Molecular and Cellular Characterization of MALT Lymphoma Across Anatomical Sites: Integrative Transcriptomic and Epigenetic Approaches

Already existing and coded tumor biological material and health-related patient data will be retrospectively collected from institutional biobanks and patients' charts or electronic medical records upon receipt of ethical approval. Each patient enrolled in the study will be assigned a unique identification numerical code upon registration in the study. The unique identification code will be used to record health-related data and to label biological samples. The coded biological material will be transferred to the coordinating center at the Institute of Oncology Research (IOR) in Bellinzona. Health-related data will be collected in the electronic case report form (eCRF) (OpenClinica). Data quality will be ensured by query generation.

Annotated baseline features will include the date of diagnosis, date of biopsy, age, gender, Eastern Cooperative Oncology Group Performance Status (ECOG PS), Ann Arbor stage, lactate dehydrogenase (LDH), number and location of extranodal sites, bone marrow involvement and percentage, peripheral blood involvement, number of nodal sites, B symptoms, lymph nodes larger than 7 cm, hemoglobin (Hb), platelets, lymphocytes, beta-2-microglobulin, albumin, infections (hepatitis C virus, Helicobacter pylori, Chlamydophila psittaci, Achromobacter xylosoxidans, Campylobacter jejuni), serum paraprotein presence and type.

Annotated follow-up features included the date of progression to a disease requiring treatment, type of first-line treatment, date of start of the first line treatment, date of progression after first line treatment, date of the second line treatment, type of second line treatment, date of transformation, date of death, cause of death, and date of last follow-up. Mutation analysis, immunoglobulin genes and T cell receptor rearrangement analysis, copy number aberration analysis, structural variant analysis, and deoxyribonucleic acid (DNA) methylation profile will be performed by next-generation sequencing of genomic DNA extracted from the biopsy. Gene expression will be assessed by next-generation sequencing of RNA extracted from the biopsy. Protein expression will be assessed by mass spectrometry of proteins extracted from the biopsy. AI-based computational pathology will be performed on digitized whole-slide images of the biopsy to extract quantitative histologic features.

Single-cell transcriptomic profiling will be used to resolve intratumoral heterogeneity and to define malignant and non-malignant cellular populations. After quality control, normalization, and batch correction, unsupervised clustering will be applied to identify transcriptionally distinct cell states. Malignant B-cell populations will be distinguished from reactive B cells using a combination of copy number inference, immunoglobulin expression patterns, and canonical marker genes. Differential expression and pathway enrichment analyses will be conducted to identify signaling programs associated with anatomical site, immune context, and disease features.

In details: