We would need to achieve a much better and comprehensive molecular understanding of the complexity of cancer in individual patients, across the spatial dimension of tumor tissues as well over time.

We believe that better prediction of cancer drug efficacies would improve and personalize patient care, help to prioritize new drugs for clinical testing, and design tailor-made drug combinations. Our lab focuses on three areas in cancer:

  • Func­tional precision can­cer medicine
  • Molecular spatial pathology
  • Personalized molecular and digital health

Func­tional precision can­cer medicine

Our research is based on a functional systems medicine strategy, originally developed as a team-effort by us in close collaboration with other FIMM research groups and hematologists at the Helsinki University Central Hospital Comprehensive Cancer Center (HUCH) for leukemias, such as acute myeloid leukemia (AML). Unlike genomics-based precision cancer medicine, our strategy involves direct testing of efficacies of drugs in patient-derived cells ex vivo, which provides opportunities for drug repositioning. Recently we have applied this approach to solid tumors, including urological cancers and ovarian cancer.

In addition, we are partners in EraPERMED -funded COMPASS project, which aims to build a standardized functional drug testing platform for pediatric solid tumors. Across all indications, we work with patient-derived cells in both 2D and 3D culture, with the aim to: i) identify effective targeted drugs by ex-vivo drug sensitivity and resistance testing (DSRT), ii) understand pharmacogenomics correlations, and iii) translate these data towards precision cancer treatment.

Molecular spatial pathology

We have developed a multi-color immunohiostochemistry (mIHC) and spatial image profiling technology and implemented it in many collaborative studies of different solid tumors. In translatiopnal cancer research, it is important to apply technologies capable of high-resolution and high-throughput spatial profiling of heterogeneous tumor tissues.

We aim to achieve a comprehensive understanding of the role of different stromal cell subtypes, such as cancer-associated fibroblasts (CAFs), immune cells, and vascular cells across different types of tumours. This could help to develop diagnostic methods and ways to define the risk of cancer progression and/or response to therapies. We have demonstrated the capability and potential of multiplex immunohistochemistry (mIHC) and digital image analysis in the spatial profiling of different immune cell subtypes in hematological and solid cancers. We have optimized more than 60 antibodies against immune cell markers and many antibodies for other components of the tumour microenvironment.

Recently, we showed that fibroblast is a critical cell type determining prognosis in prostate cancer (Blom et al., 2019). Inspired by these findings, we have initiated a pan-cancer CAF profiling project to characterize cell populations in more than 15 different cancer types. We hope to achieve a deep understanding of the specific stromal and immune cell subtypes that are responsible for the poor outcome in cancers, as well as the type of molecular and spatial cross talk that they engage in. We will also seek to identify diagnostically applicable mIHC staining cocktails as well as associated imaging/AI approaches.

Personalized molecular and digital health

We are interested in studying human biology and transitions to disease in a comprehensive manner. We aim towards a deep systems-level understanding of human biology and generate capabilities to understand and improve personal health. We perform longitudinal deep multi-omics analyses and digital health profiling of healthy and diseased people.

We aim to identify and give feedback on actionable findings that can be used to counter disease and improve health in a data-driven manner. To demonstrate the value of predictive, preventive, personalized, and participatory (P4) medicine, we have conducted a longitudinal Digital Health Revolution (DHR) Study. Over a 16-month study period, we collected a massive amount of longitudinal data on 96 people, including clinical laboratory, anthropometric, and physiological data; genomic, metabolomic, proteomic, and gut microbiome profiles; lifestyle, behavior, and wellness data (including digital monitoring data on physical activity and sleep); and food purchase data, to enable the assessment of factors impacting human health. Furthermore, we collected over 20,000 biospecimens for biobanking and molecular studies.

We have demonstrated that a strategy of returning personalized health data can change the behavior of people, and along with tailored coaching and social support, promote lifestyle changes towards positive health outcomes. Longitudinal data generated and the samples collected provide a valuable, deep biological resource for exploring human biology and health, as well as for studying the complex interconnections of the genome, proteome, metabolome, gut microbiome, digital health, and lifestyle factors.