Characterizing node-negative non-small cell lung cancer patients with similarity networks: A CancerLinQ Discovery analysis.

Abstract

e20585 Background: Patients with node-negative non-small cell lung cancer (NSCLC) whose tumors are completely resected account for approximately 17% of all patients with lung cancer, and disease recurrence occurs in approximately 1 in 5 of these patients. The lack of consensus on factors associated with risk of disease recurrence among node-negative NSCLC patients is a significant barrier to applying precision medicine strategies in this patient population. Clustering similar patients based on distances between various features of data is an emerging topic in precision medicine. Patient similarity networks represent a new model for clustering patients based on heterogeneous data, whereby any data type is converted into a similarity network by defining a similarity measure. The objective of this study was to examine the utility of patient similarity networks to identify NSCLC patients at higher risk of adverse outcomes. Methods: We conducted a retrospective, observational study of 6,020 node-negative NSCLC patients with an initial diagnosis in 2011-2014 in the CancerLinQ Discovery database. A patient similarity network was assembled based on comorbidities and network communities of patients with similar comorbidities at diagnosis were identified. Using Cox proportional-hazards modeling, we examined the extent to which patient age, sex, race, ethnicity, and network community predicted 2-year disease recurrence and 2-year mortality. Results: In the adjusted analyses, patients in the network community enriched for renal disease and congestive heart failure had an 83% increased risk of mortality (95% CI = 1.39-2.41). Patients in the network community enriched for pulmonary disease had a 37% increased risk of mortality (95% CI = 1.06-1.74) yet a lower risk of recurrence (HR = 0.5, 95% CI = 0.34-0.75). After adjusting for comorbidity network community, male patients had a 14% increased risk of mortality (95%CI = 1.02-1.28) and a 21% increased risk of recurrence (95% CI = 1.05-1.40) and black patients had a lower mortality risk (HR = 0.71, 95% CI = 0.58-0.86). Conclusions: Future studies applying patient similar networks to integrate additional diverse and high dimensional data types may afford more clarity in assigning risk of adverse outcomes.