Bibliographic record
Abstract
Lymphoid cancers represent a heterogeneous group of neoplasms composed of malignant lymphoid cells with variable infiltration by non-neoplastic, mostly immune cells (tumor microenvironment). For some subtypes of lymphoid cancers, the contribution of the microenvironment to the histological appearance is widely recognized and used for pathological classification. Although microenvironment-related biology in lymphoid cancers has been primarily explored in a core set of lymphoma subtypes, the number of entities studied has recently accelerated. The pathogenic evolution of tumor microenvironments and in particular their composition and spatial distribution can be perceived as a complex function of 1) genetic alterations within the malignant cell population, 2) the extent and dependence on the molecular crosstalk involving cyto- and chemokines, and 3) host-specific factors. As a result, three major patterns of microenvironmental architecture can be distinguished termed "Re-education", "Recruitment" and "Effacement". Hodgkin lymphoma can serve as a paradigm for an extensive crosstalk between tumor cells and a quantitatively dominant tumor microenvironment. Importantly, related prognostic implications of tumor microenvironment composition (e.g. macrophage content, representation of T cell subsets) have been extensively studied in this disease. With focus on B cell lymphomas, this talk will highlight the emerging literature about genetic alterations in malignant lymphoma cells that provide the foundation for somatically acquired immune privilege and evasion from immune surveillance. The genomic changes discussed in this talk can be broadly categorized according to the effect that they exert on the tumor microenvironment: 1) Loss or down-regulation of (surface) molecules leading to decreased immunogenicity of tumor cells (e.g. mutations of B2M, CIITA); 2) Increased expression of surface molecules suppressing immune cell function (e.g. structural genomic changes of PDL1, PDL2); 3) Recruitment or induction of a regulatory cellular milieu (e.g. mutations in JAK-STAT and NFκB signaling pathways). The discovery of gene mutations underlying immune privilege, properties of the altered molecules, downstream functional consequences and clinical rationales for therapeutic intervention will be presented in the context of specific lymphoma subtypes. It will be discussed how precise description of genomic and molecular alterations underlying immune privilege might accelerate effective targeting of microenvironment-related biology in the clinical setting. Moreover, the development and clinical implementation of predictive biomarkers will be outlined that harbor the potential to inform on companion diagnostic approaches to accompany therapies such as immunological checkpoint inhibition. Disclosures No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".