Concepts and mechanisms underlying chemotherapy induced immunogenic cell death: impact on clinical studies and considerations for combined therapies
Bibliographic record
Abstract
// Simon Gebremeskel 1,4 and Brent Johnston 1,2,3,4 1 Department of Microbiology & Immunology, Dalhousie University, Halifax, Nova Scotia, Canada 2 Department of Pediatrics, Dalhousie University, Halifax, Nova Scotia, Canada 3 Department of Pathology, Dalhousie University, Halifax, Nova Scotia, Canada 4 Beatrice Hunter Cancer Research Institute, Halifax, Nova Scotia, Canada Correspondence to: Brent Johnston, email: // Keywords : cancer therapy, immunostimulation, immunogenic cell death, immunotherapy, chemotherapy Received : September 03, 2015 Accepted : September 22, 2015 Published : October 14, 2015 Abstract Chemotherapy has historically been thought to induce cancer cell death in an immunogenically silent manner. However, recent studies have demonstrated that therapeutic outcomes with specific chemotherapeutic agents (e.g. anthracyclines) correlate strongly with their ability to induce a process of immunogenic cell death (ICD) in cancer cells. This process generates a series of signals that stimulate the immune system to recognize and clear tumor cells. Extensive studies have revealed that chemotherapy-induced ICD occurs via the exposure/release of calreticulin (CALR), ATP, chemokine (C–X–C motif) ligand 10 (CXCL10) and high mobility group box 1 (HMGB1). This review provides an in-depth look into the concepts and mechanisms underlying CALR exposure, activation of the Toll-like receptor 3/IFN/CXCL10 axis, and the release of ATP and HMGB1 from dying cancer cells. Factors that influence the impact of ICD in clinical studies and the design of therapies combining chemotherapy with immunotherapy are also discussed.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".