Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed

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BACKGROUND:The usefulness of circulating tumor DNA (ctDNA) in detecting mutations and monitoring treatment response has not been well studied beyond a few actionable biomarkers in non-small cell lung cancer (NSCLC).RESEARCH QUESTION: How does the usefulness of ctDNA analysis compare with that of solid tumor biopsy analysis in patients with NSCLC? METHODS: We retrospectively evaluated 370 adult patients with NSCLC treated at the City of Hope between November 2015 and August 2019 to assess the usefulness of ctDNA in mutation identification, survival, concordance with matched tissue samples in 32 genes, and tumor evolution.RESULTS: A total of 1,688 somatic mutations were detected in 473 ctDNA samples from 370 patients with NSCLC. Of the 473 samples, 177 showed at least one actionable mutation with currently available Food and Drug Administration-approved NSCLC therapies. MET and CDK6 amplifications co-occurred with BRAF amplifications (false discovery rate [FDR], < 0.01), and gene-level mutations were mutually exclusive in KRAS and EGFR (FDR, 0.0009). Low cumulative percent ctDNA levels were associated with longer progression-free survival (hazard ratio [HR], 0.56; 95% CI, 0.37-0.85; P ¼ .006). Overall survival was shorter in patients harboring BRAF mutations (HR, 2.35; 95% CI, 1.24-4.6; P ¼ .009), PIK3CA mutations (HR, 2.77; 95% CI, 1.56-4.9; P < .001) and KRAS mutations (HR, 2.32; 95% CI, 1.30-4.1; P ¼ .004). Gene-level concordance was 93.8%, whereas the positive concordance rate was 41.6%. More mutations in targetable genes were found in ctDNA than in tissue biopsy samples. Treatment response and tumor evolution over time were detected in repeated ctDNA samples.INTERPRETATION: Although ctDNA analysis exhibited similar usefulness to tissue biopsy analysis, more mutations in targetable genes were missed in tissue biopsy analyses. Therefore, the evaluation of ctDNA in conjunction with tissue biopsy samples may help to detect additional targetable mutations to improve clinical outcomes in advanced NSCLC.

EGFR-mutated lung adenocarcinoma patients who received tyrosine kinase inhibitors (TKIs) may initially respond to therapy, but over time, resistance eventually occurs. In a small population (5–10%), these patients can have a histological transformation to SCLC. Nine patients with EGFR-mutated lung adenocarcinoma who transformed to SCLC were evaluated at City of Hope. Patient clinical and pathology data, including multiple next-generation sequencing (NGS) results, clinical therapies, histology, and outcomes, were collected across multiple time points. Descriptive statistics were utilized to visualize and interpret the clinical therapeutic timeline and molecular transformation profiles for these patients. All patients received at least one line of EGFR TKI therapies prior to small cell lung cancer transformation, including erlotinib, afatinib, and osimertinib. Two patients also received chemotherapy prior to transformation (one with immunotherapy). The median months to small cell lung cancer transformation was 16 months, ranging from 4–49 months. The median overall survival (OS) was 29 months from diagnosis, with the minimum of 16 months and maximum of 62 months. The majority of patients had EGFR exon 19 deletion (n = 7, 77.8%), and no patients had a change of original oncogenic EGFR mutation over the different time points. Though a TP53 mutation was detected in eight patients (88.9%) either at the first biopsy or the subsequent biopsies, an RB1 alteration was only detected in one patient at presentation, and three patients upon subsequent biopsies (n = 4, 44.4%). Each patient had a unique molecular profile in the subsequent molecular testing post-transformation, but BRAF alterations occurred frequently, including BRAF rearrangement (n = 1), fusion (n = 1), and amplification (n = 1). Our results showed that EGFR-mutated lung adenocarcinoma to SCLC transformation patients have a unique histological, molecular, and clinical profile over multiple time points, with further heterogeneity that is not currently reported in the literature, and we suggest more work is required to better understand the molecular heterogeneity and clinical outcomes over time for this EGFR TKI resistance subtype.

PURPOSE: Omic-informed therapy is being used more frequently for patients with non–small-cell lung cancer (NSCLC) being treated on the basis of evidence-based decision-making. However, there is a lack of a standardized framework to evaluate those decisions and understand the association between omics-based management strategies and survival among patients. Therefore, we compared outcomes between patients with lung adenocarcinoma who received omics-driven targeted therapy versus patients who received standard therapeutic options. PATIENTS AND METHODS: This was a retrospective study of patients with advanced NSCLC adenocarcinoma (N = 798) at City of Hope who received genomic sequencing at the behest of their treating oncologists. A thoracic oncology registry was used as a clinicogenomic database to track patient outcomes. RESULTS: Of 798 individuals with advanced NSCLC (median age, 65 years [range, 22-99 years]; 60% white; 50% with a history of smoking), 662 patients (83%) had molecular testing and 439 (55%) received targeted therapy on the basis of the omic-data. A fast-and-frugal decision tree (FFT) model was developed to evaluate the impact of omics-based strategy on decision-making, progression-free survival (PFS), and overall survival (OS). We calculated that the overall positive predictive value of the entire FFT strategy for predicting decisions regarding the use of tyrosine kinase inhibitor–based targeted therapy was 88% and the negative predictive value was 96%. In an adjusted Cox regression analysis, there was a significant correlation with survival benefit with the FFT omics-driven therapeutic strategy for both PFS (hazard ratio [HR], 0.56; 95% CI, 0.42 to 0.74; P < .001) and OS (HR, 0.51; 95% CI, 0.36 to 0.71; P < .001) as compared with standard therapeutic options. CONCLUSION: Among patients with advanced NSCLC who received care in the academic oncology setting, omics-driven therapy decisions directly informed treatment in patients and was correlated with better OS and PFS.