Theodoros Foukakis

Theodoros Foukakis

Professor/Senior Physician | Docent
Visiting address: Visionsgatan 4, 17164 SOLNA
Postal address: K7 Onkologi-Patologi, K7 Forskning Foukakis, 171 77 Stockholm
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About me

  • Theodoros Foukakis is an MD and Professor of Oncology who conducts research in the field of breast cancer. He earned his PhD at Karolinska Institutet in 2005, with a thesis on thyroid cancer. Since then, he has divided his time between clinical work as an oncologist at Karolinska University Hospital and research at Karolinska Institutet.

Articles

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Grants

  • Swedish Research Council
    1 January 2024 - 31 December 2027
    Triple negative breast cancer (TNBC) and metastatic urothelial cancer (mUC) have poor prognoses with low survival rates. Treatment options for these metastatic solid tumors have been expanded through the introduction of checkpoint inhibitors (such as atezolizumab and pembrolizumab), systemic chemotherapy, or antibody-drug conjugates (ADC) directed against cell surface proteins like TROP-2 (Trophoblast cell surface antigen 2).A promising approach is to develop radiotheranostics using radiolabelled novel antibodies that target TROP-2 expression, a protein highly expressed in both TNBC and mUC. The concept utilizes  a TROP-2 specific monoclonal antibody, radiolabelled with alpha- or beta particle radiation for therapy or radiolabelled with gamma och positron radiation for diagnostics. This approach delivers therapeutic quantities of radioactivity specifically to the tumor cells while sparing normal tissues. The same antibody, radiolabelled with diagnostic radionuclides, tumors are detected by imaging modalities that are currently used in clinical practice. We believe that targeting TROP-2 with radiotheranostics might provide an effective treatment option with a favourable toxicity-benefit balance, and might be a better option for patients who do not respond to ADC or other later line systemic therapies. The project is currently in preclinical development and has high translational feasibility into patients within 4-5 years.
  • Swedish Research Council
    1 January 2023 - 31 December 2026
    Structural variants (SVs) are a pervasive trait of cancer genomes but their origin, evolution, and clinical impact remain poorly understood. In this project, I aim at unraveling the mechanisms of origin, pathogenic effects, and clinical consequences of cancer-associated SVs with the overarching goal of developing new tools and knowledge for precision cancer medicine. In Aim 1, we build on my strong track-record in method development to develop innovative assays to detect SVs and measure their effects on gene expression in single cells, as well as advanced computational tools to model the impact of SVs on the 3D genome. In Aim 2, we leverage our new tools to pioneer studies of how SVs form and evolve under various types of stress and genetic conditions, and then in Aim 3 we generate the first-ever atlas of single-cell 3D cancer genome in silico reconstructions to study how SVs rewire the 3D genome and in turn disrupt gene expression. Finally, in Aim 4, we bring our methods and knowledge closer to the clinic by assessing the predictive value of SVs for improved prognostication in luminal breast cancer patients, and by performing proof-of-principle applications of our methods to detect SVs in circulating tumor DNA and improve breast cancer molecular subtyping. This project has the potential to generate unique insights into the biology and pathological consequences of SVs in cancer, and to ultimately pave the way to routine SV profiling in precision cancer medicine.
  • Swedish Research Council
    1 January 2022 - 31 December 2025
    Purpose and aimsThis project aims to develop tools for prediction of response to neoadjuvant (pre-operative) therapy (NAT) and prognostication of post-surgery risk of recurrence in breast cancer. To this end, input from radiology, digital pathology, genomics and informative clinical variables will be integrated using a machine learning (ML)-based multi-modal fusion strategy. Project organisation, time plan and scientific methodsThree academic clinical trials and one population-based cohort of NAT (N=2500) will be used to train single-source predictive model priors that will be ensembled into integrative multi-omics predictive models. These will be validated externally in independent cohorts of ~3000 patients.The project will be divided into work packages (WP), corresponding to each of the data modalities. WP1 data and material collection (year 1-4)
    WP2-3 transcriptomics and genomics in tissue and blood (y 1-3)
    WP4 radiomics using mammography and magnetic resonance imaging (y 1-3)
    WP5 pathomics (y 1-3)
    WP6 model integration (y 3-4)
    WP7 external validation (y 4-5). ImportanceThe project will contribute with novel ML methodology for clinical medicine and a precision oncology solution for optimizing NAT selection and risk stratification that will lead to less over- and under treatment, sparing patients from unnecessary toxicities and reducing financial burden to healthcare systems, and ultimately improving prognosis for patients with breast cancer.
  • Swedish Cancer Society
    1 January 2022
    Systemic oncological treatment for breast cancer is now often given before surgery, so-called neoadjuvant therapy (NAT). The advantage of NAT is that the treatment effect can be assessed at an early stage and you have the opportunity to change treatment, alternatively give additional treatment after the operation, depending on tumor response. There is a great need for tools, so-called predictive markers that can predict which patients benefit from NAT as well as prognostic markers that can identify patients with a poor prognosis and need for additional postoperative treatment. Lack of reliable markers risks undertreatment or, more commonly, overtreatment. Artificial intelligence (AI)-based methods will be used to discover markers that can predict whether a particular NAT is effective as well as provide prognostic information in patients who have received NAT. Advanced AI methods will integrate the prognostic or predictive value of various data sources such as: - Gene expression analyzes and analysis of the tumor's genome with DNA sequencing - Analysis of tumor DNA and proteins in the patients' blood - Image analyzes of tumor sections using AI and deep learning methods that identify patterns in tumor architecture - Image analyzes of mammography, ultrasound and magnetic resonance imaging examinations with corresponding AI methods Data from >5000 patients treated with NAT in clinical trials or as routine treatment will be analyzed with AI to identify factors of importance for treatment effect and prognosis. The factors with the greatest predictive/prognostic value will then be combined in an integrated model that is expected to have a much higher predictive capacity compared to models from individual sources. In the long term, this project is expected to lead to a clinically useful tool to be able to offer, on an individual level, a tailored NAT to patients with breast cancer, with better efficacy, fewer side effects and reduced costs as a result.
  • Swedish Research Council
    1 December 2020 - 31 December 2024
  • Swedish Research Council
    1 December 2020 - 4 March 2024
  • Swedish Research Council
    1 January 2019 - 31 March 2021
  • Swedish Research Council
    1 January 2019 - 31 December 2021
  • Optimization of breast cancer treatment through better understanding of tumor biology
    Swedish Cancer Society
    1 January 2018
    Breast cancer is the most common form of cancer in women and the most common cause of death in middle-aged women in Sweden. Today, we can only roughly determine the risk of relapse and / or predict the effect of the treatment, which sometimes leads to under-treatment and more often to over-treatment, especially with cytostatics. There is a great need for tools, so-called prognostic markers, which show which patients have a high risk of relapse and, above all, predictive markers, who can predict whether a patient is benefiting from a specific treatment. Better understanding of tumor biology can be crucial to detecting clinically relevant markers. In this project, we will carry out and analyze clinical drug studies in breast cancer. The studies are characterized by the fact that tumor tissue is collected for analysis, in some cases both before and during / after treatment. A central place in the project is DNA and RNA sequencing of the collected tumor tissue, where we analyze DNA mutations and the expression of, essentially, all the genes in the tumor and put them in relation to the observed treatment result. Examination of how mutations and gene expression change during treatment should also be done on the single cell level. Last, we will examine the importance of the patient's immune cells for the treatment Through advanced molecular analyzes of tumor tissue in patients being treated in clinical research studies, I want to identify the tumor properties that are crucial for the treatment effect of cytostatics and targeted drugs in breast cancer. Based on these characteristics, I hope to be able to distinguish the patients who do not need cytostatics and the patients who should receive intensive treatment to be cured. In the long term, this project is expected to lead to clinically useful methods for offering, at an individual level, a tailor-made cancer treatment with better effect, fewer side effects and reduced costs for care as a result.
  • Translational studies for the development of predictive biomarkers in breast cancer
    Swedish Cancer Society
    1 January 2017
    Breast cancer is the most common form of cancer in women and the most common cause of death in middle-aged women in Sweden. Breast cancer is treated with surgery and a number of additional treatments (cytostatics, endocrine therapy, targeted therapies) given before or after surgery and reducing the risk of relapse and improving the chance of cure. There is a great need for tools, such as predictive markers, which can predict whether a patient benefits from a specific treatment. Today, we can only roughly determine the risk of relapse and / or predict the effect of the treatment, which sometimes leads to under-treatment and more often to over-treatment. We will use molecular methods to analyze tumor material from five clinical studies in breast cancer. Tumor tissue has been collected for analysis before and in some studies even after treatment. Through gene expression studies, we will analyze the expression of, essentially, all the genes in the tumor and also the changes in gene expression caused by the treatment. We will also study how the tumor cells differ within a tumor, so-called intra-tumor heterogeneity with newly developed methods that are characterized by being able to look at individual tumor cells. The molecular analyzes will be correlated to the treatment effects. Through advanced molecular analyzes of patients treated in clinical research studies, I want to identify the tumor properties that are crucial for the treatment effect of cytostatic drugs and angiogenesis inhibitors in breast cancer. I will also map intra-tumor heterogeneity and its significance for analysis of predictive markers but also examine whether heterogeneity per se can be used as a marker. In the long term, this project is expected to lead to clinically useful methods for offering, at an individual level, a tailor-made cancer treatment with better effect, fewer side effects and reduced costs for care as a result.
  • Translational studies for the development of predictive biomarkers in breast cancer
    Swedish Cancer Society
    1 January 2016
    Breast cancer is the most common form of cancer in women and the most common cause of death in middle-aged women in Sweden. Breast cancer is treated with surgery and a number of additional treatments (cytostatics, endocrine therapy, targeted therapies) given before or after surgery and reducing the risk of relapse and improving the chance of cure. There is a great need for tools, such as predictive markers, which can predict whether a patient benefits from a specific treatment. Today, we can only roughly determine the risk of relapse and / or predict the effect of the treatment, which sometimes leads to under-treatment and more often to over-treatment. We will use molecular methods to analyze tumor material from five clinical studies in breast cancer. Tumor tissue has been collected for analysis before and in some studies even after treatment. Through gene expression studies, we will analyze the expression of, essentially, all the genes in the tumor and also the changes in gene expression caused by the treatment. We will also study how the tumor cells differ within a tumor, so-called intra-tumor heterogeneity with newly developed methods that are characterized by being able to look at individual tumor cells. The molecular analyzes will be correlated to the treatment effects. Through advanced molecular analyzes of patients treated in clinical research studies, I want to identify the tumor properties that are crucial for the treatment effect of cytostatic drugs and angiogenesis inhibitors in breast cancer. I will also map intra-tumor heterogeneity and its significance for analysis of predictive markers but also examine whether heterogeneity per se can be used as a marker. In the long term, this project is expected to lead to clinically useful methods for offering, at an individual level, a tailor-made cancer treatment with better effect, fewer side effects and reduced costs for care as a result.
  • Translational studies for the development of predictive biomarkers in breast cancer
    Swedish Cancer Society
    1 January 2015
    Breast cancer is the most common form of cancer in women and the most common cause of death in middle-aged women in Sweden. Breast cancer is treated with surgery and a number of additional treatments (cytostatics, endocrine therapy, targeted therapies) given before or after surgery and reducing the risk of relapse and improving the chance of cure. There is a great need for tools, such as predictive markers, which can predict whether a patient benefits from a specific treatment. Today, we can only roughly determine the risk of relapse and / or predict the effect of the treatment, which sometimes leads to under-treatment and more often to over-treatment. We will use molecular methods to analyze tumor material from five clinical studies in breast cancer. Tumor tissue has been collected for analysis before and in some studies even after treatment. Through gene expression studies, we will analyze the expression of, essentially, all the genes in the tumor and also the changes in gene expression caused by the treatment. We will also study how the tumor cells differ within a tumor, so-called intra-tumor heterogeneity with newly developed methods that are characterized by being able to look at individual tumor cells. The molecular analyzes will be correlated to the treatment effects. Through advanced molecular analyzes of patients treated in clinical research studies, I want to identify the tumor properties that are crucial for the treatment effect of cytostatic drugs and angiogenesis inhibitors in breast cancer. I will also map intra-tumor heterogeneity and its significance for analysis of predictive markers but also examine whether heterogeneity per se can be used as a marker. In the long term, this project is expected to lead to clinically useful methods for offering, at an individual level, a tailor-made cancer treatment with better effect, fewer side effects and reduced costs for care as a result.

Employments

  • Professor/Senior Physician, Department of Oncology-Pathology, Karolinska Institutet, 2025-
  • Senior Lecturer/Senior Physician, Department of Oncology-Pathology, Karolinska Institutet, 2020-2025

Degrees and Education

  • Docent, Karolinska Institutet, 2014
  • Doctor Of Philosophy, Department of Molecular Medicine and Surgery, Karolinska Institutet, 2005

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