Abstract 2671
Background
Homeobox (HOX) family consists of 39 genes which act as master regulators in embryonic development. Each of the genes is also known to play key roles in progression of breast cancer, including epithelial to mesenchymal transition, tumor angiogenesis and endocrine therapy resistance. Although there are numerous reports on individual HOX genes and cancer, none of them have comprehensively analyzed the whole gene family. Since HOX genes strongly coordinate within the family during the embryonic period, we considered that the analysis of the whole HOX family is also indispensable in breast cancer.
Methods
We collected 702 breast cancer data from four publicly available array datasets (GSE11121, GSE7390, GSE3494, GSE2990) and performed unsupervised hierarchal clustering into two clusters by the expression of HOX genes. We constructed model formulas for cluster prediction by dividing the samples into learning and validation groups. We used three machine learning methods: support-vector machine (SVM), neural network and Bayes. The model formulas were validated by validation samples. We also used 512 TCGA breast cancer data to calculate covariations of the genes in breast cancer.
Results
By the clustering of four array datasets, the DFS of the two clusters in PAM50-classified luminal B patients were statistically different (p = 0.016), and the gene ontology analysis revealed that the Wnt pathway was activated in the poor prognostic cluster. All cluster prediction models for luminal B sample achieved accuracies of over 90%. From TCGA breast cancer data, we found that HOX genes covariate the most with other HOX genes, especially within the chromosomally proximal groups.
Conclusions
Comprehensive analysis of the whole HOX family lead to the prediction of luminal B breast cancer prognosis. Considering that Wnt signaling controls HOX genes during the embryonic stage, we suppose a Wnt pathway activated, poor prognostic subgroup in luminal B breast cancer which can be identified by the expression of HOX genes. The cluster prediction model by machine learning was acceptable for its future adaptation in clinical settings. We also proved that HOX genes strongly covariate within the gene family in cancer, not only during the embryonic stage.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
3630 - Results of phase 1 clinical trial of high doses of Seleno-L-methionine (SLM) in sequential combination with Axitinib in previously treated and relapsed clear cell renal carcinoma (ccRCC) patients
Presenter: Yousef Zakharia
Session: Poster Display session 3
Resources:
Abstract
2356 - Safety and Efficacy of CDX-014 , an Antibody-Drug Conjugate against T Cell immunoglobulin mucin-1 (TIM-1), in advanced Renal Cell Carcinoma
Presenter: Bradley McGregor
Session: Poster Display session 3
Resources:
Abstract
1028 - SPAZO2 (SOGUG): Outcomes and prognostic significance of IMDC intermediate prognosis subclassification in metastatic renal cell carcinoma (mRCC) in patients treated with 1st-line pazopanib (1stPz).
Presenter: Begona P. Valderrama
Session: Poster Display session 3
Resources:
Abstract
2293 - Effect of Antacid Intake on the Therapeutic Efficacy of Sunitinib (SUN) in Metastatic Renal Cell Carcinoma (mRCC) Patients (pts): a Sub-Analysis of the STAR-TOR Registry
Presenter: Katrin Schlack
Session: Poster Display session 3
Resources:
Abstract
1451 - Randomized phase 3 trial of avelumab + axitinib vs sunitinib as first-line treatment for advanced renal cell carcinoma: JAVELIN Renal 101 Japanese subgroup analysis
Presenter: Motohide Uemura
Session: Poster Display session 3
Resources:
Abstract
4399 - Overall and progression-free survival according to MSKCC scores in 1st line sunitinib treatment of metastatic renal cell carcinoma (mRCC)
Presenter: Jindrich Finek
Session: Poster Display session 3
Resources:
Abstract
1344 - Combination therapy with checkpoint inhibitors for first-line treatment of advanced renal cell carcinoma: A systematic review and meta-analysis of randomized controlled trials
Presenter: Kyaw Thein
Session: Poster Display session 3
Resources:
Abstract
3462 - A phase II trial of TKI induction followed by a randomized comparison between nivolumab or TKI continuation in renal cell carcinoma (NIVOSWITCH)
Presenter: Viktor Grünwald
Session: Poster Display session 3
Resources:
Abstract
5268 - Nivolumab (N) treatment beyond progression in a real-world cohort of patients (pts) with metastatic renal cell carcinoma (mRCC)
Presenter: Sophie Hans
Session: Poster Display session 3
Resources:
Abstract
4235 - First results of safety profile of nivolumab (NIVO) in combination with stereotactic body radiotherapy (SBRT) in II and III line of patients (pts) with metastatic renal cell carcinoma (mRCC) in NIVES Study
Presenter: Cristina Masini
Session: Poster Display session 3
Resources:
Abstract