Comprehensive Model of Lung Cancer Prediction and Prognosis

Two unresolved issues in the treatment of non-small cell lung cancer are the assessment of risk of recurrence beyond the use of tumor stage alone, and the selection of an effective chemotherapeutic agent for patients with similar tumor morphology. A prognostic model able to identify high or low-risk patients with a high degree of accuracy can be used to inform clinicians on potential improvements to the current clinical practice. Clinical presentation, pathology, demographics, and genomics have been independently verified as influencing survival. A comprehensive model capable of incorporating multiple predictors into a unified measure has the potential to simplify risk assessment and more accurately model the determinants of patient outcome.;In order to accomplish this, patient characteristics including tumor stage, grade, patient race, age, COPD status ,and sex were assessed using Cox proportional hazards modeling across combinations of surgical, radiological, and chemotherapeutic treatments. A comprehensive model combining these factors was created and showed superior prognostic ability when compared to stage alone. In order to identify miRNA markers for chemoresponse, this patient data was then compared with information on miRNA expression from both a clinical cohort and the NCl-60 anti-cancer screen. A set of predictive and prognostic miRNA were selected by measuring the association between miRNA expression and disease-specific patient survival. The sets of significant miRNA were seen to have strong associations with mechanisms of apoptosis and cell-cycle control in an analysis of networked molecules.;The results show that a comprehensive model lends itself to a more accurate assessment of patient risk, and that these improvements persist across a variety of patient profiles and treatment modalities. Additionally, miRNA expression appears to play a role in patient response to chemotherapy when assessed across categories of disease progression. Multiple miRNA showed significant associations with disease-specific survival in the population analysis. These associations were able to be corroborated in the clinical and cellular data, demonstrating that this approach may be useful for identifying broad patterns of genomic expression which influence sensitivity and resistance to chemotherapy, and hold promise in further developing clinical tools for prediction. It was shown that the large, well-annotated, and diverse patient sample derived from registry and administrative data can be leveraged to approach two of the major unresolved issues in the treatment of non-small cell lung cancer

Association of Arsenic Exposure with Lung Cancer Incidence Rates in the United States

Although strong exposure to arsenic has been shown to be carcinogenic, its contribution to lung cancer incidence in the United States is not well characterized. We sought to determine if the low-level exposures to arsenic seen in the U.S. are associated with lung cancer incidence after controlling for possible confounders, and to assess the interaction with smoking behavior.Measurements of arsenic stream sediment and soil concentration obtained from the USGS National Geochemical Survey were combined, respectively, with 2008 BRFSS estimates on smoking prevalence and 2000 U.S. Census county level income to determine the effects of these factors on lung cancer incidence, as estimated from respective state-wide cancer registries and the SEER database. Poisson regression was used to determine the association between each variable and age-adjusted county-level lung cancer incidence. ANOVA was used to assess interaction effects between covariates.Sediment levels of arsenic were significantly associated with an increase in incident cases of lung cancer (P<0.0001). These effects persisted after controlling for smoking and income (P<0.0001). Across the U.S., exposure to arsenic may contribute to up to 5,297 lung cancer cases per year. There was also a significant interaction between arsenic exposure levels and smoking prevalence (P<0.05).Arsenic was significantly associated with lung cancer incidence rates in the U.S. after controlling for smoking and income, indicating that low-level exposure to arsenic is responsible for excess cancer cases in many parts of the U.S. Elevated county smoking prevalence strengthened the association between arsenic exposure and lung cancer incidence rate, an effect previously unseen on a population level

Combining Clinical, Pathological, and Demographic Factors Refines Prognosis of Lung Cancer: A Population-Based Study

In the treatment of lung cancer, an accurate estimation of patient clinical outcome is essential for choosing an appropriate course of therapy. It is important to develop a prognostic stratification model which combines clinical, pathological and demographic factors for individualized clinical decision making.A total of 234,412 patients diagnosed with adenocarcinomas or squamous cell carcinomas of the lung or bronchus between 1988 and 2006 were retrieved from the SEER database to construct a prognostic model. A model was developed by estimating a Cox proportional hazards model on 500 bootstrapped samples. Two models, one using stage alone and another comprehensive model using additional covariates, were constructed. The comprehensive model consistently outperformed the model using stage alone in prognostic stratification and on Harrell's C, Nagelkerke's R(2), and Brier Scores in the whole patient population as well as in specific treatment modalities. Specifically, the comprehensive model generated different prognostic groups with distinct post-operative survival (log-rank P<0.001) within surgical stage IA and IB patients in Kaplan-Meier analyses. Two additional patient cohorts (n = 1,991) were used as an external validation, with the comprehensive model again outperforming the model using stage alone with regards to prognostic stratification and the three evaluated metrics.These results demonstrate the feasibility of constructing a precise prognostic model combining multiple clinical, pathologic, and demographic factors. The comprehensive model significantly improves individualized prognosis upon AJCC tumor staging and is robust across a range of treatment modalities, the spectrum of patient risk, and in novel patient cohorts

Kaplan-Meier analysis of patients with and without COPD among those treated with surgery alone.

<p>Log-rank tests were used to assess the difference in survival probabilities of two groups.</p

Comparison of patient information in good prognosis (those survived more than 5 years) and poor prognosis (those survived less than 2 years) groups when the patient sample was restricted to patients receiving surgery without radiation or chemotherapy.

<p>These patients were diagnosed with AJCC 3^rd staging edition. * indicates statistical significance at a <i>P</i><0.05 level.</p

Distribution of demographic and clinical characteristics of patients diagnosed with adenocarcinoma or squamous cell carcinoma in the original AJCC 3^rd and 6^th staging editions and recoded 7^th edition.

<p>Distribution of demographic and clinical characteristics of patients diagnosed with adenocarcinoma or squamous cell carcinoma in the original AJCC 3^rd and 6^th staging editions and recoded 7^th edition.</p

Comprehensive model for squamous cell lung carcinoma.

<p>Model coefficients used to determine the Hazard Score for each patient are shown on the forest plot (right). Patient survival at 24 months for the total population sample is shown for the range of Hazard Scores (left), with the risk-groups delimited by vertical bars. A: AJCC 3^rd Staging Edition; B: AJCC 6^th Staging Edition; C: AJCC 7^th Staging Edition.</p

An example of output from the web-based version of the comprehensive prognostic model.

<p>Given the patient information submitted by the user (left), the web-based tool will estimate survival for each treatment category using the survival observed for patients of a particular treatment modality and similar Hazard Score (right).</p

Association of each comorbid condition and NSCLC survival when the patient sample was restricted to patients receiving surgery without radiation or chemotherapy.

<p>* indicates statistical significance at a <i>P</i><0.05 level.</p