High Expression of Three-Gene Signature Improves Prediction of Relapse-Free Survival in Estrogen Receptor-Positive and Node-Positive Breast Tumors

Thakkar, Arvind; Raj, Hemanth; Ravishankar,; Muthuvelan, Bhaskaran; Balakrishnan, Arun; Padigaru, Muralidhara

doi:10.4137/bmi.s30559

Cited by 29 publications

(26 citation statements)

References 51 publications

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“…Meanwhile, three studies that focused on GATA3 mRNA expression or GATA3 mutation by PCR or RT-PCR were excluded from this meta-analysis [29–31]. There is no doubt that comparing IHC-based protein expression with microarray-based gene-expression levels can lead to quite distinct conclusions.…”

Section: Discussionmentioning

confidence: 99%

Prognostic and clinicopathological value of GATA binding protein 3 in breast cancer: A systematic review and meta-analysis

et al. 2017

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The potential prognostic value of GATA binding protein 3 (GATA3) in breast cancer has recently increased, although the evidence is inconclusive. This meta-analysis of 10 articles involving 5,080 breast cancer patients explored the prognostic and clinicopathological value of GATA3 in breast cancer. Time to tumor progression (TTP) and overall survival (OS) were primary endpoints. Pooled hazard ratio (HR), pooled risk ratio (RR), and 95% confidence interval (CI) were calculated to evaluate the association between GATA3, prognosis, and clinicopathological parameters. High GATA3 expression predicts breast cancer, with a HR (HR = 0.671; 95% CI = 0.475–0.947; P = 0.023) of TTP, but is not associated with OS (HR = 0.889; 95% CI = 0.789–1.001; P = 0.052). GATA3 overexpression is associated with positive ER (RR = 3.155; 95% CI = 1.680–5.923; P = 0.000), positive PR (RR = 3.949; 95% CI = 1.567–9.954, P = 0.004), lower nuclear grade (RR = 0.435; 95% CI = 0.369–0.514; P = 0.000), and smaller tumor size (RR = 0.816; 95% CI = 0.709–0.940; P = 0.005). High GATA3 expression may predict TTP in breast cancer, and such patients may show better clinicopathological features.

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Section: Discussionmentioning

confidence: 99%

Prognostic and clinicopathological value of GATA binding protein 3 in breast cancer: A systematic review and meta-analysis

et al. 2017

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“…Even though a systematic biological interpretation of the signatures identified is beyond the scope of this work, to ascertain the reliability of our results we compared them with published data. The top features in the BRCA-ER rSNFi signature include multiple genes known to be associated with breast carcinoma progression and outcome such as AGR3, B3GNT and MLPH (29,30,31). In addition we find the estrogen receptor gene (ESR1 from the gene and ER-alpha from the prot layer) and the transcription factor GATA3 (from both gene and prot layers) (32).…”

Section: Resultsmentioning

confidence: 92%

Integrative Network Fusion: a multi-omics approach in molecular profiling

Chierici

Bussola

Marcolini

et al. 2020

Preprint

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Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold crossvalidation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein abundances and copy number variants are used to predict estrogen receptor status (BRCA-ER, N=381) and breast invasive carcinoma subtypes (BRCA-subtypes, N=305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N=157; KIRC-OS, N=181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs 0.80; FS: 56 vs 1801) and BRCA-subtypes 1 Chierici et al. INF(0.84 vs 0.80; 302 vs 1801), improving KIRC-OS performance (0.38 vs 0.31; 111 vs 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes 1 .

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“…Data from Oncomine revealed a significant upregulation of SLC7A8 in several cancers, including breast, colorectal, head and neck, leukaemia, lymphoma, and melanoma [35]. However, this only has been validated at the mRNA level in a subset of breast tumours [15] and melanoma cell lines, which showed however more than five times increase in SLC7A5 expression compared to SLC7A8 [36]. Herein, we used large BC cohorts to reveal the significant associations between the high SLC7A8 expression, at mRNA and protein levels, and good prognostic clinicopathological parameters, including small tumour size, low tumour grade, and good NPI.…”

Section: Discussionmentioning

confidence: 99%

“…However, there is limited information whether SLC7A8 plays an equal role in BC. Previous studies showed that SLC7A8 is upregulated in ER+BC and it is controlled by oestrogen [4,15]. Luo et al also identified SLC7A8 as a novel progesterone target gene in uterine leiomyoma cells [16].…”

Section: Introductionmentioning

confidence: 98%

The solute carrier SLC7A8 is a marker of favourable prognosis in ER-positive low proliferative invasive breast cancer

Ansari

Alfarsi

Craze

et al. 2020

Breast Cancer Res Treat

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Purpose Breast cancer (BC) is a heterogeneous disease consisting of various subtypes, with different prognostic and therapeutic outcomes. The amino acid transporter, SLC7A8, is overexpressed in oestrogen receptor-positive BC. However, the consequence of this overexpression, in terms of disease prognosis, is still obscure. This study aimed to evaluate the biological and prognostic value of SLC7A8 in BC with emphasis on the intrinsic molecular subtypes. Methods SLC7A8 was assessed at the genomic, using METABRIC data (n = 1980), and proteomic, using immunohistochemistry and TMA (n = 1562), levels in well-characterised primary BC cohorts. SLC7A8 expression was examined with clinicopathological parameters, molecular subtypes, and patient outcome. Results SLC7A8 mRNA and SLC7A8 protein expression were strongly associated with good prognostic features, including small tumour size, low tumour grade, and good Nottingham Prognostic Index (NPI) (all P < 0.05). Expression of SLC7A8 mRNA was higher in luminal tumours compared to other subtypes (P < 0.001). High expression of SLC7A8 mRNA and SLC7A8 protein was associated with good patient outcome (P ≤ 0.001) but only in the low proliferative ER+/luminal A tumours (P = 0.01). In multivariate analysis, SLC7A8 mRNA and SLC7A8 protein were independent factors for longer breast cancer specific survival (P = 0.01 and P = 0.03), respectively. Conclusion SLC7A8 appears to play a role in BC and is a marker for favourable prognosis in the most predominant, ER+ low proliferative/luminal A, BC subtype. Functional assessment is necessary to reveal the specific role played by SLC7A8 in ER+ BC.

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High Expression of Three-Gene Signature Improves Prediction of Relapse-Free Survival in Estrogen Receptor-Positive and Node-Positive Breast Tumors

Cited by 29 publications

References 51 publications

Prognostic and clinicopathological value of GATA binding protein 3 in breast cancer: A systematic review and meta-analysis

Prognostic and clinicopathological value of GATA binding protein 3 in breast cancer: A systematic review and meta-analysis

Integrative Network Fusion: a multi-omics approach in molecular profiling

The solute carrier SLC7A8 is a marker of favourable prognosis in ER-positive low proliferative invasive breast cancer

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