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Computational Analysis of Breast Cancer Signatures Uncovers Gene with Roles in Metastasis and Treatment Resistance

NEW YORK (GenomeWeb News) – By analyzing the genetic signatures derived from three large-scale breast cancer studies, a team of American researchers has identified a gene involved in both breast cancer metastasis and resistance to chemotherapy.
 
In a paper appearing online today in Cancer Cell, researchers used a computational algorithm to uncover genomic changes consistently linked to poor breast cancer outcomes. By assessing signatures from three large studies, the researchers identified a region on chromosome 8 that tends to be amplified in breast cancers with poor prognoses. Their subsequent research demonstrated that over-expression of a gene in this region — called MTDH — is linked to both metastasis and chemoresistance.
 
“Most breast cancer patients resist currently available therapeutic regimens and succumb to recurrent tumors that spread to distant vital organs, such as lung, bone, liver, and brain,” senior author Yibin Kang, a molecular biologist at Princeton University, said in a statement. “Resistance to chemotherapy and metastasis remain major challenges to curative therapy.”
 
Gene signatures have already proven useful clinically, Kang told GenomeWeb Daily News. For instance, Genomic Health’s Oncotype DX gauges the expression of 21 genes to help predict how some breast cancer patients will respond to chemotherapy. Agendia’s MammaPrint test, meanwhile, uses a 70-gene signature to help forecast breast cancer metastasis in certain patients.
 
But while such profiles may be useful for helping predict treatment outcomes, Kang said, they don’t necessarily provide much information about the underlying biology of cancer progression and metastasis.
 
“Although different poor-prognosis signatures have proven to be operationally interchangeable for class prediction purposes in the clinic,” the authors noted, “the lack of overlap between different poor-prognosis signatures has posed a major challenge for understanding the biological underpinnings of cancer progression and metastasis, thereby hindering the development of targeted therapeutics.”
 
For this study, Kang and his colleagues used computational biology to find recurrent events associated with poor prognoses in multiple breast cancer data sets. Specifically, the team’s “analysis of [copy number alterations] by expression data” algorithm helped them identify regions of gain or loss in the genome using expression data and looking at neighborhood effects, Kang explained.
 
The researchers analyzed two Dutch studies published in Nature and the New England Journal of Medicine in 2002, which came up with the 70-gene signature ultimately used in the MammaPrint test, along with a third study, published in the Lancet in 2005, that uncovered a 76-gene signature for predicting metastasis. Prior to applying the algorithm, the researchers removed any samples that overlapped between the two Dutch studies, Kang noted.
 
Using their algorithm, the researchers identified a region on chromosome 8 that was consistently amplified in all three data sets: the 8q22 region is amplified in about 30 percent of breast cancers and over-expressed in roughly 40 percent.
 
Because the region is relatively small and doesn’t contain too many genes, Kang explained, it was possible to look at the 15 or so individual genes in this region one-by-one. The researchers picked out six suspicious genes in the region based on their expression and biological functions: UQCRB, PTDSS1, TSPYL5, MTDH, LAPTM4β, and SDC2.
 
Of these, the researchers found that over-expression of MTDH — a gene coding for a cell surface protein called metadherin that appears to function in mammary tumor cell adhesion to lung endothelial cells in mice — promoted metastasis in breast cancer cell lines and mouse models.
 
Their subsequent research revealed that over-expression of the gene is linked to both metastasis and resistance to chemotherapy treatment. For instance, when the team knocked down MTDH in a human breast cancer cell line called LM2 using two different short hairpin RNAs, they found that MTDH knockdown decreased the metastasis of cells to the lungs by three to five times when the cell line was transplanted into mice.
 
The researchers also saw a decrease in metastasis to the bone and brain, though that decrease was less pronounced and not statistically significant. On the other hand, over-expression of MTDH in breast cancer cells bumped up cancer metastasis to bone and brain tissue.
 
The researchers also found evidence that amplification of the 8q22 region — and particularly MTDH — contributes to chemotherapy resistance in both breast cancer cell lines and mouse models.
 
MTDH is one of the 70 genes whose expression is assessed as part of the MammaPrint test. But given the gene’s dual roles in metastasis and chemoresistance, Kang explained, MTDH may eventually act not only as an independent marker for breast cancer, but also as a target for those developing new treatments.
 
Kang said he and his team are currently doing more functional analyses on metadherin. And, he added, they are also starting to talk with some companies about the possibility of developing therapeutics targeting MTDH.
 
“Genomic gain and over-expression of MTDH can become a powerful prognosis marker independent from other well-established markers for breast cancer,” the authors concluded. “Molecular targeting of MTDH may not only prevent seeding of breast cancer cells to the lung and other vital organs but also sensitize cells to chemotherapy, thereby stopping the deadly spread of breast cancer.”

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