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Breast cancer is the most common malignancy in women worldwide, with approximately 9000 new cases per year in Sweden. Breast cancer is a heterogeneous disease at the molecular level. This heterogeneity translates into differences in clinical manifestation, patient therapies, and ultimately patient survival. Clinically, breast cancer is divided into three main subgroups based on the assessment of protein overexpression of two hormone receptors (the estrogen receptor alpha (ER) and the progesterone receptor (PR)) and gene amplification by immunohistochemistry (IHC) and fluorescence in-situ hybridization (FISH) of the human epidermal growth factor receptor 2 (HER2) protooncogene. Tumors that do not express the ER and PR receptor proteins, nor amplified HER2 gene levels are referred to as triple negative breast cancers (TNBC). The second subgroup is constituted by tumors that have amplified HER2 levels due to gene amplification. Tumors that are HER2-negative with a positive ER status make up the third subgroup, also termed the luminal subgroup due to expressing typical proteins of luminal epithelial cells. In early 2000, a groundbreaking report of five intrinsic gene expression-based molecular subtypes in breast cancer was published (today referred to as: Basal-like, Luminal A, Luminal B, HER2-enriched, and Normal-like by the PAM50 classification). Since then, there has been an intense focus on the molecular/genomic characteristics of the disease. This has revealed intriguing new insights into the biology of the disease of which some findings have also now made its way into the clinic as treatment decision support tools.

However, therapeutic progress has been different for the current clinical subtypes of breast cancer. Most breast cancer patients today receive some form of targeted therapy (e.g., endocrine or antibody-based), although chemotherapy is still used extensively for high-risk patients, often with adverse side effects and a lack good predictive markers for response.

We target current high-risk breast cancer patient subsets defined by clinical and molecular markers by integrative high-dimensional genomic methods assessing DNA, RNA, epigenetic, and protein alterations. The project involves both data generated from bulk tumor tissue as well as spatial in situ data. Our goal is to in depth molecularly profile specific poor outcome patient groups regarding their somatic alterations and subsequently the interplay between the tumor and its genomic alterations in the context of the tumor microenvironment. We believe this approach is crucial to develop new prognosticators and further tailor future therapy options for these patient groups for which we today lack effective clinical tools. The project goals are well aligned with prevailing clinical needs in breast cancer and by using integrative analyses combining >6 -omics layers with in situ pathological image data we believe the impact of the our work can be substantial.

UKÄ subject classification

  • Cancer and Oncology
  • Bioinformatics (Computational Biology)

Free keywords

  • Bioinformatics
  • Breast cancer
  • Data Science
  • Genomic characterization
  • Multiomics


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