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Molecular Pathology and Genetics of Cancer´s Profile image

Molecular Pathology and Genetics of Cancer

Research group
01.01.2022 -
Institute of Clinical Medicine, School of Medicine, Faculty of Health Sciences
Information People

Molecular Pathology, Genetics, Liquid Biopsies, and AI-Based Data Analysis in Cancer Research

Our research focuses on understanding the mechanisms of cancer development, biology, and progression by applying methods from molecular pathology, genetics, genomics, and biomarker research. We aim to identify factors that influence cancer initiation, disease prognosis, and treatment response, as well as to promote the translation of research findings into clinical practice.

The research integrates histopathological and molecular pathological analyses based on tissue samples with modern approaches in genomics, transcriptomics, and bioinformatics. Digital pathology and AI-assisted analysis of tissue and imaging data constitute a central part of our work, enabling objective, quantitative, and large-scale assessment of tissue samples. The goal is to improve understanding of the molecular heterogeneity of cancers and to develop new solutions to support diagnostics, risk assessment, prognostication, and personalized treatment selection.

A key area of research is also liquid biopsy, which allows cancer-related biomarkers to be identified from blood and other body fluids. We study, among other things, circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), RNA, extracellular vesicles, and other tumor-derived molecules. Liquid biopsies enable earlier cancer detection, monitoring of treatment response, identification of recurrence, and investigation of tumor molecular characteristics in a less invasive manner than traditional tissue biopsies.

In addition, we develop and utilize artificial intelligence and machine learning–based methods for the analysis of multimodal data. By integrating pathological images, genomic and transcriptomic data, liquid biopsy datasets, clinical variables, biobank materials, and other health data, we aim to identify novel biological associations and predictive models. AI-driven analytical methods support biomarker discovery, patient risk stratification, and the development and implementation of precision medicine solutions.

The research makes use of extensive biobank, registry, and patient datasets, enabling population-level studies of cancer risk factors, molecular mechanisms, disease processes, and treatment outcomes. Combining these datasets with molecular and imaging data creates a unique foundation for identifying new diagnostic and predictive biomarkers.

Our work is based on multidisciplinary collaboration among experts in basic research, clinical research, pathology, genetics, bioinformatics, data science, and health sciences. Close national and international research networks enable high-quality research and the development of new biomarkers, digital analytical methods, and precision medicine solutions to support cancer prevention, diagnosis, and treatment.

The long-term goal of our research is to advance personalized medicine, where cancer prevention, diagnostics, prognostic assessment, and treatment are based on the patient’s molecular, clinical, and environmental factors. By integrating biological knowledge, digital solutions, and artificial intelligence, we aim to enable more accurate risk assessment, earlier diagnosis, and more effective, individualized treatment for cancer patients.

 

Cooperation

  • Päivi Auvinen, KUH

  • Maarit Anttila, KUH

  • Katri Pylkäs, University of Oulu

  • Jan Seppälä, KUH

  • Breast Cancer Association Consortium (BCAC)

  • Sami Heikkinen, UEF

  • Doug Easton, University of Cambridge, UK

  • Paul Pharoah, Cedars-Sinai Medical Center, CA, USA

  • Confluence Project

  • Biobank of Eastern Finland

  • Genome Center of Eastern Finland

  • Tuomas Rauramaa, UEF

  • Kirsi Ketola, UEF

  • Jouni Ihalainen, VTT

  • Riitta Kaarteenaho, University of Oulu

  • Reijo Sund, UEF

  • Leo Kärkkäinen, UEF

  • Otto Jokelainen, Mikkeli Central Hospital

  • Molecular evolution: Tumor cells change genetically over time. These changes can be caused by, for example, mutations in individual DNA bases, mutations in chromosomes or epigenetic changes. Molecular evolution can affect tumor aggressiveness, spread, and treatment resistance. The development of cancer drugs is largely based on an understanding of these changes. Tumor heterogeneity: Tumors are not homogeneous cell masses, but may contain different cell types, such as rapidly dividing and dormant cells. The diversity of cells is based on the different mutation spectrum of cells found in clones. Heterogeneity can affect the effectiveness of treatment and the risk of relapse. Modern medicine seeks to understand the heterogeneity of tumors due to more individualized treatment planning.

    Our translational research focuses on the identification of molecular evolutionary mechanisms. The aim is to be able to identify the features essential for the development of the disease and monitor their occurrence during treatment, as well as to understand the molecular development of treatment resistance. Due to tumour heterogeneity, some patients are treated too intensively, while others might benefit from more aggressive treatments. Our research focuses on a better interpretation of tumour heterogeneity in order to develop targeted precision cancer therapies.

  • Our AI research team is dedicated to advancing cancer research through machine learning and artificial intelligence (AI). We leverage AI to analyze and integrate multimodal cancer related data, including imaging, biological tumor profiling data and clinicopathological data. We aim to prevent cancer and predict therapy responses, optimize treatment solutions, and estimate risk of cancer and patient outcomes. We also use machine learning to gain insights into development of immune related cancer therapies. Additionally, we develop explainable deep learning techniques for multimodal data to identify the complex interacting factors that affect cancer risk and outcome, thereby advancing the field of computational pathology.

Keywords

cancer genetics clinical medicine epigenetics genetic variants genomics mechanistic interactions metastasis tumour stroma

Professors

Senior Researchers

Post-doctoral Researchers

Doctoral Researchers

Technicians

Other group members

  • Maria Tengström Specialist in Oncology, MD, PhD
  • Ella Ikonen
  • Jouni Kujala
  • Henna Tynjälä