Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UNIPROT:P10636 (tau protein)
 5,110 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Ductal carcinoma in situ (DCIS) accounts for approximately 20% of mammographically detected breast cancers. Although DCIS is generally highly curable, some women with DCIS will develop life-threatening invasive breast cancer, but the determinants of progression to infiltrating ductal cancer (IDC) are largely unknown. In the current study, we used multiplex ligation-dependent probe amplification (MLPA), a multiplex PCR-based test, to compare copy numbers of 21 breast cancer related genes between laser-microdissected DCIS and adjacent IDC lesions in 39 patients. Genes included in this study were ESR1, EGFR, FGFR1, ADAM9, IKBKB, PRDM14, MTDH, MYC, CCND1, EMSY, CDH1, TRAF4, CPD, MED1, HER2, CDC6, TOP2A, MAPT, BIRC5, CCNE1 and AURKA.There were no significant differences in copy number for the 21 genes between DCIS and adjacent IDC. Low/intermediate-grade DCIS showed on average 6 gains/amplifications versus 8 in high-grade DCIS (p=0.158). Furthermore, alterations of AURKA and CCNE1 were exclusively found in high-grade DCIS, and HER2, PRDM14 and EMSY amplification was more frequent in high-grade DCIS than in low/intermediate-grade DCIS. In contrast, the average number of alterations in low/intermediate and high-grade IDC was similar, and although EGFR alterations were exclusively found in high-grade IDC compared to low/intermediate-grade IDC, there were generally fewer differences between low/intermediate-grade and high-grade IDC than between low/intermediate-grade and high-grade DCIS.In conclusion, there were no significant differences in copy number for 21 breast cancer related genes between DCIS and adjacent IDC, indicating that DCIS is genetically as advanced as its invasive counterpart. However, high-grade DCIS showed more copy number changes than low/intermediate-grade DCIS with specifically involved genes, supporting a model in which different histological grades of DCIS are associated with distinct genomic changes that progress to IDC in different routes. These high-grade DCIS specific genes may be potential targets for treatment and/or predict progression.
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PMID:Molecular differences between ductal carcinoma in situ and adjacent invasive breast carcinoma: a multiplex ligation-dependent probe amplification study. 2154 76

Increasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We investigated correspondence between growth inhibitory concentrations of paclitaxel and gemcitabine (GI50) and gene copy number, mutation, and expression first in breast cancer cell lines and then in patients. Genes encoding direct targets of these drugs, metabolizing enzymes, transporters, and those previously associated with chemoresistance to paclitaxel (n = 31 genes) or gemcitabine (n = 18) were analyzed. A multi-factorial, principal component analysis (MFA) indicated expression was the strongest indicator of sensitivity for paclitaxel, and copy number and expression were informative for gemcitabine. The factors were combined using support vector machines (SVM). Expression of 15 genes (ABCC10, BCL2, BCL2L1, BIRC5, BMF, FGF2, FN1, MAP4, MAPT, NFKB2, SLCO1B3, TLR6, TMEM243, TWIST1, and CSAG2) predicted cell line sensitivity to paclitaxel with 82% accuracy. Copy number profiles of 3 genes (ABCC10, NT5C, TYMS) together with expression of 7 genes (ABCB1, ABCC10, CMPK1, DCTD, NME1, RRM1, RRM2B), predicted gemcitabine response with 85% accuracy. Expression and copy number studies of two independent sets of patients with known responses were then analyzed with these models. These included tumor blocks from 21 patients that were treated with both paclitaxel and gemcitabine, and 319 patients on paclitaxel and anthracycline therapy. A new paclitaxel SVM was derived from an 11-gene subset since data for 4 of the original genes was unavailable. The accuracy of this SVM was similar in cell lines and tumor blocks (70-71%). The gemcitabine SVM exhibited 62% prediction accuracy for the tumor blocks due to the presence of samples with poor nucleic acid integrity. Nevertheless, the paclitaxel SVM predicted sensitivity in 84% of patients with no or minimal residual disease.
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PMID:Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning. 2637 58