The TP53 tumor suppressor gene is frequently mutated in human cancers. An analysis of five data platforms in 10,225 patient samples from 32 cancers reported by The Cancer Genome Atlas (TCGA) enables ...comprehensive assessment of p53 pathway involvement in these cancers. More than 91% of TP53-mutant cancers exhibit second allele loss by mutation, chromosomal deletion, or copy-neutral loss of heterozygosity. TP53 mutations are associated with enhanced chromosomal instability, including increased amplification of oncogenes and deep deletion of tumor suppressor genes. Tumors with TP53 mutations differ from their non-mutated counterparts in RNA, miRNA, and protein expression patterns, with mutant TP53 tumors displaying enhanced expression of cell cycle progression genes and proteins. A mutant TP53 RNA expression signature shows significant correlation with reduced survival in 11 cancer types. Thus, TP53 mutation has profound effects on tumor cell genomic structure, expression, and clinical outlook.
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•TP53 mutation effects analyzed by five data platforms in 32 cancers/10,225 patients•More than 91% of cancers with TP53 mutations show loss of both functional TP53 alleles•TP53 mutation affects genomic stability, global RNA, miRNA, and protein expression•Mutant p53 RNA expression signature helps prognostic predictions in 11 cancer types
Donehower et al. performed a comprehensive analysis of the effects of TP53 gene mutation in 32 cancer types and 10,225 patients from The Cancer Genome Atlas (TCGA). Data synthesized from five different analysis platforms show how mutant TP53 increases genomic instability and induces major pathway signaling changes in cancer cells.
Reverse-phase protein arrays (RPPA) represent a powerful functional proteomic approach to elucidate cancer-related molecular mechanisms and to develop novel cancer therapies. To facilitate ...community-based investigation of the large-scale protein expression data generated by this platform, we have developed a user-friendly, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA, http://tcpaportal.org), which contains two separate web applications. The first one focuses on RPPA data of patient tumors, which contains >8,000 samples of 32 cancer types from The Cancer Genome Atlas and other independent patient cohorts. The second application focuses on the RPPA data of cancer cell lines and contains >650 independent cell lines across 19 lineages. Many of these cell lines have publicly available, high-quality DNA, RNA, and drug screening data. TCPA provides various analytic and visualization modules to help cancer researchers explore these datasets and generate testable hypotheses in an effective and intuitive manner.
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Invasive bladder cancer, for which there have been few therapeutic advances in the past 20 years, is a significant medical problem associated with metastatic disease and frequent mortality. Although ...previous studies had identified many genetic alterations in invasive bladder cancer, recent genome-wide studies have provided a more comprehensive view. Here, we review those recent findings and suggest therapeutic strategies. Bladder cancer has a high mutation rate, exceeded only by lung cancer and melanoma. About 65% of all mutations are due to APOBEC-mediated mutagenesis. There is a high frequency of mutations and/or genomic amplification or deletion events that affect many of the canonical signaling pathways involved in cancer development: cell cycle, receptor tyrosine kinase, RAS, and PI-3-kinase/mTOR. In addition, mutations in chromatin-modifying genes are unusually frequent in comparison with other cancers, and mutation or amplification of transcription factors is also common. Expression clustering analyses organize bladder cancers into four principal groups, which can be characterized as luminal, immune undifferentiated, luminal immune, and basal. The four groups show markedly different expression patterns for urothelial differentiation (keratins and uroplakins) and immunity genes (CD274 and CTLA4), among others. These observations suggest numerous therapeutic opportunities, including kinase inhibitors and antibody therapies for genes in the canonical signaling pathways, histone deacetylase inhibitors and novel molecules for chromatin gene mutations, and immune therapies, which should be targeted to specific patients based on genomic profiling of their cancers.
Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumours. Therefore, direct study of the functional proteome has the potential to provide a wealth of ...information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects. Here we use reverse-phase protein arrays to analyse 3,467 patient samples from 11 TCGA 'Pan-Cancer' diseases, using 181 high-quality antibodies that target 128 total proteins and 53 post-translationally modified proteins. The resultant proteomic data are integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumour lineages. In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumour lineages. This integrative analysis, with an emphasis on pathways and potentially actionable proteins, provides a framework for determining the prognostic, predictive and therapeutic relevance of the functional proteome.
Reverse phase protein array (RPPA) technology introduced a miniaturized “antigen-down” or “dot-blot” immunoassay suitable for quantifying the relative, semi-quantitative or quantitative (if a ...well-accepted reference standard exists) abundance of total protein levels and post-translational modifications across a variety of biological samples including cultured cells, tissues, and body fluids. The recent evolution of RPPA combined with more sophisticated sample handling, optical detection, quality control, and better quality affinity reagents provides exquisite sensitivity and high sample throughput at a reasonable cost per sample. This facilitates large-scale multiplex analysis of multiple post-translational markers across samples from in vitro, preclinical, or clinical samples. The technical power of RPPA is stimulating the application and widespread adoption of RPPA methods within academic, clinical, and industrial research laboratories.
Advances in RPPA technology now offer scientists the opportunity to quantify protein analytes with high precision, sensitivity, throughput, and robustness. As a result, adopters of RPPA technology have recognized critical success factors for useful and maximum exploitation of RPPA technologies, including the following: •preservation and optimization of pre-analytical sample quality,•application of validated high-affinity and specific antibody (or other protein affinity) detection reagents,•dedicated informatics solutions to ensure accurate and robust quantification of protein analytes, and•quality-assured procedures and data analysis workflows compatible with application within regulated clinical environments.
In 2011, 2012, and 2013, the first three Global RPPA workshops were held in the United States, Europe, and Japan, respectively. These workshops provided an opportunity for RPPA laboratories, vendors, and users to share and discuss results, the latest technology platforms, best practices, and future challenges and opportunities. The outcomes of the workshops included a number of key opportunities to advance the RPPA field and provide added benefit to existing and future participants in the RPPA research community. The purpose of this report is to share and disseminate, as a community, current knowledge and future directions of the RPPA technology.
An integrated analysis of DNA, RNA and protein, so called proteogenomic studies, has the potential to greatly increase our understanding of both normal physiology and disease development. However, ...such studies are challenged by a lack of a systematic approach to credential individual samples resulting in the introduction of noise into the system that limits the ability to identify important biological signals. Indeed, a recent proteogenomic CPTAC study identified 26% of samples as unsatisfactory, resulting in a marked increase in cost and loss of information content. Based on a large-scale analysis of RNA-seq and proteomic data generated by reverse phase protein arrays (RPPA) and by mass spectrometry, we propose a protein-mRNA correlation-based (PMC) score as a robust metric to credential single samples for integrated proteogenomic studies. Samples with high PMC scores have significantly higher protein-mRNA correlation, total protein content and tumor purity. Our results highlight the importance of credentialing individual samples prior to proteogenomic analysis.
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