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Abstract LB-499: Malignancy-sustaining characteristics of cancer-associated plasma: role of systemic cytokines in maintaining tumor latency

2012· article· en· W2083572039 on OpenAlexaff
Julia Kirshner, Kyle Beamis, Zhifang Zhang, Mary M. Zheng, Linda M. Pilarski, John E. Shively

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineCytokineMalignancyMultiple myelomaCancerInternal medicineDiseaseOncologyImmunology

Abstract

fetched live from OpenAlex

Abstract BACKGROUND: While most studies investigate the biology of the active disease, the focus of our study was on the state of clinical remission, during which patients are considered healthy and tumor free. We demonstrate that the ‘disease-promoting latency’ factor(s) are sustained post therapy and likely contribute to tumor re-growth and drug-resistance that accompanies the relapse. We propose that therapies aimed at shrinking the tumor do not restore the homeostasis within the tissue; thus, tumor-associated stroma remains post therapy sustaining the production of pro-tumorigenic factors that ultimately induce a relapse. Multiple myeloma (MM) is an incurable bone marrow malignancy of B cell lineage that accounts for 20% of deaths from hematologic malignancies. Despite the development of potent new regimens, nearly all MM patients relapse and become refractory to treatment; thus the median survival rate remains 3-5 years. The goal of this study was to determine whether the balance of the systemic cytokines returns to normal during complete remission and whether we can identify a cytokine signature specific for various phases of the disease. METHODS: Utilizing multiplex technology of Luminex we measured the plasma levels of 25 cytokines in normal donors (n=177) and MM patients (n=54) either at diagnosis/relapse, treatment, or remission phases of MM. The cytokine levels were compared between normal donors and MM patients as well as between various phases of the MM, and discriminant analysis was used to create predictive classification of disease phase based on the levels of differentially expressed cytokines. RESULTS: Based on fold-change analysis, we identified 15 cytokines that were differentially expressed between normal donors and MM patients, whose profile was heavily skewed toward a pro-tumorigenic Th2 response. Moreover, chemokines (IL-8, eotaxin, MCP1) were also upregulated in plasma of patients. Significantly, the expression of cytokines in patients in clinical remission was maintained at the same levels as in patients with active disease and our predictive model placed all patients in remission in the same category as those at diagnosis/relapse. CONCLUSIONS: Our study demonstrates that remission is not a state of health, but tumor latency, and that once an organism has cancer, the system is primed for relapse and emergence of secondary neoplasms. Thus, the cytokines maintained in circulation will induce proliferation and confer drug resistance of the minimal residual disease, and chemokines will stimulate systemic dissemination of tumor cells leading to the colonization of secondary sites. We believe that such systemic alteration of tissue homeostasis is consistent for all cancers; thus our findings suggest the need to shift the paradigm of clinical intervention to include homeostasis-stabilizing interventions. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-499. doi:1538-7445.AM2012-LB-499

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.058
GPT teacher head0.392
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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