Tumor-Independent Host Secretomes Induced By Angiogenesis and Immune-Checkpoint Inhibitors
Bibliographic record
Abstract
Abstract The levels of various circulating blood proteins can change in response to cancer therapy. Monitoring therapy-induced secretomes (TIS) may have use as biomarkers for establishing optimal biological effect (such as dosing) or identifying sources of toxicity and drug resistance. Although TIS can derive from tumor cells directly, nontumor “host” treatment responses can also impact systemic secretory programs. For targeted inhibitors of the tumor microenvironment, including antiangiogenic and immune-checkpoint therapies, host TIS could explain unexpected collateral “side effects” of treatment. Here, we describe a comparative transcriptomic and proteomic analysis of host TIS in tissues and plasma from cancer-free mice treated with antibody and receptor tyrosine kinase inhibitors (RTKI) of the VEGF, cMet/ALK, and PD-1 pathways. We found that all cancer therapies elicit TIS independent of tumor growth, with systemic secretory gene change intensity higher in RTKIs compared with antibodies. Our results show that host TIS signatures differ between drug target, drug class, and dose. Notably, protein and gene host TIS signatures were not always predictive for each other, suggesting limitations to transcriptomic-only approaches to clinical biomarker development for circulating proteins. Together, these are the first studies to assess and compare “off-target” host secretory effects of VEGF and PD-1 pathway inhibition that occur independent of tumor stage or tumor response to therapy. Testing treatment impact on normal tissues to establish host-mediated TIS signatures (or “therasomes”) may be important for identifying disease agnostic biomarkers to predict benefits (or limitations) of drug combinatory approaches. Mol Cancer Ther; 17(7); 1602–12. ©2018 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".