Genetic Testing as Simple as 1-2-3

VBCC - June 2010, Volume 1, No 2 - IMPAKT Breast Cancer Conference

Brussels—A simple genetic test using only 3 genes is among the most effective means of classifying breast cancer into the subtypes that indicate pa tients’ different prognoses and response or resistance to cancer therapies, say researchers, and this finding could allow greater use of personalized treatments in breast cancer.

Breast cancer is not a single, biologically homogeneous disease but rather is composed of several molecular subtypes, each of which is characterized by distinct gene expression profiles. Several research groups have already developed a range of different genomic “fingerprints” they use to assign breast cancers into different subtypes, but questions have been raised about the reliability of these classifications.

Benjamin Haibe-Kains, PhDLead author Benjamin Haibe-Kains, PhD, a research fellow in the department of Biostatistics at the Dana-Farber Cancer Institute, described the statistical analysis comparing breast cancer subtypes, which analyzed 32 publicly available gene expression datasets, including more than 4600 breast cancer patients and 6 different classification models. He presented these findings at the IMPAKT Breast Cancer Conference.

The research team compared 2 types of classification models— the single sample predictor (SSP) and subtype classification models (SCM) —each of which analyzes different genes to assign breast cancer subtypes.

In a press release, Dr Haibe-Kains described the research parameters: “We studied these models in terms of concordance and prognostic value and, for the first time, we estimated their robustness: that is, their capacity to assign the same tumors to the same molecular subtypes whatever the gene expression data used to fit this model.

“Generally speaking, we found that SCMs yielded stronger concordance than SSPs,” Dr Haibe-Kains said, and his team observed that SCMGENE, an SCM that used only 3 genes—ESR1, ERBB2, and AURKA—was significantly more robust than SSPs.

“The robustness of SCMs,” he concluded, “makes them promising candidates for an implementation into the clinic especially in the simplest form—that is, a model using only 3 genes.” There is an urgent need for such a tool “to provide clinicians with guidance for classifying breast cancer molecular subtypes, which could… aid in making therapeutic decisions.”

This work has not yet been submitted to a peer-reveiwed journal.

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