https://www.ibioinformatics.org/Oncomine.pdf

ONCOMINE: A Cancer Microarray Database and Integrated Data-Mining Platform

Pathology and genetics of tumours of the urinary system and male genital organs

Every year the American Cancer Society estimates the number of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival, using National Cancer Institute (NCI) incidence and National Center for Health Statistics (NCHS) mortality data. Incidence and death rates are age adjusted to the 1970 US standard population. It is estimated that 1,284,900 new cases of cancer will be diagnosed and 555,500 people will die from cancer in the United States in the year 2002. From 1992 to 1998, cancer death rates declined in males and females, while cancer incidence rates decreased among males and increased slightly among females. Most notably, African-American men showed the largest decline for both incidence and mortality. Nevertheless, African Americans still carry the highest burden of cancer with later-stage cancer diagnosis and poorer survival compared with whites. Despite the continued decline in cancer death rates, the total number of recorded cancer deaths in the United States continues to increase slightly due to the aging and expanding population.

https://onlinelibrary.wiley.com/doi/pdf/10.3322/canjclin.52.1.23

Cancer statistics, 2002.

http://disciplinas.stoa.usp.br/pluginfile.php/166516/mod_resource/content/1/Radiomics.pdf

Radiomics: the process and the challenges

Prostate cancer, a leading cause of cancer death, displays a broad range of clinical behavior from relatively indolent to aggressive metastatic disease. To explore potential molecular variation underlying this clinical heterogeneity, we profiled gene expression in 62 primary prostate tumors, as well as 41 normal prostate specimens and nine lymph node metastases, using cDNA microarrays containing ≈26,000 genes. Unsupervised hierarchical clustering readily distinguished tumors from normal samples, and further identified three subclasses of prostate tumors based on distinct patterns of gene expression. High-grade and advanced stage tumors, as well as tumors associated with recurrence, were disproportionately represented among two of the three subtypes, one of which also included most lymph node metastases. To further characterize the clinical relevance of tumor subtypes, we evaluated as surrogate markers two genes differentially expressed among tumor subgroups by using immunohistochemistry on tissue microarrays representing an independent set of 225 prostate tumors. Positive staining for MUC1, a gene highly expressed in the subgroups with “aggressive” clinicopathological features, was associated with an elevated risk of recurrence (P = 0.003), whereas strong staining for AZGP1, a gene highly expressed in the other subgroup, was associated with a decreased risk of recurrence (P = 0.0008). In multivariate analysis, MUC1 and AZGP1 staining were strong predictors of tumor recurrence independent of tumor grade, stage, and preoperative prostate-specific antigen levels. Our results suggest that prostate tumors can be usefully classified according to their gene expression patterns, and these tumor subtypes may provide a basis for improved prognostication and treatment stratification.

https://www.pnas.org/content/pnas/101/3/811.full.pdf

Gene expression profiling identifies clinically relevant subtypes of prostate cancer

Critical aspects of the biology and molecular basis for prostate malignancy remain poorly understood. To reveal fundamental differences between benign and malignant growth of prostate cells, we performed gene expression profiling of primary human prostate cancer and benign prostatic hyperplasia (BPH) using cDNA microarrays consisting of 6500 human genes. Frozen prostate specimens were processed to facilitate extraction of RNA from regions of tissue enriched in either benign or malignant epithelial cell growth within a given specimen. Gene expression in each of the 16 prostate cancer and nine BPH specimens was compared with a common reference to generate normalized measures for each gene across all of the samples. Using an analysis of complete pairwise comparisons of expression profiles among all of the samples, we observed clearly discernable patterns of overall gene expression that differentiated prostate cancer from BPH. Further analysis of the data identified 210 genes with statistically significant differences in expression between prostate cancer and BPH. These genes include many not recognized previously as differentially expressed in prostate cancer and BPH, including hepsin, which codes for a transmembrane serine protease. This study reveals for the first time that significant and widespread differences in gene expression patterns exist between benign and malignant growth of the prostate gland. Gene expression analysis of prostate tissues should help to disclose the molecular mechanisms underlying prostate malignant growth and identify molecular markers for diagnostic, prognostic, and therapeutic use.

https://cancerres.aacrjournals.org/content/canres/61/12/4683.full.pdf

Human Prostate Cancer and Benign Prostatic Hyperplasia Molecular Dissection by Gene Expression Profiling

There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different underlying classes, whereas those in the same cluster come from the same class. Stochastically, the underlying classes represent different random processes. The inference is that clusters represent a partition of the sample points according to which process they belong. This paper discusses a model-based clustering toolbox that evaluates cluster accuracy. Each random process is modeled as its mean plus independent noise, sample points are generated, the points are clustered, and the clustering error is the number of points clustered incorrectly according to the generating random processes. Various clustering algorithms are evaluated based on process variance and the key issue of the rate at which algorithmic performance improves with increasing numbers of experimental replications. The model means can be selected by hand to test t...

https://bioinformatics.cs.vt.edu/~easychair/DoughertyEtAl_JCB_2002.pdf

Inference from clustering with application to gene-expression microarrays.

For small samples, classifier design algorithms typically suffer from overfitting. Given a set of features, a classifier must be designed and its error estimated. For small samples, an error estimator may be unbiased but, owing to a large variance, often give very optimistic estimates. This paper proposes mitigating the small-sample problem by designing classifiers from a probability distribution resulting from spreading the mass of the sample points to make classification more difficult, while maintaining sample geometry. The algorithm is parameterized by the variance of the spreading distribution. By increasing the spread, the algorithm finds gene sets whose classification accuracy remains strong relative to greater spreading of the sample. The error gives a measure of the strength of the feature set as a function of the spread. The algorithm yields feature sets that can distinguish the two classes, not only for the sample data, but for distributions spread beyond the sample data. For linear classifiers...

Strong feature sets from small samples.

cDNA microarrays provide simultaneous expression mea- surements for thousands of genes that are the result of processing images to recover the average signal intensity from a spot composed of pixels covering the area upon which the cDNA detector has been put down. The accuracy of the signal measurement depends on using an appropriate algorithm to process the images. This includes deter- mining spot locations and processing the data in such a way as to take into account spot geometry, background noise, and various kinds of noise that degrade the signal. This paper presents a stochastic model for microarray images. There are over 20 model parameters, each governed by a probability distribution, that control the signal intensity, spot geometry, spot drift, background effects, and the many kinds of noise that affect microarray images owing to the manner in which they are formed. The model can be used to analyze the performance of image algorithms designed to measure the true signal intensity be- cause the ground truth (signal intensity) for each spot is known. The levels of foreground noise, background noise, and spot distortion can be set, and algorithms can be evaluated under varying conditions.

Simulation of cDNA microarrays via a parameterized random signal model.

BACKGROUND

One of the difficulties confronting genetic studies of prostate cancer is the complex and heterogeneous etiology. Given the high population frequency of lesions meeting the histological definition of prostate cancer, a significant portion of men with a positive family history may be diagnosed due to increased surveillance and associated higher likelihood of biopsy. Over diagnosis decreases power to detect genes that increase susceptibility to a clinically significant prostate cancer.



METHODS

We re-evaluated all 623 men with prostate cancer in our 188 hereditary prostate cancer families and identified a subset of 244 men with more aggressive disease based upon meeting at least one of the following clinical and/or pathologic criteria: tumor grade Gleason score ≥7, tumor stage T2c or higher, pretreatment PSA ≥20 ng/ml, rising PSA after treatment, evidence of metastasis, or death from prostate cancer.



RESULTS

Genome-wide screens were re-performed by defining men as affected only if they met the criteria for clinically significant disease. The new analyses identified stronger evidence for linkage in Xq27-28 and 22q, as well as several novel loci, including 3p and 9p.



CONCLUSIONS

Although, these results need to be confirmed in independent studies, our approach represents an important step to overcome the impact of over diagnosis in genetic studies of prostate cancer. Larger studies that incorporate this approach are needed. © 2005 Wiley-Liss, Inc.

Jeffrey M. Trent

Papers

Human Prostate Cancer and Benign Prostatic Hyperplasia Molecular Dissection by Gene Expression Profiling

Inference from clustering with application to gene-expression microarrays.

Strong feature sets from small samples.

Simulation of cDNA microarrays via a parameterized random signal model.

Genome‐wide screen for prostate cancer susceptibility genes in men with clinically significant disease