prediction of patient survival and response to immunotherapy by integrating neoantigen heterogeneity, MHC binding, and TCR recognition probabilities.
The model accounts for tumor heterogeneity and clonal structure, improving prediction accuracy over neoantigen burden alone.
Documented Applications
Determining the likely responsiveness of a human cancer subject, including those with pancreatic cancer (e.g., pancreatic ductal adenocarcinoma), to checkpoint blockade immunotherapy regimens such as anti-CTLA-4, anti-PD-1, anti-PD-L1, and others.
Selecting immunotherapies for cancer treatment by identifying neoantigens with high recognition potential in clones of the tumor.
Identification of neoantigen immunogenic hotspots such as MUC16 in pancreatic cancer for targeted therapies including vaccines and adoptive T cell therapies.
Use of neoantigen quantity and immunogenicity measurements, including neoantigen-microbial homology and activated T cell numbers, to stratify cancer subjects for immunotherapy.
Development of vaccines comprising neoantigens or neoantigen-encoding polynucleotides for immunotherapy.
Adoptive T cell therapies involving expansion or engineering of neoantigen-specific T cells targeting identified neoantigens.
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