Lymph node WP
We will perform a multilayer characterization of samples, based on our published/ongoing analysis of poor outcome biomarkers and develop translational research platforms to study drug responses and resistance to understand, predict, and target early relapses after frontline BTKi. LN samples will be used for immunostains, spatial transcriptomic and nucleic acid isolation.
We will implement a digital pathology hub to:
i) gather whole slide images of diagnostic samples of TRIANGLE and other EMCLN trials. Using the existing conventionally assessed pathology (diagnosis, cytology, Ki67, TP53), molecular and clinical data we will apply deep learning (artificial intelligence) to identify the most relevant relapse predictors;
ii) implement a digital pathology hub for a semi-automated central pathology review/bio-marker scoring. The (epi)genetic/transcriptomic characterization will be performed by an MCL-oriented NGS panel including genes with known prognostic impact and related to treatment resistance; epigenetic study to distinguish cMCL/nnMCL; digital expression (Nanostring) of signatures with prognostic impact and RNAseq.
The MCL microenvironment related to early relapse will be defined by measuring +spatial metrics based on multiplex immunofluorescence data, connected to deep spatially guided mRNA profiling (GeoMX™) of tumor and immune cells, revealing cell-cell communication associated to resistance. Cryopreserved samples at relapse will be used to derive patient-validated lymphoma cell lines and patient-derived xenografts (PDX) in immunodeficient NSG mice. The PDX will be used for experimental proof-of-concept therapies focusing on rational chemotherapy-free combinations or innovative biomarker-driven strategies.