Kshivets O. Cancer, Synergetics and Immune Circuit
-
Upload
oleg-kshivets -
Category
Health & Medicine
-
view
5.503 -
download
2
description
Transcript of Kshivets O. Cancer, Synergetics and Immune Circuit
Start of Phase Transition of Early Lung Cancer into Invasive Cancer Significantly Depended on Cell Ratio Factors
Oleg Kshivets, MD, PhD
Abstract:Start of phase transition of early lung cancer into invasive cancer significantly depended on cell ratio factors Kshivets Oleg Central City Hospital, Kachkanar, Russia
Background: Significance of immune cell circuit for start of phase transition (PT) of early lung cancer into invasive cancer (LC) was investigated. Material and methods: In trial (1987-2012) consecutive cases after radical surgery (R0, bi/lobectomies=97, N2-lymphadenectomies=97; squamous=38, adenocarcinoma=54, large cell=5; G1=33, G2=40, G3=24), monitored 97 LC patients (LCP) (age=58.3±8.2 years, m=83, f=14); 48 early LCP (T1AN0M0, tumor size=1.7±0.3 cm, 5-year survival=100%), 49 invasive LCP (T1BN0M0, tumor size=2.9±0.1 cm, 5-year survival=77.6%). Variables selected for study were input levels of immunity blood parameters, sex, age, TNMG. The percentage, absolute count and total population number (per human organism) of CD3, CD19, CD4, CD8, CD16, CD1, CDw26, monocytes, CD4+2H, CD8+VV, leukocytes, lymphocytes, monocytes, eosinophils, stick and segmented neutrophils were estimated. Differences between groups were evaluated using discriminant analysis, clustering, structural equation modeling, Monte Carlo, bootstrap simulation and neural networks computing. Results: It was revealed that start of PT early—invasive cancer significantly depended on cell ratio factors (ratio between blood cells subpopulations and cancer cells-CC): segmented neutrophils/CC, stick neutrophils/CC, CD3/CC, CD4+2H/CC, CDw26/CC (P=0.001-0.039). Neural networks computing, genetic algorithm selection and bootstrap simulation revealed relationships of PT early—invasive cancer and segmented neutrophils/CC (rank=1), CD16/CC (rank=2), CD4/CC (3), CD19/CC (4), CD4/CC (5), monocytes/CC (6), CD4+2H/CC (7). Correct detection of start of PT early—invasive cancer was 100% by neural networks computing (error=0.000; urea under ROC curve=1.0). Conclusions: Start of phase transition early—invasive lung cancer significantly depended on cell ratio factors.
Data:• Males………………………………………………….83• Females………..………………………………….......14• Age=58.3±8.2 years• Bi/Lobectomies with N2-Lymphadenectomies……..97• • Early Lung Cancer Patients (T1aN0M0; Tumor
Size=1.7±0.3 cm; 5-year Survival=100%)………….48• Invasive Lung Cancer Patients (T1bN0M0; Tumor
Size=2.9±0.1 cm; 5-year Survival=77.6%)…………49
Staging:• T1a.….48 N0..…..97 G1…………33• T1b…..49 N12……0 G2…………40• M1…….0 G3…………24• Adenocarcinoma………......................................54 • Squamos Cell Carcinoma………………………38 • Large Cell Carcinoma..………………………….5• Early Cancer (T1aN0M0)………………………48 • Invasive Cancer (T1bN0M0)…………………...49
Survival Rate:• Alive………………………………………....67 (69.1%)• 5-Year Survivors…………..………………..86 (88.7%) • Losses………………………………….…….11 (11.3%)• General Life Span=3983.5±1917.1 days• For 5-Year Survivors=4398.5±1613.5 days• For Losses=739.1±302.2 days
• Cumulative 5-Year Survival………………..88.7%• Cumulative 10-Year Survival………………82.7%
Immune Testing:
Factors of Immune Cell Circuit in Recognition of Phase
Transition Early—Invasive Lung Cancer (n=97)
General Lung Cancer Patients (T1abN0N0) Survival after Complete Bi/Lobectomies (Kaplan-Meier) (n=97)
Results of Univariate Analysis of Phase Transition Early—Invasive Cancer in Prediction of Lung Cancer Patients Survival (n=97)
Results of Discriminant Fanction Analysis in Recognition
of Phase Transition Early—Invasive Lung Cancer (n=97)
Results of Multi-Factor Clustering of Immune and Blood Cell Factors in Recognition of Phase Transition Early—Invasive Lung Cancer (n=97)
Results of Multi-Factor Clustering in Recognition of Phase Transition Early—Invasive Lung Cancer (n=97)
Logic Formulas Phase Transition Early—Invasive Cancer. Early Lung Cancer 0.73 <= Leucocytes/Cancer Cells (7.4%) <= 48.59 & 0.51 <= Segmented Neutrophils/Cancer Cells (6.7%) <= 34.02 & 0.20 <= Lymphocytes/Cancer Cells (7.4%) <= 13.49 & 0.06 <= T-Cells/Cancer Cells (6.7%) <= 9.41 & 0.08 <= CD4+Cells/Cancer ells (6.1%) <= 7.53 Objects=48 Error1=0.00 (0) Error2=0.00 (0) ────────────────────────────────────────────── Phase Transition Early—Invasive Cancer. Invasive Lung Cancer 0.15 <= Leucocytes/Cancer Cells ( 7.4%) <= 0.74 & 0.07 <= Segmented Neutrophils/Cancer Cells (6.7%) <= 0.53 & 0.03 <= Lymphocytes/Cancer Cells (7.4%) <= 0.25 & 0.01 <= T-Cells/Cancer Cells (6.7%) <= 0.14 & 0.01 <= CD4+Cells/Cancer Cells (6.1%) <= 0.13 Objects=49 Error1=0.00 (0) Error2=0.02 (1) ──────────────────────────────────────────────
Results of Neural Networks Computing in Recognition of Phase Transition Early—Invasive Lung Cancer (n=97)
Error=0.000; Area under ROC Curve=1.00; Correct Classification Rate=100%
Factors: Rank Sensitivity
Segmented Neutrophils/Cancer Cells 1 32102.8
CD16+/Cancer Cells 2 15818.1
CD8+/Cancer Cells 3 1899.04
CD19+/Cancer Cells 4 921.551
CD4+/Cancer Cells 5 883.075
Monocytes/Cancer Cells 6 389.121
CD4+2H+/Cancer Cells 7 249.443
Results of Bootstrap Simulation in Recognition of Phase
Transition Early—Invasive Lung Cancer (n=97)
Number of Samples=3333Significant Factors: Rank Kendall Tau-A P<
Lymphocytes/Cancer Cells 1 -0.505 0.000Segmented Neutrophils/Cancer Cells 2 -0.505 0.000Leucocytes/Cancer Cells 3 -0.505 0.000CD3+/Cancer Cells 4 -0.500 0.000CD4+/Cancer Cells 5 -0.500 0.000CD4+2H+/Cancer Cells 6 -0.497 0.000CD19+/Cancer Cells 7 -0.486 0.000CD16+/Cancer Cells 8 -0.471 0.000CD8+VV+/Cancer Cells 9 -0.467 0.000CD8+/Cancer Cells 10 -0.462 0.000Monocytes/Cancer Cells 11 -0.433 0.000CD1+/Cancer Cells 12 -0.421 0.000CDw26+/Cancer Cells 13 -0.385 0.000Eosinophils/Cancer Cells 14 -0.311 0.000Stick Neutrophils/Cancer Cells 15 -0.180 0.010CD8+VV+ tot 16 -0.164 0.017
Holling-Tenner Models of Lung Cancer Cell Population and Cytotoxic Cell Population Dynamics
0 5 1015202530
Killer-Cell Population
11.251.51.7522.252.52.75Cancer Cell Population
00
0.10.1
0.20.2
0.30.3
0.40.40.50.50.60.6
0.70.7
0.80.8
0.90.9
11
Phas
e Tra
nsiti
on
Phas
e Tra
nsiti
on
P=0.0000z=a+bx+cx 2̂+GCUMY(d,e,f)
r 2̂=1 DF Adj r 2̂=1 FitStdErr=8.4354944e-12 Fstat=6.8151355e+22a=1 b=5.7675827e-16 c=-1.8688846e-17
d=-1 e=2.2287067 f=-0.034448505
252015105B-Cell Population 1 1.251.51.752
2.252.52.75
Cancer Cells0 00.1 0.1
0.2 0.20.3 0.30.4 0.40.5 0.50.6 0.60.7 0.70.8 0.80.9 0.91 1
Phas
e Tra
nsiti
on
Phas
e Tra
nsiti
on
P=0.0000z=EXTRVALX(a,b,c)*EXTRVALY(1,d,e)
r 2̂=0.99790554 DF Adj r 2̂=0.99779046 FitStdErr=0.023494978 Fstat=10958.344a=1.2548733 b=2.4678053 c=7.9389194
d=2.7277315 e=0.3674437
Results of Kohonen Self-Organizing Neural Networks Computing in Simulation in Recognition of Phase
Transition Early—Invasive Lung Cancer (n=97)
Lung Cancer Dynamics
Results of Structurul Equation Modeling in Recognition of Phase Transition
Early—Invasive Lung Cancer (n=97)
Conclusions:• Start of phase transition early—
invasive lung cancer significantly depended on cell ratio factors (ratio between blood cells subpopulations and cancer cells).
Address:Oleg Kshivets, M.D., Ph.D.Consultant Thoracic, Abdominal, General Surgeon & Surgical Oncologist
• e-mail: [email protected] • skype: okshivets • http: //www.ctsnet.org/home/okshivets