Bioinformatics for cancer research

Understand cancer, develop new drugs and personalize treatment with Genevia Technologies.

Our history in cancer bioinformatics is long: we have had the privilege to work with biologists seeking for a deeper understanding of cancer, companies developing new treatments to cancer, as well as oncologists wishing to optimize therapies to individual patients.

Below you will find highlights of our journey in cancer research. If you feel inspired and would like to benefit from our expertise, leave us a message and we will book you a call with our bioinformatician.

Leave us a short description of your bioinformatics needs and we will be in touch very soon!

Understanding cancer

What causes cancer? Which alterations in DNA, pathways and metabolic processes allow a tumor to grow, spread and evade treatment? How does tumorigenic reprogramming relate to normal cellular differentiation?

Our experience in cancer biology covers research into fundamental questions across cancer types and high-throughput molecular data types. Together with our collaborators and customers, we have studied heritable and somatic variants and their downstream molecular effects as well as the evolution and microenvironment of tumors, to name but a few aspects of cancer biology.

Whether you are setting out to characterize an understudied malignancy or dive deep into the molecular biology of a more common cancer, we have you covered, bioinformatically speaking.

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References and case studies

Whole-genome and RNA sequencing analyses of hereditary cancer (University of Turku)

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
Häyrinen, M. et al. (2024). Tumor RNA sequencing identifies a group of Mycosis Fungoides patients with failure of skin-directed therapies. The Journal of investigative dermatology, S0022-202X(24)02084-0. Advance online publication. https://doi.org/10.1016/j.jid.2024.06.1292
Boyd, S., et al. (2024). NGS of brush cytology samples improves the detection of high-grade dysplasia and cholangiocarcinoma in patients with primary sclerosing cholangitis: A retrospective and prospective study. Hepatology communications, 8(4), e0415. https://doi.org/10.1097/HC9.0000000000000415
Chérouvrier Hansson, V. et al. (2024). Dichotomous Effects of Glypican-4 on Cancer Progression and Its Crosstalk with Oncogenes. International journal of molecular sciences, 25(7), 3945. https://doi.org/10.3390/ijms25073945
Häyrinen, M. J. et al. (2023). The Transcription Factor Twist1 Has a Significant Role in Mycosis Fungoides (MF) Cell Biology: An RNA Sequencing Study of 40 MF Cases. Cancers, 15(5), 1527. https://doi.org/10.3390/cancers15051527
Karihtala, P. et al. (2022). Comparison of the mutational profiles of neuroendocrine breast tumours, invasive ductal carcinomas and pancreatic neuroendocrine carcinomas. Oncogenesis, 11(1), 53. https://doi.org/10.1038/s41389-022-00427-1
Song, J. et al. (2022). The ubiquitin-ligase TRAF6 and TGFβ type I receptor form a complex with Aurora kinase B contributing to mitotic progression and cytokinesis in cancer cells. EBioMedicine, 82, 104155. https://doi.org/10.1016/j.ebiom.2022.104155
Yuan, O. et al. (2022). A somatic mutation in moesin drives progression into acute myeloid leukemia. Science advances, 8(16), eabm9987. https://doi.org/10.1126/sciadv.abm9987
Wahlström, G. et al. (2022). The variant rs77559646 associated with aggressive prostate cancer disrupts ANO7 mRNA splicing and protein expression. Human molecular genetics, ddac012. Advance online publication. https://doi.org/10.1093/hmg/ddac012
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
Kundu, S. et al. (2021). Common and mutation specific phenotypes of KRAS and BRAF mutations in colorectal cancer cells revealed by integrative -omics analysis. Journal of experimental & clinical cancer research : CR, 40(1), 225. https://doi.org/10.1186/s13046-021-02025-2
Tikkanen, T. et al. (2018). Seshat: A Web service for accurate annotation, validation, and analysis of TP53 variants generated by conventional and next-generation sequencing. Human mutation, 39(7), 925–933. https://doi.org/10.1002/humu.23543

Selected publications from our team

Vlachavas, E. I. Et al. (2025). Molecular and functional profiling unravels targetable vulnerabilities in colorectal cancer. Molecular oncology, 10.1002/1878-0261.13814. Advance online publication. https://doi.org/10.1002/1878-0261.13814
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
Salcher, S. et al. (2024). Comparative analysis of 10X Chromium vs. BD Rhapsody whole transcriptome single-cell sequencing technologies in complex human tissues. Heliyon, 10(7), e28358. https://doi.org/10.1016/j.heliyon.2024.e28358
Nurminen, A. et al. (2023). Cancer origin tracing and timing in two high-risk prostate cancers using multisample whole genome analysis: prospects for personalized medicine. Genome medicine, 15(1), 82. https://doi.org/10.1186/s13073-023-01242-y
Plattner, C. et al. (2023). Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer. iScience, 26(12), 108399. https://doi.org/10.1016/j.isci.2023.108399
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
Beumers, L. et al. (2023). Clonal heterogeneity in ER+ breast cancer reveals the proteasome and PKC as potential therapeutic targets. NPJ breast cancer, 9(1), 97. https://doi.org/10.1038/s41523-023-00604-4
Ricordel, C. et al. (2023). Genomic characteristics and clinical significance of CD56+ circulating tumor cells in small cell lung cancer. Scientific reports, 13(1), 3626. https://doi.org/10.1038/s41598-023-30536-9
Simigdala, N. et al. (2023). Loss of Kmt2c in vivo leads to EMT, mitochondrial dysfunction and improved response to lapatinib in breast cancer. Cellular and molecular life sciences : CMLS, 80(4), 100. https://doi.org/10.1007/s00018-023-04734-7
Aakula, A. et al. (2023). RAS and PP2A activities converge on epigenetic gene regulation. Life science alliance, 6(5), e202301928. https://doi.org/10.26508/lsa.202301928
Dundr, P. et al. (2023). Uterine leiomyoma with RAD51B::NUDT3 fusion: a report of 2 cases. Virchows Archiv : an international journal of pathology, 10.1007/s00428-023-03603-9. Advance online publication. https://doi.org/10.1007/s00428-023-03603-9
Kontogianni, G. et al. (2023). A Comprehensive Analysis of Cutaneous Melanoma Patients in Greece Based on Multi-Omic Data. Cancers, 15(3), 815. https://doi.org/10.3390/cancers15030815
Rodriguez-Martinez, A. et al. (2022). Novel ZNF414 activity characterized by integrative analysis of ChIP-exo, ATAC-seq and RNA-seq data. Biochimica et biophysica acta. Gene regulatory mechanisms, 1865(3), 194811. Advance online publication. https://doi.org/10.1016/j.bbagrm.2022.194811
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
Kukkonen, K. et al. (2022). Nonmalignant AR-positive prostate epithelial cells and cancer cells respond differently to androgen. Endocrine-related cancer, 29(12), 717–733. https://doi.org/10.1530/ERC-22-0108
Machado-Lopez, A. et al. (2022). Integrative Genomic and Transcriptomic Profiling Reveals a Differential Molecular Signature in Uterine Leiomyoma versus Leiomyosarcoma. International journal of molecular sciences, 23(4), 2190. https://doi.org/10.3390/ijms23042190
Gao, Y. et al. (2022). Inactivation of AMPK Leads to Attenuation of Antigen Presentation and Immune Evasion in Lung Adenocarcinoma. Clinical cancer research : an official journal of the American Association for Cancer Research, 28(1), 227–237. https://doi.org/10.1158/1078-0432.CCR-21-2049
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
Hall, Z. et al. (2021). Lipid Remodeling in Hepatocyte Proliferation and Hepatocellular Carcinoma. Hepatology (Baltimore, Md.), 73(3), 1028–1044. https://doi.org/10.1002/hep.31391
Taavitsainen, S. et al. (2021). Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse. Nature communications, 12(1), 5307. https://doi.org/10.1038/s41467-021-25624-1
Filppu, P. et al. (2021). CD109-GP130 interaction drives glioblastoma stem cell plasticity and chemoresistance through STAT3 activity. JCI insight, 6(9), e141486. https://doi.org/10.1172/jci.insight.141486
Vlachavas, E. I. et al. (2021). A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research. International journal of molecular sciences, 22(6), 2822. https://doi.org/10.3390/ijms22062822
Woodcock, D. J. et al. (2020). Prostate cancer evolution from multilineage primary to single lineage metastases with implications for liquid biopsy. Nature communications, 11(1), 5070. https://doi.org/10.1038/s41467-020-18843-5
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
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
Yoosuf, N. et al. (2020). Identification and transfer of spatial transcriptomics signatures for cancer diagnosis. Breast cancer research : BCR, 22(1), 6. https://doi.org/10.1186/s13058-019-1242-9
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
Pölönen, P. et al. (2019). Hemap: An Interactive Online Resource for Characterizing Molecular Phenotypes across Hematologic Malignancies. Cancer research, 79(10), 2466–2479. https://doi.org/10.1158/0008-5472.CAN-18-2970
Vlachavas, E. I. et al. (2019). Radiogenomic Analysis of F-18-Fluorodeoxyglucose Positron Emission Tomography and Gene Expression Data Elucidates the Epidemiological Complexity of Colorectal Cancer Landscape. Computational and structural biotechnology journal, 17, 177–185. https://doi.org/10.1016/j.csbj.2019.01.007
Escobar, G. et al. (2018). Interferon gene therapy reprograms the leukemia microenvironment inducing protective immunity to multiple tumor antigens. Nature communications, 9(1), 2896. https://doi.org/10.1038/s41467-018-05315-0
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
Berglund, E. et al. (2018). Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nature communications, 9(1), 2419. https://doi.org/10.1038/s41467-018-04724-5
de Bock, C. E. et al. (2018). HOXA9 Cooperates with Activated JAK/STAT Signaling to Drive Leukemia Development. Cancer discovery, 8(5), 616–631. https://doi.org/10.1158/2159-8290.CD-17-0583
Gao, Q. et al. (2018). Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell reports, 23(1), 227–238.e3. https://doi.org/10.1016/j.celrep.2018.03.050
Määttä, K. et al. (2016). Whole-exome sequencing of Finnish hereditary breast cancer families. European journal of human genetics : EJHG, 25(1), 85–93. https://doi.org/10.1038/ejhg.2016.141

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Treating cancer

Developing a new cancer therapy is a long, costly and risky process. High-throughput measurements coupled with cutting-edge bioinformatics has a lot to offer along the way to both speed up the process and to increase the chances of success.

We can help in identifying targets for a given disease based on public and proprietary molecular and clinical data. Public data on molecular drug perturbation profiles, on the other hand, enables scanning for new applications for pharmaceuticals that are already on the market.

For both preclinical and clinical research on a new treatment, transcriptomic, epigenomic and proteomic measurements can be used to study the molecular mechanism of action. This allows for further optimizing the treatment and ruling out off-target effects.

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References and case studies

Selected publications from our team

Eigentler, A. et al. (2024). Glucocorticoid treatment influences prostate cancer cell growth and the tumor microenvironment via altered glucocorticoid receptor signaling in prostate fibroblasts. Oncogene, 43(4), 235–247. https://doi.org/10.1038/s41388-023-02901-5
Heidegger, I. et al. (2022). Comprehensive characterization of the prostate tumor microenvironment identifies CXCR4/CXCL12 crosstalk as a novel antiangiogenic therapeutic target in prostate cancer. Molecular cancer, 21(1), 132. https://doi.org/10.1186/s12943-022-01597-7
Annala, M. et al. (2021). Cabazitaxel versus abiraterone or enzalutamide in poor prognosis metastatic castration-resistant prostate cancer: a multicentre, randomised, open-label, phase II trial. Annals of oncology : official journal of the European Society for Medical Oncology, 32(7), 896–905. https://doi.org/10.1016/j.annonc.2021.03.205
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
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

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Predicting outcomes

Being able to predict the onset and development of cancer enables better treatment through early and accurate diagnosis and personalized treatment.

We use survival analyses and machine learning approaches with clinical and molecular data to predict patient-specific risks. Such analyses result in biomarkers or multi-marker signatures with clinical potential.

Biomarker discovery projects have been some of the most fruitful and clinically promising ones we have participated in, as you may see from the references and publications below!

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References and case studies

Selected publications from our customers

Karihtala, P. et al. (2024). Mutational signatures and their association with cancer survival and gene expression in multiple cancer types. International journal of cancer, 10.1002/ijc.35148. Advance online publication. https://doi.org/10.1002/ijc.35148
Nissinen, L. et al. (2024). Clustering of RNA co-expression network identifies novel long non-coding RNA biomarkers in squamous cell carcinoma. Scientific reports, 14(1), 16864. https://doi.org/10.1038/s41598-024-67808-x
Karihtala, P. et al. (2023). Mutational signatures and their association with survival and gene expression in urological carcinomas. Neoplasia (New York, N.Y.), 44, 100933. Advance online publication. https://doi.org/10.1016/j.neo.2023.100933
Cheng, F. et al. (2023). Attenuation of cancer proliferation by suppression of glypican-1 and its pleiotropic effects in neoplastic behavior. Oncotarget, 14, 219–235. https://doi.org/10.18632/oncotarget.28388
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
Karihtala, P. et al. (2022). Mutational Signatures Associate With Survival in Gastrointestinal Carcinomas. Cancer genomics & proteomics, 19(5), 556–569. https://doi.org/10.21873/cgp.20340
Pommergaard, H. C et al. (2022). Aldehyde dehydrogenase expression may be a prognostic biomarker and associated with liver cirrhosis in patients resected for hepatocellular carcinoma. Surgical oncology, 40, 101677. https://doi.org/10.1016/j.suronc.2021.101677
Tsakonas, G. et al. (2021). High Density of NRF2 Expression in Malignant Cells Is Associated with Increased Risk of CNS Metastasis in Early-Stage NSCLC. Cancers, 13(13), 3151. https://doi.org/10.3390/cancers13133151
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
Ness, C. et al. (2021). Integrated differential DNA methylation and gene expression of formalin-fixed paraffin-embedded uveal melanoma specimens identifies genes associated with early metastasis and poor prognosis. Experimental eye research, 203, 108426. https://doi.org/10.1016/j.exer.2020.108426
Pommergaard, H. C. et al. (2021). Peroxisome proliferator-activated receptor activity correlates with poor survival in patients resected for hepatocellular carcinoma. Journal of hepato-biliary-pancreatic sciences, 28(4), 327–335. https://doi.org/10.1002/jhbp.745
Lehto, T. K. et al. (2021). Transcript analysis of commercial prostate cancer risk stratification panels in hard-to-predict grade group 2-4 prostate cancers. The Prostate, 81(7), 368–376. https://doi.org/10.1002/pros.24108
Simao, F. et al. (2021). SRF-CLICAL: an approach for patient risk stratification using random forest models. bioRxiv 2021.06.22.448514; doi: https://doi.org/10.1101/2021.06.22.448514

Selected publications from our team

Fonseca, N. M. et al. (2024). Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer. Nature communications, 15(1), 1828. https://doi.org/10.1038/s41467-024-45475-w
Kallio, H. M. et al. (2024). Sensitive circulating tumor DNA-based residual disease detection in epithelial ovarian cancer. Life science alliance, 7(6), e202402658. https://doi.org/10.26508/lsa.202402658
Cao, S., Wang et al. (2022). Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression. Nature biotechnology, 10.1038/s41587-022-01342-x. Advance online publication. https://doi.org/10.1038/s41587-022-01342-x
Rautajoki, K. J. et al. (2022). PTPRD and CNTNAP2 as markers of tumor aggressiveness in oligodendrogliomas. Scientific reports, 12(1), 14083. https://doi.org/10.1038/s41598-022-14977-2
Vandekerkhove, G. et al. (2021). Plasma ctDNA is a tumor tissue surrogate and enables clinical-genomic stratification of metastatic bladder cancer. Nature communications, 12(1), 184. https://doi.org/10.1038/s41467-020-20493-6
Taavitsainen, S. et al. (2019). Evaluation of Commercial Circulating Tumor DNA Test in Metastatic Prostate Cancer. JCO precision oncology, 3, PO.19.00014. https://doi.org/10.1200/PO.19.00014
Kaikkonen, E. et al. (2018). ANO7 is associated with aggressive prostate cancer. International journal of cancer, 143(10), 2479–2487. https://doi.org/10.1002/ijc.31746

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Meet some of our experts in cancer research

I am a Professor of Bioinformatics at Tampere University, where I lead the Computational Biology research group. My research focuses on developing algorithms and computational models to study the molecular basis of human cancers and to enable new diagnostics and treatments.

We leverage diverse omics data, statistical modeling, and machine learning to uncover the biological processes and mechanisms underlying disease. I work closely with experimental groups to validate and refine our hypotheses.

In the past two decades, my collaborative research endeavors have led to the several discoveries of genomic drivers and evolutionary patterns of prostate cancer.

Prof. Matti Nykter CSO, co-founder Genevia Technologies Oy

I am a senior bioinformatics scientist with over 10 years of experience in immuno-oncology and tumor evolution, specializing in single-cell data analysis and scientific software engineering. My work centers on integrating and analyzing multimodal data, developing analysis workflows, creating scientific software, and modeling tumor evolution.

I have deep expertise in bulk and single-cell sequencing (WES/WGS, RNA-seq, ctDNA) and 3+ years of experience with spatial omics (transcriptomic and imaging-based). During my Ph.D. and postdoctoral research, I led multiple single-cell studies that resulted in high-impact publications and contributed to two cancer atlases (NSCLC and CRC).

Dr. Georgios Fotakis Scientific Project Manager Genevia Technologies Oy

I’m a senior computational biologist with 10+ years of experience in cancer bioinformatics and precision oncology, specializing in multi-omics data analysis (WES, RNA-seq, microarrays, proteomics) for biomarker discovery and translational research.

My work focuses on functional knowledge discovery—such as molecular footprinting to infer regulatory activity—and reconstructing tumor histories using public tools and clinical databases to support personalized medicine. I’m also skilled in statistics, R programming, and developing open-source, reproducible reports.

Dr. Efstathios-Iason Vlachavas Scientific Project Manager Genevia Technologies Oy

I am a bioinformatician specialised in cancer genomics and genetics with a 10-year experience analysing omics data in countless genetic, genomic, transcriptomic and epigenetic studies.

While my research focus has been in cancer, I have also gained experience in a number of other fields, such as immunology, aging and developmental biology. In recent years, I have also applied machine learning methods to harness biomedical data in various clinical applications.

Dr. Tommi Rantapero Scientific Project Manager Genevia Technologies Oy

I am a bioinformatics scientist specializing in oncology and human health data analysis, with over five years of experience in handling various omics data types. This includes bulk data (whole-exome, RNA-seq) and single-cell and spatial data (scRNA-seq, spatial transcriptomics, single-cell proteomics), as well as expertise in the experimental techniques to generate these types of data.

Throughout my career, I have successfully applied omics analysis to cancer and aging-related diseases, securing public grants, publishing in journals, and presenting at international conferences. I am highly motivated to solve complex challenges and advance healthcare research.

Dr. Alba Machado Scientific Project Manager Genevia Technologies Oy

I am a senior bioinformatics scientist with over 10 years of experience in analyzing a wide range of next-generation sequencing (NGS) data types, including spatial transcriptomics, single-cell RNA-seq, bulk RNA-seq, ChIP-seq, CUT&Tag, ATAC-seq, single-cell ATAC-seq, MeDIP-seq, and BS-seq.

With a background in both mathematics and biology, I am well-equipped to analyze and interpret complex biological datasets. My work spans various fields, with significant contributions in immunology and oncology research.

Dr. Giulia Barbiera Scientific Project Manager Genevia Technologies Oy

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To continue exploring our background and expertise in cancer bioinformatics, here are a few suggestions:

Contact us

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Antti Ylipää CEO, co-founder Genevia Technologies Oy +358 40 747 7672