Leaders
We study the molecular, cellular and tissue mechanisms that contribute to formation of cancer and cancer treatment resistance. We focus on the molecular mechanisms behind the increased ability of cancer cells to tolerate stress, for example when cancer cells to form resistance to drugs. We also query the cancer growth patterns in tissue combining this to molecular information in order to better understand the mechanisms behind cancer cells’ ability to survive in the crossfire of different stress types. In our tissue analysis collaboration projects we search for better ways to image, visualise and quantitatively analyse histology with development of digital pathology, machine learning and AI tools. Our research aims at developing better diagnostic tools and novel cancer treatments.
News
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Researchers developed an AI-based method to replace chemical staining of tissue
Researchers from the University of Eastern Finland, the University of Turku, and Tampere University have developed an artificial intelligence-based… -
Targeting nuclear size: a new approach to reduce metastasis?
For over 150 years, scientists have used changes in nuclear size prognostically, because such changes strongly correlate with increased metastasis for… -
Academy of Finland grants funding to 12 Academy Projects in biosciences, health and environmental research at UEF
The Academy of Finland has granted funding to 12 Academy Projects in biosciences, health and environmental research at the University of Eastern…
Leaders
Post-doctoral Researchers
Doctoral Researchers
Technicians
Other group members
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Siiri Sirviö
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Jenni Ruokolainen
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Salla Takkinen
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Eero Niemelä
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Pinja Mertano
Publications
31 items-
A PEG-assisted membrane coating to prepare biomimetic mesoporous silicon for PET/CT imaging of triple-negative breast cancer
Wen, Huang; Martínez, María Gómez; Happonen, Emilia; Qian, Jing; Vallejo, Vanessa Gómez; Mendazona, Helena Jorge; Jokivarsi, Kimmo; Scaravilli, Mauro; Latonen, Leena; Llop, Jordi; Lehto, Vesa-Pekka; Xu, Wujun. 2024. International journal of pharmaceutics. 652: -
Platinum-based drugs induce phenotypic alterations in nucleoli and Cajal bodies in prostate cancer cells
Batnasan, Enkhzaya; Kärkkäinen, Minttu; Koivukoski, Sonja; Sadeesh, Nithin; Tollis, Sylvain; Ruusuvuori, Pekka; Scaravilli, Mauro; Latonen, Leena. 2024. Cancer cell international. 24: . 29 -
A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics
Weitz, Philippe; Valkonen, Masi; Solorzano, Leslie; Carr, Circe; Kartasalo, Kimmo; Boissin, Constance; Koivukoski, Sonja; Kuusela, Aino; Rasic, Dusan; Feng, Yanbo; Sinius Pouplier, Sandra; Sharma, Abhinav; Ledesma Eriksson, Kajsa; Latonen, Leena; Laenkholm, Anne-Vibeke; Hartman, Johan; Ruusuvuori, Pekka; Rantalainen, Mattias. 2023. Scientific data. 10: . 562 -
Biomimetic Inorganic Nanovectors as Tumor-Targeting Theranostic Platform against Triple-Negative Breast Cancer
Wen, Huang; Poutiainen, Pekka; Batnasan, Enkhzaya; Latonen, Leena; Lehto, Vesa Pekka; Xu, Wujun. 2023. Pharmaceutics. 15: . 2507 -
Complementary analysis of proteome‐wide proteomics reveals changes in RNA binding protein‐profiles during prostate cancer progression
Aikio, Erika; Koivukoski, Sonja; Kallio, Elina; Sadeesh, Nithin; Niskanen, Einari A; Latonen, Leena. 2023. Cancer reports. 6: -
Deep learning transforms colorectal cancer biomarker prediction from histopathology images
Ruusuvuori, Pekka; Valkonen, Mira; Latonen, Leena. 2023. Cancer cell. 41: 1543-1545 -
Deformation equivariant cross-modality image synthesis with paired non-aligned training data
Honkamaa, Joel; Khan, Umair; Koivukoski, Sonja; Valkonen, Mira; Latonen, Leena; Ruusuvuori, Pekka; Marttinen, Pekka. 2023. Medical image analysis. 90: -
Identification of long noncoding RNAs with aberrant expression in prostate cancer metastases
Sattari, Mina; Kohvakka, Annika; Moradi, Elaheh; Rauhala, Hanna; Urhonen, Henna; Isaacs, William B; Nykter, Matti; Murtola, Teemu J; Tammela, Teuvo L. J.; Latonen, Leena; Bova, G. Steven; Kesseli, Juha; Visakorpi, Tapio. 2023. Endocrine-related cancer. 30: -
The effect of neural network architecture on virtual H&E staining: Systematic assessment of histological feasibility
Khan, Umair; Koivukoski, Sonja; Valkonen, Mira; Latonen, Leena; Ruusuvuori, Pekka. 2023. Patterns. 4: -
Unstained Tissue Imaging and Virtual Hematoxylin and Eosin Staining of Histologic Whole Slide Images
Koivukoski, Sonja; Khan, Umair; Ruusuvuori, Pekka; Latonen, Leena. 2023. Laboratory investigation. 103: