Dynamic Computational Model Suggests That Cellular Citizenship Is Fundamental for Selective Tumor Apoptosis

Computational models in the field of cancer research have focused primarily on estimates of biological events based on laboratory generated data. We introduce a novel in-silico technology that takes us to the next level of prediction models and facilitates innovative solutions through the mathematical system. The model's building blocks are cells defined phenotypically as normal or tumor, with biological processes translated into equations describing the life protocols of the cells in a quantitative and stochastic manner. The essentials of communication in a society composed of normal and tumor cells are explored to reveal “protocols” for selective tumor eradication. Results consistently identify “citizenship properties” among cells that are essential for the induction of healing processes in a healthy system invaded by cancer. These properties act via inter-cellular communication protocols that can be optimized to induce tumor eradication along with system recovery. Within the computational systems, the protocols universally succeed in removing a wide variety of tumors defined by proliferation rates, initial volumes, and apoptosis resistant phenotypes; they show high adaptability for biological details and allow incorporation of population heterogeneity. These protocols work as long as at least 32% of cells obey extra-cellular commands and at least 28% of cancer cells report their deaths. This low percentage implies that the protocols are resilient to the suboptimal situations often seen in biological systems. We conclude that our in-silico model is a powerful tool to investigate, to propose, and to exercise logical anti-cancer solutions. Functional results should be confirmed in a biological system and molecular findings should be loaded into the computational model for the next level of directed experiments

Real World Analysis of Small Cell Lung Cancer Patients: Prognostic Factors and Treatment Outcomes

In this observational study, we assessed treatment patterns and prognostic factors in patients with small cell lung cancer (SCLC) in a large state-mandated healthcare organization in Israel. Methods: All incident cases with histologically confirmed SCLC who initiated systemic anti-cancer treatment between 2011 and 2017 were identified. Treatment patterns and overall survival (OS) were evaluated for each line of therapy. Results: A total of 235 patients were identified (61% male, median age 64 years, 95% ever smokers, 64% had extensive stage). The first-line treatment was platinum–etoposide regimen for 98.7% of the cohort. The second and third-line regimen were given to 43% and 12% of patients, respectively. Mean OS for extensive and limited stage patients was 9.1 and 23.5 months respectively. In a multivariable model, increased risk for mortality was observed among patients with an ECOG performance status (PS) of 2 compared to a PS of 0–1 for the extensive stage patients (Hazard ratio (HR) = 1.63, 95% confidence ratios (CI): 1.00–2.65); and for males compared to females for the limited stage patients (HR = 2.17; 95% CI: 1.12–4.20). Regarding all 2nd line patients in a multivariable model incorporating relevant confounding factors, demonstrated a significantly better outcome with platinum–based regimens compared to topotecan. Median survival after initiation of 2nd line in platinum-sensitive patients was longer (p = 0.056) for those re-challenged with platinum–based regimen (n = 7): 6.8mo (6.1-not reported (NR)), compared with those switched to a different treatment (n = 27): 4.5 mo (2.6–6.6) for extensive stage patients, and a non-significant difference was also observed for limited stage patients. Conclusion: To our knowledge, this is one of the largest real-world studies of SCLC patients. OS for SCLC patients was similar to that reported in clinical trials. PS for extensive stage patients and sex for limited stage patients were significant correlates of prognosis. Re-challenge of the platinum–based doublet was associated with longer OS compared to switching treatment in extensive stage patients

Patient characteristics.

<p>Patient characteristics.</p

Treatment patterns and clinical outcomes.

<p>Treatment patterns and clinical outcomes.</p