Start of phase transition of early lung cancer into invasive cancer significantly depended on cell ratio factors

1. Start of Phase Transition of Early
Lung Cancer into
Invasive Cancer
Significantly Depended
on Cell Ratio Factors
Oleg Kshivets, MD, PhD

2. 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.

3. 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

4. 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

5. 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%

6. Immune Testing:

7. Factors of Immune Cell Circuit in Recognition of Phase
Transition Early—Invasive Lung Cancer (n=97)

8. General Lung Cancer Patients (T1abN0N0) Survival
after Complete Bi/Lobectomies (Kaplan-Meier) (n=97)

9. Results of Univariate Analysis of Phase Transition
Early—Invasive Cancer in Prediction of Lung Cancer
Patients Survival (n=97)

10. Results of Discriminant Fanction Analysis in Recognition
of Phase Transition Early—Invasive Lung Cancer (n=97)

11. Results of Multi-Factor Clustering of
Immune and Blood Cell Factors in
Recognition of Phase Transition
Early—Invasive Lung Cancer (n=97)

12. 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)
──────────────────────────────────────────────

13. 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

14. Results of Bootstrap Simulation in Recognition of Phase
Transition Early—Invasive Lung Cancer (n=97)
Number of Samples=3333
Significant Factors: Rank Kendall Tau-A P<
Lymphocytes/Cancer Cells 1 -0.505 0.000
Segmented Neutrophils/Cancer Cells 2 -0.505 0.000
Leucocytes/Cancer Cells 3 -0.505 0.000
CD3+/Cancer Cells 4 -0.500 0.000
CD4+/Cancer Cells 5 -0.500 0.000
CD4+2H+/Cancer Cells 6 -0.497 0.000
CD19+/Cancer Cells 7 -0.486 0.000
CD16+/Cancer Cells 8 -0.471 0.000
CD8+VV+/Cancer Cells 9 -0.467 0.000
CD8+/Cancer Cells 10 -0.462 0.000
Monocytes/Cancer Cells 11 -0.433 0.000
CD1+/Cancer Cells 12 -0.421 0.000
CDw26+/Cancer Cells 13 -0.385 0.000
Eosinophils/Cancer Cells 14 -0.311 0.000
Stick Neutrophils/Cancer Cells 15 -0.180 0.010
CD8+VV+ tot 16 -0.164 0.017

15. Holling-Tenner Models of Lung Cancer Cell Population and
Cytotoxic Cell Population Dynamics
P=0.0000
z=a+bx+cx^2+GCUMY(d,e,f)
r^2=1 DF Adj r^2=1 FitStdErr=8.4354944e-12 Fstat=6.8151355e+22
a=1 b=5.7675827e-16 c=-1.8688846e-17
d=-1 e=2.2287067 f=-0.034448505
1
0.9
1 0.8
Phase Transition
0.9 0.7
0.8 0.6
Phase Transition
0.7 0.5
0.6 0.4
0.5 0.3
0.4 0.2
0.3 0.1
0.2 0
30
0.1 25 ion
0 20 lat
15 pu
5
2.7 2.5 .25 2 5 10 ll Po
2 5 -Ce
Cancer C 1.7 1.5 .25 1 0 ler
ell Popu 1 Kil
lation
P=0.0000
z=EXTRVALX(a,b,c)*EXTRVALY(1,d,e)
r^2=0.99790554 DF Adj r^2=0.99779046 FitStdErr=0.023494978 Fstat=10958.344
a=1.2548733 b=2.4678053 c=7.9389194
d=2.7277315 e=0.3674437
1 1
0.9 0.9
0.8 0.8
Phase Transition
Phase Transition
0.7 0.7
0.6 0.6
0.5 0.5
0.4 0.4
0.3 0.3
0.2 0.2
0.1 0.1
0 2.750
5 2.5
B-Cell 10 15 2 2.25
1.75
1.21.5 lls
Popula 20
25 1 5 er Ce
tion Canc

16. Results of Kohonen Self-Organizing Neural
Networks Computing in Simulation in Recognition of Phase
Transition Early—Invasive Lung Cancer (n=97)

17. Lung Cancer Dynamics

18. Results of Structurul Equation Modeling in Recognition
of Phase Transition Early—Invasive Lung Cancer (n=97)

19. Conclusions:
• Start of phase transition early—
invasive lung cancer significantly
depended on cell ratio factors (ratio
between blood cells subpopulations
and cancer cells).

20. Address:
Oleg Kshivets, M.D., Ph.D.
Consultant Thoracic, Abdominal,
General Surgeon & Surgical
Oncologist
• e-mail: okshivets@yahoo.com
• skype: okshivets
• http: //www.ctsnet.org/home/okshivets