Molecular alterations in prostate cancer
We study molecular alterations involved in prostate cancer development and treatment resistance. With collaborative large-scale sequencing and proteomics efforts we have screened prostate cancer patient tumors for novel, previously unrecognized molecular alterations to study further for their significance, roles and potency in future therapeutic targeting.
Hormone-dependent cancers are driven by steroid hormone receptors, such as androgen and estrogen receptors, which function as transcription factors regulating expression of other genes. Although many current cancer drugs are targeting the activities of these receptors, treatment resistance is common and better treatments are required.
We belong to the newly founded Steroid Receptor Research Center at University of Eastern Finland.
Latonen et al. 2018 Nature Communications: Integrative proteomics in prostate cancer uncovers robustness against genomic and transcriptomic aberrations during disease progression.
Scaravilli et al. 2020 Current Opinion in Endocrine and Metabolic Research: Integrative proteomics of prostate cancer.
Uusi-Mäkelä et al. 2020 bioRxiv 2020.09.08.287268: Chromatin accessibility analysis uncovers regulatory element landscape in prostate cancer progression.
Scaravilli et al. 2021 Front. Cell Dev. Biol: Androgen-Driven Fusion Genes and Chimeric Transcripts in Prostate Cancer.
Eerola et al. 2021 (in press): Expression and ERG-regulation of PIM kinases in prostate cancer.
Stress-responsive RNA-binding proteins in cancer
We especially focus on RNA-binding proteins that regulate mRNA and other species of RNA in cells. Many RNA-binding proteins have a recognized role in several other types of diseases, such as neurodegenerative aggregation diseases, but their roles in cancer are mostly unexplored. We have evidence to support interactions of the AR pathway with certain RBPs with functional consequences in prostate cancer. We are currently studying these fascinating connections in more detail.
Non-coding RNA functions in cancer
We are interested in alterations of RNA expression and functions in cancer. There are numerous types of non-coding RNA that can contribute to cancer development. We are studying the functional role of several non-coding RNAs with aberrated expression in prostate cancer, including microRNAs and lncRNAs.
Latonen et al. 2017 Am J Pathol: In Vivo Expression of miR-32 Induces Proliferation in Prostate Epithelium.
Kohvakka et al. 2020 Oncogene: AR and ERG drive the expression of prostate cancer specific long noncoding RNAs.
The stress-responsive nucleolus
Several types of stresses induce accumulation of proteins and RNA in cells. These stress responses are well studied in the cytoplasm, but the events in the nucleus are less well known. The nucleolus is a site of accumulation and aggregation during transient stress, serving most likely as a safe harbor for unwanted molecular entities. These events are now known to involve phase separation, but their molecular mechanisms, role, and regulation are not well understood. The role of the nucleolus in long-term aggregation events is also unclear, as well as is the participation of these stress responses in formation of cancer and drug resistance.
Latonen L. 2019 Frontiers in Cellular Neuroscience: Phase-to-Phase With Nucleoli – Stress Responses, Protein Aggregation and Novel Roles of RNA.
Latonen et al. 2011 Oncogene: Proteasome inhibitors induce nucleolar aggregation of proteasome target proteins and polyadenylated RNA by altering ubiquitin availability.
Quantitative tissue analysis of cancer
We query cancer growth patterns in tissue combining this to molecular information in order to better understand how cancer develops and grows. 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.
We belong to the international ERA PerMed-funded ABCAP consortium, aim of which is to develop and validate novel state-of-the-art deep learning-based computer models for improved routine histopathology classification and for refined patient stratification in breast cancer. Within the consortium, we test the utility of different microscopic and spectroscopic methods in AI-based cancer tissue identification and grading.
Ehteshami Bejnordi et al. 2017 JAMA: Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.
Kartasalo et al. 2018 Bioinformatics: Comparative analysis of tissue reconstruction algorithms for 3D histology.
Liimatainen et al. 2020 arXiv:2003.11148: Virtual reality for 3D histology: multi-scale visualization of organs with interactive feature exploration.
Liimatainen et al. 2021 Biomolecules: Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns.
Latonen and Ruusuvuori, 2021 European Journal of Cancer (in press ): Building a central repository landmarks a new era for AI-assisted digital pathology development in Europe.