Research focus

Our work is currently focused on two main research topics in the field of cancer genomics.

  1. Characterization how the tumor microenvironment (TME) determines tumor behavior and response to response to anti-angiogenic and checkpoint immunotherapies, which are both frequently used in the clinic (alone or in combination). This exciting line of research bears a lot of translational potential, as managing the levels of hypoxia in solid tumors by anti-angiogenic strategies represents a major yet incompletely understood challenge, while the field of immunotherapy is rapidly expanding - yet desperately in need of biomarkers predicting clinical response. We are using the newest single-cell profiling technologies to study these tumor micro-environments at the greatest detail (before, during treatment and at resistance).
  2. The use of plasma cell-free DNA (cfDNA) as a diagnostic test to characterize and monitor tumors non-invasively. Specifically, we are optimizing methods to reliably detect somatic mutations, copy number changes, but also changes in DNA methylation and nucleosome positions in cfDNA from cancer patients

Single-cell RNA sequencing of TME during cancer immunotherapy

Cancer consists of (epi)genetically and phenotypically different populations of single cells. This intra-tumor cellular heterogeneity underlies therapeutic resistance to cancer treatment. As such, improving our understanding of intra-tumor cellular heterogeneity, both in cancer cells and their surrounding stromal cells down to the single-cell resolution is essential to combat therapeutic resistance and to develop more effective cancer drugs. While cancer scientists have long sought to study single cells, the means and tools at their disposal have been limited. Recently, the field has witnessed the rapid emergence of single-cell sequencing technologies, with great capacity to simultaneously characterize the genome, transcriptome and epigenome of individual cells.

We recently published the very first ‘atlas’ of cell phenotypes found in lung tumors (Lambrechts et al., Nature Medicine 2018). By subjecting almost 100,000 single cells isolated from lung cancer patients without dissociation bias to single-cell RNA-sequencing (scRNA-seq), we were able to unbiasedly assess cancerous cells and non-cancerous cells in tumors such as blood vessels, immune cells and fibrous cells. 

We discovered that the TME is more heterogenous than anticipated. We identified 52 stromal cell types versus the dozen cell types already known to be present. These cells have never before been characterized in their native environments. Because we compared with the matching non-malignant lung samples, we were moreover able to observe how each cell type is altered by the tumor. Our data indicated that the stromal cells targeted by anti-angiogenic and immunotherapies are distinct from those residing in non-malignant tissues. Highlighting such tumor-specific changes and vulnerabilities enables a more rational design of novel therapies targeting the TME.

In addition, an outstanding research question is to what extent this heterogeneity is similar between cancers affecting different organs. Therefore, we have profiled 233,591 single cells from patients with lung, colorectal, ovary and breast cancer and constructed a pan-cancer blueprint of stromal cell heterogeneity using different single-cell RNA and protein-based technologies. We identified 68 stromal cell populations, of which 46 are shared between cancer types and 22 are unique. We also characterised each population phenotypically by highlighting its marker genes, transcription factors, metabolic activities and tissue-specific expression differences. Resident cell types are characterised by substantial tissue specificity, while tumor-infiltrating cell types are largely shared across cancer types. By applying the blueprint to melanoma tumors treated with checkpoint immunotherapy and identifying a naïve CD4+ T-cell phenotype predictive of response to checkpoint immunotherapy, we illustrated how it can serve as a guide to interpret scRNA-seq data. In conclusion, by providing a comprehensive blueprint through an interactive web server, we have generated a first panoramic view on the shared complexity of stromal cells in different cancers. Our single-cell blueprint can be visualised, analysed and downloaded from an interactive web server (http://blueprint.lambrechtslab.org) (Qian et al., Accepted in Cell Research, a preprint present on bioRxiv 2020.04.01.019646).  

Cancer immunotherapy using immune checkpoint blockade has created a paradigm shift in the treatment of advanced-stage cancers. In terms of lives saved and person-years restored, these therapies promise to be more significant than any other form of cancer treatment. Leading healthcare professionals anticipate that the future of cancer therapy lies in designing novel treatments that boost the body's natural ability to fight cancer. However, one of the major limitations of immune checkpoint blockade is that it provides durable clinical responses only in a relatively small fraction of patients. Indeed, only 40% of patients with advanced metastatic melanoma respond to nivolumab (anti-PD1) and/or ipilimumab (anti-CTLA4), while response rates for other types of cancers are even lower. In addition, although long-lasting anti-tumor responses were observed, disease relapse due to resistance frequently occurs. This clearly illustrates how the success of immune checkpoint blockade varies among different cancer types, and how, even within the same cancer type, responses may hugely differ between patients. Unfortunately, it is still not possible to predict upfront which patient will respond to immune checkpoint blockade.

Nowadays, together with the UZ Leuven oncologists, we exploit the innovative single-cell technologies (scRNA-seq, CITE-seq, spatial single-cell profiling, single-cell DNA-seq, single-cell alternative splicing and single-cell CNV profiling) and apply multi-omics single-cell profiling in the context of clinical trials involving checkpoint immunotherapy.

Specifically, tumor biopsies will be collected in patients receiving checkpoint inhibitors before and during treatment, as well as at disease progression. This will allow us to monitor therapeutic response at unprecedented resolution, inform us why specific patients or cancer types are resistant and distill from this knowledge novel biomarkers predictive of response to checkpoint inhibitors. In the long run, we also expect these insights to reveal novel treatment combinations that provide long-term therapeutic responses in refractory patients. 

We are currently profiling tumor biopsies from patients receiving checkpoint immunotherapy in clinical trials of breast cancer, recurrent head-and-neck, cervical cancer, ovarium cancer, metastatic lung cancer, (colo)rectal cancer etc. Interestingly, in 2 other cancer types, i.e., renal cell carcinoma and hepatocellular carcinoma, checkpoint immunotherapy is combined with an anti-angiogenic tyrosine kinase inhibitor, allowing us to explore the interaction between anti-angiogenic and checkpoint immunotherapies.

Movie Grand Challenge

Circulating tumor DNA as a diagnostic biomarker for cancer behavior

Plasma cell-free DNA (cfDNA) can be used to characterize and monitor tumors non-invasively, providing a real-time representation of the entire tumor. While the plasma cfDNA in healthy individuals mainly originates from leukocytes, a substantial part of this DNA in cancer patients derived from tumor cells (ctDNA, i.e. circulating tumor DNA). Such a blood-based test detecting ctDNA is important, as most patients do not undergo a tumor re-biopsy. Instead, only the initial diagnostic biopsy is available and the molecular phenotype of the tumor may have changed over time. Drawing on rapid developments in the field of non-invasive prenatal testing (NIPT), cfDNA-based tests have gained momentum in the oncology space, as a potential cancer-screening tool. In this context, we have recently demonstrated the utility of the plasma-derived cfDNA low-coverage whole-genome sequencing (LC-WGS) analysis using chromosomal instability as read out in the ovarian cancer setting (Vanderstichele et al., Clin Cancer Res. 2017 ). However, this approach is limited to the detection of tumors characterized by a certain degree of chromosomal instability and these will be missed when using this approach.

In order to more generically detect ctDNA, we also explored the characterization of the tissue-of-origin of ctDNA by analyzing the patterns of the cell type-specific nucleosome positions in cfDNA using the same LC-WGS data. Fragmentation patterns of plasma-derived cfDNA are known to reflect nucleosome positions of cell types contributing to cfDNA. Since a substantial fraction of cfDNA from cancer patients originates from tumor cells, the nucleosome footprints in cfDNA are different between cancer patients and healthy controls. 

As such, LC-WGS of cfDNA represents a single diagnostic test that generate 2 independent diagnostic read outs. However, these LC-WGS approaches need sufficient cfDNA input. In addition to the cfDNA-derived LC-WGS read outs, we have also developed a unique protocol to reliably assess the methylation status of low concentrations of heavily fragmented cfDNA by targeted bisulfite sequencing. cfDNA from cancer patients also differs in its DNA methylation pattern from that of healthy patients, with a focal hypermethylation at tumor suppressor genes and a global hypomethylation.

In contrast with the DNA hypomethylation as a ubiquitous cancer mark, regions of DNA hypermethylation are more tumor-specific and therefore more amenable for tumor (sub-)typing. Preliminary data has demonstrated the potential of this targeted methylation-based cfDNA test as biomarker for detection of tumor DNA in plasma from patients with invasive ovarian cancer, hepatocellular carcinoma etc. 

Interestingly, our recent preliminary data has further demonstrated that the assessment of the different metrics (chromosomal instability, nucleosome footprinting and DNA methylation) act complementary in the ovarian cancer setting. Some tumors are missed using one approach, but can be picked up with the other approach, making combining the three metrics more reliable in detecting ctDNA than either approach alone. 

We are currently optimizing these plasma-derived ctDNA methods and apply them in other cancer types as the fraction of ctDNA depends on the type of cancer because not all cancer types release an equal amount of tumor DNA in the bloodstream.

Expertise

Access to high-throughput genomic technologies:

We have outstanding experience with high-throughput (single-cell) (epi)genomic and transcriptomic profiling. State-of-the-art genomic studies are typically driven by the rapid development of novel and expensive technologies. In our laboratory, we have direct access to and extensive experience in handling various sequencing technologies, including an Illumina iSCAN, MiSeq, NextSeq, HiSeq2500, HiSeq4000 and NovaSeq6000. This allows us to rapidly perform whole-genome sequencing (WGS), targeted (re-)sequencing (e.g., exome sequencing), (targeted) bisulphite sequencing [5(h)mC-Seq], as well as RNA sequencing. 

These techniques can be applied on fresh frozen (tumor) tissue, FFPE-based (tumor) tissue and liquid biopsies. Moreover, via a single-cell accelerator initiative at VIB, our lab has early access to the newest single-cell multi-omics technologies (scRNA-seq, ATAC-seq, DNA-seq, CITE-seq, CROP-seq and spatial profiling). We manage the access to the 10X Genomics technology and Illumina NovaSeq system for all the single-cell RNA sequencing experiments at UZ Leuven and KU Leuven via our VIB – KU Leuven Single Cell Core Facility.

Cutting edge genomic data analysis through advanced bio-informatics and data integration approaches:

The required experience to cluster the omics-type of data into molecular subtypes and develop specific classifier to be used in a clinical context is available within our lab. We have significantly invested in attracting computational biologists to analyze and integrate large genomic datasets. Currently, there are 5 experienced postdocs, > 10 PhD students (including MDs), 3 omics data analysts as well as a staff scientist in our group focusing on the analysis of several large-scale genomic data sets. To perform these large computational analyses, our lab has a state-of-the-art computing infrastructure in-house (updated beginning 2019 to over 340 processors and 66 TB storage), and for high-performance computing we have access to the Flemish Supercomputer Center (Vlaams Supercomputer Centrum, VSC).

Numerous collaborative projects at the national and international level: 

  • We have longstanding ongoing collaborations with most oncologists at UZ Leuven, which are often internationally highly recognized leaders in the field, such as E Van Cutsem (gastro-intestinal cancer), I Vergote and D Timmerman (gynaecological cancer), P Schöffski (solid tumors and lymphomas), J Van steenkiste (lung cancer), S Jacobs (paediatric cancer), M Garmyn (melanoma) etc. These collaborations are of tremendous value for my group, as they guarantee access to a large number of clinically well-characterized patient samples for our genomic studies, positioning ourself at the center of translational cancer research in Leuven. 
  • Diether Lambrechts is a board member of the directors of the Genomics Core Facility from UZ Leuven – KU Leuven, a founding member of the Single Cell Omics Institute (SCOI) from KU Leuven and a board member of the steering committee of the Single Cell Core Facility from the VIB-KU Leuven Center for Cancer Biology (in collaboration with the Genomics Core Leuven).
  • Our publication about single-cell analysis in Lung Cancer (Nature Medicine, 2018) spurred interest with Chinese collaborators (Fudan and Zhejiang University, both top 5 Chinese universities), with whom we have now set-up 3 China-based laboratories that mirror our single-cell analysis set-up in Leuven, with the ambition to embark on joint projects using the same experimental infrastructure. Diether Lambrechts is Scientific Director of the Sino-Belgian Lab for Single-Cell Analysis Technologies.
  • We have ongoing collaborations with many other institutions for our expertise in next generation sequencing and cancer genomics, e.g. J De Greve (UZ Brussels); C Sotiriou (Jules Bordet Institute, Brussels); C Blanpain (ULB, Brussels); P Fasching (University Hospital Erlangen, Germany); Jeremy Cheadle (Cardiff University, UK); Darran O’Connor, Jochen Prehn and Annette Byrne (University College Dublin, Ireland); Vessela Kristensen (University of Oslo, Norway); VHIO, Spain); INSERM, France; Louis Chesler (The Institute of Cancer Research in London, UK); the EORTC; Comprehensive Cancer Center Zurich, the University Hospital of Zurich, Switzerland; Fudan University in Shanghai, the School of Medicine, Zheijiang University in Hangzhou, China etc.
  • We are an active contributor to international genetic consortia (BCAC, OCAC, E2C2) and currently partner (work package leader) of different European collaboration projects:
    • 2015-2020: EU H2020-PHC-2014-2015 – Breast cancer stratification: understanding the determinants of risk and prognosis of molecular subtypes  (B_CAST) (51k€; promoter)
    • 2017-2021: EU H2020-MSCA-ITN-2017 – Exploiting GLIOblastoma intractability to address European research TRAINing needs in translational brain tumour research, cancer systems medicine and integrative multi-omics (GLIOTRAIN) (207k€; promoter)
    • 2018-2022: EU H2020-SC1-2016-2017 - Advancing a Precision Medicine Paradigm in metastatic Colorectal Cancer: Systems based patient stratification solutions (COLOSSUS) – (481k€; co- promoter) 
    • 2019-2024: EU H2020-SC1-BHC-2018-2020 - Resistance under combinatorial treatment in ER+ and ER- breast cancer (RESCUER) – project is accepted, but not active (co-promoter)
    • 2019-2022: ERACoSysMed - Resistance under combinatorial treatment in ER+ and ER- breast cancer (RESCUER) – project is accepted, but not active (co-promoter)

Excellent contacts and collaborations with the Flemish Biotech scene and international key pharmaceutical companies:

We are very keen on collaborating with the pharmaceutical and biotechnical companies (Hoffman-La-Roche, Sanofi-Aventis, Bayer AG, Eisai, Eli-Lilly, Novartis Oncology, Boehringer Mannheim, Merck Serono, Brystol Myers Squibb, Agilent, Biocartis, J&J etc.). We were able to file 5 patent applications (as co-inventor and co-owner) with them. Following an intense collaboration with Biocartis, a 7-marker assay fully compatible with the Idylla platform was launched in 2018 to detect microsatellite instability (MSI). Idylla™ MSI is nowadays the only MSI test that already received CE-IVD clearance, and it is distributed on a world-wide scale (sub-licenced to Wondfo in China and Nichirei Biosciences in Japan). In 2018, the FDA approved checkpoint immunotherapy as the first cancer treatment for any solid tumor with a specific genetic feature (MSI). Currently, Idylla™ MSI is used in 2 registration studies from Bristol-Myers-Squib to seek FDA registration of the test as a companion diagnostic. 

In 2016, we started a VLAIO project with Multiplicom NV (now Agilent Technologies). As a result of our efforts in this project, a new sequencing kit encompassing 19 genes involved in homologous recombination deficiency (HRD) has been commercialized (launch: early 2020). Additionally, an HRD scarring assay to detect HRD-driven genomic instability has been optimized and will be launched. HRD status is guiding the treatment of ovarian and triple-negative breast cancer with PARP inhibitors. These success stories illustrate how we combine top-level research while keeping a strong focus on the clinical impact and technology transfer potential of our research.

Technology 

Different sequencing technologies are available at our lab, including an Illumina iSCAN, MiSeq, NextSeq, HiSeq2500, HiSeq4000 and NovaSeq6000. These technologies make it possible to rapidly perform whole-genome sequencing (WGS), targeted (re-)sequencing (e.g., exome sequencing), (targeted) bisulphite sequencing [5(h)mC-Seq], as well as RNA sequencing. These techniques can be applied on fresh frozen (tumor) tissue, FFPE-based (tumor) tissue and liquid biopsies. Via a single-cell accelerator initiative at VIB,  we have early access to the newest single-cell multi-omics technologies (scRNA-seq, ATAC-seq, DNA-seq, CITE-seq, CROP-seq and spatial profiling), including the 10X Genomics technology. 

All technologies and bioinformatics pipelines are accessible collaboratively (contact: Diether Lambrechts) or on a fee-for-service basis (https://www.genomicscore.be/). The 10X Genomics technology and Illumina NovaSeq system for the single-cell RNA sequencing experiments at UZ Leuven and KU Leuven is accessible via our VIB – KU Leuven Single Cell Core Facility

Data and Resources 

The genomics core is a facility that advances the development and implementation of novel genomic technologies. The facility provides technological and intellectual support for scientific applications as well as next generation genetic diagnosis.

Genomics core

Expertise center of the Single Cell Platform from VIB – KU Leuven Center for Cancer Biology. 

Single Cell Platform

Expertise center of the Bio-Informatics from VIB – KU Leuven Center for Cancer Biology. 

Bio Informatics

An interactive web server where you can visualize, analyze and download our pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling.

Blueprint Lambrechts Lab

The Catalogue Of Somatic Mutations In Cancer database stores and displays information about somatic mutations in cancer based on data published in the scientific literature

The Catalogue Of Somatic Mutations In Cancer database

The cBio Cancer Genomics Portal provides visualization, analysis and download of large-scale cancer genomics data sets

The cBio Cancer Genomics Portal

The Cancer Genome Atlas aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer

The Cancer Genome Atlas

The Genomics of Drug Sensitivity in Cancer project is an academic research program to identify molecular features of cancers that predict response to anti-cancer drugs

The Genomics of Drug Sensitivity in Cancer project

The Ensembl genome browser

The 1000 Genomes Project aims to establish a detailed catalog of human genetic variation

The 1000 Genomes Project

The Single Nucleotide Polymorphism Database dbSNP is a public archive for genetic variations

The Single Nucleotide Polymorphism Database

OCTIPS-Ovarian Cancer Therapy - Innovative Models Prolong Survival is a FP7 research project funded by the European Commission and aims at identifying molecular characteristics of recurrent ovarian cancer and developing new therapeutic strategies in innovative model systems

OCTIPS-Ovarian Cancer Therapy - Innovative Models Prolong Survival

Tumorscape portal is designed to facilitate the use and understanding of high resolution copy number data amassed from multiple cancer types

Cancer program

The GTEx eQTL database and browser aims to provide a central resource to archive and display association between genetic variation and high-throughput molecular-level phenotypes

The GTEx eQTL database and browser

Grand Challenges

This research is part of our Grand Challenges Program.

More info