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
5105 - Fresh blood Immune cell monitoring in patients treated with nivolumab in the GETUG-AFU26 NIVOREN study: association with toxicity and treatment outcome
Presenter: Aude DESNOYER
Session: Poster Display session 3
Resources:
Abstract
1877 - Advanced clear-cell renal cell carcinoma (accRCC): association of microRNAs (miRNAs) with molecular subtypes, mRNA targets and outcome.
Presenter: Annelies Verbiest
Session: Poster Display session 3
Resources:
Abstract
5543 - Prior tyrosine kinase inhibitors (TKI) and antibiotics (ATB) use are associated with distinct gut microbiota ‘guilds’ in renal cell carcinoma (RCC) patients
Presenter: Valerio Iebba
Session: Poster Display session 3
Resources:
Abstract
2689 - mTOR mutations are not associated with shorter PFS and OS in patients treated with mTOR inhibitors
Presenter: Cristina Suarez Rodriguez
Session: Poster Display session 3
Resources:
Abstract
3069 - Efficacy of immune checkpoint inhibitors (ICI) and genomic alterations by body mass index (BMI) in Advanced Renal Cell Carcinoma (RCC)
Presenter: Aly-Khan Lalani
Session: Poster Display session 3
Resources:
Abstract
5089 - Finding the Right Biomarker for Renal Cell Carcinoma (RCC): Nivolumab treatment induces the expression of specific peripheral lymphocyte microRNAs in patients with durable and complete response.
Presenter: Lorena Incorvaia
Session: Poster Display session 3
Resources:
Abstract
2594 - Algorithms derived from quantitative pathology can be a gatekeeper in patient selection for clinical trials in localised clear cell renal cell carcinoma (ccRCC)
Presenter: In Hwa Um
Session: Poster Display session 3
Resources:
Abstract
2566 - High baseline blood volume is an independent favorable prognostic factor for overall and progression-free survival in patients with metastatic renal cell carcinoma
Presenter: Aska Drljevic-nielsen
Session: Poster Display session 3
Resources:
Abstract
2675 - Impact of estimand selection on adjuvant treatment outcomes in renal cell carcinoma (RCC)
Presenter: Daniel George
Session: Poster Display session 3
Resources:
Abstract
1541 - TERT gene fusions characterize a subset of metastatic Leydig cell tumors
Presenter: Bozo Kruslin
Session: Poster Display session 3
Resources:
Abstract