Alternative isoform discrimination by the next generation of expression profiling microarrays

A method for identifying discriminative isoform-specific peptides for clinical proteomics application

Background

Clinical proteomics application aims at solving a specific clinical problem within the context of a clinical study. It has been growing rapidly in the field of biomarker discovery, especially in the area of cancer diagnostics. Until recently, protein isoform has not been viewed as a new class of early diagnostic biomarkers for clinical proteomics. A protein isoform is one of different forms of the same protein. Different forms of a protein may be produced from single-nucleotide polymorphisms (SNPs), alternative splicing, or post-translational modifications (PTMs). Previous studies have shown that protein isoforms play critical roles in tumorigenesis, disease diagnosis, and prognosis. Identifying and characterizing protein isoforms are essential to the study of molecular mechanisms and early detection of complex diseases such as breast cancer.

However, there are limitations with traditional methods such as EST sequencing, Microarray profiling (exon array, Exon-exon junction array), mRNA next-generation sequencing used for protein isoform determination: 1) not in the protein level, 2) no connectivity about connection of nonadjacent exons, 3) no SNPs and PTMs, and 4) low reproducibility. Moreover, there exist the computational challenges of clinical proteomics studies: 1) low sensitivity of instruments, 2) high data noise, and 3) high variability and low repeatability, although recent advances in clinical proteomics technology, LC-MS/MS proteomics, have been used to identify candidate molecular biomarkers in diverse range of samples, including cells, tissues, serum/plasma, and other types of body fluids.

Results

Therefore, in the paper, we presented a peptidomics method for identifying cancer-related and isoform-specific peptide for clinical proteomics application from LC-MS/MS. First, we built a Peptidomic Database of Human Protein Isoforms, then created a peptidomics approach to perform large-scale screen of breast cancer-associated alternative splicing isoform markers in clinical proteomics, and lastly performed four kinds of validations: biological validation (explainable index), exon array, statistical validation of independent samples, and extensive pathway analysis.

Conclusions

Our results showed that alternative splicing isoform makers can act as independent markers of breast cancer and that the method for identifying cancer-specific protein isoform biomarkers from clinical proteomics application is an effective one for increasing the number of identified alternative splicing isoform markers in clinical proteomics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2907-8) contains supplementary material, which is available to authorized users.

Upregulation of RNA Processing Factors in Poorly Differentiated Lung Cancer Cells

Intratumoral heterogeneity in non–small cell lung cancer (NSCLC) has been appreciated at the histological and cellular levels, but the association of less differentiated pathology with poor clinical outcome is not understood at the molecular level. Gene expression profiling of intact human tumors fails to reveal the molecular nature of functionally distinct epithelial cell subpopulations, in particular the tumor cells that fuel tumor growth, metastasis, and disease relapse. We generated primary serum-free cultures of NSCLC and then exposed them to conditions known to promote differentiation: the air-liquid interface (ALI) and serum. The transcriptional network of the primary cultures was associated with stem cells, indicating a poorly differentiated state, and worse overall survival of NSCLC patients. Strikingly, the overexpression of RNA splicing and processing factors was a prominent feature of the poorly differentiated cells and was also observed in clinical datasets. A genome-wide analysis of splice isoform expression revealed many alternative splicing events that were specific to the differentiation state of the cells, including an unexpectedly high frequency of events on chromosome 19. The poorly differentiated cells exhibited alternative splicing in many genes associated with tumor progression, as exemplified by the preferential expression of the short isoform of telomeric repeat-binding factor 1 (TERF1), also known as Pin2. Our findings demonstrate the utility of the ALI method for probing the molecular mechanisms that underlie NSCLC pathogenesis and provide novel insight into posttranscriptional mechanisms in poorly differentiated lung cancer cells.

Novel alternative splicing isoform biomarkers identification from high-throughput plasma proteomics profiling of breast cancer

BACKGROUND

In the biopharmaceutical industry, biomarkers define molecular taxonomies of patients and diseases and serve as surrogate endpoints in early-phase drug trials. Molecular biomarkers can be much more sensitive than traditional lab tests. Discriminating disease biomarkers by traditional method such as DNA microarray has proved challenging. Alternative splicing isoform represents a new class of diagnostic biomarkers. Recent scientific evidence is demonstrating that the differentiation and quantification of individual alternative splicing isoforms could improve insights into disease diagnosis and management. Identifying and characterizing alternative splicing isoforms are essential to the study of molecular mechanisms and early detection of complex diseases such as breast cancer. However, there are limitations with traditional methods used for alternative splicing isoform determination such as transcriptome-level, low level of coverage and poor focus on alternative splicing.

RESULTS

Therefore, we presented a peptidomics approach to searching novel alternative splicing isoforms in clinical proteomics. Our results showed that the approach has significant potential in enabling discovery of new types of high-quality alternative splicing isoform biomarkers.

CONCLUSIONS

We developed a peptidomics approach for the proteomics community to analyze, identify, and characterize alternative splicing isoforms from MS-based proteomics experiments with more coverage and exclusive focus on alternative splicing. The approach can help generate novel hypotheses on molecular risk factors and molecular mechanisms of cancer in early stage, leading to identification of potentially highly specific alternative splicing isoform biomarkers for early detection of cancer.

Drug treatment of Alzheimer's disease patients leads to expression changes in peripheral blood cells

BACKGROUND

Increasing cholinergic activity has been the primary mechanism for treating dementia due to Alzheimer's disease. However, the effectiveness of cholinesterase inhibitors (ChEIs) is still widely debated. The identification of specific biomarkers capable of identifying patients more likely to respond to these treatments could potentially provide specific evidence to clearly address this controversy through patient stratification. The goal of this study was to determine the feasibility of discovering biomarkers specific for the treatment of Alzheimer's disease.

METHODS

Peripheral blood was collected from a cohort of patients treated with different ChEIs. Total RNA was isolated and profiled on the human Genome-Wide SpliceArray (GWSA) to test the feasibility of discriminating the different treatment subgroups of subjects based on the expression patterns generated from the Genome-Wide SpliceArray.

RESULTS

Specific expression differences were identified for the various treatment groups that lead to a clear separation between patients treated with ChEIs versus naïve patients when Principal Component Analysis was performed on probe sets selected for differential expression. In addition, specific probe sets were identified to be dependent on the inhibitor used among the treated patients.

CONCLUSIONS

Distinct separation between non-treated, galantamine, donepezil, and rivastigmine-treated patients was clearly identified based on small sets of expression probes. The ability to identify drug-specific treatment expression differences strengthens the potential for using peripheral gene signatures for the identification of individuals responding to drug treatment.

High resolution analysis of the human transcriptome: detection of extensive alternative splicing independent of transcriptional activity

BACKGROUND

Commercially available microarrays have been used in many settings to generate expression profiles for a variety of applications, including target selection for disease detection, classification, profiling for pharmacogenomic response to therapeutics, and potential disease staging. However, many commercially available microarray platforms fail to capture transcript diversity produced by alternative splicing, a major mechanism for driving proteomic diversity through transcript heterogeneity.

RESULTS

The human Genome-Wide SpliceArray(TM) (GWSA), a novel microarray platform, utilizes an existing probe design concept to monitor such transcript diversity on a genome scale. The human GWSA allows the detection of alternatively spliced events within the human genome through the use of exon body and exon junction probes to provide a direct measure of each transcript, through simple calculations derived from expression data. This report focuses on the performance and validation of the array when measured against standards recently published by the Microarray Quality Control (MAQC) Project. The array was shown to be highly quantitative, and displayed greater than 85% correlation with the HG-U133 Plus 2.0 array at the gene level while providing more extensive coverage of each gene. Almost 60% of splice events among genes demonstrating differential expression of greater than 3 fold also contained extensive splicing alterations. Importantly, almost 10% of splice events within the gene set displaying constant overall expression values had evidence of transcript diversity. Two examples illustrate the types of events identified: LIM domain 7 showed no differential expression at the gene level, but demonstrated deregulation of an exon skip event, while erythrocyte membrane protein band 4.1 -like 3 was differentially expressed and also displayed deregulation of a skipped exon isoform.

CONCLUSION

Significant changes were detected independent of transcriptional activity, indicating that the controls for transcript generation and transcription are distinct, and require novel tools in order to detect changes in specific transcript quantity. Our results demonstrate that the SpliceArray(TM) design will provide researchers with a robust platform to detect and quantify specific changes not only in overall gene expression, but also at the individual transcript level.

Gene expression predictors in breast cancer: current status, limitations and perspectives

Breast cancer is characterised by a wide heterogeneity regarding outcome and drug sensitivity. A better prediction of these two parameters at the individual level should improve patient management and therefore also improve both the quality of life and the overall survival of the patient. Several molecular predictors for prognosis (MammaPrint or Oncotype DX) and drug prediction (DLD30, SET index) have been generated using DNA-based arrays or RT-PCR, some of these being tested in phase III trials. Although they exhibit good metric performance and should improve the quality of care in the next decade, these predictors are considered suboptimal regarding the potential of the technology. New study design and arrays should generate more powerful second generation gene signatures.