Pathogenic germline variants are associated with poor survival in stage III/IV melanoma patients

Patients with late stage resected cutaneous melanoma have poor overall survival (OS) and experience irreversible adverse events from systemic therapy. There is a clinical need to identify biomarkers to predict outcome. Performing germline/tumour whole-exome sequencing of 44 stage III/IV melanoma patients we identified pathogenic germline mutations in CDKN2A, CDK4, ATM, POLH, MRE11A, RECQL4 and XPC, affecting 7/44 patients. These mutations were associated with poor OS (p = 0.0082). We confirmed our findings in The Cancer Genome Atlas (TCGA) human skin cutaneous melanoma cohort where we identified pathogenic variants in 40/455 patients (p = 0.0203). Combining these cohorts (n = 499) further strengthened these findings showing germline carriers had worse OS (p = 0.0009). Additionally, we determined whether tumour mutation burden (TMB) or BRAF status were prognostic markers of survival. Low TMB rate (< 20 Mut/Mb; p = 0.0034) and BRAF p.V600 mutation (p = 0.0355) were associated with worse progression-free survival. Combining these biomarkers indicated that V600 mutant patients had significantly lower TMB (p = 0.0155). This was confirmed in the TCGA (n = 443, p = 0.0007). Integrative analysis showed germline mutation status conferred the highest risk (HR 5.2, 95% CI 1.72–15.7). Stage IV (HR 2.5, 0.74–8.6) and low TMB (HR 2.3, 0.57–9.4) were similar, whereas BRAF V600 status was the weakest prognostic biomarker (HR 1.5, 95% CI 0.44–5.2).

The development of cutaneous melanoma is heavily associated with ultraviolet radiation. This environmental influence makes melanoma the most highly mutated cancer type 1 . A subset of patients harbor germline mutations which increases their susceptibility 2 . The predominant high-risk familial melanoma genes are CDKN2A and CDK4 [3][4][5] . Pan-cancer analysis of 10,389 patients from The Cancer Genome Atlas (TCGA) reported approximately 8% of cases, across 33 cancer types, carried a pathogenic predisposition variant 6 . Importantly, they identified shared variants and genes across several cancer types. Few studies have addressed the clinical impact of pathogenic germline mutations on melanoma patients 7,8 .
The somatic landscape of cutaneous melanoma has been well characterized 9 . BRAF p.V600 is the most commonly mutated coding hotspot present in approximately 40% of patients 9,10 .
Advances in medical research have led to the development of targeted BRAF and MEK inhibitors (BRAFi/ MEKi) used as standard treatments for stage IV patients with BRAF p.V600 mutations [11][12][13][14][15] . Recently, adjuvant BRAFi/MEKi have demonstrated a survival benefit for resected stage III patients 16 , however, relapse after complete response to BRAFi/MEKi is common. Another therapeutic option is immunotherapy 17,18 . For BRAF wildtype stage IV patients, immunotherapy is first line treatment 19 and up to 50% of patients achieve 5-year survival 20 . However, a high proportion of stage III/IV patients have early disease progression and poor survival. Biomarkers are needed to identify patients who benefit from existing therapy and those in need of novel approaches.
To date there is no consensus on the TMB cut-off that best predicts response or prognosis. In melanoma, Mar et al. described the correlation between the presence of a BRAF/NRAS mutation and a low mutation load 23 . Their study separated BRAF p.V600E and p.V600K mutations. In the clinic, all patients with p.V600 mutations are eligible for targeted therapies regardless of the amino acid change, p.V600E/K/D/R. Collectively, these are class 1 mutations which are the most effective targets of BRAFi/MEKi therapy 14 .
The aim of our study was to determine genomic biomarkers as adverse prognostic factors in 499 melanoma patients. Candidate genomic biomarkers for rapid clinical translation may be germline or somatic 31,32 , therefore we focused on loss-of-function germline mutations in cancer predisposition genes, as well as somatic biomarkers: BRAF mutation status and TMB. We used whole-exome sequencing (WES) data from a Queensland cohort as well as an independent cohort from The Cancer Genome Atlas Program (TCGA) to confirm our findings.

Results
Melanoma patient cohort. We conducted a prospective study of late stage cutaneous melanoma patients with completely resected tumours ( Table 1). The cohort had a median age of 60 years (range 28-88 years). Thirtyseven patients (stage III, n = 35; stage IV, n = 2) underwent lymph node dissection from which research samples were acquired. For the remaining 7 stage IV patients, tumours were sampled from: omentum, small intestine, gastric, ovary, intransit lesion as well as 2 subcutaneous lesions (Table S1). WES was performed on these tumours as well as the matching blood. The median overall survival (OS) was 27.4 months (range 3.5-50.2 months). The median progression-free survival (PFS) was 9.1 months (range 0.5-47.0 months). The median follow up time for survivors was 32.0 months (range 13.2-50.2 months).

Characterization of germline mutations.
To determine whether germline variants were associated with survival, we analyzed 166 cancer predisposing genes (Table S2). In our cohort (n = 44), we identified 11 deleterious germline variants in 10 patients. Six of these were loss-of-function variants that are predicted to truncate the resultant protein.
Two patients had germline mutations in high-risk melanoma genes described as pathogenic in ClinVar and in the literature 2,4 . (Table 2). MelR054 carried a heterozygous missense variant CDK4 p.R24C. MelR191 had a heterozygous CDKN2A c.-34 g > t which leads to aberrant ATG translation at the initiation codon. This results in a truncated protein and decreases translation from the wild-type ATG 33,34 . Four patients had mutations at hotspots in MITF p.E318K (MelR06, MelR191) and TYR p.T373K (MelR014, MelR219) ( Table 2). These variants are pathogenic in ClinVar and confer a more modest-risk of developing melanoma 4,35,36 .
Five patients had pathogenic heterozygous germline mutations in genes predisposing to other cancer types ( Table 2). XPC p.R579* was identified in MelR041, ATM p.H2555_T2556delinsQ* was found in MelR082, RECQL4 splice acceptor variant (c.2464-1G > C) in MelR158 and POLH in-frame deletion (p.Asp74_Leu75del) in MelR162. Additionally, MelR049 carried nonsense mutation MRE11A p.R364*, conferring a risk of breast/ ovarian cancer [37][38][39] . These four genes, POLH, MRE11A, RECQL4 and XPC, are autosomal recessive. They are involved in DNA damage repair pathways and have a very broad impact. We have included these loss-of-function mutations in the analysis as these genes are in pathways that have the potential to undergo a 2 nd hit which may contribute to tumor development. Mutations in the DNA damage repair genes increase the risk of subsequent mutations and therefore confer high cancer susceptibility.
Germline mutations and survival. We performed survival analyses (Mantel-Cox) to determine the prognostic significance of germline mutations ( Table 2). We excluded MITF p.E318K and TYR p.T373K from our www.nature.com/scientificreports/ analysis as they are associated with moderate-risk melanoma susceptibility. They are involved in the differentiation of melanocytes/melanoma cells.
Survival analysis (Mantel-Cox) combining the TCGA and the Queensland cohorts (n = 499) further strengthened the findings showing carriers had worse OS than the wild-type group (33.9 months vs 91.14 months, p = 0.0009, Fig. 1d).
BRAF and survival. We next determined whether somatic biomarkers had prognostic significance. Aside from the American Joint Committee on Cancer (AJCC) staging system, BRAF status is the only biomarker commonly used to guide clinical treatment. In our cohort, we confirmed BRAF status using WES. In line with pathology reports, 16 patients harbored a p.V600E/K mutation (p.V600E, n = 14; p.V600K, n = 2) and 3 patients had an alternate mutation: p.L601E, p.T599I and p.L584F.

TMB and survival.
Previous studies have shown TMB is a predictive biomarker in a variety of cancers 21,22 .
In melanoma, patients have been categorized into high/low TMB using thresholds ranging from 4.81 to 43.2 Mut/Mb 21,43,44 . In our study, the mean TMB was 34.5 Mut/Mb (median 14.5 Mut/Mb). Recursive partitioning methods determined the optimal PFS cut off was 19.75 Mut/Mb, therefore we used 20 Mut/Mb to categorize patients into high/low groups. Univariable survival analysis showed that patients with high TMB had a better BRAF wild-type tumours are associated with a higher TMB. We found an association between BRAF status and TMB. V600 wild-type patients (n = 28) had a significantly higher TMB rate (mean 49.3 Mut/ Mb) than the V600 mutant group (n = 16, mean 8.5 Mut/Mb) (p = 0.0155, two-tailed unpaired t test, Figs. 3a,b). This was confirmed in the TCGA SKCM, where V600 wild-type patients (n = 246) had a higher mean TMB than the V600 mutant group (n = 197), 34.8 Mut/Mb vs. 17.9 Mut/Mb (p = 0.0007, Fig. 3c).
Genomic biomarkers and risk of recurrence. We next determined whether TMB was related to disease recurrence (Fig. 3a). 7/10 patients (9 stage IIIB/C, 1 stage IV) with highest TMB (> 40 Mut/Mb) were disease free (only 2 of these patients received adjuvant immunotherapy). In contrast, 25/27 (92.6%) patients with lowest TMB (< 20 Mut/Mb) had recurrent disease ( Figure S1). Significant differences in disease status were observed when we compared TMB values between V600 mutant and wild-type groups. Focusing on patients who were disease free, the V600 wild-type group had a significantly higher TMB rate (p = 0.0338). In BRAF wild-type patients, low TMB was associated with recurrence (p = 0.0011, Fig. 3d). V600 mutation carriers had a low TMB and were more likely to recur. In this group, TMB had no predictive power in regards to disease status. Publicly available TCGA data did not include sufficient information on patient treatment and recurrence to be used as a comparable dataset.
We performed Kaplan Meier OS analysis using the biomarkers with the highest risk: germline mutation status and TMB rate. We first defined germline variant carriers as a distinct sub-group. The remaining patients were classified according to TMB high/low. Analysis showed that the poorest OS was observed in the germline group (p = 0.0152, log-rank) while the TMB high patients had the longest survival (Fig. 4b).

Discussion
We assessed genomic biomarkers as adverse prognostic factors in melanoma in an unselected clinical cohort. In our study, patients have been treated with a variety of therapy combinations. This includes BRAFi/MEKi and/ or immunotherapy, which can be given in the adjuvant or palliative setting. Furthermore, a proportion of our www.nature.com/scientificreports/ patients did not receive systemic therapy and had curative surgery. One of the limitations of this study is that we were not able to assess each therapy individually due to low patient numbers in each group. Our data has shown that the presence of a pathogenic germline variant was the strongest prognostic factor for OS (p = 0.0082, n = 44). Germline carriers progressed at the same rate as the non-germline cohort however, once patients relapsed, therapy appeared less effective. This was confirmed in the TCGA SKCM cohort where loss-of-function mutations conferred worse OS (p = 0.0203). Notably, TCGA patients were treated as far back as 1977 when most did not benefit from modern systemic therapies. Despite these limitations, the OS was still significantly different. Germline assessment showed 15.9% (7/44) of the Queensland cohort and 9.9% (45/455) of the TCGA cohort carried a deleterious mutation. The high mutation rate in the Queensland cohort may be attributed to patients having late stage disease which contrasts to the TCGA, comprising all stages. Our observation is in keeping with Mandelker et al. who reported pathogenic variants in 19.7% of patients (205/1040) with advanced cancers (prostate, renal, pancreatic, breast and colon) 45 . Better understanding of the impact of these individual genes on survival would be a very useful clinical tool. For a gene to be included in a genetic test, extensive literature is needed to confirm the consequences of such mutations. Therefore, more studies need to be conducted to validate findings and determine how it can impact clinical practice.

MelR041
MelR065  Stage IIIB IIIB IIIC IIIC IIIB IV IIIC IIIC IIIB IIIB IIIB IV IIIC IIIC IV IV IIIC IIIC IIIB IV IV IIIB IIIC IIIB IIIC IIIB IIIB IIIC IIIC IIIC IIIC IIIB IIIB IIIB IIIB IV IIIB IIIB IIID IV IIIC IV IIIC IIIB BRAFi/ MEKi N N N N N N N N N N N N N N N N N N N Y N N N N N 46 . Although this appears contradictory, this study tested for CDKN2A mutational status at recruitment then patients were assigned to a follow-up scheme accordingly. Germline patients were followed-up more frequently than the wild-type patients. When assessing overall survival, they found no difference between the two groups. This provides further evidence that altering treatment for the germline carriers may improve their overall survival.
Interestingly, MelR041was the only germline carrier (XPC p.R579*) with long OS. This BRAF wild-type patient has the highest TMB rate in our study (285.9 Mut/Mb). They have had multiple primary melanomas (age of onset 23 years) and approximately 50 non-melanoma skin cancers excised. This reflects the clinical features of xeroderma pigmentosum, a condition causing DNA repair defects resulting in photosensitivity and an increased rate of skin cancer 47 . Despite the truncating mutation, the extremely high TMB rate in this patient may contribute to the favorable survival.
When examining TMB rate, we found no correlation between TMB and germline mutation status (data not shown). Further statistical analysis examining the rate of multiple melanomas found no difference in the rate of multiple melanomas between germline carriers and wild-type patients (data not shown). We also analyzed whether germline carriers had a younger age of melanoma onset and found no statistical difference (data not shown). This may be a limitation of our sample size however this observation has been confirmed in the TCGA SKCM by Qing et al. 48 .
We assessed the prognostic value of alternate genomic markers: BRAF status and TMB. BRAF V600 wild-type patients had significantly longer PFS than the V600 mutant group (p = 0.0317). This confirms previous clinical studies associating BRAF status with poor OS in stage III patients treated prior to BRAFi/MEKi availability 10,49 . The routine use of targeted therapy in the advanced setting for BRAF mutant patients may explain the improved OS in our study. For stage III/IV resected patients, TMB was also significantly associated with longer PFS (p = 0.0034). These results are consistent with recent pan-cancer analysis associating favorable OS with high TMB 50 . This is also reflected in melanoma focused studies 24,51-53 .
Combining BRAF status and TMB, we showed that V600 wild-type patients had a significantly higher TMB rate (our cohort, p = 0.0155; TCGA cohort, p = 0.0007). This is in concordance with Mar et al. who reported a statistically different tumour mutation rate between BRAF mutant, NRAS mutant and triple wild-type patients (p = 0.0004) 23 . In our cohort, NRAS mutant patients (n = 12) were embedded within the wild-type group, in order to reflect the clinical decision-making process.
Using the somatic biomarkers, we assessed risk of melanoma recurrence. V600 wild-type patients with a high TMB rate had low risk of recurrence (p = 0.0011). The results showed that for the V600 mutant group, TMB had no predictive power in regards to risk of recurrence post-surgery.
Multivariate survival analysis incorporating the genomic biomarkers with stage found germline mutation status was the most significant biomarker for OS (HR 5.2). When analyzed in parallel with TMB rate, three subgroups emerged: germline mutation carriers, TMB high patients and TMB low patients. The germline carriers had the shortest OS (p = 0.0152) while the longest survival was observed in the TMB high sub-group. Interestingly, all BRAF V600 mutant patients were TMB low. BRAF status is a prognostic factor in other studies 10 . In our cohort, these patients are all TMB low. This raises the question of whether the low TMB status is driving poor survival in this group rather than BRAF status.  www.nature.com/scientificreports/ In our study, germline status was the most prognostically significant biomarker for OS. Survival outcomes for germline carriers are poor with the current standards of care. Our observations support routine germline testing where mutation carriers should be considered high-risk and put on more intensive follow up.

Conclusions
We identified protein-truncating germline mutations in cancer genes occurring in 15.9% of late stage melanoma patients. While the treatment of patients is currently based upon BRAF status and AJCC stage, routine germline testing may be incorporated into future iterations of the staging for melanoma to improve prognosis. Combining germline status with BRAF and TMB rate may offer additional tools to stratify patients in the clinic.

Materials and methods
Melanoma patient cohort. The study included 44 patients with completely resected stage III/IV melanoma that underwent standard care at the Princess Alexandra Hospital (PAH) Melanoma Unit, Queensland, Australia. Samples were collected at surgery between July 2014 and April 2018. Patients provided informed written consent. Ethics approval was granted by the Metro South Human Research Ethics Committee overseeing research projects at the PAH (HREC/10/PAH/153, HREC/16/PAH/671). All experiments were performed in accordance with approved protocols and regulations. Routine BRAF testing was performed by hospital pathology services. Table S1 details stage, histology and site of primary.
Following surgery, 30 patients received immunotherapy (Pembrolizumab, Ipilimumab, Nivolumab), of these, 20 were V600 wild-type (Table S1). Fourteen patients received BRAFi/MEKi therapy (Dabrafenib, Trametinib, Vemurafenib, Cobimetinib). Ten of these patients also received immunotherapy following progression. MelR209 (stage IV) had BRAF p.L584F and was treated with BRAFi/MEKi. Patients were followed up every 3 months by history and clinical examination as well as CT scan of the head, chest, abdomen and pelvis or whole body PET/ CT scan. No patients were lost to follow up.
Tumour DNA was extracted from fresh-frozen tissue stored in RNAlater. Normal DNA was extracted from buffy coat isolated from blood.
Whole-exome sequencing and SNP array. WES was performed on two platforms. Thirty-three matched tumour/blood samples were sequenced on an Illumina Hiseq4000 using the Agilent sureselect V5 kit. Their overall tumour content was assessed using qpure 54 by comparing tumour SNP array data (2.5 M Illumina) with the matching normal blood. Eleven matched tumour/blood samples were sequenced on the Illumina Next-Seq500 using the IDT pan-cancer spike in. Their cellularity was determined using the mean allele fraction. All samples contained > 20% tumour content. The mean tumour read depth was 439 × (range 254.78-1053.35) for the tumour samples and 216 × (range 52.98-1173.04) for the normal samples (Table S1).
The cancer genome Atlas data. The TCGA human skin cutaneous melanoma cohort was downloaded and reanalyzed with approval from the QIMR Berghofer Ethics Committee (HREC/P2905). Acral and mucosal samples were excluded. Where patients had more than one tumour sequenced, data from the metastatic site was used. For the germline analysis, 455 patients had sufficient OS data to be included in our study. For TMB and BRAF analysis, patients (n = 443) were included if they had a TMB > 1.0. WES analysis. Sequence data was adapter trimmed using Cutadapt v1.9 55 and aligned to the GRCh37 using BWA-MEM v0.7.15 and SAMtools v1.3 56,57 . Duplicate reads were marked with Picard v2.18.15 MarkDuplicates (https ://broad insti tute.githu b.io/picar d). Sample read groups were merged using qbammerge. qProfiler v1.0 and qCoverage v0.7pre performed quality assessment and coverage estimation (sourceforge.net/projects/adamajava).
Variants had a minimum 8 reads in the normal data and 12 in the tumour data. Where the variant was identified on both strands, variants required a minimum 4 reads that were not within the first or last 5 bases. The analysis only included rare or novel germline variants with a minor allele frequency < 0.01 in gnomAD.
TMB, reported as mutations per megabase (Mut/Mb) was calculated as a quantitative measurement of all somatic mutations in the coding regions covered by the capture kit.
Selection of cancer predisposition genes. The germline mutation analysis comprised 166 genes (Table S2). The list included melanoma predisposition genes, cancer predisposition genes reported by COSMIC or the TCGA pan-cancer analysis 6 , as well as genes established through a medical literature review. Additionally, we included genes recommended by The American College of Medical Genetics and Genomics (ACMG) 60 .
Germline variants in this study are pathogenic or likely pathogenic in ClinVar or have strong evidence in the literature to support their pathogenicity. www.nature.com/scientificreports/ clinical data. OS was calculated from date of surgery to date of death from disease. PFS was the time from surgery until disease recurrence confirmed through radiology or tumour biopsy. The Kaplan Meier method was used to analyze OS and PFS (GraphPad Prism 7). Log-rank Mantel-cox tests determined statistical differences between groups. Recursive partitioning defined the optimal high/low cut off for TMB (R Foundation for Statistical Computing). TMB rate between the V600 mutant and V600 wild-type groups, was assessed using an unpaired t-test (two-tailed). Hazard ratio (HR) was determined using a multivariate Cox proportional hazards regression model including established (e.g. tumour stage and BRAF status) as well as proposed biomarkers (TMB and germline mutation status; R Foundation for Statistical Computing).

Data availability
The TCGA dataset is available in the cBioPortal for Cancer Genomics repository (https ://www.cbiop ortal .org/). The dataset from the Queensland cohort are available in the European Genome-Phenome Archive (EGAD00001006374). www.nature.com/scientificreports/