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. 2020 Jun 8;11(16):4870-4883.
doi: 10.7150/jca.46174. eCollection 2020.

Identification of the Mutational Landscape of Gynecological Malignancies

Suresh Chava  1 , Romi Gupta  1
Affiliations

Identification of the Mutational Landscape of Gynecological Malignancies

Suresh Chava et al. J Cancer. .

Abstract

Background: Cancer is a complex disease that arises from the accumulation of multiple genetic and non-genetic changes. Advances in sequencing technologies have allowed unbiased and global analysis of patient-derived tumor samples and the discovery of genetic and transcriptional changes in key genes and oncogenic pathways. That in turn has facilitated a better understanding of the underlying causes of cancer initiation and progression, resulting in new therapeutic targets. Methods: In our study, we have analyzed the mutational landscape of gynecological malignancies using datasets from The Cancer Genome Atlas (TCGA). We have also analyzed Oncomine datasets to establish the impact of their alteration on disease recurrence and survival of patients. Results: In this study, we analyzed a series of different gynecological malignancies for commonly occurring genetic and non-genetic alterations. These studies show that white women have higher incidence of gynecological malignancies. Furthermore, our study identified 16 genes that are altered at a frequency >10% among all of the gynecological malignancies and tumor suppressor TP53 is the most altered gene in these malignancies (>50% of the cases). The top 16 genes fall into the categories of either tumor suppressor or oncogenes and a subset of these genes are associated with poor prognosis, some affecting recurrence and survival of ovarian cancer patients. Conclusion: In sum, our study identified 16 major genes that are broadly mutated in a large majority of gynecological malignancies and in some cases predict survival and recurrence in patients with gynecological malignancies. We predict that the functional studies will determine their relative role in the initiation and progression of gynecological malignancies and also establish if some of them represents drug targets for anti-cancer therapy.

Keywords: Oncomine; TCGA; biomarker; gynecological malignancies; therapeutic.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
Analysis of ethnicity and diagnosis age of patients with gynecological malignancies (A) Percentage of each of type of gynecological malignancy in the TCGA dataset from cBioportal. (B) Percentage of each race/ethnicity among patients with any gynecological malignancy in the TCGA dataset from cBioportal. (C) Diagnosis age of patients with different kinds of gynecological malignancies in the TCGA dataset from cBioportal. NA shown in panel B and C stands for not applicable.
Figure 2
Figure 2
Analysis of mutation counts in gynecological malignancies (A) Mutation counts in patients with different kinds of gynecological malignancies in the TCGA dataset from cBioportal. (B) Mutation counts versus fraction of genomes altered by copy-number changes for different kinds of gynecological malignancies in the TCGA dataset from cBioportal. NA shown in panel A stands for not applicable.
Figure 3
Figure 3
Frequently altered genes in gynecological malignancies. (A) Genetic alterations (somatic mutations and copy-number changes) were analyzed in different kinds of gynecological malignancies in the TCGA dataset from cBioportal. The table shows the genes that are commonly altered at a frequency >10% among all of the gynecological malignancies. (B-E) Missense, in-frame, truncating, amplification, deletion, and fusion mutations were analyzed in the genes that were altered at a frequency >10% in panel a. Missense, in-frame, truncating, amplification, deletion, and fusion mutations are shown separately for (B) vulvar/vaginal cancer, (C) uterine cancer, (D) ovarian/fallopian tube cancer, and (E) cervical cancer in different TCGA datasets in cBioportal.
Figure 4
Figure 4
Specific genes and their alteration status in gynecological malignancies (A) Percentage alteration (mutation and copy-number changes) in the genes mutated at >10% frequency in different kinds of gynecological malignancies and in the MSK-IMPACT cohort containing 10,945 samples of different kinds of cancer. (B-F) Number of missense, truncating, in-frame, and other mutations present in different kinds of gynecological malignancies and the MSK-IMPACT cohort.
Figure 5
Figure 5
Genes that are overexpressed or repressed in ovarian cancer. (A) Overall survival in months of patients with ovarian/fallopian tube cancer, cervical cancer, and uterine cancer in the TCGA dataset from cBioportal. (B) Missense, in-frame, truncating, amplification, deletion, and fusion mutations, putative copy-number alterations from GISTIC algorithms and mRNA expression (upregulation and downregulation) of the shown genes are analyzed using ovarian serous cystadenocarcinoma (TCGA, Provisional; 606 samples) datasets. (C) Bar graph for mRNA expression of the genes that were either highly upregulated or downregulated along with mRNA expression data for all the genes in samples from ovarian serous cystadenocarcinoma (TCGA, Provisional; 606 samples) datasets is shown. (D) mRNA expression of the selected genes analyzed using the Oncomine TCGA dataset. Fold-change values and their significance are shown.
Figure 6
Figure 6
Effects of overexpressed or repressed genes on the survival of patients with ovarian cancer. (A) The Tothill ovarian dataset from Oncomine analyzed for the effect of the expression levels of the selected genes on disease recurrence at 5 years. (B) The Lu ovarian dataset from Oncomine analyzed for the effect of the expression levels of the selected genes on survival at 3 years. (C) The Bild ovarian dataset from Oncomine analyzed for the effects of PTEN and ARID1A expression on survival at 1 year. The Denkert ovarian dataset from Oncomine analyzed for the effect of ARID1A expression on survival at 3 years. (D) The effect of upregulated genes on overall survival (OS) of patients with ovarian cancer as measured by KM plotter.
Figure 7
Figure 7
Model showing the important common genetic alterations/signaling pathways that affect the growth and progression of gynecological malignancies.
See this image and copyright information in PMC

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