Largest real-world dataset on small cell lung cancer subtypes reveals different expression of clinically relevant biomarkers
This is the largest real-world analysis of SCLC evaluated by whole transcriptome sequencing
The dominant expression of four lineage-defining transcription factors (ASCL1, NEUROD1, YAP1 or POU2F3) allows the classification of small cell lung cancer (SCLC) into four subtypes (SCLC-A/N/Y/P, respectively). Recent evidence suggests that YAP1 expression is associated with an inflamed T-cell phenotype, and SCLC has significant intratumoral heterogeneity mediated by NOTCH signaling through MYC activation. Accordingly, during ASCO World Congress 2021, a real-world analysis of SLCL patient samples evaluating the expression of clinically relevant biomarkers in these tumors was presented.
A comprehensive molecular profile of 437 neuroendocrine SCLC (including 7.3% of high-grade neuroendocrine lung carcinomas) was performed using next-generation DNA sequencing (panel of 592 genes), RNA sequencing (whole-transcriptome), and immunohistochemistry. Tumors were stratified into five subgroups (SCLC-A/N/Y/P and mixed) based on the relative expression of the four transcription factors. RNA expression of key genes and previously validated immune signatures (inflamed T cells, NK cells, and STING pathway signatures) were evaluated in subgroups.
Findings:
The average age of the study cohort was 66 years and 50.6% of the patients were female. The majority (67.3%) of samples were derived from metastatic sites. Tumor stratification by expression resulted in 35.7% SCLC-A, 17.6% SCLC -N, 21.1% SCLC-Y, 6.4% SCLC -P, and 19.2% mixed SCLC samples. Compared to specimens from metastatic sites, YAP1 expression was significantly higher (p < 0.001) in primary tumors. Among the 14 tumors obtained from the central nervous system, SCLC-N (36%, n = 5) was the most common subtype.
MMR deficiency or microsatellite instability was rare overall (0.5%, n = 2); TMB was similar among SCLC subtypes. SCLC-Y was associated with higher expression of inflamed T cells, NK cells, and STING pathway signatures (p < 0.0001 each). The expression of MYC and NOTCH genes (NOTCH1/2/3/4) was strongly correlated with the expression of YAP1.
Commutation analysis revealed that EGFR sensitization mutations (L858R and exon 19) were recurrent (5.2%, n = 4) in SCLC-N tumors. The expression of SNF11, SSTR2, and MYC varied significantly between the SCLC subtypes (p < 0.001 each), with the highest expression of SNF11 and SSTR2 observed in SCLC-N, while MYC expression was higher in SCLC-P.
The researchers conclude that the analysis represents the largest real-world SCLC dataset evaluated by whole transcriptome sequencing. Differential expression of immune genes and predictive biomarkers in SCLC subtypes may inform therapeutic vulnerabilities for rational and personalized treatment approaches in SCLC.
