Bioinformatics For Immuno-oncology

Uncover how tumors escape immune surveillance — and develop the next generation of immunotherapies with us.

The immune system has the power to recognize and destroy cancer, yet tumors often find ways to evade immune attack. At the intersection of immunology and oncology, bioinformatics provides the critical tools to decode these escape mechanisms and pave the way for new immunotherapies.

We collaborate closely with our customers in academia and pharma industry, empowering them with the expertise and latest computational approaches to dissect the tumor-immune interface and drive translational discoveries.

Scroll down to explore our references in immuno-oncology and immunotherapies, including papers that acknowledge our support or are coauthored by scientists from our team. Leave us a message if you would like to learn more about how we can support your research!

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Tumor immune microenvironments

Computational analysis of spatial and single-cell data from tumors enables mapping their complex cellular landscapes. Profiling tumor-infiltrating immune cells and their spatial organization reveals how these cells interact with tumor cells and the surrounding stroma.

A high-resolution cellular and molecular characterization helps us understand immune cell dysfunction, immune exclusion, and the formation of immunosuppressive niches.

Mechanisms of tumor immune evasion

Tumors employ diverse strategies to circumvent immune detection and destruction, including:

• Impaired antigen presentation through the loss, mutation, or downregulation of MHC genes and associated components.
• Recruitment of immunosuppressive cells, such as regulatory T cells and myeloid-derived suppressor cells.
• Suppression of cytotoxic immunity through expression of immune checkpoint ligands such as PD-L1 and immunomodulatory cytokines.

A detailed understanding of how a tumor escapes immune control may require investigating these mechanisms using multi-omic approaches, including:

• DNA sequencing to uncover genomic alterations impacting antigen presentation,
• Transcriptomics to characterize immune cell populations and their functional states,
• Proteomics and immunopeptidomics to detect immunosuppressive factors and tumor-specific peptides.

Tumor antigen discovery

Antigens uniquely or preferentially expressed by tumors serve as targets for cancer vaccines and immunotherapies based on T-cells, CAR T cells, and bispecific antibodies. Such antigens include:

• Tumor-associated antigens (TAAs), which are commonly shared across tumors but not entirely cancer-specific
• Neoantigens, which most commonly arise from tumor-specific mutations

Transcriptomic, proteomic, and immunopeptidomic analyses help identify TAAs, while whole-exome or whole-genome DNA sequencing can be used to predict candidate neoantigens for personalized therapies.

Characterizing immune repertoires

Lymphocyte receptor sequencing, particularly single-cell TCR sequencing, enables dissecting the adaptive immune response in cancer patients or animal models. By analyzing the clonal diversity, expansion, and antigen specificity of T cells, we may gain insights into ongoing (or lacking) anti-tumor responses. These analyses can also uncover tumor-reactive T cell populations for potential therapeutic use.

Predicting responses to immunotherapy

Integrating molecular findings, such as immune cell infiltration, defects in antigen presentation, and expression of immunosuppressive mediators, can help in predicting a patient’s response to immunotherapy. Response-predicting biomarkers or combinations thereof can be used to stratify patients and improve clinical outcomes.

Learn more

• Bioinformatics for cancer research
• Bioinformatics for immunology
• Bioinformatics for drug development
• DNA-seq data analysis
• RNA-seq data analysis
• Single-cell RNA-seq data analysis
• Spatial data analysis
• Epigenomic data analysis
• Discovering targets and biomarkers for immunotherapies

All analyses

References and case studies

• Characterizing a Cancer Immunotherapy Target with Genomic Data (CDR-Life)Cancer Risk Prediction With Machine Learning (Karolinska Institute)
• Detecting Tumor Immune Cells With Multiplex Immunofluorescence (University of Uppsala)
• Macrophage Cell-Therapy Biomarker Discovery (Kuopio Center for Gene and Cell Therapy)

All references

Selected publications from our customers

• Chang, Y. T. et al. (2024). MHC-I upregulation safeguards neoplastic T cells in the skin against NK cell-mediated eradication in mycosis fungoides. Nature communications, 15(1), 752. https://doi.org/10.1038/s41467-024-45083-8
• Peeters, J. G. C. et al. (2024). Hyperactivating EZH2 to augment H3K27me3 levels in regulatory T cells enhances immune suppression by driving early effector differentiation. Cell reports, 43(9), 114724. Advance online publication. https://doi.org/10.1016/j.celrep.2024.114724
• Adebamowo, S. N. et al. (2024). Genome, HLA and polygenic risk score analyses for prevalent and persistent cervical human papillomavirus (HPV) infections. European journal of human genetics : EJHG, 10.1038/s41431-023-01521-7. Advance online publication. https://doi.org/10.1038/s41431-023-01521-7
• Backman, M. et al. (2023). Spatial immunophenotyping of the tumour microenvironment in non-small cell lung cancer. European journal of cancer (Oxford, England : 1990), 185, 40–52. https://doi.org/10.1016/j.ejca.2023.02.012
• Mezheyeuski, A. et al. (2023). An immune score reflecting pro- and anti-tumoural balance of tumour microenvironment has major prognostic impact and predicts immunotherapy response in solid cancers. EBioMedicine, 88, 104452. Advance online publication. https://doi.org/10.1016/j.ebiom.2023.104452
• Tusup, M. et al. (2022). Epitranscriptomics modifier pentostatin indirectly triggers Toll-like receptor 3 and can enhance immune infiltration in tumors. Molecular therapy : the journal of the American Society of Gene Therapy, 30(3), 1163–1170. https://doi.org/10.1016/j.ymthe.2021.09.022
• Ribeiro, R. et al. (2022). Synchronous Epidermodysplasia Verruciformis and Intraepithelial Lesion of the Vulva is Caused by Coinfection with α-HPV and β-HPV Genotypes and Facilitated by Mutations in Cell-Mediated Immunity Genes. Preprint at https://doi.org/10.21203/rs.3.rs-1991512/v1
• Madonna, G. et al. (2021). Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy. Cancers, 13(16), 4164. https://doi.org/10.3390/cancers13164164
• Gurvich, O. L. et al. (2020). Transcriptomics uncovers substantial variability associated with alterations in manufacturing processes of macrophage cell therapy products. Scientific reports, 10(1), 14049. https://doi.org/10.1038/s41598-020-70967-2

Selected publications from our team members

• Marteau, V. et al. (2024). Single-cell integration and multi-modal profiling reveals phenotypes and spatial organization of neutrophils in colorectal cancer. bioRxiv 2024.08.26.609563; doi: https://doi.org/10.1101/2024.08.26.609563
• Kiviaho, A. et al. (2024). Single cell and spatial transcriptomics highlight the interaction of club-like cells with immunosuppressive myeloid cells in prostate cancer. Nature communications, 15(1), 9949 https://doi.org/10.1038/s41467-024-54364-1
• Obacz, J. et al. (2024). IRE1 endoribonuclease signaling promotes myeloid cell infiltration in glioblastoma. Neuro-oncology, 26(5), 858–871. https://doi.org/10.1093/neuonc/noad256
• Caronni, N. et al. (2023). IL-1β+ macrophages fuel pathogenic inflammation in pancreatic cancer. Nature, 623(7986), 415–422. https://doi.org/10.1038/s41586-023-06685-2
• Papadopoulou, A. et al. (2023). SARS-CoV-2-specific T cell therapy for severe COVID-19: a randomized phase 1/2 trial. Nature medicine, 10.1038/s41591-023-02480-8. Advance online publication. https://doi.org/10.1038/s41591-023-02480-8
• Salcher, S. et al. (2022). High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. Cancer cell, 40(12), 1503–1520.e8. https://doi.org/10.1016/j.ccell.2022.10.008
• Rieder, D. et al. (2022). nextNEOpi: a comprehensive pipeline for computational neoantigen prediction. Bioinformatics (Oxford, England), 38(4), 1131–1132. https://doi.org/10.1093/bioinformatics/btab759
• Fotakis, G. et al. (2021). Computational cancer neoantigen prediction: current status and recent advances. Immuno-oncology technology, 12, 100052. https://doi.org/10.1016/j.iotech.2021.100052
• Andersson, A. et al. (2021). Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nature communications, 12(1), 6012. https://doi.org/10.1038/s41467-021-26271-2
• Sturm, G. et al. (2020). Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data. Bioinformatics (Oxford, England), 36(18), 4817–4818. https://doi.org/10.1093/bioinformatics/btaa611
• Fotakis, G. et al. (2020). NeoFuse: predicting fusion neoantigens from RNA sequencing data. Bioinformatics (Oxford, England), 36(7), 2260–2261. https://doi.org/10.1093/bioinformatics/btz879
• Mehtonen, J. et al. (2020). Single cell characterization of B-lymphoid differentiation and leukemic cell states during chemotherapy in ETV6-RUNX1-positive pediatric leukemia identifies drug-targetable transcription factor activities. Genome medicine, 12(1), 99. https://doi.org/10.1186/s13073-020-00799-2
• Dufva, O. et al. (2020). Immunogenomic Landscape of Hematological Malignancies. Cancer cell, 38(3), 380–399.e13. https://doi.org/10.1016/j.ccell.2020.06.002
• Lehtipuro, S. et al. (2019). Modes of immunosuppression in glioblastoma microenvironment. Oncotarget, 10(9), 920–921. https://doi.org/10.18632/oncotarget.26643
• Luoto, S. et al. (2018). Computational Characterization of Suppressive Immune Microenvironments in Glioblastoma. Cancer research, 78(19), 5574–5585. https://doi.org/10.1158/0008-5472.CAN-17-3714
• Havunen, R. et al. (2018). Abscopal Effect in Non-injected Tumors Achieved with Cytokine-Armed Oncolytic Adenovirus. Molecular therapy oncolytics, 11, 109–121. https://doi.org/10.1016/j.omto.2018.10.005
• Norelli, M. et al. (2018). Monocyte-derived IL-1 and IL-6 are differentially required for cytokine-release syndrome and neurotoxicity due to CAR T cells. Nature medicine, 24(6), 739–748. https://doi.org/10.1038/s41591-018-0036-4

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