Genetic Alteration and Their Significance on Clinical Events in Small Cell Lung Cancer

Figures & data

Table 1 Clinicopathological Characteristics of the 18 SCLC Patients

	N/Median	%/Range
Case	18
Ages (years) at diagnosis	60.5	47.0–74.0
Gender
Male	14	77.8%
Female	4	22.2%
Smoking history
Yes	12	66.7%
No	5	27.8%
Null	1	5.6%
Period of diagnosis
2010s	18	100%
Specimen source
Primary biopsy	6	33.3%
Metastatic biopsy	12	66.7%
Clinical stage (VASOG)
Limited stage	0	0
Extensive stage	18	100%
Optimal treatment response
Partial remission (PR)	12	66.7%
Stable disease (SD)	4	22.2%
Progression disease (PD)	2	11.1%
Vital status at last follow-up
Deceased	17	94.4%
Alive	1	5.6%

Figure 1 Mutation landscape of SCLC. (A) Genomic alteration profiling. X axis is specific specimens and Y axis is the detected mutations for a different gene. The percentage of mutation of a specific gene in total patients. (B) Distribution of mutations in SCLC.

Figure 2 (A) Genetic alteration in our cohort consistent with Burning Rock Biotech SCLC database (RS_SCLC). (B) Genetic differences between SCLC and non-small cell lung cancer (NSCLC) (C). Single nucleotide variation analysis. *in (B and C) represents statistically significant differences (p < 0.05). **in (B and C) represents statistically significant differences (0.01<p < 0.05). *** in (B and C) represents statistically significant differences (p < 0.01). **** in (B and C) represents statistically significant differences (p < 0.001).

Figure 3 Gene copy number variation (CNV) (A) and tumor mutation burden (TMB) (B) in our SCLC cohort differ from lung adenocarcinoma.

Figure 4 The correlation between mutant gene and clinicopathological data including gender, smoking, specimen origins, metastasis site, and progression free survival and overall survival. Red indicates statistically significant differences (p < 0.05).

Figure 5 Kaplan–Meier curve indicated that SCLC patients with LRP1B (A) or MAP3K13 (B) mutation had significantly shorter PFS. SCLC patients with MSH6 mutation had significantly longer OS (C) while OS was significantly shorter in patients with SPEN mutation (D).

Figure 6 The proportion of mutant-PIK3CA is higher in patients with bone metastases (A), the proportion of mutant-FAT1 is higher in patients with liver metastases (B).

Table 2 The Clinical Factors are Associated with Signaling Pathway Based on KEGG Pathway to Some Extent

Clinical Factors	Related Signaling Pathway Based on KEGG
Gender	Mismatch repair signaling pathway
Bone metastasis	T cell receptor signaling pathway, HIF1 signaling pathway, Estrogen signaling pathway, Chemokine signaling pathway
Lymph node metastasis	T/B cell receptor signaling pathway, HIF1 signaling pathway, Estrogen signaling pathway, Chemokine signaling pathway
PFS	ErbB signaling pathway, T/B cell receptor signaling pathway, Estrogen signaling pathway, NK cell mediated cytotoxicity signaling pathway, Chemokine signaling pathway, Progesterone mediated oocyte maturation, cAMP signaling pathway
OS	Mismatch repair signaling pathway

Figure 7 The correlation between signaling pathways involved in mutant genes (based on KEGG pathway) and clinicopathological data including gender, smoking, specimen origins, metastasis site, and progression free survival and overall survival. Red indicates statistically significant differences (p < 0.05).