MiXCR: software for comprehensive adaptive immunity profiling

Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma

Sade-Feldman, M., Yizhak, K., Bjorgaard, S. L., Ray, J. P., de Boer, C. G., Jenkins, R. W., Lieb, D. J., Chen, J. H., Frederick, D. T., Barzily-Rokni, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.038 · Cited by 2,053

Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial

Keskin, D. B., Anandappa, A. J., Sun, J., Tirosh, I., Mathewson, N. D., Li, S., Oliveira, G., Giobbie-Hurder, A., Felt, K., Gjini, E., et al. (2019) · Nature · doi:10.1038/s41586-018-0792-9 · Cited by 1,432

A transcriptionally and functionally distinct PD-1(+) CD8(+) T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade

Thommen, D. S., Koelzer, V. H., Herzig, P., Roller, A., Trefny, M., Dimeloe, S., Kiialainen, A., Hanhart, J., Schill, C., Hess, C., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0057-z · Cited by 1,161

Best practices for single-cell analysis across modalities

Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051

An RNA vaccine drives immunity in checkpoint-inhibitor-treated melanoma

Sahin, U., Oehm, P., Derhovanessian, E., Jabulowsky, R. A., Vormehr, M., Gold, M., Maurus, D., Schwarck-Kokarakis, D., Kuhn, A. N., Omokoko, T., et al. (2020) · Nature · doi:10.1038/s41586-020-2537-9 · Cited by 1,052

Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis

Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer (kConFab), Savas, P., Virassamy, B., Ye, C., Salim, A., Mintoff, C. P., Caramia, F., Salgado, R., Byrne, D. J., Teo, Z. L., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0078-7 · Cited by 992

Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma

Zhao, J., Chen, A. X., Gartrell, R. D., Silverman, A. M., Aparicio, L., Chu, T., Bordbar, D., Shan, D., Samanamud, J., Mahajan, A., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0349-y · Cited by 903

A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer

Bassez, A., Vos, H., Van Dyck, L., Floris, G., Arijs, I., Desmedt, C., Boeckx, B., Vanden Bempt, M., Nevelsteen, I., Lambein, K., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01323-8 · Cited by 867

Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer

Meylan, M., Petitprez, F., Becht, E., Bougoüin, A., Pupier, G., Calvez, A., Giglioli, I., Verkarre, V., Lacroix, G., Verneau, J., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.02.001 · Cited by 831

A phase Ib trial of personalized neoantigen therapy plus anti-PD-1 in patients with advanced melanoma, non-small cell lung cancer, or bladder cancer

Ott, P. A., Hu-Lieskovan, S., Chmielowski, B., Govindan, R., Naing, A., Bhardwaj, N., Margolin, K., Awad, M. M., Hellmann, M. D., Lin, J. J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.053 · Cited by 738

Regulatory T Cells Exhibit Distinct Features in Human Breast Cancer

Plitas, G., Konopacki, C., Wu, K., Bos, P. D., Morrow, M., Putintseva, E. V., Chudakov, D. M., & Rudensky, A. Y. (2016) · Immunity · doi:10.1016/j.immuni.2016.10.032 · Cited by 695

Intratumoral CD4+ T Cells Mediate Anti-tumor Cytotoxicity in Human Bladder Cancer

Oh, D. Y., Kwek, S. S., Raju, S. S., Li, T., McCarthy, E., Chow, E., Aran, D., Ilano, A., Pai, C.-C. S., Rancan, C., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.017 · Cited by 734

Comprehensive mapping of immune perturbations associated with severe COVID-19

Kuri-Cervantes, L., Pampena, M. B., Meng, W., Rosenfeld, A. M., Ittner, C. A. G., Weisman, A. R., Agyekum, R. S., Mathew, D., Baxter, A. E., Vella, L. A., et al. (2020) · Science Immunology · doi:10.1126/sciimmunol.abd7114 · Cited by 723

A Multifunctional AAV-CRISPR-Cas9 and Its Host Response

Chew, W. L., Tabebordbar, M., Cheng, J. K. W., Mali, P., Wu, E. Y., Ng, A. H. M., Zhu, K., Wagers, A. J., & Church, G. M. (2016) · Nature Methods · doi:10.1038/nmeth.3993 · Cited by 617

Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer

Ganesan, A.-P., Clarke, J., Wood, O., Garrido-Martin, E. M., Chee, S. J., Mellows, T., Samaniego-Castruita, D., Singh, D., Seumois, G., Alzetani, A., et al. (2017) · Nature Immunology · doi:10.1038/ni.3775 · Cited by 528

T cell fate and clonality inference from single-cell transcriptomes

(2016) · doi:10.1038/nmeth.3800 · Cited by 434

Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR

Zemmour, D., Zilionis, R., Kiner, E., Klein, A. M., Mathis, D., & Benoist, C. (2018) · Nature Immunology · doi:10.1038/s41590-018-0051-0 · Cited by 384

Cross-reactivity between tumor MHC class I–restricted antigens and an enterococcal bacteriophage

Fluckiger, A., Daillère, R., Sassi, M., Sixt, B. S., Liu, P., Loos, F., Richard, C., Rabu, C., Alou, M. T., Goubet, A.-G., et al. (2020) · Science · doi:10.1126/science.aax0701 · Cited by 396

Peripheral CD8+ T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma

Fairfax, B. P., Taylor, C. A., Watson, R. A., Nassiri, I., Danielli, S., Fang, H., Mahé, E. A., Cooper, R., Woodcock, V., Traill, Z., et al. (2020) · Nature Medicine · doi:10.1038/s41591-019-0734-6 · Cited by 341

Two subsets of stem-like CD8+ memory T cell progenitors with distinct fate commitments in humans

Galletti, G., De Simone, G., Mazza, E. M. C., Puccio, S., Mezzanotte, C., Bi, T. M., Davydov, A. N., Metsger, M., Scamardella, E., Alvisi, G., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0791-5 · Cited by 299

Overview of methodologies for T-cell receptor repertoire analysis

Rosati, E., Dowds, C. M., Liaskou, E., Henriksen, E. K. K., Karlsen, T. H., & Franke, A. (2017) · BMC Biotechnology · doi:10.1186/s12896-017-0379-9 · Cited by 322

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning

Mason, D. M., Friedensohn, S., Weber, C. R., Jordi, C., Wagner, B., Meng, S. M., Ehling, R. A., Bonati, L., Dahinden, J., Gainza, P., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00699-9 · Cited by 308

Endogenous retroviral signatures predict immunotherapy response in clear cell renal cell carcinoma

Smith, C. C., Beckermann, K. E., Bortone, D. S., De Cubas, A. A., Bixby, L. M., Lee, S. J., Panda, A., Ganesan, S., Bhanot, G., Wallen, E. M., et al. (2018) · Journal of Clinical Investigation · doi:10.1172/jci121476 · Cited by 303

Deep learning-based prediction of the T cell receptor–antigen binding specificity

Lu, T., Zhang, Z., Zhu, J., Wang, Y., Jiang, P., Xiao, X., Bernatchez, C., Heymach, J. V., Gibbons, D. L., Wang, J., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00383-2 · Cited by 295

Genomic and Transcriptomic Determinants of Therapy Resistance and Immune Landscape Evolution during Anti-EGFR Treatment in Colorectal Cancer

Woolston, A., Khan, K., Spain, G., Barber, L. J., Griffiths, B., Gonzalez-Exposito, R., Hornsteiner, L., Punta, M., Patil, Y., Newey, A., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.05.013 · Cited by 254

Spatial immunophenotypes predict response to anti-PD1 treatment and capture distinct paths of T cell evasion in triple negative breast cancer

Hammerl, D., Martens, J. W. M., Timmermans, M., Smid, M., Trapman-Jansen, A. M., Foekens, R., Isaeva, O. I., Voorwerk, L., Balcioglu, H. E., Wijers, R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25962-0 · Cited by 262

Slide-tags enables single-nucleus barcoding for multimodal spatial genomics

Russell, A. J. C., Weir, J. A., Nadaf, N. M., Shabet, M., Kumar, V., Kambhampati, S., Raichur, R., Marrero, G. J., Liu, S., Balderrama, K. S., et al. (2024) · Nature · doi:10.1038/s41586-023-06837-4 · Cited by 225

Computational approaches to therapeutic antibody design: established methods and emerging trends

Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz095 · Cited by 219

In vivo CRISPR screens reveal the landscape of immune evasion pathways across cancer

Dubrot, J., Du, P. P., Lane-Reticker, S. K., Kessler, E. A., Muscato, A. J., Mehta, A., Freeman, S. S., Allen, P. M., Olander, K. E., Ockerman, K. M., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01315-x · Cited by 218

Bioinformatic and Statistical Analysis of Adaptive Immune Repertoires

Greiff, V., Miho, E., Menzel, U., & Reddy, S. T. (2015) · Trends in Immunology · doi:10.1016/j.it.2015.09.006 · Cited by 210

Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires

Miho, E., Yermanos, A., Weber, C. R., Berger, C. T., Reddy, S. T., & Greiff, V. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.00224 · Cited by 189

PIRD: pan immune repertoire database

Zhang, W., Wang, L., Liu, K., Wei, X., Yang, K., Du, W., Wang, S., Guo, N., Ma, C., Luo, L., et al. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz614 · Cited by 166

Targeting of multiple tumor-associated antigens by individual T cell receptors during successful cancer immunotherapy

Dolton, G., Rius, C., Wall, A., Szomolay, B., Bianchi, V., Galloway, S. A. E., Hasan, M. S., Morin, T., Caillaud, M. E., Thomas, H. L., et al. (2023) · Cell · doi:10.1016/j.cell.2023.06.020 · Cited by 153

Immune Profiling of Human Gut-Associated Lymphoid Tissue Identifies a Role for Isolated Lymphoid Follicles in Priming of Region-Specific Immunity

Fenton, T. M., Jørgensen, P. B., Niss, K., Rubin, S. J. S., Mörbe, U. M., Riis, L. B., Da Silva, C., Plumb, A., Vandamme, J., Jakobsen, H. L., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.02.001 · Cited by 145

Technology dictates algorithms: recent developments in read alignment

Alser, M., Rotman, J., Deshpande, D., Taraszka, K., Shi, H., Baykal, P. I., Yang, H. T., Xue, V., Knyazev, S., Singer, B. D., et al. (2021) · Genome Biology · doi:10.1186/s13059-021-02443-7 · Cited by 144

Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes

Zaitsev, A., Chelushkin, M., Dyikanov, D., Cheremushkin, I., Shpak, B., Nomie, K., Zyrin, V., Nuzhdina, E., Lozinsky, Y., Zotova, A., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.07.006 · Cited by 139

Large-scale network analysis reveals the sequence space architecture of antibody repertoires

Miho, E., Roškar, R., Greiff, V., & Reddy, S. T. (2019) · Nature Communications · doi:10.1038/s41467-019-09278-8 · Cited by 138

Biochemical and functional characterization of mutant KRAS epitopes validates this oncoprotein for immunological targeting

Bear, A. S., Blanchard, T., Cesare, J., Ford, M. J., Richman, L. P., Xu, C., Baroja, M. L., McCuaig, S., Costeas, C., Gabunia, K., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24562-2 · Cited by 113

TCRdb: a comprehensive database for T-cell receptor sequences with powerful search function

Chen, S.-Y., Yue, T., Lei, Q., & Guo, A.-Y. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa796 · Cited by 106

Cellular origins and genetic landscape of cutaneous gamma delta T cell lymphomas

Daniels, J., Doukas, P. G., Escala, M. E. M., Ringbloom, K. G., Shih, D. J. H., Yang, J., Tegtmeyer, K., Park, J., Thomas, J. J., Selli, M. E., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15572-7 · Cited by 105

Rapid idiosyncratic mechanisms of clinical resistance to KRAS G12C inhibition

Tsai, Y. S., Woodcock, M. G., Azam, S. H., Thorne, L. B., Kanchi, K. L., Parker, J. S., Vincent, B. G., & Pecot, C. V. (2022) · Journal of Clinical Investigation · doi:10.1172/jci155523 · Cited by 103

Clonal relationships of CSF B cells in treatment-naive multiple sclerosis patients

Eggers, E. L., Michel, B. A., Wu, H., Wang, S.-Z., Bevan, C. J., Abounasr, A., Pierson, N. S., Bischof, A., Kazer, M., Leitner, E., et al. (2017) · JCI Insight · doi:10.1172/jci.insight.92724 · Cited by 93

Simultaneous multiplexed amplicon sequencing and transcriptome profiling in single cells

Saikia, M., Burnham, P., Keshavjee, S. H., Wang, M. F. Z., Heyang, M., Moral-Lopez, P., Hinchman, M. M., Danko, C. G., Parker, J. S. L., & De Vlaminck, I. (2019) · Nature Methods · doi:10.1038/s41592-018-0259-9 · Cited by 75

Bacterial induction of B cell senescence promotes age-related changes in the gut microbiota

Kawamoto, S., Uemura, K., Hori, N., Takayasu, L., Konishi, Y., Katoh, K., Matsumoto, T., Suzuki, M., Sakai, Y., Matsudaira, T., et al. (2023) · Nature Cell Biology · doi:10.1038/s41556-023-01145-5 · Cited by 91

T‐cell receptor and B‐cell receptor repertoire profiling in adaptive immunity

Minervina, A., Pogorelyy, M., & Mamedov, I. (2019) · Transplant International · doi:10.1111/tri.13475 · Cited by 65

CXCR3 Identifies Human Naive CD8+ T Cells with Enhanced Effector Differentiation Potential

De Simone, G., Mazza, E. M. C., Cassotta, A., Davydov, A. N., Kuka, M., Zanon, V., De Paoli, F., Scamardella, E., Metsger, M., Roberto, A., et al. (2019) · The Journal of Immunology · doi:10.4049/jimmunol.1901072 · Cited by 49

Preexisting Skin-Resident CD8 and γδ T-cell Circuits Mediate Immune Response in Merkel Cell Carcinoma and Predict Immunotherapy Efficacy

Reinstein, Z. Z., Zhang, Y., Ospina, O. E., Nichols, M. D., Chu, V. A., Pulido, A. D. M., Prieto, K., Nguyen, J. V., Yin, R., Moran Segura, C., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0798 · Cited by 33

Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

Bashour, H., Smorodina, E., Pariset, M., Zhong, J., Akbar, R., Chernigovskaya, M., Lê Quý, K., Snapkow, I., Rawat, P., Krawczyk, K., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06561-3 · Cited by 32

RNA-seq analysis of gene expression, viral pathogen, and B-cell/T-cell receptor signatures in complex chronic disease

Bouquet, J., Gardy, J. L., Brown, S., Pfeil, J., Miller, R. R., Morshed, M., Avina-Zubieta, A., Shojania, K., McCabe, M., Parker, S., et al. (2017) · Clinical Infectious Diseases · doi:10.1093/cid/ciw767 · Cited by 25

Variant-specific vaccination induces systems immune responses and potent in vivo protection against SARS-CoV-2

Peng, L., Renauer, P. A., Ökten, A., Fang, Z., Park, J. J., Zhou, X., Lin, Q., Dong, M. B., Filler, R., Xiong, Q., et al. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100634 · Cited by 21

Phage display enables machine learning discovery of cancer antigen–specific TCRs

Croce, G., Lani, R., Tardivon, D., Bobisse, S., de Tiani, M., Bragina, M., Perez, M. A. S., Michaux, J., Pak, H. S., Michel, A., et al. (2025) · Science Advances · doi:10.1126/sciadv.ads5589 · Cited by 23

Murine MHC-Deficient Nonobese Diabetic Mice Carrying Human HLA-DQ8 Develop Severe Myocarditis and Myositis in Response to Anti–PD-1 Immune Checkpoint Inhibitor Cancer Therapy

Racine, J. J., Bachman, J. F., Zhang, J.-G., Misherghi, A., Khadour, R., Kaisar, S., Bedard, O., Jenkins, C., Abbott, A., Forte, E., et al. (2024) · The Journal of Immunology · doi:10.4049/jimmunol.2300841 · Cited by 17

Restoration of T and B Cell Differentiation after RAG1 Gene Transfer in Human RAG1 Defective Hematopoietic Stem Cells

Sorel, N., Díaz-Pascual, F., Bessot, B., Sadek, H., Mollet, C., Chouteau, M., Zahn, M., Gil-Farina, I., Tajer, P., van Eggermond, M., et al. (2024) · Biomedicines · doi:10.3390/biomedicines12071495 · Cited by 5

Somatic Mutations in HLA Class Genes and Antigen-Presenting Molecules in Malignant Glioma

Schulte, S. C., Peter, W., Rosenberger, G., Schäfer, M., Maire, C. L., Rünger, A., Ryba, A., Riecken, K., Fita, K. D., Matschke, J., et al. (2025) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-24-0419 · Cited by 3

Learning antibody sequence constraints from allelic inclusion

Jagota, M., Hsu, C., Mazumder, T., Sung, K., DeWitt, W. S., Listgarten, J., Matsen, F. A., Ye, C. J., & Song, Y. S. (2024) · doi:10.1101/2024.10.22.619760 · Cited by 2

Learning antibody sequence constraints from allelic inclusion

Jagota, M., Hsu, C., Mazumder, T., Sung, K., DeWitt, W. S., Listgarten, J., Matsen IV, F. A., Ye, C. J., & Song, Y. S. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101368 · Cited by 2