Background: Nuclear mitochondrial pseudogenes (numts) are a potential source of contamination during mitochondrial DNA PCR amplification. This possibility warrants careful experimental design and cautious interpretation of heteroplasmic results.

Studies of somatic mitochondrial DNA mutations have become an important aspect of cancer research because these mutations might have functional significance and/or serve as a biosensor for tumor detection. Here we report somatic mitochondrial DNA mutations from three specific tissue types (tumor, adjacent benign, and distant benign) recovered from 24 prostatectomy samples. Needle biopsy tissue from 12 individuals referred for prostate biopsy, yet histologically benign (symptomatic benign), were used as among individual control samples. We also sampled blood (germplasm tissue) from each patient to serve as within individual controls relative to the somatic tissues sampled (malignant, adjacent, and distant benign). Complete mitochondrial genome sequencing was attempted on each sample. In contrast to both control groups [within patient (blood) and among patient (symptomatic benign)], all of the tissue types recovered from the malignant group harbored significantly different mitochondrial DNA (mtDNA) mutations. We conclude that mitochondrial genome mutations are an early indicator of malignant transformation in prostate tissue. These mutations occur well before changes in tissue histo-pathology, indicative of prostate cancer, are evident to the pathologist.

We report the usefulness of a 3.4-kb mitochondrial genome deletion (3.4 mtdelta) for molecular definition of benign, malignant, and proximal to malignant (PTM) prostate needle biopsy specimens. The 3.4 mtdelta was identified through long-extension polymerase chain reaction (PCR) analysis of frozen prostate cancer samples. A quantitative PCR assay was developed to measure the levels of the 3.4 mtdelta in clinical samples. For normalization, amplifications of a nuclear target and total mitochondrial DNA were included. Cycle threshold data from these targets were used to calculate a score for each biopsy sample. In a pilot study of 38 benign, 29 malignant, and 41 PTM biopsy specimens, the difference between benign and malignant core biopsy specimens was well differentiated (P & .0001), with PTM indistinguishable from malignant samples (P = .833). Results of a larger study were identical. In comparison with histopathologic examination for benign and malignant samples, the sensitivity and specificity were 80% and 71%, respectively, and the area under a receiver operating characteristic (ROC) curve was 0.83 for the deletion. In a blinded external validation study, the sensitivity and specificity were 83% and 79%, and the area under the ROC curve was 0.87. The 3.4 mtdelta may be useful in defining malignant, benign, and PTM prostate tissues.

Several cancers are characterized by large-scale mtDNA deletions. We previously provided evidence that one of these deletions has potential utility in resolving false from true-negative prostate needle biopsies. This study was to assess the clinical value of this deletion in predicting re-biopsy outcomes. We used a quantitative polymerase chain reaction assay to measure the levels of the deletion in individual negative needle biopsies from 101 patients who had a repeat biopsy within a year with known outcomes. Using an empirically established cycle threshold (Ct) cutoff of 31, and the lowest Ct for each patient as diagnostic of prostate cancer, as well as the histopathologic diagnosis on second biopsy, we calculated the clinical performance of the deletion. The Ct cutoff at 31 gave a sensitivity and specificity of 84 and 54%, respectively, with the area under a receiver-operating characteristics curve of 0.749. The negative predictive value was 91%. The assay was able to predict the presence of a missed tumor in 17 out of 20 men a year before diagnosis. This ancillary test appears to identify men who do not require a repeat biopsy with a high degree of certainty. The results suggest that the majority of men with atypical small acinar proliferation have a concurrent missed tumor and therefore require close monitoring for early detection.

Optimal sample handling techniques for tissue preparation and storage, RNA extraction and quantification, and target gene detection are crucial for reliable gene expression analysis. Methods for measuring low-expressing genes, such as interferons, in human cervical samples are not described in the scientific literature. To detect interferon mRNA in human cervical samples we obtained normal and dysplastic frozen and formalin-fixed cervical biopsies from colposcopy. Histopathological diagnosis was performed by one pathologist. Cervical keratinocytes were isolated using laser capture microdissection. Immortalized keratinocytes transduced with or devoid of an HPV oncogene were used for initial method development. RNA from samples was extracted and integrity tested to compare tissue storage and extraction methods. The expression of five housekeeping genes was analyzed in cell lines and patient tissue to permit normalization between samples using quantitative real-time polymerase chain reaction. The usefulness of cDNA amplification was assessed for the detection of low-expressing interferon κ in cervical tissue. Here we report optimal tissue storage conditions, RNA extraction, sample normalization, and transcript amplification, as well as the sensitivity of quantitative real-time polymerase chain reaction and laser capture microdissection, for interferon κ detection in cervical tissue. Without these optimized techniques, interferon κ detection would be unattainable in cervical samples. KeywordsCervical keratinocytes; Frozen and formalin-fixed cervical tissue; Housekeeping genes; Interferons; Interferon κ; Laser capture microdissection; Quantitative real-time polymerase chain reaction Quantitative real-time polymerase chain reaction (qRT-PCR) 1 for the molecular analysis of disease is a powerful and widely used tool. Extraction of high-quality RNA for use in gene expression analysis techniques such as reverse transcription (RT), qRT-PCR, and cDNA microarrays is of great importance. Although obtaining high-quality RNA from cell lines is relatively straightforward, the complexity and heterogeneity of human tissue pose considerable challenges for linking gene expression patterns with disease state. Nonetheless,