Cancer

Genome Instability and Cancer Genetics

Coordinators:

• Marcel Tijsterman (m.tijsterman@lumc.nl)
• Maaike P.G. Vreeswijk (m.p.g.vreeswijk@lumc.nl)

Within this sub-theme, researchers concentrate on two main areas: fundamental research on DNA repair mechanisms and genomic stability maintenance, alongside investigations into genetic factors that contribute to cancer predisposition. This comprehensive approach facilitates a more profound comprehension of the root causes behind cancer development. From a translational and clinical standpoint, the insights derived from this research play a crucial role in identifying individuals with an increased risk of cancer development. As a result, these individuals receive specialized and attentive follow-up care in a clinical setting, enabling effective monitoring and management of their cancer risk.

Highlights:

• Functional analysis the CHEK2 gene reveals damaging variants associated with increased cancer risk
• Functional Analysis Identifies Damaging CHEK2 Missense Variants Associated with Increased Cancer Risk. - Haico
• Modulating mutational outcomes and improving precise gene editing at CRISPR-Cas9-induced breaks by chemical inhibition of end-joining pathways. – Marcel
• Enhancing accurate gene editing with CRISPR-Cas9 by controlling DNA repair pathways.

Cancer Immunology & Immunotherapy

Coordinators:

• Sjoerd van der Burg (shvdburg@lumc.nl)
• Mirjam Heemskerk (m.h.m.heemskerk@lumc.nl)

This sub-theme focuses on researchers dedicated to unraveling the intricate relationship between the immune system and cancer. The scientists investigate how the immune system can recognize and combat cancer cells, as well as how tumors develop mechanisms to evade immune recognition and how to overcome those. The insights gained from studying these dynamics are crucial for the development of effective immunotherapeutic strategies. These strategies aim to enhance anti-tumor immune responses in cancer patients, or to create therapeutic tools inspired by immune cells and molecules. Based on these insights several investigator-driven clinical trials have been initiated. These include trials with LUMC-developed and on-site produced vaccines and cellular therapies that are currently being evaluated as “advanced therapeutic medical products” (ATMP). By harnessing the power of the immune system, these approaches hold promise for improved cancer treatment and management.

Highlights:

• γδ T cells are effectors of immunotherapy in cancers with HLA class I defects - Noel
• PD-L1 checkpoint blockade promotes regulatory T cell activity that underlies therapy resistance

Pharmacogenomics and Molecularly Targeted Therapies:

Coordinators:

• Jesse Swen (j.j.swen@lumc.nl)
• Alfred Vertegaal (a.c.o.vertegaal@lumc.nl)

This sub-theme plays a pivotal role in the advancement of personalized strategies for cancer patients. Researchers within this area explore two key aspects. Firstly, they investigate how germline genetic variations influence the response of patients to cancer therapies, and subsequently, how these treatments can be tailored based on individual genetic profiles. Secondly, other research groups delve into the exploration of therapeutic vulnerabilities that arise from the dependence of cancer cells on specific molecular pathways. By identifying these vulnerabilities, novel targets for intervention can be discovered, leading to more effective and targeted treatment options. This research aims to optimize cancer therapies and improve patient outcomes by tailoring treatments to individual characteristics and exploiting weaknesses in cancer cells.

Highlights:

• Swen et al. Lancet 2023
• SUMO wrestling pancreatic cancer with TAK-981: an immune-boosting, cell cycle-stalling SUMOylation inhibitor

Image-Guided Cancer Diagnostics & Therapy:

Coordinators:

• Tanja Alderliesten (t.alderliesten@lumc.nl)
• Dennis Vriens (d.vriens@lumc.nl)

In the quest for more precise and sensitive diagnostic and therapeutic tools, imaging modalities and computational approaches, including artificial intelligence, are becoming increasingly significant. Within the LUMC, research groups are actively involved in developing innovative methods in this field. Their work ranges from automated classification and prognostication of cancers using histopathology slides to the creation of biological probes that enable the detection and guided removal of tumors. By harnessing the power of advanced imaging techniques and computational analysis, these research efforts aim to enhance the accuracy and efficacy of cancer diagnosis and treatment.

Highlights:

• AI model predicts endometrial cancer molecular classification of pathology images
• First-in-Human Assessment of cRGD-ZW800-1, a Zwitterionic, Integrin-Targeted, Near-Infrared Fluorescent Peptide in Colon Carcinoma

Computational Oncology

Coordinator:

• Erik van den Akker (e.b.van_den_akker@lumc.nl)

Over the past decade, cancer research has witnessed remarkable technological advancements that have brought us in the era of "big data." The ability to generate vast amounts of multidimensional imaging, single cell data, and complete genome sequences has revolutionized the field and demonstrated their potential to impact clinical patient management. However, the sheer volume and complexity of these datasets necessitate the development of sophisticated computational tools that can extract the biological and clinical significance hidden within them. In this sub-theme, computational biologists, biologists, and clinicians collaborate closely to decipher the meaning behind complex data and translate it into tangible improvements in patient care. A central effort aims at creating a “digital twin” of an individual tumor in the patient’s environment to accurately predict the tumor’s behaviour and the response to molecularly or immunologically targeted therapies.

Leading Initiatives in Clinical Research

Coordinators:

• Tjalling Bosse (t.bosse@lumc.nl)

By embracing the "from bench to bedside" approach, numerous clinical researchers are engaged in groundbreaking research within the realm of clinical trials. These researchers form multidisciplinary teams, collaborating through specialized "tumor-working groups," to drive the translation of cutting-edge translational research into tangible advancements in patient care. This bidirectional process fosters a continuous feedback loop, as observations and insights gleaned from the clinical setting stimulate new avenues of research in laboratories ("from bedside to bench"). This dynamic interplay between the clinic and the laboratory not only facilitates the validation and implementation of innovative approaches but also fuels the exploration of novel frontiers in cancer research.

Highlights:

• RAINBO Research Consortium. Int J Gynecol Cancer 2023;33:109–117.
• Adjuvant chemoradiotherapy versus radiotherapy alone in women with high-risk endometrial cancer (PORTEC-3): patterns of recurrence and post-hoc survival analysis of a randomised phase 3 trial
• Breast Cancer Index Predicts Extended Endocrine Benefit to Individualize Selection of Patients with HR+ Early-stage Breast Cancer for 10 Years of Endocrine Therapy
• Pancreatic Cancer Surveillance in Carriers of a Germline CDKN2A Pathogenic Variant: Yield and Outcomes of a 20-Year Prospective Follow-Up
• Surveillance for Pancreatic Cancer in High-Risk Individuals Leads to Improved Outcomes
• Computed tomography-based preoperative muscle measurements as prognostic factors for anastomotic leakage following oncological sigmoid and rectal resections

For PhDs and Postdocs

Information on mandatory, highly recommended and other relevant but not mandatory courses are presented here. You can browse through all the known courses. You can filter for courses relevant for the 10 LUMC Research Themes, and you can also search using keywords. Information such as the number of study credits (EC), the course duration, study year, website and background information are provided when known. Feedback on missing courses, incorrect or outdated information is welcome.

A selection of courses specifically tailored for Cancer can be found here.