Mapping genetic alterations in tumors with single nucleotide polymorphisms

Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as candidate drug targets

Background

Aberrant DNA methylation is often associated with cancers. Thus, screening genes with cancer-associated aberrant DNA methylation is a useful method to identify candidate cancer-causing genes. Aberrant DNA methylation is also genotype dependent. Thus, the selection of genes with genotype-specific aberrant DNA methylation in cancers is potentially important for tailor-made medicine. The selected genes are important candidate drug targets.

Results

The recently proposed principal component analysis based selection of genes with aberrant DNA methylation was applied to genotype and DNA methylation patterns in squamous cell carcinoma measured using single nucleotide polymorphism (SNP) arrays. SNPs that are frequently found in cancers are usually highly methylated, and the genes that were selected using this method were reported previously to be related to cancers. Thus, genes with genotype-specific DNA methylation patterns will be good therapeutic candidates. The tertiary structures of the proteins encoded by the selected genes were successfully inferred using two profile-based protein structure servers, FAMS and Phyre2. Candidate drugs for three of these proteins, tyrosine kinase receptor (ALK), EGLN3 protein, and NUAK family SNF1-like kinase 1 (NUAK1), were identified by ChooseLD.

Conclusions

We detected genes with genotype-specific DNA methylation in squamous cell carcinoma that are candidate drug targets. Using in silico drug discovery, we successfully identified several candidate drugs for the ALK, EGLN3 and NUAK1 genes that displayed genotype-specific DNA methylation.

Proteomics for identifying mechanisms and biomarkers of drug resistance in cancer

A major problem in chemotherapy of cancer patients is drug resistance as well as unpredictable response to treatment. During chemotherapy, multiple alterations of genetics and epigenetics that contribute to chemoresistance take place, eventually impacting on disease outcome. A more complex picture of the mechanisms of drug resistance is now emerging through application of high-throughput proteomics technology. We have entered an exciting time where proteomics are being applied to characterize the mechanisms of drug resistance, and to identify biomarkers for predicting response to chemotherapy, thereby leading to personalized therapeutic strategies of cancer patients. Comparative proteomics have identified a large number of differentially expressed proteins associated with chemoresistance. Although roles and mechanisms of such proteins in chemoresistance need to be further proved, at least some of them may be potential biomarkers for predicting chemotherapeutic response. Herein, we review the recent advancements on proteomic investigation of chemoresistance in human cancer, and emphasize putative biomarkers for predicting chemotherapeutic response and possible mechanisms of chemoresistance identified through proteomic approaches. Suggested avenues for future work are discussed.

Application of SELDI-TOF-MS to identify serum biomarkers for renal cell carcinoma

PURPOSE

Early diagnosis is critical for improving the outcome of patients with renal cell carcinoma (RCC). In this study, we applied a proteomic approach to identify serum biomarkers associated with different stages of renal tumor development.

MATERIALS AND METHODS

The protein expression profiles in patient serum samples were analyzed using surface enhanced laser desorption/ionization time-of-flight mass spectroscopy (SELDI-TOF-MS). The subjects included 65 patients with renal cell carcinomas, 34 with benign renal tumors, and 69 normal controls. A diagnostic decision tree was developed and validated based on the differentially expressed proteins between the serum of patients with small (<or=3 cm) RCC tumors and normal controls.

RESULTS

The numbers of proteins differentially expressed in the serum samples were 29 between the patients with RCC and normal controls, 18 between patients with RCC and benign renal tumors, and 35 between patients with small RCC tumors and normal controls, respectively. The diagnostic decision tree generated from the differentially expressed proteins between patients with small RCC tumors and normal controls proved efficient in the early diagnosis of renal cell carcinoma, with a sensitivity of 81.8% and specificity of 100%. The eukaryotic initiation factor 2B delta subunit (eIF2B-delta) was identified as the most highly up-regulated protein in the serum of patients with RCC.

CONCLUSIONS

SELDI-TOF-MS is a simple, sensitive and highly reproducible technique that can be used to identify serum biomarkers. The serum biomarkers identified by this study may facilitate early diagnosis of RCC and offer new targets for mechanistic understanding and clinical therapy of this disease.

DNA base mismatch detection with bulky rhodium intercalators: synthesis and applications

This protocol describes the syntheses and applications of two metallointercalators, Rh(bpy)2(chrysi)3+ and Rh(bpy)2(phzi)3+, that target single base mismatches in DNA. The complexes bind mismatched DNA sites specifically and, upon photoactivation, promote strand scission neighboring the mismatch. Owing to their high specificity and sequence context independence, targeting mismatches with these complexes offers an attractive alternative to current mismatch- and SNP-detection methodologies. This protocol also describes the synthesis of these complexes and their use in marking mismatched sites. Irradiation of 32P-labeled duplex DNA with either intercalator followed by denaturing PAGE allows the detection of mismatches in oligonucleotides. The protocol also outlines a method for efficient detection of single nucleotide polymorphisms (SNPs) in larger genes or plasmids. Pooled genes are denatured and re-annealed to form heteroduplexes; they are then incubated with either complex, irradiated and analyzed using capillary electrophoresis to probe for mismatches (SNP sites). The synthesis of the metallointercalators requires approximately 5-7 d. The mismatch- and SNP-detection experiments each require approximately 3 d.

The emerging importance of DNA mapping and other comprehensive screening techniques, as tools to identify new drug targets and as a means of (cancer) therapy personalisation

Every human being is genetically unique and this individuality is not only marked by morphologic and physical characteristics but also by an individual's response to a particular drug. Single nucleotide polymorphisms (SNPs) are largely responsible for one's individuality. A drug may be ineffective in one patient, whereas the exact same drug may cure another patient. Recent advances in DNA mapping and other screening technologies have provided researchers and drug developers with crucial information needed to create drugs that are specific for a given individual. In the future, physicians will be able to prescribe individualised drugs adjusted to, for example, activities of specific enzymatic pathways that would either be targeted by these drugs, or would be responsible for drug conversion or inactivation. Furthermore, the mapping of the human genome allows broader development and application of drugs that act on the level of gene transcription rather than as simple biochemical inhibitors or activators of certain enzymes. Such new approaches will maximise desired therapeutic results and may completely eliminate severe side effects. To illustrate the potential of genetic translational research, the authors discuss available analytical methodologies such as; gene arrays, flow cytometry-based screening for SNPs, proteomics, metabolomics, real-time PCR, and other methods capable of detecting both SNPs, as well as more profound changes in cell metabolism. Finally, the authors provide several examples that focus mostly on targeting protein-DNA interactions, but also other processes.

Getting more from digital SNP data

The digital SNP method has been proposed for identifying loss of heterozygosity (LOH) in tumour tissue and correlating it with patients' clinical characteristics. The method evaluates a tumour's allelic count at a single nucleotide polymorphism (SNP) for which the patient's normal tissue is heterozygous. The count is used to classify the tumour as positive or negative for LOH, using the sequential probability ratio test (SPRT). However, the SPRT was not developed for analysing digital SNP experiments. When applied to digital SNP data, the SPRT has several anomalies that can result in both loss of data and tumour misclassification. The anomalies are caused by discrepancies between the design of digital SNP experiments and the setting for which SPRT was developed. We propose an alternative classification scheme based on the false discovery rate, and show that it outperforms the SPRT when applied to Digital SNP data.

Allelic imbalances of chromosomes 8p and 18q and their roles in distant relapse of early stage, node-negative breast cancer

Introduction

Identification of breast cancer patients at risk for postoperative distant relapse is an important clinical issue. Existing pathological markers can predict disease recurrence only to a certain extent, and there is a need for more accurate predictors.

Methods

Using 'counting alleles', a novel experimental method, we determined allelic status of chromosomes 8p and 18q in a case-control study with 65 early stage, node negative, invasive ductal carcinomas (IDCs). The association between allelic imbalance (AI) of both chromosomal markers and distant relapses was examined.

Results

Eighty percent of tumors contained 8pAI and sixty-eight percent of tumors contained 18qAI. However, none of the tumor samples retained both chromosome 8p and 18q alleles. More importantly, tumors with 8pAI but not 18qAI were more likely to have distant relapse compared to tumors with 18qAI but not 8pAI.

Conclusion

Our finding suggests that differential allelic loss of chromosomes 8p and 18q may represent subtypes of early stage IDC with different tumor progression behaviors.

Genomic and proteomic technologies for individualisation and improvement of cancer treatment

The development of microarray-based technologies for characterising tumours, both at the genomic and proteomic levels, has had a significant impact on the field of oncology. Gene expression profiling of various human tumour tissues has led to the identification of expression patterns related to disease outcome and drug resistance, as well as to the discovery of new therapeutic targets and insights into disease pathogenesis. Protein microarray technologies, such as reverse-phase protein arrays, provide the unique opportunity to profile tissues and assess the activity of signalling pathways within isolated cell populations. This technology can be used to identify patients likely to benefit from specific treatment modalities and also to monitor therapeutic response in samples obtained during and after treatment. Routine application of genomic and proteomic microarray technologies in clinical practice will require significant efforts to standardise the techniques, controls and reference standards, and analytical tools used. Extensive, independent validation using large, statistically-powered datasets will also be necessary. Inclusion of concomitant genomic and proteomic-based molecular profiling techniques into clinical trial protocols will bring us closer to the reality of patient-tailored therapy.

Molecular Markers for Early Detection of Renal Carcinoma

Molecular markers for renal cell carcinoma could guide early detection of localized disease in defined populations at high risk for the disease or early disease recurrence after nephrectomy for renal cell carcinoma. In addition, sensitive and specific markers may provide surrogate end points for clinical trials of treatment and/or disease prevention. Powerful techniques of genomic and proteomic analysis of human renal carcinoma cell lines, tumor samples, and biological fluids, such as plasma and urine, obtained from patients with renal cell carcinoma, are likely to identify candidate markers. Careful selection among early candidate markers and further testing in independent patient populations are required for marker validation.

Surgical margin and Gleason score as predictors of postoperative recurrence in prostate cancer with or without chromosome 8p allelic imbalance

BACKGROUND

Identification of prostate cancer patients at risk for postoperative disease recurrence is an important clinical issue. Existing pathological markers can predict disease recurrence only to a certain extent, and there is a need for more accurate predictors.

METHODS

Using "counting alleles," a novel experimental method, we determined allelic status of chromosome 8p in 107 prostatectomy specimens. Statistical analyses examined the association between pathologic predictors (Gleason score, stage, surgical margin, etc.) and cancer recurrence in patients with and without 8p allelic imbalance (8p AI).

RESULTS

8p AI cancers were more likely to recur in the presence of a positive surgical margin, whereas recurrence of 8p retaining tumors was associated with the Gleason score, but not with the surgical margin.

CONCLUSIONS

Our findings suggest that chromosome 8p allelic status affects the predictive value of "traditional" markers of prostate cancer recurrence. If confirmed by larger studies, these results may have important clinical implications.