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Whole-genome and transcriptome analysis of advanced adrenocortical cancer highlights multiple alterations affecting epigenome and DNA repair pathways
Jean-Michel Lavoie,1,6 Veronika Csizmok,2,6 Laura M. Williamson,2 Luka Culibrk,2 Gang Wang,3 Marco A. Marra,2,4 Janessa Laskin,5 Steven J.M. Jones,2 Daniel J. Renouf,5 and Christian K. Kollmannsberger5
1Department of Medical Oncology, BC Cancer, Surrey, British Columbia V3V 1Z2, Canada; 2Canada’s Michael Smith Genome Sciences Centre, 3Department of Pathology and Laboratory Medicine, BC Cancer, Vancouver, British Columbia V5Z 4E6, Canada; 4Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada; 5Department of Medical Oncology, BC Cancer, Vancouver, British Columbia V5Z 4E6, Canada
Abstract Adrenocortical cancer (ACC) is a rare cancer of the adrenal gland. Several driver mutations have been identified in both primary and metastatic ACCs, but the therapeutic options are still limited. We performed whole-genome and transcriptome sequencing on seven patients with metastatic ACC. Integrative analysis of mutations, RNA expression changes, mutation signature, and homologous recombination deficiency (HRD) analysis was performed. Mutations affecting CTNNB1 and TP53 and frequent loss of heterozygosity (LOH) events were observed in our cohort. Alterations affecting genes involved in cell cycle (RB1, CDKN2A, CDKN2B), DNA repair pathways (MUTYH, BRCA2, ATM, RAD52, MLH1, MSH6), and telomere maintenance (TERF2 and TERT) consisting of somatic and germline mutations, structural variants, and expression outliers were also observed. HRDetect, which aggregates six HRD-associated mutation signatures, identified a subset of cases as HRD. Genomic alterations affecting genes involved in epigenetic regulation were also identified, including structural variants (SWI/SNF genes and histone methyltransferases), and copy gains and concurrent high expression of KDM5A, which may contribute to epigenomic deregulation. Findings from this study highlight HRD and epigenomic pathways as potential therapeutic targets and suggest a subgroup of patients may benefit from a diverse array of molecularly targeted therapies in ACC, a rare disease in urgent need of therapeutic strategies.
Corresponding author: jeanmichel.lavoie@ bccancer.bc.ca
@ 2022 Lavoie et al. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial License, which permits reuse and redistribution, except for commercial purposes, provided that the original author and source are credited.
Ontology term: neoplasm of the adrenal gland
Published by Cold Spring Harbor Laboratory Press
doi:10.1101/mcs.a006148
INTRODUCTION
Adrenocortical cancer (ACC) is a rare genitourinary malignancy, with an incidence of 1-2 cas- es per million patient-years; of these, ~40% present with metastatic disease (Ng and Libertino 2003). Although most cases are localized, metastatic disease has a poor prognosis,
6These authors contributed equally to this work.
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with a 5-yr survival rate of ~15% (Fassnacht et al. 2009). In addition, in more than one-half of cases, the tumors secrete steroid hormones, which can lead to Cushing’s syndrome and medical complications thereof (Ng and Libertino 2003).
The current standard treatment of ACC is limited to cytotoxic chemotherapy (etoposide, doxorubicin, and cisplatin [EDP]) and the adrenolytic agent mitotane. The FIRM-ACT phase 3 clinical trial has established the combination of mitotane and triplet chemotherapy as a stan- dard treatment for advanced ACC, with a median overall survival of 14.8 mo (Fassnacht et al. 2012). There is no established second-line therapy, and small phase 2 trials evaluating different targeted agents in all comers have shown no clear clinical benefit. The vascular en- dothelial growth factor (VEGF) antibody bevacizumab in combination with capecitabine (Wortmann et al. 2010), VEGF receptor inhibitors axitinib (O’Sullivan et al. 2014) or sunitinib (Kroiss et al. 2012), epidermal growth factor receptor (EGFR) inhibitor erlotinib in combina- tion with gemcitabine (Quinkler et al. 2008), or the insulin-like growth factor type 1 receptor (IGF1R) antibody cixutumumab (Lerario et al. 2014) or inhibitor linsitinib (Fassnacht et al. 2015) have shown limited activity. Immune checkpoint inhibitors have also been investigat- ed. A phase Ib expansion cohort from the JAVELIN solid tumor trial showed a response rate of 6% to the PD-L1 antibody avelumab (Le Tourneau et al. 2018). A phase II study of the PD-1 antibody pembrolizumab had an overall response rate of 23%; two of six patients with mis- match repair-deficient tumors (identified by immunohistochemistry [IHC]) had a response to therapy (Raj et al. 2020). In this context, better treatments are sorely needed.
Although studies of targeted agents have shown poor overall outcomes, molecular char- acterization of ACC may inform genetic changes involved in cancer progression and reveal potential therapeutic targets. Studies leveraging whole-exome sequencing and single- nucleotide polymorphism (SNP) and expression arrays in ACC have revealed recurrent mu- tations in CTNNB1, TP53, MEN1, PRKAR1A, RPL22, TERF2, TERT, CCNE1, RB1, and NF1 genes, frequent whole-genome doubling events, large-scale loss of heterozygosity (LOH), and increased ERBB4, IGF2, WNT, retinoic acid receptor, GPCR (Gq, G, Gi), and PDGFR sig- naling (Giordano et al. 2003; Tissier et al. 2005; Assié et al. 2014; Zheng et al. 2016; Gara et al. 2018; Fojo et al. 2020).
Here we describe the genomic profiles from whole-genome and transcriptome analysis (WGTA) of seven advanced ACC tumors, with detailed clinical and therapeutic annotations. These patients underwent molecular analysis as part of the personalized medicine program at BC Cancer (NCT02155621; Pleasance et al. 2020). In addition to identifying previously described recurrent alterations affecting genes involved in the WNT pathway, DNA repair, and cell cycle regulation, our analysis revealed frequent alterations in genes involved in epi- genetic regulation. Specifically, we identified multiple copy gains affecting KDM5A, a lysine demethylase that antagonizes the histone methyltransferase activity associated with the tu- mor suppressor MEN1 (Feng et al. 2017), a well-described tumor suppressor in ACC. Multiple potentially actionable therapeutic targets were revealed from profiling metastatic ACC tumors, and treatment and response to systemic therapies are reported.
RESULTS
Clinical Presentations and Treatment Outcomes
Baseline characteristics at the time of diagnosis of metastatic ACC are summarized in Table 1. Overall, six of seven patients were female. The median age was 40 (range, 19-54), and five of seven patients had symptoms of cortisol excess, with one patient also having symptoms of aldosterone excess and another also having symptoms of testosterone excess. The clinical course is outlined in Figure 1. Standard chemotherapy and mitotane were given to four of seven patients, with an additional patient receiving this treatment in the adjuvant setting
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| Table 1. Presenting clinical and histopathological characteristics of seven patients with metastatic adrenocortical carcinoma | |||||||
| Case number | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Age, gender | 40, M | 19, F | 24, F | 54, F | 46, F | 34, F | 48, F |
| Elevated cortisol | No | Yes | Yes | No | Yes | Yes | Yes |
| Other hormone elevation | No | No | No | No | Aldosterone | Testosterone | No |
| Source of tissue for WGTA | Primary | Primary | Soft tissue | Lymph node | Lung | Primary | Primary |
| metastasis | |||||||
| Histopathology score | |||||||
| >5 mitotic figures/50 HPF | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Clear or vacuolated cells <25% | n/a | 1 | 0 | 1 | n/aª | 1 | n/a |
| Atypical mitotic figures | n/a | 1 | 0 | 1 | 0 | 1 | 1 |
| Necrosis | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Capsular invasion | n/a | 1 | 1 | 0 | 0 | 1 | n/a |
| Nuclear grade III or IV | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Diffuse architecture | 1 | 1 | 1 | 1 | 1 | 1 | n/a |
| Venous invasion | n/a | 1 | 0 | 0 | 0 | 1 | n/a |
| Sinusoidal invasion | n/a | 1 | 0 | 1 | 0 | 1 | n/a |
| Specimen type | Biopsy | Resection | Resection | Resection | Resection | Resection | Biopsy |
| Score: Weiss (modified Weiss) | 4/9 (3/7) | 9/9 (7/7) | 5/9 (4/7) | 7/9 (6/7) | ∗ | 9/9 (7/7) | 4/9 (4/7) |
(M) Male, (F) female, (WGTA) whole-genome and transcriptome analysis, (HPF) high-power field, (n/a) not applicable, (*) oncocytic variant.
CASE
1
×
2
*
Į
3
*
×
1
4
*
5
*
6
7
*
-30
0
Time from biopsy (months)
30
☐ EDP chemotherapy
☐ Mitotane
☐ TTK Inhibitor
* Adjuvant therapy
☐ Sunitinib
☐ Streptozocin
☐ Olaparib
Į Radiotherapy
☐ Docetaxel
☐ Checkpoint inhibitor
☐ Carboplatin + Paclitaxel
# Surgical resection
☐ Cisplatin
☒ EDP + Mitotane
☐ Olaparib + Temozolomide)
* Death
* Pt declined treatment
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(Case 3). There were no responses to treatment except for Case 5 (response to single-agent mitotane, which had also been used in the adjuvant setting) and Case 6 (response to EDP chemotherapy without mitotane). Case 5 also showed stable disease for 8.5 mo on olaparib. There were no adverse events leading to treatment discontinuation, except for Case 6 in whom an immune checkpoint inhibitor led to autoimmune hepatitis requiring treatment dis- continuation and corticosteroids.
Genomic Analyses
Sequencing coverage, tumor content, and tumor ploidy for all seven cases is shown in Table 2. All patients had advanced disease, with four of the biospecimens for sequencing acquired from the primary site and three from metastatic sites. Somatic alterations including small mu- tations/indels, copy-number alterations (CNAs), structural variant data, and the gene expres- sion (the number of reads per kilobase of transcript per million mapped reads [RPKM]) data for each case presented in the paper have been deposited at the Michael Smith Genome Sciences Centre at BC Cancer (BCGSC; https://bcgsc.ca/downloads/POG_ACC/) and are highlighted below.
Somatic Copy-Number Alterations and Loss of Heterozygosity
Large-scale LOH affecting multiple whole chromosomes was observed in six of the seven samples (Fig. 2A). Chromosome 17 was affected in all cases (with lesser extent in Case 2) and Chromosomes 2, 4, and 6 were also largely affected in five cases. Frequent copy-number gains were observed in cases 4, 6, and 7 and these were accompanied by frequent copy- number losses in Cases 4 and 6 (Fig. 2B). Large-scale copy-number losses were also detect- ed in Case 5. Gene amplification events were not seen in Case 7, and Cases 1 and 2 have very few amplification events. In Case 5, arm-level amplification was observed on Chromosome 12 (Ch12p) together with focal amplification events on the other arm (Ch12q).
Evaluation of genes affected by copy gains and losses revealed recurrent gains affecting KDM5A. A gain of two or more copies of the KDM5A gene on Chromosome 12 was ob- served in four cases and a single copy gain was observed in one case (Case 4) (Fig. 3A). KDM5A encodes a demethylase that antagonizes the histone methyltransferase activity pro- moted by MEN1, a gene that is recurrently altered in ACC. KDM5A gene expression was higher in cases with multiple copies gains, supporting the hypothesis that KDM5A activity may be elevated and contribute to epigenetic dysregulation in cases with KDM5A copy gains (Fig. 3B). TERT promoter hotspot mutations were frequently identified in primary ACC tumors (Liu et al. 2014). We did not observe TERT hotspot mutations; however, all sam- ples had copy gains affecting TERT on Chromosome 5 and one sample was found to have a
| Table 2. Sequencing coverage, tumor content, and tumor ploidy | |||||
|---|---|---|---|---|---|
| Case ID | Tumor genome sequencing coverage | RNA sequencing coverage (no. of paired reads) | Tumor content (%) | Ploidy | Ts/Tv |
| Case 1 | 74× | N/A | 40 | Triploid | 0.691 |
| Case2 | 98× | 331M | 86 | Triploid | 0.749 |
| Case3 | 84× | 167M | 54 | Diploid | 0.541 |
| Case4 | 91 × | 192M | 80 | Triploid | 0.79 |
| Case5 | 59× | 213M | 84 | Triploid | 1.177 |
| Case6 | 107× | 286M | 80 | Tetraploid | 0.964 |
| Case7 | 96× | 208M | 80 | Diploid | 2.005 |
(Ts/Tv) Transition to transversion ratio, (N/A) not applicable, (M) million.
| Case 7 | Case 6 | Case 5 | Case 4 | Case 3 | Case 2 | Case 1 | B | Case 7 | Case 6 | Case 5 | Case 4 | Case 3 | Case 2 | Case 1 | A | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 2 3 4 | 1 | |||||||||||||||
| 2 3 4 | ||||||||||||||||
| 5 6 7 2 9 10 | Chromosome | 5 6 7 8 9 10 | ||||||||||||||
| 11 12 13 14 15 16 78902 1 7 | 11 12 13 14 15 16 1 17 | |||||||||||||||
| İç | ||||||||||||||||
| - | ||||||||||||||||
| M | ₼ | |||||||||||||||
| X | 1 | |||||||||||||||
Chromosome
Figure 2. Loss of heterozygosity (LOH) events (A) and copy-number alterations (B) in seven ACC tumors. LOH
events are indicated in green, copy gains are indicated in red, and copy losses are indicated in blue.
somatic variant of unknown significance affecting TERT (R447C) (Fig. 3A). Copy gains on Chromosome 16 affecting TERF2, a member of the telomere nucleoprotein complex in- volved in telomerase regulation and telomere maintenance, was also observed in four cases. Copy losses affecting the genes involved in the cell cycle were observed including homo- zygous loss of CDKN2A and CDKN2B (Case 4) and RB1 (Cases 2 and 6). Copy gains, but not amplification, of CCNE1 were noted in Cases 6 and 7. NF1, another recurrently altered gene in ACC, was subjected to a homozygous deletion in one case (Case 3).
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☒ mutation
deletion
loss
gain
☒ homozygous loss
☒ structural variant
ADS
Weiss
1.5
9
1.5
0
Steroid-pheno-high
☒ Steroid-pheno-high+prolif
Steroid-pheno-low
Steroid-pheno-low+prolif
A
Case 1
Case 2
Case 3
Case 4
Case 5
Case 6
Case 7
B
D
20
T-test, p = 0.1
CCNE1
0.8
cell cycle
KDM5A Expression (RPKM)
RB1
15
0.6
CDKN2B
0.4
telomere maintenance
CDKN2A
10
0.2
TERF2
TP53lof
CTNNB19of
5
TERT
A
4
CHEK2
<2
BRCA2
Copy gains
≥2
4
DNA repair
RAD52
C
Case 7
Case 1
Case 6
Case 3
Case 4
Case 5
Case 2
8
ATM
1.0
* MUTYH
E
HRDetect score
0.5
MSH6
MLH1
30
A
ATRX
HRD score
20
epigenetic regulation
ARID1B
10
SMARCC2
0.075
SBS3
0.05
SMARCA4
0.025
KMT2A
KMT2C
SBS8
0.05
0.025
KDM5A
JAK2
0.3
SV signature 3
0.2
NF1
0.1
PIK3CA
Case 7
(primary)
Case 3
(soft tissue)
Case 6
(primary)
Case 5
(lung)
Case 4
(lymph node)
Case 2 (primary)
TP53
SV signature 5
0.4
CTNNB1
0.2
ADS ☒
Exp subtype ☒
del microhomology
0.2
Weiss
0.1
Single-Nucleotide Variants and Indels
The variants highlighted in this section are listed in Table 3. Several alterations predicted to affect diverse DNA repair pathways were observed. Somatic alterations in genes involved in the homologous recombination repair pathways include a homozygous in-frame deletion of ATM (Case 6), a heterozygous BRCA2 nonsense mutation (Case 4); other alterations affecting DNA repair pathways include a pathogenic germline mutation affecting a base excision re- pair gene, MUTYH (Case 1), and a homozygous frameshift mutation affecting MSH6 and a homozygous somatic mutation in MLH1 (Case 7; Fig. 3A).
Somatic mutations affecting CTNNB1 and TP53 are the most recurrent driver events in ACC (Zheng et al. 2016) and were observed in Cases 1, 5, 6, and 7, and co-occurred in Case 1 (Fig. 3A). The TP53 variants comprised a nonsense mutation, a missense mutation associated with loss of function and a homozygous deletion. The recurrent gain-of-function S45P mutation affecting CTNNB1 was detected in two samples, with a third sample affected
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| Gene | Chromosome | HGVS DNA reference | HGVS protein reference | Variant type | Predicted effect | dbSNP/dbVar ID | Genotype | ClinVar accession number |
|---|---|---|---|---|---|---|---|---|
| CTNNB1 | 3 | c. 133T > G | p.Ser45Ala | SNV | Missense | rs121913407 | Het | |
| CTNNB1 | 3 | c. 133T > G | p.Ser45Ala | SNV | Missense | rs121913407 | Hom | |
| TERT | 5 | c. 1339C> T | p.Arg447Cys | SNV | Missense | rs1304418053 | Het | |
| ATM | 11 | c.1837_1839delGTG | p.Val613del | DEL | In-frame deletion | Hom | SCV002062047 | |
| BRCA2 | 13 | c.2368G > T | p.Glu790* | SNV | Nonsense | rs398122746 | Het | |
| MUTYH | 1 | c. 1145G > A | p.G382D | SNV | Missense | rs36053993 | Het | |
| MSH6 | 2 | c.3644T> A | p.Leu1215* | SNV | Nonsense | Hom | SCV002062052 | |
| MLH1 | 3 | c.884G >T | p.Ser295Ile | SNV | Splice | Hom | SCV002062048 | |
| region variant | ||||||||
| TP53 | 17 | c.499C> T | p.GIn167* | SNV | Nonsense | rs 1555526097 | Het | |
| TP53 | 17 | c.742C>T | p.Arg248Trp | SNV | Missense | rs121912651 | Hom | |
| ARID1B | 6 | c.4084C>T | p.GIn1362* | SNV | Nonsense | Hom | SCV002062056 | |
| SMARCA4 | 19 | c.276G >C | p.Gln92His | SNV | Missense | Het | SCV002062051 | |
| SMARCC2 | 12 | c.3221delC | p.Pro1074fs | SNV | Frameshift variant | rs752788954 | Het | |
| KMT2C | 7 | c.8390delA | p.Lys2797fs | SNV | Frameshift variant | rs747256476 | Het | |
| KMT2A | 11 | c.2318dupC | p.Ser774fs | SNV | Frameshift variant | Hom | SCV002062055 | |
| ATRX | X | c.6235C>T | p.Arg2079* | SNV | Nonsense | Het | SCV002062053 | |
| ATRX | X | c.4377_4379delGGA | p.Glu1460del | DEL | Disruptive in-frame deletion | rs587780286 | Het | |
| ATRX | X | c.3904delA | p.Arg1302fs | SNV | Frameshift variant | Het | SCV002062054 | |
| PIK3CA | 3 | c.2086G > A | p.Gly696Arg | SNV | Missense | Het | SCV002062049 | |
| JAK2 | 9 | c.2385G > T | p.Arg795Ser | SNV | Missense | Hom | SCV002062050 |
(HGVS) Human Genome Variation Society, (SNV) single-nucleotide variant, (Het) heterozygous, (Hom) homozygous.
by an in-frame deletion of exons 2-3 of CTNBB1 that deletes the first 80 amino acids of the protein, leading to the loss of inhibitory amino-terminal phosphorylation sites (Maharjan et al. 2018).
Mutations affecting MEN1, a gene involved in epigenetic regulation through its associ- ation with the histone methyltransferase MLL1/KMT2A, are recurrent in ACC but were not observed in this cohort. On the other hand, we observed several somatic mutations affecting SWI/SNF chromatin remodeling genes (ARID1B, SMARCA4, SMARCC2) and histone meth- yltransferase genes (KMT2C, KMT2A, and ATRX) (Fig. 3A).
Alterations affecting genes involved in oncogenic signaling pathways, including a somatic missense mutation of unknown significance in PIK3CA (Case 2), were detected (Fig. 3A). A novel homozygous missense mutation affecting the kinase domain of JAK2 was also observed in Case 5 and may affect JAK2-mediated cytokine signaling (Fig. 3A).
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Structural Variant Analysis
Few structural variants affecting cancer genes were observed in this case series. Case 1 was found to harbor a translocation between CDKN2C and FAF1 (1:50978798| 1:51436870), which may disrupt the function of CDKN2C involved in cell cycle inhibition. Case 5 harbored a structural variant in RAD52 (12:1029459|12:1095898), which is predict- ed to cause a duplication of one-half of the gene and the creation of an additional trun- cated RAD52 protein that lacks the carboxy-terminal 181-412 residues (Fig. 3A). The same case was also found to harbor an interchromosomal translocation affecting STK11 and a noncoding region that was predicted to disrupt the first 300 residues of STK11 and is an inferred loss-of-function mutation. A complete list of high-quality structural var- iants (see Methods) have been made publicly accessible for download (https://bcgsc.ca/ downloads/POG_ACC/).
Transcriptome Analysis
EZH2, histone methyltransferase, and catalytic subunit of the PRC2 complex were highly ex- pressed in two patients compared to the Cancer Genome Atlas (TCGA) ACC data set (96th percentile in Cases 2 and 91st percentile in Case 6). Both cases also had homozygous loss of RB1, in agreement with previous findings that loss of this tumor suppressor promotes EZH2 expression in ACC (median EZH2 expression is 7.4 RPKM in RB1 mutant vs. 2.6 RPKM in RB1 wild-type cases in our cohort) (Drelon et al. 2016).
IGF1R signaling has been shown to be frequently activated in ACCs (Weber et al. 1997), and high expression of IGF1R (Cases 3 and 7; 100th and 95th percentile, respectively), IGF2R (Cases 3, 6, and 7; 100th, 85th, and 89th percentile, respectively), IGF2 (Case 5; 82th percen- tile) and IGF1 (Cases 2, 3, and 5; 86th, 91st, and 85th percentile, respectively) compared to the TCGA ACC data set was observed, indicating potential increased activation of the IGF1R signaling axis.
Clustering based on genomic, methylation, and transcriptome profiles has revealed mo- lecular subgroups of ACC as well as signatures associated with adrenocortical differentiation (Zheng et al. 2016). Transcriptome clustering using previously defined gene sets identified three different expression subtypes, with the rarest steroid-phenotype-low + proliferation ex- pression subtype lacking in our cohort (Fig. 3A). Three patients (Cases 4, 5, and 7) clustered with the steroid-phenotype-high group, two patients (Cases 2 and 6) with the steroid-pheno- type-high + proliferation group, and one patient with the steroid-phenotype-low group. Evaluation of the adrenocortical differentiation gene expression signature score (ADS) re- vealed four of six patients with positive ADS values supporting the differentiated state of these cases, one of which harbored a CTNNB1 somatic mutation and exhibited a very high ADS value.
Previously described expression signatures were not closely aligned with driver muta- tions in our cohort; thus, we sought to determine if hierarchical clustering in our cohort may reveal expression profiles associated with driver status. Hierarchical clustering of tran- scriptome profiles from six patients with available RNA sequencing (RNA-seq) distin- guished three pairs (Fig. 3D). The three pairs were characterized by different driver gene mutations, TP53 mutation or loss was detected in pair 1 (Cases 7 and 3), CTNNB1 mutation or deletion was identified in pair 2 (Cases 5 and 6), whereas tumors in pair 3 (Cases 2 and 4) had no mutations in either TP53 or CTNNB1 but had other gene alter- ations. In addition, pair 1 and 2 were clustered together and were distinguished from tu- mors with no TP53 and CTNNB1 alterations. These data suggest genomic alterations might lead to gene expression profiles that are characteristic of tumors with specific driver gene mutations.
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Integrative Analysis
Our observations, and previous reports in ACC (Zheng et al. 2016; Fojo et al. 2020), have indicated a significant subset of ACC may be associated with homologous recombination (HR) repair deficiency, although the impact of mutations affecting several HR genes is un- clear. To investigate the impact of HR mutations in our cohort, we performed mutation sig- nature analysis and investigated homologous recombination repair using HRDetect that aggregates four single-nucleotide variant (SNV) and structural variant (SV) mutation signa- tures, homologous recombination deficiency (HRD) index, and the proportion of small dele- tions associated with microhomology (Fig. 3C; Davies et al. 2017; Zhao et al. 2017). Case 5 had a high proportion of mutations attributed to Catalog Of Somatic Mutations in Cancer (COSMIC) single base substitution 3 (SBS3) signature, which previously has been associated with HRD (Figs. 3A and 4A), and HRDetect results were consistent with this tumor being de- ficient in homologous recombination repair (Fig. 3C), despite the absence of a BRCA1 or BRCA2 pathogenic mutation (Davies et al. 2017). In addition to the structural variant in RAD52, copy losses of several DNA repair genes including BRCA2, CHEK2, and ATM were detected in this case (Fig. 3A). The patient received the PARP inhibitor olaparib and had disease stabilization for 8.5 mo. Case 2 was also predicted to be HRD by HRDetect and similarly lacked a BRCA1/2 alteration. Interestingly, Case 4 with the heterozygous BRCA2 mutation and strong SBS3 signature was predicted to be HR-proficient by HRDetect, in line with the presence of a functional wild-type allele. Apart from HRD-associ- ated signatures, Case 3 and Case 6 had a high proportion of mutations attributed to SBS31, SBS35, and DBS5, all previously associated with prior platinum drug treatment (Fig. 4A,B). Case 3 received platinum treatment prior to metastatic disease (86 d), whereas Case 6, which exhibited very high SBS31 and SBS35 exposures (ranked second and first, respective- ly, compared against 570 cancer genomes of mixed origins), was treated with platinum for 163 d prior to biopsy. The presence of genome signatures consistent with HRD provides support for involvement of the HR pathway in ACC pathogenesis and may represent a com- pelling target in ACC as in other tumors of the endocrine system.
Case 1 with the pathogenic germline MUTYH mutation was characterized by a high propor- tion of mutations attributed to SBS18 (ranked 20th compared against 570 cancer genomes), pre- viously associated with MUTYH deficiency (Fig. 4A; Thibodeau et al. 2019). Case 7 with deleterious mutations in MSH6 and MLH1 demonstrated a high mutation burden (whole-ge- nome tumor mutation burden of 28.6 mutations/Mb), and microsatellite instability (39% of mi- crosatellites were unstable; Niu et al. 2014). Mutation signature analysis also revealed a high proportion of mutations attributed to SBS26, SBS44, DBS7, ID2, and ID7, all previously associ- ated with mismatch repair deficiency (MMRd) (Fig. 4; Drost et al. 2017). SBS44 (ranked eighth), DBS7 (ranked sixth), ID2 (ranked 11th), and ID7 (ranked fourth) were particularly high compared to a large, previously sequenced cancer cohort. Interestingly, Case 7 also harbored mutations consistent with signatures SBS7c and SBS38 (Fig. 4A), both of which are primarily observed in melanomas (Saini et al. 2016). We and others have reported SBS38 in non-UV-associated mel- anoma subtypes, raising the possibility of a common underlying mutational process in ACC and a subset of non-UV-exposed melanomas (Newell et al. 2020; Pleasance et al. 2020). After pro- gressing on EDP chemotherapy and mitotane, the patient was initiated on avelumab, but unfor- tunately died before completing one cycle because of rapidly progressing disease.
DISCUSSION
There are relatively few studies of comprehensive genomic characterizations of ACC (Zheng et al. 2016; Fojo et al. 2020). Current exome sequencing studies have revealed a few
ACC tumors are compared against 570 cancer genomes of mixed origins (Pleasance et al. 2020).
Figure 4. The contribution of previously reported single base substitution (SBS) (A), doublet base substitution
Mutation fraction contributed
0.0
0.2
0.4
0.6
0.8
ID1
ID2
ID3
ID4
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Cosmic indel signatures
ID7
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Cosmic single base substitution signatures
Mutation fraction contributed
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SBS7d
SBS8
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SBS10b
SBS11
SBS12
SBS13
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SBS17b
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SBS41
SBS42
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SBS84
SBS85
SBS86
SBS87
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SBS90
| 0.0 0.1 | 0.2 | Mutation fraction contributed 0.3 0.4 0.5 | 0.6 | 0.7 | |
|---|---|---|---|---|---|
| Case7 Case6 Case5 Case3 Case4 | Case2 Case1 | ||||
A
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Lavoie et al. 2022 Cold Spring Harb Mol Case Stud 8: a006148
(DBS) (B), and indel (ID) (C) mutational signatures (Catalog of Somatic Mutations in Cancer [COSMIC v3.1]) in
DBS2
Cosmic doublet base substitution signatures
DBS3
DBS4
DBS5
DBS6
DBS7
DBS8
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DBS10
DBS11
Case7
Case6
Case5
Case4
Case3
Case2
Case1
C
B
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recurrently altered genes in ACC, with examples of variants of uncertain significance impact- ing multiple genes aligned to several cancer associated pathways. Here we describe a cohort of patients with metastatic ACC profiled using whole-genome and transcriptome analysis (WGTA) and provide detailed clinical annotation.
Our study is limited by the small sample size and heterogeneity of the cohort. Four out of seven samples were obtained from primary adrenal tumors, whereas the remaining three were obtained through biopsies of metastatic lesions. This introduces significant heteroge- neity. The fact that four patients received systemic therapy prior to biopsy, including two pa- tients who received cytotoxic chemotherapy, also creates significant bias. Our analysis highlights the genomic diversity of ACC, which makes inferring specific conclusions about the management of this rare disease challenging. The clinical-genomic correlates are pre- sented as hypothesis-generating findings.
Whole-genome sequencing allows for detection of potentially disruptive structural variants, including the STK11 and RAD52 variants detected here, and evaluation of genome-wide mu- tation signature phenotypes including HRDetect and microsatellite instability. Transcriptome sequencing informs on the impact of observed genomic alterations, differentiation, and re- vealed distinct expression profiles that were correlated with driver gene mutation status.
Consistent with previous studies, somatic mutations in CTNNB1 and/or TP53 were ob- served in our cohort, including an exon level deletion affecting CTNNB1 similar to that de- scribed in 7% of ACC cases (Maharjan et al. 2018). Although no somatic mutations in other previously reported driver genes were detected in our cohort, we observed the activation of pathways implicated in ACCs. We observed copy-number gains of CCNE1 in six cases and detected homozygous loss of RB1 in two cases and homozygous loss of CDKN2A and CDKN2B in one case, suggesting the deregulation of the cell cycle and suggesting that targeting the cell cycle pathway may be beneficial in a subset of patients. We did not detect MEN1 mutations in our cohort; however, we did observe multiple copy gains and concurrent high expression of KDM5A, a demethylase that antagonizes MEN1-medi- ated methylation. A single TCGA ACC sample harbored a KDM5A amplification, and this case was also negative for MEN1 mutation (Zheng et al. 2016). Increased KDM5A expres- sion may represent an alternate mechanism of epigenetic dysregulation compared to MEN1 loss of function and is the subject of active investigation as an oncology target (Yang et al. 2021).
In addition to alterations in ACC driver genes, alterations consistent with deregulation of epigenetic signaling and DNA repair processes were observed. We identified somatic mu- tations in SMARCA4, SMARCC2, and ARID1B, suggesting deregulation of the chromatin re- modeling complex, SWI/SNF. Inactivating mutations in genes encoding SWI/SNF subunits are frequently observed in various cancer types and have been associated with deficiency in DNA repair as well as response to multiple targeted therapies (Mittal and Roberts 2020; Ribeiro-Silva et al. 2019), but the role of the SWI/SNF complex in ACC tumorigenesis and therapeutic response has not yet been investigated. In addition, somatic variants were de- tected in KMT2A and KMT2C histone methyltransferases and in ATRX and DAXX as previ- ously reported in primary ACCs (Zheng et al. 2016). We also observed increased EZH2 expression, particularly in cases with RB1 loss. EZH2 inhibitors are approved for use in EZH2 mutant follicular lymphoma and in SMARCB1/INI1 deficient epitheliod sarcoma (Groisberg 2020) and may be therapeutically beneficial in a subset of SWI/SNF deficient, or EZH2 overexpressing ACCs.
Four out of the seven cases were associated with mutations in DNA repair genes, includ- ing somatic alterations in BRCA2, MLH1, MSH6, ATM, and a germline pathogenic mutation affecting MUTYH. Mutations in the DNA-mismatch repair pathway have been observed in primary ACCs (Zheng et al. 2016) and ACC has been shown to be a Lynch syndrome-asso- ciated cancer (Raymond et al. 2013). Germline MUTYH mutations are also known to be
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associated with ACC predisposition; however, germline alterations affecting HR-related genes have only been rarely attributed to ACC (El Ghorayeb et al. 2016; Xie et al. 2018), sug- gesting a potential subset of ACCs is characterized by HRD derived from somatic alterations as opposed to germline mutation. Two cases in our cohort demonstrating a strong HRD sig- nature lacked mutations in BRCA1/2, suggesting either epigenomic dysregulation or alter- ations affecting other HR genes may contribute to the HRD in ACC. Profiling mutational signatures associated with HRD may reveal ACC patients that can benefit from platinum- based therapy or PARP inhibitors, as was observed in other tumor types (Zhao et al. 2017; Chopra et al. 2020; Golan et al. 2021).
Despite numerous shared alterations, this cohort also illustrates the high degree of hetero- geneity that exists within ACC and represents a challenge to successfully propose targeted therapy for ACCs. One patient (Case 6) had a partial response to combination chemotherapy (etoposide, doxorubicin, and cisplatin), and another patient (Case 5) has had sustained benefit from mitotane and subsequently from olaparib, whereas other treatments only led to minimal clinical benefit. Therapy informed by WGTA analysis was given for Case 5, Case 6, and Case 7. Case 5 had evidence of HRD despite the absence of a BRCA1 or BRCA2 mutation and re- ceived the PARP inhibitor olaparib resulting in disease stabilization for 8.5 mo. This patient had a potential disruptive structural variant in RAD52, which participates in HR. In mammals RAD52 deletion alone does not compromise HR repair; however, depletion of human RAD52 is syn- thetically lethal with mutations in other members of the HR pathways including BRCA1, BRCA2, PALB2, and RAD51 paralogs (Feng et al. 2011; Chun et al. 2013; Lok et al. 2013). In this case the structural variant in RAD52 together with the copy losses of other HR genes might have resulted in the HRD phenotype and partial response to PARPi. The patient showed mixed response to the subsequent carboplatin and paclitaxel combination with disease pro- gression 5 mo after initiation of treatment. Case 6 was enrolled in a phase 1 clinical trial of a TTK inhibitor based on the presence of a CTNNB1 activating mutation and preclinical evi- dence (Zaman et al. 2017), but did not demonstrate clinical benefit. This patient subsequently received the PARP inhibitor olaparib based on the presence of an in-frame deletion affecting ATM with loss of heterozygosity and low ATM gene expression based on transcriptome and immunohistochemistry, although this sample was not classified as HRD by HRDetect. A phase 2 study in gastric cancer (Bang et al. 2015) suggested improved overall survival for patients treated with a PARP inhibitor and a taxane, when compared to a taxane alone. This benefit was preferentially seen in a cohort of patients with low ATM expression by immunohistochem- istry, although a confirmatory phase 3 study was negative (Bang et al. 2017). In this case, the PARP inhibitor olaparib was combined with temozolomide based on the recommended dose and schedule derived from a phase 1 clinical trial in small-cell lung cancer (Farago et al. 2018), but unfortunately the patient did not benefit from this combination. Case 7 had evidence of microsatellite instability and mismatch repair deficiency; and based on these findings the pa- tient was initiated on an immune checkpoint inhibitor (Le et al. 2017), but the patient’s clinical status had rapidly deteriorated and she passed away within a few days of initiating treatment without a response assessment.
Overall, the findings of this cohort suggest that molecular profiling of ACC could be uti- lized to target specific treatments to certain alterations affecting several potentially action- able pathways including DNA repair, cell cycle, oncogenic signaling pathways, and genes involved in epigenetic regulation found in specific subpopulations. Comprehensive geno- mic and transcriptome analysis of ACC has the potential to reveal therapeutic strategies tai- lored to patients’ individual tumors. Although the lack of response seen in previous studies of targeted agents may be related to the poor activity of these drugs, it is also possible that the heterogeneity of the underlying disease contributed to the disappointing results observed. To better guide future studies, more information is required on the molecular makeup of metastatic ACC.
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METHODS
Sample Collection and Preparation
Whole-genome sequencing of tumors and matched normal tissue was performed in seven cases; four of the biopsies were obtained from primary adrenal tumors, whereas the remain- ing three were biopsies of metastatic lesions, as outlined in Table 1. Whole-transcriptome sequencing was performed on six of the seven tumors. Patients with locally advanced or met- astatic ACC with a life expectancy of greater than 6 mo were enrolled on the Personalized OncoGenomics (POG) project (ClinicalTrials.gov identified: NCT02155621) at BC Cancer, in Vancouver, Canada (Laskin et al. 2015; Jones et al. 2017).
Tumor sections were reviewed by a pathologist to determine tumor content and cellular- ity, and remaining sections were used for DNA and RNA preparation. Peripheral blood was collected for use as a normal tissue reference and pathogenic and likely pathogenic germline variants in 98 known cancer predisposition genes were analyzed as outlined in Dixon et al. (2020). DNA and RNA were coextracted for the construction of polymerase chain reaction (PCR)-free DNA libraries and poly(A) selected RNA libraries as described previously (Sheffield et al. 2015).
Sequencing and Bioinformatics
Tumor genomes sequencing was performed to 80x coverage and normal genomes to 40x coverage on Illumina HiSeq platform with 125- or 150-bp paired-end reads. Illumina HiSeq2500 or NextSeq500 were used for RNA sequencing with 150-200 million 75-bp paired-end reads. Sequence reads were aligned to the human reference genome (hg19) by the BWA tool (Li and Durbin 2010) and somatic SNVs and small indels were identified using SAMtools (v0.1.17) (Li et al. 2009) and Strelka (v1.0.6) (Saunders et al. 2012). Regions of copy-number alteration (CNA) and LOHs were determined using CNAseq (v0.0.6) (Jones et al. 2010) and APOLLOH (v0.1.1) (Ha et al. 2012), respectively. Amplification was defined as the ploidy-corrected copy-number gain being greater than the estimated tumor ploidy. Copy gains are defined as an increase in gene copies equiva- lent to or less than the estimated average ploidy. SVs in RNA-seq data were detected by ABySS v1.3.4 and TransABySS (v1.4.10) (Birol et al. 2009; Simpson et al. 2009) and Chimerascan (v0.4.5) (Iyer et al. 2011) and DeFUSE (v0.6.2) (McPherson et al. 2011); SVs in the DNA were identified using ABySS and TransABySS followed by Manta v1.0.0 (Chen et al. 2016) and Delly v0.7.3 (Rausch et al. 2012). SV calls from multiple algorithms were merged into a consensus caller MAVIS (v2.1.1) that performs a subsequent validation by lo- cal assembly (Reisle et al. 2019). Structural variants were filtered for events that were called by multiple tools and were supported by an assembled contig and were deposited for download (https://bcgsc.ca/downloads/POG_ACC/). The contribution of previously report- ed mutational signatures in COSMIC v3.1 (https://cancer.sanger.ac.uk/signatures; Alexandrov et al. 2020) was calculated using nonnegative least squares optimization. HRDetect scores were computed using a logistic regression model described previously in Davies et al. (2017). Six HRD-associated mutation signatures were used in the algorithm: (i) Signature 3, (ii) Signature 8, (iii) SV signature 3, (iv) SV signature 5, (v) the HRD index, and (vi) the fraction of deletions with microhomology. Somatic SNVs called by Strelka (v1.0.6) were used for single base substitution signature calculation. The contribution of previously reported mutational signatures in COSMIC v3.1 (https://cancer.sanger.ac.uk/cosmic/ signatures) was calculated using Monte Carlo Markov chain (MCMC) sampling (https ://github.com/eyzhao/SignIT). Somatic, DNA-derived SVs identified by the consensus caller MAVIS were used for SV signature calculation (Reisle et al. 2019). MAVIS calls that were de- tected by more than one tool and for which the contig could be assembled were included in
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the analysis, and the contribution of the previously reported SV mutational signatures was calculated using MCMC sampling (https://github.com/eyzhao/SignIT) (Nik-Zainal et al. 2016). HRD index was computed as the arithmetic sum of LOH, telomeric-allelic imbalance (TAI), and large scale transitions (LST) scores (Zhao et al. 2017). All signatures were normal- ized and log transformed, and a threshold of 0.7 was used as previously reported (Davies et al. 2017; Zhao et al. 2017).
Microsatellite instability (MSI) was calculated by MSIsensor (v0.2) (Niu et al. 2014).
Gene Expression Analysis
A transcriptome of an average depth of 200 million reads was generated from the tumor sample. The RNA sequencing reads were analyzed with JAGuaR (Butterfield et al. 2014) to include alignments to a database of exon junction sequences and subsequent reposition- ing onto the genomic reference. The RNA sequencing data were processed using the Genome Sciences Centre’s wtss (whole-transcriptome shotgun sequencing) pipeline cover- age analysis (version 1.1) with the “stranded” option to determine gene and exon read counts and normalized expression level. The level of expression of each gene was deter- mined as the number of RPKM.
Gene expression level was evaluated by converting RPKM of select genes into percen- tile ranks against transcriptomes of ACC tumors from The Cancer Genome Atlas project (https://portal.gdc.cancer.gov/). Expression subtypes classification shown in Figure 3A (Exp subtype track) was inferred by calculating spearman correlation factor between indi- vidual TCGA samples and POG samples. Hierarchical clustering shown in Figure 3D was performed after selecting the top 2500 genes according to the magnitude (from largest to smallest in absolute values) of their coefficients contributing to the first principal com- ponent analysis (PCA) component and after conversion of the RPKM values to z-scores us- ing the expression data of six patients with RNA-seq results. Agglomerative clustering method was used to obtain clusters, computing the distance matrix with the Euclidean dis- tance. The adrenocortical differentiation score (ADS) shown in Figure 3A (ADS score track) was calculated by using 25 gene expression as described by Zheng et al. (2016). To obtain the expression subtypes described in Zheng et al. (2016), kmeans clustering was per- formed on the publicly available mRNA data of 76 TCGA ACC samples with expression subtypes classification (downloaded from https://pancanatlas.xenahubs.net) and the POG mRNA data that was calculated using the TOIL RNA-seq pipeline (Vivian et al. 2017) to minimize computational batch effects between POG samples and recomputed TCGA samples. The TOIL pipeline uses CutAdapt for adapter trimming and STAR (Dobin et al. 2013) to generate alignments and performs quantification using RSEM (Li and Dewey 2011) and Kallisto (Bray et al. 2016).
ADDITIONAL INFORMATION
Data Deposition and Access
The data sets generated and analyzed during the current study have been deposited at the Michael Smith Genome Sciences Centre at BC Cancer (BCGSC; https://bcgsc.ca/ downloads/POG_ACC/). The genomic and transcriptome sequencing data sets have been deposited at the European Genome-phenome Archive (EGA; https://ega-archive.org/) as part of the study EGAS00001001159 (Patient 1 EGAD00001002037, Patient 2 EGAD0000 1002049, Patient 3 EGAD00001003053, Patient 4 EGAD00001004905, Patient 5 EGAD00 001003676, Patient 6 EGAD00001005844, Patient 7 EGAD00001005897).
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Ethics Statement
The study was conducted in accordance with the principles of the Declaration of Helsinki and with approval of the Research Ethics Board at BC Cancer, and all participants provided writ- ten consent.
Acknowledgments
This work would not be possible without the participation of our patients and families, the POG team, Canada’s Michael Smith Genome Sciences Centre technical platforms, the gen- erous support of the BC Cancer Foundation and their donors and Genome British Columbia. The results published here are in part based upon analyses of data generated by the follow- ing projects and obtained from dbGaP (http://www.ncbi.nlm.nih.gov/gap): TCGA managed by the National Cancer Institute and National Human Genome Research Institute (http:// cancergenome.nih.gov). Data from the Pan-Cancer Analysis of Whole Genomes (PCAWG) managed by the International Cancer Genome Consortium was retrieved from https://dcc .icgc.org/pcawg.
Author Contributions
J .- M.L. and V.C. conceptualized the study. Analyses were performed by J .- M.L., V.C., L.M.W., L.C., and G.W. Provision of patient samples and curation of data was conducted by J .- M.L., C.K.K., D.J.R., and J.L. The original draft was written by J .- M.L., V.C., L.M.W., and M.A.M., J.L., C.K.K., D.J.R., and S.J.M.J. reviewed and edited the manuscript. Funding was acquired by M.M., J.L., and S.J.M.J.
Competing Interest Statement
The authors have declared no competing interest.
Received September 10, 2021; accepted in revised form January 18, 2022.
Funding
This work was supported by the BC Cancer Foundation and Genome British Columbia (pro- ject B20POG).
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