MSI Screening with Whole-Genome or -Exome Sequencing Data

Microsatellites are simple tandem repeats that are present at millions of loci in the human genome. Microsatellite instability (MSI) refers to DNA slippage events on microsatellites that occur frequently in cancer genomes when there is a defect in the DNA-mismatch repair system. These somatic mutations can result in inactivation of tumor-suppressor genes or disrupt other noncoding regulatory sequences, thereby playing a role in carcinogenesis. Here, we will discuss the ways in which high-throughput sequencing data can facilitate genome- or exome-wide discovery and more detailed investigation of MSI events in microsatellite-unstable cancer genomes. We will address the methodologic aspects of this approach and highlight insights from recent analyses of colorectal and endometrial cancer genomes from The Cancer Genome Atlas project. These include identification of novel MSI targets within and across tumor types and the relationship between the likelihood of MSI events to chromatin structure. Given the increasing popularity of exome and genome sequencing of cancer genomes, a comprehensive characterization of MSI may serve as a valuable marker of cancer evolution and aid in a search for therapeutic targets. Cancer Res; 74(22); 1–6. ©2014 AACR.

Gradual Dysregulation of the VHL-HIF Pathway in Oncogenesis

The classic model of tumor suppression implies that malignant transformation requires full “two-hit” inactivation of a tumor-suppressor gene. However, more recent work in mice has led to the proposal of a “continuum” model that involves more fluid concepts such as gene dosage-sensitivity and tissue specificity. Mutations in the tumor-suppressor gene von Hippel-Lindau (VHL) are associated with a complex spectrum of conditions. Homozygotes or compound heterozygotes for the R200W germline mutation in VHL have Chuvash polycythemia, whereas heterozygous carriers are free of disease. Individuals with classic, heterozygous VHL mutations have VHL disease and are at high risk of multiple tumors (e.g., CNS hemangioblastomas, pheochromocytoma, and renal cell carcinoma). We report here an atypical family bearing two VHL gene mutations in cis (R200W and R161Q), together with phenotypic analysis, structural modeling, functional, and transcriptomic studies of these mutants in comparison with classical mutants involved in the different VHL phenotypes. We demonstrate that the complex pattern of disease manifestations observed in VHL syndrome is perfectly correlated with a gradient of VHL protein (pVHL) dysfunction in hypoxia signaling pathways. Thus, by studying naturally occurring familial mutations, our work validates in humans the “continuum” model of tumor suppression. Cancer Res; 74(22); 1–11. ©2014 AACR.

Correction: Diffusion-Weighted Imaging in Cancer: Physical Foundations and Applications of Restriction Spectrum Imaging

Early-Stage Detection of Malignant Vocal Fold Lesions

About two thirds of laryngeal cancers originate at the vocal cords. Early-stage detection of malignant vocal fold alterations, including a discrimination of premalignant lesions, represents a major challenge in laryngology as precancerous vocal fold lesions and small carcinomas are difficult to distinguish by means of regular endoscopy only. We report a procedure to discriminate between malignant and precancerous lesions by measuring the characteristics of vocal fold dynamics by means of a computerized analysis of laryngeal high-speed videos. Ten patients with squamous cell T1a carcinoma, ten with precancerous lesions with hyperkeratosis, and ten subjects without laryngeal disease underwent high-speed laryngoscopy yielding 4,000 images per second. By means of wavelet-based phonovibrographic analysis, a set of three clinically meaningful vibratory measures was extracted from the videos comprising a total number of 15,000 video frames. Statistical analysis (ANOVA with post hoc two-sided t tests, P < 0.05) revealed that vocal fold dynamics is significantly affected in the presence of precancerous lesions and T1a carcinoma. On the basis of the three measures, a discriminating pattern was extracted using a support vector machine-learning algorithm performing an individual classification in respect to the different clinical groups. By applying a leave-one-out cross-validation strategy, we could show that the proposed measures discriminate with a very high performance between precancerous lesions and T1a carcinoma (sensitivity, 100%; specificity, 100%). Although a large-scale study will be necessary to confirm clinical significance, the set of vibratory measures derived in this study may be applicable to improve the accuracy and reliability of noninvasive diagnostics of vocal fold lesions. Cancer Res; 75(1); 1–9. ©2014 AACR.

A non-invasive procedure for early stage discrimination of malignant and precancerous vocal fold lesions based on laryngeal dynamics analysis

About two thirds of laryngeal cancers originate at the vocal cords. Early stage detection of malignant vocal fold alterations, including a discrimination of premalignant lesions, represents a major challenge in laryngology as precancerous vocal fold lesions and small carcinomas are difficult to distinguish by means of regular endoscopy only. We report a procedure to discriminate between malignant and precancerous lesions by measuring the characteristics of vocal fold dynamics by means of a computerized analysis of laryngeal high-speed videos. Ten patients with squamous cell T1a carcinoma, ten with precancerous lesions with hyperkeratosis, and ten subjects without laryngeal disease underwent high-speed laryngoscopy yielding 4,000 images per second. By means of wavelet-based phonovibrographic analysis, a set of three clinically meaningful vibratory measures were extracted from the videos comprising a total number of 15,000 video frames. Statistical analysis (ANOVA with post-hoc two-sided t-tests, P <0.05) revealed that vocal fold dynamics is significantly affected in presence of precancerous lesions and T1a carcinoma. On the basis of the three measures a discriminating pattern was extracted using a support vector machine-learning algorithm performing an individual classification in respect to the different clinical groups. By applying a leave-one-out cross-validation strategy, we could show that the proposed measures discriminate with a very high performance between precancerous lesions and T1a carcinoma (sensitivity: 100%, specificity: 100%). Although a large-scale study will be necessary to confirm clinical significance, the set of vibratory measures derived in this study may be applicable to improve the accuracy and reliability of non-invasive diagnostics of vocal fold lesions.