5 results on '"Sivakumar IKA"'
Search Results
2. High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.
- Author
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Shao XM, Bhattacharya R, Huang J, Sivakumar IKA, Tokheim C, Zheng L, Hirsch D, Kaminow B, Omdahl A, Bonsack M, Riemer AB, Velculescu VE, Anagnostou V, Pagel KA, and Karchin R
- Subjects
- Algorithms, Antigens, Neoplasm genetics, Antigens, Neoplasm metabolism, Artificial Intelligence, CD8-Positive T-Lymphocytes immunology, Cancer Vaccines therapeutic use, Computational Biology methods, Data Mining, Histocompatibility Antigens Class I genetics, Histocompatibility Antigens Class I metabolism, Histocompatibility Antigens Class II genetics, Histocompatibility Antigens Class II metabolism, Humans, Mutation, Missense, Neoplasms drug therapy, Neoplasms metabolism, Neoplasms pathology, Predictive Value of Tests, Protein Binding, Software, Antigens, Neoplasm immunology, Cancer Vaccines immunology, Histocompatibility Antigens Class I immunology, Histocompatibility Antigens Class II immunology, Neoplasms immunology, Neural Networks, Computer
- Abstract
Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low predictive value for actual peptide presentation, inadequate support for rare MHC alleles, and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method that predicts peptide-MHC binding. MHCnuggets can predict binding for common or rare alleles of MHC class I or II with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is faster than other methods. When compared with methods that integrate binding affinity and MHC-bound peptide (HLAp) data from mass spectrometry, MHCnuggets yields a 4-fold increase in positive predictive value on independent HLAp data. We applied MHCnuggets to 26 cancer types in The Cancer Genome Atlas, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique predicted immunogenic missense mutations (IMM). Predicted IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration ( P < 2 × 10
-16 ), including CD8+ T cells. Only 0.16% of predicted IMMs were observed in more than 2 patients, with 61.7% of these derived from driver mutations. Thus, we describe a method for neoantigen prediction and its performance characteristics and demonstrate its utility in data sets representing multiple human cancers., (©2019 American Association for Cancer Research.)- Published
- 2020
- Full Text
- View/download PDF
3. The Immune Landscape of Cancer.
- Author
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Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA, Ziv E, Culhane AC, Paull EO, Sivakumar IKA, Gentles AJ, Malhotra R, Farshidfar F, Colaprico A, Parker JS, Mose LE, Vo NS, Liu J, Liu Y, Rader J, Dhankani V, Reynolds SM, Bowlby R, Califano A, Cherniack AD, Anastassiou D, Bedognetti D, Mokrab Y, Newman AM, Rao A, Chen K, Krasnitz A, Hu H, Malta TM, Noushmehr H, Pedamallu CS, Bullman S, Ojesina AI, Lamb A, Zhou W, Shen H, Choueiri TK, Weinstein JN, Guinney J, Saltz J, Holt RA, Rabkin CS, Lazar AJ, Serody JS, Demicco EG, Disis ML, Vincent BG, and Shmulevich I
- Published
- 2019
- Full Text
- View/download PDF
4. Dynamics of Tumor and Immune Responses during Immune Checkpoint Blockade in Non-Small Cell Lung Cancer.
- Author
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Anagnostou V, Forde PM, White JR, Niknafs N, Hruban C, Naidoo J, Marrone K, Sivakumar IKA, Bruhm DC, Rosner S, Phallen J, Leal A, Adleff V, Smith KN, Cottrell TR, Rhymee L, Palsgrove DN, Hann CL, Levy B, Feliciano J, Georgiades C, Verde F, Illei P, Li QK, Gabrielson E, Brock MV, Isbell JM, Sauter JL, Taube J, Scharpf RB, Karchin R, Pardoll DM, Chaft JE, Hellmann MD, Brahmer JR, and Velculescu VE
- Subjects
- Antineoplastic Agents, Immunological therapeutic use, Carcinoma, Non-Small-Cell Lung drug therapy, Carcinoma, Non-Small-Cell Lung genetics, Carcinoma, Non-Small-Cell Lung pathology, Circulating Tumor DNA genetics, Cohort Studies, DNA, Neoplasm genetics, Follow-Up Studies, Humans, Lung Neoplasms drug therapy, Lung Neoplasms genetics, Lung Neoplasms pathology, Neoplasm, Residual drug therapy, Neoplasm, Residual genetics, Neoplasm, Residual pathology, Prognosis, Survival Rate, Carcinoma, Non-Small-Cell Lung immunology, Circulating Tumor DNA analysis, DNA, Neoplasm analysis, Lung Neoplasms immunology, Neoplasm, Residual immunology, Nivolumab therapeutic use
- Abstract
Despite the initial successes of immunotherapy, there is an urgent clinical need for molecular assays that identify patients more likely to respond. Here, we report that ultrasensitive measures of circulating tumor DNA (ctDNA) and T-cell expansion can be used to assess responses to immune checkpoint blockade in metastatic lung cancer patients ( N = 24). Patients with clinical response to therapy had a complete reduction in ctDNA levels after initiation of therapy, whereas nonresponders had no significant changes or an increase in ctDNA levels. Patients with initial response followed by acquired resistance to therapy had an initial drop followed by recrudescence in ctDNA levels. Patients without a molecular response had shorter progression-free and overall survival compared with molecular responders [5.2 vs. 14.5 and 8.4 vs. 18.7 months; HR 5.36; 95% confidence interval (CI), 1.57-18.35; P = 0.007 and HR 6.91; 95% CI, 1.37-34.97; P = 0.02, respectively], which was detected on average 8.7 weeks earlier and was more predictive of clinical benefit than CT imaging. Expansion of T cells, measured through increases of T-cell receptor productive frequencies, mirrored ctDNA reduction in response to therapy. We validated this approach in an independent cohort of patients with early-stage non-small cell lung cancer ( N = 14), where the therapeutic effect was measured by pathologic assessment of residual tumor after anti-PD1 therapy. Consistent with our initial findings, early ctDNA dynamics predicted pathologic response to immune checkpoint blockade. These analyses provide an approach for rapid determination of therapeutic outcomes for patients treated with immune checkpoint inhibitors and have important implications for the development of personalized immune targeted strategies. Significance: Rapid and sensitive detection of circulating tumor DNA dynamic changes and T-cell expansion can be used to guide immune targeted therapy for patients with lung cancer. See related commentary by Zou and Meyerson, p. 1038 ., (©2018 American Association for Cancer Research.)
- Published
- 2019
- Full Text
- View/download PDF
5. The Immune Landscape of Cancer.
- Author
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Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA, Ziv E, Culhane AC, Paull EO, Sivakumar IKA, Gentles AJ, Malhotra R, Farshidfar F, Colaprico A, Parker JS, Mose LE, Vo NS, Liu J, Liu Y, Rader J, Dhankani V, Reynolds SM, Bowlby R, Califano A, Cherniack AD, Anastassiou D, Bedognetti D, Mokrab Y, Newman AM, Rao A, Chen K, Krasnitz A, Hu H, Malta TM, Noushmehr H, Pedamallu CS, Bullman S, Ojesina AI, Lamb A, Zhou W, Shen H, Choueiri TK, Weinstein JN, Guinney J, Saltz J, Holt RA, Rabkin CS, Lazar AJ, Serody JS, Demicco EG, Disis ML, Vincent BG, and Shmulevich I
- Subjects
- Adolescent, Adult, Aged, Aged, 80 and over, Child, Female, Humans, Interferon-gamma genetics, Interferon-gamma immunology, Macrophages immunology, Male, Middle Aged, Prognosis, Th1-Th2 Balance physiology, Transforming Growth Factor beta genetics, Transforming Growth Factor beta immunology, Wound Healing genetics, Wound Healing immunology, Young Adult, Genomics methods, Neoplasms classification, Neoplasms genetics, Neoplasms immunology
- Abstract
We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)
- Published
- 2018
- Full Text
- View/download PDF
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