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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

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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).

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Factors: <ul><li>1) Antropometric Factors…………… 4 </li></ul><ul><li>2) Immune Testing...……………….. 54 </li></ul><ul><li>3) Blood Analysis…………………… 26 </li></ul><ul><li>4) Hemostasis Factors……………….. 8 </li></ul><ul><li>5) Cell Ratio Factors……………….. 18 </li></ul><ul><li>6) Lung Cancer Characteristics……. 9 </li></ul><ul><li>7) Biochemic Factors………………... 5 </li></ul><ul><li>8) Treatment Characteristics……….. 5 </li></ul><ul><li>9) Survival Data……………………… 4 </li></ul><ul><li>In All………………………………. 133 </li></ul>

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Main Problem of Analysis of Alive Supersystems (e.g. Lung Cancer Patient Homeostasis): Phenomenon of «Combinatorial Explosion» <ul><li>Number of Clinicomorphological Factors:……...….. 133 </li></ul><ul><li>Number of Possible Combination for Random Search:……………..……………… n!=133!=1.487e+226 </li></ul><ul><li>Operation Time of IBM Blue Gene/L Supercomputer (135.5TFLOPS) ………………………… 4.7e+218 Years </li></ul><ul><li>The Age of Our Universe………..... 1.3e+10 Years </li></ul>

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Basis: <ul><li>NP  RP  P </li></ul><ul><li>   </li></ul><ul><li>n!  n*n*2(e+n) or n log n  n </li></ul><ul><li>   </li></ul><ul><li>AI  CSA+S+B  SM </li></ul>

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Samplings: <ul><li>Lung Cancer Patients Lived More than 5 Years after Radical Procedures………. 59 </li></ul><ul><li>Lung Cancer Patients Died Because Generalization During First 5 Years After Radical Procedures……………... 49 </li></ul><ul><li>In All…………………………………...108 </li></ul><ul><li>5-Year Survival…………………….54.6% </li></ul>

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Complete Pulmonary Resections: <ul><li>Pneumonectomy………………….. 45 </li></ul><ul><li>Upper/Lower Bilobectomy……...… 7 </li></ul><ul><li>Upper Lobectomy……………….... 44 </li></ul><ul><li>Lower Lobectomy……………….... 11 </li></ul><ul><li>Middle Lobectomy…………………. 1 </li></ul><ul><li>In All…………………………...…108 </li></ul>

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Staging: <ul><li>T1……38 N0……63 G1……30 </li></ul><ul><li>T2……43 N1……20 G2……34 </li></ul><ul><li>T3……23 N2……25 G3……44 </li></ul><ul><li>T4……..4 Stage II…34 Stage III...74 </li></ul><ul><li>Squamo u s Cell Carcinoma……… .. ...…56 </li></ul><ul><li>Adenocarcinoma……………………….46 </li></ul><ul><li>Large Cell Carcinoma…………………..6 </li></ul>

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Immune Testing:

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

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Results of Multivariate Proportional Hazard Cox Regression Analysis: Chi2=127.643; df=22; n=108; P=0.000000 <ul><li>Factors Wald df P Exp(B) 95%CI for Exp(B) Lower Upper </li></ul><ul><li>CD8+VV+% 46.050 1 0.000 1.124 1.087 1.162 </li></ul><ul><li>CD8+VV+abs 32.925 1 0.000 0.004 0.001 0.025 </li></ul><ul><li>CD1+abs 10.199 1 0.001 0.000 0.000 0.000 </li></ul><ul><li>CD8+% 5.552 1 0.018 0.966 0.939 0.994 </li></ul><ul><li>CD8+abs 11.101 1 0.001 11.521 2.736 48.518 </li></ul><ul><li>B+% 20.465 1 0.000 0.924 0.892 0.956 </li></ul><ul><li>CD16+abs 11.348 1 0.001 1.2e+5 133.00 1.1e+8 </li></ul><ul><li>NSTs 18.574 1 0.000 1.094 1.050 1.139 </li></ul><ul><li>St.Neutrophils abs 28.981 1 0.000 1.6e+4 487.83 5.8e+5 </li></ul><ul><li>Monocytes abs 4.316 1 0.038 3.6e+3 1.590 8.3e+6 </li></ul><ul><li>Lymphocytes% 22.636 1 0.000 1.086 1.050 1.124 </li></ul><ul><li>CIC 21.396 1 0.000 0.963 0.948 0.979 </li></ul>

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Results of Multivariate Proportional Hazard Cox Regression Analysis: Chi2=127.643; df=22; n=108; P=0.000000 <ul><li>Factors Wald df P Exp(B) 95%CI for Exp(B) Lower Upper </li></ul><ul><li>CD16+tot 10.521 1 0.001 0.119 0.033 0.431 </li></ul><ul><li>CD4+2H+tot 30.228 1 0.000 1.745 1.431 2.128 </li></ul><ul><li>CD1+tot 7.721 1 0.005 76.662 3.592 1.6e+3 </li></ul><ul><li>Monocytes tot 5.645 1 0.018 0.159 0.035 0.725 </li></ul><ul><li>T1-4 13.012 3 0.005 </li></ul><ul><li>T1-4(1) 0.151 1 0.697 1.334 0.312 5.699 </li></ul><ul><li>T1-4(2) 0.484 1 0.487 0.616 0.158 2.409 </li></ul><ul><li>T1-4(3) 2.158 1 0.142 0.329 0.075 1.450 </li></ul><ul><li>N0-2 27.425 2 0.000 </li></ul><ul><li>N0-2(1) 7.972 1 0.005 0.344 0.164 0.722 </li></ul><ul><li>CD4+/Cancer Cells 14.547 1 0.000 0.196 0.085 0.453 </li></ul>

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Results of Discriminant Analysis in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=108) <ul><li>Discriminant Function Analysis Summary </li></ul><ul><li>Wilks' Lambda: 0.230 approx. F (60,47)=2.619 p< 0.0004 </li></ul><ul><li>Wilks' Partial F-remove P-level </li></ul><ul><li>Lambda Lambda (1,421) </li></ul><ul><li>N0-2 .288494 .798046 11.89384 .001199 </li></ul><ul><li>CD8+VV+% .272237 .845700 8.57524 .005240 </li></ul><ul><li>B+% .254813 .903530 5.01819 .029850 </li></ul><ul><li>CIC .249275 .923603 3.88769 .054543 </li></ul><ul><li>CD4+2H+% .246179 .935218 3.25564 .077588 </li></ul><ul><li>CD8+VV+tot .245170 .939066 3.04971 .087285 </li></ul><ul><li>NST2 .242024 .951274 2.40743 .127469 </li></ul><ul><li>CDw26+abs .240159 .958661 2.02673 .161158 </li></ul><ul><li>PHN .240297 .958112 2.05478 .158348 </li></ul><ul><li>Seg.Neutrophils% .239719 .960422 1.93680 .170568 </li></ul><ul><li>Lymphocytes% .239040 .963149 1.79829 .186365 </li></ul><ul><li>NA .238421 .965648 1.67198 .202313 </li></ul><ul><li>CD8+% .237814 .968113 1.54806 .219592 </li></ul><ul><li>PFT .238148 .966758 1.61608 .209895 </li></ul><ul><li>ISF .237157 .970797 1.41385 .240391 </li></ul><ul><li>CDw26+/CC .236674 .972780 1.31515 .257268 </li></ul><ul><li>CD4+/CC .237153 .970813 1.41305 .240522 </li></ul><ul><li>B+/CC .230458 .999018 0.04621 .830731 </li></ul>

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Results of Logistic Regression Analysis in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108); Chi2=88.714; df=19; P=0.00000; Odds ratio=64.800 <ul><li> Est. S.E. Wald P Odds 95.0% .Od.Ratio Ratio Lower Upper </li></ul><ul><li>Const.B 5.619 3.193 3.095 .0785 275.578 0.483 1.5e+5 </li></ul><ul><li>T1-4 -0.321 0.623 0.265 0.6067 0.725 0.210 2.504 </li></ul><ul><li>CD8+VV+% -0.216 0.077 7.814 0.0052 0.806 0.691 0.940 </li></ul><ul><li>CD8+VV+abs 8.332 3.987 4.367 0.0367 4152.486 1.504 1.1e+7 </li></ul><ul><li>CD1+abs 46.682 22.224 4.412 0.0357 </li></ul><ul><li>CD8+% 0.141 0.056 6.345 0.0118 1.151 1.030 1.287 </li></ul><ul><li>CD4+abs -0.771 2.419 0.102 0.7499 0.462 0.004 56.624 </li></ul><ul><li>B+% 0.257 0.086 8.983 0.0027 1.294 1.091 1.534 </li></ul><ul><li>CD16+abs -22.709 10.715 4.491 0.0341 0.000 0.000 0.243 </li></ul><ul><li>NSTs -0.053 0.072 0.529 0.4672 0.949 0.822 1.095 </li></ul><ul><li>St.Neutroph abs -12.805 6.249 4.200 0.0404 0.000 0.000 0.679 </li></ul><ul><li>Monocytes abs -8.340 14.650 0.320 0.5700 0.000 0.000 1.0e+9 </li></ul><ul><li>Lymphocytes abs-2.866 1.491 3.694 0.0546 0.057 0.003 1.102 </li></ul><ul><li>CIC 0.031 0.028 1.264 0.2610 1.032 0.129 14.327 </li></ul><ul><li>CD4+/CC 0.309 1.184 0.068 0.7942 1.036 0.129 14.327 </li></ul><ul><li>CD16+tot 4.908 2.106 5.429 0.0198 135.315 2.059 8894.403 </li></ul><ul><li>CD4+2H+tot -0.617 0.359 2.944 0.0862 0.540 0.264 1.102 </li></ul><ul><li>CD1+tot -9.299 4.555 4.167 0.0412 0.000 0.000 0.782 </li></ul><ul><li>Monocytes tot 1.733 2.902 0.357 0.5503 5.658 0.018 1807.255 </li></ul><ul><li>N0-2 -1.425 0.520 7.521 0.0061 0.241 0.086 0.675 </li></ul>

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

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

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Neural Networks in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108) <ul><li>Losses 5-year survivors Baseline Errors=0.001074; </li></ul><ul><li>Total 49 59 Area under ROC curve=1 .00; </li></ul><ul><li>Correct 49 59 Correct Classification Rate= 100% </li></ul><ul><li>Wrong 0 0 </li></ul><ul><li>Genetic Algorithm Selection </li></ul><ul><li>Useful for Lymphocytes/CC B/CC CD16/CC CD4+2H+/CC CD8+VV+/CC CD8/CC </li></ul><ul><li>Survival Yes Yes Yes Yes Yes Yes </li></ul>

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Results of Neural Networks Computing in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108) Error=0.001074; Area under ROC Curve=1.0; Correct Classification Rate=100% <ul><li>Factor Rank Error Ratio </li></ul><ul><li>CD8+VV+% 1 0.183 169.33 </li></ul><ul><li>N0-2 2 0.129 120.30 </li></ul><ul><li>B (CD19+)% 3 0.128 118.93 </li></ul><ul><li>CD4+2H+abs 4 0.122 113.60 </li></ul><ul><li>NA 5 0.104 97.261 </li></ul><ul><li>LC Cells 6 0.095 88.299 </li></ul><ul><li>Thymus FI 7 0.093 86.738 </li></ul><ul><li>Protein 8 0.084 78.483 </li></ul><ul><li>Monocytes/CC 9 0.081 75.471 </li></ul><ul><li>CDw26+tot 10 0.079 73.741 </li></ul><ul><li>Heparin Tol. 11 0.063 58.667 </li></ul><ul><li>Gender 12 0.053 49.388 </li></ul><ul><li>LC Growth 13 0.051 47.182 </li></ul><ul><li>CDw26+/CC 14 0.036 33.921 </li></ul><ul><li>Monocytes abs 15 0.036 33.883 </li></ul><ul><li>Factor Rank Error Ratio </li></ul><ul><li>Hb 16 0.031 29.625 </li></ul><ul><li>PI 17 0.031 29.098 </li></ul><ul><li>CIC 18 0.026 24.520 </li></ul><ul><li>G1-3 19 0.023 21.495 </li></ul><ul><li>D 20 0.018 16.520 </li></ul><ul><li>Weight 21 0.012 11.467 </li></ul><ul><li>T1-4 22 0.011 10.427 </li></ul><ul><li>Lymphocytes%23 0.008 7.385 </li></ul><ul><li>Recalcif.Time 24 0.008 7.348 </li></ul><ul><li>Eosinophils % 25 0.007 6.312 </li></ul><ul><li>Erythrocytes 26 0.006 5.510 </li></ul><ul><li>Fibrinogen-B 27 0.004 4.040 </li></ul><ul><li>Coagul.Time 28 0.004 3.903 </li></ul><ul><li>IgM 29 0.004 3.318 </li></ul><ul><li>Eosinophils/CC30 0.003 2.991 </li></ul>

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

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

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

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Results of Bootstrap Simulation in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108) <ul><li>Number of Samples=3333 </li></ul><ul><li>Significant Factors Rank Kendall’s Tau-A P< </li></ul><ul><li>CD8+VV+ % 1 -0.239 0.000 </li></ul><ul><li>C D8+VV+ abs 2 -0.239 0.000 </li></ul><ul><li>CD8+VV+tot 3 -0.219 0.000 </li></ul><ul><li>B % 4 0.216 0.001 </li></ul><ul><li>CD8+ % 5 0.199 0.01 </li></ul><ul><li>CD1+ abs 6 -0.191 0.01 </li></ul><ul><li>CD1+ % 7 -0.183 0.01 </li></ul><ul><li>N0-2 8 -0.179 0.01 </li></ul><ul><li>Erythrocytes 9 0.174 0.01 </li></ul><ul><li>CD1+ tot 10 -0.172 0.01 </li></ul><ul><li>CD8+VV+/CC 11 -0.163 0.05 </li></ul><ul><li>CD1+/CC 12 -0.153 0.05 </li></ul><ul><li>CD8+ abs 13 0.152 0.05 </li></ul><ul><li>CD4+ abs 14 -0.148 0.05 </li></ul><ul><li>CD4+ % 15 -0.146 0.05 </li></ul><ul><li>Heparin Tolerance 16 -0.145 0.05 </li></ul><ul><li>CD8+/CC 17 0.144 0.05 </li></ul><ul><li>CD8+ tot 18 0.143 0.05 </li></ul><ul><li>CD16+ abs 19 -0.142 0.05 </li></ul><ul><li>CD4+2H+ % 20 -0.140 0.05 </li></ul><ul><li>CD4+2H+ abs 21 -0.140 0.05 </li></ul><ul><li>CD4+2H+ tot 22 -0.135 0.05 </li></ul><ul><li>Hb 23 0.135 0.05 </li></ul>

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

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

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

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Prediction of Lung Cancer Patients Survival after Complete Resections (n=108) <ul><li>Classification of Cases by Logistic Regression, n=108 </li></ul><ul><li>(5-Year Survivors--Losses) Odds Ratio=68.8 </li></ul><ul><li>Observed Pred.Losses Pred.Survivors Correct </li></ul><ul><li>Losses 42 7 85.7% </li></ul><ul><li>5-Year Survivors 5 5 4 91.5% </li></ul><ul><li>Total 96 12 88 .9% </li></ul><ul><li>Classification of Cases by Discriminant Analysis, n=108 </li></ul><ul><li>(5-Year Survivors--Losses) </li></ul><ul><li>Observed Pred.Losses Pred.Survivors Correct </li></ul><ul><li>Losses 46 3 93.9% </li></ul><ul><li>5-Year Survivors 2 57 96.6% </li></ul><ul><li>Total 103 5 95 .4% </li></ul><ul><li>Classification of Cases by Neural Networks, n=108 </li></ul><ul><li>(5-Year Survivors--Losses) </li></ul><ul><li>Observed Pred.Losses Pred.Survivors Correct </li></ul><ul><li>Losses 49 0 100% </li></ul><ul><li>5-Year Survivors 0 59 100% </li></ul><ul><li>Total 49 59 100 % </li></ul>

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

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

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

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Lung Cancer Dynamics

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Conclusions: <ul><li>5-year survival of lung cancer patients after complete resections significantly depended on: </li></ul><ul><li>1) level of T-, B- and K-cell circuit; </li></ul><ul><li>2) value of monocyte- and macrophage-circuit; </li></ul><ul><li>3) level of humoral immunity; </li></ul><ul><li>4) ratio of malignant cell population quantity to immunity cell subpopulations in integral patient’s organism (immune cell ratio factors); </li></ul><ul><li>5) cancer characteristics; </li></ul><ul><li>6) blood cell circuit; </li></ul><ul><li>7) hemostasis system; </li></ul><ul><li>8) biochemic homeostasis; </li></ul><ul><li>9) anthropometric data. </li></ul>

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<ul><li>Oleg Kshivets, M.D., Ph.D. Thoracic Surgeon, Department of Surgery </li></ul><ul><li>Siauliai Cancer Center, Tilzes:42-16, LT5400 Siauliai, Lithuania </li></ul><ul><li>Tel. (37041)416614; Fax 1(270)9687098 </li></ul><ul><li>[email_address] http//:myprofile.cos.com/Kshivets </li></ul>