Abstract LB-499: Malignancy-sustaining characteristics of cancer-associated plasma: role of systemic cytokines in maintaining tumor latency
Bibliographic record
Abstract
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
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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 | 0.000 | 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) | 0.001 | 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 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".