Biomedical subjects

Philip M Long

Publications and source records attributed to Philip M Long.

3 recordsLinked to original sources

PubMed2003

Breast cancer classification and prognosis based on gene expression profiles from a population-based study.

Comprehensive gene expression patterns generated from cDNA microarrays were correlated with detailed clinico-pathological characteristics and clinical outcome in an unselected group of 99 node-negative and node-positive breast cancer patients. Gene expression patterns were found to be strongly associated with estrogen receptor (ER) status and moderately associated with grade, but not associated with menopausal status, nodal status, or tumor size. Hierarchical cluster analysis segregated the tumors into two main groups based on their ER status, which correlated well with basal and luminal characteristics. Cox proportional hazards regression analysis identified 16 genes that were significantly associated with relapse-free survival at a stringent significance level of 0.001 to account for multiple comparisons. Of 231 genes previously reported by others [van't Veer, L. J., et al. (2002) Nature 415, 530-536] as being associated with survival, 93 probe elements overlapped with the set of 7,650 probe elements represented on the arrays used in this study. Hierarchical cluster analysis based on the set of 93 probe elements segregated our population into two distinct subgroups with different relapse-free survival (P < 0.03). The number of these 93 probe elements showing significant univariate association with relapse-free survival (P < 0.05) in the present study was 14, representing 11 unique genes. Genes involved in cell cycle, DNA replication, and chromosomal stability were consistently elevated in the various poor prognostic groups. In addition, glutathione S-transferase M3 emerged as an important survival marker in both studies. When taken together with other array studies, our results highlight the consistent biological and clinical associations with gene expression profiles.

Breast Neoplasms↗

PubMed2003

Comparative full-length genome sequence analysis of 14 SARS coronavirus isolates and common mutations associated with putative origins of infection.

BACKGROUND: The cause of severe acute respiratory syndrome (SARS) has been identified as a new coronavirus. Whole genome sequence analysis of various isolates might provide an indication of potential strain differences of this new virus. Moreover, mutation analysis will help to develop effective vaccines. METHODS: We sequenced the entire SARS viral genome of cultured isolates from the index case (SIN2500) presenting in Singapore, from three primary contacts (SIN2774, SIN2748, and SIN2677), and one secondary contact (SIN2679). These sequences were compared with the isolates from Canada (TOR2), Hong Kong (CUHK-W1 and HKU39849), Hanoi (URBANI), Guangzhou (GZ01), and Beijing (BJ01, BJ02, BJ03, BJ04). FINDINGS: We identified 129 sequence variations among the 14 isolates, with 16 recurrent variant sequences. Common variant sequences at four loci define two distinct genotypes of the SARS virus. One genotype was linked with infections originating in Hotel M in Hong Kong, the second contained isolates from Hong Kong, Guangzhou, and Beijing with no association with Hotel M (p<0.0001). Moreover, other common sequence variants further distinguished the geographical origins of the isolates, especially between Singapore and Beijing. INTERPRETATION: Despite the recent onset of the SARS epidemic, genetic signatures are emerging that partition the worldwide SARS viral isolates into groups on the basis of contact source history and geography. These signatures can be used to trace sources of infection. In addition, a common variant associated with a non-conservative aminoacid change in the S1 region of the spike protein, suggests that immunological pressures might be starting to influence the evolution of the SARS virus in human populations.

Amino Acid Sequence↗

PubMed2002

Optimal gene expression analysis by microarrays.

DNA microarrays make possible the rapid and comprehensive assessment of the transcriptional activity of a cell, and as such have proven valuable in assessing the molecular contributors to biological processes and in the classification of human cancers. The major challenge in using this technology is the analysis of its massive data output, which requires computational means for interpretation and a heightened need for quality data. The optimal analysis requires an accounting and control of the many sources of variance within the system, an understanding of the limitations of the statistical approaches, and the ability to make sense of the results through intelligent database interrogation.

Algorithms↗