The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Small Cell Lung Cancer (SCLC) provide recommended management for patients with SCLC, including diagnosis, primary treatment, surveillance for relapse, and subsequent treatment. This selection for the journal focuses on metastatic (known as extensive-stage) SCLC, which is more common than limited-stage SCLC. Systemic therapy alone can palliate symptoms and prolong survival in most patients with extensive-stage disease. Smoking cessation counseling and intervention should be strongly promoted in patients with SCLC and other high-grade neuroendocrine carcinomas. The “Summary of the Guidelines Updates” section in the SCLC algorithm outlines the most recent revisions for the 2022 update, which are described in greater detail in this revised Discussion text.

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BackgroundPD-L1 immunohistochemistry (IHC) has been traditionally used for predicting clinical responses to immune checkpoint inhibitors (ICIs). However, there are at least 4 different assays and antibodies used for PD-L1 IHC, each developed with a different ICI. We set to test if next generation RNA sequencing (RNA-seq) is a robust method to determine PD-L1 mRNA expression levels and furthermore, efficacy of predicting response to ICIs as compared to routinely used, standardized IHC procedures.MethodsA total of 209 cancer patients treated on-label by FDA-approved ICIs, with evaluable responses were assessed for PD-L1 expression by RNA-seq and IHC, based on tumor proportion score (TPS) and immune cell staining (ICS). A subset of serially diluted cases was evaluated for RNA-seq assay performance across a broad range of PD-L1 expression levels.ResultsAssessment of PD-L1 mRNA levels by RNA-seq demonstrated robust linearity across high and low expression ranges. PD-L1 mRNA levels assessed by RNA-seq and IHC (TPS and ICS) were highly correlated (p < 2e-16). Sub-analyses showed sustained correlation when IHC results were classified as high or low by clinically accepted cut-offs (p < 0.01), and results did not differ by tumor type or anti-PD-L1 antibody used. Overall, a combined positive PD-L1 result (≥1% IHC TPS and high PD-L1 expression by RNA-Seq) was associated with a 2-to-5-fold higher overall response rate (ORR) compared to a double negative result. Standard assessments of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) showed that a PD-L1 positive assessment for melanoma samples by RNA-seq had the lowest sensitivity (25%) but the highest PPV (72.7%). Among the three tumor types analyzed in this study, the only non-overlapping confidence interval for predicting response was for “RNA-seq low vs high” in melanoma.ConclusionsMeasurement of PD-L1 mRNA expression by RNA-seq is comparable to PD-L1 expression by IHC both analytically and clinically in predicting ICI response. RNA-seq has the added advantages of being amenable to standardization and avoidance of interpretation bias. PD-L1 by RNA-seq needs to be validated in future prospective ICI clinical studies across multiple histologies.Electronic supplementary materialThe online version of this article (10.1186/s40425-018-0489-5) contains supplementary material, which is available to authorized users.

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8521 Background: The role of EGFR tyrosine kinase inhibitors (TKIs) as adjuvant therapy in EGFR-mutated NSCLC is still controversial. Identifying biomarkers associated with increased risk of recurrence may help stratify pts & guide adjuvant therapy. We hypothesized that tumor immune microenvironment (TME) alterations could predict disease-free survival (DFS) in these pts. Methods: The Cancer Genome Atlas (TCGA) lung Adenocarcinoma (LUAD) data at Genomic Data Common (GDC) was accessed for pt phenotype, updated outcome & normalized gene expression profile (RNA seq). Immune landscape data was obtained from PANCAN. We chose to focus on 54 key TME genes, identified from a commercially available immune report card from OmniSeq (Inc.). Pt groups were divided via K-mean clustering. Group comparison was done via Likelihood Ratio (LR, categorical), Mann Whitney (continuous), log-rank (survival) & Cox regression (outcome). Bonferroni correction was used to correct for multiple comparisons. Results: 877 pts with LUAD were identified, 32 of whom had EGFR mutations and were at stages I to III. The mutations were mostly in the TK domain, involving exons 18 (12%), 19 (27%), 20 (6%), & 21 (33%). Only 3% harbored the T790M mutation. None of the pts received adjuvant TKI. Analysis of the impact of individual genes on DFS yielded a group of 8 genes whose high expression was associated with improved DFS: IL10 (HR 0.58, p 0.029), BTLA (HR 0.66, p 0.07), CD8A (HR 0.6252, p 0.099), CD39 (HR 0.454, p 0.037), CCR2 (HR 0.729, p 0.039), CSF1R (HR 0.70, p 0.087), ICOS (HR 0.66, p 0.062), & CD4 (HR 0.67, p 0.059). K-mean clustering of the pts using these genes demonstrated 2 groups with distinct immune profiles. Group 1 was characterized by higher leukocyte and stromal fractions, lymphocyte infiltration score, macrophage regulation, TGF-ß response, & T cell richness with less proliferation, pointing towards a more “inflamed” phenotype. Significant difference between the two groups in the immune subtypes was found (LR 10, p = 0.039). 90% of pts in the inflamed group had tumors with IFN-γ dominant, inflammatory, and TGF-ß dominant subtypes, while 45% of the “non-inflamed” group had lymphocyte depleted & wound healing signatures. DFS was significantly longer in the inflamed group (median DFS 1480 vs 772 days, p = 0.002). Conclusions: In pts with resected EGFR-mutated LUAD, an inflamed TME is associated with prolonged DFS. Identifying these pts may help select those who would benefit from adjuvant therapy.