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Record W4249005193 · doi:10.4161/cbt.4.9.2071

TRPS-1, a DNA-Binding Protein and Potential Tumor Antigen, is Overexpressed in Breast Cancer

2005· article· en· W4249005193 on OpenAlexaboutno aff

Bibliographic record

VenueCancer Biology & Therapy · 2005
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerCancerCA 15-3CA15-3Cancer researchGeneOncologyMedicineAntigenDuctal carcinomaInternal medicineBiologyImmunologyGenetics

Abstract

fetched live from OpenAlex

AbstractA gene that appears to help regulate normal embryonic development is found at high levels in virtually all forms of breast cancer, according to a new study led by Laszlo Radvanyi, Ph.D., associate professor of breast and melanoma medical oncology at The University of Texas M. D. Anderson Cancer Center.The finding, published in the Aug. 2, 2005, issue of the Proceedings of the National Academy of Sciences and available on-line July 25, shows that the gene, normally made in small amounts in normal breast tissue, somehow becomes over-expressed in breast cancer cells. Researchers hope to use the cancer-specific protein to train the immune system to specifically attack breast cancer cells."There is a tremendous need for new molecular targets to treat breast cancer," Radvanyi says. "There are very few bona fide targets that are exquisitely specific for breast cancer. We believe this is one of them."Radvanyi and his collaborators at Sanofi Pasteur, Toronto, Canada, zeroed in on the gene, called TRPS-1, after an exhaustive search for targets that are found at higher levels in breast cancer than in normal tissue. The researchers compared the gene levels of more than 50,000 known genes in 54 breast cancer specimens and 289 normal samples representing 75 tissues or organs. The breast cancer specimens included 10 examples of early breast cancer or ductal carcinoma in situ (DCIS), 38 locally invasive breast cancers and six representing metastatic disease. They narrowed down their search by eliminating genes commonly found in normal tissue and those predicted to encode proteins that are excreted from the cell."We were interested in identifying proteins that could be potential tumor antigens activating cytotoxic T-cells or tumor killer cells," Radvanyi says. "We wanted proteins that would make good targets for a cancer vaccine."Finally, they zeroed in on TRPS-1, a gene they found at high levels in all forms of breast cancer, from DCIS to invasive disease, but in none of the normal tissues tested, except for low levels found in normal breast tissue.The TRPS-1 gene turned out to be associated with a rare, inherited genetic disease in which loss of the gene function results in muscle and bone deformities. The gene is located on human chromosome 8 in a region previously known to be associated with breast cancer and other oncogenes. The scientists don't yet know what the TRPS-1 protein is doing during the development of breast cancer, but they have started gathering clues to its role. Scientists at other institutions have shown TRPS-1 is a DNA-binding protein that regulates how other proteins get produced. It also appears to be involved in recognition of steroids such as estrogen. Radvanyi speculates that the protein may help regulate cell growth and perhaps estrogen recognition."Based on our findings, we believe that TRPS-1 is involved in the earliest stages of breast cancer," he says.The success of the breast cancer drug Herceptin, an antibody that specifically attacks breast cancer cells in which the Her2/neu gene is active, has made immunotherapy an attractive option for treating breast cancer. However, only about one-third of breast cancer patients are candidates for Herceptin treatment. Radvanyi's technique does not use antibodies, but instead attempts to get powerful immune system cells called T-cells to attack the cancer cells.Once they had identified TRPS-1, the researchers wanted to test its ability to act as an antigen, a protein that could prime the immune system to attack breast cancer cells. They used portions of the TRPS-1 protein as antigens to train human T-cells to attack cells containing TRPS-1. T-cells are white blood cells of the immune system that recognize and destroy bacteria, viruses and other foreign tissue. The scientists showed T-cells trained to detect TRPS-1 would attack and kill breast cancer cells containing the protein in laboratory experiments."This is exciting because TRPS-1 appears to be over-expressed only in cancers and not in normal tissue," Radvanyi says. "This makes it much less likely that normal tissue would be attacked in an immunotherapy setting."The researchers' next steps will be to test more patient samples at M. D. Anderson and try to correlate levels of TRPS-1 to other known breast cancer markers, such as HER2/neu and the estrogen receptor. They also want to understand what the protein's targets are inside the cell."If we understand its targets, we might be able to design inhibitors that disrupt its action, which could be clinically important given its early appearance in breast cancer," Radvanyi says.The research was funded by Sanofi Pasteur and in part by a Technology Partnerships Canada loan to Sanofi Pasteur, which has patented TRPS-1. Senior author Neil Berinstein, M.D., Ph.D., with Devender Singh-Sandhu, Ph.D., Scott Gallichan, Ph.D., Corey Lovitt, M.Sc., Artur Pedyczak, Ph.D., Jalil Hakimi, B.Sc., Jean Shortreed, B.Sc., Melinda Donovan, M.Sc., Mark Parrington, Ph.D., Pamela Dunn, Ph.D., Ray Oomen, Ph.D., James Tartaglia, Ph.D., and Gustavo Mallo, Ph.D. of Sanofi Pasteur as additional authors; Kurt Gish, Ph.D. of Protein Design Labs, Fremont, Calif.; Jane Armes, M.D., and Deon Venter, M.D., of MacCallum Cancer Institute, University of Melbourne, Melbourne, Australia; and Kevin Kwok, B.Sc., Wedad Hanna, M.D., and Judith Zubovits, M.D., of Sunnybrook and Women's College Hospital, Toronto, also contributed to the research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.342
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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