Background: Significance of blood immune cell circuit for start of phase transition (PT) “norm---early lung cancer” (LC) was investigated.

Material and methods: In trial (1987-2012) consecutive cases after radical surgery (R0, bi/lobectomies=48, N2-lymphadenectomies=48; squamous=21, adenocarcinoma=25, large cell=2; G1=16, G2=21, G3=11), monitored 48 early LC patients (LCP) (age=59±6.5 years, m=40, f=8; T1AN0M0=48, tumor size=1.7±0.3 cm, 5-year survival=100%) and 120 healthy donors (HD) (m=69, f=51) were reviewed. Variables selected for study were input levels of immunity blood parameters. The percentage, absolute count and total population number (per human organism) of T, B, CD4, CD8, CD16, CD1, CDw26, monocytes, CD4+2H, CD8+VV, leukocytes, lymphocytes, monocytes, eosinophils, stick and segmented neutrophils were estimated. The laboratory blood studies also included input levels of NST (tests of oxygen dependent metabolism of neutrophils spontaneous and stimulated by Staphylococcus aureus or by Streptococcus pyogenes), index of stimulation of leukocytes by Staphylococcus aureus or Streptococcus pyogenes, index of thymus function, phagocytic number, phagocyte index, index of complete phagocytosis. 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 “norm---early lung cancer” significantly depended on T-, B-, CD16-, CD8- cell circuit, neutrophils, monocyte circuit (P=0.000-0.027). Neural networks computing, genetic algorithm selection and bootstrap simulation revealed relationships of PT “norm---early lung cancer” and neutrophils (rank=1), CD16 (rank=2), monocytes (3), lymphocytes (4), CD4 (5), CD8 (6), B-cells (7), T-cells (8), eosinophils (9). Correct detection of start of PT “norm---early lung cancer” was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).

START OF PHASE TRANSITION “NORM---EARLY LUNG CANCER” SIGNIFICANTLY DEPENDED ON BLOOD IMMUNE CELL CIRCUIT

KSHIVETS OLEG SURGERY DEPARTMENT, KALUGA CANCER CENTER, KALUGA, RUSSIA

Conclusions: Start of phase transition “norm---early lung cancer” significantly depended on blood immune cell circuit.

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Significant Factors:Norm, n=120---Early Lung Cancer, n=48

t-value p

T-lymphocytes (%) 2.27612 0.024116

CD4+2H+ (%) 2.51792 0.012752

CDw26+ (%) 3.51127 0.000574

CDw26+ (abs) 3.16388 0.001852

CD1+ (%) 3.19223 0.001689

CD1+ (abs) 2.10134 0.037121

CD8+ (abs) -2.02769 0.044191

B-lymphocytes (abs) -2.51135 0.012983

CD16+ (%) -3.17310 0.001797

CD16+ (abs) -3.36676 0.000945

Index Complete Phagocytosis 3.41913 0.000791

Phagocyte Number 2.29011 0.023273

Phagocyte Index 2.80538 0.005626

Leucocytes (abs) -3.36661 0.000946

Segmented Neutrophils (abs) -3.70224 0.000291

Monocytes (abs) -2.25531 0.025421

Segmented Neutrophils (tot) -4.12532 0.000058

Monocytes (tot) -2.80480 0.005636

B-lymphocytes (tot) -2.74665 0.006685

CD16+ (tot) -3.49627 0.000605

CDw26+ (tot) 3.18346 0.001738

CD8+ (tot) -2.28863 0.023362

Leucocytes (tot) -3.89109 0.000144

Segmented Neutrophils (%) -2.37483 0.018699

Lymphocytes (%) 2.39982 0.017510

Circulating Immune Complexes -3.45602 0.000696

Bootstrap Simulation

Significant Factors:

Norm=120; Early Lung Cancer=48;

Number of Samples=3333

Rank Kendall Tau-A P<

CD16+Cells 1 -0.174 0.001

Segmented Neutrophils 2 -0.159 0.01

Leucocytes 3 -0.142 0.01

CDw26+Cells 4 0.140 0.05

B-Cells 5 -0.102 0.05

Neural Networks Simulation Factors (Correct Classification=100%; error=0.0: area under ROC curve=1.0):

Rank Sensitivity

Segmented Neutrophils 1 336.276

CD16+ Cells 2 319.895

Monocytes 3 221.115

Lymphocytes 4 188.612

CD4+Cells 5 183.271

CD8+Cells 6 173.591

B-Cells

T-Cells

Eosinophils

Kohonen Self-Organizing Neural Networks Computing

7

8

9

155.698

138.340

82.668