Metabolomics of primary cutaneous melanoma and matched adjacent extratumoral microenvironment.

BACKGROUND: Melanoma causes the vast majority of deaths attributable to skin cancer, largely due to its propensity for metastasis. To date, few studies have examined molecular changes between primary cutaneous melanoma and adjacent putatively normal skin. To broaden temporal inferences related to initiation of disease, we performed a metabolomics investigation of primary melanoma and matched extratumoral microenvironment (EM) tissues; and, to make inferences about progressive disease, we also compared unmatched metastatic melanoma tissues to EM tissues.
METHODS: Ultra-high performance liquid chromatography-mass spectrometry-based metabolic profiling was performed on frozen human tissues.
RESULTS: We observed 824 metabolites as differentially abundant among 33 matched tissue samples, and 1,118 metabolites as differentially abundant between metastatic melanoma (n = 46) and EM (n = 34) after false discovery rate (FDR) adjustment (p<0.01). No significant differences in metabolite abundances were noted comparing primary and metastatic melanoma tissues.
CONCLUSIONS: Overall, pathway-based results significantly distinguished melanoma tissues from EM in the metabolism of: ascorbate and aldarate, propanoate, tryptophan, histidine, and pyrimidine. Within pathways, the majority of individual metabolite abundances observed in comparisons of primary melanoma vs. EM and metastatic melanoma vs. EM were directionally consistent. This observed concordance suggests most identified compounds are implicated in the initiation or maintenance of melanoma.

Background

Melanoma is a neoplasm that evolves through discrete stages distinguished by clinical and pathological features [1, 2]; however, the underlying molecular biology of disease initiation and progression is not well understood. The development and progression of melanoma are influenced by endogenous compounds—metabolites—which combined represent distinct metabolic phenotypes with respect to glucose uptake, glycolytic, and mitochondrial activity [3]. Collectively, metabolites and their interactions constitute the metabolome, a de facto representation of cellular physiology that is not subject to epigenetic regulation or post-transcriptional/-translational modification, as might be expected when examining genes and proteins, respectively. The metabolome describes metabolic patterns that are most proximal to disease or a phenotype of interest, and may better distinguish key biological mechanisms influencing disease etiology and progression compared to other molecular approaches [4].

To date, most molecular studies of cancer have focused on malignant tissue and have given less consideration to putative histologically-normal tissue surrounding solid tumors, which we hereafter refer to as extratumoral microenvironment (EM). The reasons for this inattention are related to concerns over whether the surrounding tissue is truly normal or, as we posit, represents a subclinical permissive environment for tumor formation on a molecular scale. Previous studies of breast cancer [5, 6], as well as recent pathway-based findings using data from the TCGA [7], are supportive of the latter.

A better temporal understanding of metabolic perturbations influencing melanoma development and progression could further characterize melanoma heterogeneity and identify influential biological pathways, as well as potential therapeutic targets. To identify and evaluate metabolic patterns and differences among temporally distinct tissues, we conducted a metabolomics investigation of frozen primary melanoma tissue and matched EM tissue. And, to better characterize advanced disease, we analyzed a cohort of frozen unmatched metastatic melanoma tissues.

Methods

Patient selection

Participants for this study were adults aged 19 to 96 years who were treated for primary cutaneous malignant melanoma at the H. Lee Moffitt Cancer Center and Research Institute (Moffitt)—a National Cancer Institute-designated comprehensive cancer center—and had signed informed written consent for participation in Moffitt’s Total Cancer Care Protocol. Study participants’ medical records were accessed between July, 2015 and September, 2017 to obtain relevant patient level data. Ethics approval was obtained from Liberty IRB (Liberty IRB Tracking #: 15.06.0013) prior to the start of the study (even after an initial "Non-Human Subjects Research" determination by the Moffitt Cancer Center’s Protocol Support Office: MCC#18133) and all research was performed in accordance with relevant regulations. Melanoma is primarily a disease of Caucasian populations, so eligible cases were limited to patients who self-identified as white. Efforts were made to include participants equitably according to gender, but there were no restrictions on vital status or tumor stage. Eligible cases had existing frozen primary cutaneous melanoma with matching EM tissue available for research purposes. We also selected a different cohort of patients for whom metastatic melanoma tissue (unmatched) was available. Metastases from different organ sites were included to reflect a representative distribution of metastatic lesions observed at the Moffitt Cancer Center and minimize the potential systematic bias that could be introduced by the inclusion of analytes specific to cells other than skin.

Tissue specimen handling

Primary melanoma and EM specimens as well as metastatic melanoma lesions were procured from patients at the time of surgical excision, and were flash frozen within 30 minutes. H&E-stained frozen sections from each specimen were reviewed by the board-certified study pathologist (JLM) to ensure the presence of tumor at ≥70% cellularity, or in the case of EM tissues, <1% malignant cells. During macrodissection of specimens, efforts were made to exclude tissue regions directly exposed to optimal cutting temperature compound (OCT). OCT is a potentially adulterating compound containing polyvinyl alcohol and polyethylene glycol that is routinely used by pathologists to enable frozen tissue sectioning. To facilitate reproducibility of analyses and ensure adequate tissue availability for future assays, specimens were required to have a minimum mass of 15mg. Tumor specimens were required to exhibit at least 70% tumor cellularity and efforts were made to exclude regions of necrosis and small heterogeneous regions not representative of cells present in the total specimen.

Frozen tissue specimens were stored at -80°C before processing, which involved biopulverization (Biospec Biopulverizer). After pulverization, each sample was divided into aliquots for metabolomics and future molecular studies, and again stored at -80°C prior to further analysis.

IROA and internal standardization

Isotopic Ratio Outlier Analysis (IROA) is a mass spectrometry-based analytic technique that discriminates compounds of biological origin from non-biological artifacts in a two-group or multi-group study [8]. The IROA process was employed in this study and involves the selection of a standardized control cell group that is grown on a medium in which the carbon isotope 13C is randomly distributed into all nutrients at 95% abundance (normal or, natural, abundance ~1%). The labeled control cells are then pooled with an equivalent measure of tissue primarily containing the carbon isotope 12C (natural abundance ~99%). The pooled samples are then analyzed using liquid chromatography—mass spectrometry (LC-MS) methods. This protocol allows for unambiguous detection of 12C and 13C monoisotopic peaks for all compounds present in the pooled samples, leading to the accurate differentiation of metabolites found in the standardized control cells versus the tissue specimens. The primary advantage of this method is that only biologically relevant compounds will exhibit incorporation of the 13C label in the standard control; thus, only relevant metabolic peaks identified in the tissue specimens will contain matching peaks in the standard control. As a result, significant data reduction is achieved and artifacts that commonly interfere with analysis of mass spectrometry in traditional metabolomics approaches [9] can be readily identified and excluded. This protocol also benefits from unbiased relative quantitation of compounds present in the tissue compared to the standard control cell line and reduced sample-to-sample variability due to the simultaneous preparation and pooling of standard control and tissue samples.

IROA standardized control preparation

Yeast (Saccharomyces cerevisiae) was selected as the standard control for this study due to the range of metabolites exhibited. Yeast was grown on 5% and 95% 13C labeled media containing randomized labeled glucose as a single carbon source to enrich the natural abundance of 13C of all biologically formed metabolites. An extract from 95% 13C yeast was added as an Internal Standard (IS) to a dried, prepared extract of each homogenized tissue specimen (e.g. EM, primary melanoma, or metastatic melanoma) for phenotypic IROA injections, and extracts from both 5% and 95% 13C yeasts were pooled for the combined IROA injections.

In order to determine the optimal quantities of tissue and labeled control yeast in the pooled analytic sample, we conducted a titration pilot using stand-in meat homogenates. The protein concentration of the pooled meat homogenates was measured. From the stock, 250, 200, 150, 100, 50, and 25μg protein/mL samples were prepared at 50μL. Proteins were precipitated with 400μL 8:1:1 acetonitrile:methanol:acetone and centrifuged at 20,000xg for 10 minutes at 8°C. The supernatant (375μL) was dried under nitrogen gas at 30°C, and 10x diluted yeast IROA IS was used to reconstitute samples at a final volume of 40μL. After mass spectra were acquired, 50μg protein/mL was determined to be the optimal protein concentration for sample pre-normalization.

Metabolite extraction and LC-MS data processing

Pooled samples were pre-normalized at 50μg protein/mL, 25μL prepared as described below, randomized and batched into seven sets that included balanced numbers of samples from each group. Detailed procedures for metabolite extraction and analysis by ultra-high performance liquid chromatography—mass spectrometry (UHPLC-MS) have been previously described [10]. Briefly, the first batch was extracted and immediately placed on LC-MS for analysis. Extraction of subsequent batches was scheduled immediately following the end of the previous batch. Samples (25μL) were extracted using 200μL of 8:1:1 acetonitrile:methanol:acetone and centrifuged at 20,000xg for 10 minutes at 8°C. 188μL of supernatant was dried down under nitrogen gas at 30°C and 10x diluted yeast IROA IS was used to reconstitute samples to a final volume of 25μL, thereby assuring that all samples received identical quantities of the IROA IS.

Mass spectra were acquired on a Thermo Scientific Q Exactive orbitrap equipped with a heated electrospray ionization (HESI II) probe in positive ion mode. Full scan mode data were collected in profile mode from m/z 70–1000 corresponding to the mass range of most expected metabolites; external calibration (of both mass and mass resolution) was applied before each run to allow for liquid chromatography—high resolution mass spectrometry (LC-HRMS) at 35,000 resolution (m/z 200). Chromatographic separation of metabolites was achieved by coupling a Thermo Dionex UltiMate 3000 RS UHPLC system to the Q Exactive Orbitrap. Separation was achieved under gradient elution on an Ace Excel C18-pfp column (100 x 2.1mm, 2μm) at 25°C. Mobile phase A was 0.1% formic acid in water and B was acetonitrile. The flow rate was 350μL/min starting at 100% A and holding for 3 minutes, followed by an increase in B to 80% over 10 minutes. Mobile phase B was then held constant at 80% for 3 minutes before returning to 100% A for equilibration.

Processing of raw LC-MS data was performed by the Thermo Xcalibur Workstation software (v2.2.44) and Proteowizard’s MS Convert (v3.0.5759) was used to centroid and convert Thermo (.raw) files in mzXML data files for further analysis. The mzXML files were analyzed using ClusterFinder (version 3, IROA Technologies) in order to locate all IROA isotopic peaks and their associated natural abundance companions; results were compared to known metabolite peaks contained within the ClusterFinder library. Quantitation of the natural abundance peak areas and their ratios to the corresponding IROA IS was performed by ClusterFinder.

Statistical analysis

Principal component analysis (PCA) was performed using the NIPALS [11] algorithm as implemented in the pcaMethods [12] R package, and indicated a significant batch effect after metabolite extraction. The observed batch effect was mitigated by fitting partial least squares (PLS), with the response Y being the batch number (from 1–7). To account for missing data when fitting PLS, we used the R package mixOmics [13]. After visual inspection confirmed that the first component of the PLS analysis contained the batch effect, we computed the resultant residual PLS data matrix with one component which was used for the primary analysis. We repeated PCA using this corrected data to verify no clear separation of batches (Fig 1).

Fig 1

Identification and mitigation of batch effects.

Results of principal components analysis (PCA) illustrating an observed sample batch effect associated with mass spectrometry (A) and mitigation of that batch effect by fitting partial least squares (PLS) (B).

Primary analyses focused on discriminating mean metabolite abundance differences between primary melanoma and matched EM and between metastatic melanoma and unmatched EM. We also explored differences in mean metabolite abundances between primary melanoma and metastatic melanoma. To account for data skewness and an over-representation of zero counts, we conducted both a non-parametric Wilcoxon signed rank test and a parametric t-test; a metabolite was considered noteworthy only if it was found to be significantly differentially abundant between tissue types according to both statistical tests. Paired tests were used for the analysis of matched tissues and unpaired tests were performed for analyses of unmatched tissues. We observed a high level of concordance between the resultant p-values from the two tests when comparing primary melanoma vs. matched EM (Pearson correlation = 0.96) and metastatic melanoma vs. unmatched EM (Pearson correlation = 0.92). To account for multiple comparisons, p-values from each test were adjusted to control the false discovery rate (FDR) using the Benjamini-Yakutieli approach [14] at α = 0.01. Heatmaps illustrating metabolite abundances were generated using the ComplexHeatmap R package [15].

We performed pathway-based analyses comparing primary melanoma vs. EM as well as metastatic melanoma vs. EM, including as input all statistically significant results from both positive and negative ion modes from previous analyses, using Mummichog 1 interfaced through MetaboAnalyst [16-25]. We adopted the Mummichog default p-value cutoff of 1×10−05 to delineate between significantly enriched and non-significantly enriched metabolites. The KEGG pathways program was then used to identify the most strongly influenced metabolic pathways. We report enrichment factors—the ratio of the number of significant pathway hits to the expected number of compound hits within the pathway—to summarize the influence of individual KEGG pathways.

To further explore functional characteristics of metabolite differences, we performed metabolite set enrichment analysis (MSEA) within MetaboAnalyst. We used the RefMet conversion tool within Metabolomics Workbench [26] to standardize metabolite names prior to MSEA. Enrichment was performed using the Pathway-associated metabolite sets (SMPDB) [27], which consist of 99 metabolite sets from normal pathways. Network analysis was performed in MetaboAnalyst using the metabolite-metabolite interaction network based on associations extracted from STITCH [28].

Results

Study samples and patient characteristics

A total of 34 patients with matching frozen primary cutaneous melanoma and EM tissues (mean age at melanoma diagnosis = 64 years), as well as 46 patients with unmatched frozen metastatic melanoma tissue (mean age at melanoma diagnosis = 62 years), were eligible for inclusion in the final analytic cohort. Table 1 describes patient and tissue characteristics of those included in the analytic cohort. Among the 34 patients with matching primary melanoma and EM tissues, 4 patients also had matching metastatic melanoma tissue; these 4 metastatic melanoma tissue specimens were included with the unmatched metastatic melanoma tissue specimens in unmatched analyses only. One patient was excluded from the matched primary melanoma—EM analysis due to primary melanoma sample contamination prior to MS analysis, leaving a total of 33 patients (66 tissue samples) in the final matched cohort (S1 Fig).

Table 1

Descriptive characteristics of subjects and tissue specimens contributing to study results.

	Patients providing primary melanoma tissue	Patients providing metastatic melanoma tissue
# Subjects	Total (N = 35)	Total (N = 50)
Gender
Men	24 (69%)	28 (56%)
Women	11 (31%)	18 (36%)
Missing		4 (8%)
Vital status
Alive	21 (60%)	21 (42%)
Dead	14 (40%)	25 (50%)
Missing		4 (8%)
Average age at diagnosis (years)*	64	61
Histology*
Malignant melanoma, NOS	15 (43%)
Nodular melanoma	14 (40%)
Lentigo maligna melanoma	1 (3%)
Superficial spreading melanoma	4 (11%)
Acral lentignous melanoma	1 (3%)
Pathologic T Staging (Primaries)
pT1	1 (3%)
pT2	0 (0%)
pT3	8 (23%)
pT4	15 (43%)
pTX	11 (31%)
Anatomic Site of primary
Trunk	9 (26%)
Head/Neck	12 (34%)
Arm/Hand	7 (20%)
Leg/Foot	7 (20%)
Skin, NOS
Anatomic Site of metastasis
Brain		5 (10%)
Large Bowel		1 (2%)
Lung		6 (12%)
Lymph node		13 (26%)
Salivary gland		1 (2%)
Skin		5 (10%)
Small intestine		5 (10%)
Soft tissue		9 (18%)
Spleen		2 (4%)
Stomach		1 (2%)
Throacic		1 (2%)
Vulva		1 (2%)

* Data correspond to primary lesion.

Metabolite detection

In total, 9,599 peaks were detected by UHPLC-MS among the 118 frozen tissue samples included in the study. The final analytic data set excluded 1,540 peaks (16%) due to missing data among at least 50% of the tissue samples.

Comparison of metabolites in primary melanoma and EM

For the analysis comparing primary melanoma to EM tissues, an additional 552 peaks were excluded from the matched analysis due to failure to detect them among at least 50% of samples contributing to a specific analysis. Of the 7,507 peaks tested among matching tissue samples, 824 (11%) were differentially abundant after FDR adjustment (p<0.01). Approximately 75% of statistically significant metabolites were noted with higher abundance among primary cutaneous melanoma compared to matched EM (S2 Fig). Table 2 lists the top ten percent most significant of the 824 differentially abundant metabolites by mass (m/z) and retention time in the matched comparison of primary melanoma vs. EM. A heat map of these peaks is given in Fig 2A, and the paired differences (primary vs. EM) are shown in Fig 2B. Five metabolites falling in the top ten percent of statistically significant results had verified KEGG Compound IDs. Three of these five metabolites had significantly higher abundances among primary melanoma vs. EM in both positive and negative ion modes: N-Acetyl-DL-glutamic acid (KEGG ID: C00624); Uracil (KEGG ID: C00106); and Dihydroxyacetone phosphate (KEGG ID: C00111) (S3 Fig). Additionally, N-Methyl-D-aspartic acid (KEGG ID: C12269) and Fructose 1,6-bisphosphate (KEGG ID: C00354) had significantly higher abundances in primary melanoma vs. EM in positive ion mode and negative ion mode, respectively (S3 Fig). Supplemental bar graphs illustrate all metabolites that were identified as significantly differentially abundant between primary melanoma tissue and paired EM (S4 Fig).

Table 2

Top ten percent of differentially abundant metabolites among primary melanoma and matched EM tissues according to mass (m/z) and retention time.

Row*	Ion mode	P-value**	Direction vs. EM†	m/z	Retention time (seconds)	Compound Name‡	KEGG ID
1	Negative	9.10E-06	1	188.0565	2.1	N-Acetyl-DL-glutamic acid	C00624
2	Positive	5.30E-06	1	190.0707	2.1	N-Acetyl-DL-glutamic acid	C00624
3	Positive	3.40E-06	1	113.0346	1.3	Uracil	C00106
4	Negative	2.90E-06	1	111.0199	1.3	Uracil	C00106
5	Positive	6.60E-06	1	148.0603	0.7	N-Methyl-D-aspartic acid	C12269
6	Positive	3.00E-06	1	131.0339	0.7
7	Positive	6.60E-06	1	150.0651	0.7
8	Positive	1.83E-05	1	148.1092	0.6
9	Positive	1.78E-05	1	171.0051	0.7	Dihydroxyacetone phosphate	C00111
10	Negative	6.70E-06	1	168.9901	0.7	Dihydroxyacetone phosphate	C00111
11	Positive	7.80E-06	1	210.9975	0.7
12	Positive	3.40E-06	1	192.9872	0.7
13	Negative	1.17E-05	1	338.9892	0.7	Fructose 1,6-bisphosphate	C00354
14	Negative	9.70E-06	1	444.0363	2.0
15	Negative	7.80E-06	1	372.0843	6.3
16	Negative	1.87E-05	1	362.0508	3.7
17	Positive	3.50E-06	1	152.0566	3.6
18	Negative	9.10E-06	1	346.0558	2.7
19	Positive	2.90E-06	1	348.0693	2.6
20	Positive	1.69E-05	1	247.0570	0.8
21	Negative	6.40E-06	1	245.0431	0.8
22	Negative	6.60E-06	1	333.0593	0.7
23	Negative	3.40E-06	1	216.9390	0.7
24	Negative	3.00E-06	1	126.9440	0.7
25	Negative	5.00E-06	1	218.9359	0.7
26	Negative	2.90E-06	1	296.8818	0.6
27	Negative	3.80E-06	1	346.8808	0.6
28	Negative	3.00E-06	1	278.8926	0.7
29	Negative	6.70E-06	1	500.8455	0.7
30	Negative	3.00E-06	1	432.8575	0.6
31	Negative	3.00E-06	1	280.9070	0.7
32	Negative	2.90E-06	1	128.9597	0.6
33	Negative	1.18E-05	1	445.0531	0.8
34	Positive	5.40E-06	1	324.0584	0.9
35	Negative	5.90E-06	1	533.1057	0.9
36	Positive	3.00E-06	1	245.0603	0.9
37	Negative	1.47E-05	1	577.0958	0.9
38	Positive	1.15E-05	1	213.0163	1.2
39	Positive	1.18E-05	1	123.5941	0.9
40	Positive	6.40E-06	1	186.0758	3.0
41	Positive	6.60E-06	1	292.1018	1.2
42	Positive	1.18E-05	1	186.0761	0.7
43	Positive	1.13E-05	1	130.1227	1.7
44	Negative	1.60E-05	1	98.9737	0.7
45	Negative	2.90E-06	1	96.9693	0.7
46	Negative	5.00E-06	1	186.9649	0.7
47	Positive	5.40E-06	1	267.0562	0.8
48	Negative	1.72E-05	1	451.1265	0.8
49	Positive	6.60E-06	1	177.9573	0.7
50	Positive	3.50E-06	1	193.9296	0.7
51	Positive	9.80E-06	1	195.9679	0.7
52	Positive	1.45E-05	1	154.9414	0.7
53	Positive	3.00E-06	-1	218.9191	0.6
54	Negative	2.90E-06	-1	312.9429	0.6
55	Positive	7.80E-06	-1	349.2111	0.7
56	Negative	7.70E-06	-1	315.9351	0.6
57	Negative	6.70E-06	-1	358.9227	0.6
58	Negative	1.60E-05	-1	468.8964	0.6
59	Negative	5.40E-06	-1	536.8855	0.6
60	Negative	5.80E-06	-1	248.9604	0.6
61	Negative	3.80E-06	-1	316.9480	0.6
62	Negative	6.40E-06	-1	446.9031	0.6
63	Negative	2.90E-06	-1	314.9326	0.6
64	Negative	2.90E-06	-1	246.9449	0.6
65	Negative	3.00E-06	-1	380.9042	0.6
66	Positive	3.50E-06	-1	158.9615	0.9
67	Positive	3.40E-06	-1	226.9510	0.6
68	Positive	5.00E-06	-1	90.9766	0.7
69	Positive	5.80E-06	-1	156.9483	0.6
70	Positive	5.80E-06	-1	82.0264	0.6
71	Positive	6.60E-06	-1	288.9207	0.6
72	Negative	1.88E-05	-1	94.9247	0.7
73	Negative	7.00E-06	-1	92.9278	0.7
74	Negative	7.80E-06	-1	96.9218	0.7
75	Negative	1.66E-05	-1	154.8986	0.8
76	Negative	6.40E-06	-1	181.9661	0.7
77	Negative	3.00E-06	-1	113.0243	0.7
78	Negative	2.90E-06	-1	89.0239	0.7
79	Negative	7.10E-06	-1	119.0353	0.7
80	Negative	5.80E-06	-1	143.0354	0.7
81	Negative	2.90E-06	-1	217.0299	0.7	Glucose/Fructose
82	Negative	2.90E-06	-1	215.0327	0.7	Glucose/Fructose

*Corresponds to row in Fig 2.

**Minimum FDR adjusted P-value among Wilcoxon signed rank test and T-test.

†1 = metabolite is more abundant in tumor relative to EM; -1 = metabolite is less abundant in tumor relative to EM.

‡ Compound identification according to IROA ClusterFinder library.

Fig 2

Metabolite abundance profiles differ significantly between primary melanoma and matched EM tissues.

(A) Heatmap of the top 10% of significant metabolites (n = 824). Tissue type (EM: Green or primary melanoma: Red) is noted in the top color bar. Unidentified metabolites are annotated with retention time (RT) and mass (m/z). (B) Heatmap of the metabolite differences between matched primary melanoma and EM tissues from the same patient for the top 10% of significant metabolites.

Comparison of metabolites in metastatic melanoma and EM

The analysis comparing metastatic melanoma to EM tissues was performed after an additional 294 peaks were excluded due to failure to detect them among at least 50% of EM samples. Among the 7,765 peaks subsequently tested in the comparison of metastatic melanoma and EM tissues, 1,118 (14%) were differentially abundant after FDR adjustment (p<0.01). Approximately 67% of statistically significant metabolites were observed at higher abundances among metastatic melanoma compared to EM (S2 Fig). Of the 1,118 differentially abundant metabolites identified, the top ten percent most significant are presented in Table 3 according to mass (m/z) and retention time and are mapped in Fig 3. Four metabolites falling in the top ten percent of statistically significant results had verified KEGG IDs based on m/z and retention times; one of four metabolites was significantly higher in abundance among metastatic melanoma vs. EM in both positive and negative ion modes: Uracil (KEGG ID: C00106). The remaining three, Cytidine 5’-diphosphocholine (KEGG ID: C00307), Cytosine (KEGG ID: C00380), and N-Methyl-D-aspartic acid (KEGG ID: C12269) were higher in abundance among metastatic melanoma compared to EM in positive ion mode (S5 Fig). Supplemental box plots illustrate all metabolites that were identified as significantly differentially abundant between EM and unmatched metastatic melanoma (S6 Fig). A comparison of metabolite abundances found in primary melanomas and unmatched metastatic melanomas yielded no statistically significant differences at the adjusted p<0.01 threshold (S7 Fig).

Table 3

Top ten percent of differentially abundant metabolites among metastatic melanoma and EM tissues according to mass (m/z) and retention time.

Row*	Ion mode	P-value**	Direction vs. EM†	m/z	Retention time (seconds)	Compound Name	KEGG ID
1	Negative	4.28E-08	1	218.0671	3.2
2	Positive	4.00E-11	1	230.1129	1.5
3	Positive	2.59E-08	1	264.0341	0.9
4	Positive	5.80E-10	1	272.0199	0.9
5	Negative	1.15E-08	1	533.1057	0.9
6	Positive	5.89E-09	1	245.0603	0.9
7	Positive	1.98E-08	1	489.1135	0.9	Cytidine 5’-diphosphocholine	C00307
8	Negative	3.61E-09	1	577.0958	0.9
9	Positive	1.50E-10	1	112.0505	0.8	Cytosine	C00380
10	Positive	9.60E-09	1	204.0864	3.0
11	Positive	1.19E-09	1	186.0758	3.0
12	Negative	5.66E-09	1	606.0745	3.0
13	Positive	4.91E-09	1	204.0863	3.1
14	Negative	4.80E-08	1	606.0745	3.2
15	Positive	1.41E-09	1	204.0863	3.3
16	Negative	3.80E-10	1	333.0593	0.7
17	Positive	1.10E-10	1	335.0726	0.7
18	Negative	4.40E-10	1	245.0431	0.8
19	Positive	1.50E-10	1	247.0570	0.8
20	Positive	8.91E-08	1	148.0603	0.7	N-Methyl-D-aspartic acid	C12269
21	Positive	3.63E-08	1	131.0339	0.7
22	Positive	5.00E-10	1	150.0651	0.7
23	Negative	1.98E-08	1	228.0639	0.7
24	Negative	1.39E-08	1	272.0540	0.7
25	Positive	1.10E-07	1	706.0822	7.8
26	Positive	1.10E-07	1	706.2252	7.8
27	Negative	9.21E-08	1	160.0823	0.7
28	Negative	4.40E-10	1	126.9440	0.7
29	Negative	1.50E-10	1	216.9390	0.7
30	Negative	6.00E-11	1	218.9359	0.6
31	Negative	6.00E-11	1	128.9597	0.6
32	Negative	6.00E-11	1	296.8818	0.6
33	Negative	1.65E-08	1	346.8808	0.6
34	Negative	1.27E-09	1	278.8926	0.6
35	Negative	1.36E-09	1	432.8575	0.6
36	Negative	2.40E-10	1	280.9070	0.6
37	Negative	8.20E-08	1	500.8455	0.6
38	Positive	6.00E-10	1	156.9981	1.3
39	Positive	1.10E-10	1	123.5234	1.3
40	Negative	3.00E-11	1	173.0203	1.3
41	Positive	1.00E-11	1	113.0346	1.3	Uracil	C00106
42	Negative	1.00E-11	1	111.0199	1.3	Uracil	C00106
43	Positive	4.40E-10	1	114.5181	1.3
44	Positive	9.00E-11	1	214.9969	1.3
45	Positive	3.18E-08	1	126.0268	1.4
46	Positive	1.50E-10	1	230.9694	1.3
47	Positive	9.00E-11	1	212.9581	1.3
48	Positive	1.98E-08	1	268.1033	6.4
49	Positive	1.59E-09	1	290.0852	6.4
50	Negative	6.26E-09	1	266.0898	6.4
51	Negative	8.20E-10	1	306.0498	1.2
52	Positive	2.80E-10	1	395.4083	7.2
53	Positive	2.59E-08	1	325.0422	2.0
54	Positive	2.28E-09	1	213.0159	2.0
55	Negative	8.15E-09	1	323.0286	2.0
56	Positive	8.25E-09	1	347.0240	2.0
57	Negative	8.25E-09	1	435.0059	2.0
58	Negative	1.85E-08	1	346.0558	2.6
59	Positive	1.16E-08	1	348.0693	2.6
60	Positive	1.20E-07	1	152.0566	3.6
61	Positive	2.55E-08	1	357.0994	5.7
62	Positive	1.67E-08	1	358.1028	5.7
63	Negative	8.71E-09	1	356.0893	5.7
64	Positive	1.90E-10	1	374.0977	6.3
65	Negative	1.10E-10	1	372.0843	6.3
66	Negative	8.30E-08	1	332.0587	2.5
67	Negative	7.58E-08	1	105.0016	15.8
68	Positive	7.06E-08	1	334.0722	2.0
69	Positive	2.69E-08	1	356.0540	2.0
70	Negative	1.61E-08	1	444.0363	2.0
71	Negative	1.06E-08	1	332.0589	2.0
72	Positive	2.69E-08	1	218.0323	2.0
73	Negative	1.06E-08	-1	340.9375	0.6
74	Negative	3.70E-10	-1	312.9429	0.6
75	Negative	1.59E-09	-1	181.9661	0.7
76	Positive	4.32E-08	-1	216.9216	0.6
77	Positive	3.20E-10	-1	218.9191	0.6
78	Positive	1.14E-07	-1	150.0139	0.7
79	Negative	5.30E-10	-1	315.9351	0.6
80	Negative	3.70E-10	-1	358.9227	0.6
81	Positive	1.50E-10	-1	226.9510	0.6
82	Positive	1.40E-10	-1	90.9766	0.7
83	Positive	9.00E-11	-1	158.9615	1.0
84	Positive	7.40E-10	-1	82.0264	0.6
85	Positive	1.21E-09	-1	288.9207	0.6
86	Negative	3.80E-10	-1	446.9031	0.6
87	Positive	2.54E-08	-1	236.9065	0.6
88	Negative	2.04E-09	-1	246.9449	0.6
89	Negative	1.59E-09	-1	314.9326	0.6
90	Negative	1.00E-10	-1	380.9042	0.6
91	Negative	1.67E-08	-1	468.8964	0.6
92	Negative	1.58E-09	-1	536.8855	0.6
93	Negative	2.90E-09	-1	248.9604	0.6
94	Negative	4.60E-10	-1	316.9480	0.6
95	Positive	7.40E-10	-1	156.9483	0.6
96	Negative	1.31E-08	-1	144.8697	0.8
97	Negative	1.50E-10	-1	146.8667	0.8
98	Negative	6.80E-10	-1	154.8809	0.8
99	Negative	1.11E-08	-1	94.9247	0.7
100	Negative	4.91E-09	-1	92.9278	0.7
101	Negative	1.31E-08	-1	154.8986	0.8
102	Negative	7.12E-08	-1	96.9218	0.7
103	Positive	1.10E-07	-1	241.1588	2.3
104	Positive	2.63E-09	-1	256.1775	6.3
105	Positive	2.58E-08	-1	257.1614	1.8
106	Positive	1.10E-07	-1	200.0912	4.7
107	Negative	4.28E-08	-1	113.0243	0.7
108	Negative	2.82E-08	-1	143.0354	0.7
109	Negative	8.29E-08	-1	89.0239	0.7
110	Negative	3.61E-09	-1	217.0299	0.7	Glucose/Fructose
111	Negative	3.30E-09	-1	215.0327	0.7	Glucose/Fructose
112	Positive	3.16E-08	-1	180.0865	0.7

*Corresponds to row in Fig 3.

**Minimum FDR adjusted P-value among Wilcoxon signed rank test and T-test.

†1 = metabolite is more abundant in tumor relative to EM; -1 = metabolite is less abundant in tumor relative to EM.

‡ Compound identification according to IROA ClusterFinder library.

Fig 3

Metabolite abundance profiles differ significantly between metastatic melanoma and unmatched EM tissues.

Heatmap of the top 10% of significant metabolites (n = 1,118). Tissue type (EM:green or metastatic melanoma: Black) is noted in the top color bar.

KEGG pathway-based analysis of metabolites

KEGG pathway-based analysis of metabolites distinguishing primary melanoma and matched EM showed significant influence of compounds involved in the metabolism of: ascorbate and aldarate (Enrichment Factor = 1.67, p = 0.003); propanoate (Enrichment Factor = 0.96, p = 0.04); and tryptophan (Enrichment Factor = 0.65, p = 0.04). Metabolites involved in vitamin B6 metabolism (Enrichment Factor = 1.21, p = 0.06) and pyruvate metabolism (Enrichment Factor = 1.37, p = 0.08) were identified as strongly enriched, but were not statistically significant (Table 4).

Table 4

Top KEGG biological pathways distinguishing primary melanoma from EM and metastatic melanoma from EM.

Primary melanoma vs. EM						Metastatic melanoma vs. EM
Pathway name	Total metabolites*	Hits (all/sig)**	Expected†	Enrichment factor‡	P-value	Total metabolites*	Hits (all/sig)**	Expected†	Enrichment factor‡	P-value
Ascorbate and aldarate	45	32/29	17.41	1.67	0.003	45	32/32	24.1	1.33	0.002
Tryptophan	79	23/20	30.56	0.65	0.04	79	26/23	42.31	0.54	0.33
Propanoate	35	14/13	13.54	0.96	0.04	35	15/12	18.74	0.64	0.76
Vitamin B6	32	17/15	12.38	1.21	0.06	32	19/17	17.14	0.99	0.35
Pyruvate	32	20/17	12.38	1.37	0.08	32	20/14	17.14	0.82	0.96
Pyrimidine	60	35/24	23.21	1.03	0.57	60	34/33	32.13	1.03	0.01
Histidine	44	17/12	17.02	0.71	0.54	44	19/19	23.56	0.81	0.03
Pentose phosphate	32	24/14	12.38	1.13	0.90	32	26/25	17.14	1.46	0.05

* Total number of KEGG metabolites considered by Mummichog/Metaboanalyst according to pathway.

** Total number of hits per pathway and total number of statistically significant hits per pathway (p<1x10-05).

† Expected number of hits.

‡ The ratio of the number of significant pathway hits to the expected number of compound hits within the pathway.

KEGG pathway-based analysis including metabolites identified in the unmatched analysis of EM and metastatic melanoma showed pronounced influence of compounds involved in ascorbate and aldarate metabolism (Enrichment Factor = 1.33, p = 0.002). Additionally, pyrimidine (Enrichment Factor = 1.03, p = 0.01) and histidine (Enrichment Factor = 0.81, p = 0.03) metabolism were significantly influential in distinguishing metastatic melanoma from EM. Pentose phosphate metabolism (Enrichment Factor = 1.46, p = 0.05) was notably enriched, but marginally statistically significant (Table 4).

MSEA pathway-based analysis of metabolites

We conducted MSEA for metabolites with statistically significantly higher abundance (or lower abundance) in primary (or metastatic) melanoma compared to EM; and for each condition, the MSEA of pathway-associated metabolite sets—based on the small molecule pathway database (SMPDB) [27]–was considered. A network of metabolite-metabolite interactions was plotted for each condition, with nodes representing metabolites and edges indicating a relationship between metabolites based on STITCH databases [28]. L-glutamic acid and pyruvic acid exhibited the most interactions when we considered significantly overabundant metabolites among primary melanoma compared to EM (S8 Fig), while D-glucose and D-galactose exhibited the most interactions when we considered metabolites that were significantly less abundant among primary melanoma compared to EM (S9 Fig).

For those metabolites that were overabundant among metastatic melanoma vs. EM, we again noted that L-glutamic acid and pyruvic acid exhibited the most interactions in the MSEA generated metabolite network (S10 Fig). Similarly to results from our comparison of primary melanoma and EM, D-glucose and D-galactose exhibited the most interactions when we considered metabolites that were significantly less abundant among metastatic melanoma compared to EM (S11 Fig).

Discussion

To date, metabolomics investigations of frozen human tissue specimens in the context of cancer have been limited; especially scant are those involving non-malignant tissue. To our knowledge, this study represents the largest examination of extratumoral and matched primary cutaneous melanoma tissues as well as a cohort of unmatched metastatic melanomas.

Our evaluation showed significantly differentially abundant metabolites comparing either primary melanoma or metastatic melanoma to EM, and that 75% and 68% of differences, respectively, represented higher levels of metabolite in the melanoma tissue. In contrast, comparison of metabolite abundances between primary and metastatic melanoma yielded no statistically significant differences. In a study of melanoma cell lines, Yu et al. reported 12 metabolites different between primary melanoma and descendent metastatic melanoma [29]. This study applied a less conservative significance level (α = 0.05) whereas our approach was deliberately conservative to identify differences with high confidence focused on malignant vs. EM conditions. Our data suggest that primary vs. metastatic differences may be more subtle than differences observed between malignant tissues and EM, and future studies are needed to fully characterize these differences.

Overall, pathway-based analysis yielded three significantly influential pathways in distinguishing primary melanoma from EM (ascorbate and aldarate metabolism, propanoate metabolism, and tryptophan metabolism) and three significantly influential pathways in distinguishing metastatic melanoma from EM (ascorbate and aldarate metabolism, pyrimidine metabolism and histidine metabolism) that may reflect important foci of carcinogenesis and cancer progression, respectively. Within these pathways, the majority of individual metabolite abundances observed in comparisons of primary melanoma vs. EM and metastatic melanoma vs. EM were directionally consistent; that is, the majority of compounds that were observed to be higher in abundance (or less abundant) in primary melanoma compared to EM were also higher in abundance (or less abundant) in metastatic melanoma compared to EM. This observed concordance suggests most identified compounds are implicated in the initiation or maintenance of melanoma, as opposed to progression of disease. Nevertheless, we observed several key metabolites within pathways that were in opposing directions according to primary melanoma vs. EM or metastatic melanoma vs. EM. These discordant observations are suggestive of metabolites that may be influential in disease progression.

Ascorbate and aldarate metabolism showed notable differences between both malignant tissue types and EM. Interestingly, within this pathway, arabinose was observed at lower abundances in primary melanoma compared to EM, whereas it was at higher levels in metastatic melanoma compared to EM. An overabundance of arabinose was reported in colorectal tumors compared to matched normal mucosa, where the majority of malignant tissues sampled (56%) represented metastatic disease [30]. Similarly, in a study comparing serum metabolite concentrations between advanced pancreatic cancer patients (79% stage III/IV) and healthy volunteers, arabinose was found at nearly 2-fold higher concentrations among pancreatic cancer patients [31]. In contrast, a study comparing relatively early staged esophageal carcinomas (60% stage I/II; 70% non-metastatic) to matched normal mucosae reported a lower abundance of arabinose among the tumor tissue [32]. These results may indicate that arabinose and related metabolic pathways should be more thoroughly investigated. In addition, arabinose has been the target of bacteria-based cytotoxic therapies in murine colorectal tumors involving Salmonella typhimurium. The attenuated strain is engineered to exclude the ara operon that is responsible for metabolizing arabinose to D-xylulose-5-phosphate. When affected mice were treated with the operon-deleted strain, arabinose accumulation resulted, facilitating expression of the cytotoxic protein cytolysin A in the tumors [33]. In conjunction with arabinose supplementation, such therapies may show promise among earlier staged melanomas, as well as in the treatment of metastatic melanomas that accumulate high levels of arabinose. It should be noted that compounds involved in the KEGG annotated ascorbate and aldarate metabolic pathway are involved in several other pathways, leading some to suggest the pathway is redundant and should not be considered as an independent pathway [34].

We noted a significant influence of compounds involved in propanoate metabolism on distinguishing primary melanoma from EM. Propanoate was notably reduced among primary melanoma compared to EM, whereas it was at significantly higher levels among metastatic melanoma compared to EM. Propanoate has been shown to inhibit tumor cell proliferation and promote apoptosis in different cancer cell types [35-37]; one possible explanation for our observation may be related to reduced expression of free fatty acid receptor 2 (FFAR2, also known as GPR43)—a receptor for which propanoate is the most potent ligand [38]—leading to an unproductive pool of propanoate. Loss of FFAR2 has been shown to promote colon cancer and leukemia in murine models [39-41], and expression levels are relatively diminished among TCGA melanoma tissues vs. several other cancers according to the Human Protein Atlas [42]. Loss of FFAR2 (or obstruction of ligand binding) may be a significant marker of melanoma progression, and is already being examined as a therapeutic target for metabolic and inflammatory diseases [43]. Surprisingly, in comparisons of both primary melanoma and metastatic melanoma vs. EM, we observed a reduced abundance of L-lactate. Lactate is involved in propanoate metabolism as a contributor to the pool of propionyl-CoA, which is converted to succinyl-CoA and enters the Krebs cycle. It is also an important factor in glycolysis. Increased production of lactate via glycolysis is a hallmark of cancer in general, as the vast majority of human cancers exhibit overexpression of glycolytic genes [44, 45]. Melanoma cells are known to exhibit the Warburg effect in cell line studies, but have also demonstrated functional oxidative phosphorylation, even under hypoxic conditions [46]. However, in the context of tumor microenvironment, newer research suggests metabolic heterogeneity across tumors [47] that may be attributed to a preference for oxidative phosphorylation—a so-called “reverse Warburg effect” [48-51]. This theory involves aberrant induction of aerobic glycolysis in adjacent cells and utilization of the products for mitochondrial oxidative phosphorylation in tumor cells [52-55]. The proposed mechanism involves tumor cell stimulation of oxidative stress among adjacent fibroblasts, leading to downregulation of caveolin-1 (CAV1)—a plasma membrane protein associated with increased mitochondrial activity [55, 56]. Downregulated CAV1 promotes glycolysis in adjacent cells leading to increased tumor growth via the reverse Warburg effect [57]. Loss of stromal CAV1 has been associated with poorer prognosis among metastatic melanoma patients [58], and clinical studies of squamous cell carcinoma have demonstrated reconstitution of CAV1 and increased tumor cell apoptosis in patients treated with Metformin [59]—a drug known to inhibit mitochondrial oxidative phosphorylation.

Endogenous molecules related to tryptophan metabolism could also distinguish primary melanoma from EM. Notably, N-acetylserotonin was less abundant among primary melanoma vs. EM and more abundant in metastatic melanoma vs. EM, whereas melatonin levels were not significantly different between melanoma tissues and EM. Melanoma cells can possess high and low affinity binding sites for melatonin, which is known to inhibit cell proliferation [60]. Souza et al. demonstrated equally potent antiproliferative effects for N-acetylserotonin and melatonin [60]; however, N-acetylserotonin only binds low affinity receptors whereas melatonin binds both high and low affinity sites. As N-acetylserotonin is a precursor of melatonin, and both ligands have similar binding affinities, our results may suggest inhibited binding of an extant, but limited, pool of melatonin.

Pyrimidine and histidine metabolic pathways accounted for some of the differences between the metabolomes of metastatic melanoma and EM. With respect to pyrimidine metabolism, cytidine was notably increased among metastatic melanoma relative to EM, but reduced among primary melanoma vs. EM. One explanation may be reduced expression of cyditine deaminase (CDA), which is responsible for the irreversible conversion of cytidine to uridine. CDA has recently been reported as downregulated in approximately 60% of cancer cells and tissues, making it a promising therapeutic target [61]. The histidine metabolic pathway also exhibited individual metabolite differences when comparing metastatic melanoma vs. EM to primary melanoma vs. EM results. Notably, urocanic acid (UCA) was detected at higher levels among primary melanoma vs. EM and was less abundant in metastatic melanoma vs. EM, whereas histidine was less abundant in primary melanoma vs. EM and conversely more abundant in metastatic melanoma vs. EM. UCA is present in the stratum corneum and has been called a “natural sunscreen” due to its photoprotective properties [62]. UCA is synthesized from histidine by the enzyme histidine ammonia lyase [63]. Changes in the expression of histidine ammonia lyase may be related to a transition from primary melanoma to metastatic melanoma.

Our exploratory MSEA pathway-based analysis, which drew on a larger number of physiological pathways and was not limited to KEGG pathways alone, yielded comparable influential network nodes when we compared primary melanoma to EM and metastatic melanoma to EM. L-glutamic acid and pyruvic acid were influential in distinguishing networks of overabundant metabolites in both primary melanoma and metastatic melanoma vs. EM, whereas D-glucose and D-galactose were influential in distinguishing networks of less abundant metabolites in both primary melanoma and metastatic melanoma vs. EM. Glutamic acid influences more metabolic reactions than any other amino acid and serves as a key source of glucose. Glutamic acid has exhibited anti-cancer properties when conjugated with current pharmaceutical treatments (e.g. paclitaxel, cisplatin) [64]. In a recent study aimed at differentiating slow and fast proliferative states among melanoma cell lines compared to control media, glutamic acid was observed to play a supportive role in significantly accelerating proliferation, migration, and invasiveness among early stage melanoma cells, but not among metastatic melanoma cells [65].

The overabundance of pyruvic acid we observed among melanoma cells compared to EM is consistent with recent research that suggests it may accumulate in the cytosol as a result of sustained ERK1/2 activation in melanoma, leading to downregulation of pyruvate dehydrogenase and promotion of lactic fermentation—a hallmark of cancer [66].

The relatively diminished abundance of glucose and galactose among melanoma cells vs. EM is not surprising; melanoma cells are known to metabolize a large proportion of glucose into lactate under both normoxic and hypoxic conditions via the Warburg effect, and galactose is a precursor to glucose production by conversion to glucose 6-phosphate.

Conclusions

In summary, compounds in the ascorbate and aldarate metabolic pathway distinguish the metabolomes of both primary and metastatic melanoma from that of EM. The tryptophan and propanoate metabolic pathways distinguished primary melanoma from EM, but not metastatic melanoma from EM, suggesting that these pathways may be important to the initiation or maintenance of the carcinogenic process for melanoma. In contrast, pyrimidine and histidine metabolic pathways could distinguish metastatic melanoma from EM, but not primary melanoma from EM. This finding indicates that these pathways may be important to the progression of melanoma rather than its initiation, and that the proteins or metabolites in these pathways may play a role as potential therapeutic targets to inhibit metastasis.

Study participant and tissue eligibility.

Flow chart illustrating the number of eligible study participants contributing eligible tissue to the study according to matched and unmatched analysis.

(TIF)

Click here for additional data file.

Number of mass spectrometry peaks identified among eligible tissue specimens.

Flow chart illustrating the number of metabolites (MS peaks) identified among eligible tissue specimens and the final number of metabolites (MS peaks) considered for matched and unmatched analyses after batch correction and quality control measures.

(TIF)

Click here for additional data file.

Noteworthy differentially abundant metabolites between primary melanoma and EM.

Bar charts reporting noteworthy differentially abundant metabolites between primary melanoma and matched EM.

(PDF)

Click here for additional data file.

All differentially abundant metabolites between primary melanoma and EM.

Bar charts reporting all differentially abundant metabolites between primary melanoma and matched EM.

(PDF)

Click here for additional data file.

Noteworthy differentially abundant metabolites between metastatic melanoma and EM.

Bar charts reporting noteworthy differentially abundant metabolites between metastatic melanoma and EM.

(PDF)

Click here for additional data file.

All differentially abundant metabolites between metastatic melanoma and EM.

Bar charts reporting all differentially abundant metabolites between metastatic melanoma and EM.

(PDF)

Click here for additional data file.

Metabolite abundance profiles do not differ significantly between primary melanoma and metastatic melanoma tissues.

Heatmap of all metabolites with tissue type (primary melanoma: Red or metastatic melanoma: Black) noted in the top color bar.

(TIF)

Click here for additional data file.

Metabolite set enrichment analysis results of overabundant metabolites in primary melanoma vs. EM.

Network of metabolite-metabolite interactions is shown among metabolites that were significantly overabundant in primary melanoma vs. EM.

(TIFF)

Click here for additional data file.

Metabolite set enrichment analysis results of metabolites that were less abundant in primary melanoma vs. EM.

Network of metabolite-metabolite interactions is shown among metabolites that were significantly less abundant in primary melanoma vs. EM.

(TIFF)

Click here for additional data file.

Metabolite set enrichment analysis results of overabundant metabolites in metastatic melanoma vs. EM.

Network of metabolite-metabolite interactions is shown among metabolites that were significantly overabundant in metastatic melanoma vs. EM.

(TIFF)

Click here for additional data file.

Metabolite set enrichment analysis results of metabolites that were less abundant in metastatic melanoma vs. EM.

Network of metabolite-metabolite interactions is shown among metabolites that were significantly less abundant in metastatic melanoma vs. EM.

(TIFF)

Click here for additional data file.

15 Jun 2020

PONE-D-20-09783

Metabolomics of primary cutaneous melanoma and matched adjacent extratumoral microenvironment

PLOS ONE

Dear Dr. Nicholas Taylor,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 30 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Ch Ratnasekhar, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. In the ethics statement in the manuscript and in the online submission form, please provide additional information about the tissue samples used in your retrospective study, including:

a) the date range (month and year) during which patients' medical records were accessed and

b) the date range (month and year) during which patients whose medical records were selected for this study sought treatment.

If patients provided informed written consent to have data from their tissue samples used in research, please include this information.

3. Our staff editors have determined that your manuscript is likely within the scope of our Cancer Metastasis Call for Papers. This editorial initiative is headed by a team of Guest Editors for PLOS ONE: Joe Ramos (University of Hawai'i), Shengyu Yang (Penn State University), Helen Fillmore (University of Portsmouth) and Tobias Zech (University of Liverpool). The Collection will encompass a diverse range of research articles about metastasis, including mechanisms of cell motility, invasion and the tumor microenvironment, as well as advances in the development of anti-metastatic therapies.  Additional information can be found on our announcement page: https://collections.plos.org/s/cancer-metastasis

If you would like your manuscript to be considered for this collection, please let us know in your cover letter and we will ensure that your paper is treated as if you were responding to this call.

Please note that being considered for the Collection does not require additional peer review beyond the journal’s standard process and will not delay the publication of your manuscript if it is accepted by PLOS ONE.

If you would prefer to remove your manuscript from collection consideration, please specify this in the cover letter.

4. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

5. Thank you for stating the following in the Competing Interests section:

'I have read the journal's policy and the authors of this manuscript have the following competing interests: Chris Beecher is the Founder and Chief Science Officer of IROA Technologies.  Timothy J. Garrett is a member of the Scientific Advisory Board of IROA Technologies.  IROA® Long Term Reference Standard and Internal Standard were donated by IROA® Technologies.  Timothy J Garrett's laboratory received no funding from IROA® Technologies.'

We note that one or more of the authors are employed by a commercial company: IROA Technologies

a. Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

b. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and  there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

c. Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

6. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Metabolomics-based strategies have become an integral part of present clinical research studies and has been instrumental in cancer precision medicine. This manuscript entitling ‘‘Metabolomics of primary cutaneous melanoma and matched adjacent extratumoral microenvironment’’ by Taylor et al, is an interesting study and authors have collected a unique dataset using an ultra-high-performance liquid chromatography. Overall, this manuscript is well written. However, in my opinion the article has some minor concerns regarding data analyses, and I feel this dataset has not been utilized to its full extent. Furthermore, I would prefer more in-depth functional analyses of the data.

1. Authors did not show any functional analysis of the data. For this, authors can utilize PROFILE clustering to order the metabolites based on their mutual proximity in the metabolic network. This analysis will provide useful insights in the interpretation of metabolomic changes in primary and metastatic melanoma and EM.

2. Authors should create a metabolic map based on the differential expression of metabolites in the primary tumor and EM by using a molecular interaction software like Cytoscape. This metabolic map can be used for predicting predominant metabolic processes in melanoma and will help in building strategies for therapeutic targeting of metabolic pathways in cancer cells.

Reviewer #2: The current manuscript describing the LC/MS base metabolomics analysis of melanoma and its matched adjacent extratumoral microenvironment tackles an especially interesting subject. The work done is substantial well designed and executed whereas the statistical treatment of the results was in my opinion accurate and also well designed. The IROA technology used has also led to increased confidence concerning the identification of features. Finally, the authors explained their findings convincingly, rendering the work o significant merit for the readers.

A couple of minor points should be taken care of.

line 157 please specify if the external calibration refers to mass

line 174 using pooled QC samples is a frequently used procedure in metabolomics analyses. Did the authors consider to employ this methodology in order to control the intra batch variation as well? Please comment

Table 1 Does the second digit in retention time hold a meaning? Please comment

Please also add the statistics of multivariate analyses i.e. at least the R2 and Q2, to indicate their statistical significance.

Reviewer #3: Comments

Article no. Ms. Ref. No. PONE-D-20-09783, Research Article entitled ‘Metabolomics of primary cutaneous melanoma and matched adjacent extratumoral microenvironment’.

In the present study, authors have analysed the metabolomics of primary cutaneous melanoma and matched adjacent extratumoral microenvironment using ultra-high-performance liquid chromatography-mass spectrometry. There are few novel findings and may also hold promising translational relevance to develop the novel biomarker for melanoma patients. However, I do notice many important concerns that should be addressed to further enhance the manuscript quality:

Comment 1: First of all, authors need to provide high resolution images for all figures as it’s very hard to figure out anything out of any heatmaps.

Comment 2: Authors really need to explain their results in different subheadings and need to cite figure numbers in text rather than in headings.

Comment 3: It’s better to provide the box plot or bar diagram of potential differentially altered metabolites with their respective p-values in addition to heatmap.

Comment 4: Authors need to analyse and correlate the altered level of their identified metabolites with other pathophysiological characteristics such as TNM staging and survival if possible.

Comment 5: Authors need to provide demographic and clinical-pathological details of all the clinical bio specimens collected in their study.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Manish Charan

Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Submitted filename: plosone.docx

Click here for additional data file.

14 Sep 2020

Reviewer #1

1) “Authors did not show any functional analysis of the data. For this, authors can utilize PROFILE clustering to order the metabolites based on their mutual proximity in the metabolic network. This analysis will provide useful insights in the interpretation of metabolomic changes in primary and metastatic melanoma and EM.”

“Authors should create a metabolic map based on the differential expression of metabolites in the primary tumor and EM by using a molecular interaction software like Cytoscape. This metabolic map can be used for predicting predominant metabolic processes in melanoma and will help in building strategies for therapeutic targeting of metabolic pathways in cancer cells.”

We have given these suggestions careful consideration. Our original functional analysis was a pathway-based approach via Metaboanalyst and Mummichog focused on mapping compounds to known KEGG pathways in human beings. Our intention was, and remains, to highlight potentially meaningful differences in biological pathways between tissue types. We retain this approach as our primary analysis in our revised manuscript.

However, in order to fully address the Reviewer’s comments, and to further explore the functional characteristics of the metabolite differences between tissue types, we performed a metabolite set enrichment analysis (MSEA) within Metaboanalyst 4.0 as a secondary analysis. Again, we considered metabolites with statistically significantly higher abundance (or lower abundance) in primary melanoma (or metastatic melanoma) compared to EM. For each condition, the MSEA of pathway-associated metabolite sets—based on the small molecule pathway database (SMPDB—smpdb.ca)—was considered. A network of metabolite-metabolite interactions was plotted for each condition, with nodes representing metabolites and edges indicating a relationship (from STITCH—an interaction network database for small molecules and proteins) between metabolites. STITCH incorporates several databases (including KEGG) when producing the “interactome” of metabolites (e.g. Protein Data Bank, Reactome, NCI-Nature Pathway Interaction Database, DrugBank, and MATADOR) We highlighted specific KEGG pathways within the network for comparison to our main analysis.

We have made appropriate additions to the Methods and Results sections describing these secondary analyses, and we have highlighted the relevant text in the revised manuscript. We also present the results of these exploratory analyses in supplemental material (Supplemental Figures 8-11), and add some discussion as highlighted in the manuscript.

Reviewer #2

1) “line 157 please specify if the external calibration refers to mass.”

External calibration refers to both mass calibration and mass resolution calibration; we have added this clarification to the methods section of the revised manuscript.

2) “line 174 using pooled QC samples is a frequently used procedure in metabolomics analyses. Did the authors consider to employ this methodology in order to control the intra batch variation as well? Please comment.”

We did not use pooled QC samples in this study. The melanoma tissue samples are a scarce resource (especially the primary melanoma samples) and were generally low in mass; the decision was made to preserve tissue for multiple omics studies and other assays rather than using a pool for batch-to-batch quality control. We used a pooled red cross plasma sample to assure instrument performance in both retention time and mass accuracy. Our process of detecting and mitigating inter-batch bias is presented in detail in the manuscript from lines 175-182, as well as Figure 1.

3) “Table 1 Does the second digit in retention time hold a meaning? Please comment.”

We thank the reviewer for pointing out this oversight. We have removed the unnecessary digits from all reports of retention times in the manuscript.

4) “Please also add the statistics of multivariate analyses i.e. at least the R2 and Q2, to indicate their statistical significance.”

The Authors are confused by the Reviewer’s comment; we did not perform multivariate analyses of these high dimensional data.

Reviewer #3

1) “First of all, authors need to provide high resolution images for all figures as it’s very hard to figure out anything out of any heatmaps.”

We previously (and again) provided the journal with high resolution images conforming to PLoS ONE guidelines and requirements. Please note that images/figures contained in the manuscript pdf file appear in lower resolution; however, the url links contained within the pdf file allow for images/figures to be viewed externally at full resolution.

2) “Authors really need to explain their results in different subheadings and need to cite figure numbers in text rather than in headings.”

We have added subheadings to the results section per the Reviewer’s suggestion. The formatting of figure citations in the text of the manuscript was made to adhere to PLoS ONE requirements.

3) “It’s better to provide the box plot or bar diagram of potential differentially altered metabolites with their respective p-values in addition to heatmap.”

We now provide bar diagrams illustrating a) significantly differentially abundant metabolites between primary melanoma and paired EM tissues which we highlight as being identified and named in both positive and negative ion modes, as well as two identified and named metabolites that are differentially abundant between primary melanoma and paired EM in separate ion modes (Supplemental Figure 3)—from results section lines 237-239; and, b) all significantly differentially abundant metabolites between primary melanoma and paired EM (Supplemental Figure 4).

Further, we now include box plots illustrating differentially abundant metabolites between EM and unmatched metastatic melanoma tissues. Supplemental Figure 5 illustrates results described in the Metastatic melanoma vs. EM section in lines 255-261; Supplemental Figure 6 illustrates all significantly differentially abundant metabolites between EM and unmatched metastatic melanoma.

4) “Authors need to analyse and correlate the altered level of their identified metabolites with other pathophysiological characteristics such as TNM staging and survival if possible.”

We appreciate the Reviewer’s suggestion, and we have previously considered this approach. We decided against including results from such analyses in the manuscript for important reasons: 1) Due to the scarcity of primary melanoma tissue, our study sample essentially represents a convenience sample. This limits our ability to select optimal tissue from those patients who developed multiple primary melanomas; moreover, we were limited in obtaining data indicating whether or not a patient had multiple primary melanomas. This limitation hinders our ability to make reasonable inferences from survival analyses; 2) Our sample size is relatively small in comparison to the number of metabolites identified, which makes external validation highly variable and power low.

Nevertheless, we convey results from 3 different analyses of the data with respect to survival below.

1) We performed a Cox proportional hazard model on the raw difference between tumor and EM (i.e. tumor minus EM) and the fold difference (i.e. tumor divided by EM) for each of the 824 statistically significant metabolites identified. The resulting p-values are all non-significant after adjustment for FDR, indicating they are either not important for survival (unlikely) or we have insufficient power to detect their effects (likely).

2) We binned 33 subjects into two clusters based on the profiles of the 824 statistically significant metabolites identified and compared the resulting Kaplan-Meier survival curves. There is no significant survival separation between the two clusters (again, likely due to insufficient power).

3) We performed a cross-validation with penalized Cox regression model on the 824 differentially abundant metabolites (meaning ridge regression so that all metabolites are used in the prediction). The 33 samples were split into 10 folds (100 splits total, each has 10 folds); the best model was fit on 9 folds and the risk evaluated on the remaining one. The measure of model prediction is Harrel’s concordance (https://statisticaloddsandends.wordpress.com/2019/10/26/what-is-harrells-c-index/), with values of 0.5 equating to a completely random guess and 1.0 being maximum prediction. Results from this approach were highly variable across 100 splits and range from 0.3 to 0.8. On average, the best model with 824 metabolites on EM tissue gave better prediction than the best model with 824 metabolites on tumor tissue (concordance means 0.58 vs. 0.52), though both means were similar and close to 0.5, indicating metabolite abundances in EM and primary tissues are not predictive of survival in our dataset (or likely we have insufficient power to detect their effects).

Lastly, the Reviewer suggested analyses of associations between metabolites and pathophysiologic characteristics. Unfortunately, we were unable to reliably perform these analyses due to the limited data we had on these factors.

5) “Authors need to provide demographic and clinical-pathological details of all the clinical bio specimens collected in their study.”

We have added a new Table 1, which now includes descriptive summaries of sex, vital status, age at diagnosis, histology, TNM staging, anatomic site of primary, and anatomic site of metastasis.

Submitted filename: (Response to Reviewers)(9-7-20)(PLoS One).docx

Click here for additional data file.

5 Oct 2020

Metabolomics of primary cutaneous melanoma and matched adjacent extratumoral microenvironment

PONE-D-20-09783R1

Dear Dr. Nicholas,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Ch Ratnasekhar, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

16 Oct 2020

PONE-D-20-09783R1

Metabolomics of primary cutaneous melanoma and matched adjacent extratumoral microenvironment

Dear Dr. Taylor:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Ch Ratnasekhar

Academic Editor

PLOS ONE