Clinical characteristics and patient outcomes of molecular subtypes of small cell lung cancer (SCLC)

Abstract

Background Recent studies have shown that according to the expression levels of achaete-scute homolog 1 (ASCL1), neurogenic differentiation factor 1 (NEUROD1), and POU class 2 homeobox 3 (POU2F3), small cell lung cancer (SCLC) can be divided into four subtypes: SCLC-A (ASCL1-dominant), SCLC-N (NEUROD1-dominant), SCLC-P (POU2F3-dominant), and SCLC-I (triple negative or SCLC-inflamed). However, there are limited data on the clinical characteristics and prognosis of molecular subtypes of SCLC. Methods Immunohistochemistry (IHC) was used to detect the expression levels of ASCL1, NEUROD1, and POU2F3 in 53 patient samples of resectable SCLC. The subtype was defined by the differential expression of the transcription factors for ASCL1, NEUROD1, and POU2F3 or the low expression of all three factors with an inflamed gene signature (SCLC-A, SCLC-N, SCLC-P, and SCLC-I, respectively). The clinicopathological characteristics, immunological features (programmed death ligand 1 [PD-L1] expression and CD8+ tumor infiltrating lymphocyte [TIL] density), and patient outcomes of the four subtypes of SCLC were analyzed. Results Positive ASCL1, NEUROD1, and POU2F3 staining was detected in 43 (79.2%), 27 (51.0%), and 17 (32.1%) SCLC specimens by IHC. According to the results of IHC analysis, SCLC was divided into four subtypes: SCLC-A (39.6%), SCLC-N (28.3%), SCLC-P (17.0%), and SCLC-I (15.1%). The 5-year overall survival (OS) rates of these four subtypes were 61.9%, 69.3%, 41.7%, and 85.7%, respectively (P=0.251). There were significant differences in smoking status among different subtypes of SCLC (P= 0.031). However, we did not confirm the correlation between subtypes of SCLC and other clinicopathological factors or immune profiles. Cox multivariate analysis showed that N stage (P=0.025), CD8+ TILs (P=0.024), Ki-67 level (P=0.040), and SCLC-P (P=0.023) were independent prognostic factors for resectable SCLC. Conclusions Our IHC-based study validated the proposed classification of SCLC using the expression patterns of key transcriptional regulatory factors. We found that SCLC-P was associated with smokers and was one of the poor prognostic factors of limited-stage SCLC. In addition, no correlation was found between PD-L1 expression or CD8+ TIL density and SCLC subtypes.

Ring finger protein 180 suppresses cell proliferation and energy metabolism of non-small cell lung cancer through downregulating C-myc

“…Moreover, circular RNAs are reported to play an essential role in the progression of human lung cancer [ 27 ]. Recently, Wang et al has discovered that SCLC-P was associated with smokers and is one of the poor prognostic factors of limited-stage small cell lung cancer [ 28 ]. Furthermore, AURKB, CCNB2, CDC20, CDCA5, CDCA8, CENPF, and KNTC1 are were highly expressed in lung cancer tissues and were associated with poor prognosis [ 29 ].…”

A nomogram diagnostic prediction model of pancreatic metastases of small cell lung carcinoma based on clinical characteristics, radiological features and biomarkers

“…The final study population consisted of 93 patients with PM-SCLC, 74 men and 12 women, which was not statistically significant compared to PDCA. Because small cell lung cancer (SCLC) usually occurs in older men (60-70 years of age) and has the worst prognosis due to the rapid growth of disseminated features before the first diagnosis ( 39 , 40 ). In addition, similar tendencies were observed for CEA value, CA125 value, CA199 value, tumor size, number, tumor shape, retention cyst, peripancreatic fluid, peritumoral lymph nodes, enhancement pattern, enhancement degree, enhancement type, enhancement type1 between the two groups, respectively, in the three cohorts, although not always statistically significant in univariate analysis ( Table 2 ).…”

UCHL1 acts as a potential oncogene and affects sensitivity of common anti-tumor drugs in lung adenocarcinoma

“…Some scholars have also found some genes related to the differences and prognosis through biological information analysis [ 15 , 22 – 24 ], for example, EGFR, KRAS, PIK3CA, STEAP1/2, HOXA11, and TCN1. In addition, some scholars analyze its application value in clinical diagnosis and treatment by constructing a new prognostic model [ 14 , 21 , 25 , 26 ]. Therefore, the combination of bioinformatics technology to find possible diagnostic and prognostic markers of lung adenocarcinoma has become the key point.…”