Tumor spread through air spaces (STAS): prognostic significance of grading in non-small cell lung cancer.

Tumor spread through air spaces (STAS) is an invasive pattern of lung cancer that was recently described. In this study, we investigated the association between the extent of STAS and clinicopathological characteristics and patient outcomes in resected non-small cell lung cancers (NSCLCs). STAS has been prospectively described from 2008 and graded its extent with a two-tiered system (STAS I: <2500 µm [one field of ×10 objective lens] from the edge of tumor and STAS II: ≥2500 µm from the edge of tumor) from 2011 in Seoul National University Bundang Hospital. We retrospectively analyzed the correlations between the extent of STAS and clinicopathologic characteristics and prognostic significance in 1869 resected NSCLCs. STAS was observed in 765 cases (40.9%) with 456 STAS I (24.4%) and 309 STAS II (16.5%). STAS was more frequently found in patients with adenocarcinoma (ADC) (than squamous cell carcinoma), pleural invasion, lymphovascular invasion, and/or higher pathologic stage. In ADC, there were significant differences in recurrence free survival (RFS), overall survival (OS), and lung cancer specific survival (LCSS) according to the extent of STAS. In stage IA non-mucinous ADC, multivariate analysis revealed that STAS II was significantly associated with shorter RFS and LCSS (p < 0.001 and p = 0.006, respectively). In addition, STAS II was an independent poor prognostic factor for recurrence in both limited and radical resection groups (p = 0.001 and p = 0.023, respectively). In conclusion, presence of STAS II was an independent poor prognostic factor in stage IA non-mucinous ADC regardless of the extent of resection.

Estrogen receptor α as a predictive biomarker for survival in human papillomavirus-positive oropharyngeal squamous cell carcinoma.

BACKGROUND: Although oropharyngeal squamous cell carcinoma (OPSCC) with human papillomavirus (HPV) infection has a good prognosis, the accurate prediction of survival and risk of treatment failure is essential to design deintensification regimens. Here, we investigated estrogen receptor α (ERα) as a prognostic biomarker with therapeutic implications in OPSCC alongside factors associated with HPV infection. METHODS: We performed immunohistochemistry for ERα and p53 using formalin-fixed, paraffin-embedded tissues and assessed the HPV status using p16 immunohistochemistry and HPV DNA testing in 113 consecutive patients with OPSCC treated with surgical resection or radiotherapy/chemoradiotherapy. RESULTS: ERα expression and p53 alteration was observed in 35.4% and 21.2% OPSCCs; 45.6% and 1.3% p16+/HPV+ OPSCCs; and 11.5% and 76.9% p16- OPSCCs, respectively. These data suggest that OPSCC pathogenesis varies with HPV status. Furthermore, ERα expression was associated with improved overall survival (OS) in both HPV+ (p16+/HPV+ OPSCC) and p16+ (p16+ OPSCC irrespective of HPV status) models (p = 0.005 and p = 0.006, respectively) and with improved OS adjusted for stage (p = 0.037, hazard ratio: 0.109, 95% confidence interval 0.013-0.871) in the p16+ model. CONCLUSIONS: ERα is a potential predictive biomarker for improved survival in both HPV+ and p16+ OPSCC models.

Genomic Landscape of Pulmonary Sarcomatoid Carcinoma.

PURPOSE: Pulmonary sarcomatoid carcinoma (PSC) is a rare aggressive subtype of non-small cell lung cancer (NSCLC) with limited therapeutic strategies. We attempted to elucidate the evolutionary trajectories of PSC using multiregional and longitudinal tumor samples. MATERIALS AND METHODS: A total of 31 patients were enrolled in this study and 11 longitudinal samples were available from them. Using whole exome sequencing data, we analyzed the mutational signatures in both carcinomatous and sarcomatous areas in primary tumors of the 31 patients and longitudinal samples obtained from 11 patients. Furthermore, digital droplet polymerase chain reaction (ddPCR), and programmed death-ligand 1 (PD-L1) immunohistochemistry using the Ventana SP263 assay were performed. RESULTS: TP53 was identified as the most frequently altered gene in the primary (74%) and metastatic (73%) samples. MET exon 14 skipping mutations, confirmed by ddPCR, and TP53 mutations were mutually exclusive; whereas, MET exon 14 skipping mutations frequently co-occurred with MDM2 amplification. Metastatic tumors showed dissimilar genetic profiles from either primary component. During metastasis, the signatures of APOBEC decreased in metastatic lesions compared with that in primary lesions. PSC showed higher MET and KEAP1 mutations and stronger PD-L1 protein expression compared with that recorded in other NSCLCs. CONCLUSION: Decreased APOBEC signatures and subclonal diversity were detected during malignant progression in PSC. Frequent MET mutations and strong PD-L1 expression distinguished PSC from other NSCLCs. The aggressiveness and therapeutic difficulties of PSC were possibly attributable to profound intratumoral and intertumoral genetic diversity. Next-generation sequencing could suggest the appropriate treatment strategy for PSC.

Assessment of TP53 and CDKN2A status as predictive markers of malignant transformation of sinonasal inverted papilloma.

The mechanism and predictive biomarkers of sinonasal inverted papilloma (IP) transformation into squamous cell carcinoma (SCC) are still unclear. We investigated the genetic mutations involved and the predictive biomarkers. Fourteen patients with SCC arising from IP and six patients with IPs without malignant transformation (sIP) were included. DNA was extracted separately from areas of normal tissue, IP, dysplasia, and SCC. Whole exome sequencing and immunohistochemistry was performed. Major oncogenic mutations were observed in the progression from IP to SCC. The most frequently mutated genes were TP53 (39%) and CDKN2A (27%). Mutations in TP53 and/or CDKN2A were observed in three of six IPs with malignant transformation (cIP); none were observed in sIPs. Tumor mutational burden (TMB) increased from IP to SCC (0.64/Mb, 1.11/Mb, and 1.25 for IP, dysplasia, and SCC, respectively). TMB was higher in the cIPs than in the sIPs (0.64/Mb vs 0.3/Mb). Three cIPs showed a diffuse strong or null pattern in p53, and one showed a total loss of p16, a distinct pattern from sIPs. Our result suggests that TP53 and CDKN2A status can be predictive markers of malignant transformation of IP. Furthermore, immunohistochemistry of p53 and p16 expression can be surrogate markers for TP53 and CDKN2A status.

Comparison of the Predictive Power of a Combination versus Individual Biomarker Testing in Non-Small Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors.

PURPOSE: Since tumor mutational burden (TMB) and gene expression profiling (GEP) have complementary effects, they may have improved predictive power when used in combination. Here, we investigated the ability of TMB and GEP to predict the immunotherapy response in patients with non-small cell lung cancer (NSCLC) and assessed if this combination can improve predictive power compared to that when used individually. MATERIALS AND METHODS: This retrospective cohort study included 30 patients with NSCLC who received immune checkpoint inhibitors (ICI) therapy at the Seoul National University Bundang Hospital. programmed cell death-ligand-1 (PD-L1) protein expression was assessed using immunohistochemistry, and TMB was measured by targeted deep sequencing. Gene expression was determined using NanoString nCounter analysis for the PanCancer IO360 panel, and enrichment analysis were performed. RESULTS: Eleven patients (36.7%) showed a durable clinical benefit (DCB), whereas 19 (63.3%) showed no durable benefit (NDB). TMB and enrichment scores (ES) showed significant differences between the DCB and NDB groups (p=0.044 and p=0.017, respectively); however, no significant correlations were observed among TMB, ES, and PD-L1. ES was the best single biomarker for predicting DCB (area under the curve [AUC], 0.794), followed by TMB (AUC, 0.679) and PD-L1 (AUC, 0.622). TMB and ES showed the highest AUC (0.837) among other combinations (AUC [TMB and PD-L1], 0.777; AUC [PD-L1 and ES], 0.763) and was similar to that of all biomarkers used together (0.832). CONCLUSION: The combination of TMB and ES may be an effective predictive tool to identify patients with NSCLC patients who would possibly benefit from ICI therapies.