KRAS status is related to histological phenotype in gastric cancer: results from a large multicentre study.

BACKGROUND: Gastric cancer (GC) is histologically a very heterogeneous disease, and the temporal development of different histological phenotypes remains unclear. Recent studies in lung and ovarian cancer suggest that KRAS activation (KRASact) can influence histological phenotype. KRASact likely results from KRAS mutation (KRASmut) or KRAS amplification (KRASamp). The aim of the study was to investigate whether KRASmut and/or KRASamp are related to the histological phenotype in GC.
METHODS: Digitized haematoxylin/eosin-stained slides from 1282 GC resection specimens were classified according to Japanese Gastric Cancer Association (JGCA) and the Lauren classification by at least two observers. The relationship between KRAS status, predominant histological phenotype and clinicopathological variables was assessed.
RESULTS: KRASmut and KRASamp were found in 68 (5%) and 47 (7%) GCs, respectively. Within the KRASmut and KRASamp cases, the most frequent GC histological phenotype was moderately differentiated tubular 2 (tub2) type (KRASmut: n = 27, 40%; KRASamp: n = 21, 46%) or intestinal type (KRASmut: n = 41, 61%; KRASamp: n = 23, 50%). Comparing individual histological subtypes, mucinous carcinoma displayed the highest frequency of KRASmut (JGCA: n = 6, 12%, p = 0.012; Lauren: n = 6, 12%, p = 0.013), and KRASamp was more frequently found in poorly differentiated solid type (n = 12, 10%, p = 0.267) or indeterminate type (n = 12, 10%, p = 0.480) GC. 724 GCs (57%) had intratumour morphological heterogeneity.
CONCLUSIONS: This is the largest GC study investigating KRAS status and histological phenotype. We identified a relationship between KRASmut and mucinous phenotype. The high level of intratumour morphological heterogeneity could reflect KRASmut heterogeneity, which may explain the failure of anti-EGFR therapy in GC.

Introduction

Gastric cancer (GC) is histologically a very heterogeneous disease, and this is reflected in the numerous proposed histological classification schemes [1]. The temporal development of different histological phenotypes in GC remains unclear. Recent studies suggest that Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) activation and downstream signalling can impact on the properties and functions of the tumour microenvironment [2], and thus may influence histological phenotype. Likely mechanisms of KRAS activation include KRAS mutation (KRASmut) and KRAS amplification (KRASamp) [3].

Mutations in KRAS have been identified in many human cancers and result in the constitutive activation of KRAS and the receptor tyrosine kinase (RTK) pathway [4]. The frequency of KRASmut is variable across different cancer types, with the highest frequency in pancreatic cancer (90%) followed by colon (34.6%), lung (16.5%) and ovarian (11%) cancer and the lowest frequencies in cervical (6.6%), prostate (5%) and oesophageal cancer (2%) [5]. In a review of the literature we identified, on average, only 6.5% of GC have a KRASmut [6]. In colorectal cancer, routine testing for KRASmut is now implemented as a predictor of response to anti-epidermal growth factor receptor (EGFR) therapy [7].

Several studies have demonstrated a relationship between KRASmut status and histological phenotype in lung and ovarian cancer. In the subgroup of invasive mucinous adenocarcinoma of the lung, KRAS is mutated in up to 86% of cases [8]. In ovarian cancer, KRASmut has been identified in almost all cases with a mucinous histological phenotype [9]. The relationship between KRASmut status and histological phenotype in GC remains to be clarified [6].

The reported frequency of KRASamp is 1–9% in GC [10-16]. There are no reports of a relationship between KRAS DNA copy number and histological phenotype in other cancer types and in GC it has not been investigated in a large study. There is increasing recognition of the clinical importance of KRASamp in GC. KRASamp is also associated with a worse survival [3, 10, 12], whereas KRASmut do not appear to influence survival of GC patients [17].

Recently, image analysis on lung cancer haematoxylin and eosin (H&E) stained sections using deep learning was predictive of mutation status [18], thus suggesting that morphological phenotype is reflective of molecular phenotype. Investigating the relationship between KRAS activation by KRASmut and/or KRASamp and histological phenotype may provide some insight into gastric adenoma–carcinoma sequence progression and the origin of histological heterogeneity. Based on the studies in lung and ovarian cancer, we hypothesise that KRAS activation influences histological phenotype and is associated with a mucinous phenotype in GC. This would suggest that KRAS activation is an early event in GC, occurring before the phenotype is determined.

The aim of this multicentre GC study was to investigate the relationship of KRAS activation status (KRASmut and/or KRASamp) with the histological phenotype in a large series of GCs from UK, Japan and The Cancer Genome Atlas (TCGA). In addition, the relationship between KRAS status, clinicopathological variables, survival and microsatellite instability status was assessed.

Material and methods

Patients

Kanagawa Cancer Center Hospital (KCCH), Yokohama, Japan

This cohort included 250 patients with TNM stage II/III GC who underwent potentially curative surgery at Kanagawa Cancer Center Hospital (Yokohama, Japan) between 2001 and 2010. One hundred and six (43%) patients were treated with surgery alone, 108 (43%), 22 (9%), 14 (6%) patients received S-1, tegafur–uracil or S-1 combined with other cytotoxic drug therapy, respectively. Demographical, clinical and pathological data were retrieved from hospital records. The study was approved by the Local Research Ethics Committee.

Leeds Teaching Hospitals NHS Trust (LTHT), Leeds, UK

This cohort included 277 patients with GC who underwent potentially curative surgery at the Department of Surgery, Leeds General Infirmary (Leeds, UK), between 1970 and 2004. Seven (3%) patients were treated by chemotherapy followed by surgery and the remaining 270 (98%) by surgery alone. Clinical and pathological data were retrieved from histopathology reports, electronic patient hospital records and the Northern and Yorkshire Cancer Registry. The study was approved by the Local Research Ethics Committee (LREC no. CA01/122).

The Cancer Genome Atlas

The TCGA stomach adenocarcinoma (STAD) clinicopathological and molecular dataset of 295 patients was obtained from the publically available TCGA database portal [19].

Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology (TMGH), Tokyo, Japan

This cohort included 420 patients with 460 GC who were treated by surgery in the Tokyo Metropolitan Geriatric Hospital between 2000 and 2008. Three hundred and eighty patients had single carcinoma, and 36 had 2 or more carcinomas. Patients with Lynch syndrome were excluded from the current study. None of the patients underwent neoadjuvant chemotherapy. Histopathological examination and medical research were performed with informed written consent by the patients, and this work was approved by the ethics committee of the Tokyo Metropolitan Geriatric Hospital (#230,225, R16-23).

Histopathological classification

pT and pN stages were reported according to 7th edition of the UICC TNM classification for GC [20].

In all cohorts, H&E stained formalin fixed paraffin embedded (FFPE) tissue sections from the resection specimens were reviewed. In the KCCH and LTHT cohorts, H&E stained slides were scanned at 40 × magnification using an Aperio AT2 scanner for review. In the TCGA cohort, H&E stained slides were viewed online using the cancer digital slide archive (https://cancer.digitalslidearchive.net/). In the TMGH cohort, classification was performed using the glass slides.

Histological classification according to JGCA scheme was performed [21]. Mucinous carcinoma were defined as tumour cells located in mucinous pools comprising an area greater than 50% of the total tumour. GC were classified as signet-ring cell carcinoma when signet-ring cells were present in more than 50% of the tumour volume. In cases where more than one histological phenotype was identified, the most predominant phenotype was recorded, and these GCs were categorised as heterogeneous. JGCA classification was converted to Lauren classification [22] according to Table 1. As there is no Lauren classification for mucinous GC, we retained mucinous carcinomas as a separate category to distinguish them from other histological types.

Table 1

Japanese Gastric Cancer Association histological classification of common types of gastric cancers in relation to Lauren classification

Histological classification
Lauren	Japanese Gastric Cancer Association (JGCA)
Intestinal	Differentiated:  Papillary adenocarcinoma (pap)  Tubular adenocarcinoma (tub)   Well-differentiated (tub1)   Moderately differentiated (tub2)
Diffuse	Undifferentiated:  Poorly differentiated adenocarcinoma (por)   Non-solid type (por2)  Signet-ring carcinoma (sig)
Mucinous	Differentiated/undifferentiated:  Mucinous adenocarcinoma (muc)
Indeterminate	Undifferentiated:  Poorly differentiated adenocarcinoma (por)   Solid type (por1)

Table created after personal communication with H. Grabsch, March 12, 2019

DNA extraction

The area with the highest tumour cell density was identified on H&E stained sections and the whole tumour area, irrespective of subregions with different histological phenotypes was microdissected after staining with Shandon instant haematoxylin (Thermo Scientific, Cheshire, UK) using a sterile surgical blade. Tumour DNA from FFPE material was extracted from KCCH and LTHT GCs using the QIAmp DNA Micro Kit (Qiagen, Hilden, Germany) as previously described [23]. DNA concentration was measured by ND-100 Spectrophotometer (Labtech International) and samples were diluted using Tris-EDTA buffer. In the TMGH cohort, DNA was extracted using a phenol–chloroform procedure as described previously [24].

KRAS gene copy number and data analyses

KRAS copy number status was investigated in KCCH, LTHT and TCGA cohorts.

In the KCCH and LTHT cohort, KRAS gene copy number was determined by multiplex ligation-dependent probe amplification (MLPA) using the Salsa-FAM-labeled MLPA reagent kit and probemix P458-A1 or the updated version -B1 (MRC Holland, Amsterdam, The Netherlands) as previously described [25]. For further details on the KRAS probes included in this probemix see Supplementary Table 1. Fragment analysis of the MLPA reaction product was performed using capillary electrophoresis ABI-3130 XL (Applied Biosystems, California, USA) as previously described [25]. Failed experiments were repeated at least twice before a case was finally excluded from the analyses.

KRAS DNA copy number data from 237 KCCH GC has been previously published [25], but was re-analysed using a different methodology in the current study. The output files (FSA files) from the sequencer were initially imported into Coffalyser.net for fragment analysis and results were exported as csv files. Subsequent analyses were performed using the MLPAInter method, as previously described [26], implemented in R. Samples were normalised per batch using reference samples processed in each batch. Quality control was performed to exclude samples with low overall intensity, with a large difference in intensity between short probes and long probes, with low intensity of denaturation controls, or high within gene variation, defined as the average of the standard deviation of log-transformed values. Final values were calculated by averaging the peak height of each probe and then averaging the results of replicates. Copy number thresholds were set based on previously published studies [25, 27, 28], with a DNA copy number > 1.31 categorised as amplification. This analysis was performed separately for KCCH and LTHT cohorts.

In TCGA, KRASamp were determined by array-based somatic copy number analysis [29]. Level 3 copy number segmentation data was downloaded from the TCGA data portal [19] and used to estimate copy number for KRAS. Based on previous studies, a LogRatio > 0.4 was categorised as amplification [30].

KRAS mutation status

KRASmut data from a previous study were available for 230 KCCH and 275 LTHT GC patients [17]. KRASmut testing was performed on an additional 12 KCCH GCs as previously described [17]. In TCGA, KRASmut status was determined by whole-exome sequencing [29] and results were downloaded from the TCGA database portal [19] for 289 patients. In the TMGH cohort, KRAS (codon 12 and 13) was examined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), using primers and methods previously described [31, 32].

Microsatellite instability (MSI) status

Immunohistochemistry of DNA mismatch repair proteins were used as a surrogate marker of MSI status. Results for MLH1, MSH2, MSH6 and PMS2 were available from 230 KCCH GCs, and MLH1 and MSH2 from 253 LTHT GCs from a previous study [17]. MLH1, MSH2, MSH6 and PMS2 immunohistochemistry was performed on additional 13 GCs from the KCCH cohort for this study, as previously described [17].

In TCGA, MSI was determined by a DNA based MSI-Mono-Derived-Dinucleotide Assay using four mononucleotide repeat loci and three dinucleotide repeat loci using a multiplex fluorescent-labeled PCR and capillary electrophoresis [29]. Results were obtained from the TCGA database portal [19] for 295 GC patients. MSI-low GCs were grouped with microsatellite stable (MSS) GCs for further analyses following current guidelines [33].

In the TMGH cohort, mononucleotide repeats BAT25 and BAT26 were investigated, as previously described [34-36] and GC were classified as MSS or MSI.

Statistical analyses

All statistical analyses were performed using SPSS software version 23 (SPSS Inc., Chicago, III). The relationship between KRASmut or KRASamp and clinicopathological variables (age, gender, depth of invasion (pT), lymph node status (pN), TNM stage, Lauren classification [22], JGCA classification [21], MSI status and morphological heterogeneity status) was assessed using Chi-squared or Fisher’s exact test. The relationship between KRASmut and survival in LTHT and KCCH cohorts has been published previously [17]. Combining all cohorts, the relationship between KRASmut or KRASamp and 5-year overall survival was analysed using the Kaplan–Meier method and differences were assessed using the log rank test. A p value of < 0.05 was considered significant.

Results

Patient characteristics

The median (range) age of GC patients was as follows; KCCH: 65 years (35–85 years), LTHT: 72 years (14–96 years), TCGA: 68 years (35–90 years), TMGH: 78 years (51–96 years). For a summary of other patient clinicopathological variables in each cohort see Table 2.

Table 2

Comparison of clinicopathological variables in each gastric cancer cohort

	Total (n)1282	Total (%)	KCCH (n)250	KCCH (%)20	LTHT (n)277	LTHT (%)22	TCGA (n)295	TCGA (%)23	TMGH (n)460	TMGH (%)36
Age (years)
< 65	343	27	122	49	78	28	123	42	20	4
≥ 65	936	73	128	51	199	72	169	58	440	96
Gender
Male	769	60	175	70	164	59	182	62	248	54
Female	513	40	75	30	113	41	113	38	212	46
T stage
pT1	272	21	6	2	20	7	11	4	235	51
pT2	138	11	43	17	24	9	44	15	27	6
pT3	350	28	34	14	79	29	155	54	82	18
pT4	512	40	167	67	154	56	75	26	116	25
N stage
pN0	489	39	42	17	87	31	97	34	263	57
pN1	247	19	58	23	52	19	64	23	73	16
pN2	229	18	67	27	54	20	58	20	50	11
pN3	306	24	83	33	84	30	65	23	74	16
TNM stage
I	307	24	0		34	12	32	12	241	53
II	384	30	97	39	81	29	116	42	90	20
III	507	40	153	61	151	55	111	40	92	20
IV	67	5	0		11	4	20	7	36	8
Lauren classification
Diffuse	293	23	83	34	60	22	73	25	77	17
Intestinal	698	55	103	42	145	54	156	53	294	64
Mucinous	51	4	10	4	10	4	20	7	11	2
Indeterminate	229	18	51	21	56	21	44	15	78	17
JGCA classification
Pap	71	6	5	2	9	3	17	6	40	9
Tub1	219	17	18	7	55	20	23	8	123	27
Tub2	408	32	80	32	81	30	116	40	131	29
Por1	229	18	51	21	56	21	44	15	78	17
Por2	227	18	63	26	52	19	71	24	41	9
Sig	66	5	20	8	8	3	2	1	36	8
Muc	51	4	10	4	10	4	20	7	11	2
Morphological heterogeneity
Homogenous	542	43	102	42	82	30	185	63	173	38
Heterogeneous	724	57	140	58	189	70	108	37	287	62
KRAS mutation status
Mutant	68	5	10	4	16	6	28	10	14	3
Wild type	1198	95	232	96	259	94	261	90	446	97
KRAS gene copy number
Amplified	47	7	12	6	17	8	18	8	-	-
Other	602	93	196	94	199	92	207	92	-	-
Microsatellite instability status
MSI	199	16	23	9	31	12	64	22	81	18
MSS	1057	84	223	91	224	88	231	78	379	82

Some variables do not add up to 1282 due to missing data

JGCA Japanese Gastric Cancer Association, Pap papillary adenocarcinoma, Tub1 well-differentiated tubular adenocarcinoma, Tub2 moderately differentiated tubular adenocarcinoma, Por1 poorly differentiated adenocarcinoma solid type, Por2 poorly differentiated adenocarcinoma non-solid type, Sig signet-ring cell carcinoma, Muc mucinous adenocarcinoma, MSI microsatellite instable, MSS microsatellite stable, KCCH Kanagawa Cancer Center Hospital, LTHT Leeds Teaching Hospital Trust, TCGA The Cancer Genome Atlas, TMGH Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology

Histological classification of gastric cancer

Histological classification was available for 1271 GCs. Using the JGCA classification, the most predominant phenotype was moderately differentiated tubular  [tub2] (n = 408, 32%), followed by poorly differentiated solid type [por1] (n = 229, 18%), poorly differentiated non-solid type [por2] (n = 227, 18%), well-differentiated tubular [tub1] (n = 219, 17%), papillary [pap] (n = 71, 6%), signet-ring cell [sig] (n = 66, 5%) and mucinous [muc] (n = 51, 4%). According to Lauren classification, 293 (23%) GCs were classified as diffuse type, 698 (55%) as intestinal type, 51 (4%) as mucinous and 229 (18%) as indeterminate. Seven hundred and twenty-four GCs (57%) had intratumour morphological heterogeneity (see Table 2).

KRAS mutation status and relationship with clinicopathological variables

KRASmut status was available from 1266 GCs (KCCH n = 242; LTHT n = 275; TCGA n = 289, TMGH n = 460). In total, 68 (5%) GCs were KRAS mutant, with the highest frequency of KRASmut in the TCGA cohort (10%) and lowest frequency in the TMGH cohort (3%), see Table 2. Within the KRASmut GC, the most frequent histological phenotype was intestinal type (n = 41, 61%) or tub2 (n = 27, 40%) by Lauren and JGCA classification, respectively (see Fig. 1a). Comparing individual histological subtypes, mucinous phenotype displayed the highest frequency of KRASmut by Lauren (p = 0.013) and JGCA (p = 0.012) classification, respectively (see Fig. 1b). KRASmut was more frequent in MSI GC (p < 0.001). For the comparison of KRASmut status and other clinicopathological variables, see Table 3. The 5-year overall survival rate in patients with KRASmut or KRAS wild type GC was 63.6% and 54.8%, respectively, p = 0.541, see Fig. 2a.

Fig. 1

Example of KRAS mutated GC with a moderately differentiated tubular (tub2) phenotype and b mucinous (muc) phenotype

Table 3

Comparison of clinicopathological variables and KRAS mutation status in all gastric cancer cohorts combined

	KRAS mutation status				p value
	M n	M %	WT n	WT%
Age (years)
< 65	13	4	319	96	0.167
≥ 65	55	6	876	94
Gender
Male	36	5	723	95	0.225
Female	32	6	475	94
T stage
pT1/pT2	20	5	388	95	0.639
pT3/pT4	47	6	802	95
N stage
pN0	31	6	455	94	0.158
pN1–pN3	35	5	734	95
TNM stage
I–II	35	5	651	95	0.756
III–IV	31	6	533	95
Lauren classification
Diffuse	7	2	283	98	0.013
Intestinal	41	6	652	94
Mucinous	6	12	43	88
Indeterminate	13	6	215	94
JGCA classification
Pap	7	10	64	90	0.012
Tub1	7	3	212	97
Tub2	27	7	376	93
Por1	13	6	215	94
Por2	6	3	219	97
Sig	1	2	64	99
Muc	6	12	43	88
Morphological heterogeneity
Homogeneous	31	6	506	94	0.550
Heterogeneous	36	5	683	95
Microsatellite instability status
MSI	33	17	165	83	< 0.001
MSS	32	3	1010	97

Some variables do not add up to 1282 due to missing data

JGCA Japanese Gastric Cancer Association, Pap papillary adenocarcinoma, Tub1 well-differentiated tubular adenocarcinoma, Tub2 moderately differentiated tubular adenocarcinoma, Por1 poorly differentiated adenocarcinoma solid type, Por2 poorly differentiated adenocarcinoma non-solid type, Sig signet-ring cell carcinoma, Muc mucinous adenocarcinoma, MSI microsatellite instable, MSS microsatellite stable, M mutated, WT wild type

Fig. 2

Kaplan–Meier plots showing probability of overall survival in GC patients stratified by KRAS gene activation status. a Kaplan–Meier survival analysis showed no difference in survival when patients were stratified by KRAS mutation status. b Kaplan–Meier survival analysis showed no difference in survival when patients were stratified by KRAS amplification status

KRAS amplification and relationship with clinicopathological variables

KRAS gene copy number status was available from 649 GCs (KCCH n = 208, LTHT n = 216, TCGA n = 225). In total, 47 (7%) GCs had a KRASamp [TCGA (8%), LTHT (8%) and KCCH (6%)], see Table 2. Within KRASamp GC, intestinal type (n = 23, 50% or tub2 (n = 21, 46%) was the most frequent histological phenotype by Lauren and JGCA classification, respectively (see Fig. 3a). Comparing individual histological subtypes, KRASamp was more frequently found in indeterminate type (n = 12, 10%) or por1 (n = 12, 10%) phenotype by Lauren and JGCA classification, respectively (see Fig. 3b). There was no relationship between KRASamp and histological phenotype or any other clinicopathological variables, see Table 4. The 5-year overall survival rate in GC patients with and without KRASamp was 47.6% versus 55.6%, respectively, p = 0.166, see Fig. 2b.

Fig. 3

Example of KRAS amplified GC with a moderately differentiated tubular (tub2) adenocarcinoma and b poorly differentiated solid-type  (por1) adenocarcinoma

Table 4

Comparison of clinicopathological variables and KRAS copy number status in KCCH, LTHT and TCGA gastric cancer cohorts combined

	KRAS amplified (n)	KRAS amplified (%)	KRAS other (n)	KRAS other (%)	p value
Age (years)
< 65	21	8	235	92	0.462
≥ 65	26	7	364	93
Gender
Male	29	7	383	93	0.792
Female	18	8	219	92
T stage
pT1/pT2	8	7	109	93	0.867
pT3/pT4	38	7	484	93
N stage
pN0	7	4	163	96	0.058
pN1–pN3	40	9	428	92
TNM stage
I–II	14	5	262	95	0.061
III–IV	32	9	325	91
Lauren classification
Diffuse	10	6	168	94	0.480
Intestinal	23	7	298	93
Mucinous	1	3	29	97
Indeterminate	12	10	107	90
JGCA classification
Pap	0	0	24	100	0.267
Tub1	2	3	70	97
Tub2	21	9	204	91
Por1	12	10	107	90
Por2	9	6	144	94
Sig	1	4	24	96
Muc	1	3	29	97
Morphological heterogeneity
Homogeneous	19	6	282	94	0.437
Heterogeneous	27	8	315	92
Microsatellite instability status
MSI	3	3	90	97	0.093
MSS	44	8	494	92

Some variables do not add up to 822 due to missing data

JGCA Japanese Gastric Cancer Association, Pap papillary adenocarcinoma, Tub1 well-differentiated tubular adenocarcinoma, Tub2 moderately differentiated tubular adenocarcinoma, Por1 poorly differentiated adenocarcinoma solid type, Por2 poorly differentiated adenocarcinoma non-solid type, Sig signet-ring cell carcinoma, Muc mucinous adenocarcinoma, MSI microsatellite instable, MSS microsatellite stable, KCCH Kanagawa Cancer Center Hospital, LTHT Leeds Teaching Hospital Trust, TCGA The Cancer Genome Atlas

Only two GCs from the TCGA cohort had a concurrent KRASamp and KRASmut; one was a mucinous GC, the other was a por2 GC according to JGCA classification.

Discussion

This is the largest multicentre study to date to investigate the relationship between KRAS activation by mutation and/or amplification and histological phenotype in GC. The frequency of KRASamp (7%) was slightly higher than that of KRASmut (5%) which is consistent with other GC studies [10, 11, 37]. The higher frequency of KRASmut in the TCGA GC cohort compared to the other cohorts could be related to the methodology as TCGA used whole-exome sequencing to test non-hotspot regions, whereas other studies used less-sensitive Sanger sequencing/PCR–RFLP. We found KRASamp and KRASmut were exclusive in > 99% of GC, which is consistent with previous reports [11–13, 38].

The relationship between KRASmut and histological phenotype has not been investigated in great detail and previous studies were limited by small sample sizes and hence lack of statistical power [6]. In our study, we identified a relationship between KRASmut and mucinous histological phenotype, which is concordant with higher frequencies of KRASmut being reported in mucinous lung [8], ovarian [9] and colorectal cancer [39, 40]. However, due to the relatively low frequency of GC with mucinous phenotype and KRASmut (12%), it would not be feasible to use the presence of a mucinous phenotype as a predictor for the presence of a KRASmut in GC. The main component of mucinous GCs is extracellular mucin, which consists of high molecular weight glycoproteins regulated by expression of the MUC2, MUC5AC and MUC6 genes in humans [41]. In mouse models with constitutively activated KRAS in the stomach, irregular MUC4+ cells were found with abnormal mucins confirmed by Alcian-blue staining [42]. Interestingly, our study suggests a relationship between KRASmut and mucinous phenotype, which is characterised by extracellular mucin, but is not related to signet-ring cell type GC, which is characterised by intracellular mucin. Our study confirmed the relationship between KRASmut and the presence of MSI, which our group and others have described previously in a smaller GC cohort [43, 44].

The prognostic significance of KRASmut in GC remains controversial [6]. In our study, there was no association with the presence of KRASmut and survival. Interestingly, in lung and colorectal cancer, KRASmut has been associated with a poor prognosis [45, 46], whereas in ovarian cancer, KRASmut has been associated with an improved prognosis [47].

The relationship between KRASamp and clinicopathological variables, including histological phenotype in cancer is not well studied. In GC, we found no statistically significant relationship between KRASamp and histological phenotype, or any other clinicopathological variables. In contrast, others found that the presence of KRASamp is associated with a poor prognosis in GC [3, 10, 12]. This difference might be due to case selection and methodology used.

In our study, we used the JGCA scheme for the histological classification of GC and performed a conversion to the Lauren scheme, which is the most widely used histological classification system in Western countries [22]. Previous studies investigating the relationship between KRASmut and histological phenotype performed classification according to the Lauren scheme [6], for which there is no separate category for mucinous GC. The relatively large number of GCs classified as indeterminate according to the Lauren scheme comes from conversion from the JGCA por1 histological phenotype. Direct classification according to the Lauren scheme, would likely result in a higher proportion of GCs classified as either intestinal or diffuse.

In colorectal cancer, KRASmut is known to be an early event in the progression from normal colonic epithelial cell to adenoma, and finally to carcinoma [48]. The evidence of sequential development by accumulation of genetic alterations, including KRASmut, is still controversial in GC [49-51]. We were unable to make any comments regarding the role of KRAS activation in gastric carcinogenesis in our cohort as we did not investigate precancerous lesions in the current study. However, evidence from mouse models suggest that KRASmut is one of the key molecular alterations involved in the development of stomach dysplasia [52] and GC [53]. Based on the evidence from other cancer types that KRASmut influence the progression of a mucinous histological phenotype, we therefore speculate based on our results, that KRASmut in GC is an early event in GC development, whereas KRASamp is likely to be a late event occurring after the histological phenotype has been established. This would correspond with experiments in mice expressing oncogenic KRAS in combination with E-cadherin and p53 loss, which resulted in a rapid progression of GC compared to wild type mice [53].

Our study has some limitations. This is a retrospective study. Histological phenotyping was performed on a single slide. Given the high frequency of intra-tumoural morphological heterogeneity in this study and the previously reported intra-tumoural heterogeneity in KRASmut status in GC [54], the sensitivity of some of the techniques used in the current study may not be sufficient to detect KRAS activation in subclones of tumour cells. As we did not perform microdissection of tumour subregions, we cannot comment on KRAS status heterogeneity within the same tumour. Furthermore, we used different techniques for DNA extraction, KRASmut status analysis and MSI analysis in different cohorts included in the current study, each with differing sensitivities [55, 56].

In summary, we identified a relationship between KRASmut and mucinous histological phenotype in GC. The high level of intratumour morphological heterogeneity could reflect KRASmut heterogeneity, which may explain the failure of anti-EGFR therapy in GC.

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 15 kb)