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Small-cell lung cancers (SCLC) and large-cell neuroendocrine carcinomas (LCNEC) are two types of high-grade pulmonary neuroendocrine carcinomas (NECs). Diagnostic neuroendocrine markers commonly include synaptophysin, chromogranin A, CD56, and insulinoma-associated protein 1 (INSM1). In this study, the utility of secretagogin (SCGN) was examined in the context of pulmonary NEC diagnosis. The study included 71 pulmonary NEC cases (18 SCLCs, 13 combined-SCLCs, 23 LCNECs, and 17 combined-LCNECs). Immunohistochemical stains of SCGN, synaptophysin, chromogranin A, CD56, and INSM1 were performed on whole tumor sections. The stains were evaluated based on combined staining intensity and the proportion of positive tumor cells. At least mild staining intensity in at least 1% of the cells was considered positive. Bioinformatic studies showed specific SCGN expression in neuroendocrine cells and NECs. SCGN showed diffuse nuclear and cytoplasmic staining in NECs with intra-tumoral heterogeneity. The non-neuroendocrine components were negative. The sensitivity of SCGN was no better than the other established neuroendocrine markers based on all NECs combined or LCNECs/c-LCNECs only. However, the sensitivity of SCGN (71%) was higher than chromogranin A (68%) for SCLCs/c-SCLCs only. The average proportion of SCGN positive tumor cells was 8% higher than chromogranin A (22% versus 14%, P = 0.0332) in all NECs and 18% higher for SCLC and c-SCLC cases only (32% versus 13%, P = 0.0054). The above data showed that SCGN could be used as a supplemental neuroendocrine marker to diagnose SCLC.

e21017 Background: KRAS is the most commonly mutated oncogene in lung adenocarcinoma. Different KRAS mutations (mts) have unique prognostic and therapeutic implications. New drugs to target KRAS G12C have raised interest in studying the unique clinical and molecular characteristics of the different KRAS subtypes. We sought to evaluate the clinical outcomes of KRAS G13C/D (*) mutant (mut) non-small cell lung cancers (NSCLC) patients (pts), compared to those with G12C and other non-G12C mutations. Methods: We identified 255 KRAS mt NSCLC pts at Brown University-Lifespan Cancer Institute that were diagnosed between 06/2016 to 12/2021. Demographics, therapies and outcomes were retrospectively recorded. Progression-free (PFS) and overall survival (OS) was compared using log-rank test. For clinical relevance, we divided pts into G12C, G13*, and other non-G12C groups. Treatments (tt) received were divided into chemotherapy alone and immune-checkpoint inhibitor (IO) containing regimens. Results: Across the KRAS cohort (n = 255), median age was 71 years and 62% were females. Most pts had advanced disease (85%) at diagnosis (dx) and > 90% were smokers. Most common KRAS mt was G12C (42%), and 7% of pts had G13*. Compared to other KRAS mut groups, G13 * had worse PFS counted from dx (median 4 vs 12 m, p = .037). When comparing outcomes of any tt among pts with G13*, G12C and other non-G12C KRAS mut, G13 group had the worst PFS (2.1 vs 10.3 vs 6.4 m, p = .004) and OS (4.1 vs 27.8 vs 18.1 m, p = .027). When treated with IO-based regimens, pts with G13 had also worse outcomes than those with other KRAS mts: PFS 1.8 vs 7.3 m, respectively, (p < 0.001) and OS 1.8 vs 16.5 m, respectively (p = .006). Within the G13 group, pts who received IO-based regimens had worse PFS (1.8 vs 7.9 m, p = .037) and OS (1.8 vs 16.1 m, p = .093), compared to chemotherapy alone. While response to all systemic therapies was poor in G13* group (disease control rate [DCR] 10%, p = .004), DCR was particularly low with IO-based tt in G13* (0%) compared to other groups (p = .023). Conclusions: Pts with KRAS G13C/D had poor survival and response to therapies in comparison with G12C and other non-G12C KRAS mts, particularly with IO-containing regimens. Recently (Judd et al, MCT-21-0201), STK11 and KEAP1 were reported to be frequently co-mutated with KRAS G13* (37.3 and 12.6%) compared to other KRAS mts, and this genotype may confer primary resistance to IO. We hypothesize that the poor outcomes and immune resistance in G13* mut may be secondary to higher prevalence of STK11 and KEAP1 co-mutations. Larger data sets should confirm our observations and correlate them with PD-L1 scores and co-mutations.[Table: see text]

Lung adenocarcinoma is currently staged based on invasive tumor size, excluding areas of lepidic (in situ) growth. Invasive tumor size may be determined by pathologic assessment of a surgical specimen or radiographic assessment on computerized tomography (CT) scan. When invasive tumor size is the primary stage determinate, radiographic-pathologic discordance or discordant interpretation among pathologists may alter tumor stage and treatment. We reviewed 40 cases of non-mucinous pulmonary adenocarcinoma in which tumor size was the only stage-determinant. We determined the inter-observer variability when microscopically assessing architectural patterns and its effect on pathologic stage and treatment. Additionally, we correlated pathologic and radiographic assessment of invasive tumor size and its effect on tumor stage and treatment. The intraclass correlation among three pathologists was 0.9879; all three pathologists agreed on T-stage in 75% of cases. Four cases of pathologic disagreement had the potential to alter therapy. Intraclass correlation between the pathologists and invasive tumor size determined by CT scan was 0.8482. In 23 cases (57.5%) the pathologic T-stage differed (it increased >90% of the time) from clinical T-stage (determined by CT scan) based on invasive tumor size. Five of the radiographically-pathologically discrepant cases resulted in a stage change that had the potential to alter adjuvant therapy. Our findings suggest the stage differences in pathologic staging are prognostically relevant, but unlikely to impact routine selection of adjuvant therapy, and the observed variability in clinical stage tends to select against overuse of neoadjuvant therapy when invasive tumor size is the primary stage-determinant.