Detection of somatic TP53 mutation in surgically resected small-cell lung cancer by targeted exome sequencing: association with longer relapse-free survival.

OBJECTIVES: Few reports have explored clinical biomarkers, including those identified by targeted exome sequencing (TES) of surgically resected small-cell lung cancer (SCLC) and correlation with patient survival. PATIENTS AND METHODS: We collected formalin-fixed paraffin-embedded tumor samples from 127 patients with SCLC who had undergone surgery and analysed nonsynonymous somatic gene mutation profiles by TES of 26 cancer-related genes using next-generation sequencing (NGS) and web databases (UMIN Registration No. 000010117).
RESULTS: We detected 38 nonsynonymous somatic tumor protein p53 (TP53) mutations in 43 (54.4%) patients. Among these TP53 lesions, we identified clinically relevant mutations including those encoding Y220C, R248W, R249M, M237I, and R273L substitutions in the p53 protein. These mutations have been reported to be associated with certain clinical outcomes or biology in other types of malignancies but not in SCLC. Moreover, nonsynonymous somatic mutations of TP53 were positively associated with relapse-free survival (RFS) (median, 17.33 months [95% confidence interval (CI), 3.86-30.79] in a mutation-positive group vs 10.39 months (6.96-13.82) in a mutation-negative group, p = 0.042). Multivariate analysis revealed that nonsynonymous somatic TP53 mutation was an independent factor of prolongation of RFS (hazard ratio: 0.51, 95% CI: 0.29-0.89, p = 0.019) but not overall survival (OS).
CONCLUSION: These data suggested that TES may play a critical role for promoting reverse-translational studies, including investigations of the biology of TP53 mutations in different stages of SCLC. Accumulation of the data using cancer panels with a broader range of genes, including TP53, is expected to be useful for future clinical applications for patients with SCLC.

Introduction

Small-cell lung cancer (SCLC) accounts for approximately 13–15% of all lung cancers [1, 2]. Overcoming SCLC remains a large obstacle due to the limited numbers of available treatments and the high proliferative index of this cancer. Thus, thorough exploration of novel treatment strategies is needed.

Recent discovery of relevant gene alterations in non-small-cell lung cancer (NSCLC) has accelerated the development of treatments for patients with NSCLC [3]. Indeed, a significant increase in survival was demonstrated in patients harboring tumors with such gene alterations and who received genotype-directed therapy [4]. In the field of SCLC, whole-genome or whole-exome sequence (WES) analysis using next-generation sequencing (NGS) systems has revealed that SCLC also harbors potential targets with gene alterations, including SOX-2 amplification [5], mutations in genes responsible for histone modification [6, 7, 8], and changes genes encoding components of the PI3K/AKT/mTOR pathway [9], suggesting that novel treatment strategies directed to these targets have potential for treating patients with SCLC. Indeed, several ongoing clinical trials for the treatment of SCLC are examining the role of mutations in the genes encoding components of the PI3K/AKT/mTOR pathway in tumors [9]. Separately, experiments have shown that classification of gene copy-number aberrations in circulating tumor cells from pretreatment SCLC blood samples can predict chemosensitivity [10]. However, there is an ongoing debate regarding the utility of comprehensive whole-genome sequencing or WES in clinical use compared with targeted exome sequencing (TES) from the perspectives of data interpretation, time, and cost due to the high volume of information generated by NGS systems [11].

Recently, various TES studies using clinical samples from patients with SCLC have identified mutations for drug targets [12], prediction of response to immune checkpoint inhibitors [13], and gene mutation profiling for diagnosis [14]. A previous paper demonstrated that TP53 mutation is associated with unfavorable overall survival (OS) in patients with limited disease (LD)-SCLC [15]. However, few reports have attempted to validate the clinical utility of TES using a number of surgically resected SCLC tumor specimens in combination with corresponding clinical data, including survival times.

Given these findings, the objective of the present study was two-fold. The first goal was to use our TES system to explore clinically meaningful somatic mutations, including drug targets. The second goal was to assess the relationship between mutation profiles and clinical variables including relapse-free survival (RFS) and/or OS. Together, these results were expected to address whether TES is applicable for clinical use and as an aid in establishing treatment strategies in individual patients with early-stage SCLC.

Patients and methods

Patient data

Our eligibility criteria allowed the inclusion of patients with primary SCLC who had undergone complete surgical resection of the primary lung tumor. The study represented patients subjected to surgery from January 2003 through January 2013 at the participating institutions, including either the Fukushima Investigative Group for Healing Thoracic Malignancy (FIGHT) or the Hokkaido Lung Cancer Clinical Study Group Trial (HOT). Written informed consent was obtained only from patients who were still alive at the time of data accrual (from February 2013 through January 2014).

The requirement for consent was waived if the patient had died or could not be contacted. In such cases, investigators of each participating institution were required to provide subjects with a written statement regarding the research in the outpatient department or via a website.

This study was registered with the University Hospital Medical Information Network (UMIN) Clinical Trials Registry as Identification Number UMIN000010117; this trial included immunohistochemistry, results of which were reported previously [16]. The study protocol was approved by the Institutional Review Boards of the respective participating institutions. All individual data were obtained from medical records and de-identified. Each tissue sample was anonymized by assigning a randomized code number. Stages were determined or reclassified according to the seventh edition of the tumor, node, metastasis (TNM) staging system [17].

This is a retrospective non-interventional genetic association study. Thus, we used the STREGA checklist when writing our report [18].

Samples

All the cases that were included in the present study met the following criteria: a complete surgical resection of the primary tumors had been performed; and a central re-review confirmed a pathological diagnosis of SCLC or combined SCLC according to the 2004 World Health Organization classification [19]. Each formalin-fixed paraffin-embedded (FFPE) tissue block was cut so as to yield five sections with 20-μm thicknesses, obtained as a paraffin roll, for use in NGS. Total DNA was obtained from each of the samples. Preparation of DNA and NGS analysis were performed at the Department of Translational Pathology, Hokkaido University Graduate School of Medicine. This is a retrospective observational study. In addition, the number of SCLC patients who underwent surgery is generally limited. Thus, we did not set an appropriate sample size for this study, and instead we attempted to collect as many samples as possible that annotated to clinical data from the institutions.

TES and mutation profiling

Genomic DNA was extracted from FFPE tissues using QIAamp DNA FFPE Tissue Kits (Qiagen, Hilden, Germany) in accordance with the manufacturer's protocol. The quality of genomic DNA was assessed using Qubit dsDNA BR assay kits, a Qubit 2.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA), and GeneRead DNA QuantiMIZE Assay Kits (Qiagen). The TruSight Tumor Sequencing Panel (Illumina) was used for library preparation with genomic DNA following the manufacturer's instructions. The quality of the libraries was assessed using an Agilent 2100 bioanalyser (Agilent Technologies, Santa Clara, CA, USA) with Agilent DNA 1000 Kits (Agilent Technologies). The libraries were sequenced using MiSeq (Illumina, San Diego, CA, USA) to produce 150-bp paired-end reads. The target exons of 26 cancer-related genes (Table 1) were loaded on the TruSight Tumor Sequencing Panel (Illumina), which allows detection of hotspot somatic mutations across 14 Kb of exons (21 Kb total length of exons and introns) in genes that are commonly mutated across multiple forms of cancer. The 26 genes selected under the supervision of the College of American Pathologists and The National Comprehensive Cancer Network were all cancer related. Base calling of variant frequency (VF) was performed using Miseq Reporter v2.3 (Illumina) with the default parameter of VF >3.0%. The paired-end sequence reads that passed the quality-control metrics determined by the pipeline were included in the analysis.

Table 1

List of genes on TruSight Tumor Sequencing Panel.

AKT1	EGFR	GNAS	NRAS	STK11
ALK	ERBB2	KIT	PDGFRA	TP53
APC	FBXW7	KRAS	PIK3CA
BRAF	FGFR2	MAP2K1	PTEN
CDH1	FOXL2	MET	SMAD4
CTNNB1	GNAQ	MSH6	SRC

Gene products: ALK, anaplastic lymphoma kinase; APC, adenomatous polyposis coli; CDH1, cadherin 1; CTNNB1, catenin beta 1; EGFR, epidermal growth factor receptor; FBXW7, F-box and WD repeat domain containing 7; FGFR2, fibroblast growth factor receptor 2; FOXL2, forkhead box L2; GNAQ, guanine nucleotide binding protein, Q polypeptide; GNAS, guanine nucleotide binding protein, alpha stimulating; MSH6, MutS homolog 6; PDGFRA, platelet-derived growth factor receptor, alpha; PIK3CA, phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha; PTEN, phosphatase and tensin homologue; STK11, serine/threonine kinase 11, also known as liver kinase B1 (LKB1); TP53, tumor protein P53.

BAM files obtained from Miseq Reporter v2.3 were processed by the BioReT System (Amelieff, Tokyo, Japan) for analysis of mutations. In the BioReT System, BAM files were realigned and recalibrated with the Genome Analysis Toolkit (GATK) (version 1.6.13), using RealignerTargetCreator, IndelRealigner, CountCovariates, and TableRecalibration. Single-nucleotide variants (SNVs) and small indels were detected using the GATK UnifiedGenotyper, followed by filtering for low-quality variants using the GATK VariantFiltration. All analysis was performed with the default settings except for the minIndelFrac parameter for indel call using GATK UnifiedGenotyper, which was set to 0.05. After variant detection, VCF files were annotated by the SnpEff genetic variant annotation and effect prediction toolbox (version 4.0). Information from the Catalogue of Somatic Mutations in Cancer (COSMIC) database (version 72) and IntOGen (Integrative Onco Genomics, version 1412) were used to annotate the VCF sequences using SnpSift, a package tool of SnpEff, and variants on targeted genes were extracted. SNVs were limited to protein-altering mutations at ≥10% VF with read-depths of >100. The resulting mutations detected by our TES were stratified into three categories: i) major mutations that were annotated in the COSMIC database and were recognized as driver genes by IntOGen, ii) sub-major mutations that were annotated only in COSMIC, and iii) minor mutations that were not annotated in either COSMIC or IntOGen. All sub-major mutations were synonymous mutations, and all minor mutations were not annotated in the COSMIC database. Therefore, the lesions that were categorized as sub-major and minor mutations were excluded from further consideration in the present study. Next, potential germline variants were manually excluded by reference to gnomAD (http://gnomad.broadinstitute.org), a web database that spans 125748 exomes and 15708 genomes from individuals. In an attempt to remove additional germline mutations and to determine the pathogenicity, evidence level, clinical relevance, and description of putative SCLC-associated somatic gene mutations, we consulted several web databases, including ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/), CIViC (Clinical Interpretation of Variants in Cancer, https://civic.genome.wustl.edu/#/home), OncoKB (A Precision Oncology Knowledge Base, https://oncokb.org/), IARC TP53 database (p53.iarc.fr/TP53GeneVariations.aspx), and ICGC Data Portal (https://dcc.icgc.org/). The day of last data acquisition from these web databases was March 31, 2020. The analyst (H.M. in the Acknowledgements) was not informed of any data with regard to patient survival and other clinical results.

Statistical analysis

Univariate and multivariate Cox proportional hazard model analyses were performed to examine the association between clinical variables, including gene mutations, and either the RFS or OS. For factors that were significant in univariate analysis, we confirmed the Spearman's rank correlation coefficient (rs) and avoided entering multiple variables with a high correlation (rs ≥ 0.6) and similar significance. RFS was calculated from the date of surgery to the date of first disease recurrence or death. OS was calculated from the date of surgery to the date of death. Patients who had survived through the observation period were censored at the date for which last available information on status was available. Survival curves were estimated using the Kaplan–Meier method, and differences in survival distributions were evaluated using the log-rank test. All statistical analyses were performed using SPSS version 20 (IBM Corporation, Armonk, NY, USA). A p value of <0.05 was considered statistically significant.

Data statement

Raw data of major mutations detected by BioReT System were uploaded in Mendeley Data, V1 (https://doi.org/10.17632/pczf7nwxp8.1). Synonymous and potential germline mutations were omitted because those mutations were not allowed to be disclosed according to the initial research plan which had been submitted to Institutional Review Boards of the respective participating institutions and the UMIN Clinical Trials Registry.

Results

Patient characteristics

A flow chart schematic diagram of the study is provided in Figure 1. Between January 2003 and January 2013, 157 patients were enrolled from 17 institutions. One hundred twenty-seven tumor samples were obtained from 16 institutions. Of the samples, 48 (37.8%) were unfit for sequencing due to the poor quality of the DNA. Baseline characteristics of the remaining 79 patients are listed in Table 2. Median age was 69 years, 22 (27.8%) patients were female, and 6 (7.6%) were never-smokers. Eastern Cooperative Oncology Group performance status was 0 in 52 (65.8%) patients. The median maximum tumor diameter was 21 mm. The numbers of patients with SCLC and combined SCLC were 62 (78.5%) and 17 (21.5%), respectively. In terms of pathological stage, 31 cases were IA, 20 were IB, 10 were IIA, 2 were IIB, 13 were IIIA, 1 was IIIB, and 2 were IV. Adjuvant chemotherapy was conducted in 49 (62.0%) patients, including 6 patients who received adjuvant chemoradiotherapy.

Figure 1

Flow chart diagram. NGS, next-generation sequencing; RFS, relapse-free survival; OS, overall survival.

Table 2

Demographic and clinical characteristics of patients included in this study.

Variables	Patients (n = 79)
	No.	%
Age, median (range in years)	69 (44–85)
Sex
Female	22	27.8
Male	57	72.2
Smoking status
Never-smoker	6	7.6
Smoker (current or former)	68	86.1
Unknown	5	6.3
ECOG PS
0	52	65.8
1	23	29.1
Unknown	4	5.1
Maximum tumor diameter, median (mm)	21 (9–64)
Histology
SCLC	62	78.5
Combined SCLC	17	21.5
Clinical stage (TNM, version 7·0)
IA	51	64.6
IB	8	10.1
IIA	11	13.9
IIB	3	3.8
IIIA	5	6.3
IIIB	1	1.3
Pathologic stage (TNM, version 7·0)
IA	31	39.2
IB	20	25.3
IIA	10	12.7
IIB	2	2.5
IIIA	13	16.5
IIIB	1	1.3
IV	2	2.5
Adjuvant chemotherapy
Yes	49	62.0
No	29	36.7
Unknown	1	1.3
Comorbidity or past history
Interstitial pneumonitis	9	11.4
Other types of cancer	25	31.6
Serum level of LDH
< ULN	58	73.4
≥ ULN	21	26.6
Approach
VATS	45	57.0
Open surgery	34	43.0
Type of surgical resection
Lobectomy	55	69.6
Partial resection	22	27.8
Pneumonectomy	2	2.5
PCI
Yes	6	7.6
No	71	89.9
Unknown	2	2.5

ECOG PS, Eastern Cooperative Oncology Group performance status; SCLC, small-cell lung cancer; TNM, tumor-node-metastasis; LDH, lactate dehydrogenase; ULN, upper limit of normal range; TNM, tumor-node-metastasis; VATS, video-assisted thoracoscopic surgery; PCI, prophylactic cranial irradiation.

Mutations detected by TES

DNA libraries of 79 samples (62.2%) were successfully subjected to NGS. We detected 38 nonsynonymous somatic TP53 mutations in 43 (54.4%) patients. A summary of the detected and confirmed mutations in TP53 is provided in Figure 2, and the same lists are shown with clinical data in Table 3 and Table 4. The vast majority of TP53 mutations corresponded to missense mutations within the DNA-binding domain of the protein, irrespective of the degree of VF. As shown in Table 3, the TP53 mutations that we identified included clinically relevant mutations that encoded proteins with Y220C, R248W, R249M, M237I, and R273L substitutions. All of these mutations have been implicated (by clinical or preclinical evidence; as described in CIViC) in other types of malignancies but not in SCLC.

Figure 2

Distribution of 38 nonsynonymous somatic TP53 mutations identified in this study. Missense mutations are indicated in blue, nonsense mutations in red, splice site mutations in purple, and frameshift (fs) mutations in orange. Each circle represents a detected mutation. Numbers in the white bar denote the respective exon, and numbers below the bar show corresponding amino acid sequence. For mutation designations, single-letter abbreviations are used for amino acids, except where C > T and G > T notations are used to indicate nucleotide substitutions; an asterisk indicates a stop codon.

Table 3

Clinically relevant or potentially clinically relevant somatic mutations of TP53 mutation and clinical features in our cohort.

Exon	Mutation	Variant pattern	VF	COSMICID	ClinVar annotation	CIViC evidence level	OncoKB description	Clinical relevance	Histology	Sex	Age (years)	Smoking status	p-stage
6	Y220C	missense	0.28713	10758	Likely pathogenic	C	Oncogenic	TP53-independent response to bortezomib in breast cancer	SCLC	M	78	unknown	IA
			0.72078						SCLC	M	76	ever	IB
			0.84699						w/Sq	M	60	ever	IV
7	M237I	missense	0.44355	10834	Likely pathogenic	D	Likely oncogenic	Resistance to chemotherapeutic agents in AML cell lines	SCLC	M	65	ever	IA
			0.69928						SCLC	M	62	ever	IA
7	R248W	missense	0.2816	10656	Likely pathogenic	B	Likely oncogenic	Worse prognosis in breast cancer	SCLC	M	85	ever	IB
7	R249M	missense	0.2845	43871	Likely pathogenic	B	Likely oncogenic	Better response to doxorubicin in breast cancer	SCLC	M	71	never	IA
8	R273L	missense	0.22636	3675521	Likely pathogenic	C	Likely oncogenic	Refractory to platinum-based chemotherapy and shorter time to disease progression and reduction of survival in ovarian cancer	w/La	M	61	unknown	IA

VF, variant frequency; Age, age at diagnosis; p-stage, pathological stage; SCLC, small-cell lung cancer; w/Sq, combined with squamous cell carcinoma; AML, acute myeloid leukemia; w/La, combined with large cell carcinoma; CIViC evidence level B, clinical: clinical trials or other primary patient data supports association; CIViC evidence level C, case study: individual case reports from clinical journals; CIViC evidence level D, preclinical: in vivo or in vitro models support association.

Table 4

Nonsynonymous somatic mutations of TP53 detected using target exome sequence, potential clinical relevance, and clinical features.

Exon	Mutation	Variant pattern	VF	COSMIC ID	ClinVar annotation	CiVIC evidence level	OncoKB description	Our cohort
								Histology	Sex	Age	Smoking status	p-stage
3–4	c.97-1G > T	Splice site	0.12389	1610881	NA	NA	NA	combined with la	F	64	ever	IA
4	R65∗	Nonsense	0.31438	1646878	Pathogenic	NA	Likely oncogenic	SCLC	M	74	ever	IIIA
			0.23908					combined with ln	M	76	ever	IIA
4–5	c.376-13C > T	Splice site	0.10526	44442	NA	NA	NA	SCLC	M	74	ever	IA
5	c.378C > T	Splice site	0.11648	44196	NA	NA	NA	combined with sq	F	68	ever	IIIA
5	C135Y	Missense	0.33231	10801	Likely pathogenic	NA	Likely oncogenic	combined with ln	M	76	ever	IB
5	V147Lfs∗23	Frameshift	0.34438	44698	NA	NA	Likely oncogenic	SCLC	M	70	ever	IB
5	S149fs∗32	Frameshift	0.48219	1324767	NA	NA	Likely oncogenic	combined with ad	F	63	never	IIB
5	G154V	Missense	0.38025	342245	NA	NA	Likely oncogenic	SCLC	M	52	ever	IIIA
5	R158S	Missense	0.80265	3970361	NA	NA	Likely oncogenic	SCLC	M	74	ever	IA
5	K164∗	Nonsense	0.57135	10750	NA	NA	Likely oncogenic	SCLC	F	64	ever	IB
			0.63432					SCLC	M	60	ever	IIA
5	S183∗	Nonsense	0.90281	10706	Pathogenic	NA	Likely oncogenic	SCLC	F	61	ever	IB
5	H179Q	Missense	0.75284	1649385	NA	NA	Likely oncogenic	SCLC	F	73	never	IIIA
6	c.560-1G > T	Splice site	0.45194	43841	NA	NA	NA	SCLC	M	58	ever	IIIA
6	P190Lfs∗57	Frameshift	0.29657	45320	NA	NA	Likely oncogenic	SCLC	M	73	ever	IA
6	H193R	Missense	0.52225	10742	Likely pathogenic	NA	Likely oncogenic	SCLC	M	74	ever	IA
			0.31318					SCLC	M	66	ever	IB
6	H193Y	Missense	0.33631	10672	Likely pathogenic	NA	Likely oncogenic	SCLC	M	66	ever	IA
6	L194P	Missense	0.28781	437527	Likely pathogenic	NA	Likely oncogenic	SCLC	F	58	ever	IB
6	L194R	Missense	0.46984	117647	Likely pathogenic	NA	Likely oncogenic	SCLC	M	71	ever	IV
			0.41548					SCLC	M	69	ever	IA
6	I195N	Missense	0.23674	44877	Likely pathogenic	NA	Likely oncogenic	SCLC	F	73	unknown	IA
6	E204∗	Nonsense	0.38789	165087	NA	NA	Likely oncogenic	combined with sq	M	74	ever	IB
7	Y236C	Missense	0.13005	10731	Likely pathogenic	NA	Likely oncogenic	combined with sq	F	68	ever	IIIA
7	S241F	Missense	0.31855	10812	Likely pathogenic	NA	Likely oncogenic	combined with ad	F	63	never	IIB
7	S241Y	Missense	0.6879	10935	Likely pathogenic	NA	Likely oncogenic	SCLC	M	76	ever	IA
7	G245C	Missense	0.40776	11081	Likely pathogenic	NA	Likely oncogenic	combined with ad	M	64	ever	IA
7	G245D	Missense	0.77484	3388189	Likely pathogenic	NA	Likely oncogenic	SCLC	M	75	ever	IA
7	G245R	Missense	0.70946	10957	Likely pathogenic	NA	Likely oncogenic	SCLC	M	76	ever	IIA
8	c.783-1G > T	Splice site	0.63082	6913	Likely pathogenic	NA	NA	SCLC	M	64	ever	IIIA
8	F270I	Missense	0.69492	437484	Likely pathogenic	NA	Likely oncogenic	SCLC	M	69	ever	IB
8	E298Q	Missense	0.45481	45938	NA	NA	NA	combined with sq	F	53	ever	IA
8	T304I	Missense	0.58434	45128	NA	NA	NA	SCLC	M	77	ever	IB
9	c.919+1G > T	Splice site	0.84719	2744491	Likely pathogenic	NA	NA	SCLC	M	75	ever	IIA
9	T329Hfs∗8	Frameshift	0.7753	5002556	NA	NA	Likely oncogenic	combined with sq	M	75	ever	IIA
10	G334V	Missense	0.16953	11514	NA	NA	Oncogenic	SCLC	M	72	ever	IB
			0.53128					combined with la	M	62	ever	IB

VF, variant frequency; COSMIC, Catalogue of Somatic Mutations in Cancer; ID, identification; p-stage, pathological stage; NA, not available; la, large cell carcinoma; SCLC, small-cell lung cancer; ln, large cell neuroendocrine carcinoma; sq, squamous cell carcinoma; ad, adenocarcinoma. For mutation designations, single-letter abbreviations are used for amino acids, except where C > T and G > T notations are used to indicate nucleotide substitutions; an asterisk indicates a stop codon.

The detailed results for five pathogenic or potentially pathogenic somatic mutations in genes other than TP53 that were detected in 5 patients are shown in Table 5 with corresponding clinical data. The mutations included an AKT1 E17K mutation that has been reported to render tumors sensitive to AZD5363 (capivasertib), an Akt inhibitor [20]; an EGFR E746_A750del mutation that has been reported to render tumors sensitive to an EGFR tyrosine kinase inhibitor [21], a FBXW7 R505G mutation that is likely pathogenic [22, 23]; and KRAS G12D and Q61H mutations, both of which are associated with shorter progression-free survival and overall survival by anti-EGFR antibody in colorectal cancer [24, 25] and responsiveness to MEK inhibitor in combination with cyclin dependent kinase 4/6 inhibitor [26, 27]. The four of the five patients who harbored these gene mutations were those with combined small-cell carcinoma.

Table 5

Clinically relevant or potentially clinically relevant somatic mutations except for TP53 mutation and clinical features in our cohort.

Gene	Exon	Mutation	Variant pattern	VF	COSMICID	ClinVar annotation	CIViC evidencelevel	OncoKBEvidence level	Clinical relevance	Histology	Sex	Age (years)	Smoking status	p-stage
AKT1	4	E17K	missense	0.32817	33765	Pathogenic/Likely pathogenic	B	3A	Drug response to AZD5363 (capivasertib, Akt-i)	w/Ad	F	56	never	IIIA
EGFR	19	E746_A750 Del	indel	0.73915	6223	Drug response	A	1	Longer PFS by EGFR-TKI in NSCLC	w/Ad	F	63	never	IIB
FBXW7	10	R505G	missense	0.30651	99604	Likely pathogenic	NA	NA (Likely oncogenic)	NA	w/Ad	M	72	ever	IIIA
KRAS	2	G12D	missense	0.3599	521	Pathogenic	B	R1, 3A, 4	Poor PFS and OS by anti-EGFR Ab in CRCDrug response to MEK-i and CDK 4/6-i in CRC	SCLC	F	73	never	IIIA
KRAS	3	Q61H	missense	0.18395	1135364	Pathogenic/Likely pathogenic	B	R1, 3A, 4	Poor PFS and OS by anti-EGFR Ab in CRCDrug response to MEK-i and CDK 4/6-i in CRC	w/La	M	61	unknown	IA

VF, variant frequency; Age, age at diagnosis; p-stage, pathological stage; Akt-i, Akt inhibitor; w/Ad, combined with adenocarcinoma; EGFR, epidermal growth factor receptor; indel, insertion and deletion; PFS, progression free survival; TKI, tyrosine kinase inhibitor; NSCLC, non-small cell lung cancer; FBXW7, F-box and WD repeat domain containing 7; NA, not available; OS, overall survival; CRC, colorectal cancer; Ab, antibody; MEK-i, MEK inhibitor; CDK 4/6-i, cyclin dependent kinase 4/6 inhibitor; SCLC, small cell lung cancer; w/La, combined with large cell carcinoma.

Association between somatic TP53 mutations and RFS or OS in SCLC

The median follow-up time of 79 patients was 24.13 months (range, 0.36–119.97). Nonsynonymous somatic mutations of TP53 were positively associated with RFS [median, (95% confidence interval): 17.33 months (3.86–30.79) in mutation-positive group vs 10.39 months (6.96–13.82) in mutation-negative group, p = 0.042]. The OS was nominally but not statistically longer in the mutation-positive group compared with mutation-negative group [median, (95% confidence interval): 44.88 months (20.00–69.76) in mutation-positive group vs 29.06 months (21.64–36.49) in mutation-negative group, p = 0.127] (Figure 3). Univariate analysis of RFS revealed that this parameter was statistically larger in patients who underwent lobectomy, those with p-stage IA, those who underwent adjuvant chemotherapy, and those who harbored a TP53 mutation (Table 6). We did not identify any confounding factors among these four variables using Spearman's rank correlation coefficient (data not shown). Multivariate analysis using the four variables demonstrated that TP53 mutation was an independent factor of prolongation of RFS (hazard ratio: 0.51, 95% confidence interval: 0.29–0.89, p = 0.019) (Table 7). However, univariate and multivariate analysis revealed that OS was not significantly associated with TP53 mutation (Table 8 and Table 9).

Figure 3

Kaplan–Meier curves of relapse-free survival (RFS) (A) and overall survival (OS) (B). Bold lines denote patients with nonsynonymous somatic TP53 mutations, and dashed lines indicate those without such mutations. Vertical bars indicate the censored cases at the data cutoff point. Mu+, mutation positive; WT, wild type.

Table 6

Univariate analysis of the association between clinical variables and RFS.

Variables	HR	95%CI	p value
Age <70 years	1.24	0.72–2.13	0.437
Female	1.00	0.55–1.83	0.990
Never-smoker	1.01	0.40–2.54	0.989
ECOG PS: 0	0.80	0.44–1.45	0.464
Combined SCLC	1.77	0.94–3.33	0.078
Without history or presence of other types of cancer	0.93	0.51–1.67	0.799
Without IP complication	0.95	0.41–2.23	0.909
Serum level of LDH < ULN	0.63	0.35–1.12	0.113
VATS approach	0.60	0.35–1.03	0.064
Lobectomy	0.45	0.26–0.78	0.005
p-stage IA	0.40	0.22–0.74	0.003
Adjuvant chemotherapy	0.55	0.31–0.96	0.036
PCI	0.34	0.08–1.39	0.133
TP53 mutation	0.57	0.33–0.99	0.044

RFS, relapse-free survival; HR, hazard ratio; CI, confidence interval; ECOG PS, Eastern Cooperative Oncology Group performance status; SCLC, small-cell lung cancer; IP, interstitial pneumonitis; LDH, lactate dehydrogenase; ULN, upper limit of normal range; VATS, video-assisted thoracoscopic surgery; p-stage, pathological stage; PCI, prophylactic cranial irradiation. Cox proportional hazard model analysis was used to obtain p values.

Table 7

Multivariate analysis of the association between clinical variables and RFS.

Variables	HR	95%CI	p value
Lobectomy	0.46	0.26–0.80	0.007
p-stage IA	0.36	0.19–0.66	0.001
Adjuvant chemotherapy	0.55	0.32–0.97	0.038
TP53 mutation	0.51	0.29–0.89	0.019

RFS, relapse-free survival; HR, hazard ratio; CI, confidence interval; p-stage, pathological stage; Cox proportional hazard model analysis was used to obtain p values.

Table 8

Univariate analysis of the association between clinical variables and OS.

Variables	HR	95% CI	p value
Age <70 years	1.01	0.55–1.84	0.983
Female	0.80	0.40–1.59	0.523
Never-smoker	0.99	0.35–2.79	0.987
ECOG PS: 0	0.86	0.44–1.68	0.661
Combined SCLC	1.71	0.86–3.42	0.129
Without history or presence of other types of cancer	0.69	0.37–1.31	0.258
Without IP complication	0.68	0.29–1.62	0.383
Serum level of LDH < ULN	1.01	0.51–2.00	0.986
VATS approach	0.77	0.42–1.40	0.386
Lobectomy	0.49	0.27–0.89	0.020
p-stage IA	0.41	0.21–0.82	0.011
Adjuvant chemotherapy	0.61	0.33–1.14	0.119
PCI	0.48	0.12–2.00	0.313
TP53 mutation	0.63	0.34–1.15	0.130

OS, overall survival; HR, hazard ratio; CI, confidence interval; ECOG PS, Eastern Cooperative Oncology Group performance status; SCLC, small-cell lung cancer; IP, interstitial pneumonitis; LDH, lactate dehydrogenase; ULN, upper limit of normal range; VATS, video-assisted thoracoscopic surgery; p-stage, pathological stage; PCI, prophylactic cranial irradiation. Cox proportional hazard model analysis was used to obtain p values.

Table 9

Multivariate analysis of the association between clinical variables and OS.

Variables	HR	95% CI	p value
Lobectomy	0.46	0.24–0.85	0.013
p-stage IA	0.42	0.21–0.85	0.015
Adjuvant chemotherapy	0.62	0.33–1.16	0.135
TP53 mutation	0.62	0.33–1.15	0.126

OS, overall survival; HR, hazard ratio; CI, confidence interval; p-stage, pathological stage. Cox proportional hazard model analysis was used to obtain p values.

To examine the association between TP53 mutation and response to chemotherapy, we generated Kaplan–Meier curves for patients with and without TP53 mutations, performing this analysis separately for patients who did (Figure 4A) and did not (Figure 4B) receive adjuvant chemotherapy. RFS was nominally, but not significantly, prolonged in patients with TP53 mutations compared with those lacking TP53 mutations, both in the cohort of patients who underwent adjuvant chemotherapy (median: 54.74 months [95% CI, NR (not reached)–NR] vs 12.33 months [95% CI, 3.33–21.33], p = 0.070) and in the cohort of patients who did not receive adjuvant chemotherapy (median: 15.42 months [95% CI, 11.63–19.21] vs 6.90 months [95% CI, 1.63–12.17], p = 0.415).

Figure 4

Kaplan–Meier curves of relapse-free survival (RFS) in patients who underwent adjuvant chemotherapy (A) and those who did not (B). Bold lines denote patients with nonsynonymous somatic TP53 mutations, and dashed lines indicate those without such mutations. Vertical bars indicate the censored cases at the data cutoff point. Mu+, mutation positive; WT, wild type.

Discussion

We analyzed hotspot mutations in 26 cancer-related genes using a TES system in tumors from patients with surgically resected SCLC. Specifically, we found 54.4% of the patients harbored tumors containing TP53 nonsynonymous somatic mutations. Among these lesions, we found several clinically relevant somatic mutations. However, the development of treatments that target TP53 has not been clinically successful. In addition, as shown in Table 5, only five pathogenic or potentially pathogenic somatic mutations in genes other than TP53 were identified. These results suggested that the limited number of genes included in the cancer panel in the present study were not sufficient for practical identification of novel drug targets.

All of the five clinically relevant mutations of TP53, which are shown in Table 3, have been reported to have a relationship with prognosis, sensitivity or insensitivity to chemotherapy. These previous TP53 results were described in the literature in the context of acute myeloid leukemia (AML), or breast or ovarian cancer, but not in the context of SCLC.

The existence or the number of TP53 mutations has been demonstrated to be associated with unfavorable OS in patients with LD-SCLC [15], lung adenocarcinoma [28], NSCLC [29, 30], and other malignancies [31, 32, 33]. However, in our study, nonsynonymous somatic mutations of TP53 were positively associated with RFS and nominally with improved OS. The distinct associations between TP53 mutations and survival may be attributable to basic TP53 biology.

TP53 is known as a master transcription factor and critical tumor suppressor. Wild-type (WT) TP53 in cancer has generally been described as an inducer of cell cycle arrest and apoptosis by cellular stress such as chemotherapy. In many cancer types, a strong correlation exists between the presence of TP53 mutations and reduced responses to chemotherapeutic agents and, thus, a poor prognosis [34, 35]. We, therefore, examined whether chemotherapy might be associated with the shorter survival of patients with TP53 mutation than those without in our cohort. However, there was no difference in RFS between patients with TP53 WT and those with TP53 mutation irrespective of chemotherapy (Figure 4), suggesting that the difference in RFS between patients with TP53 WT and those with TP53 mutation might be due to other factors.

An enormous amount of research has established multiple aspects of TP53 functionality and its network in the context of cells [36]. We summarized the potential pathobiological factors of TP53 which were expected and lacking in the present study.

i) TP53 target: conversely, TP53 has been demonstrated to regulate proteins that exert an anti-apoptotic potential. Anti-apoptotic TP53 targets include genes related to DNA repair, cell cycle control, oxidative stress response, co-transcriptional factors, TP53-binding proteins, and MAPK signaling. The expression levels and duration of occupancy of these targets in tumor cells are context dependent [37].

ii) Tumor microenvironment: senescence-associated secretory phenotype (SASP) is a phenotype associated with senescent cells regulated by specific transcription factors including TP53, and can be responsible for chronic inflammation and age-linked diseases including cancer [38]. TP53-driven SASP in tumor stroma can create a tumor-suppressive immune milieu that influences the incidence of cancer [39, 40]; however, the SASP can mediate chronic inflammation and stimulate the growth and survival of tumor cells in a cell context-dependent manner [41, 42]. The TP53 WT in our patients might induce the SASP, thereby creating an environment that promotes tumor proliferation.

iii) Cancer immunity: TP53 regulates the expression of the natural-killer group 2, member D ligands, either positively or negatively as a transcriptional target through the upregulation of miR-34a. The miR34 family suppresses programmed death ligand 1 expression, an inhibitor of T cell activity [43]. These results suggest that exploring the difference in the tumor immune microenvironment within our SCLC samples with or without TP53 mutation might be important.

iv) Autophagy regulation: TP53-driven cellular senescence may be supported by activation of autophagy [44]. In some settings, autophagy has the potential to delay apoptosis by reducing the levels of the pro-apoptotic BH3-only protein PUMA [45]. The dual roles of autophagy in cancer, including tumor progression and promotion, are also cell reliant [46]. Autophagy regulation and related factors might affect the survival of our SCLC patients.

v) Cancer stem cell (CSC)-like features: CSCs are associated with aggressive cancer behavior, metastatic progression, resistance to therapy and relapse. CD133+ cancer stem-like cells in SCLC are highly tumorigenic and resistant to chemotherapy [47, 48, 49]. Although TP53 was previously reported to transcriptionally suppress CD133 expression [50], TP53 may function with different transcription factors in colorectal cancer to maintain the stem cell properties [51], which may be independent of the tumor suppressor role of TP53. TP53 WT in early-stage SCLC might correlate with the nature of CSC. Examining the expression of CSC-like markers including CD133 in combination with TP53 WT/mutation, may be one of the methods for clarifying the reason for differences in RFS.

vi) Intratumor genetic heterogeneity: a previous report described that cancer cells with TP53 WT and TP53 mutation resided as different clusters in the same tumor sample of prostate cancer [52]. The use of surgical specimens, as in the present study, can avoid the clonal heterogeneity that is observed in small biopsy samples, an aspect that is a strength of our study.

This study has several limitations that require cautious interpretation; we consider three of those limitations here. First, paired normal tissues could not be obtained for the specimens examined in our study. Thus, it remains possible that some of the mutations classified as somatic events in the present study may in fact be germline mutations in this Japanese cohort, although we screened for such lesions using various web databases.

Second, our TES system does not cover all exons of each of the 26 template genes, which might have compromised our ability to assess less-frequent, non-canonical gene mutations.

Third, ours was a retrospective observational study recruiting a heterogeneous population with a variety of treatments before and after surgery and representing a limited number of patients. Thus, it is possible that we would not have been able to detect precise associations between gene mutation profiles and survival.

In conclusion, TES of cancer-related genes by NGS and comparison with web databases, as used in this study, permitted us to identify several meaningful gene mutations that were predicted to alter drug response and survival in SCLC. However, our analysis surveyed a limited number of clinically relevant genes. Thus, an investigation of a larger gene panel in the TES system is recommended. The OncoGuide NCC Oncopanel System (Sysmex Corporation, Kobe, Japan), a panel that covers 114 genes, and FoundationOne (Foundation Medicine Inc. Cambridge, MA, USA), a panel that covers 324 genes, both received insurance coverage for use in cancer genome profiling in Japan in June 2019. Accumulation of gene alteration data and correlation with various clinical variables by these panels for research use may facilitate exploration of drug targets and promote reverse-translational research, including that on TP53 mutations. These efforts are expected eventually to assist the development of precision treatments for patients with SCLC.

Declarations

Author contribution statement

H. Yokouchi: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

H. Nishihara: Conceived and designed the experiments; Analyzed and interpreted the data.

T. Harada: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.

S. Yamazaki, H. Kikuchi, H. Uramoto, M. Harada, K. Akie, F. Sugaya, Y. Fujita, K. Takamura, T. Kojima, M. Higuchi, O. Honjo, Y. Minami, and N. Watanabe: Contributed reagents, materials, analysis tools or data.

S. Oizumi, F. Tanaka, H. Suzuki, H. Dosaka-Akita, H. Isobe, and M. Nishimura: Analyzed and interpreted the data.

Funding statement

This work was supported by research funding the Department of Translational Pathology, Hokkaido University Graduate School of Medicine; the Center for Respiratory Diseases, JCHO Hokkaido Hospital; and the Department of Pulmonary Medicine, Fukushima Medical University.

Competing interest statement

The authors declare no conflict of interest.

Additional information

The clinical trial described in this paper was registered at UMIN-CTR Clinical Trial under the registration number UMIN000010117.