Subclonal heterogeneitY and MicroenvironMEntal engagement as predictors for Treatment Resistance in lYmphoma
In this project FRRB finances Partner number 3: Fondazione IRCCS Istituto Nazionale Tumori (INT). The Principal Investigator is Dr. Sabina Sangaletti.
|Pathology of interest:||Lymphoma|
|Area of research:||Oncology|
|Start date:||1st May 2022|
|End date:||30th April 2025|
- University Hospital Heidelberg (Germany)
- Karolinska Institut - Science for Life Laboratory (Sweden)
- Single-Cell Technologies Ltd. (Hungary)
- Fondazione IRCCS Istituto Nazionale dei Tumori (INT) (Italy)
- University of Oslo (Norway)- Evang. Landeskirche Hannovers - Zentrum für Gesundheitsethikan der Ev. Akademie Loccum (Germany)
- Rīga Stradiņš University (Latvia)
While heterogeneity between patients (inter-tumour heterogeneity) is known to affect treatment efficacy, 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 genetic and non-genetic ITH.
We will use viably frozen lymph node biopsies to thoroughly characterize lymphoma subpopulations and their cellular microenvironment. As a starting point we will use CITE-Seq, characterizing the transcriptome and surface proteome of single cells, and single-cell DNA-Seq, revealing ITH at high resolution. Distinct lymphoma subpopulations will be isolated by single-cell-Seq informed flow cytometry followed by genome sequencing, in depth proteomics and ex-vivo drug sensitivity profiling.
We will determine the minimal number of features needed to characterize ITH using the multi-omics data of fresh frozen samples. 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. The transcriptome, genome and proteome of tumour cell subpopulations in FFPE samples will be evaluated by digital spatial profiling (DSP) and laser microcapture microscopy followed by sequencing and proteome analysis.
We will use these 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. Ideally, we aim to link recurrent treatment resistant subpopulations to resistance mechanisms and clinically exploitable drug sensitivity profiles.
Our results will demonstrate how to overcome the limited applicability of state-of-the-art single cell techniques in routine diagnostic biopsies and will foster the development of patient stratification tools for cancer.