Integrative analysis of mutational and transcriptional profiles reveals driver mutations of metastatic breast cancers.
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ABSTRACT:
SUBMITTER: Lee JH
PROVIDER: S-EPMC5004232 | biostudies-literature | 2016
REPOSITORIES: biostudies-literature
Publications
Integrative analysis of mutational and transcriptional profiles reveals driver mutations of metastatic breast cancers.
Lee Ji-Hyun JH Zhao Xing-Ming XM Yoon Ina I Lee Jin Young JY Kwon Nam Hoon NH Wang Yin-Ying YY Lee Kyung-Min KM Lee Min-Joo MJ Kim Jisun J Moon Hyeong-Gon HG In Yongho Y Hao Jin-Kao JK Park Kyung-Mii KM Noh Dong-Young DY Han Wonshik W Kim Sunghoon S
Cell discovery 20160830
Despite the explosion in the numbers of cancer genomic studies, metastasis is still the major cause of cancer mortality. In breast cancer, approximately one-fifth of metastatic patients survive 5 years. Therefore, detecting the patients at a high risk of developing distant metastasis at first diagnosis is critical for effective treatment strategy. We hereby present a novel systems biology approach to identify driver mutations escalating the risk of metastasis based on both exome and RNA sequenci ...[more]
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Project description:BackgroundEvolutionary models of breast cancer progression differ on the extent to which metastatic potential is pre-encoded within primary tumors. Although metastatic recurrences often harbor putative driver mutations that are not detected in their antecedent primary tumor using standard sequencing technologies, whether these mutations were acquired before or after dissemination remains unclear.MethodsTo ascertain whether putative metastatic driver mutations initially deemed specific to the metastasis by whole exome sequencing were, in actuality, present within rare ancestral subclones of the primary tumors from which they arose, we employed error-controlled ultra-deep sequencing (UDS-UMI) coupled with FFPE artifact mitigation by uracil-DNA glycosylase (UDG) to assess the presence of 132 "metastasis-specific" mutations within antecedent primary tumors from 21 patients. Maximum mutation detection sensitivity was ~1% of primary tumor cells. A conceptual framework was developed to estimate relative likelihoods of alternative models of mutation acquisition.ResultsThe ancestral primary tumor subclone responsible for seeding the metastasis was identified in 29% of patients, implicating several putative drivers in metastatic seeding including LRP5 A65V and PEAK1 K140Q. Despite this, 93% of metastasis-specific mutations in putative metastatic driver genes remained undetected within primary tumors, as did 96% of metastasis-specific mutations in known breast cancer drivers, including ERRB2 V777L, ESR1 D538G, and AKT1 D323H. Strikingly, even in those cases in which the rare ancestral subclone was identified, 87% of metastasis-specific putative driver mutations remained undetected. Modeling indicated that the sequential acquisition of multiple metastasis-specific driver or passenger mutations within the same rare subclonal lineage of the primary tumor was highly improbable.ConclusionsOur results strongly suggest that metastatic driver mutations are sequentially acquired and selected within the same clonal lineage both before, but more commonly after, dissemination from the primary tumor, and that these mutations are biologically consequential. Despite inherent limitations in sampling archival primary tumors, our findings indicate that tumor cells in most patients continue to undergo clinically relevant genomic evolution after their dissemination from the primary tumor. This provides further evidence that metastatic recurrence is a multi-step, mutation-driven process that extends beyond primary tumor dissemination and underscores the importance of longitudinal tumor assessment to help guide clinical decisions.
| S-EPMC10848417 | biostudies-literature
Project description:BackgroundMajor advances have been achieved in the characterization of early breast cancer (eBC) genomic profiles. Metastatic breast cancer (mBC) is associated with poor outcomes, yet limited information is available on the genomic profile of this disease. This study aims to decipher mutational profiles of mBC using next-generation sequencing.Methods and findingsWhole-exome sequencing was performed on 216 tumor-blood pairs from mBC patients who underwent a biopsy in the context of the SAFIR01, SAFIR02, SHIVA, or Molecular Screening for Cancer Treatment Optimization (MOSCATO) prospective trials. Mutational profiles from 772 primary breast tumors from The Cancer Genome Atlas (TCGA) were used as a reference for comparing primary and mBC mutational profiles. Twelve genes (TP53, PIK3CA, GATA3, ESR1, MAP3K1, CDH1, AKT1, MAP2K4, RB1, PTEN, CBFB, and CDKN2A) were identified as significantly mutated in mBC (false discovery rate [FDR] < 0.1). Eight genes (ESR1, FSIP2, FRAS1, OSBPL3, EDC4, PALB2, IGFN1, and AGRN) were more frequently mutated in mBC as compared to eBC (FDR < 0.01). ESR1 was identified both as a driver and as a metastatic gene (n = 22, odds ratio = 29, 95% CI [9-155], p = 1.2e-12) and also presented with focal amplification (n = 9) for a total of 31 mBCs with either ESR1 mutation or amplification, including 27 hormone receptor positive (HR+) and HER2 negative (HER2-) mBCs (19%). HR+/HER2- mBC presented a high prevalence of mutations on genes located on the mechanistic target of rapamycin (mTOR) pathway (TSC1 and TSC2) as compared to HR+/HER2- eBC (respectively 6% and 0.7%, p = 0.0004). Other actionable genes were more frequently mutated in HR+ mBC, including ERBB4 (n = 8), NOTCH3 (n = 7), and ALK (n = 7). Analysis of mutational signatures revealed a significant increase in APOBEC-mediated mutagenesis in HR+/HER2- metastatic tumors as compared to primary TCGA samples (p < 2e-16). The main limitations of this study include the absence of bone metastases and the size of the cohort, which might not have allowed the identification of rare mutations and their effect on survival.ConclusionsThis work reports the results of the analysis of the first large-scale study on mutation profiles of mBC. This study revealed genomic alterations and mutational signatures involved in the resistance to therapies, including actionable mutations.
| S-EPMC5189935 | biostudies-literature
Project description:As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.
| S-EPMC7685479 | biostudies-literature
Project description:BackgroundMale breast cancer (MBC) is still poorly understood with a large proportion arising in families with a history of breast cancer. Genomic studies have focused on germline determinants of MBC risk, with minimal knowledge of somatic changes in these cancers.MethodsUsing a TruSeq amplicon cancer panel, this study evaluated 48 familial MBCs (3 BRCA1 germline mutant, 17 BRCA2 germline mutant and 28 BRCAX) for hotspot somatic mutations and copy number changes in 48 common cancer genes.ResultsTwelve missense mutations included nine PIK3CA mutations (seven in BRCAX patients), two TP53 mutations (both in BRCA2 patients) and one PTEN mutation. Common gains were seen in GNAS (34.1%) and losses were seen in GNAQ (36.4%), ABL1 (47.7%) and ATM (34.1%). Gains of HRAS (37.5% vs 3%, P=0.006), STK11 (25.0% vs 0%, P=0.01) and SMARCB1 (18.8% vs 0%, P=0.04) and the loss of RB1 (43.8% vs 13%, P=0.03) were specific to BRCA2 tumours.ConclusionsThis study is the first to perform high-throughput somatic sequencing on familial MBCs. Overall, PIK3CA mutations are most commonly seen, with fewer TP53 and PTEN mutations, similar to the profile seen in luminal A female breast cancers. Differences in mutation profiles and patterns of gene gains/losses are seen between BRCA2 (associated with TP53/PTEN mutations, loss of RB1 and gain of HRAS, STK11 and SMARCB1) and BRCAX (associated with PIK3CA mutations) tumours, suggesting that BRCA2 and BRCAX MBCs may be distinct and arise from different tumour pathways. This has implications on potential therapies, depending on the BRCA status of MBC patients.
| S-EPMC4264438 | biostudies-literature
Project description:The closely related T-box transcription factors TBX2 and TBX3 are frequently overexpressed in melanoma and various types of human cancers, in particular, breast cancer. The overexpression of TBX2 and TBX3 can have several cellular effects, among them suppression of senescence, promotion of epithelial-mesenchymal transition, and invasive cell motility. In contrast, loss of function of TBX3 and most other human T-box genes causes developmental haploinsufficiency syndromes. Stephens and colleagues (1), by exome sequencing of breast tumor samples, identified five different mutations in TBX3, all affecting the DNA-binding T-domain. One in-frame deletion of a single amino acid, p.N212delN, was observed twice. Due to the clustering of these mutations to the T-domain and for statistical reasons, TBX3 was inferred to be a driver gene in breast cancer. Since mutations in the T-domain generally cause loss of function and because the tumorigenic action of TBX3 has generally been attributed to overexpression, we determined whether the putative driver mutations had loss- or gain-of-function properties. We tested two in-frame deletions, one missense, and one frameshift mutant protein for DNA-binding in vitro, and for target gene repression in cell culture. In addition, we performed an in silico analysis of somatic TBX mutations in breast cancer, collected in The Cancer Genome Atlas (TCGA). Both the experimental and the in silico analysis indicate that the observed mutations predominantly cause loss of TBX3 function.
| S-EPMC4625211 | biostudies-literature
Project description:Driver mutations are the genetic variants responsible for oncogenesis, but how specific somatic mutational events arise in cells remains poorly understood. Mutational signatures derive from the frequency of mutated trinucleotides in a given cancer sample, and they provide an avenue for investigating the underlying mutational processes that operate in cancer. Here we analyse somatic mutations from 7,815 cancer exomes from The Cancer Genome Atlas (TCGA) across 26 cancer types. We curate a list of 50 known cancer driver mutations by analysing recurrence in our cohort and annotations of known cancer-associated genes from the Cancer Gene Census, IntOGen database and Cancer Genome Interpreter. We then use these datasets to perform binary univariate logistic regression and establish the statistical relationship between individual driver mutations and known mutational signatures across different cancer types. Our analysis led to the identification of 39 significant associations between driver mutations and mutational signatures (P < 0.004, with a false discovery rate of < 5%). We first validate our methodology by establishing statistical links for known and novel associations between driver mutations and the mutational signature arising from Polymerase Epsilon proofreading deficiency. We then examine associations between driver mutations and mutational signatures for AID/APOBEC enzyme activity and deficient mismatch repair. We also identify negative associations (odds ratio < 1) between mutational signatures and driver mutations, and here we examine the role of aging and cigarette smoke mutagenesis in the generation of driver mutations in IDH1 and KRAS in brain cancers and lung adenocarcinomas respectively. Our study provides statistical foundations for hypothesised links between otherwise independent biological processes and we uncover previously unexplored relationships between driver mutations and mutagenic processes during cancer development. These associations give insights into how cancers acquire advantageous mutations and can provide direction to guide further mechanistic studies into cancer pathogenesis.
| S-EPMC6249022 | biostudies-literature
Project description:Cancer is one of the leading cause of death of women worldwide, and breast, ovarian, endometrial and cervical cancers contribute significantly to this every year. Developing early genetic-based diagnostic tools may be an effective approach to increase the chances of survival and provide more treatment opportunities. However, the current cancer genetic studies are mainly conducted independently and, hence lack of common driver genes involved in cancers in women. To explore the potential common molecular mechanism, we integrated four comprehensive literature-based databases to explore the shared implicated genetic effects. Using a total of 460 endometrial, 2,068 ovarian, 2,308 breast and 537 cervical cancer-implicated genes, we identified 52 genes which are common in all four types of cancers in women. Furthermore, we defined their potential functional role in endogenous hormonal regulation pathways within the context of four cancers in women. For example, these genes are strongly associated with hormonal stimulation, which may facilitate rapid diagnosis and treatment management decision making. Additional mutational analyses on combined the cancer genome atlas datasets consisting of 5,919 gynaecological and breast tumor samples were conducted to identify the frequently mutated genes across cancer types. For those common implicated genes for hormonal stimulants, we found that three quarter of 5,919 samples had genomic alteration with the highest frequency in MYC (22%), followed by NDRG1 (19%), ERBB2 (14%), PTEN (13%), PTGS2 (13%) and CDH1 (11%). We also identified 38 hormone related genes, eight of which are associated with the ovulation cycle. Further systems biology approach of the shared genes identified 20 novel genes, of which 12 were involved in the hormone regulation in these four cancers in women. Identification of common driver genes for hormone stimulation provided an unique angle of involving the potential of the hormone stimulants-related genes for cancer diagnosis and prognosis.
| S-EPMC6556371 | biostudies-literature
Project description:Tumors act systemically to sustain cancer progression, affecting the physiological processes in the host and triggering responses in the blood circulating cells. In this study, we explored blood transcriptional patterns of patients with two subtypes of HER2 negative breast cancers, with different prognosis and therapeutic outcome. Peripheral blood samples from seven healthy female donors and 29 women with breast cancer including 14 triple-negative breast cancers and 15 hormone-dependent breast cancers were evaluated by microarray. We also evaluated the stroma in primary tumors. Transcriptional analysis revealed distinct molecular signatures in the blood of HER2- breast cancer patients according to ER/PR status. Our data showed the implication of immune signaling in both breast cancer subtypes with an enrichment of these processes in the blood of TNBC patients. We observed a significant alteration of "chemokine signaling," "IL-8 signaling," and "communication between innate and adaptive immune cells" pathways in the blood of TNBC patients correlated with an increased inflammation and necrosis in their primary tumors. Overall, our data indicate that the presence of triple-negative breast cancer is associated with an enrichment of altered systemic immune-related pathways, suggesting that immunotherapy could possibly be synergistic to the chemotherapy, to improve the clinical outcome of these patients.
| S-EPMC4738716 | biostudies-literature
Project description:The mutational profiles of primary colorectal cancers (CRCs) and corresponding ovarian metastases were compared. Using a custom-made next generation sequencing panel, 115 cancer-driving genes were analyzed in a cohort of 26 primary CRCs and 30 matching ovarian metastases (four with bilateral metastases). To obtain a complete overview of the mutational profile, low thresholds were used in bioinformatics analysis to prevent low frequency passenger mutations from being filtered out. A subset of variants was validated using Sanger and/or hydrolysis probe assays. The mutational landscape of CRC that metastasized to the ovary was not strikingly different from CRC in consecutive series. When comparing primary CRCs and their matching ovarian metastases, there was considerable overlap in the mutations of early affected genes. A subset of mutations demonstrated less overlap, presumably being passenger mutations. In particular, primary CRCs showed a substantially high number of passenger mutations. We also compared the primary CRCs and matching metastases for stratifying variants of six genes (KRAS, NRAS, BRAF, FBXW7, PTEN and PIK3CA) that select for established (EGFR directed) or future targeted therapies. In a total of 31 variants 12 were not found in either of the two locations. Tumours thus differed in the number of discordant variants between the primary tumours and matching metastases. Half of these discordant variants were definitive class 4/5 pathogenic variants. However, in terms of temporal heterogeneity, no clear relationship was observed between the number of discordant variants and the time interval between primary CRCs and the detection of ovarian metastases. This suggests that dormant metastases may be present from the early days of the primary tumours.
| S-EPMC4958737 | biostudies-literature
Project description:BackgroundAlthough triple-negative breast cancer (TNBC) is associated with an increased risk of recurrence and metastasis, the molecular mechanisms underlying metastasis in TNBC remain unknown. To identify transcriptional changes and genes regulating metastatic progression in TNBC, we compared the transcriptomic profiles of primary and matched metastatic tumors using massively parallel RNA sequencing.MethodsWe performed gene expression profiling using formalin-fixed paraffin-embedded (FFPE) TNBC tissues of patients from two cohorts: the Zurich cohort (n = 31) and the Stavanger cohort (n = 5). Among the 31 patients in the Zurich cohort, 18 had primary TNBC tumors that did not metastasize, and 13 had primary tumors that metastasized (11 paired primary and locoregional recurrences). The Stavanger cohort included five matched primary and metastatic TNBC tumors. Significantly differentially expressed genes (DEGs; absolute fold change ≥2, p < 0.05) were identified and subjected to functional analyses. We investigated if there was any overlap between DEGs from both the cohorts with epithelial-to-mesenchymal-to-amoeboid transition (EMAT) gene signature. xCell was used to estimate relative fractions of 64 immune and stromal cell types in each RNA-seq sample.ResultsIn the Zurich cohort, we identified 1624 DEGs between primary TNBC tumors and matched metastatic lesions. xCell analysis revealed a significantly higher immune scores for metastatic lesions compared to paired primary tumors in the Zurich cohort. We also found significant upregulation of three MammaPrint signature genes (HRASLS, TGFB3 and RASSF7) in primary tumors that metastasized compared to primary tumors that remained metastasis-free. In the Stavanger cohort, we identified 818 DEGs between primary tumors and matched metastatic lesions. No significant differences in xCell immune scores were observed. We found that 21 and 14 DEGs from Zurich and Stavanger cohort, respectively, overlapped with the EMAT gene signature. In both cohorts, genes belonging to the MMP, FGF, and PDGFR families were upregulated in primary tumors compared to matched metastatic lesions.ConclusionsOur results suggest that distinct gene expression patterns exist between primary TNBCs and matched metastatic tumors. Further studies are warranted to explore whether these discrete expression profiles underlie or result from disease status.
| S-EPMC9139196 | biostudies-literature