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Kaplan, Jennifer

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Kaplan

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Jennifer

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Kaplan, Jennifer

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2010 - 20132

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Author's Publications: search.filters.isAuthorOfPublication.45b740f3-7b3f-4abc-9c40-809e8336ff34

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    Significance Analysis of Prognostic Signatures
    (Public Library of Science (PLoS), 2013) Beck, Andrew; Knoblauch, Nicholas W.; Hefti, Marco; Kaplan, Jennifer; Schnitt, Stuart; Culhane, Aedin; Schroeder, Markus S.; Risch, Thomas; Quackenbush, John; Haibe-Kains, Benjamin
    A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that ‘‘random’’ gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date) of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically annotated genomic datasets.
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    Prevalence and Predictors of Loss of Wild Type BRCA1 in Estrogen Receptor Positive and Negative BRCA1-Associated Breast Cancers
    (BioMed Central, 2010) Fetten, Katharina; Yassin, Yosuf; Buraimoh, Ayodele; Kim, Ji-Young; Legare, Robert D; Tung, Nadine; Miron, A; Schnitt, Stuart; Gautam, Shiva; Kaplan, Jennifer; Szasz, Attila M.; Tian, R; Wang, Zhigang C.; Collins, Laura; Brock, Jane; Krag, Karen; Sgroi, Dennis; Ryan, Paula D.; Silver, Daniel P.; Garber, Judy; Richardson, Andrea
    Introduction: The majority of breast cancers that occur in BRCA1 mutation carriers (BRCA1 carriers) are estrogen receptor-negative (ER-). Therefore, it has been suggested that ER negativity is intrinsic to BRCA1 cancers and reflects the cell of origin of these tumors. However, approximately 20% of breast cancers that develop in BRCA1 carriers are ER-positive (ER+); these cancers are more likely to develop as BRCA1 carriers age, suggesting that they may be incidental and unrelated to BRCA1 deficiency. The purpose of this study was to compare the prevalence of loss of heterozygosity due to loss of wild type (wt) BRCA1 in ER+ and ER- breast cancers that have occurred in BRCA1 carriers and to determine whether age at diagnosis or any pathologic features or biomarkers predict for loss of wt BRCA1 in these breast cancers. Methods: Relative amounts of mutated and wt BRCA1 DNA were measured by quantitative polymerase chain reaction performed on laser capture microdissected cancer cells from 42 ER+ and 35 ER- invasive breast cancers that developed in BRCA1 carriers. BRCA1 gene methylation was determined on all cancers in which sufficient DNA was available. Immunostains for cytokeratins (CK) 5/6, 14, 8 and 18, epidermal growth factor receptor and p53 were performed on paraffin sections from tissue microarrays containing these cancers. Results: Loss of wt BRCA1 was equally frequent in ER+ and ER- BRCA1-associated cancers (81.0% vs 88.6%, respectively; P = 0.53). One of nine cancers tested that retained wt BRCA1 demonstrated BRCA1 gene methylation. Age at diagnosis was not significantly different between first invasive ER+ BRCA1 breast cancers with and without loss of wt BRCA1 (mean age 45.2 years vs 50.1 years, respectively; P = 0.51). ER+ BRCA1 cancers that retained wt BRCA1 were significantly more likely than those that lost wt BRCA1 to have a low mitotic rate (odds ratio (OR), 5.16; 95% CI, 1.91 to ∞). BRCA1 cancers with loss of wt BRCA1 were more likely to express basal cytokeratins CK 5/6 or 14 (OR 4.7; 95% CI, 1.85 to ∞). Conclusions: We found no difference in the prevalence of loss of wt BRCA1 between ER+ and ER- invasive BRCA1-associated breast cancers. Our findings suggest that many of the newer therapies for BRCA1 breast cancers designed to exploit the BRCA1 deficiency in these cancers may also be effective in ER+ cancers that develop in this population.