Oncogenes and cancer associated thrombosis: what can we learn from single cell genomics about risks and mechanisms?
Bibliographic record
Abstract
Single cell analysis of cancer cell transcriptome may shed a completely new light on cancer-associated thrombosis (CAT). CAT causes morbid, and sometimes lethal complications in certain human cancers known to be associated with high risk of venous thromboembolism (VTE), pulmonary embolism (PE) or arterial thromboembolism (ATE), all of which worsen patients' prognosis. How active cancers drive these processes has long evaded scrutiny. While "unspecific" microenvironmental effects and consequences of patient care (e.g., chemotherapy) have been implicated in pathogenesis of CAT, it has also been suggested that oncogenic pathways driven by either genetic (mutations), or epigenetic (methylation) events may influence the coagulant phenotype of cancer cells and stroma, and thereby modulate the VTE/PE risk. Consequently, the spectrum of driver events and their downstream effector mechanisms may, to some extent, explain the heterogeneity of CAT manifestations between cancer types, molecular subtypes, and individual cases, with thrombosis-promoting, or -protective mutations. Understanding this molecular causation is important if rationally designed countermeasures were to be deployed to mitigate the clinical impact of CAT in individual cancer patients. In this regard, multi-omic analysis of human cancers, especially at a single cell level, has brought a new meaning to concepts of cellular heterogeneity, plasticity, and multicellular complexity of the tumour microenvironment, with profound and still relatively unexplored implications for the pathogenesis of CAT. Indeed, cancers may contain molecularly distinct cellular subpopulations, or dynamic epigenetic states associated with different profiles of coagulant activity. In this article we discuss some of the relevant lessons from the single cell "omics" and how they could unlock new potential mechanisms through which cancer driving oncogenic lesions may modulate CAT, with possible consequences for patient stratification, care, and outcomes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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".