Groups at the German Cancer Research Center (DKFZ) and Heidelberg University Hospital (UKHD) that are involved with activities at the NCT Heidelberg and offer to host an HSO² Fellow in the current call are listed below. The list of hosting groups will be regularly updated during the call.
Potential Hosting Groups
Computer Assisted Medical Interventions
Principle investigator: Prof. Dr. Lena Maier-Hein, Division of Computer Assisted Medical Interventions, DKFZ
More information: Division of Computer Assisted Medical Interventions
The mission of our division is to improve the quality of interventional healthcare and its value through computational measures. Our vision is to support physicians throughout the entire process of diagnosis, therapy and follow-up with the right information at the right time. To this end, our multidisciplinary group builds upon principles and knowledge from a diversity of research fields including computing, physics, mathematics and medicine.
A postdoctoral position in medical informatics is available in the group of Prof. Dr. Lena Maier-Hein (Division of Computer Assisted Medical Interventions, https://www.dkfz.de/en/cami/index.php) at the German Cancer Research Center in Heidelberg, Germany.
We seek to solve seminal problems in surgical oncology by using computational approaches to develop a decision support system that functions both pre-operatively (as a virtual tumor board) and intra-operatively (as tool for fast re-planning of operations) in liver surgery. By applying state of the art methods in knowledge representation and machine learning, we aim to create complete models of treatment processes, patient-individual data and factual (published) knowledge and infer optimal procedures and associated risks and outcomes for the patient.
The data science driven surgical oncology project is part of an interdisciplinary collaboration between the NCT, clinicians of the University Hospital Heidelberg, particularly the Department for General, Visceral and Tranplant Surgery, Division for Minimally invasive and Robot-Assisted Surgery (Prof. Dr. med. B. Müller) and scientists of the DKFZ. The research environment will be highly collaborative and involves regular interactions with the project partners.
PhD in medical or applied informatics, mathematics, physics or related studies
Advanced programming skills (e.g. C++, Python)
Excellent skills and practical experience in one or more of the following research areas is beneficial:
Proficiency in spoken and written English
Ability to collaborate well in an interdisciplinary environment
Principle investigator: Prof. Dr. Carsten Müller-Tidow, Department of Internal Medicine V: Hematology, Oncology and Rheumatology, UKHD
More information: Epigenomics, Epitranscriptomics and novel therapy approaches in AML
The aim of our group is to identify pathogenetic mechanisms in acute leukemias and other cancers to ultimately develop and test novel therapy approaches in early phase clinical trials. Clinical specimens from these trials are then analyzed to further understand mechanisms of disease and therapy resistance. Epigenetic alterations are an important target of novel therapeutics and we are translating basic research findings into clinical trials. Several clinical trials are ongoing and additional trials are at the planning stage. Successful candidates will perform research at the interface of clinical trials and laboratory research to identify biomarkers for response and novel therapy targets. Hence, close integration in clinical and basic research is envisioned. This program is best suited for clinician scientists aiming at a career in academic medicine with a focus on target discovery/validation and early clinical trials.
HIRO Research Database
Principal investigator: Nina Bougatf, Department of Radiation Oncology, UKHD
More information: Heidelberg Institut for Radiation Oncology
Our HIRO Research Database team focuses on the integration and automatic analysis of heterogeneous radiooncological data. We work on data from multiple source systems in clinical routine and research. The Database already includes relevant clinical routine and imaging data as well as study data. Researchers are currently working on the integration of information from unstructured reports using NLP tools. Another aspect is the extraction and integration of information with radiomics tools out of radiooncological imaging data.
Furthermore, we are working on the integration of patient reported outcomes and molecular diagnostic data. Our overall goal is the development of new analysis tools based on machine learning techniques to generate new knowledge from our data.
For a future project we are looking for a clinical/medical data scientist. After the integration of all molecular diagnostic data of our patients, the focus of the work will be the combined analysis of clinical routine and molecular diagnostic data to identify new correlations between molecular markers and clinical outcomes looking on different studies, tumor entities or irradiation techniques.
We are looking for a candidate with biological, molecular genetic or molecular pathological background. Experience in data science, data integration and analysis are appreciated.
Hopp Children's Cancer Center Heidelberg (KiTZ), Translational Pediatric Oncology
Principal investigator: Prof. Dr. Olaf Witt, Hopp Children's Cancer Center Heidelberg (KiTZ)
More information: Translational Pediatric Oncology
In November 2016, the Heidelberg University Hospital and the German Cancer Research Center (DKFZ) founded the Hopp Children’s Cancer Center Heidelberg (short KiTZ). KiTZ is a therapy and research center for oncology and hematology in children and adolescents.
The KiTZ program „Translational Pediatric Oncology” (headed by Prof. Dr. Olaf Witt) focuses on innovative and biomarker driven treatments. Novel research results are translated into early phase biomarker driven clinical trials, primarily for treatment of children that have exhausted established standard therapies. We offer a fellowship to supporting clinicians wishing to specialize in early phase clinical trials and drug development in pediatric oncology. The fellow will perform clinical tasks in our early phase clinical trial center by participating in the daily care for phase I/II trial patients including INFORM molecular tumor boards. In parallel, the fellow will work in our translational trial group and be involved in clinical trial development of one of our INFORM2 biomarker driven phase I/II basket trial. This requires close interaction with various preclinical research groups of the KiTZ in order to understand the biology of newly identified drug targets and mechanisms, which is key for designing a trial. Since pediatric oncology trials are dealing with small patient cohorts, our trials are developed in international collaborative networks. As such, The translational trial group therefore strongly collaborates with other clinical trial groups in Germany (Society for Pediatric Oncology and Hematology (GPOH)) and is embedded in several international networks (European and transatlantic). Also, strong collaborations with the pharmaceutical industry guarantee access to new innovative drugs. This covers all aspects of trial development in pediatric oncology, but also offers great opportunities to participate in a variety of educational workshops and courses provided by these networks.
In order to apply for the fellowship, a candidate should have a pediatric board certification or comparable experience and a strong interest in precision medicine
The fellow will be exposed to all aspects of translational trial protocol development including drug discovery, preclinical testing, genomics and personalised medicine, biomarkers, trial designs, protocol development, regulatory and ethical aspects.
Medical Image Computing
Principal investigator: PD Dr. Klaus Maier-Hein, Division of Medical Image Computing, DKFZ
More information: Division of Medical Image Computing
The Division of Medical Image Computing (MIC) pioneers research in machine learning and information processing, with the particular aim of improving cancer patient care by systematic image data analytics. We structure and quantify imaging information from multiple time-points and imaging technologies, e.g. magnetic resonance imaging or computer tomography, and link it with clinical and biological parameters. As an initiator and co-coordinator of the Helmholtz Imaging Platform (HIP) we pursue cutting-edge developments at the core of computer science, with applications in but also beyond medicine. We are particularly interested in techniques for semantic segmentation and object detection as well as in unsupervised learning and probabilistic modeling.
Methodologic excellence can only be achieved on the basis of a sophisticated research software system and infrastructure, for example to facilitate highly scalable data analysis in a federated setting. Our technological portfolio in this regard builds the foundation of various national and international clinical research networks, such as the National Center for Tumor Diseases (NCT), the German Cancer Consortium (DKTK) and the Cancer Core Europe (CCE). In collaboration with our clinical partners, we work on the direct translation of the latest machine learning advances into relevant clinical applications.
Our vision is to advance the quality of healthcare through methodological advances in artificial intelligence research and their large-scale clinical implementation. We therefore have a particular interest in techniques that improve the applicability of data science in clinical settings, e.g. by providing more interpretable decision-making, by explicitly dealing with data uncertainty, by increasing the generalizability of algorithms or by learning more powerful representations. We further study image computing concepts that combine mathematical modelling approaches with current machine learning techniques. We are dedicated to open science and committed to maintaining several open source projects in order to share our advances with developers and the scientific community and to promote leveraging synergies.
The Division of Medical Image Computing has a keen interest in collaborating with clinical and medical scientist to tackle interesting and medically relevant clinical research questions in the areas of “Digital Oncology and Big Data” and is eager to apply our expertise in translational research by participating in clinical study programs. The project details would be defined according to the specific research interests of the potential HSO clinical fellow.
Medical Image Computing / Institute of Pathology
Principal investigator: PD Dr. Klaus Maier-Hein, Division of Medical Image Computing, DKFZ; Dr. Mic hael Götz, DKFZ
More information: Division of Medical Image Computing
The Division of Medical Image Computing (MIC) – part of the German cancer research center (DKFZ) - focusses on machine learning strategies, e.g. for semantic segmentation and object detection as well as unsupervised learning. As an initiator and co-coordinator of the Helmholtz Imaging Platform (HIP) we train artificial neuronal networks (ANN) to structure and quantify imaging data from multiple imaging technologies. We successfully established an automated quantitative tumor response assessment of MRI in neuro-oncology using ANN. This technology may help to standardize the diagnostic processes and therefore improving the clinical decision making.
The Institute of Pathology Heidelberg (IPH) provides a special expertise in liver pathology and has a strong interest in molecular pathogenesis of liver tumors. The Liver Cancer Center Heidelberg (LCCH) is administered by the IPH and several research groups are part of the transregional research consortium SFB/TR209 (www.livercancer.de).
Histopathological classification of tumors requires a standardized evaluation of both cytological as well as architectural features and is thus comparable to non-invasive imaging diagnostics. Importantly, the histological differences between highly differentiated tumors and the surrounding normal tissue may only be subtle requiring ancillary immunohistochemical or molecular analyses.
However, automated tumor typing may be possible by training an image analysis algorithm with histological sections of highly differentiated hepatocellular tumors as well as normal liver and premalignant cirrhotic liver tissues. This project demands both bioinformatical and histological expertise, for which a collaboration between both divisions has already been initiated. The medical scientist with expertise in histopathology works in the intersection of both disciplines and supports the MIC bioinformatics team. Annotated slides will be digitized and used to train an ANN for the automated typing of hepatocellular lesions. The medical scientist will be supported and supervised by an experienced histopathologist, thereby ensuring that only correctly typed and adequately stained samples free of artifacts will be included in the bioinformatical analysis. With his medical expertise he/she is also an important adviser for bioinformatic group to guarantee a detailed training and adjustment of the algorithm which will be performed by feedback loops between the project groups in the IPH and the DKFZ.
Medical Physics in Radiation Oncology - Computational Patient Models
Principal investigator: Prof. Dr. Oliver Jäkel, Dr. Kristina Giske, Division of Medical Physics in Radiation Oncology, DKFZ
More information: Medical Physics in Radiation Oncology - Computational Patient Models
Research of our division focuses on improvement of radiotherapy techniques using photons and ion beams. Image-guided and time-adapted strategies require fast and reliable computational models to monitor the 3D anatomy of the patient and adapt the treatment.
For image understanding, modern deep learning correlation methodologies enter the stage promising to predict target volume delineations, motion detection, as well as resulting dose distributions in real-time, competing with accurate physical-rules-based simulation models. Within the research group Computational Patient Models, we focus to exploit the advantages of both approaches to combine the accuracy and reliability of classical rule-based simulation methods and the speed of data-correlates established utilizing supervised learning.
Clinician scientists can contribute to two projects dedicated to radiotherapy improvement: The first project is focusing to generate electron densities and stopping power maps directly from planning MRI scans. Here, the clinical challenge is the judgement on the reliability of the generated tissue distributions and the impact of MRI-artifacts. The second project is dedicated to incorporate human expert guidelines in automated target volume localization and delineation into the supervised training method to guarantee the AI-based prediction compliance with evidence-derived human expert knowledge.
Medical Informatics for Translational Oncology
Principal investigator: Prof. Dr. Frank Ückert, Division of Medical Informatics for Translational Oncology, DKFZ
More information: Medical Informatics for Translational Oncology
The Department of Medical Informatics for Translational Oncology (MITRO) has many years of experience in the development of IT tools and concepts for the consolidation of data from prevention, diagnostics, treatment, aftercare and research. As an interface between scientists and clinicians at the DKFZ, UKHD and NCT, MITRO contributes significantly to the further development of the research landscape. It has successfully focused on the topics of semantics as a basis for data interoperability and integration, data protection, data warehousing and the secondary use of medical data. With these solutions, the MITRO department has opened up and integrated the extensive data stock from translational oncology at the Heidelberg site. It can be used by translational scientists and translational physicians to explore the molecular, genetic, radiological and clinical data it contains.
Especially due to the increasing amount of molecular and radiological OMICS data in precision oncology, the collection of data points relevant for therapy decisions using classical analytical methods is becoming difficult and time-consuming. Therefore, in close cooperation with oncologists and scientists from the NCT and the DKFZ, we are trying to develop new methods and procedures that address these new challenges. In doing so, we would like to simplify (or make entire work steps superfluous)
- the medical screening of thousands of genetic variants and their complex interrelationships among themselves
- the intensive research in external knowledge databases (CIViC, COSMIC, Reactome etc.), publication databases (Pubmed, Embase, among others) and study registries (e.g. ClinicalTrials.gov)
- the preparation of therapy recommendations with molecular changes, predictive values, degrees of evidence and prioritization
- the documentation of the Molecular Tumour Board recommendation
by automatically querying external and internal databases, annotating molecular biomarkers with extensive information (in which pathways a gene is involved, which drugs target a specific variant, etc.) and further using the data underlying the therapy recommendations of the past to facilitate decision-making in future patients and improve therapy recommendations by applying and adapting AI-Models and Machine Learning approaches.
Minimally invasive and Robot-Assisted Surgery
Principal investigator: Prof. Dr. med. Beat Müller, Division for Minimally invasive and Robot-Assisted Surgery, UKHD
More information: Division for Minimally invasive and Robot-Assisted Surgery
The aim of our working group is to combine minimally invasive surgery and innovative technologies to improve surgical treatment for cancer.
Digitalization and artificial intelligence made autonomous driving, personalized digital assistants and smart factories possible. In surgical oncology, however, we lack similar support facing vast amounts of diagnostic information for every single patient, a continuously growing number of publications and clinical trials and a myriad of medical devices in our operating rooms. To tackle this problem, in late 2018 NCT Heidelberg initiated a specific data science related cancer therapy program in the field of surgical oncology.
Together with our surgical data science partners at the German Cancer Research Center (DKFZ, Division for Computer-Assisted Medical Interventions, Prof. Lena Maier-Hein) we are developing a computer-based system to improve overall survival in cancer therapy by means of clinical decision support prior to and during surgery based on artificial intelligence methods processing holistic data from patient, imaging, medical devices and clinical trials. Our envisioned system will focus on liver surgery and will support both, pre-operative decision making as a virtual tumor board and intra-operative decision making as a tool for fast re-planning of operations.
We offer protected research time embedded in a challenging surgical residency program at a world-renowned center for surgical oncology. We are looking for a clinician scientist who is willing to combine surgical excellence with an understanding for the potential of medical informatics, data science and machine learning to improve surgical treatment for cancer. The translational research project we will define together with the clinician scientist will focus on workflow integration of novel decision support systems in close interdisciplinary collaboration with our research partners at DKFZ.
NCT Clinical Cancer Research Program Upper GI Cancer
Principal investigator: Dr. Georg Martin Haag, Medical Oncology, NCT Heidelberg/UKHD
More information: CP Upper GI Cancer
The scientific program within the CP Upper GI Cancer focuses on integrative characterization of esophageal and gastric carcinomas including response prediction in terms of oncological systemic treatment based on molecular and immunological signatures.
Within the last years several investigator-initiated trials (IITs) have been performed in cooperation with national and international partner sites (NCT02128243, NCT03429816): the multinational MATEO trial explores the role of a maintenance therapy in Her-2 negative gastric cancer with a concurrent translational program to identify molecular subgroups. The OPPOSITE trial, a NCT Proof-of Concept Trial, explores the predictive role of molecular subgroups in patients receiving perioperative treatment; in addition the predictive value of a patient-derived organoid model is explored. Regarding immunological signatures, a translational project (NCT Proof of Concept trial IMMUNOGUIDE, cooperation with AG PD Halama) focusing on the immunological characterization of resected gastric cancer (including analysis of tumor micro-milieu) and the association with clinical outcome is planned to start in 2020.
In a prospective cohort study molecular alterations in MSI-high GI tumors and the association with clinical response are being explored in collaboration with the department of applied tumor biology, Pathological Institute Heidelberg.
Regarding the current application, the respective project of the Clinician Scientist focuses on translational analyses within the currently running and upcoming trials including, but not limited to the NCT Proof of Concept trials OPPOSITE and IMMUNOGUIDE. In addition the CS will participate in the development of new clinical trials.
In detail the following working steps are planned:
- Correlation of clinical outcome in the perioperative setting with molecular alterations including the proposed molecular subtype
- Correlation of histological response with preclinical response observed in patient-derived organoid models in collaboration with University Hospital Dresden and NCT Dresden.
- Analysis of efficacy of systemic treatment in metastatic disease in correlation with molecular alterations with a focus on MSI-H tumors.
- Analysis of the tumor micro-milieu as well as blood-based biomarkers in terms of response to cytotoxic chemotherapy and immunotherapy in the perioperative and palliative setting.
- Participation in the development of new clinical trials (biomarker trials as well as interventional trials) within the CP Upper GI Cancer.
NCT Trial Center - HeLeNe Clinical Trials Office
Principal investigator: Prof. Dr. med. Richard F. Schlenk, NCT Trial Center
More information: NCT Trial Center
The HeLeNe Clinical Trials Office brings together investigator initiated clinical trial activities of several project partners on acute leukemias (Heidelberg University Hospital, German Cancer Research Center - DKFZ Heidelberg, National Center for Tumor Diseases (NCT) Heidelberg, Medical Faculty Mannheim - University Medical Centre Mannheim, HI-STEM - Heidelberg Institute for Stem Cell Technology and Experimental Medicine, EMBL - European Molecular Biology Laboratory). Recently two clinical phase-II trials in refractory/relapsed acute myeloid leukemia (AML) have been approved by the ethical review board (ERB) and the national competent authority with start of recruitment in 2020; i) the TEAM trial evaluates the combination of high dose cytarabine, gemtuzumab ozogamicin and bortezomib, whereby proteasome inhibition with bortezomib appears to be a promising treatment strategy to restore chemo-sensitivity via EZH2 stabilization; ii) the Q-HAM study evaluates the combination of the FLT3 inhibitor quizartinib with intensive chemotherapy in FLT3 internal tandem duplication (ITD) positive AML. Furthermore, two large randomized phase-III trials in newly diagnosed AML will be activated in Q2/2020; i) the GnG study evaluates different dosages of Gemtuzumab Ozogamicin as adjunct to standard induction therapy and Glasdegib as hedgehog inhibitor versus placebo as adjunct to intensive consolidation therapy and as single agent maintenance therapy; ii) the Q-SOC study evaluates quizartinib as adjunct to intensive induction and consolidation as well as single agent maintenance therapy versus standard of care in newly diagnose FLT3-ITD positive AML. While TEAM, Q-HAM and GnG are national studies running in Germany, the Q-SOC study will be activated in Germany, Spain and Portugal via the voluntary harmonized procedure including ERB (VHP-plus). Additional study proposals are currently under review such as CD19.CAR T-cells in first line acute lymphocytic leukemia and treatment de-escalation in AML with mutated NPM1. Thus we offer for clinician scientists an environment to get insides how clinical trials in acute leukemia are planned, set-up and conducted not only in the national German but also in the European perspective; presentations of clinical trials in progress as well as interim analyses on national and international conferences will be an integrated component of a focused mentoring program.
NCT Trial Center - NCT Precision Medicine in Oncology
Principle investigator: Prof. Dr. med. Richard F. Schlenk, NCT Trial Center
More information: NCT Trial Center
The NCT Precision Medicine in Oncology (PMO) clinical trial platform links multidimensional profiling (whole-genome and transcriptome sequencing, genome-wide DNA methylation analysis) of advanced solid tumors within the NCT/DKTK MASTER (Molecularly Aided Stratification for Tumor Eradication Research) program with prospective clinical studies focusing on distinct molecular targets/pathways. Currently, two trials are recruiting: The PMO-1603/TOP-ART study evaluates whether patients whose tumors exhibit “BRCAness”, as defined by whole-genome sequencing, benefit from combination treatment with trabectedin and olaparib compared to physician’s choice; the PMO-1601 study evaluates CDK4/6 inhibition with palbociclib in patients with chordoma and a distinct response signature. In Q2/2020, two additional protocols will be activated: The CRAFT basket study will comprise six treatment arms that are defined by cancer-driving alterations affecting BRAF, ERBB2, ALK, PI3K-AKT, and MAPK signaling as well as changes resulting in immune evasion, such as high tumor mutational burden and/or specific alterations predicting sensitivity to PD-1/PD-L1 inhibition; the PMO-1604 study will focus on three distinct cohorts of patients with NRG1-rearranged malignancies. Furthermore, new therapeutic approaches, such as bispecific antibodies in patients with solid tumors, are evaluated in first-in-human phase-I studies. The combination of deep molecular tumor and germline characterization coupled with controlled clinical trials provides a unique opportunity for exploratory, hypothesis-generating data analysis. Thus, we offer clinician scientists a multidisciplinary and highly collaborative environment to learn how clinical trials in a cross-entity setting are planned, set up, and conducted – not only in Germany, but also on the European level within the Cancer Core Europe consortium. Furthermore, successful candidates will gain insights into the planning and conduct of first-in-human phase-I studies, and presentations of clinical trials in progress as well as interim analyses on national and international conferences will be an integral component of a focused mentoring program.
Institut of Pathology (IPH)
The Institute of Pathology Heidelberg (IPH) and its Center for Molecular Pathology are among the largest institutions in Europe. Our pathologists, molecular diagnosticians, bioinformaticians, and researchers customize each treatment based on integrated diagnostics. With a strong expertise in a range of solid tumor types as well as in hematopathology, IPH performs biomedical research ranging from basic science to translational and trial-based projects. Specifically, we investigate the clinical and functional impact of molecular profiles in several cancer entities with a strong focus on Hepatobiliary, Lung, Prostate Cancer, and Cancer of Unknown Primary (CUP) in order to inform basic science projects, discover new biomarkers and drug targets, and support innovative clinical trials.
At the Liver Cancer Center Heidelberg (LCCH) of the NCT, patients with primary tumors of the hepatobiliary system are diagnosed and treated following interdisciplinary tumor board decisions support by comprehensive molecular diagnostic umbrella concepts and a clinical trial portfolio. The joint LCCH registry combines the diagnostic, treatment and follow-up data from all clinical and diagnostic disciplines. It provides the basis for improved treatment decisions and translational research.
Following the same approach, IPH joined forces with the Thoraxklinik Heidelberg, the Department of Urology, as well as the DKFZ and NCT to establish registry study programs for patients with lung cancer, prostate cancer and cancers of unknown primary. Within this framework deep clinical data with long term follow-up are linked with cancer-specific genetic make-ups. These datasets inform the clinical impact of molecular lesions, provide a source for reverse translation to basic science projects and drives phase1/2 clinical trials including IITs.
Collectively, these programs constitute the data basis for the continuous improvement of patient care and a rich resource for basic biomedical and translational research. Our expertise in the integration and analysis of big data is further expanded by our close collaboration with the NCT cancer registry, the DKFZ, MASTER program, The German Centers for Health Research (DKTK, DZL) the HiGHmed consortium of the Medical Informatics Initiative, and the Centers for Personalized Medicine (ZPM) Program of the Federal State of Baden-Wuerttemberg.
The fellow will be exposed to a unique multidisciplinary and thriving working environment that combines knowledge about molecular diagnostic, mechanistic and translational research as well as molecular targeted therapy in an interdisciplinary manner providing research projects centered on patient informed data.
Possible projects include:
- Integration of morphological and molecular data to identify and define new clinically relevant cancer subtypes
- Integration of morpho-molecular data with clinical outcomes to discover new biomarkers that predict prognosis, therapy response and patterns of resistance.
- Functional analysis of specific molecular settings observed in patients.
- Development of new assays for diagnostic application and clinical trials.
Pattern Analysis and Learning
Principal investigator: PD Dr. Klaus Maier-Hein, Head of Pattern Analysis and Learning Group, UKHD
More information: Pattern Analysis and Learning Group, UKHD
The field of digital oncology has brought the large-scale introduction of data science and artificial intelligence into the health care sector and cancer patient care. Today's advanced radiotherapy methods involve an enormous wealth of multimodal medical patient and especially image data, requiring the careful use of computer science to efficiently manage and facilitate information for the best treatment options. Artificial intelligence and deep learning technologies are applied to advance the current state of the art in radiooncology towards better assessment of diagnosis, risk and prognosis as well as planning and delivery of accurate treatments.
The section of “Pattern Analysis and Learning” pioneers research in machine learning and information processing, with the particular aim of improving cancer patient care by systematic data analytics. The section pursues cutting-edge developments at the core of computer science with applications in radiation oncology and is particularly interested in techniques for semi- and unsupervised learning and probabilistic modeling.
Our vision is to advance the quality of radiation oncology through methodological advances in artificial intelligence research and their large-scale clinical implementation. We therefore have a particular interest in techniques that improve the applicability of data science in clinical settings, e.g. by providing more interpretable decision-making, by explicitly dealing with data uncertainty, by increasing the generalizability of algorithms or by learning more powerful representations. We further study image computing concepts that combine mathematical modelling approaches with current machine learning techniques.
The section of “Pattern Analysis and Learning” has a keen interest in collaborating with motivated clinical and medical scientist to tackle interesting and medically relevant clinical research questions in the areas of “Digital Oncology and Big Data” and is eager to apply our expertise in translational research by participating in clinical study programs. The project details would be defined according to the specific research interests of the potential HSO clinical fellow.
Radiation Oncology and Radiation Therapy
Principal investigator: Prof. Dr. Dr. Jürgen Debus, Department of Radiation Oncology, UKHD
More information: Department of Radiation Oncology
The research activities of the Department of Radiation Oncology and Radiotherapy are focused on the optimization of radiation therapy including all its facets: research and development of ion beam therapy at the Heidelberg Ion-beam Therapy center (HIT), image guided radiotherapy and adaptive planning techniques. These approaches are complemented by research projects based at the NCT on the individualization of radiotherapy and multimodal cancer therapies.
For a future project we are looking for a clinical/medical scientist with high motivation in crossing-over traditional research fields. Candidates should have a multidisciplinary background in medicine and/or physics and high affinity to computer science or statistics.
Section Gynaecologic Oncology / Division Molecular Genetics
Breast cancer is the leading cause of cancer deaths in women in Western societies. Current standard clinical classifiers provide a robust backbone to stratify patients, yet are limited in capturing the large variety of anticipated molecular and genomic alterations that drive tumor progression. We have implemented integrative diagnostic genomics into the clinics within two molecular diagnostic platforms for metastatic (CATCH) and early high-risk (COGNITION) breast cancer patients based on the tight interaction between the Schneeweiss and the Lichter groups.
The Schneeweiss group runs a dedicated breast and gynaecological cancer unit comprising comprehensive standard-of care treatment as well as long-standing experience in conduction of innovative clinical trials on large patient numbers. The molecular genomic part is accomplished employing structures of the Molecular Diagnostics NCT / Lichter group: the latter applies oncogenomic approaches for the elucidation of pathomechanisms of tumor aetiology and progression as a basis for novel treatment strategies, and for the identification of prognostic and predictive genes and gene signatures along with pre-clinical functional analyses.
Beyond directly impacting the optimized management of breast and gynaecological cancer patients, this precision oncology framework allows systematic collection of clinical and genomic/molecular data along with corresponding biomaterial. This strategy pursues a precious backbone ideally suited to host candidates to conduct clinical and pre-clinical scientific companion projects from different areas.
Principal investigator: Prof. Dr. med. Karin Jordan, Prof. Dr. Carsten Müller-Tidow, Department of Internal Medicine V: Hematology, Oncology and Rheumatology
More information: AG Supportive Care
Despite growing awareness of the need to develop patient-centred care interventions more and better scientific evidence, tailored to individual and fluctuating patient needs is required. Along with antitumour treatment, most patients need help to prevent and alleviate side-effects and toxicities of the corresponding antitumour treatment. The overall aim of our interdisciplinary and multidisciplinary “Supportive Care” research group is to improve supportive care in cancer patients with the ultimate goal to decrease cancer therapy side effects. Research topics include:
- Evaluating short- and long-term side-effects of new therapeutic interventions.
- Studying supportive care agents in randomized controlled trials.
- Integrating new technologies such as web-based programs and apps for selfmonitoring and reporting symptoms, further evaluate congruent outcome assessments on symptoms and side-effects (health care assessment versus patient reported outcomes). Requesting digitalized patient-reported outcomes with corresponding side effect management strategies in the continuum of cancer care has shown to be associated with better quality of life, fewer hospitalizations and even increased survival compared with usual care.
- Classification of side effects: Certain side effects need to be classified into risk categories to adapt the corresponding prophylactic supportive intervention. Our group is now leading the regular international update for the classification of the emetic potential of the new antineoplastic agents and places them in an appropriate level in the four-level classification schema.
- Coordination and development of the S3 guidelines on “Supportive Therapy in the oncological patient” and right now the ESMO (European Society of Medical Oncology) supportive care guidelines.
Potential project in our group: Painful peripheral neuropathy due to systemic antineoplastic therapy is very common and often a dose-limiting side effect. Efficacious pharmacological therapeutic options for patients with established painful neuropathy are still limited. This might be due to its multidimensional nature of pain (total pain concept). As such a “one fits all concept” might therefore not be useful and a more individualized pharmacological intervention approach is warranted. According to this concept we would like to plan a randomized study testing the efficacy of an adaptive pharmacological intervention.
SYMPATHY: An integrated systems medicine approach to personalized and targeted therapy in lymphoma
Principle investigator: PD. Dr. med. Sascha Dietrich, Department of Internal Medicine V: Hematology, Oncology and Rheumatology, UKHD
More information: Molecular Medicine Partnership Unit: Systems Medicine of Cancer Drugs
Our aim is to bring biology-based individualized treatment of lymphoma and leukemia into clinical practice. To achieve this ambitious aim, we establish a systems medicine program that integrates systematic functional assays, multi-omic profiling, bioinformatic analysis, mathematical modeling and setup towards a clinical exploitation. The basis of the program is a platform to map patient specific pathway activity and drug sensitivity of their primary tumor cells ex-vivo. By comparing drug responses across patients with detailed molecular characterization (e.g. whole exome- and RNA-sequencing) and the clinical parameters, we obtain a rich set of associations of drug sensitivity with biology, biomarkers and outcome.
A particular focus of our research is on signals provided by the microenvironment, and how these signals modify pathway activities targeted by drugs. We aim to generate a systems-level understanding of how the microenvironment and the individual genetic and molecular make-up of a tumor interact and modify drug response using a high throughput automated microscopy platform mimicking microenvironment conditions. In parallel, we investigate further dimensions of in-vivo drug response (3D-space, timing of drug response) with a hierarchy of increasingly complex culture models and readouts in suitable tumors.
Translational Myeloma Research Program
Principal investigator: Prof. Dr. Marc-Steffen Raab, Myeloma Center, Department of Internal Medicine V: Hematology, Oncology and Rheumatology
More information: Translational Myeloma Research Program
The Myeloma Center Heidelberg is one of the largest centers of its kind dedicated to the clinical, translational and basic science to improve our patients' lives. We conduct large scale clinical trials from early phase targeted therapies to randomized multi-center phase III studies inlcuding stem cell transplantation.
Within the Translational Myeloma Research Program, we are dedicated to gain deep knowledge on the pathophysiology of the myeloma cell and on mechanisms of drug resistance in the context of clinical trials. To this end, the program builds on an extensive network of expert groups accross the campus to employ state of the art methodologies including single cell RNA and ATAC seq, WGS, proteomics, imaging and bioinformatics in order to comprehensively analyze therapy-induced biological processes within tumor cells and their immune-microenvironment alike.
Finally, we aim at translating our lab-based results from bench to bedside by designing clinical protocols that allow for proof of concept and provide the basis for practice changing trial design.
Translational Medical Oncology
Principal investigator: Prof. Dr. Stefan Fröhling, Division of Translational Medical Oncology, NCT Heidelberg/DKFZ
More information: Division of Translational Medical Oncology
Our ambition is to improve the way we practice oncology towards a more rational and personalized approach. Our division, therefore, engages in all aspects of the translational research process, including one of the most comprehensive cancer molecular diagnostics programs worldwide (NCT/DKTK MASTER), clinically guided exploratory research projects, and – of particular relevance to prospective Heidelberg School of Oncology Fellows – the implementation of innovative clinical trials.
Within the NCT/DKTK MASTER program, we have analyzed more than 1,800 tumor samples by whole-exome/genome and RNA sequencing and genome-wide DNA methylation profiling and discovered previously unrecognized recurrent genetic alterations – including complex genomic, epigenomic, and transcriptomic signatures – in various tumor types. In several cases, we have been able to decipher the functional and mechanistic consequences of genetic alterations identified in human cancer patients and, in select cases, feed the results back into the clinic, as exemplified by our recent discoveries of pharmacologically tractable NRG1 rearrangements in pancreatic ductal adenocarcinoma (Heining et al. Cancer Discov 2018) and genomic imprints of defective homologous recombination DNA repair (“BRCAness”) in bone and soft-tissue sarcomas (Chudasama et al. Nat Commun 2018, Gröschel et al. Nat Commun 2019). The ultimate clinical goal of MASTER is to move away from assessing the therapeutic activity of targeted, molecular mechanism-based therapeutic interventions on an individual-case basis. To this end, we are working, in close collaboration with the NCT Clinical Trial Center, on a continuously growing portfolio of cross-institutional basket trials that are linked to the MASTER platform and assume that the presence of a specific molecular marker predicts response to a targeted therapy independent of tumor histology.
Other achievements include the development of a system for prioritizing clinically relevant genomic alterations regarding their association with response to a particular therapy and the definition of novel clinical endpoints that could aid in the design and interpretation of future precision oncology trials. To help realize the promise of personalized oncology based on scientific inquiry and biology-guided clinical decision making, we are looking for highly motivated candidates with a passion for applied cancer research.
Principal investigator: Prof. Dr. med. Michael Schmitt, AG Zelluläre Immuntherapie/GMP Labor; Prof. Dr. Carsten Müller-Tidow, Department of Internal Medicine V: Hematology, Oncology and Rheumatology, UKHD
More information: AG Zelluläre Immuntherapie/GMP Labor
A main focus of our group is the adoptive immunotherapy, especially in the genetically modified T cells. Currently the GMP lab produces chimeric antigen receptor (CAR) modified T cells for CD19 positive cancer (NHL and ALL), which will be given back to the respective patient in a clinical phase I/II study (HD-CAR-1).
A second clinical trial HD-CAR-2 will recruit patients with ALL with refractory minimal residual disease (MRD) at early stages of the treatment algorithm. Furthermore we are focusing in the preclinical CAR science on mainly two aspects: the optimization of cell production and on the development of different combination therapies. This comprises cytokine-armored CARs, transduction of natural killer (NK) cells with CARs and the conjunction of CARs and TCRs using the CRISPR/Cas technology. Another project track is the development of therapies with other targets like b cell maturation antigen (BCMA).