Goedgekeurde dataverzoeken

Lieke en Stef bio informatici

De DNA-databank van Hartwig Medical Foundation helpt onderzoekers in hun onderzoek naar een betere behandeling voor kankerpatiënten.

Onderzoekers kunnen data aanvragen voor wetenschappelijk onderzoek. Hieronder volgen, per jaar, de goedgekeurde dataverzoeken: een korte samenvatting van de aanvraag, de naam van de hoofdaanvrager en het instituut waar hij / zij werkzaam is (ten tijde van de aanvraag). 

Small intestinal neuroendocrine tumours (SI-NETs) represent a heterogenous group of rare tumours. At time of diagnosis  approximately 50% of patients present with metastasized disease (Frilling 2014)1. Patients with metastasized disease are (usually) ineligible for curative surgery and have few treatment options with limited survival benefit. Unraveling the molecular events underlying SI-NET tumorigenesis could facilitate the identification of therapeutic targets and improve prognosis. In order to expand the limited existing knowledge on molecular factors in SI-NETs and move towards personalized treatment, we would like to study somatic and germline mutations of SI-NETs by using DNA next generation sequencing.

Margot Tesselaar, NKI/AvL, the Netherlands

1 Frilling A, Modlin I, Kidd M, Russel C, Breitenstein S, Salem R et al (2014). Recommendations for management of patients with neuroendocrine liver metastases. The Lancet Oncology15(1), pp. e8-e21

Cancer is a disease of the genome. Random factors, environmental exposures and also certain biological mechanisms have the potential to cause mutations in DNA, while the sophisticated DNA repair mechanisms available to human cells prevent such changes in genes. We will use the whole-genome sequences from metastatic tumors to examine how the balance between DNA damage and DNA repair shapes the mutation patterns observed in cancer. This is relevant to patients because we will further investigate how such mutational patterns are associated with risk of cancer progression and with response to various drugs, with implications to prevention and treatment.

Fran Supek, IRB Barcelona, Spain

Breast cancer is the most common malignancy among women wordwide. To reveal the genomic landscape of metastatic breast cancer, we recently performed an extensive genomic analysis which provided important novel insights into the biology of metastatic breast cancer  and identified clinically relevant subgroups of patients who might benefit in the future from specific therapies. With this project we aim to correlate the findings on the DNA level with data generated from RNA. This will lead to increased knowledge of metastatic breast cancer and probably provides new predictive and prognostic information by integrating these two data sources. Finally, our goal is to improve personalized cancer treatment for patients with metastatic breast cancer.

John Martens, Erasmus MC, the Netherlands

Cancer affects millions of people worldwide. Current DNA sequencing technologies allow routine screening of patients for cancer-associated mutations that can be targeted in personalized cancer treatments. Accurate computational methods for the identification of mutations, in particular structural variants (SVs), are lacking, together with methods that can prioritize mutations for further intervention. To solve this problem, we are developing a new set of machine learning approaches that can learn to detect SVs directly from ‘raw’ sequencing data and prioritize them for clinical intervention based on multiple sources of genomic information. Our work will open up a new avenue of machine-learned SV detection and prioritization, and accelerate the introduction of SV-based screenings for new treatments in clinical settings.

Jeroen de Ridder, UMC Utrecht

Despite the fact that completion of human genome project that was announced over 15 years ago, our knowledge of human genomes is still incomplete. The most challenging pieces missing from our genome “blueprint” are large insertions that are not present in genome of every individual or located in genomic regions that are difficult to assemble. The aim of this project is to identify such segments, finemap them to chromosomes and characterize their molecular function. The completeness of genome reference will greatly simplify, speedup and improve analysis of genomic information from wide range of samples: from healthy subjects to cancer patients.

Victor Guryev, UMCG, the Netherlands

Malignant pleural mesothelioma (MPM) is a rare and aggressive malignancy that arises from the mesothelial surfaces of the pleural cavity. Currently no curative treatment options are available. Recently, promising new treatment options have emerged, including immune checkpoint inhibitors and cell based vaccines. In order to give the best therapy to each patient, a better understanding of the tumor cells and their interaction with their microenvionment is necessary. We would like to investigate how abberations in the genetic material of the tumor can lead to the development, maintenance and progression of  the tumor and how different therapies can influence these processes.

Joachim Aerts, Erasmus MC, the Netherlands

We aim to apply computational tools to Hartwig Medical Foundation cancer genomes to establish the order in which harmful mutations were acquired, and approximately when they were acquired during a patient’s lifetime. Furthermore, we can infer what caused the mutations (for example, smoking, UV light, etc.), and estimate when the cell was exposed to these damaging processes. Across many cancer samples, this approach lets us build a picture of how different cancers evolve over time. By comparing these results to earlier analyses we performed on primary tumours (observed at initial cancer diagnosis), we seek to understand the development of cancer as it spreads from the initial site to another site (known as metastasis). This will show whether tumours that metastasise evolve in the same way as non-metastatic cancers, and may give insight that helps to predict the metastatic step in cancer evolution.

Peter van Loo, The Francis Crick Institute, United Kingdom

Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumor in adults. Since 2005, standard treatment consists of surgery followed by radiation therapy with concurrent and adjuvant temozolomide chemotherapy, taking ± 8.5 months in total. Despite optimal treatment, GBM outcome is almost inevitably fatal, usually within 16 months after diagnosis. There is an urgent need to improve treatment of patients with GBM, both by selection of patients most likely to respond to standard therapy, as well as through the identification of novel targets for [combination] treatment.
Whole-genome sequencing is a personalized medicine approach that can be applied to characterize the complete DNA sequence of individual patient’s tumors. Thus far, tumor specimen of 80 patients with GBM have been collected in the Dutch Center for Personalized Cancer Treatment [CPCT] biopsy study, together with information on the response to temozolomide-based chemoradiation. In this research proposal, a multidisciplinary team of Dutch neurologists, oncologists and neuro-surgeons propose to investigate the relation between the genetic properties of these tumors and response to temozolomide-based treatment.

Filip De Vos, UMC Utrecht, the Netherlands

The treatment of inoperable metastatic colorectal cancer consists of systemic treatment through chemotherapy with or without additional targeted treatment. Until now, no good model exists to provide personalized treatment and to predict which treatment could benefit individual patients the most. With this project we aim to reveal the genetic landscape of metastatic colorectal cancer. This will not only lead to an increased knowledge of the metastatic disease, but it will provide new information on the possible predictive potential of this information. With this combined knowledge we want to improve personalized medicine options for patients with metastasized colorectal cancer.

Saskia M. Wilting, Erasmus MC, the Netherlands

We aim to identify and characterize the molecular and cellular factors that distinguish cancer cells that metastasize from cancer cells that do not migrate. Discovering and better understanding these factors will enable the development of treatment options that specifically target the metastatic process.

Roel Verhaak, The Jackson Laboratory, United States

The use of molecular pathology (for example DNA-testing) has increasingly allowed the selection of the right treatment for the right patient (personalized medicine). Nonetheless, the majority of anticancer treatments remains not patient-specific. As a result, patients still receive ineffective treatments. A possible explanation for this might be found in the tumoral heterogeneity (ie. the presence of different types of cells). Recently, an computional algorithm (Tumor Cell Deconvolution (TCD)) has been developed in breast cancer patients to assess the cellular heterogeneity of a tumor, for every patient specific. With the TCD, we can identify specific cell types, and can explore whether these specific cell types determine the response to therapy.

Emile Voest, NKI/AvL, the Netherlands

Biliary tract cancer (BTC) is malignancy of bile ducts (also called cholangiocarcinoma) and gallbladder. BTC is a rare malignancy in Western countries with a poor prognosis. Surgical resection in the only available treatment that potentially can cure patients with BTC, but most patients present with advanced BTC (including metastatic disease) that can not be completely removed by an operation. Chemotherapy is the standard treatment for those patients with advanced BTC. Treatments with agents directed to DNA changes (targeted therapy) did not show better survival in different studies because of the heterogeneous nature of BTC. To improve the treatment of advanced BTC, a more comprehensive overview of the genomic changes in advanced BTC is required. If we can identify subgroups of patients with different prognosis based on genomic changes, new clinical studies could be initiated to study a specific treatment in one or more of these subgroup of patients.

Heinz-Josef Klümpen, Amsterdam UMC locatie VUmc, the Netherlands

The changes to the DNA (mutations) found in a tumour are expected to be largely unique to each tumour as there are 3 billion places in the DNA that can be changed. However, in a cancer study with 2,583 participants we observed over a million non-unique mutations. We used characteristics of these mutations and all mutations detected, to classify the tumours into 16 groups that have clinical relevance. We want to refine these classes to gain further insights into the biological processes inducing mutations (‘mutagenesis’), inform on treatment, help in diagnosis, and predict the origin of a metastatic tumour.

Ivo Gut, Centro Nacional de Análisis Genómico, Spain

We use whole cancer genome sequences to understand the order of mutations in cancer development so that we can infer which mutations arise early versus late during the process. The ability to determine the order and importance of mutations is critical for early detection and treatment of disease. We can also understand the environmental exposires that cause cancer based on the pattern of mutations.

Paul Spellman, Oregon Health & Science University, United States


In this project we aim at combining mathematical modelling of tumour evolution with cancer genomic data to measure fundamental evolutionary characteristics of metastatic tumours. These measurements will allow better understanding of how tumours spread to distant parts of the body. Importantly, we anticipate that the patient-specific ‘evolutionary rules’ we will measure from the data may be informative in terms of stratifying patients between good versus poor prognosis. Moreover, some of these measurements may also be predictive of the effectiveness of certain treatments.

Andrea Sottoriva, The Institute of Cancer Research, United Kingdom

The two strongest factors predicting a human cancer’s clinical behaviour are the primary tumour’s anatomic organ of origin and its histopathology. However, roughly 3% of the time a cancer presents with metastatic disease and no primary can be determined even after a thorough radiological survey. A related dilemma arises when a radiologically defined mass is sampled by cytology yielding cancerous cells, but the cytologist cannot distinguish between a primary tumour and a metastasis from elsewhere. Here we use whole genome sequencing (WGS) data from the ICGC/TCGA PanCancer Analysis of Whole Genomes (PCAWG) and Hartwig Medical Foundation to develop a machine learning classifier able to accurately distinguish among 24 major cancer types using information derived from somatic mutations alone. This demonstrates the feasibility of automated cancer type discrimination based on next-generation sequencing of clinical samples. In addition, this work opens the possibility of determining the origin of tumours detected by the emerging technology of deep sequencing of circulating cell-free DNA in blood plasma.

Lincoln Stein, Ontario Institute for Cancer Research, Canada

Cancer genomes typically harbor a substantial number of somatic mutations. However, only few of these mutations actually contribute to tumor development. To identify these so-called driver mutations, large-scale aggregated sequencing datasets are needed. Over the past decade, the search for driver mutations has been mostly focussed on coding regions. Substantially less is known whether and to which degree mutations in non-coding regions are functionally involved in tumor formation. With the drop in sequencing costs over the past years, it became possible to sequence the entire genomes of large cohorts of cancer patients (e.g. the dataset provided by Hartwig Medical Foundation). However, the avaialalbity of these datasets has not been met with the of algorithms that are particularly designed to the underlying biology of these data yet. The goal of this project is to develop a statistical framework which systematically identifies driver mutations in non-coding regions in aggregated whole-genome sequencing data. For this purpose, we will particularly consider the biological differences between coding an non-coding regions, in order to specifically taylor our algorithm to non-coding regions. Applying this refined approach to ~2500 Whole Genome Sequencing data, we will systematically characterize which mutations in non-coding regions are relevant to oncogenesis.

Eliezer van Allen, Dana-Farber Cancer Institute, United States

Anticancer drugs can (in)directly interfere with DNA and thereby potentially generate a characteristic pattern of somatic mutations (mutational signature). Identifying treatment-specific mutational signatures in a cancer genome will not only improve the mechanistic understanding of the mutational processes of the drug itself, but will also delineate the proportion of the mutational side effects. This research seeks to characterize the mutational patterns that are generated by anticancer drugs and tries to advance our understanding of the mutational signatures with unknown etiology.

Edwin Cuppen, UMC Utrecht, the Netherlands

Resistance to therapy is one of the main challenges in treatment of metastatic breast cancer. We know that treatment resistance is often driven by changes in the genetic material of the tumor. Tumor DNA is, to some extend, detectable in the blood of cancer patients as circulating tumor DNA (ctDNA). Analysis of ctDNA might enable monitoring of response to anti cancer therapy over time and at low impact. The aim of this study is therefore to identify aberrations in ctDNA related to treatment outcome and to see whether those aberrations change during treatment. Furthermore, we aim to explore to what extend ctDNA represents the genomic landscape of metastastic breast cancer. Therefore, our findings in ctDNA and in metastatic tumor tissue will be compared in a subset of patients.

John Martens, Erasmus MC, the Netherlands

Immunotherapies stimulate the patients own immune system to kill tumor cells. These relatively new therapies have shown impressive treatment results in many different tumor types. However, these impressive responses are restricted to only a subset of patients; many patients do not have benefit from these treatments, as the tumors keep growing and the patients suffer from treatment side-effects. We set up an ongoing project in which we try to understand why particular patients are not responding based on their DNA. If we understand this, we could (1) prevent treatment of patients with tumors resistant to immunotherapy and (2) develop strategies to overcome such resistance.

Emile Voest, NKI/AvL, the Netherlands

DR-045 is een uitbreiding van DR-009 en DR-023

Cancer develops by accumulation of mutations in the DNA of individual cells. In many patients, DNA repair mechanisms are often perturbed early in cancer development, leading to an increased mutation rate and faster development of the cancer. However, repair deficiencies can also make the cancer cells more susceptible to certain types of treatments. Better understanding and detection of DNA repair deficiencies may thus help improve treatment of cancer patients.

Using the cancer genomics data from Hartwig Medical Foundation, we aim to characterize the pattern of DNA deficiencies across different cancer types. We further aim to improve the detection of DNA repair deficiencies through their effect on mutation rates and patterns across the genomes of cancer cells. Finally, we aim further clarify the relationship between DNA repair deficiencies and cancer treatment efficacy.

Jakob Skou Pedersen, Aarhus University, Denmark

Some cancers are the result of viral infections. A novel type of cancer therapy, called immunotherapy, has been shown to work exceptionally well against such virus-driven cancers. These therapies stimulate the patients own immune system to kill the virus-infected tumor cells. Many viruses have, like humans, a genome of DNA. In this project we are searching for viral DNA within the tumors of patients, to identify the patients with virus-driven tumors. These patients are potential candidates for immutherapy treatment.

Emile Voest, NKI/AvL, the Netherlands

Cancer is driven by accumulation of single DNA mutations, but also by catastrophic events involving multiple simultaneous changes. One such catastrophic event is called kataegis (thunder), which concerns multiple DNA mutations occurring in a small region of the DNA. Using Hartwig Medical Foundation whole-genome repository, we can improve our sophisticated computational methods to better detect such somatic events in cancer patients. By identifying and characterizing these events in cancer we gain insight in the molecular mechanisms that give rise to kataegis. These molecular insights will improve our understanding of the biology of (metastatic) cancer.

Harmen van de Werken, Erasmus MC, the Netherlands

Recent Whole Genome Sequencing efforts have identified a broad spectrum of gains and losses of DNA in cancer genomes. This has led to the identification of novel mechanisms for genome rearrangement like chromothripsis, where chromosomes are shattered and stitched back together in a different order. However, little is known about the prevalence of these types of events throughout different tumor types and their relevance for patient outcome. Furthermore, the data provided by Hartwig Medical Foundation provides unprecedented detail and resolution, enabling us to detect small gains/losses of DNA that would have previously remained unnoticed.

Bauke Ylstra, Amsterdam UMC locatie VUmc, the Netherlands

Endometrium cancer is one of the most common gynecological cancer and accounts for -6% of all cancers in women. The vast majority of enometrium cancers are dependent on a hormone receptor, Estrogen Receptor a (ERa). We aim to identify how changes in the DNA sequence of the tumor would affect the function of ERa, and how this would ultimately impact gene activity and tumor cell growth.

Wilbert Zwart, NKI/AvL, the Netherlands

Tumor-specific defect in the DNA repair offer great potential for DNA damage based chemo/radio therapy. Present data implicate that this therapeutic intervention strategy has not been explored sufficiently. To provide optimal therapy with less side effects, there is an unmet need tot take further advantage of cancer-specific defect within the molecular network that controls DNA repair. This project will reveal the novel therapeutic opportunities for patients with tumor-specific DNA repair defects. The mission, optimize tumor intervention strategies to achieve higher cue rates with less side effects by developing optimised pesonalised medicine.

Heinz Jacobs, NKI/AvL, the Netherlands

Neuroendocrine tumors are rare cancers most often arising in the lungs, pancreas or gastro-intestinal tract. Its behavior can vary greatly among patients, from very slowly growing tumors that can be kept under control with a relatively low-impact treatment, to very aggressive tumors that quickly progress despite intensive (chemotherapeutic) treatment. Currently, we cannot optimally select the right treatment for the right patient, possibly because the characteristics we are using to select this treatment are not based on the genetic make-up of the tumor.
A minimally-invasive method to determine which patient will benefit from a certain treatment, is by taking so-called liquid biopsies. In liquid biopsies we look at tumor-specific genetic variations in the blood of our patients. Thus far, the implementation of liquid biopsies in neuroendocrine tumors have been hampered by the lack of a known genetic variation which occurs in the majority of our patients and is thus a suitable liquid biopsy marker.
In this project, we want to explore the genetic charactertistics of metastases of neuroendocrine tumors and use these data to develop a liquid biopsy test by which we can better determine who will benefit from a certain treatment, and who should be spared unnecessary toxicity.

Bianca Mostert, Erasmus MC, the Netherlands

Histological, immunohistochemical and gene expression markers are routinely used to stratify tumours into clinically distinct subtypes, some of which have targeted therapies that improve prognosis. Recent work has shown that unsupervised clustering using genomic profiling can reveal novel subtypes with distinct clinical outcomes. We will explore if stratifying patients directly from whole genome mutation profiles can identify novel subtypes and driver pathways that could lead to improved personalised therapy opportunities.

David Wedge, University of Oxford, United Kingdom

To date, translational cancer genomics has focused on somatic genetics, driven by the desire to identify mutated targets that indicate specific therapies. Therapeutic information is also present within the germline genome. However, this germline information is currently ‘subtracted’ in clinical analytic pipelines, discarding valuable information relevant to therapy. Within this project we will identify and study the value of this inherited germline information.

David Thomas, Garvan Institute Medical Research, Australia

Patients with head and neck cancer too advanced for surgical resection have a very poor prognosis, despite the currently standard, very intensive treatment which consists of a combination of chemotherapy and radiotherapy. Until now we have not been successful in identifying patients with a very poor prognosis. To spare these patients unnecessary treatment and its side-effects, better upfront patient selection is urgently warranted.
A minimally-invasive method to determine which patient will benefit from a certain treatment, is by taking so-called liquid biopsies. In liquid biopsies, we look at tumor-specific genetic variations in the blood of our patients. We are planning to do a study in which we correlate the level of a specific mutation which is often found in head and neck cancer, TP53, with the chance of disease recurrence. We also want to look at other genetic variants in liquid biopsies that are associated with poor prognosis. So far, the known genetic variants have been identified in the original primary tumors. However, genes that are mutated specifically in metastases, which we know can differ from the original primary tumor, are likely largely responsible for its aggressive behavior. Therefore, we will identify recurrent genetic variations in the metastases of patients with head and neck cancer. These variations will then be included in an elaborate liquid biopsy test, which we will correlate with prognosis in our observational study.

Bianca Mostert, Erasmus MC, the Netherlands

This project aims to unravel molecular mechanisms that drive the high metastatic potential of urothelial cancer. In addition predictive markers that correspond with response to chemo- or targeted therapy are urgently needed both in the neoadjuvant and metastatic setting to improve patient stratification.

Joost Boormans, Erasmus MC, the Netherlands

Irinotecan is a widely used cytostatic treatment for a broad range of tumor types. However, response is highly variable making the identifiation of biomarkers that can predict treatment outcome highly desirable. In this project Whole Genome Sequencing characteristics will be explored for all patients that received irinotec treatment and compared with clinical response to the drug.

Martijn Lolkema, Erasmus MC, the Netherlands

This project aims to characterize the genetic landscape of sarcoma tumors and relate this to treatment outcome data. Improving patient care for this relatively rare cancer indication requires the identification of biomarkers that enable stratification of patients towards the most effective therapies. Understanding underlying mechansisms of carcinogenesis, notably driver genes and pathways, is imperative to reach these goals.

Eric Wiemer, Erasmus MC, the Netherlands

The effect of anti-cancer drugs depends on the amount of drug that is present in the tumor cells. If these tumor cells have decreased uptake or increased excretion of a drug, the tumor has less exposure to the drug and therefore the drug might be less effective. Uptake and excretion of drugs is regulated by drug transporters, which we will investigate at DNA level. If (subtypes of) tumors have reduced exposure via this mechanism, it might be beneficial to give higher doses of that drug or to switch to an alternative drug.

Stefan Sleijfer, Erasmus MC, the Netherlands

This project aims to create a systematic overview of the mutation landscape of metastatic breast cancer patients and identify similarities and differences with comparable primary breast cancer data set obtained from elsewhere. This resource may identify patient subcategories that may benefit from different treatment strategies.

John Martens, Erasmus MC, the Netherlands

The aim of our Bioinformatics project is to investigate the presence, and length distribution in tumors of encoded peptides that result from frameshift mutations. We refer to them as Novel Open Reading Frame Peptides, or NOPs. Such peptides may be important triggers of the immune response.

Koos Zwinderman, Academisch Medisch Centrum, the Netherlands

Most studies have focused on mutations in the few percent of the genome that encode proteins. In this study, large amounts of tumor genomes will be used to identify loci in the non-coding regulatory part of the genome that contribute to tumor development when mutated.

Nuria López-Bigas, IRB Barcelona, Spain

The response to checkpoint inhibitors (immunotherapy) is highly variable between tumor types. The researchers will search for novel biomarkers based on Whole Genome Sequencing and RNA sequencing data accross and within all tumor types to better stratify patients for this treatment type and to understand the underlying biology driving treatment sensitivity or response.

Emile Voest, NKI/AvL, the Netherlands

DR-023 is een uitbreiding van DR-009

The DNA in normal – and tumor cells are continuously exposed to internal and external challenges. The objective of this project is to computationally derive specific footprints (signatures) that are left in the tumor genomes. Previous work has shown that environmental challenges like UV radiation and smoking leave very characteristic patterns, as well as intrinsic process like DNA repair deficiencies. Such knowledge is of importance for cancer prevention and treatment strategies.

Serena Nik-Zainal, University of Cambridge, United Kingdom

The goal is to investigate the presence of actionable biomarkers in Whole Genome Sequencing data of glioblastoma cases and compare these with results obtained with standard diagnostic targeted gene panels. This research should provide insight in what the best diagnostic strategy would be for this category of patients.

Paul van Diest, UMC Utrecht, the Netherlands

The objective of this project is to assess the value of ‘big data’ approaches for interpreting cancer genomes for diagnostic purposes and treatment decision making. Results of IBM Watson interpretation will be compared with automated curated database approach as commonly used at Hartwig Medical Foundation and other diagnostic centers.

Hans van Snellenberg, Hartwig Medical Foundation, the Netherlands

The goal of this study is to systematically compare the mutation landscape and underlying mutagenic mechanisms that are active in primary and advanced metastatic tumors. This study is expected to provide insight in the molecular mechanisms that drive different stages of tumor development and in the mutagenic effects of specific treatments.

Edwin Cuppen, UMC Utrecht, the Netherlands

Deficiency in DNA repair pathways is a key characteristic in many tumors. The goal of this project is to develop tools based on genome-wide mutation characterists that better detect defects in the homologous recombination DNA repair pathway. This is relevant as such patients may better respond to platinum drug-based therapies or PARPinhibitor targeted treatment.

Edwin Cuppen, UMC Utrecht, the Netherlands

Most studies have focused on mutations in the few percent of the genome that encode proteins. In this study, large amounts of tumor genomes will be used to identify loci in the non-coding regulatory part of the genome that contribute to tumor development when mutated.

Nuria López-Bigas, IRB Barcelona, Spain

The researchers will study the involvement of structural variation in the tumor genomes of colorectal cancer patients and investigate their role in tumor development and treatment response.

Remond Fijneman, NKI/AvL

In this study genetic factors will be explored that may explain response to standard chemotherapy (FOLFOX/CAPOX/Irinotecan/capecitabine) in coloroctal cancer patients. These results will be compared and integrated with results obtained with in vitro treated tumor organoids.

Louisa Hoes, NKI/AvL, the Netherlands

DR-015 is een uitbreiding van DR-003

This project is focused on biomarker discovery for specific targeted treatments in breast cancer patients. Specific focus will be on patients treated with aromatase inhibitors.

John Martens, Erasmus MC, the Netherlands

The research question is focused on the improvement and development of novel treatment approaches in prostate cancer patients that have become resistant to androgen deprivation therapy. The goal is to identify mechansisms that explain resistance and which could be developed in a circulating tumor DNA test for easy monitoring of treatment response.

Michiel van der Heijden, NKI/AvL, the Netherlands

The goal of this project is to correlate systematic DNA and protein measurement data from the same patients. This pilot may provide indications if multi-level measurements are feasible and provide additive information and form jointly stronger biomarkers. Furthermore, this work may provide insight in the molecular mechanisms that are perturbed by cancer mutations.

Henk Verheul, Amsterdam UMC locatie VUmc, the Netherlands

This project aims to create a systematic overview of the mutation landscape of metastatic prostate cancer patients with a special focus on structural variation. This resource may identify patient subcategories that may benefit from different treatment strategies.

Martijn Lolkema, Erasmus MC, the Netherlands

The goal of this project is to identify inherited mutations in genes that result in higher chances to develop cancer (cancer pre-disposition genes).

Edwin Cuppen, UMC Utrecht, the Netherlands

The response to check-point inhibitors (immunotherapy) is highly variable between tumor types. The researchers will search for novel biomarkers based on Whole Genome Sequencing and RNA sequencing data accross and within all tumor types to better stratify patients for this treatment type and to understand the underlying biology driving treatment sensitivity or response.

Joachim Aerts, Erasmus MC, the Netherlands

This project aims to identify biomarkers that are predictive for response to immunotherapy (check-point inhibitors) in lung cancer patients. As only ~20 to 30% of patients respond to this very expensive treatment, ability to identify these patients before start of treatment would have an enormous impact on quality of life and care budgets, which will also be studied as part of this project.

Joachim Aerts, Erasmus MC, the Netherlands

In this project, pairs of tumor biopsies are requested to study evolution of tumors over time. This project may provide insights in potential mutagenic effects of specific tumor treatments and resulting from selection pressure and shed light on treatment resistance mechanisms.

Emile Voest, NKI/AvL, the Netherlands

The researchers will explore the prevalance of mutations in the KRAS gene in GIST patients and study the response to specific treatments. This work may result in better stratifiation of patients towards targeted treatments.

Neetlje Steeghs, NKI/AvL, the Netherlands

The goal of this request is to enable Hartwig Medical Foundation Australia team to optimize bio informatic tools and pipelines for the identification of all types of somatic variants to obtain the most complete genetic picture of every individual tumor genome. Data of Hartwig Medical Foundation allows the researchers to test and validate their tools on real-world data.

Hans van Snellenberg, Hartwig Medical Foundation, the Netherlands

The goal of this project is to identify genetic factors that are involved in chemotherapy resistence in ovarium cancer and to identify novel targets for treatment. To this end, the spectrum of genetic aberrations in chemoresistant ovarium tumors will be compared with chemo sensitive tumors.

Wigard Kloosterman, UMC Utrecht, the Netherlands

The objective of this request is to connect genetic information obtained in the CPCT-02 study and in vitro tumor organoid data from the TUMEROID and SENSOR studies form the same patient. This connection allows for the identification and exploration of the potential genetic basis for (in vitro) treatment sensitvity for a broad range of drugs.

Fleur Weeber, NKI/AvL, the Netherlands

In this project, data from CRC patients treated with anti-EGFR therapy will be used to identify biomarkers that predict treatement response. A specific hypothesis on the involvement of mutations in the MAPK will be tested which provides novel treatment strategies involving existing MEK inhibitors.

René Bernards, NKI/AvL, the Netherlands

The objective of this project is to determine if the microenvironment of metastases influences mutation patterns. The investigators will test if colorectal cancer tumor lesions that develop as metastases in different tissues show differences in the type of mutations that are accumulated.

Ruben van Boxtel, UMC Utrecht, the Netherlands