Identification of prognostic signatures in breast cancer microarray data using Bayesian techniques

Convergence of Prognostic Gene Signatures Suggests Underlying Mechanisms of Human Prostate Cancer Progression

The highly heterogeneous clinical course of human prostate cancer has prompted the development of multiple RNA biomarkers and diagnostic tools to predict outcome for individual patients. Biomarker discovery is often unstable with, for example, small changes in discovery dataset configuration resulting in large alterations in biomarker composition. Our hypothesis, which forms the basis of this current study, is that highly significant overlaps occurring between gene signatures obtained using entirely different approaches indicate genes fundamental for controlling cancer progression. For prostate cancer, we found two sets of signatures that had significant overlaps suggesting important genes (p < 10⁻³⁴ for paired overlaps, hypergeometrical test). These overlapping signatures defined a core set of genes linking hormone signalling (HES6-AR), cell cycle progression (Prolaris) and a molecular subgroup of patients (PCS1) derived by Non Negative Matrix Factorization (NNMF) of control pathways, together designated as SIG-HES6. The second set (designated SIG-DESNT) consisted of the DESNT diagnostic signature and a second NNMF signature PCS3. Stratifications using SIG-HES6 (HES6, PCS1, Prolaris) and SIG-DESNT (DESNT) classifiers frequently detected the same individual high-risk cancers, indicating that the underlying mechanisms associated with SIG-HES6 and SIG-DESNT may act together to promote aggressive cancer development. We show that the use of combinations of a SIG-HES6 signature together with DESNT substantially increases the ability to predict poor outcome, and we propose a model for prostate cancer development involving co-operation between the SIG-HES6 and SIG-DESNT pathways that has implication for therapeutic design.

A novel stratification framework for predicting outcome in patients with prostate cancer

Background

Unsupervised learning methods, such as Hierarchical Cluster Analysis, are commonly used for the analysis of genomic platform data. Unfortunately, such approaches ignore the well-documented heterogeneous composition of prostate cancer samples. Our aim is to use more sophisticated analytical approaches to deconvolute the structure of prostate cancer transcriptome data, providing novel clinically actionable information for this disease.

Methods

We apply an unsupervised model called Latent Process Decomposition (LPD), which can handle heterogeneity within individual cancer samples, to genome-wide expression data from eight prostate cancer clinical series, including 1,785 malignant samples with the clinical endpoints of PSA failure and metastasis.

Results

We show that PSA failure is correlated with the level of an expression signature called DESNT (HR = 1.52, 95% CI = [1.36, 1.7], P = 9.0 × 10⁻¹⁴, Cox model), and that patients with a majority DESNT signature have an increased metastatic risk (X² test, P = 0.0017, and P = 0.0019). In addition, we develop a stratification framework that incorporates DESNT and identifies three novel molecular subtypes of prostate cancer.

Conclusions

These results highlight the importance of using more complex approaches for the analysis of genomic data, may assist drug targeting, and have allowed the construction of a nomogram combining DESNT with other clinical factors for use in clinical management.

DESNT: A Poor Prognosis Category of Human Prostate Cancer

BACKGROUND

A critical problem in the clinical management of prostate cancer is that it is highly heterogeneous. Accurate prediction of individual cancer behaviour is therefore not achievable at the time of diagnosis leading to substantial overtreatment. It remains an enigma that, in contrast to breast cancer, unsupervised analyses of global expression profiles have not currently defined robust categories of prostate cancer with distinct clinical outcomes.

OBJECTIVE

To devise a novel classification framework for human prostate cancer based on unsupervised mathematical approaches.

DESIGN, SETTING, AND PARTICIPANTS

Our analyses are based on the hypothesis that previous attempts to classify prostate cancer have been unsuccessful because individual samples of prostate cancer frequently have heterogeneous compositions. To address this issue, we applied an unsupervised Bayesian procedure called Latent Process Decomposition to four independent prostate cancer transcriptome datasets obtained using samples from prostatectomy patients and containing between 78 and 182 participants.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Biochemical failure was assessed using log-rank analysis and Cox regression analysis.

RESULTS AND LIMITATIONS

Application of Latent Process Decomposition identified a common process in all four independent datasets examined. Cancers assigned to this process (designated DESNT cancers) are characterized by low expression of a core set of 45 genes, many encoding proteins involved in the cytoskeleton machinery, ion transport, and cell adhesion. For the three datasets with linked prostate-specific antigen failure data following prostatectomy, patients with DESNT cancer exhibited poor outcome relative to other patients (p=2.65×10-5, p=4.28×10-5, and p=2.98×10-8). When these three datasets were combined the independent predictive value of DESNT membership was p=1.61×10-7 compared with p=1.00×10-5 for Gleason sum. A limitation of the study is that only prediction of prostate-specific antigen failure was examined.

CONCLUSIONS

Our results demonstrate the existence of a novel poor prognosis category of human prostate cancer and will assist in the targeting of therapy, helping avoid treatment-associated morbidity in men with indolent disease.

PATIENT SUMMARY

Prostate cancer, unlike breast cancer, does not have a robust classification framework. We propose that this failure has occurred because prostate cancer samples selected for analysis frequently have heterozygous compositions (individual samples are made up of many different parts that each have different characteristics). Applying a mathematical approach that can overcome this problem we identify a novel poor prognosis category of human prostate cancer called DESNT.

Genomic organization and control of the grb7 gene family

Grb7 and their related family members Grb10 and Grb14 are adaptor proteins, which participate in the functionality of multiple signal transduction pathways under the control of a variety of activated tyrosine kinase receptors and other tyrosine-phosphorylated proteins. They are involved in the modulation of important cellular and organismal functions such as cell migration, cell proliferation, apoptosis, gene expression, protein degradation, protein phosphorylation, angiogenesis, embryonic development and metabolic control. In this short review we shall describe the organization of the genes encoding the Grb7 protein family, their transcriptional products and the regulatory mechanisms implicated in the control of their expression. Finally, the alterations found in these genes and the mechanisms affecting their expression under pathological conditions such as cancer, diabetes and some congenital disorders will be highlighted.

Targeted therapy in the treatment of solid tumors: practice contradicts theory

The basic principle of targeted therapy formulated about ten years ago consists in the design and application of drugs specifically directed against well-defined targets that are critical for tumor survival and not compromising for normal organs and tissues. The past decade has been marked by the appearance of an immense diversity of novel antitumor agents with claimed targeted action. Unfortunately, despite indisputable progress in clinical settings, some popular drugs against solid tumors (e.g. bevacizumab, trastuzumab, erlotinib, gefitinib) nominally assigned to targeted-action drugs, cannot actually be classified with this group being nonconforming to a priori stated goals of targeted therapy. The state-of-the-art and current problems in targeted therapy of solid tumors are reviewed.

Identification of the first intragenic deletion of the PITX2 gene causing an Axenfeld-Rieger Syndrome: case report

BACKGROUND

Axenfeld-Rieger syndrome (ARS) is characterized by bilateral congenital abnormalities of the anterior segment of the eye associated with abnormalities of the teeth, midface, and umbilicus. Most cases of ARS are caused by mutations in the genes encoding PITX2 or FOXC1. Here we describe a family affected by a severe form of ARS.

CASE PRESENTATION

Two members of this family (father and daughter) presented with typical ARS and developed severe glaucoma. The ocular phenotype was much more severe in the daughter than in the father. Magnetic resonance imaging (MRI) detected an aggressive form of meningioma in the father. There was no mutation in the PITX2 gene, determined by exon screening. We identified an intragenic deletion by quantitative genomic PCR analysis and characterized this deletion in detail.

CONCLUSION

Our findings implicate the first intragenic deletion of the PITX2 gene in the pathogenesis of a severe form of ARS in an affected family. This study stresses the importance of a systematic search for intragenic deletions in families affected by ARS and in sporadic cases for which no mutations in the exons or introns of PITX2 have been found. The molecular genetics of some ARS pedigrees should be re-examined with enzymes that can amplify medium and large genomic fragments.