Immunologic Predictors of the Risc of Generalization in Non-Small Cell Lung Cancer Patients after Comlete Resections

1. Immunologic Predictors of the Risc of Generalization in Non-Small Cell Lung Cancer Patients after Comlete Resections Oleg Kshivets, M.D., Ph.D. Department of Surgery , Siauliai Public Hospital, Lithuania The 15th World Congress of World Society of Cardio-Thoracic Surgeons, Vilnius, Lithuania, 2005

2. Abstract: IMMUNOLOGIC PREDICTORS OF THE RISK OF GENERALIZATION IN NON-SMALL CELL LUNG CANCER PATIENTS AFTER COMPLETE RESECTIONS Oleg Kshivets Department of Surgery, Siauliai Public Hospital, Siauliai, 5400, Lithuania Background : some non-small cell lung cancer (LC) patients (LCP) after complete resections are known to be rapidly progressive and fatal requiring adjuvant treatment while others are not. We examined the immunologic factors associated with the low- and high-risk of generalization of LC after surgery. Methods : We analyzed data of 108 consecutive LCP radically operated and monitored in 1987-2004 (males – 94, females – 14; pneumonectomy=45, upper/lobectomy=44, lower/lobectomy=11, upper/lower bilobectomy=7, middle lobectomy=1; stage II=34, stage III=74; squamos cell LC=56, adenocarcinoma=46, large cell=6; T1=38, T2=43, T3=23, T4=4; N0=63, N1=20, N2=25; G1=30, G2=34, G3=44). 59 LCP (age=56.7  0.9 years; tumor size: D=4.3  0.3 cm; life span: LS=1903.8  21.0 days) lived more than 5 years without any features of LC progressing. 49 LCP (age=56.6  1.2 years; D=4.6  0.3 cm; LS=542.7  55.2 days) died because of generalization of LC during the first 5 years after radical procedures. Variables selected for 5YS study were input levels of 64 immunity blood parameters, sex, age, TNMG, cell type, D. Survival curves were estimated by the Kaplan-Meier method. Differences in curves between groups of LCP were evaluated using a log-rank test. Multivariate Cox modeling, multi-factor clustering, discriminant analysis, structural equation modeling, Monte Carlo, bootstrap simulation and neural networks computing were used to determine any significant dependence. Results : Cox modeling displayed that 5-year survival of LCP (n=108) after complete resections significantly depended on: N0-2 (P=0.000), T1-4 (P=0.005), lymphocytes (P=0.000), monocytes (P=0.018), CD19 (P=0.000), CD16 (P=0.001), CD4+2H (P=0.000), CD8VV (P=0.000), CD1 (P=0.001), CD8 (P=0.018), CD4 (P=0.001), stick nuclear neutrophils (P=0.000), NST (P=0.000), circular immune complexes (P=0.000). Neural networks computing, genetic algorithm selection and bootstrap simulation revealed relationships between 5-year survival of LCP and CD8VV (rank=1), N0-2 (2), CD19 (3), CD4+2H (4), natural antibodies (5), LC cell population (6), index thymus function index (7), protein (8), ratio of monocytes to LC cell population (9), CDw26 (10), heparin tolerance (11), gender (12), LC growth (13), ratio of CDw26 to LC cell population (14), monocytes (15), hemoglobin (16), prothrombin index (17), circular immune complexes (18), G1-3 (19), D (20), weight (21), T1-4 (22), lymphocytes (23), recalcification time (24), eosinophils (25), erythrocytes (26), fibrinogen-B (27), coagulation time (28), IgM (29), ratio of eosinophils to LC cell population (30). Correct prediction of LCP survival after radical procedures was 88.9% by logistic regression (odds ratio=64.8), 95.4% by discriminant analysis and 100% by neural networks computing (area under ROC curve=1.0; error=0.001).

9. Immune Testing:

10. Cumulative Proportion Lung Cancer Patients Surviving after Complete Resections (Kaplan-Meier) (n=108)

15. Clastering in in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108)

16. Clastering in in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108)

19. Results of Kohonen Self-Organizing Neural Networks Computing in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108)

20. Results of Kohonen Self-Organizing Neural Networks Computing in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108)

21. Results of Kohonen Self-Organizing Neural Networks Computing in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108)

23. Ratio of B+, CD8+VV+ and CD16+Cell and Cancer Cell Populations in Prediction of Lung Cancer Patients Survival after Complete Resections (n= 108 )

24. Ratio of B+, CD8+VV+ and CD16+Cell and Cancer Cell Populations in Prediction of Lung Cancer Patients Survival after Complete Resections (n= 108 )

25. Ratio of B+, CD8+VV+ and CD16+Cell and Cancer Cell Populations in Prediction of Lung Cancer Patients Survival after Complete Resections (n= 108 )

27. Immunity Networks and 5-Year Survival of Lung Cancer Patients, n=108

28. Holling-Tenner Models of Alive Supersystem “Lung Cancer-Cytotoxic Cell Population ”

29. Holling-Tenner Models of Alive Supersystem “Lung Cancer-Cytotoxic Cell Population ”

30. Lung Cancer Dynamics