Bioinformatics for cancer research

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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

Selected publications from our customers

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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
  • 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
  • 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

  • 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

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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. (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

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