Målet med studien är att bredda den biologiska förståelsen för hur sådan behandlingsresistens uppkommer, samt identifiera markörer som kan användas i klinisk rutin för att välja rätt behandling till varje lymfompatient. Detta ska uppnås genom ny och förbättrad förståelse för mekanismerna bakom behandlingsresistens mot Imbruvica och Venclyxto för patienter som behandlas för mantelcellslymfom. I syfte att utveckla kliniska verktyg för att välja rätt immunterapi så kommer vi även att beskriva undergrupper av lymfom med liknande cellulär och molekylär sammansättning av tumörens immunologiska mikromiljö.

The lymphoma cellular neighbourhood shape distinct tumor immune microenvironment with implications for tumor resistance and outcome. The aim of the current application is to advance treatment strategies for patients afflicted by lymphoid malignancies. This will be achieved by enhancing the understanding of the tumor immuno microenvironment (TIME) in lymphomas and the biological response to novel targeted therapies such as ibrutinib, Venetoclax and immune-directed treatment. Advanced and novel research strategies will be applied to address such questions and include image analysis, spatial omic and serum-based profiling. The scientific learning and output will be maximized using novel machine learning strategies to analyse images and robustly classify patients based on integrated and complex biological data from large international clinical trial cohorts. The three specific aims are: (i) to decipher the role of cellular abundance and molecular mechanisms in the TIME for primary Ibrutinib resistance in MCL, (ii) to define biomarkers that can predict response to Venetoclax and to validate the prognostic role of soluble CD163, and (iii) to develop tissue-based descriptors to classify TIMEs based on image analyses in subtypes of lymphomas for stratification of patients to different immunotherapeutic alternatives. The overarching scientific scope is to understand and use the interplay between the cellular and molecular make-up of TIME in lymphomas and the association to targeted immunotherapy response using novel and advanced technical and bioinformatic strategies.