T93
Bibliographic record
Abstract
Risk of metastasis formation is provided by both tumor cell biological characteristics and the microenvironment features within the primary tumor along with local and systemic conditions for metastatic niche formation. The inflammatory infiltration has been shown to strongly impact on tumor progression (Whiteside, 2013). Dronca et al. (2011) showed that immunosuppressive factors in the tumor microenvironment may impair not only local immune responses but also disturb systemic immunity. Zitvogel et al. anticipate that the comprehension of the mechanisms governing the immunogenicity of cell death will have a profound impact on the design of anticancer therapies.To study the impact of immune system on clinical response to neoadjuvant chemotherapy and metastasis-free survival in breast cancer patients. 350 patients with newly diagnosed invasive breast cancer treated with neoadjuvant chemotherapy (NAC) were enrolled into the study. The procedures were made in accordance with the Helsinki Declaration. Clinical response to chemotherapy, the 5-year metastasis-free survival and all major clinical and morphological parameters were determined. The original method of multidimensional data visualization was applied to present the immune system state as integral entirety in visual image for classification of patients with different risk of metastasis (NovoSpark Corporation, Canada). Copy number aberrations (CNA) of cytokine gene regions in tumor specimens were tested using high-density microarray platform CytoScanTM HD Array (Affymetrix, USA). Cytokine gene polymorphism was analyzed. Subpopulations of lymphocytes and macrophages were determined within the primary tumors by IHC. We found, that favorable clinical immediate response to preoperative chemotherapy was related to the high levels of IL-1beta, TNF-alpha and IL-10 production by peripheral mononuclear cells before the treatment. This correlation was further confirmed by data from the study on association between cytokine gene functional polymorphism and response to NAC. We used NovoSpark Corporation visualization approach allowing the representation the immune system state as integral unit and to discriminate breast cancer patients with high and low risk of haematogeneic metastasis. When estimated before cancer treatment, 95% of breast cancer patients had risk of metastasis. The neoadjuvant chemotherapy and surgical tumor removal reduced the risk of tumor progression to 62–71%. However, in a year after adjuvant chemo- and radiotherapy, the patient group with high risk of metastases increased to 81% again. Thus, the cancer treatment can change the primarily estimated outcome prognosis in breast cancer patients, and the monitoring of immune system is a promising approach to predict the risk of cancer progression or resistance to the therapy. We have found the connection between the profile of intra-tumor inflammatory elements and chemotherapy efficacy.Cytokine gene expression may be influenced by the chromosome anomalies (CNA – Copy Number Aberration) – deletion and amplification – of cytokine gene loci in tumor cells. We found the close relation between the clinical response to NAC and gain of function of IL-10 and CHI3L1 (YKL40) genes. In contrast, loss of TNF-alpha and IL-17 gene function due to corresponding CNA was associated with good response to NAC. Metastasis-free survival of breast cancer patients was shown to be closely related to CNA. The parameters of the activation of systemic and intra-tumoral immune system by growing tumor and its dissemination have to be validated in order to identify the new prognostic markers for the efficiency of the neoadjuvant chemotherapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.656 | 0.541 |
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".