Table 1.

Priority areas for lymphoma discovery and translation, divided into infrastructure and research areas

Priority area	Examples for specific targets
Infrastructure
Model development  Develop disease models, including cell lines, patient-derived xenografts, and genetically engineered mouse and zebrafish models. Reliable models are essential tools for interrogating disease biology as well as experimental therapeutics.	• Establish ≥5 cell lines for each lymphoma subtype and for each common genetic aberration, with characterization by RNA and exome sequencing. • Establish ≥5 in vivo models for each lymphoma subtype and for each common genetic aberration, with characterization by RNA and exome sequencing.
Collaborative biorepositories  Create repositories of biospecimens and disease models to organize, validate, and distribute well-annotated reagents. Broad access expands the impact of specimens and models, whereas collaborative banking allows for adequate numbers to capture disease heterogeneity.	• Establish a central repository of biospecimens, cell lines, and in vivo models with open access. • Establish a central portal for genomic, proteomic, metabolomic, compound sensitivity, and other data from lymphoma cell lines, building on the Cancer Cell Line Encyclopedia (www.CCLE.org) and other repositories.
Advocacy and development  Organize patient advocacy to support research. Advocacy promotes fundraising, sample collection, government lobbying and disease visibility, while aligning research priorities with community goals.	• Establish educational and interactive websites for each lymphoma subtype. • Establish lymphoma advocacy groups through existing organizations (eg, ASH, Leukemia & Lymphoma Society, Lymphoma Research Foundation).
Research
Molecular characterization	• Perform whole genome sequencing, RNA sequencing, and phosphoproteome analysis on ≥500 primary specimens (with paired germ line sequencing) from each common lymphoma subtype and ≥50 from each less common subtype.
Comprehensively catalog genetic, transcriptional, epigenetic, proteomic, and metabolomic alterations across lymphoma subtypes. This characterization will provide the critical foundation to understand disease pathobiology, including intratumoral heterogeneity, and to identify targets for new treatments.
Genetic dependences  Define genetic dependences using genome-wide libraries for knockdown/knockout. Loss-of-function screening can establish novel targets and elucidate lymphoma biology.	• Perform genome-wide screens using Cas9/guide RNA and/or shRNA libraries in all relevant lymphoma cell lines. • Define synthetic lethal interactions that overcome resistance to current therapies or target “undruggable” genetic alterations.
Experimental therapeutics  Identify novel compound activities in lymphoma using cell line and in vivo models. As with genetic screens, compound screening can establish novel targets as well as mechanisms of action.	• Screen existing bioactive libraries against all relevant lymphoma cell lines. • Establish biomarkers for de novo sensitivity and resistance using genomic and other data. • Identify mechanisms of in vivo resistance to therapeutics.
Patient stratification  Develop strategies to identify and target high-risk subsets of patients. Patient stratification can expedite clinical trials by targeting patients with specific biology.	• Establish next-generation prognostic indices that incorporate genomics and other data for individual lymphoma subtypes. • Develop a therapeutic strategy to target MYC in DLBCL. • Develop approaches to predict de novo resistance to BTK inhibitors.
Immune therapies  Turn the power of the immune system against lymphoma. This includes the identification of synergistic combinations of immune therapies, targeted therapies, and chemotherapy.	• Enhance the effectiveness of therapeutic monoclonal antibodies. • Combine therapeutic antibodies and small molecules with agents that block immune checkpoints. • Perform high-throughput screening for synergy between checkpoint inhibitors and small molecule–targeted drugs. • Develop strategies for therapeutic vaccination to eradicate minimal residual disease. • Develop off-the-shelf engineered therapeutic T cells. • Engineered therapeutic T cells that target novel epitopes created by recurrent driver mutations.
Microenvironment  Understand the protumoral crosstalk between neoplastic lymphoma cells and tissue-specific microenvironments.	• Develop strategies to disrupt angiogenesis within lymphomas. • Target critical adhesion molecules to disrupt lymphoma survival signals. • Abrogate the nurturing effects of cytokines and chemokines released by tumor-associated stromal and immune cells.
Normal lymphocyte development  Define the common features and unique traits of specific lymphoid malignancies in comparison with their developmentally related normal lymphoid counterparts.	• Define all molecules necessary to initiate and sustain the germinal center response. • Define all key protein–protein interactions and posttranslational modifications that regulate B-cell receptor signaling. • Define critical survival signals in each T- and NK-cell subset and distinct precursor.
Clinical translation  Develop robust biomarkers that can be translated into the clinical laboratory using platforms suitable to routinely available formalin-fixed, paraffin-embedded biopsy material.	• Perform clinical studies that integrate the mutational landscape and are powered to identify/validate molecular correlates of survival. • Develop transcriptional, epigenetic, and/or metabolomic signatures downstream of genetic aberrations that can be tested using large patient cohorts that received uniform therapy.

Examples of specific targets in each priority area are outlined to guide funding and advocacy. The table is not intended to be comprehensive across all aspects of lymphoma-related research, but instead to serve as a focused catalog of high-priority areas.

DLBCL, diffuse large B-cell lymphoma; NK, natural killer; shRNA, short hairpin RNA.