High-throughput mutational analysis of the human cancer genome

Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification

Non-negative Matrix Factorization (NMF) is a dimensionality reduction approach for learning a parts-based and linear representation of non-negative data. It has attracted more attention because of that. In practice, NMF not only neglects the manifold structure of data samples, but also overlooks the priori label information of different classes. In this paper, a novel matrix decomposition method called Hyper-graph regularized Constrained Non-negative Matrix Factorization (HCNMF) is proposed for selecting differentially expressed genes and tumor sample classification. The advantage of hyper-graph learning is to capture local spatial information in high dimensional data. This method incorporates a hyper-graph regularization constraint to consider the higher order data sample relationships. The application of hyper-graph theory can effectively find pathogenic genes in cancer datasets. Besides, the label information is further incorporated in the objective function to improve the discriminative ability of the decomposition matrix. Supervised learning with label information greatly improves the classification effect. We also provide the iterative update rules and convergence proofs for the optimization problems of HCNMF. Experiments under The Cancer Genome Atlas (TCGA) datasets confirm the superiority of HCNMF algorithm compared with other representative algorithms through a set of evaluations.

Analysis of circulating tumor DNA in breast cancer as a diagnostic and prognostic biomarker

Despite all the advances in diagnosis and treatment of breast cancer, a large number of patients suffer from late diagnosis or recurrence of their disease. Current available imaging modalities do not reveal micrometastasis and tumor biopsy is an invasive method to detect early stage or recurrent cancer, signifying the need for an inexpensive, non-invasive diagnostic modality. Cell-free tumor DNA (ctDNA) has been tried for early detection and targeted therapy of breast cancer, but its diagnostic and prognostic utility is still under investigation. This review summarizes the existing evidence on the use of ctDNA specifically in breast cancer, including detection methods, diagnostic accuracy, role in genetics and epigenetics evaluation of the tumor, and comparison with other biomarkers. Current evidence suggests that increasing levels of ctDNA in breast cancer can be of significant diagnostic value for early detection of breast cancer although the sensitivity and specificity of the methods is still suboptimal. Additionally, ctDNA allows for characterizing the tumor in a non-invasive way and monitor the response to therapy, although discordance of ctDNA results with direct biopsy (i.e. due to tumor heterogeneity) is still considered a notable limitation.

Challenges for the implementation of high-throughput testing and liquid biopsies in personalized medicine cancer trials

During recent decades, major advances in the comprehension of biology and in biotechnologies have paved the way for what is commonly named personalized medicine. For cancer therapy, personalized medicine represents a paradigm shift in which patient treatment is based on biology in addition to histology and tumor location. Here, we report the major personalized medicine trials in oncology that are either based on molecular alterations from tumor tissue or from circulating blood markers. We next review important challenges facing the implementation of personalized medicine in daily clinical practice, including tumor heterogeneity, reliability of high-throughput technologies, the key role of bioinformatics and the assessment of biomarkers and synthetic models, in order to use big data in actual cancer biology.

Protein phosphorylation profiling using an in situ proximity ligation assay: phosphorylation of AURKA-elicited EGFR-Thr654 and EGFR-Ser1046 in lung cancer cells

The epidermal growth factor receptor (EGFR), which is up-regulated in lung cancer, involves the activation of mitogenic signals and triggers multiple signaling cascades. To dissect these EGFR cascades, we used 14 different phospho-EGFR antibodies to quantify protein phosphorylation using an in situ proximity ligation assay (in situ PLA). Phosphorylation at EGFR-Thr654 and -Ser1046 was EGF-dependent in the wild-type (WT) receptor but EGF-independent in a cell line carrying the EGFR-L858R mutation. Using a ProtoAarray™ containing ∼5000 recombinant proteins on the protein chip, we found that AURKA interacted with the EGFR-L861Q mutant. Moreover, overexpression of EGFR could form a complex with AURKA, and the inhibitors of AURKA and EGFR decreased EGFR-Thr654 and -Ser1046 phosphorylation. Immunohistochemical staining of stage I lung adenocarcinoma tissues demonstrated a positive correlation between AURKA expression and phosphorylation of EGFR at Thr654 and Ser1046 in EGFR-mutant specimens, but not in EGFR-WT specimens. The interplay between EGFR and AURKA provides an explanation for the difference in EGF dependency between EGFR-WT and EGFR-mutant cells and may provide a new therapeutic strategy for lung cancer patients carrying EGFR mutations.

Identifying mutated proteins secreted by colon cancer cell lines using mass spectrometry

Secreted proteins encoded by mutated genes (mutant proteins) are a particularly rich source of biomarkers being not only components of the cancer secretome but also actually implicated in tumorigenesis. One of the challenges of proteomics-driven biomarker discovery research is that the bulk of secreted mutant proteins cannot be identified directly and quantified by mass spectrometry due to the lack of mutated peptide information in extant proteomics databases. Here we identify, using an integrated genomics and proteomics strategy (referred to iMASp - identification of Mutated And Secreted proteins), 112 putative mutated tryptic peptides (corresponding to 57 proteins) in the collective secretomes derived from a panel of 18 human colorectal cancer (CRC) cell lines. Central to this iMASp was the creation of Human Protein Mutant Database (HPMD), against which experimentally-derived secretome peptide spectra were searched. Eight of the identified mutated tryptic peptides were confirmed by RT-PCR and cDNA sequencing of RNA extracted from those CRC cells from which the mutation was identified by mass spectrometry. The iMASp technology promises to improve the link between proteomics and genomic mutation data thereby providing an effective tool for targeting tryptic peptides with mutated amino acids as potential cancer biomarker candidates. This article is part of a Special Issue entitled: Integrated omics.

Hereditary breast cancer: from bench to bedside

PURPOSE OF REVIEW

The proportion of breast cancers directly attributable to determinant hereditary factors is estimated to be 5-10%. A number of recent findings with regard to hereditary breast cancer should affect the criteria and scope of routine genetic testing and, soon, breast cancer therapy.

RECENT FINDINGS

The number of genes causing genetic cancer has expanded, mostly with genes that encode proteins that function in the BRCA1/2 pathways. The risk level associated with some genes is still under investigation, but is high for specific mutations. Some mutant alleles occur frequently, some are rare. High-throughput technologies will progressively allow investigating all genes involved in genetic (breast) cancer risks in all individuals for whom this information could be relevant. This and the emerging novel treatment options specific for cancers in mutation carriers will oblige us to progressively drop all currently used selection criteria such as familial phenotype for genomic testing. A major challenge remains the effective penetration of this knowledge in the professional and lay community, the broad application and financing of this high-throughput technology, and the identification of as yet unknown breast cancer predisposition genes.

SUMMARY

The assessment of breast cancer predisposition genes, previously only an optional predictive genetic test, is growing in importance as it also becomes a therapeutic predictive test.

Focus on ERBB2

The ERBB2 (HER2) gene codes for a tyrosine kinase receptor that activates pathways involved in cell division, differentiation and apoptosis. Gene amplification, and as a result protein overexpression, are commonly seen in breast tumors and correlate with poor prognosis. The overexpressed protein is the target of trastuzumab, a monoclonal antibody routinely used in clinical practice. A new tyrosine kinase inhibitor, lapatinib, is already an alternative in women progressing despite treatment with trastuzumab. Using comprehensive tagging approaches, highly-powered association studies concluded that ERBB2 was unlikely to be a breast cancer predisposition gene. ERBB2 pharmacogenomics are of little relevance at present, since we have no knowledge of polymorphisms in the gene that could affect the binding, efficacy or tolerability of trastuzumab or lapatinib. There is minor contribution from hepatic cytochrome CYP2C19 to the metabolism of lapatinib, whereas in vitro studies have shown the drug to be a substrate for the transporter P-glycoprotein. If, and how, the pharmacokinetics of lapatinib would be altered in individuals carrying polymorphisms in CYP2C19 or ABCB1 – the gene that codes for the P-glycoprotein – is yet to be determined.

Nuclear epidermal growth factor receptor (EGFR) interacts with signal transducer and activator of transcription 5 (STAT5) in activating Aurora-A gene expression

Loss of the maintenance of genetic material is a critical step leading to tumorigenesis. It was reported that overexpression of Aurora-A and the constitutive activation of the epidermal growth factor (EGF) receptor (EGFR) are implicated in chromosome instability. In this study, we examined that when cells treated with EGF result in centrosome amplification and microtubule disorder, which are critical for genetic instability. Interestingly, the expression of Aurora-A was also increased by EGF stimulus. An immunofluorescence assay indicated that EGF can induce the nuclear translocation of EGFR. Chromatin immunoprecipitation (ChIP) and re-ChIP assays showed significant EGF-induced recruitment of nuclear EGFR and signal transducer and activator of transcription 5 (STAT5) to the Aurora-A promoter. A co-immunoprecipitation assay further demonstrated that EGF induces nuclear interaction between EGFR and STAT5. A small interfering (si)RNA knockdown assay also showed that EGFR and STAT5 are indeed involved in EGF-increased Aurora-A gene expression. Altogether, this study proposes that the nuclear EGFR associates with STAT5 to bind and increase Aurora-A gene expression, which ultimately may lead to chromosome instability and tumorigenesis. The results also provide a novel linkage between the EGFR signaling pathway and overexpression of Aurora-A in tumorigenesis and chromosome instability.

Aspects of coverage in medical DNA sequencing

Background

DNA sequencing is now emerging as an important component in biomedical studies of diseases like cancer. Short-read, highly parallel sequencing instruments are expected to be used heavily for such projects, but many design specifications have yet to be conclusively established. Perhaps the most fundamental of these is the redundancy required to detect sequence variations, which bears directly upon genomic coverage and the consequent resolving power for discerning somatic mutations.

Results

We address the medical sequencing coverage problem via an extension of the standard mathematical theory of haploid coverage. The expected diploid multi-fold coverage, as well as its generalization for aneuploidy are derived and these expressions can be readily evaluated for any project. The resulting theory is used as a scaling law to calibrate performance to that of standard BAC sequencing at 8× to 10× redundancy, i.e. for expected coverages that exceed 99% of the unique sequence. A differential strategy is formalized for tumor/normal studies wherein tumor samples are sequenced more deeply than normal ones. In particular, both tumor alleles should be detected at least twice, while both normal alleles are detected at least once. Our theory predicts these requirements can be met for tumor and normal redundancies of approximately 26× and 21×, respectively. We explain why these values do not differ by a factor of 2, as might intuitively be expected. Future technology developments should prompt even deeper sequencing of tumors, but the 21× value for normal samples is essentially a constant.

Conclusion

Given the assumptions of standard coverage theory, our model gives pragmatic estimates for required redundancy. The differential strategy should be an efficient means of identifying potential somatic mutations for further study.