Hematologic dysfunction in cancer: Mechanisms, effects on antitumor immunity, and roles in disease progression
Bibliographic record
Abstract
With the major advances in cancer immunology and immunotherapy, it is critical to consider that most immune cells are short-lived and need to be continuously replenished from hematopoietic stem and progenitor cells. Hematologic abnormalities are prevalent in cancer patients, and many ground-breaking studies over the past decade provide insights into their underlying cellular and molecular mechanisms. Such studies demonstrate that the dysfunction of hematopoiesis is more than a side-effect of cancer pathology, but an important systemic feature of cancer disease. Here we review these many advances, covering the cancer-associated phenotypes of hematopoietic stem and progenitor cells, the dysfunction of myelopoiesis and erythropoiesis, the importance of extramedullary hematopoiesis in cancer disease, and the developmental origins of tumor associated macrophages. We address the roles of many secreted mediators, signaling pathways, and transcriptional and epigenetic mechanisms that mediate such hematopoietic dysfunction. Furthermore, we discuss the important contribution of the hematopoietic dysfunction to cancer immunosuppression, the possible avenues for therapeutic intervention, and highlight the unanswered questions and directions for future work. Overall, hematopoietic dysfunction is established as an active component of the cancer disease mechanisms and an important target for therapeutic intervention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".