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Integrative molecular profiling of triple negative breast cancers identifies amplicon drivers and potential therapeutic targets.

ABSTRACT: Triple negative breast cancers (TNBCs) have a relatively poor prognosis and cannot be effectively treated with current targeted therapies. We searched for genes that have the potential to be therapeutic targets by identifying genes consistently overexpressed when amplified. Fifty-six TNBCs were subjected to high-resolution microarray-based comparative genomic hybridization (aCGH), of which 24 were subjected to genome-wide gene expression analysis. TNBCs were genetically heterogeneous; no individual focal amplification was present at high frequency, although 78.6% of TNBCs harboured at least one focal amplification. Integration of aCGH and expression data revealed 40 genes significantly overexpressed when amplified, including the known oncogenes and potential therapeutic targets, FGFR2 (10q26.3), BUB3 (10q26.3), RAB20 (13q34), PKN1 (19p13.12) and NOTCH3 (19p13.12). We identified two TNBC cell lines with FGFR2 amplification, which both had constitutive activation of FGFR2. Amplified cell lines were highly sensitive to FGFR inhibitor PD173074, and to RNAi silencing of FGFR2. Treatment with PD173074 induced apoptosis resulting partly from inhibition of PI3K-AKT signalling. Independent validation using publicly available aCGH data sets revealed FGFR2 gene was amplified in 4% (6/165) of TNBC, but not in other subtypes (0/214, P=0.0065). Our analysis demonstrates that TNBCs are heterogeneous tumours with amplifications of FGFR2 in a subgroup of tumours.

SUBMITTER: Turner N

PROVIDER: S-EPMC2852518 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Integrative molecular profiling of triple negative breast cancers identifies amplicon drivers and potential therapeutic targets.

Turner N N Lambros M B MB Horlings H M HM Pearson A A Sharpe R R Natrajan R R Geyer F C FC van Kouwenhove M M Kreike B B Mackay A A Ashworth A A van de Vijver M J MJ Reis-Filho J S JS

Oncogene 20100118 14

Triple negative breast cancers (TNBCs) have a relatively poor prognosis and cannot be effectively treated with current targeted therapies. We searched for genes that have the potential to be therapeutic targets by identifying genes consistently overexpressed when amplified. Fifty-six TNBCs were subjected to high-resolution microarray-based comparative genomic hybridization (aCGH), of which 24 were subjected to genome-wide gene expression analysis. TNBCs were genetically heterogeneous; no individ ...[more]

PMID: 20101236

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Project description:Triple negative breast cancer (TNBC) is high-risk due to its rapid drug resistance and recurrence, metastasis, and lack of targeted therapy. So far, no molecularly targeted therapeutic agents have been clinically approved for TNBC. It is imperative that we discover new targets for TNBC therapy.A large volume of cancer genomics data are emerging and advancing breast cancer research. We may integrate different types of TNBC genomic data to discover molecular targets for TNBC therapy.We used publicly available TNBC tumor tissue genomic data in the Cancer Genome Atlas database in this study.We integratively explored genomic profiles (gene expression, copy number, methylation, microRNA [miRNA], and gene mutation) in TNBC and identified hyperactivated genes that have higher expression, more copy numbers, lower methylation level, or are targets of miRNAs with lower expression in TNBC than in normal samples. We ranked the hyperactivated genes into different levels based on all the genomic evidence and performed functional analyses of the sets of genes identified. More importantly, we proposed potential molecular targets for TNBC therapy based on the hyperactivated genes.Some of the genes we identified such as FGFR2, MAPK13, TP53, SRC family, MUC family, and BCL2 family have been suggested to be potential targets for TNBC treatment. Others such as CSF1R, EPHB3, TRIB1, and LAD1 could be promising new targets for TNBC treatment. By utilizing this integrative analysis of genomic profiles for TNBC, we hypothesized that some of the targeted treatment strategies for TNBC currently in development are more likely to be promising, such as poly (ADP-ribose) polymerase inhibitors, while the others are more likely to be discouraging, such as angiogenesis inhibitors.The findings in this study need to be experimentally validated in the future.This is a systematic study that combined 5 different types of genomic data to molecularly characterize TNBC and identify potential targets for TNBC therapy. The integrative analysis of genomic profiles for TNBC could assist in identifying potential new therapeutic targets and predicting the effectiveness of a targeted treatment strategy for TNBC therapy.

Project description:PurposeTriple-negative breast cancer (TNBC) is an aggressive tumor with poor prognosis, it has higher recurrence and metastatic rates than other breast cancer subtypes. This study aims to investigate biomarkers and potential targets for TNBC related to ferroptosis through data mining and bioinformatics analysis. The findings may provide new insights for treating TNBC.MethodsThe TNBC patients' data from the Cancer Genome Atlas (TCGA) database were extracted for differential expression and prognosis analysis. Consensus genes obtained by intersecting differential expressed and ferroptosis-related genes was used to establish the prognostic model by the univariate and multivariate Cox analyses. Besides, TNBC data from the Gene Expression Omnibus (GEO) database was used to confirm the reliability of the prognosis model. Moreover, clinical information was analyzed by multifactorial independent analysis to identify independent prognostic factors. The expression of genes constituting the prognostic model was further validated using the Human Protein Atlas (HPA) database. Finally, the Comparative Toxicogenomic Data (CTD) database was used to explore possible treatment drugs for TNBC.ResultsWe obtained 13,245 differential expressed genes, and 177 consensus genes. 98 genes with prognostic implication were obtained by univariable Cox. Then, a prognostic model including 12 ferroptosis-related genes was constructed by multivariable Cox. The area under curve (AUC) value of the prognostic model for TNBC was 0.82. The GEO database validated that the model (AUC = 0.77) could predict the patient outcomes. The staining results of 10 out of 12 prognostic model genes in HPA database showed that their expression was consistent with our predictions. Clinical risk analysis indicated that risk score of patients could act as an independent prognostic factor. Finally, six drugs that may have interaction with 12 ferroptosis-related genes were obtained using the CTD database.ConclusionThe prognostic model composed of 12 ferroptosis-related genes could predict the prognosis of TNBC patients, and seven genes (ASNS, LAMP2, CAV1, DPP4, HELLS, TF, ZFP69B) could be potential new therapeutic targets for TNBC, and two drugs (1-methyl-3-isobutylxanthine, rosiglitazone) could act as potential therapeutic drugs for the treatment of TNBC.

Project description:BACKGROUND:Abundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy. METHODS:Using four publicly available TNBC genomics datasets, we classified TNBC on the basis of the immunogenomic profiling of 29 immune signatures. Unsupervised and supervised machine learning methods were used to perform the classification. RESULTS:We identified three TNBC subtypes that we named Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L) and demonstrated that this classification was reliable and predictable by analyzing multiple different datasets. Immunity_H was characterized by greater immune cell infiltration and anti-tumor immune activities, as well as better survival prognosis compared to the other subtypes. Besides the immune signatures, some cancer-associated pathways were hyperactivated in Immunity_H, including apoptosis, calcium signaling, MAPK signaling, PI3K-Akt signaling, and RAS signaling. In contrast, Immunity_L presented depressed immune signatures and increased activation of cell cycle, Hippo signaling, DNA replication, mismatch repair, cell adhesion molecule binding, spliceosome, adherens junction function, pyrimidine metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and RNA polymerase pathways. Furthermore, we identified a gene co-expression subnetwork centered around five transcription factor (TF) genes (CORO1A, STAT4, BCL11B, ZNF831, and EOMES) specifically significant in the Immunity_H subtype and a subnetwork centered around two TF genes (IRF8 and SPI1) characteristic of the Immunity_L subtype. CONCLUSIONS:The identification of TNBC subtypes based on immune signatures has potential clinical implications for TNBC treatment.

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Integrative molecular profiling of triple negative breast cancers identifies amplicon drivers and potential therapeutic targets.

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Integrative molecular profiling of triple negative breast cancers identifies amplicon drivers and potential therapeutic targets.

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