Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.

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Gene expression profiling predicts clinical outcome of breast cancer

Since the publication of the American Association for the Study of Liver Diseases (AASLD) practice guidelines on the management of hepatocellular carcinoma (HCC) in 2005, new information has emerged that requires that the guidelines be updated. The full version of the new guidelines is available on the AASLD Web site at http://www.aasld.org/practiceguidelines/ Documents/Bookmarked%20Practice%20Guidelines/ HCCUpdate2010.pdf. Here, we briefly describe only new or changed recommendations.

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AASLD PRACTICE GUIDELINE Management of Hepatocellular Carcinoma: An Update

Background A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy. Methods Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node–negative disease, and 144 had lymph-node–positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses. Results Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (±SE) overall 10-year survival rates were 54.6±4.4 percent and 94.5±2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6±4.5 percent in the group with a...

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A Gene-Expression Signature as a Predictor of Survival in Breast Cancer

Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

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Survey of clustering algorithms

https://aasldpubs.onlinelibrary.wiley.com/doi/pdf/10.1002/hep.20933

Management of hepatocellular carcinoma.

Background
Urothelial carcinoma of the bladder (UC) is a common malignancy. Although extensive transcriptome analysis has provided insights into the gene expression patterns of this tumor type, the mechanistic underpinnings of differential methylation remain poorly understood. Multi-level genomic data may be used to profile the regulatory potential and landscape of differential methylation in cancer and gain understanding of the processes underlying epigenetic and phenotypic characteristics of tumors.

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Integrative epigenomic analysis of differential DNA methylation in urothelial carcinoma

Purpose Cigarette smoking is the major pathogenic factor for lung cancer. The precise mechanisms of tobacco-related carcinogenesis, and its effect on the genomic and transcriptional landscape in lung cancer are not fully understood. Experimental Design 1398 (277 never-smokers, 1121 smokers) genomic and 1449 (370 never-smokers, 1079 smokers) transcriptional profiles were assembled from public lung adenocarcinoma cohorts, including matched next-generation DNA-sequencing data (n=423). Unsupervised and supervised methods were used to identify smoking-related copy number alterations (CNAs), predictors of smoking status, and molecular subgroups. Results Genomic meta-analyses showed that never-smokers and smokers harbored a similar frequency of total CNAs, although, specific regions (5q, 8q, 16p, 19p, and 22q) displayed a 20-30% frequency difference between the two groups. Importantly, supervised classification analyses based on CNAs or gene expression could not accurately predict smoking status (balanced accuracies ~60-80%). However, unsupervised multicohort transcriptional profiling stratified adenocarcinomas into distinct molecular subgroups with specific patterns of CNAs, oncogenic mutations, and mutation transversion frequencies that were independent of the smoking status. One subgroup included ~55-90% of never-smokers and ~20-40% of smokers (both current and former) with molecular and clinical features of a less aggressive and smoking-unrelated disease. Given the considerable intra-group heterogeneity in smoking-defined subgroups, especially among former-smokers, our results emphasize the clinical importance of accurate molecular characterization of lung adenocarcinoma. Conclusions The landscape of smoking-related CNAs and transcriptional alterations in adenocarcinomas is complex, heterogeneous, and with moderate differences. Our results support a molecularly distinct less aggressive adenocarcinoma entity, arising in never-smokers and a subset of smokers.

https://clincancerres.aacrjournals.org/content/clincanres/20/18/4912.full.pdf

Genomic and transcriptional alterations in lung adenocarcinoma in relation to smoking history.

Introduction Human breast tumors are heterogeneous and consist of phenotypically diverse cells. Breast cancer cells with a CD44+/CD24- phenotype have been suggested to have tumor-initiating properties with stem cell-like and invasive features, although it is unclear whether their presence within a tumor has clinical implications. There is also a large heterogeneity between tumors, illustrated by reproducible stratification into various subtypes based on gene expression profiles or histopathological features. We have explored the prevalence of cells with different CD44/CD24 phenotypes within breast cancer subtypes.

The CD44 + /CD24 - phenotype is enriched in basal-like breast

Gene expression signatures consisting of tens to hundreds of genes have been found to be informative for different biological states. Recently, many computational methods have been proposed for biological interpretation of such signatures. However, there is a lack of methods for identifying cell signaling pathways whose deregulation results in an observed expression signature. We present a strategy for identifying such signaling pathways and evaluate the strategy using six human and mouse gene expression signatures.

/pdf/revealing-signaling-pathway-deregulation-by-using-gene-3y1k2wf11p.pdf

Revealing signaling pathway deregulation by using gene expression signatures and regulatory motif analysis

Purpose Primary lung adenocarcinoma remains a deadly disease. Gene expression phenotypes (GEPs) in adenocarcinoma have potential to provide clinically relevant disease stratification for improved prognosis and treatment prediction, given appropriate clinical and methodological validation. Experimental Design 2395 transcriptional adenocarcinoma profiles were assembled from 17 public cohorts and classified by a nearest centroid GEP classifier into three subtypes: terminal respiratory unit (TRU), proximal-proliferative, and proximal-inflammatory, and additionally scored by five transcriptional metagenes representing different biological processes, including proliferation. Prognostic and chemotherapy predictive associations of the subtypes were analyzed by univariate and multivariate analysis using overall survival or distant metastasis-free survival as endpoints. Results Overall, GEPs were associated with patient outcome in both univariate and multivariate analyses, although not in all individual cohorts. The prognostically relevant division was between TRU and non-TRU classified cases, with expression of proliferation-associated genes as a key prognostic component. In contrast, GEP classification was not predictive of adjuvant chemotherapy response. GEP classification showed stability to random perturbations of genes or samples and alterations to classification procedures (typically 20% of cases switching subtype) was observed when removing larger or entire fractions of a single subtype, due to gene-centering shifts not addressable by the classifier. Conclusions In a large-scale evaluation we show that GEPs add prognostic value to standard clinicopathological variables in lung adenocarcinoma. Subject to classifier refinement and confirmation in prospective cohorts, GEPs have potential to impact the prognostication of adenocarcinoma patients through a molecularly driven disease stratification.

https://clincancerres.aacrjournals.org/content/clincanres/22/1/218.full.pdf

Markus Ringnér

Papers

Integrative epigenomic analysis of differential DNA methylation in urothelial carcinoma

Genomic and transcriptional alterations in lung adenocarcinoma in relation to smoking history.

The CD44 + /CD24 - phenotype is enriched in basal-like breast

Revealing signaling pathway deregulation by using gene expression signatures and regulatory motif analysis

Prognostic and Chemotherapy Predictive Value of Gene-Expression Phenotypes in Primary Lung Adenocarcinoma