\MENT OF HEALTH & HUMAN
Published in final edited form as: Endocr Relat Cancer. ; 28(10): 671-681. doi:10.1530/ERC-21-0040.
Targeted Genomic Analysis of 364 Adrenocortical Carcinomas
Nikita Pozdeyev1,2,*, Lauren Fishbein1,2,*, Laurie M. Gay3, Ethan S. Sokol3, Ryan
Hartmaier3, Jeffrey S. Ross3,4, Sourat Darabi5, Michael J. Demeure5,6, Adwitiya Kar1, Lindsey Foust1, Katrina Koc1, Daniel W. Bowles7, Stephen Leong7, Margaret E. Wierman1,8, Katja Kiseljak-Vassiliades 1,8
1Division of Endocrinology, Metabolism and Diabetes, University of Colorado School of Medicine at Colorado Anschutz Medical Campus Aurora, Colorado;
2Division of Biomedical Informatics & Personalized Medicine, Department of Medicine, University of Colorado School of Medicine at Colorado Anschutz Medical Campus Aurora, Colorado;
3Foundation Medicine Inc. Cambridge Massachusetts;
4Departments of Pathology and Urology, Upstate Medical University, Syracuse, New York,
5Hoag Family Center Institute, Newport Beach, California,
6Translational Genomics Research Institute, Phoenix, Arizona,
7Division of Medical Oncology, Department of Medicine, University of Colorado School of Medicine at Colorado Anschutz Medical Campus Aurora, Colorado
8Research Service Veterans Affairs Medical Center, Aurora Colorado 80045
Abstract
Despite recent advances in elucidating molecular pathways underlying adrenocortical carcinoma (ACC), this orphan malignancy is associated with poor survival. Identification of targetable genomic alterations is critical to improve outcomes. The objective of this study was to characterize the genomic profile of a large cohort of patient ACC samples to identify actionable genomic alterations. 364 individual patient ACC tumors were analyzed. The median age of the cohort was 52 years and 60.9% (n=222) were female. ACC samples had common alterations in epigenetic pathways with 38% of tumors carrying alterations in genes involved in histone modification, 21% in telomere lengthening, and 21% in SWI/SNF complex. Tumor suppressor genes and WNT signaling pathway were each mutated in 51% of tumors. Fifty (13.7%) ACC tumors had a genomic alteration in genes involved in the DNA mismatch repair (MMR) pathway with many tumors also displaying an unusually high number of mutations and a corresponding MMR mutation signature. In addition, genomic alterations in several genes not previously associated with ACC were observed, including IL7R, LRP1B, FRS2 mutated in 6%, 8% and 4% of tumors, respectively. In total, 58.5% of ACC (n=213) had at least one potentially actionable genomic
alteration in 46 different genes. As more than half of ACC have one or more potentially actionable genomic alterations, this highlights the value of targeted sequencing for this orphan cancer with a poor prognosis. In addition, significant incidence of MMR gene alterations suggests that immunotherapy is a promising therapeutic for a considerable subset of ACC patients.
INTRODUCTION
Adrenocortical carcinoma (ACC) is an orphan malignancy affecting individuals across a broad age spectrum with bimodal distribution from pediatric to adults. The rarity of the disease, limited preclinical models and lack of clinical trials have contributed to slow progress in the field and continued poor outcomes with 5-year survival rate of 35% (Else, et al. 2014; Icard, et al. 2001; Luton, et al. 1990).
Over the last decade, progress has been made in characterization of the genomic and genetic landscape of human adrenocortical carcinoma. Two large scale multi-omic studies confirmed that heterogonous molecular signatures underlying human ACC tumors are associated with variable genomic clusters and prognosis in patients (Assie, et al. 2014; Zheng, et al. 2016). These two comprehensive studies confirmed three known alterations in ACC including Insulin-like Growth Factor (IGF2) overexpression occurring in the majority of ACC tumors, and the Wnt and p53 pathways that are the most commonly altered in ACC (Giordano, et al. 2003; Tissier, et al. 2005). These studies have identified a few novel candidate driver genes including ZNRF3, RPL22, CCNE1 and TERF2; however, none of these are targetable to date (Assie et al. 2014; Zheng et al. 2016). Most studies to date show great variability in the phenotype and genotype of ACC.
For rare cancers, uncovering targetable genetic drivers and pathways in order to identify precision medicine approaches is especially important because of the difficulty in recruiting large number of subjects for clinical trials. Current therapies for ACC are limited. Mitotane, an adrenolytic drug, is the only FDA approved therapy with significant toxicity and limited effectiveness (Grubbs, et al. 2010; Megerle, et al. 2018; Terzolo, et al. 2007). In patients with advanced disease, EPD+M (etoposide, doxorubicin, cisplatin + mitotane) chemotherapy regiment is commonly used, but response rate is low at 23.2% with median progression- free survival of 5 months (Fassnacht, et al. 2012). No effective targeted therapy has been identified to date (Adam, et al. 2010; Berruti, et al. 2012; Fassnacht, et al. 2015; Gross, et al. 2006; Quinkler, et al. 2008; Weigel, et al. 2014; Wortmann, et al. 2010).
FoundationOne (Foundation Medicine Inc .; FMI, Cambridge, MA) is a next-generation sequencing-based platform that analyzes single nucleotide variants (SNV), indels, copy number alterations (CNA), and selected gene rearrangements in a targeted panel of cancer- associated genes (Frampton, et al. 2013). Here, we present genomic analysis of the largest known cohort of ACCs (N=364) genotyped with FoundationOne with the goal of identifying novel genetic alterations and understanding the percent targetable alterations for therapeutic intervention. Our analysis confirmed the commonly known pathway alterations in ACC and identified several novel somatic alterations. Importantly, we identified that 13.7% of ACC have alterations in the DNA mismatch repair gene pathway, which is higher than previously reported in adults with ACC (3-7%) (Lippert, et al. 2018; Raymond, et al. 2013; Zheng et al.
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2016). Immune checkpoint inhibitor therapy was recently approved for MMR/MSI-H solid tumors (Lemery, et al. 2017), and may be a clinically relevant therapeutic option in a subset of patients with ACC.
METHODS
Data source
Genomic and limited demographic data for 364 ACCs from unique patients tested using FoundationOne (Frampton et al. 2013) through 2018 were provided by Foundation Medicine Inc. (FMI), Cambridge, MA (Supplementary Table 1 and 2; referred to as FMI dataset). Approval for this study, including a waiver of informed consent and Health Insurance Portability and Accountability Act waiver of authorization, was obtained from the Western Institutional Review Board (protocol #20152817). Prior to sequencing, all specimens were reviewed by the FMI pathologist for consistency with the previously established diagnosis of ACC. Data were generated as part of the clinical care; therefore, there may be ascertainment bias as some physicians may send all ACC tumors for analysis, whereas others may only send a subset from patients who have aggressive disease and are out of standard therapeutic options. Clinical information on stage, prior treatments and clinical course is not available. The FoundationOne baits version (T7 with 396 genes, T5a with 288 genes or D2 with 456 genes) used was accounted for when calculating the prevalence of alterations (Supplementary Table 3). There were 553 genes potentially investigated and 286 genes in common across all bait sets. Genes included in a specific bait set and assigned into annotation/pathway groups (Cancer Genome Atlas Research 2014; Landa, et al. 2016; Pozdeyev, et al. 2018) are listed in Supplementary Table 3.
Variant filtering and annotation
Germline DNA was not available for comparison. Therefore, to remove germline variants, synonymous variants and non-pathogenic variants, a stringent filtering strategy was used as described previously (Pozdeyev et al. 2018) and which are similar to the strategy used by the American Association for Cancer Research Project GENIE (Consortium 2017). In brief, germline variants reported by 1000 Genomes Project (Abecasis, et al. 2010) or present in any of the eight Exome Aggregation Consortium (Lek, et al. 2016) databases with the frequency of ≥ 0.00002 were excluded from further analysis. However, variants reported as “pathogenic”, “likely pathogenic” or of “uncertain significance” by Clin Var (Landrum, et al. 2016) were left in the database regardless of the frequency to prevent removal of the relatively frequent pathogenic germline variants causing cancer syndromes. Despite this rigorous filtering, it is possible that a few non-pathogenic or germline variants remained. After filtering, alterations were annotated using default settings in ANNOVAR (Wang, et al. 2010).
Mutation signature analysis
R package deconstructSigs was used to analyze mutation signatures in the adrenocortical cancers samples (Rosenthal, et al. 2016). Synonymous SNVs were included in this analysis only. COSMIC mutation signatures served as the reference (https://cancer.sanger.ac.uk/ cosmic/signatures). Mutation signatures were evaluated only in specimens with at least 10
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single nucleotide variants and small indels (71 tumors). As previously reported (Pozdeyev et al. 2018), this threshold was empirically identified as a minimum requirement to make reliable mutation signature calls. Mutation signature was assigned to a specific specimen when at least 60% of mutations were specific to a particular signature or mechanism (such as DNA mismatch repair deficit).
RESULTS
FoundationOne ACC cohort
Among 364 individual patient tumors analyzed, 222 were females and 141 were males (for 1 specimen the sex was unknown) (Figure 1A). The mean age (SD) was 48.6 (13.6) for females and 50.6 (12.20) for males with overall median age of 52 years at the time the specimens were submitted for genotyping, not different between sexes (Wilcoxon, p=0.44). Distribution by age and sex is shown in Figure 1B and is similar in distribution to the TCGA cohort (Zheng et al. 2016), although the bimodal distribution seen in TCGA female cohort was not observed in the FMI (Foundation Medicine Inc.) patient cohort.
Genomic Landscape
A total of 3117 genomic alterations were found in 457 genes. The median number of alterations per tumor was 7 (range 1-56). SNVs and indels were the most common alterations (median=4), followed by CNA (median=1) and rearrangements (median=0) (Figure 2A) The distribution of types of genetic alterations associated with most frequently alerted genes is displayed in Figure 2B.
The most frequently altered genes were TP53 (38%), CTNNB1 (28%), ZNRF3 (17%), CDKN2A (13%), RB1 (12%), MEN1(12%), ATRX(11%), TERT promoter (10%), APC (10%) and NF1 (9%) (Figure 2B, Supplementary Figure 1). Of note, of the 138 TP53 alterations, 8 were found in the pediatric (<18 years old) cases (Supplementary Table 4). In addition, several novel recurrent alterations, not previously reported in ACC, were found affecting LRP1B (8%), IL7R (6%), FRS2 (4%), ARID1A1 (4%) and KRAS (3%).
Tumor suppressor genes (51%) and Wnt signaling pathway genes (51%) were commonly mutated (Figure 2C, Supplementary Figure 1). Epigenetic dysregulation plays a significant role in ACC tumorigenesis (Else, et al. 2008; Zheng et al. 2016). In our cohort, TERT, ATRX or DAXX were altered in 78 ACCs (21%); all were mutually exclusive except in one tumor, suggesting that mutation of any one gene in this pathway is sufficient to contribute to ACC development. Genes involved in histone modification, SWI/SNF (Switch/Sucrose Non-Fermentable) and DNA methylation were affected in 38%, 21 % and 8% of ACC, respectively (Figure 2C, Supplementary Figure 1).
Fifty ACCs (13.7%) exhibited 60 genomic alterations in MMR genes, MLH1, MSH2, MSH6 and PMS2 (Supplementary Table 2), which included 49 SNVs/indels, 10 CNAs (nine deletions, one amplification) and one MSH2-MSH2 truncating rearrangement. Eight of these are known pathogenic or likely pathogenic variants per Clin Var associated with Lynch syndrome (Supplementary Table 2). Overall, the 50 ACCs with MMR gene alterations had a significantly higher number of total SNV/indels compared to tumors with wild type
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MMR genes (Figure 3A, Wilcoxon test, p=1.14e-14). Interestingly, the majority (N=10/14) of MMR gene alterations in MMR altered tumors with low number (<10) of SNV/indels were still predicted damaging (predicted frameshifts, splice site variants or missense variants predicted damaging using in silico callers) (Supplementary Table 1 and 2). Surprisingly, 49 of the 50 ACC were microsatellite stable (Supplementary Table 1).
Mutational signature analysis (Alexandrov, et al. 2013) demonstrated, for the first time, that some ACC tumors display mutational signatures 6, 15 and 26, associated with defective MMR (Supplementary Figure 2). Eight of nine of these tumors had putative loss-of-function variants in MMR genes, supporting the functional significance of these variants (Figure 3B). None of the MMR altered tumors had a CTTNB1 hotspot mutation in exon 3, a known somatic ACC driver, observed in 66 other specimens (18.6 %) (Figure 3B). Mutational signature 1 caused by the spontaneous deamination of 5-methylcytosine (Alexandrov et al. 2013) associated with aging, was most prevalent in ACC. Surprisingly, these tumors were from younger patients (median age = 45 years) than the rest of the cohort (median age = 52 years, Wilcoxon, p=0.02).
Potentially actionable genomic alterations
Using OncoKB criteria (www.oncokb.org), this analysis identified 46 drug-targetable genetic alterations, with at least one found in 213 (58.5%) of samples in this cohort (Supplementary Table 5). Of these, the most common were found in CDKN2A (13.5%), NF1(10.2%), CDK4(7.7%) and MDM2(5.8%), suggesting that drugs targeting components of cell cycle regulation might be helpful in subpopulations of ACC tumors (Supplementary Table 4). Most of the specific alterations found in each gene were not recurrent, with the exception of CDK4R24L and NF1 R1276, which were found in two tumors each. Other recurrent alterations were found in PTCH1 S1203fs*52 in three tumors and KRAS G12C hotspot variant in two tumors. Also of interest, one tumor had a KIT/KDR/PDGFRA amplification, which is potentially druggable and was previously reported in anaplastic thyroid cancer (Pozdeyev et al. 2018). Fusion were rare and non-recurrent events in this cohort (N=13 fusions) confirming TCGA findings. FGFR2-CIT fusion reported in one tumor was likely oncogenic and a potential target for FGFR inhibitors.
DISCUSSION
This study represents the largest cohort of ACC to date and further establishes the prevalence of specific genomic alterations and identifies additional rare genomic events. A subset of this patient cohort has been previously published and described a series of 22 ACCs in which 76% featured at least 1 genomic alteration and there were 2.6 alterations per case (Ross, et al. 2014). In our current study of 364 ACC, there was an unexpectedly high prevalence of MMR gene alterations affecting 13.7% of ACCs, whereas prior studies found only 3-7% (Darabi, et al. 2020; Else et al. 2014; Lippert et al. 2018; Zheng et al. 2016). These tumors were associated with a greater mutation burden and MMR mutational signatures, supporting a functional significance. In addition, mutual exclusivity of MMR gene alterations and activating CTNNB1 mutations suggests that MMR gene loss is the primary carcinogenic event in this subset of tumors. Collectively, these findings are
clinically relevant as immune checkpoint inhibitor therapy is approved for MMR-mutated and/or MSI-H solid tumors (Lemery et al. 2017).
We recently reported a positive response to immunotherapy in a patient with MMR pathway mutation and matched PDX mouse model of ACC, and we reported additional positive responses in a cohort of six patients with advanced ACC when immune checkpoint inhibitor was given in combination with mitotane (Head, et al. 2019; Lang, et al. 2020). Other data in the literature about the role of immunotherapy in ACC has been mixed (Carneiro, et al. 2019; Habra, et al. 2019; Le Tourneau, et al. 2018; Raj, et al. 2020). It is not yet clear if ACC tumors with MMR mutational signatures would have similar results to MMR-mutated solid tumors, but the correlation with tumor mutational burden implies that they might. Therefore, genomic profiling and selecting ACC tumors with high tumor mutation burden and MMR gene alterations might improve response to immunotherapy, offering a more personalized medicine approach.
Similar to prior studies (Assie et al. 2014; Juhlin, et al. 2015; Lippert et al. 2018; Zheng et al. 2016), this study confirmed that the Wnt/B-Catenin and p53 are the most commonly dysregulated pathways in ACC. Eight of the 138 TP53 gene alterations, detected in the cohort, were found within the subset of 12 pediatric cases. It is known that 50-80% of pediatric ACC are associated with Li Fraumeni syndrome caused by germline TP53 alterations (Rodriguez-Galindo, et al. 2005; Wagner, et al. 1994). The pediatric cases in this cohort represent a small subset of tumors, and do not significantly impact overall conclusions and alteration frequencies in the study cohort. We found that 10% (39/364) of ACC tumors carried an alteration in both CTNNB1 and TP53, and up to 30% (109/364) had alterations in both the p53 and Wnt signaling pathways, which is at higher frequencies than in prior studies (Lippert et al. 2018; Zheng et al. 2016). Our data is similar to previous work by Zheng et al (Zheng et al. 2016), showing alterations in ZNRF3 and CTNNB1 are almost always mutually exclusive, with only <1% (2/364) tumors containing alterations in both genes.
Recent studies also identified recurrent TERT promoter alterations in ACC tumors (Assie et al. 2014; Juhlin et al. 2015; Lippert et al. 2018; Zheng et al. 2016), and TCGA analysis found that TERT expression was significantly higher in the whole genome doubling tumor group (Zheng et al. 2016). We identified TERT alterations in 10% of ACC tumors. Collectively, alterations in telomere maintenance genes, TERT, ATRX or DAXX, were altered in 78 (21%) ACCs and all were mutually exclusive except in one tumor, suggesting that alterations in this pathway are oncogenic events.
Over half of the ACC tumors had potentially actionable alterations. Similar to published work, several potentially druggable alterations were identified in cell cycle and tumors suppressor genes, including alterations in CDK4, CDKN2A, NF1 and MDM2 gene. While limited therapeutic options are currently available for these dysregulated genes, targeting CDK4 using CDK4/6 inhibitors might be a relevant clinical option. Three CDK4/6 inhibitors, palbociclib, ribociclib and abemaciclib have recently been approved for hormone receptor positive and human epidermal growth factor receptor 2 (HER2) - negative metastatic breast cancer and are being investigated, alone or in combinations, in
clinical trials in other solid tumors (Shah, et al. 2018). Several small clinical trials have also examined CDK4/6 inhibitors targeting solid tumors with CDKN2A loss, although responses have been modest (Ahn, et al. 2020; Al Baghdadi, et al. 2019). Furthermore, recent preclinical studies showed that ACC cell lines are sensitive to CDK4/6 inhibition (Fiorentini, et al. 2018; Hadjadj, et al. 2017; Liang, et al. 2020). CDK4 upregulation has been reported in 62% of ACC tumors (Liang et al. 2020), and in this study, CDK4 amplification occurred in in 6% of tumors and CDKN2A alterations occurred in 14%. Taken together, these data suggest that CDK4/6 inhibitors may have a place for novel treatment in ACC tumors.
A large subgroup of ACC had alterations in genes involved in epigenetic regulation, providing additional evidence of its importance in ACC tumorigenesis as previously reported (Zheng et al. 2016). Histone modification, telomere lengthening and SWI/SNF pathway genes were commonly altered (21-38%), while DNA methylation pathway gene alterations were observed in 8% of samples. Genetic alterations in ARID1A was another novel finding in our study for ACC, and has been shown in other endocrine tumors such as pituitary tumors (Bi, et al. 2017), thyroid cancers (Pozdeyev et al. 2018) and pheochromocytomas/ paragangliomas (Toledo, et al. 2016). ARID1A acts as a tumor suppressor, where its loss disrupts SWI/SNF complex targeting of gene enhancers to alter gene expression and drive tumorigenesis (Mathur, et al. 2017). Although targeting epigenetic regulators for therapeutic intervention has shown only limited success so far (Shah, et al. 2006), alterations in this pathway may have prognostic implications (Davidson, et al. 2018; Seligson, et al. 2005; Weischer, et al. 2013; Zhou, et al. 2021). Furthermore, future studies on the impact of epigenome disruption might contribute to better understanding of environmental and genetic influences on ACC tumorigenesis.
Several other novel genetic alterations occurred in small subsets of ACC tumors. IL7R was mutated in 6% of samples and is normally expressed in immune cells and plays a role in the development and survival of immune cells (DeKoter, et al. 2007; Gregory, et al. 2007). Its absence results in combined immunodeficiency and overexpression results in T cell acute lymphoblastic leukemia (Oliveira, et al. 2019). IL7R is also amplified in squamous cell cancer of the esophagus and downregulation of IL7R decreased tumorigenicity in esophageal cell lines (Kim, et al. 2018). Taken together, mutations in IL7R might represent a novel potential therapeutic target in ACC. LRP1B a member of the low density lipoprotein (LDL) receptor family and interacts with multiple ligands including fibrinogen and apoE carrying lipoproteins (Liu, et al. 2000). Its promoter is hypermethylated in gastric cancer, oral squamous cell cancer, lung and ovarian cancer suggesting a role as a tumor suppressor gene (Beer, et al. 2016). LRP1B deletion is associated with poor prognosis in patients with glioblastomas (Tabouret, et al. 2015) and the gene is also downregulated in colon cancer where its loss increases cell growth and migration (Wang, et al. 2017). Perhaps novel therapeutics of this pathway may be future options for treatment of ACC. FRS2 in an adaptor protein that mediates FGFR signaling. FRS2 is ubiquitously expressed, and suppression of FRS2 expression decreases mitotgenic signaling in prostate cancer cells (Valencia, et al. 2011). FRS2 is amplified in various types of human cancer including liposarcomas, prostate and ovarian cancer (Jing, et al. 2018; Luo, et al. 2015; Valencia et al. 2011). Previous studies in ACC have identified FGFR signaling dysregulation and 57%
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of ACC with FGFR4 overexpression correlated with worse outcomes (Brito, et al. 2012). This study found four tumors with FGR4 amplification and one with FGFR2-CIT fusion predicted to be oncogenic. This suggests that FGFR pathway targeting might be relevant in a subset of ACC tumors.
Our study has several limitations. Targeted sequencing approaches may miss relevant genomic alterations. In addition, clinical data are not available and correlation with prior therapy and clinical outcomes could not be performed. Selection bias of the cohort may be present based on provider referral to the platform. However, FoundationOne is one of the most utilized platforms for clinical tumor genomic analysis adding to the relevance.
In summary, in the largest ACC cohort to date, analysis identified that 58.5% of ACC have at least one potentially actionable alteration, and importantly, a significant percentage of tumors (13.7%) have alterations in the MMR pathway. These data suggest that over half of ACC may have clinically relevant therapeutic options and a subset may respond to immunotherapy.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Funding sources:
This work was supported by NIH K08CA222620 (to K.K.V.), Cancer League of Colorado Award (to K.K.V. and S.L.), Golfers Against Cancer (to K.K.V.), Veterans Affairs Merit Review Award 001 (to M.E.W.), American Cancer Society MRSG-15-063-01-TBG (to L.F.), NIH R01CA246586 (to L.F.), University of Colorado Cancer Center Support Grant P30-CA046934. The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data or in writing the manuscript.
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A.
B.
60
Female (FMI- ACC)
Male (FMI-ACC)
Number of patients
40
75-
20
0
1-10
1-20
21-30
31-40
41-50
51-60
61-70
71-80
81-90
Age, years
50
Age quartiles
15
8X Female (TCGA-ACC)
2
Male (TCGA - ACC)
Number of patients
10
25
5
0
0
1-10
1-20
21-30
31-40
41-50
51-60
61-70
71-80
81-90
Patient
Age quartiles
Author Manuscript
Pozdeyev et al.
A.
Author Manuscript
Number of genetic alterations
40
20
B.
0
Author Manuscript
All
40-
Specific Genetic Alterations/Gene in Patient Population (%)
Frame Shift, Nonsense, Splice
Genetic alteration type
SNV/Indel
C.
30-
Copy Number Loss
Copy Number Gain
Fusion
CNA
20
Author Manuscript
Missense
Duplication, Rearrangement
Fusion/Rearrangement
10-
pathway mutations
of the most altered genes and their genomic alterations; C. Frequency of tumor signaling
ACC genomic landscape, A. Distribution of genomic alteration by types; B. Frequency
Figure 2:
Double strand break repair-
DNA base excision repair- DNA mismatch repair-
Hedgehog signalling pathway- Notch signalling pathway-
Telomere lengthening-
PI3K/AKT signalling pathway- MAPK signalling pathway-
WNT signalling pathway- Histone modification- Cell cycle-
Immune evasion-
DNA methylation- SWI/SNF-
Tumor suppressor genes-
0
TP53BP1
FRS2 POLE
Pathway Enrichment
NSD1
SLIT2
FANCA
NOSLI1 NOTCH Sp41 SPTA BAA1 SRCA ONMES
Page 14
O
PARA “ARPA PTCA CH1
SETDE 92 ET
CCNE
FAT
FLL BLT4
20
PTPRD
DOT1L
PRKDC
TRRAP
GNAS
Endocr Relat Cancer. Author manuscript; available in PMC 2022 August 16.
% of genes in category
SPEN
LZR
KAT6A PIK3C20 ARID1
AO
CREBR TEBBP
Gene
RICTOR
MDM2
MSH6
KMT2A
60
SMARCA4
TBX: ARID1B
MSH
CAT3 KMT20 SA2C SOHA
CDK4
CKER CDKNS KNZB
KMT2D
NF1
APC
LRPİ
MEN-
ATRX
RR
CDKNS W2 ENRE: CTNNB1
TP53
A.
50
40
Number of SNV/indels
30
20
10
8 00
0
Altered
MMR gene status
Wild type
B.
| Tissue ID | Somatic variant | Sig 6 | Sig 15 | Sig 6 + Sig 15 | MMR gene mutation | CTNNB1 mutation |
|---|---|---|---|---|---|---|
| ACC 19 | 11 | 0 | 0.6 | 0.6 | None | No |
| ACC 119 | 19 | 0.31 | 0.33 | 0.64 | MSH2 E658* | No |
| ACC 171 | 17 | 0.73 | 0.07 | 0.8 | MSH6 P590fs*8 | No |
| ACC 267 | 18 | 0.91 | 0 | 0.91 | MSH2 copy number loss | No |
| ACC 269 | 21 | 0.87 | 0.13 | 1 | MSH6 C1158del | No |
| ACC 270 | 20 | 0.37 | 0.3 | 0.68 | MSH2 copy number loss | No |
| ACC 275 | 20 | 0.8 | 0 | 0.8 | MSH6 S564* | No |
| ACC 279 | 12 | 0.07 | 0.7 | 0.76 | MSH2 R711* | No |
| ACC 361 | 19 | 0.47 | 0.15 | 0.62 | MSH6 copy number loss | No |
Figure 3: Mismatch repair (MMR) alterations in ACC A. Frequency of genomic alteration in tumors with MMR alterations compared to tumors with no MMR alterations; B. Number of somatic variants and mutational signatures in tumors with MMR gene alterations.
| Gene Symbol | Description | Samples (%) | Potential Targeted Therapy | Level of Evidence |
|---|---|---|---|---|
| ALK | Receptor tyrosine kinase | 2.2 | Crizotinib - F, M | 1, R2 |
| Alectinib - F, M | 1, R2 | |||
| Certinib - F | 2 | |||
| Lorlatinib - O | 1 | |||
| Brigatinib - O | 1 | |||
| ATM | DNA damage repair related kinase | 8 | Olaparib - O | 4 |
| BRCA2 | DNA damage involved TS | 3.8 | Rucaparib - O | 1 |
| Niraparib - O | 1 | |||
| Olaparib - O | 2 | |||
| Talazoparib - O | 2 | |||
| CDK4 | Intracellular kinase | 7.7 | Abemaciclib - A | 2 |
| Palbociclib - A | 2 | |||
| CDKN2A | Protein for cell growth and survival | 13.5 | Abemaciclib - O | 4 |
| Palbociclib - O | 4 | |||
| Ribociclib - O | 4 | |||
| EGFR | Receptor tyrosine kinase | 2.7 | Afatinib - M, De, Du, I | 1, R1 |
| Erlotinib - De, M, I, Du | 1, R1 | |||
| Osimertinib - De, M | 1, R2 | |||
| Dacomitinib - De, M | 1 | |||
| Gefitinib - De, M,I , Du | 1, R2 | |||
| Poziotinib - I | 3A | |||
| Lapatinib - A, M | 4 | |||
| KRAS | GTPase regulator of MAPK and PI3K | 2.7 | Regorafenib - W | 1 |
| Cobimetinib - O | 4,3A | |||
| AMG-510 - M | 3A | |||
| Cetuximab - W, O | 1, R1 | |||
| Panitumumab - W, O | 1, R1 | |||
| Binimetinib - O | 4 | |||
| Trametinib - O | 4 | |||
| MDM2 | Ubiquitin ligase and p53 inhibitor | 5.8 | Milademetan Tosylate - A | 3A |
| RO5045337 - A | 3A | |||
| NF1 | Negative regulator of RAS | 10.2 | Selumetinib - O | 1 |
| Cobimetinib - O | 4 | |||
| Trametinib - O | 4 | |||
| NTRK2 | Receptor tyrosine kinase | 2.5 | Larotrectinib - F | 1 |
| Entrectinib - F | 1 | |||
| PDGFRA | Receptor tyrosine kinase | 4.7 | Imatinib - F, O,M | 1, R1 |
Endocr Relat Cancer. Author manuscript; available in PMC 2022 August 16.
| Gene Symbol | Description | Samples (%) | Potential Targeted Therapy | Level of Evidence |
|---|---|---|---|---|
| Avapritinib - M, De, I | 1 | |||
| Dasatinib - M | 2 | |||
| PTCH1 | Inhibitor of the hedgehog pathway | 4.1 | Sonidegib - T | 3A |
| Vismodegib - T | 3A | |||
| TSC1 | TS in the mTOR pathway | 2.7 | Everolimus - O | 1 |
| TSC2 | TS in the mTOR pathway | 2.7 | Everolimus - O | 1 |
Abbreviations: Tumor suppressor (TS), Fusion (F), Missense mutation (M), Oncogenic mutation (O), Amplification (A), Deletion (De), Duplication (Du), Insertion (I), Wildtype (W).
Levels of significance are from OnkoKB website and denote the following: 1 - FDA recognized biomarker predictive of response to an FDA approved drug in this indication. 2 - Standard care biomarker recommended by the NCCN or other expert panels predictive of response to an FDA approved drug in this indication. 3A - Compelling clinical evidence supports the biomarker as being predictive of response to a drug in this indication. 3B - Standard care or investigational biomarker predictive of response to an FDA approved or investigational drug in another indication. 4 - Compelling biological evidence supports the biomarker as being predictive response to a drug. R1 - Standard care biomarker predictive of resistance to an FDA approved drug in this indication. R2 - Compelling clinical evidence supports the biomarker as being predictive of resistance to a drug.