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Thomas LaFramboise

Publications and source records attributed to Thomas LaFramboise.

6 recordsLinked to original sources

Sensitive mutation detection in heterogeneous cancer specimens by massively parallel picoliter reactor sequencing.

The sensitivity of conventional DNA sequencing in tumor biopsies is limited by stromal contamination and by genetic heterogeneity within the cancer. Here, we show that microreactor-based pyrosequencing can detect rare cancer-associated sequence variations by independent and parallel sampling of multiple representatives of a given DNA fragment. This technology can thereby facilitate accurate molecular diagnosis of heterogeneous cancer specimens and enable patient selection for targeted cancer therapies.

Base Sequence↗

Allele-specific amplification in cancer revealed by SNP array analysis.

Amplification, deletion, and loss of heterozygosity of genomic DNA are hallmarks of cancer. In recent years a variety of studies have emerged measuring total chromosomal copy number at increasingly high resolution. Similarly, loss-of-heterozygosity events have been finely mapped using high-throughput genotyping technologies. We have developed a probe-level allele-specific quantitation procedure that extracts both copy number and allelotype information from single nucleotide polymorphism (SNP) array data to arrive at allele-specific copy number across the genome. Our approach applies an expectation-maximization algorithm to a model derived from a novel classification of SNP array probes. This method is the first to our knowledge that is able to (a) determine the generalized genotype of aberrant samples at each SNP site (e.g., CCCCT at an amplified site), and (b) infer the copy number of each parental chromosome across the genome. With this method, we are able to determine not just where amplifications and deletions occur, but also the haplotype of the region being amplified or deleted. The merit of our model and general approach is demonstrated by very precise genotyping of normal samples, and our allele-specific copy number inferences are validated using PCR experiments. Applying our method to a collection of lung cancer samples, we are able to conclude that amplification is essentially monoallelic, as would be expected under the mechanisms currently believed responsible for gene amplification. This suggests that a specific parental chromosome may be targeted for amplification, whether because of germ line or somatic variation. An R software package containing the methods described in this paper is freely available at http://genome.dfci.harvard.edu/~tlaframb/PLASQ.

Alleles↗

A transforming MET mutation discovered in non-small cell lung cancer using microarray-based resequencing.

We have designed resequencing microarrays to test the performance of this platform when interrogating a large number of exons (164 total) from genes associated with cancer. To evaluate false positive and negative rates, dideoxy sequencing was done for 335,420 bases interrogated by the arrays. From the array data, calls could be made for approximately 97.5% of the bases, and false positive rates were very low with only a single mutation reported from the array dataset for which the corresponding dideoxy trace had a clean wildtype sequence. For the nucleotide positions where array calls were made, false negative rates were 1.41% for heterozygous mutations. All the homozygous mutations were detected, but 8.11% were erroneously reported as heterozygous changes from the reference sequence by the array analysis software. In addition, 20 non-small cell lung cancer (NSCLC) samples were analyzed using the arrays, and both somatic and germline mutations were found. The most interesting findings were two MET mutations that have recently been implemented in NSCLC. Large scale MALDI-TOF genotyping indicated that one of these mutations (T1010I) might represent a true cancer-causing genotype, whereas the other (N375S) appears to be a common germline polymorphism.

Carcinoma, Non-Small-Cell Lung↗

Homozygous deletions and chromosome amplifications in human lung carcinomas revealed by single nucleotide polymorphism array analysis.

Genome-wide copy number changes were analyzed in 70 primary human lung carcinoma specimens and 31 cell lines derived from human lung carcinomas, with high-density arrays representing approximately 115,000 single nucleotide polymorphism loci. In addition to previously characterized loci, two regions of homozygous deletion were found, one near the PTPRD locus on chromosome segment 9p23 in four samples representing both small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC) and the second on chromosome segment 3q25 in one sample each of NSCLC and SCLC. High-level amplifications were identified within chromosome segment 8q12-13 in two SCLC specimens, 12p11 in two NSCLC specimens and 22q11 in four NSCLC specimens. Systematic copy number analysis of tyrosine kinase genes identified high-level amplification of EGFR in three NSCLC specimens, FGFR1 in two specimens and ERBB2 and MET in one specimen each. EGFR amplification was shown to be independent of kinase domain mutational status.

Carcinoma, Non-Small-Cell Lung↗

Genomic representations using concatenates of Type IIB restriction endonuclease digestion fragments.

We have developed a method for genomic representation using Type IIB restriction endonucleases. Representation by concatenation of restriction digests, or RECORD, is an approach to sample the fragments generated by cleavage with these enzymes. Here, we show that the RECORD libraries may be used for digital karyotyping and for pathogen identification by computational subtraction.

Bacteria↗

A graph-theoretic approach to testing associations between disparate sources of functional genomics data.

MOTIVATION: The last few years have seen the advent of high-throughput technologies to analyze various properties of the transcriptome and proteome of several organisms. The congruency of these different data sources, or lack thereof, can shed light on the mechanisms that govern cellular function. A central challenge for bioinformatics research is to develop a unified framework for combining the multiple sources of functional genomics information and testing associations between them, thus obtaining a robust and integrated view of the underlying biology. RESULTS: We present a graph-theoretic approach to test the significance of the association between multiple disparate sources of functional genomics data by proposing two statistical tests, namely edge permutation and node label permutation tests. We demonstrate the use of the proposed tests by finding significant association between a Gene Ontology-derived predictome and data obtained from mRNA expression and phenotypic experiments for Saccharomyces cerevisiae. Moreover, we employ the graph-theoretic framework to recast a surprising discrepancy presented elsewhere between gene expression and knockout phenotype, using expression data from a different set of experiments. AVAILABILITY: An R software package, GraphAT, containing the data and statistical procedures is available from Bioconductor: http://www.bioconductor.org.

Algorithms↗