Subpopulation heterogeneitY and MicroenvironMental Engagement as predictors for Treatment Resistance in IYmphoma

Project Details


While heterogeneity between patients (inter-tumour heterogeneity) is known to affect the efficacy of cancer therapy, most personalized treatment approaches do not account for intra-tumour heterogeneity (ITH). New single-cell omics assays provide us, for the first time, with the opportunity to molecularly detect and characterise both types of ITH: genetic and non-genetic.
We will use viably frozen single cell solutions isolated from lymph node biopsies to characterize lymphoma subpopulations and their cellular microenvironment thoroughly. As a starting point we will use CITE-Seq, which simultaneously characterizes the transcriptome and surface proteome of single cells, revealing ITH at high resolution. Transcriptionally distinct lymphoma subpopulations will be isolated by CITE-Seq informed flow cytometry followed by genome sequencing, in depth proteomics and ex-vivo drug sensitivity profiling.
Multi-omics data generated with fresh frozen samples will be used to determine the minimal number of features needed to characterize ITH. This set of markers will be used to trace ITH in formalin fixed paraffin embedded (FFPE) tissue by multiplexed immunofluorescence, which enables, if successful, the investigation of ITH in clinical routine samples. In FFPE samples the transcriptome of tumour cell populations will be evaluated by digital spatial profiling (DSP) and the genome and proteome of FFPE lymphoma specimens will be investigated by laser microcapture microscopy followed by sequencing or proteome analysis.
We will use the data generated in this consortium to develop an automated image analysis pipeline, which estimates the degree of ITH in routine clinical samples and which is suitable to risk stratify lymphoma patients.
Our results will demonstrate how to overcome the limited applicability of state-of-the-art single cell techniques in routine diagnostic FFPE material and will foster the development of patient stratification tools for cancer.
Effective start/end date1/02/2231/01/25


  • Tumour Subpopulation
  • Lymphoma
  • Single-cell
  • histopathology
  • drug-sensitivity
  • proteogenomics
  • precision medicine

Field of Science

  • 3.3 Health sciences

Smart Specialization Area

  • Biomedicine, medical technologies and biotechnology


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