Genomic and Transcriptomic Characterization of Relapsed SCLC Through Rapid Research Autopsy

Abstract

Introduction: Relapsed SCLC is characterized by therapeutic resistance and high mortality rate. Despite decades of research, mechanisms responsible for therapeutic resistance have remained elusive owing to limited tissues available for molecular studies. Thus, an unmet need remains for molecular characterization of relapsed SCLC to facilitate development of effective therapies.

Methods: We performed whole-exome and transcriptome sequencing of metastatic tumor samples procured from research autopsies of five patients with relapsed SCLC. We implemented bioinformatics tools to infer subclonal phylogeny and identify recurrent genomic alterations. We implemented immune cell signature and single-sample gene set enrichment analyses on tumor and normal transcriptome data from autopsy and additional primary and relapsed SCLC data sets. Furthermore, we evaluated T cell-inflamed gene expression profiles in neuroendocrine (ASCL1, NEUROD1) and non-neuroendocrine (YAP1, POU2F3) SCLC subtypes.

Results: Exome sequencing revealed clonal heterogeneity (intertumor and intratumor) arising from branched evolution and identified resistance-associated truncal and subclonal alterations in relapsed SCLC. Transcriptome analyses further revealed a noninflamed phenotype in neuroendocrine SCLC subtypes (ASCL1, NEUROD1) associated with decreased expression of genes involved in adaptive antitumor immunity whereas non-neuroendocrine subtypes (YAP1, POU2F3) revealed a more inflamed phenotype.

Conclusions: Our results reveal substantial tumor heterogeneity and complex clonal evolution in relapsed SCLC. Furthermore, we report that neuroendocrine SCLC subtypes are immunologically cold, thus explaining decreased responsiveness to immune checkpoint blockade. These results suggest that the mechanisms of innate and acquired therapeutic resistances are subtype-specific in SCLC and highlight the need for continued investigation to bolster therapy selection and development for this cancer.

Keywords: Research autopsy; Small cell lung cancer; Treatment resistance; Tumor heterogeneity.

Figure 1

Identification of SMGs and CNVs in SCLC autopsy patients. (A) Oncoplot of SMGs identified in our SCLC cohort using MuSiC (FDR < 0.05). Multiple tumor samples per patient with SCLC were sequenced, resulting in exome data from 63 samples including three pretreatment biopsy samples (marked by ∗). Vertical bar graphs (top) reveal total number of Mut. per corresponding tumor sample below. Horizontal bar graphs and percentages (left) reveal mutational frequency of the corresponding gene (right). Type of somatic variant is defined by colored box key at the bottom, with black boxes indicating multiple variants detected in a specific gene in a given tumor sample. (B) Uncurated CNVs in SCLC autopsy patients detected by FALCON. Data from all tumor samples per patient were pooled into a composite CNV profile as illustrated for each patient. Gains were defined as allele number greater than 2.0 (e.g., SOX2) and loss less than 0.5 (e.g., APC). Red line, major allele. Blue line, minor allele. Green line, no change in one or both alleles. CNV, copy number variation; FDR, false discovery rate; Mut., mutation; SMG, significantly mutated gene. #, number.

Figure 2

Inferred clonal phylogeny and heterogeneity in four SCLC autopsy patients. (A–D) (Left) Tumor samples procured from research autopsy as labeled on the human figure. Three large tumors from SCLC1 and SCLC3 were divided into multiple regions for sequencing. (A–D) (Middle) Phylogenetic trees depicting clonal evolution from a normal cell (gray solid circle) are as illustrated, with branch length corresponding to mutational burden. Numbers in parentheses after genes reveal predicted order of occurrence in a truncal branch (e.g., A, CREBBP, third mut. to occur of 273 truncal alterations). The COSMIC tobacco mut. Sig.4 was detected in the trunk of all five phylogenetic trees. Clonal and subclonal predicted driver mut., indicated by ∗, in Wnt signaling genes include CREBBP and TRRAP in (A) SCLC1, (B) FZD in SCLC2, DVL1, and (C) XPO1 in SCLC3, and (D) AXIN1 in SCLC4. (A–D) (Right) Percentage of different clones that compose each tumor sample are as revealed, demonstrating increased clonal heterogeneity. #T1, residual primary tumor in SCLC1; Adr., adrenal; Bx, pretreatment biopsy; del, deletion; Liv., liver; LN, lymph node; LOH, loss of heterozygosity; Lu, lung; mut., mutation; Sig., signature.

Figure 3

Concordance between ctD (ctDNA) and tumor profiling in a fifth SCLC autopsy patient. (A) Inferred clonal phylogeny and clonal composition of metastatic tumors from a fifth SCLC autopsy patient. Truncal predicted driver alterations include point mut. in RB1 and Wnt pathway gene PSMC1 and copy number losses of TP53 and APC. Tumor clonal heterogeneity was recapitulated in a ctD sample collected shortly before death of this patient. The ctD sample revealed a high proportion of clone 5, which was detected in increased proportions only in liver metastases T6 to T11. (B) (Left) Bar graph reveals concordant numbers of SNVs between tumor and ctD. (Right) Average VAF of Ubi. SNVs was compared with VAF of ctD variants. amp, amplification; ctD, circulating tumor DNA; del, deletion; mut., mutation; Sig., signature; SNV, single-nucleotide variant; Ubi., ubiquitous; VAF, variant allele frequency. #: number.

Figure 4

Transcriptome analyses reveal decreased expression of genes involved in antitumor immune responses in SCLC. (A) Box plots of ESs from ssGSEA analysis of gene sets derived from NanoString PanCancer IO 360 panel. Statistically significant differences in ES were detected in pairwise comparisons between normal-primary and normal-relapse. ∗, adjusted p < 0.0001. (B) Box plots of ImSig scores for different immune cell types in the adaptive and innate immune systems. Statistically significant differences in ES were detected in pairwise comparisons between normal-primary and normal-relapse. ∗, adjusted p < 0.0001. ES, enrichment score; ImSig, Immune signature; IO, Immune-Oncology; Mac., macrophage; Mono., monocyte; Neutro., neutrophil; NK, natural killer; Pretx, pretreatment; sig., signature; ssGSEA, single-sample gene set enrichment.

Figure 5

T cell-inflamed GEP evaluation reveals different immune phenotypes in neuroendocrine versus non-neuroendocrine SCLC. (A) Heatmap of log-transformed expression values (TPM) of genes representing the analytically validated T cell-inflamed GEP. Hierarchical clustering was performed using Qlucore Omics Explorer v3.6. Most primary and relapsed SCLC tumor samples revealed low GEP expression relative to normal lung tissue. Sample type and data source indicated as labeled. (B) Violin plot of GEP scores for normal, primary, and relapsed SCLC samples. Higher scores indicate “hot” tumors, whereas lower scores indicate “cold” tumors. Two-tailed unpaired t test ∗p < 0.001; ∗∗p < 0.01. (C) Violin plot of TPM values of selected T cell-inflamed GEP and checkpoint genes in normal and SCLC tumor samples. (D) Heatmap of log-transformed expression values (TPM) of T cell-inflamed GEP genes in SCLC tumor samples annotated by subtype and data source as indicated. (E) Violin plot of GEP scores in different subtypes of SCLC and normal lung. Each subtype was compared against normal using two-tailed unpaired t test ∗, p < 0.01. (F) Violin plot of IDO1/2 and ARG1/2 expression in normal, primary, and relapsed SCLC. Primary/pretx or relapsed samples were compared against normal using two-tailed unpaired t test ∗p < 0.001; ∗∗p < 0.005. (G) Violin plot of ARG2 expression in SCLC tumor samples annotated by subtype. Each subtype was compared against normal using two-tailed unpaired t test. ∗p < 0.001; ∗∗p < 0.005. ARG, arginase; GEP, gene expression profile; IDO, indoleamine 2,3-dioxygenase; Pretx, pretreatment; TPM, transcript reads per million.