Morphometric prognostic index in breast cancer

OBJECTIVE: To assess the ability of the morphometric prognostic index (MPI) in predicting clinical outcome in a group of breast cancer patients with short-term follow-up and to assess the relationship between MPI and other prognosticators. STUDY DESIGN: The study group consisted of 63 cases of breast cancer. Follow-up data were available for 48 patients. MPI values were calculated, and degree of nuclear and tubular differentiation was investigated in each tumor. S-phase fraction (SPF), estrogen and progesterone receptors were also studied. RESULTS: The group of patients with MPI values < 0.60 had percent values of disease-free survival significantly higher than did those with MPI values > or = 0.60. Furthermore, significant direct correlations were found between MPI and degree of nuclear atypia and between MPI and SPF. Significant inverse relationships were found between MPI and tumor progesterone receptor levels and between MPI and degree of histologic tubular differentiation. CONCLUSION: The validity of MPI as a prognosticator in breast cancer was confirmed, even in a limited number of patients observed in short-term follow-up. MPI seems to be a reliable and economical prognosticator in selecting breast cancer patients for adjuvant chemotherapy.     

A novel autophagy-related lncRNA prognostic risk model for breast cancer

Long non-coding RNAs (lncRNAs) are well known as crucial regulators to breast cancer development and are implicated in controlling autophagy. LncRNAs are also emerging as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy-related lncRNAs with prognostic value in breast cancer. In this study, we identified autophagy-related lncRNAs in breast cancer by constructing a co-expression network of autophagy-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA). We evaluated the prognostic value of these autophagy-related lncRNAs by univariate and multivariate Cox proportional hazards analyses and eventually obtained a prognostic risk model consisting of 11 autophagy-related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2-DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further validated as a novel independent prognostic factor for breast cancer patients based on the calculated risk score by Kaplan-Meier analysis, univariate and multivariate Cox regression analyses and time-dependent receiver operating characteristic (ROC) curve analysis. Moreover, based on the risk model, the low-risk and high-risk groups displayed different autophagy and oncogenic statues by principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation. Taken together, these findings suggested that the risk model of the 11 autophagy-related lncRNAs has significant prognostic value for breast cancer and might be autophagy-related therapeutic targets in clinical practice.     

Identifying sigma factors in Mycobacterium smegmatis by comparative genomic analysis

Mycobacterium smegmatis is a saprophytic species that has been used for 15 years as a model to perform heterologous regulation and virulence studies of Mycobacterium tuberculosis. Members of the extracytoplasmic sigma factors family, which are required for adaptive responses to various environmental stresses, are responsible for some of the virulence traits of M. tuberculosis. A bioinformatic search on the genome of M. smegmatis has predicted the existence of 26 sigma factors, which is twice the number that are present in M. tuberculosis. A phylogenetic analysis has shown that despite this high number of sigma factors the orthologs of the genes sigC, sigI and sigK of M. tuberculosis are absent in the M. smegmatis genome. Several sigma factors are specific for M. smegmatis, with a special enrichment in the sigH and, to a lesser extent, in the sigJ and sigL subfamily, pinpointing the potential variability of the repertoire of adaptive response in this saprophytic species.     

Identifying prognostic pairwise relationships among bacterial species in microbiome studies

In recent literature, the human microbiome has been shown to have a major influence on human health. To investigate this impact, scientists study the composition and abundance of bacterial species, commonly using 16S rRNA gene sequencing, among patients with and without a disease or condition. Methods for such investigations to date have focused on the association between individual bacterium and an outcome, and higher-order pairwise relationships or interactions among bacteria are often avoided due to the substantial increase in dimension and the potential for spurious correlations. However, overlooking such relationships ignores the environment of the microbiome, where there is dynamic cooperation and competition among bacteria. We present a method for identifying and ranking pairs of bacteria that have a differential dichotomized relationship across outcomes. Our approach, implemented in an R package PairSeek, uses the stability selection framework with data-driven dichotomized forms of the pairwise relationships. We illustrate the properties of the proposed method using a published oral cancer data set and a simulation study.     

The role of pretreatment prognostic nutritional index in esophageal cancer: A meta-analysis

Clinicopathological characteristics and prognosis of esophageal cancer (EC) patients with decreased prognostic nutritional index (PNI) have not been well investigated. So, we conducted this meta-analysis. We performed comprehensive research in PubMed, Embase, and Cochrane databases. The effect size was hazard ratio (HR) with 95% confidence interval (CI) for overall survival (OS) and cancer-specific survival (CSS). The pooled odds ratio (OR) with 95% CI were used to assess the association between PNI and clinicopathological features. A total of 3,425 EC patients were included in the present meta-analysis. Male patients, advanced age, higher tumor stage, and lymph node metastases were associated with reduced PNI level (OR = 1.40, 95% CI: 1.10-1.79; OR = 1.35, 95% CI: 1.10-1.66; OR = 2.37, 95% CI: 1.91-2.94; OR = 1.63, 95% CI: 1.04-2.56). And, the EC patients with decreased PNI held a worse OS and CSS compared with those who carried a higher PNI (HR = 1.29, 95% CI: 1.10-1.50; HR = 2.53, 95% CI: 1.15-5.57). This meta-analysis demonstrated PNI level was associated with tumor stage and lymph nodes metastases and was an independent prognostic factor in EC.     

Ranking prognosis markers in cancer genomic studies

In cancer research, high-throughput genomic studies have been extensively conducted, searching for markers associated with cancer diagnosis, prognosis and variation in response to treatment. In this article, we analyze cancer prognosis studies and investigate ranking markers based on their marginal prognosis power. To avoid ambiguity, we focus on microarray gene expression studies where genes are the markers, but note that the methodology and results are applicable to other high-throughput studies. The objectives of this study are 2-fold. First, we investigate ranking markers under three commonly adopted semiparametric models, namely the Cox, accelerated failure time and additive risk models. Data analysis shows that the ranking may vary significantly under different models. Second, we describe a nonparametric concordance measure, which has roots in the time-dependent ROC (receiver operating characteristic) framework and relies on much weaker assumptions than the semiparametric models. In simulation, it is shown that ranking using the concordance measure is not sensitive to model specification whereas ranking under the semiparametric models is. In data analysis, the concordance measure generates rankings significantly different from those under the semiparametric models.     

A novel approach for determining cancer genomic breakpoints in the presence of normal DNA

CDKN2A (encodes p16(INK4A) and p14(ARF)) deletion, which results in both Rb and p53 inactivation, is the most common chromosomal anomaly in human cancers. To precisely map the deletion breakpoints is important to understanding the molecular mechanism of genomic rearrangement and may also be useful for clinical applications. However, current methods for determining the breakpoint are either of low resolution or require the isolation of relatively pure cancer cells, which can be difficult for clinical samples that are typically contaminated with various amounts of normal host cells. To overcome this hurdle, we have developed a novel approach, designated Primer Approximation Multiplex PCR (PAMP), for enriching breakpoint sequences followed by genomic tiling array hybridization to locate the breakpoints. In a series of proof-of-concept experiments, we were able to identify cancer-derived CDKN2A genomic breakpoints when more than 99.9% of wild type genome was present in a model system. This design can be scaled up with bioinformatics support and can be applied to validate other candidate cancer-associated loci that are revealed by other more systemic but lower throughput assays.     

A novel data mining method to identify assay-specific signatures in functional genomic studies

Background:  

The highly dimensional data produced by functional genomic (FG) studies makes it difficult to visualize relationships between gene products and experimental conditions (i.e., assays). Although dimensionality reduction methods such as principal component analysis (PCA) have been very useful, their application to identify assay-specific signatures has been limited by the lack of appropriate methodologies. This article proposes a new and powerful PCA-based method for the identification of assay-specific gene signatures in FG studies.     

Biomarkers as prognostic factors in endometrial cancer

Endometrial cancer is the most common gynecologic malignancy in more developed countries. Approximately 75% of cases are diagnosed at an early stage with a tumor confined to the uterine corpus. Although most patients are cured by surgery alone, about 15-20% with no signs of locally advanced or metastatic disease at primary treatment recurs, with limited responsiveness to systemic therapy. The most common basis for determining the risk of recurrent disease has been classification of endometrial cancers into two subtypes. Type I, associated with a good prognosis and endometrioid histology and type II, associated with a poor prognosis and non-endometrioid histology. This review will focus primarily on the molecular biomarkers that have supported the dualistic model of endometrial carcinoma and help determine which patients would benefit from either adjuvant therapy or more aggressive primary treatment.     

The UBC-40 Urothelial Bladder Cancer cell line index: a genomic resource for functional studies

Background

Urothelial bladder cancer is a highly heterogeneous disease. Cancer cell lines are useful tools for its study. This is a comprehensive genomic characterization of 40 urothelial bladder carcinoma (UBC) cell lines including information on origin, mutation status of genes implicated in bladder cancer (FGFR3, PIK3CA, TP53, and RAS), copy number alterations assessed using high density SNP arrays, uniparental disomy (UPD) events, and gene expression.

Results

Based on gene mutation patterns and genomic changes we identify lines representative of the FGFR3-driven tumor pathway and of the TP53/RB tumor suppressor-driven pathway. High-density array copy number analysis identified significant focal gains (1q32, 5p13.1-12, 7q11, and 7q33) and losses (i.e. 6p22.1) in regions altered in tumors but not previously described as affected in bladder cell lines. We also identify new evidence for frequent regions of UPD, often coinciding with regions reported to be lost in tumors. Previously undescribed chromosome X losses found in UBC lines also point to potential tumor suppressor genes. Cell lines representative of the FGFR3-driven pathway showed a lower number of UPD events.

Conclusions

Overall, there is a predominance of more aggressive tumor subtypes among the cell lines. We provide a cell line classification that establishes their relatedness to the major molecularly-defined bladder tumor subtypes. The compiled information should serve as a useful reference to the bladder cancer research community and should help to select cell lines appropriate for the functional analysis of bladder cancer genes, for example those being identified through massive parallel sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1450-3) contains supplementary material, which is available to authorized users.     

Clinicodemographic aspect of resectable pancreatic cancer and prognostic factors for resectable cancer

ABSTRACT: BACKGROUND: Pancreatic adenocarcinoma (PCA) is one of the most lethal human malignancies, and radical surgery remains the cornerstone of treatment. After resection, the overall 5-year survival rate is only 10% to 29%. At the time of presentation, however, about 40% of patients generally have distant metastases and another 40% are usually diagnosed with locally advanced cancers. The remaining 20% of patients are indicated for surgery on the basis of the results of preoperative imaging studies; however, about half of these patients are found to be unsuitable for resection during surgical exploration. In the current study, we aimed to determine the clinicopathological characteristics that predict the resectability of PCA and to conduct a prognostic analysis of PCA after resection to identify favorable survival factors. METHODS: We retrospectively reviewed the medical files of 688 patients (422 men and 266 women) who had undergone surgery for histopathologically proven PCA in the Department of Surgery at Chang Gung Memorial Hospital in Taiwan from 1981 to 2006. We compared the clinical characteristics of patients who underwent resection and patients who did not undergo resection in order to identify the predictive factors for successful resectability of PCA, and we conducted prognostic analysis for PCA after resection. RESULTS: A carbohydrate antigen 19-9 (CA 19-9) level of 37 U/ml or greater and a tumor size of 3 cm or more independently predicted resectability of PCA. In terms of survival after resection, PCA patients with better nutritional status (measured as having an albumin level greater than 3.5 g/dl), radical resection, early tumor stage and better-differentiated tumors were associated with favorable survival. CONCLUSIONS: Besides traditional imaging studies, preoperative CA 19-9 levels and tumor size can also be used to determine the resectability of PCA. Better nutritional status, curative resection, early tumor stage and well-differentiated tumors predict the favorable prognosis of PCA patients after resection.     

Meta-analysis of several gene lists for distinct types of cancer: A simple way to reveal common prognostic markers

Background  

Although prognostic biomarkers specific for particular cancers have been discovered, microarray analysis of gene expression profiles, supported by integrative analysis algorithms, helps to identify common factors in molecular oncology. Similarities of Ordered Gene Lists (SOGL) is a recently proposed approach to meta-analysis suitable for identifying features shared by two data sets. Here we extend the idea of SOGL to the detection of significant prognostic marker genes from microarrays of multiple data sets. Three data sets for leukemia and the other six for different solid tumors are used to demonstrate our method, using established statistical techniques.