Genomic Landscape of Endometrial, Ovarian, and Cervical Cancers in Japan from the Database in the Center for Cancer Genomics and Advanced Therapeutics
by
, , ,
Qian Xi
1
,
Hidenori Kage
2,
Miho Ogawa
3,
Asami Matsunaga
1,4,
Akira Nishijima
5,
Kenbun Sone
5,
Kei Kawana
4 and
Katsutoshi Oda
1,*
1
Division of Integrative Genomics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
2
Department of Respiratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
3
Next-Generation Precision Medicine Development Laboratory, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
4
Department of Obstetrics and Gynecology, Graduate School of Medicine, Nihon University, Tokyo 173-8610, Japan
5
Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
*
Author to whom correspondence should be addressed.
Cancers 2024, 16(1), 136; https://doi.org/10.3390/cancers16010136
Submission received: 18 November 2023
/
Revised: 17 December 2023
/
Accepted: 23 December 2023
/
Published: 27 December 2023
(This article belongs to the Special Issue Genomic Characterization of Gynecological Cancer)
Abstract
:Simple Summary
This study comprehensively investigated the genomic landscape of >3000 gynecological malignancies (endometrial, cervical, and ovarian cancers) in Japan. The Center for Cancer Genomics and Advanced Therapeutics database used in this study is a useful tool containing real-world data of patients with poor prognoses, as comprehensive genomic profiling tests are limited to patients with cancer who have completed standardized treatments in Japan. Genomic profiling based on histological subtypes, tumor mutational burden, and microsatellite instability highlights actionable mutations for future drug development for each gynecological cancer.
Abstract
This study aimed to comprehensively clarify the genomic landscape and its association with tumor mutational burden-high (TMB-H, ≥10 mut/Mb) and microsatellite instability-high (MSI-H) in endometrial, cervical, and ovarian cancers. We obtained genomic datasets of a comprehensive genomic profiling test, FoundationOne® CDx, with clinical information using the “Center for Cancer Genomics and Advanced Therapeutics” (C-CAT) database in Japan. Patients can undergo the tests only after standardized treatments under universal health insurance coverage. Endometrial cancers were characterized by a high frequency of TMB-H and MSI-H, especially in endometrioid carcinomas. The lower ratio of POLE exonuclease mutations and the higher ratio of TP53 mutations compared to previous reports suggested the prognostic effects of the molecular subtypes. Among the 839 cervical cancer samples, frequent mutations of KRAS, TP53, PIK3CA, STK11, CDKN2A, and ERBB2 were observed in adenocarcinomas, whereas the ratio of TMB-H was significantly higher in squamous cell carcinomas. Among the 1606 ovarian cancer samples, genomic profiling of serous, clear cell, endometrioid, and mucinous carcinomas was characterized. Pathogenic mutations in the POLE exonuclease domain were associated with high TMB, and the mutation ratio was low in both cervical and ovarian cancers. The C-CAT database is useful for determining the mutational landscape of each cancer type and histological subtype. As the dataset is exclusively collected from patients after the standardized treatments, the information on “druggable” alterations highlights the unmet needs for drug development in major gynecological cancers.
1. Introduction
Comprehensive genomic profiling (CGP) tests broadly explore treatments based on individual genomic information [1]. Until June 2023, three CGP tests have been clinically applicable in Japan, including a tumor-only panel, the FoundationOne® CDx (F1CDx) assay; a liquid biopsy panel, the FoundationOne Liquid® CDx assay; and a tumor/normal paired panel, the OncoGuideTM NCC Oncopanel System [2,3]. All genomic profiling data and clinical information are transferred to the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) with written informed consent (agreement ratio, 99.7%), and the data are available for research use [3]. As the CGP tests under the universal health insurance system in Japan are only applicable to patients who have (already or almost) finished standardized treatments, the dataset is composed of patients with a poor prognosis for all cancer types. Liquid biopsy is limited to patients whose tissue specimens are not available or not suitable for CGP, and to date, F1CDx has been broadly tested (>75%) in Japan. The C-CAT database enables us to understand the mutational landscape, tumor mutational burden (TMB), and microsatellite instability (MSI) status of any type of advanced solid tumor [3].
Endometrial, cervical, and ovarian cancers are the major types of gynecological malignancies. Platinum-based chemotherapy is typically used for these three cancers, and CGP tests are anticipated to identify novel treatment options. In endometrial cancer, genomic alterations are common in the phosphatidylinositol-3 kinase (PI3K) pathway (such as PTEN, PIK3CA, and PIK3R1) and the receptor tyrosine kinase/RAS pathway [4,5]. Notably, four major molecular subtypes have been identified: (i) POLE ultramutated (in the exonuclease domain), (ii) MSI-high (hypermutated), (iii) copy number low (mainly endometrioid), and (iv) copy number-high (serous-like) [4,6]. Immunohistochemistry for mismatch repair (MMR) genes and TP53 can alternatively be considered MSI-high (MSI-H) and copy number-high, respectively [7]. In cervical cancer, genomic alterations in PIK3CA are the most common (26%), followed by EP300 (11%) and FBXW7 (11%) [8]. Genomic alterations in BRCA1/2 (both germline and somatic) and TP53 are common in high-grade serous ovarian carcinomas [9,10]. Genomic alterations of ARID1A and PIK3CA have been detected in 30–60% of endometriosis-associated ovarian carcinomas, that is, endometrioid and clear cell ovarian carcinomas [11]. Genomic alterations in KRAS and BRAF in the MAPK pathway and TP53 are common in mucinous ovarian carcinomas [12].
Both MSI-high and TMB-high (TMB-H, ≥10 mutations/megabase [mut/Mb]) are used as companion diagnostics for an immune checkpoint inhibitor (ICI), pembrolizumab, in solid tumors [13,14]. In addition to these tumor-agnostic indications, since December 2022, cemiplimab monotherapy (anti-programmed cell death 1 antibody) has been approved in recurrent cervical cancer as a second-line or later treatment in Japan, regardless of PD-L1 status [15]. Since December 2021, lenvatinib (a multi-tyrosine kinase inhibitor) plus pembrolizumab has been approved in Japan for the treatment of advanced/recurrent endometrial cancer, regardless of MSI status [16]. Recently, ICI plus platinum-based chemotherapy has shown significantly better overall survival and/or progression-free survival in both endometrial and cervical cancers (either primary advanced or recurrent) [17,18,19]. However, the prognostic benefits of ICI-containing regimens are significantly greater in the presence of MSI-H and/or deficient MMR (dMMR) in endometrial cancer and PD-L1 markers in cervical cancer [17,18,19]. In ovarian cancer, TMB-H or MSI-H remains the only indication for pembrolizumab, although several ongoing clinical trials include ICIs [20].
In the present study, we aimed to focus on the mutational landscape, TMB, and MSI status of endometrial, cervical, and ovarian cancers in Japanese patients using the C-CAT database of F1CDx (registered from June 2019 to May 2022; https://www.ncc.go.jp/jp/c_cat/use/index.html, (accessed on 1 June 2022)).
2. Materials and Methods
2.1. Patient Samples of FoundationOne® CDx (F1CDx) from the Center for Cancer Genomics and Advanced Therapeutics Database
This Japanese cohort study included 561 endometrial, 839 cervical, and 1606 ovarian cancers that were analyzed using F1CDx under health insurance coverage. The data were obtained from the C-CAT database organized by the National Cancer Center of Japan, which stores the CGP data tests [3]. The CGP tests in Japan are limited to patients with solid cancers who have finished (or are expected to finish) standard treatments for advanced unresectable diseases. Therefore, the patients enrolled generally had poor prognoses and were resistant to platinum-based chemotherapies for all three gynecological cancers. We logged into the C-CAT system to collect 3006 of 25,504 patients’ F1CDx data for the three gynecological cancers (between June 2019 and May 2022). We accessed the database on 1 June 2022. The workflow of this study is shown in Figure 1. The histological subtypes of each cancer are summarized in Supplementary Table S1. In this study, pure sarcomas were not included in endometrial cancer, whereas 2 sarcomas and 63 non-epithelial tumors were included in cervical and ovarian cancers, respectively. This study was approved by our institutional ethics committee (#2021341G) and the Information Utilization Review Board of C-CAT (#CDU2022-026N).
2.2. F1CDx Testing
F1CDx is a tumor-only panel using DNA isolated from formalin-fixed, paraffin-embedded tumor tissue specimens, which can detect substitutions, insertions, and deletions (indels); copy number alterations (CNAs) in 324 genes; gene rearrangements in 36 genes; and genomic signatures, including MSI and TMB [21]. MSI status is reported as “cannot be determined” when the quality is insufficient. TMB by F1CDx is determined by counting all synonymous and non-synonymous variants, except for hotspot genomic alterations, and is considered TMB-H when reported as ≥10 mut/Mb. In our study, all genetic variants, including single nucleotide variants, CNAs, and gene fusions, were annotated as pathogenic or likely pathogenic based on CIViC, BRCAExchange, ClinVar, and COSMIC [3]. MSI-H and TMB-H are tumor-agnostically approved as CDx for pembrolizumab in solid cancers in Japan. In this study, cases with “cannot be determined” for either TMB or MSI were excluded from the analysis (31 endometrial, 70 cervical, and 80 ovarian cancers).
2.3. Statistical Analyses and Graphical Representations
Quantitative variables were analyzed using one-way analysis of variance (ANOVA) (when normality was assumed) and the Kruskal–Wallis H test (when normality could not be assumed) for comparisons among the three groups. Pearson’s correlation test was used for correlation analysis between the two groups. All reported p values were two-tailed, and p < 0.05 was considered significant unless otherwise specified. All the graphs, calculations, and statistical analyses were performed using GraphPad Prism software 9.3.0 and R 4.2.0 software. The collation and visual analysis of alteration data were implemented using the “ComplexHeatmap” package in R.
3. Results
3.1. Genomic Alteration Profiles across Cancer Types
We analyzed the genomic alterations (pathogenic or likely pathogenic) in F1CDx from the C-CAT database in 561 endometrial, 839 cervical, and 1606 ovarian cancer samples. The mutational landscape of frequently mutated (pathogenic or likely pathogenic) genes (top 30) in each cancer type and histological subtype is summarized in Supplementary Figure S1, and Figure 2, respectively (A: endometrial, B: cervical, and C: ovarian cancers).
3.1.1. Endometrial Cancer
Genomic alterations were common in TP53 (n = 305, 54.4%), PIK3CA (n = 231, 41.2%), PTEN (n = 194, 34.6%), ARID1A (n = 172, 30.7%), and KRAS (n = 146, 26.0%) (Supplementary Figure S1A). The ratio of TP53 (p < 0.001) was significantly higher, and the ratios of PTEN (p < 0.001) and PIK3CA (p = 0.0028) were significantly lower in the C-CAT database compared with The Cancer Genome Atlas (TCGA) database. In addition, the ratio of pathogenic/likely pathogenic alterations in POLE in the exonuclease domain was only 1.4% (7.3% in the TCGA), supporting the favorable prognosis of POLE-mutated endometrial carcinomas [4].
Endometrioid endometrial carcinoma, accounting for 49.0% of our study, was characterized by genomic alterations of PTEN (47.6% vs. 13.7%, p < 0.001), KRAS (30.9% vs. 17.8%, p = 0.0037), CTNNB1 (23.6% vs. 2.1%, p < 0.001), and ARID1A (37.8% vs. 22.6%, p = 0.0015), compared with non-endometrioid endometrial carcinomas (serous, clear cell, and mixed carcinomas) (Figure 2A). The high frequency of PIK3CA genomic alterations, regardless of the histological types, suggested the need for potential therapies targeting the PI3K pathway (Figure 3A and Table 1).
Genomic alterations of both TP53 (80.8% vs. 35.3%, p < 0.001) and ERBB2 (27.4% vs. 6.9%, p < 0.001) were more frequent in non-endometrioid carcinomas (Figure 3A).
3.1.2. Cervical Cancer
Among the 839 samples, genomic alterations of PIK3CA were the most prevalent (n = 270, 32.2%), followed by STK11 (n = 170, 20.3%), TP53 (n = 166, 19.8%), KRAS (n = 117, 13.9%), and CDKN2A (n = 96, 11.4%) (Supplementary Figure S1B). ERBB2 genomic alterations were observed at 9.7% (amplifications at 6.3% and pathogenic variants at 4.1%), which might lead to clinical trials (Table 1). Squamous cell carcinomas (n = 389) exhibited a significantly higher PIK3CA mutation rate of 45.2% compared with 19.8% in non-squamous cell carcinomas (n = 420) (Figure 3B). In adenocarcinomas (n = 180), KRAS genomic alterations were most frequently observed (32.2%), followed by TP53 (29.4%), PIK3CA (22.2%), STK11 (22.2%), CDKN2A (18.3%), ERBB2 (16.7%), and ARID1A (11.7%) (Figure 2B).
3.1.3. Ovarian Cancer
Among the 1606 samples, TP53 genomic alterations (n = 1054, 65.6%) were the most frequent, followed by ARID1A (n = 407, 25.3%), PIK3CA (n = 406, 25.3%), KRAS (n = 272, 16.9%), KMT2D (n = 272, 16.9%), and NOTCH3 (n = 270, 16.8%) (Supplementary Figure S1C and Table 1).
In serous carcinomas, genomic alterations of BRCA1 and BRCA2 accounted for 21.2% (166/784) and 14.7% (115/784) of cases, respectively (Figure 2C). The coexistence rate of these two alterations was 4.8% (38/784), which was significantly higher than those reported by 0.6% (2/316) [12] and 0% (0/205) [30]. Genomic alterations in other homologous recombination repair genes included ATM (8.8%), PALB2 (7.1%), and CDK12 (6.6%) (Figure 2C). Genomic alterations in TP53, NF1, KRAS, and PIK3CA were detected in 90.4% (n = 709), 15.8% (n = 124), 11.9% (n = 93), and 11.7% (n = 92) of cases, respectively (Figure 3C).
Clear cell carcinomas were examined in 20.7% (n = 333) of the cases, with genomic alterations in ARID1A (n = 231, 69.4%) and PIK3CA (n = 190, 57.1%), consistent with previous reports [15] (Figure 2C). Genomic alterations of TP53 were observed in 16.5% (n = 55) of the cases and were negatively associated with alterations in both ARID1A (p < 0.001) and PIK3CA (p < 0.001) (Figure 2C). Genomic alterations of ERBB2 (primarily amplification) and KRAS were detected in 25% and 15% of the cases, respectively.
In endometrioid carcinomas, the ratios of genomic alterations in TP53, PIK3CA, KRAS ARID1A, PTEN, and CTNNB1 were 55.4%, 43.5%, 31.5%, 29.3%, 27.2%, and 19.6%, respectively. TP53 alterations were negatively associated with alterations in ARID1A (p = 0.0006), KRAS (p = 0.0017), PTEN (p = 0.0002), and CTNNB1(p < 0.001).
In mucinous carcinomas, genomic alterations of TP53, KRAS, CDKN2A, and CDKN2B were detected in 61.5%, 59.3%, 44.0%, and 26.4% of the cases, respectively. Although genomic alterations of BRAF were approximately 20% [16], the ratio was only 5.5% (n = 5) in this study. Genomic alterations in ERBB2 were detected in 16.5% of the cases.
3.2. Prevalence of Microsatellite Instability-High (MSI-H) and Tumor Mutation Burden-High (TMB-H)
Among the 561 endometrial cancer samples, 78 (13.9%) were TMB-H and 61 (10.9%) were MSI-H. A total of 58 of the 61 MSI-H tumors were TMB-H, whereas 20 of the 78 (25.6%) TMB-H tumors were non-MSI-H tumors (Figure 4A).
Among the 839 cervical cancer samples, 119 (14.2%) and 13 (1.5%) were TMB-H and MSI-H, respectively (Figure 4B). Only 1 of 13 (7.7%) cervical cancers with MSI-H was TMB-low (TMB-L) (Figure 4B). Among the 1606 ovarian cancer samples, 80 (5.0%) were MSI-H and 19 (1.2%) were TMB-H (Figure 4C). All 19 MSI-H ovarian cancer samples were classified as TMB-H (Figure 4C).
The TMB value in endometrial cancer was significantly higher than that in cervical cancer (p < 0.001 by one-way ANOVA with the Kruskal–Wallis test) and ovarian cancer (p < 0.001) (Figure 4D). The median TMB values in MSI-H tumors were 21.4 mut/Mb in endometrial, 23.0 mut/Mb in cervical, and 40.4 mut/Mb in ovarian cancers (Figure 4E), with a strong correlation between MSI and TMB in these three cancer types (p < 0.001) (Figure 4E).
The TMB and MSI statuses were distinct among the histological subtypes of each cancer (Supplementary Table S2).
In endometrial cancer, the MSI-H ratio was significantly higher in endometrioid carcinomas (40/275, 14.5%) compared to serous carcinomas, clear cell carcinomas, and carcinosarcomas (5/215, 2.3%) (p < 0.001) (Figure 5A).
In cervical cancer, the MSI-H ratio was not significantly different between squamous cell carcinomas (1.3%) and adenocarcinomas (1.1%) (Figure 5A). In ovarian cancer, the MSI-H ratio was <4.0% in all histological subtypes and was significantly lower in serous carcinomas (2/784, 0.3%) compared with non-serous carcinomas (15/571, 2.6%) (p = 0.0002) (Figure 5A). In endometrial cancer, the ratio of TMB-H was high in adenosquamous carcinomas (5/17, 29.4%), mixed carcinomas (5/18, 27.8%), and endometrioid carcinomas (47/275, 17.1%), whereas it was only 4.9–7.7% in serous carcinomas, clear cell carcinomas, and carcinosarcomas (Figure 5B). In cervical cancer, the TMB-H ratio was significantly higher in squamous cell carcinomas (80/389, 20.6%) compared with adenocarcinomas (8.3%, 15/180) and mucinous carcinomas (5.0%, 4/80) (p = 0.0002 and p = 0.0004, respectively) (Figure 5B). In ovarian cancer, the TMB-H ratio was 3.3–6.5% in all histological subtypes.
3.3. Mismatch Repair-Related Mutations in Tumors with MSI-H and TMB-H
We analyzed the correlation between genomic alterations in MMR genes (dMMR, defined as genomic alterations in MLH1, PMS2, MSH2, and MSH6) and the MSI status. In endometrial cancer, the dMMR ratio was 31.1% (19/61) in MSI-H, which was significantly higher than the 2.8% (13/469) reported in microsatellite stable (MSS) tumors (p < 0.001) (Supplementary Figure S2A). The dMMR ratios in MSI-H and MSS in cervical cancer were 61.5% (8/13) and 4.6% (35/756) (p < 0.001), respectively, whereas those in ovarian cancer were 84.2% (16/19) and 13.5% (203/1507) (p < 0.001), respectively (Supplementary Figure S2A).
Next, we analyzed the dMMR ratio in MSS tumors. The dMMR ratio was significantly higher in TMB-H tumors (25%) compared with TMB-L tumors (2.5%) in MSS endometrial cancer (p = 0.0003) (Supplementary Figure S2B). In MSS cervical cancer, dMMR was also more frequent in TMB-H (9.4%) compared with TMB-L (4.3%) (p = 0.0302). No statistically significant difference was detected in ovarian cancer (22.4% vs. 13.5%, p = 0.0769) (Supplementary Figure S2B).
The highest prevalence of genomic alterations in MSI-H endometrial cancer was observed in MSH6 (n = 14, 23.0%), followed by MSH2 (n = 8, 13.1%), MLH1 (n = 4, 6.6%), and PMS2 (n = 1, 1.6%) (Supplementary Table S3). Similarly, this prevalence was confirmed in ovarian cancer with MSI-H, with genomic alteration rates of MSH6, MSH2, MLH1, and PMS2 of 52.6%, 36.8%, 31.6%, and 10.5%, respectively. In MSI-H cervical cancer, the genomic alteration rates of MSH6 and MLH1 were the highest (n = 4, 30.8%) (Supplementary Table S3).
3.4. Distribution of POLE Genomic Alterations in the Exonuclease Domain among TMB-H and Microsatellite Stable Subsets
All POLE variants (including variants of unknown significance [VUS]) are listed in Table 2.
The ultramutated genotype (TMB > 100 mut/Mb) was identified in eight tumors (five endometrial and three ovarian cancers). In endometrial cancer, all eight (1.4%) POLE exonuclease-mutated tumors were TMB-H (median TMB, 90.78 mut/Mb), of which only one was MSI-H (Table 2). Three MSI-H and TMB-H tumors showed VUS of POLE outside the exonuclease domain, which should be categorized as MSI-H, not as a POLE subgroup (Table 2). Pathogenic/likely pathogenic variants in the POLE exonuclease domain were detected in one case (0.12%) of cervical cancer and three cases (0.19%) of ovarian cancer. None of the POLE variants outside the exonuclease domain were annotated as pathogenic or likely pathogenic (Table 2).
3.5. Correlation among Genomic Alterations, MSI, and TMB
Finally, we focused on the mutational landscape of “TMB-H with MSS” and “MSI-H” tumors in each cancer type. The most frequent genomic alteration in the “MSI-H” group was ARID1A in all three cancer types. The ratios were 96.7% (59/61) in endometrial, 76.9% (10/13) in cervical, and 89.5% (17/19) in ovarian cancers (Supplementary Figure S3A–C). PTEN was another MSI-H-related gene. The ratios of PTEN alterations in the “MSI-H” group were 85.2% (52/61) in endometrial, 69.2% (9/13) in cervical, and 57.9% (11/19) in ovarian cancers, whereas the ratios of PTEN alterations in the “MSS with TMB-L” group were 28.2% (127/451) in endometrial, 7.5% (49/650) in cervical, and 6.3% (92/1449) in ovarian cancers.
In “TMB-H with MSS” tumors, the ratio of genomic alterations in PIK3CA was the most or the second highest, which was 61.1% in endometrial, 51.4% in cervical, and 31.0% in ovarian cancers (Supplementary Figure S3). Genomic alterations of TP53 were most common in the TMB-H with MSS group in endometrial (61.1%) and ovarian (82.8%) cancers, whereas the rate was 12.0% in cervical cancer (usually human papillomavirus [HPV], which relates to the impairment of TP53 by the ubiquitin–proteasome pathway). The ratio of genomic alterations in CDKN2A and CDKN2B was also high in endometrial and ovarian cancers (Supplementary Figure S3).
4. Discussion
In this study, we analyzed 3006 endometrial, cervical, and ovarian cancers using a tumor-only panel, F1CDx. The Japanese CGP test dataset is unique in terms of eligible patients and insurance coverage. All the patients have finished or are expected to finish the standardized treatments and take the CGP tests under universal health insurance coverage [3,31]. Thus, any poor prognosis in Japanese patients with cancer may allow them to undergo CGP tests. Furthermore, a sufficient number of tumor specimens are usually available through surgery and/or biopsy. Therefore, the C-CAT database is suitable for analyzing the genomic profiles of patients with gynecological cancer with a poor prognosis.
In endometrial cancer, a comparison with the TCGA database highlighted the high incidence of genomic alterations of TP53 (54.4%) and the low incidence of genomic alterations of POLE (1.4%) in this database. This discrepancy supports the significance of the molecular classification of “Proactive Molecular Risk Classifier for Endometrial Cancer” in endometrial cancer by POLE, dMMR, and TP53 [32]. Drug development is highly warranted in genomic alterations of the PI3K (PTEN and PIK3CA), RAS (KRAS), and wnt/β-catenin (CTNNB1) pathways in endometrioid carcinomas and TP53, ERBB2, and PIK3CA in non-endometrioid carcinomas (Table 1). A WEE1 inhibitor, adavosertib, showed an objective response rate of 29.4% in recurrent uterine serous carcinomas (usually TP53 mutated), and an international phase IIb study is ongoing [33,34]. Further development of precision medicine in endometrial cancer is warranted.
In cervical cancer, the C-CAT dataset was helpful for elucidating the genomic profiling of adenocarcinomas, as the ratio of non-squamous cell carcinomas was significantly lower in the TCGA dataset (19.1%) than in the C-CAT database (53.6%) [8]. Key molecular targets, especially in adenocarcinomas, include KRAS, ERBB2, and ARID1A. According to the recently published 5th edition of the World Health Organization classification, cervical cancer is classified as HPV-associated and HPV-independent for each histological type [35]. As both the TP53 and RB pathways are impaired by HPV-E6 and HPV-E7 oncoproteins, respectively, genomic alterations of TP53, RB, and CDKN2A/2B are informative for speculating HPV-independent cervical cancers, especially in gastric-type mucinous adenocarcinomas [36,37].
One limitation of the C-CAT database is that data on low-grade serous ovarian carcinomas are mixed with those on high-grade serous carcinomas. Genomic alterations of TP53 in 90% of serous carcinomas suggest that these tumors represent high-grade serous carcinomas. The RAS-MAPK signaling pathway (genomic alterations of NF1 at 16% and KRAS at 12% with mutual exclusivity), the PI3K-mTOR pathway (PIK3CA at 12% and TSC2 at 8%), and certain receptor tyrosine kinases (ROS1 at 9% and ERBB2 at 8%) might be candidates for targeted therapy in serous carcinomas. The pathogenicity of each alteration, especially in BRCA1 and BRCA2, should be carefully addressed [9,30]. Drug development targeting ARID1A and PIK3CA in clear-cell ovarian carcinomas is also warranted. Currently, a p110alpha selective inhibitor, CYH33, is under phase 2 clinical trials (NCT05043922, jRCT2031210216), which recruits patients with clear cell ovarian carcinoma with hotspot mutations in PIK3CA (Table 1) [29]. Targeting the RAS-MAPK pathway should be key in mucinous carcinomas.
Candidate tumor-agnostic molecular targets in the three gynecological malignancies included ERBB2, PIK3CA, ARID1A, and KRAS. An antibody-drug conjugate, trastuzumab deruxtecan, showed an overall response rate of 54.5–70.0% in endometrial carcinosarcomas positive for HER2 in the STATICE trial [28]. Genomic alterations in ARID1A may lead to novel molecular-targeted therapies, including an EZH2 inhibitor and an enzyme for antioxidant glutathione synthesis (Table 1) [23,24]. p110alpha selective inhibitors (alpelisib), KRASG12C inhibitors (sotorasib), KRASG12D degraders (ASP3082), and a CBP/β-catenin inhibitor (E7386) may be candidates [22,25,26,27]. The Japanese Gynecologic Oncology Group is currently conducting a basket trial on niraparib monotherapy for any gynecological cancer (except ovarian cancer) with BRCA1/2 genomic alterations, which targets a rare fraction of each cancer type [38].
In agreement with previous findings, MSI-H in this study was the main causative genomic finding for TMB-H induction in endometrial cancer, whereas it shared only 10% and 24% of TMB-H in cervical and ovarian cancers, respectively [39,40]. A low TMB-H ratio (5.0%) in ovarian cancer may be associated with limited sensitivity to ICIs [41]. A comparison between “TMB-H with MSS” and “MSI-H” in each cancer type is informative to elucidate real “driver” alterations. In endometrial and ovarian cancers, the frequency of genomic alterations in TP53 and CDKN2A/2B was significantly higher in the group of “TMB-H with MSS”. These findings suggest that TMB-H should be subclassified according to the MSI status. Although pembrolizumab has been approved in any solid cancers with either TMB-H and MSI-H, combination therapies with immune checkpoint inhibitors may be developed separately according to the status of TMB and MSI.
This study has some limitations. First, CGP tests in Japan are reimbursed only for patients who have (almost) finished standardized treatments, suggesting that patients with rapid progression may miss the opportunity to undergo CGP tests. In addition, this study lacks data from patients without medical insurance due to the universal health insurance system in Japan. Second, the response to genome-matched therapies was not analyzed in this study because of the low accessibility of the recommended drugs. Third, the C-CAT database was deposited at designated hospitals located in Japan. Therefore, most of the patients were Japanese.
5. Conclusions
This study uniquely illustrates the genomic landscape of three major gynecological cancers in the Japanese cohort. It highlights the necessity of future drug developments in each cancer type and each histological subtype. ERBB2, PIK3CA, ARID1A, and KRAS would be key molecular targets in gynecological cancers. Furthermore, the prevalence and correlation between TMB and MSI may influence future immunotherapy, including combination therapies. These insights reinforce the necessity of molecular classification in understanding tumor biology and developing personalized therapies, underlining the potential of genomic profiling in precision oncology.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers16010136/s1, Figure S1. Genomic landscape of three gynecological cancers. Recurrently mutated genes are listed with the status of TMB and MSI and with information about types of alterations and histological subtypes in (A) endometrial, (B) cervical, and (C) ovarian cancers. The upper plot represents the TMB scores by F1CDx.Waterfall plot of genetic alteration profiles in endometrial (A), cervical (B), and ovarian cancer (C). Figure S2. Frequency of genomic alterations in the mismatch repair (MMR) genes according to the MSI and TMB stauts in each cancer type. (A) Frequency of MMR alterations according to the MSI status, (B) Frequency of MMR alterations according to the TMB status. Comparisons between the groups were performed by Fisher’s exact test (* p < 0.05; ** p < 0.01; *** p < 0.001). MMR-pv, pathogenic variants in MMR genes, MMR-wt, no pathogenic variants (wild-type) in MMR genes. Figure S3. Genomic landscape according to the TMB and MSI status in (A) endometrial, (B) cervical, and (C) ovarian cancer. Each cancer was categorized as TMB-H with MSS, MSI-H (regardless of TMB status), and TMB-L with MSS. Table S1. Distribution of histological subtypes in each cancer. Table S2. Frequency of MSI-H and TMB-H according to the histological subtypes in each cancer. Table S3. Genomic alterations of MMR genes in each cancer with MSI-H.
Author Contributions
Conceptualization and validation, Q.X., H.K., M.O., A.M., A.N., K.S., K.K. and K.O.; data curation, formal analysis, investigation, methodology, project administration, and resources, Q.X. and K.O.; funding acquisition, K.O.; software and visualization, Q.X.; supervision, H.K., M.O., K.S. and K.O.; writing—original draft, Q.X. and K.O.; and writing—review and editing, Q.X., H.K., M.O., A.M., A.N., K.S., K.K. and K.O. All authors have read and agreed to the published version of the manuscript.
Funding
This study was funded by a Grant-in-Aid for Scientific Research (B) (grant number: 21H03074 to K.O.).
Institutional Review Board Statement
This study was approved by our institutional ethics committee (#2021341G) and the Information Utilization Review Board of C-CAT (#CDU2022-026N).
Informed Consent Statement
Written informed consent has been obtained from all the patients in each hospital to deposit the data to the C-CAT database for research use.
Data Availability Statement
https://www.ncc.go.jp/jp/c_cat/use/index.html (accessed on 1 June 2022).
Acknowledgments
The manuscript has been edited by a native English speaker.
Conflicts of Interest
H.K. and K.O. received research funds from Konica Minolta, Inc. K.O. received research funds and lecture fees from AstraZeneca plc, and lecture fees from Chugai Pharmaceutical Co., Ltd., and Takeda Pharmaceutical Co., Ltd. The other authors have no conflicts of interest.
References
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Figure 1.
A workflow of the study to analyze the C-CAT database. Clinical information from each hospital and genomic data from certified laboratories (i.e., Foundation Medicine, Inc. for F1CDx) are collected and sent to the C-CAT data center. C-CAT reports with annotations of each alteration and therapeutic options are returned to each hospital. The C-CAT datasets with clinical information can be used for research purposes with the permission of institutional ethics committees and the Information Utilization Review Board of C-CAT.
Figure 1.
A workflow of the study to analyze the C-CAT database. Clinical information from each hospital and genomic data from certified laboratories (i.e., Foundation Medicine, Inc. for F1CDx) are collected and sent to the C-CAT data center. C-CAT reports with annotations of each alteration and therapeutic options are returned to each hospital. The C-CAT datasets with clinical information can be used for research purposes with the permission of institutional ethics committees and the Information Utilization Review Board of C-CAT.
Figure 2.
Genomic landscape of three gynecological cancers according to histological subtypes. Recurrently mutated genes (up to 20) with types of alterations are listed with the status of tumor mutational burden (TMB) and microsatellite instability in (A) endometrial, (B) cervical, and (C) ovarian cancers. The upper plot represents the TMB scores using FoundationOne® CDx (registered from June 2019 to May 2022; https://www.ncc.go.jp/jp/c_cat/use/index.html, (accessed on 1 June 2022)).
Figure 2.
Genomic landscape of three gynecological cancers according to histological subtypes. Recurrently mutated genes (up to 20) with types of alterations are listed with the status of tumor mutational burden (TMB) and microsatellite instability in (A) endometrial, (B) cervical, and (C) ovarian cancers. The upper plot represents the TMB scores using FoundationOne® CDx (registered from June 2019 to May 2022; https://www.ncc.go.jp/jp/c_cat/use/index.html, (accessed on 1 June 2022)).
Figure 3.
Frequency of genomic alterations in the key signaling pathways (mainly focusing on the KRAS and PI3K pathways), according to major histological types in (A) endometrial, (B) cervical, and (C) ovarian cancers. EEC, endometrioid endometrial carcinoma; NEEC, non-endometrioid endometrial carcinoma; SCC, squamous cell carcinoma; NSCC, non-squamous cell carcinoma; SOC, serous ovarian carcinoma; NSOC, non-serous ovarian carcinoma.
Figure 3.
Frequency of genomic alterations in the key signaling pathways (mainly focusing on the KRAS and PI3K pathways), according to major histological types in (A) endometrial, (B) cervical, and (C) ovarian cancers. EEC, endometrioid endometrial carcinoma; NEEC, non-endometrioid endometrial carcinoma; SCC, squamous cell carcinoma; NSCC, non-squamous cell carcinoma; SOC, serous ovarian carcinoma; NSOC, non-serous ovarian carcinoma.
Figure 4.
Venn diagrams of tumor mutational burden (TMB)-high and microsatellite instability-high in (A) endometrial, (B) cervical, and (C) ovarian cancers. (D) Box plots of TMB levels and (E) scatter dot plots of TMB distribution according to the MSI status in each cancer. ** p < 0.01, *** p < 0.001 using one-way analysis of variance with the Kruskal–Wallis test.
Figure 4.
Venn diagrams of tumor mutational burden (TMB)-high and microsatellite instability-high in (A) endometrial, (B) cervical, and (C) ovarian cancers. (D) Box plots of TMB levels and (E) scatter dot plots of TMB distribution according to the MSI status in each cancer. ** p < 0.01, *** p < 0.001 using one-way analysis of variance with the Kruskal–Wallis test.
Figure 5.
Frequency of (A) microsatellite instability-high and (B) tumor mutational burden-high according to the major histological subtypes in endometrial, ovarian, and cervical cancers.
Figure 5.
Frequency of (A) microsatellite instability-high and (B) tumor mutational burden-high according to the major histological subtypes in endometrial, ovarian, and cervical cancers.
Table 1.
Genomic Alterations and Potential Targeted Therapies in Gynecological Cancers.
| Cancer Type | Genomic Alteration | Frequency | Histological Type | Potential Targeted Therapies | References |
|---|---|---|---|---|---|
| Endometrial | TP53 | 54.4% | Serous (91%); Carcinosarcoma (78%); Clear cell (50%), etc. | - | - |
| PIK3CA | 41.2% | Adenosquamous (53%); Clear cell (46%); Serous (41%), etc. | p110alpha selective inhibitor (alpelisib) | [22] | |
| PTEN | 34.6% | Adenosquamous (59%); Endometrioid (48%); Carcinosarcoma (21%), etc. | - | - | |
| ARID1A | 30.7% | Endometrioid (38%); Clear cell (31%); Adenosquamous (29%), etc. | EZH2 inhibitor; Enzyme for antioxidant glutathione synthesis | [23,24] | |
| KRAS | 26.0% | Adenosquamous (53%); Endometrioid (31%); Carcinosarcoma (21%), etc. | KRAS-G12C inhibitors (sotorasib); KRAS-G12D degrader (ASP3082) | [25,26] | |
| CTNNB1 | 15.0% | Endometrioid (24%), etc. | CBP/β-catenin inhibitor (E7386) | [27] | |
| ERBB2 | 14.0% | Serous (30%); Clear cell (19%); Adenosquamous (18%), etc. | Trastuzumab deruxtecan | [28] | |
| TMB-H | 13.9% | Adenosquamous (29%); Endometrioid (17%); Clear cell (8%), etc. | Pembrolizumab; Lenvatinib plus pembrolizumab (regardless of MSI status); Pembrolizumab plus chemotherapy; Dostarlimab | [13,14,16,17,18] | |
| MSI-H | 10.9% | Adenosquamous (18%); Endometrioid (15%), etc. | |||
| Cervical | PIK3CA | 32.2% | Squamous cell (45%); Adenocarcinoma (22%); Small cell (17%), etc. | p110alpha selective inhibitor (alpelisib) | [22] |
| STK11 | 20.3% | Mucinous (34%); Adenocarcinoma (22%); Squamous cell (19%), etc. | - | - | |
| TP53 | 19.8% | Mucinous (60%); Adenocarcinoma (29%); Squamous cell (10%), etc. | - | - | |
| KRAS | 13.9% | Adenocarcinoma (32%); Mucinous (29%); Small cell (10%), etc. | KRAS-G12C inhibitors (sotorasib); KRAS-G12D degrader (ASP3082) | [25,26] | |
| CDKN2A | 11.4% | Mucinous (48%); Adenocarcinoma (18%) Small cell (4%), etc. | - | - | |
| TMB-H | 14.2% | Squamous cell (21%); Adenocarcinoma (8%), etc. | Pembrolizumab | [13,14] | |
| MSI-H | 1.5% | Adenocarcinoma (2%), etc. | |||
| Ovarian | TP53 | 65.6% | Serous (90%); Mucinous (62%); Endometrioid (55%), etc. | - | - |
| BRCA1/ BRCA2 | 25.4% | Serous (33%); Endometrioid (18%); Mucinous and Clear cell (13%), etc. | PARP inhibitors | - | |
| ARID1A | 25.3% | Clear cell (69%); Endometrioid (29%); Mucinous (21%), etc. | EZH2 inhibitor; Enzyme for antioxidant glutathione synthesis | [23,24] | |
| PIK3CA | 25.3% | Clear cell (57%); Endometrioid (43%); Mucinous (15%), etc. | p110alpha selective inhibitors (CYH33; alpelisib) | [22,29] | |
| KRAS | 16.9% | Mucinous (59%); Endometrioid (32%); Clear cell (15%), etc. | KRAS-G12C inhibitors (sotorasib); KRAS-G12D degrader (ASP3082) | [25,26] | |
| TMB-H | 5.0% | - | Pembrolizumab | [13,14] | |
| MSI-H | 1.2% | - |
Table 2.
List of POLE variants with their pathogenicity, MSI, and TMB status in each cancer.
| Endometrial Cancer (n = 561) | |||||||
| Histology | Gene | Variants | Clinical Significance | MSI Status | TMB Status | TMB Value (mut/Mb) | |
| Exonuclease Domain (n = 8; 1.4%) | MSI-H 12.5% | TMB-H 100% | Median 146.89 | ||||
| 1 | Endometrioid | POLE | P286R | Likely pathogenic | Stable | high | 253.4 |
| 2 | Endometrioid | POLE | P286R | Likely pathogenic | Undetermined | high | 247.1 |
| 3 | Adenosquamous | POLE | V411L | Likely pathogenic | Stable | high | 174.0 |
| 4 | Carcinosarcoma | POLE | P286R | Likely pathogenic | Stable | high | 162.7 |
| 5 | Endometrioid | POLE | P286R | Likely pathogenic | Stable | high | 131.1 |
| 6 | Endometrioid | POLE | P286R | Likely pathogenic | Stable | high | 90.8 |
| 7 | Endometrioid | POLE | V411L | Likely pathogenic | Stable | high | 25.2 |
| 8 | Adenosquamous | POLE | W243 * | Pathogenic | High | high | 18.9 |
| Non-Exonuclease Domain (n = 3; 0.5%) | MSI-H 100% | TMB-H 100% | Median 30.26 | ||||
| 1 | Endometrioid | POLE | K1170fs*49 | High | high | 20.2 | |
| 2 | Endometrioid | POLE | P172fs*3 | High | high | 36.6 | |
| 3 | Endometrioid | POLE | S2173fs*130 | High | high | 30.3 | |
| Cervical Cancer (n = 839) | |||||||
| Histology | Gene | Variants | Clinical Significance | MSI Status | TMB Status | TMB Value (mut/Mb) | |
| Exonuclease Domain (n = 1; 0.1%) | MSI-H 100% | TMB-H 100% | Median 23.0 | ||||
| 1 | Squamous Cell | POLE | L283fs*5 | Pathogenic | high | high | 23.0 |
| Non-Exonuclease Domain (n = 6; 0.7%) | MSI-H 0% | TMB-H 66.7% | Median 11.81 | ||||
| 1 | Squamous Cell | POLE | E179 * | stable | high | 22.7 | |
| 2 | Squamous Cell | POLE | E586 * | stable | high | 17.7 | |
| 3 | Squamous Cell | POLE | Q670 * | stable | high | 12.6 | |
| 4 | Squamous Cell | POLE | E1085K | stable | high | 11.0 | |
| 5 | Adenocarcinoma | POLE | E18K | stable | low | 7.6 | |
| 6 | Adenocarcinoma | POLE | R1077fs*43 | stable | low | 1.3 | |
| Ovarian Cancer (n = 1606) | |||||||
| Histology | Gene | Variants | Clinical Significance | MSI Status | TMB Status | TMB Value (mut/Mb) | |
| Exonuclease Domain (n = 4; 0.2%) | MSI-H 50% | TMB-H 75% | Median 146.89 | ||||
| 1 | Endometrioid | POLE | V411L | Likely pathogenic | high | high | 223.2 |
| 2 | Clear cell | POLE | T278K | Pathogenic | high | high | 218.1 |
| 3 | Unknown | POLE | P286R | Likely pathogenic | stable | high | 75.7 |
| 4 | Serous | POLE | L432V | stable | low | 5.0 | |
| Non-Exonuclease Domain (n = 64; 4.0%) | MSI-H 9.4% | TMB-H 17.2% | Median 3.78 | ||||
| 1 | Clear cell | POLE | H1356R; N1369S | high | high | 286.0 | |
| 2 | Unknown | POLE | c.4551 + 2_4551 + 3 delTG | high | high | 61.8 | |
| 3 | Clear cell | POLE | W1251 * | high | high | 60.5 | |
| 4 | Carcinosarcoma | POLE | T41M | high | high | 42.9 | |
| 5 | Mucinous | POLE | L1983fs*76 | high | high | 29.0 | |
| 6 | Carcinosarcoma | POLE | V1368M; V1929fs*70 | high | high | 17.7 | |
| 7 | Others | POLE | D934Y | stable | high | 22.7 | |
| 8 | Endometrioid | POLE | R847L | stable | high | 11.0 | |
| 9 | Clear cell | POLE | R2131C | stable | high | 10.1 | |
| 10 | Serous | POLE | T880L | stable | high | 10.1 | |
| 11 | Serous | POLE | N1521S | stable | high | 10.1 | |
| 12 | Serous | POLE | G2046R | stable | low | 9.0 | |
| 13 | Serous | POLE | R1059H | stable | low | 8.0 | |
| 14 | Serous | POLE | F695L | stable | low | 8.0 | |
| 15 | Serous | POLE | A1854S | stable | low | 7.6 | |
| 16 | Serous | POLE | L53V | stable | low | 7.6 | |
| 17 | Serous | POLE | amplification | stable | low | 6.3 | |
| 18 | Clear cell | POLE | A2192V | stable | low | 6.3 | |
| 19 | Unknown | POLE | A1778T | stable | low | 6.3 | |
| 20 | Serous | POLE | I238F | stable | low | 6.0 | |
| 21 | Serous | POLE | L1245V | stable | low | 6.0 | |
| 22 | Serous | POLE | E2155Q | stable | low | 6.0 | |
| 23 | Serous | POLE | I622M | stable | low | 5.0 | |
| 24 | Unknown | POLE | T737A | stable | low | 5.0 | |
| 25 | Serous | POLE | K1877E | stable | low | 5.0 | |
| 26 | Clear cell | POLE | Q394 * | stable | low | 5.0 | |
| 27 | Clear cell | POLE | R1382C | stable | low | 4.0 | |
| 28 | Endometrioid | POLE | R1284Q | stable | low | 3.8 | |
| 29 | Serous | POLE | L32P | stable | low | 3.8 | |
| 30 | Serous | POLE | D934G | stable | low | 3.8 | |
| 31 | Unknown | POLE | S1598C | stable | low | 3.8 | |
| 32 | Endometrioid | POLE | Q394fs*18 | stable | low | 3.8 | |
| 33 | Serous | POLE | R1324H | stable | low | 3.8 | |
| 34 | Endometrioid | POLE | V1736I | stable | low | 3.8 | |
| 35 | Clear cell | POLE | T1196M | stable | low | 3.0 | |
| 36 | Clear cell | POLE | V1512I | stable | low | 2.5 | |
| 37 | Serous | POLE | amplification | stable | low | 2.5 | |
| 38 | Clear cell | POLE | R847Q | stable | low | 2.5 | |
| 39 | Unknown | POLE | E1554K | stable | low | 2.5 | |
| 40 | Serous | POLE | A1140T | stable | low | 2.5 | |
| 41 | Serous | POLE | D1516G | stable | low | 2.5 | |
| 42 | Clear cell | POLE | S171F | stable | low | 2.5 | |
| 43 | Granulosa cell | POLE | A788V | stable | low | 2.5 | |
| 44 | Clear cell | POLE | G702R | stable | low | 2.5 | |
| 45 | Serous | POLE | C2187Y | stable | low | 2.5 | |
| 46 | Serous | POLE | R1077fs*43 | undetermined | low | 2.5 | |
| 47 | Endometrioid | POLE | F1435L | stable | low | 1.3 | |
| 48 | Serous | POLE | T1666R | stable | low | 1.3 | |
| 49 | Serous | POLE | T1196M | stable | low | 1.3 | |
| 50 | Serous | POLE | R1284Q | stable | low | 1.3 | |
| 51 | Serous | POLE | I218M | stable | low | 1.3 | |
| 52 | Serous | POLE | R1485C | stable | low | 1.3 | |
| 53 | Clear cell | POLE | T1313M | stable | low | 1.3 | |
| 54 | Unknown | POLE | G541R | stable | low | 1.3 | |
| 55 | Serous | POLE | T41M | stable | low | 1.3 | |
| 56 | Unknown | POLE | R1284W | stable | low | 1.3 | |
| 57 | Serous | POLE | L3V | stable | low | 1.0 | |
| 58 | Mucinous | POLE | E1964D | stable | low | 0.0 | |
| 59 | Clear cell | POLE | R1382C | stable | low | 0.0 | |
| 60 | Mucinous | POLE | T1196M | stable | low | 0.0 | |
| 61 | Clear cell | POLE | D1700V | stable | low | 0.0 | |
| 62 | Serous | POLE | R1382C | stable | low | 0.0 | |
| 63 | Clear cell | POLE | A1778T | stable | low | 0.0 | |
| 64 | Clear cell | POLE | A1260T | undetermined | low | 0.0 | |
Blank: Pathogenicity is not defined in the C-CAT database. *: Genetic mutation notation indicates a stop codon, leading to premature termination of the protein. This results in a truncated protein with potential functional alterations or loss.
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MDPI and ACS Style
Xi, Q.; Kage, H.; Ogawa, M.; Matsunaga, A.; Nishijima, A.; Sone, K.; Kawana, K.; Oda, K. Genomic Landscape of Endometrial, Ovarian, and Cervical Cancers in Japan from the Database in the Center for Cancer Genomics and Advanced Therapeutics. Cancers 2024, 16, 136. https://doi.org/10.3390/cancers16010136
AMA Style
Xi Q, Kage H, Ogawa M, Matsunaga A, Nishijima A, Sone K, Kawana K, Oda K. Genomic Landscape of Endometrial, Ovarian, and Cervical Cancers in Japan from the Database in the Center for Cancer Genomics and Advanced Therapeutics. Cancers. 2024; 16(1):136. https://doi.org/10.3390/cancers16010136
Chicago/Turabian StyleXi, Qian, Hidenori Kage, Miho Ogawa, Asami Matsunaga, Akira Nishijima, Kenbun Sone, Kei Kawana, and Katsutoshi Oda. 2024. "Genomic Landscape of Endometrial, Ovarian, and Cervical Cancers in Japan from the Database in the Center for Cancer Genomics and Advanced Therapeutics" Cancers 16, no. 1: 136. https://doi.org/10.3390/cancers16010136
APA StyleXi, Q., Kage, H., Ogawa, M., Matsunaga, A., Nishijima, A., Sone, K., Kawana, K., & Oda, K. (2024). Genomic Landscape of Endometrial, Ovarian, and Cervical Cancers in Japan from the Database in the Center for Cancer Genomics and Advanced Therapeutics. Cancers, 16(1), 136. https://doi.org/10.3390/cancers16010136
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