The term ‘Lymphoma’ encompasses a diverse group of malignancies, which require very different approaches to ensure optimal patient outcomes. However, even within individual lymphoma types tumour behaviour and patient responses remain highly variable. To some extent this is influenced by patient specific factors (e.g. age, fitness, comorbidity), but there remains significant heterogeneity of tumour biology, which is not adequately identified by standard diagnostic techniques and expert pathology review. This fellowship investigated novel diagnostic techniques in lymphoma in an attempt to identify clinically relevant tissue- based factors, which could improve disease classification and characterise the underlying tumour biology more objectively. Two novel techniques were developed in lymphoma subtypes which best suited the technological platform. Firstly, gene expression-based classifiers were developed for aggressive B-cell lymphoma, using RNA extracted from formalin-fixed, paraffin-embedded (FFPE) biopsy samples. Secondly, multiplexed immunofluorescence and digital image analysis were leveraged to characterise immune checkpoints and the tumour microenvironment (TME) of classical Hodgkin lymphoma (CHL). In the first validation series of patient samples (n=55) a diagnostic molecular classifier was able to classify 92% of tumours correctly as molecular Burkitt lymphoma (mBL) and molecular diffuse large B-cell lymphoma (mDLBCL), with a sensitivity and specificity of 1, using pathological diagnosis as gold standard (15% of tumours classified as ‘molecular intermediate’). A classifier of ‘MYC activity’ predicted clinical outcome following R-CHOP chemotherapy in a small subgroup of patients, with a sensitivity I specificity of 0.771 0.83, using MYC IHC as gold standard.