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Refereed/Peer-reviewed Aim: The island species–area relationship (ISAR) assumes that the area of islands is homogeneous, or scales with environmental heterogeneity across an archipelago, which is not always the case. We compare the performance of models that adjust or substitute for island area with measures of habitat diversity, island age and resource availability for two taxonomic groups. Location: Five hotspot archipelagos (Azores, Galapagos, Hawaii, Cape Verde, Canary Islands). Taxa: Vascular plants, birds. Methods: We used the mathematical framework of the power law to compare relevant models, treating the one containing only area as a null model against which others were compared. Data were collated from databases and the literature. Models were compared using linear regression within archipelagos and via mixed effect models with archipelago as a random effect. Results: Weighting of island area by habitat diversity and resource availability systematically improved statistical significance and model fits versus the area only power law. Models including island age did not show the same systematic improvement in model fits. For vascular plants, weighting islands by resource availability (energy and water) performed better than weighting by habitat diversity, although for birds these weightings performed equally well. Main Conclusions: Given that islands within archipelagos are fairly heterogeneous in climate, topography and geology, it is worth accounting for this in ISARs. Our results suggest that, for islands in volcanic hotspot archipelagos, this is best done by using direct measures of habitat diversity and resource availability rather than using island age as a proxy. We, therefore, recommend using direct measures, rather than proxies, when investigating the drivers of biodiversity patterns on islands.

Purpose: We studied the expression levels of cyclins B1, D1, and E1 and the implications of cyclin overexpression for patient outcomes in distinct breast cancer subtypes defined by clinical variables and transcriptional profiling.Experimental Design: The expression levels of cyclins B1, D1, and E1 were quantified in 779 breast tumors and 53 cell lines using reverse phase protein arrays and/or transcriptional profiling.Results: Whereas cyclin E1 overexpression was a specific marker of triple-negative and basal-like tumors, cyclin B1 overexpression occurred in poor prognosis hormone receptor–positive, luminal B and basal-like breast cancers. Cyclin D1 overexpression occurred in luminal and normal-like cancers. Breast cancer subgroups defined by integrated expression of cyclins B1, D1, and E1 correlated significantly (P < 0.000001) with tumor subtypes defined by transcriptional profiling and clinical criteria. Across three hormone receptor–positive data sets, cyclin B1 was the dominant cyclin associated with poor prognosis in univariate and multivariate analyses. Although CCNE1 was present in significantly higher copy numbers in basal-like versus other subtypes (ANOVA P < 0.001), CCNB1 gene copy number did not show gain in breast cancer. Instead, cyclin B1 expression was increased in tumors with co-occurrence of TP53 mutations and MYC amplification, a combination that seems to characterize basal-like and luminal B tumors. CCNB1 gene expression was significantly correlated with PLK, CENPE, and AURKB gene expression.Conclusion: Cyclins B1, D1, and E1 have distinct expressions in different breast cancer subtypes. Novel PLK, CENPE, and AURKB inhibitors should be assessed for therapeutic utility in poor prognosis cyclin B1–overexpressing breast cancers.

Supplementary Tables 1-4 from Integrative Analysis of Proteomic Signatures, Mutations, and Drug Responsiveness in the NCI 60 Cancer Cell Line Set

Aberrations in oncogenes and tumor suppressors frequently affect the activity of critical signal transduction pathways. To analyze systematically the relationship between the activation status of protein networks and other characteristics of cancer cells, we did reverse phase protein array (RPPA) profiling of the NCI60 cell lines for total protein expression and activation-specific markers of critical signaling pathways. To extend the scope of the study, we merged those data with previously published RPPA results for the NCI60. Integrative analysis of the expanded RPPA data set revealed five major clusters of cell lines and five principal proteomic signatures. Comparison of mutations in the NCI60 cell lines with patterns of protein expression showed significant associations for PTEN, PIK3CA, BRAF, and APC mutations with proteomic clusters. PIK3CA and PTEN mutation enrichment were not cell lineage-specific but were associated with dominant yet distinct groups of proteins. The five RPPA-defined clusters were strongly associated with sensitivity to standard anticancer agents. RPPA analysis identified 27 protein features significantly associated with sensitivity to paclitaxel. The functional status of those proteins was interrogated in a paclitaxel whole genome small interfering RNA (siRNA) library synthetic lethality screen and confirmed the predicted associations with drug sensitivity. These studies expand our understanding of the activation status of protein networks in the NCI60 cancer cell lines, demonstrate the importance of the direct study of protein expression and activation, and provide a basis for further studies integrating the information with other molecular and pharmacological characteristics of cancer. Mol Cancer Ther; 9(2); 257–67

Supplementary Data from Integrative Analysis of Cyclin Protein Levels Identifies Cyclin B1 as a Classifier and Predictor of Outcomes in Breast Cancer

Supplementary Figure Legends 1-4 from Integrative Analysis of Proteomic Signatures, Mutations, and Drug Responsiveness in the NCI 60 Cancer Cell Line Set

Supplementary Figures 1-4 from Integrative Analysis of Proteomic Signatures, Mutations, and Drug Responsiveness in the NCI 60 Cancer Cell Line Set

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Supplementary Tables 1-3 from An Integrative Genomic and Proteomic Analysis of PIK3CA, PTEN, and AKT Mutations in Breast Cancer

Supplementary Table 4 from Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics

Supplementary Table Legends 1-3 from An Integrative Genomic and Proteomic Analysis of PIK3CA, PTEN, and AKT Mutations in Breast Cancer

Phosphatidylinositol 3-kinase (PI3K)/AKT pathway aberrations are common in cancer. By applying mass spectroscopy–based sequencing and reverse-phase protein arrays to 547 human breast cancers and 41 cell lines, we determined the subtype specificity and signaling effects of PIK3CA, AKT, and PTEN mutations and the effects of PIK3CA mutations on responsiveness to PI3K inhibition in vitro and on outcome after adjuvant tamoxifen. PIK3CA mutations were more common in hormone receptor–positive (34.5%) and HER2-positive (22.7%) than in basal-like tumors (8.3%). AKT1 (1.4%) and PTEN (2.3%) mutations were restricted to hormone receptor–positive cancers. Unlike AKT1 mutations that were absent from cell lines, PIK3CA (39%) and PTEN (20%) mutations were more common in cell lines than tumors, suggesting a selection for these but not AKT1 mutations during adaptation to culture. PIK3CA mutations did not have a significant effect on outcome after adjuvant tamoxifen therapy in 157 hormone receptor–positive breast cancer patients. PIK3CA mutations, in comparison with PTEN loss and AKT1 mutations, were associated with significantly less and inconsistent activation of AKT and of downstream PI3K/AKT signaling in tumors and cell lines. PTEN loss and PIK3CA mutation were frequently concordant, suggesting different contributions to pathophysiology. PTEN loss rendered cells significantly more sensitive to growth inhibition by the PI3K inhibitor LY294002 than did PIK3CA mutations. Thus, PI3K pathway aberrations likely play a distinct role in the pathogenesis of different breast cancer subtypes. The specific aberration present may have implications for the selection of PI3K-targeted therapies in hormone receptor–positive breast cancer. [Cancer Res 2008;68(15):6084–91]

Supplementary Tables 1-3 from Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics

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Metaplastic breast cancers (MBC) are aggressive, chemoresistant tumors characterized by lineage plasticity. To advance understanding of their pathogenesis and relatedness to other breast cancer subtypes, 28 MBCs were compared with common breast cancers using comparative genomic hybridization, transcriptional profiling, and reverse-phase protein arrays and by sequencing for common breast cancer mutations. MBCs showed unique DNA copy number aberrations compared with common breast cancers. PIK3CA mutations were detected in 9 of 19 MBCs (47.4%) versus 80 of 232 hormone receptor–positive cancers (34.5%; P = 0.32), 17 of 75 HER-2–positive samples (22.7%; P = 0.04), 20 of 240 basal-like cancers (8.3%; P < 0.0001), and 0 of 14 claudin-low tumors (P = 0.004). Of 7 phosphatidylinositol 3-kinase/AKT pathway phosphorylation sites, 6 were more highly phosphorylated in MBCs than in other breast tumor subtypes. The majority of MBCs displayed mRNA profiles different from those of the most common, including basal-like cancers. By transcriptional profiling, MBCs and the recently identified claudin-low breast cancer subset constitute related receptor-negative subgroups characterized by low expression of GATA3-regulated genes and of genes responsible for cell-cell adhesion with enrichment for markers linked to stem cell function and epithelial-to-mesenchymal transition (EMT). In contrast to other breast cancers, claudin-low tumors and most MBCs showed a significant similarity to a “tumorigenic” signature defined using CD44+/CD24− breast tumor–initiating stem cell–like cells. MBCs and claudin-low tumors are thus enriched in EMT and stem cell–like features, and may arise from an earlier, more chemoresistant breast epithelial precursor than basal-like or luminal cancers. PIK3CA mutations, EMT, and stem cell-like characteristics likely contribute to the poor outcomes of MBC and suggest novel therapeutic targets. [Cancer Res 2009;69(10):4116–24]

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Supplementary Legends for Figures and Tables 1-8 from Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics

Supplementary Tables 5-8 from Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics

Supplemental Figure 7 from Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics

Supplementary Figure 4 from Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics

Supplementary Figures 5 and 6A-B from Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics

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Conflict of Interest Form 1 from Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics

Supplemental Figure 5 from Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics

Contains fulltext : 283536.pdf (Publisher’s version ) (Closed access) The Gynecologic Cancer InterGroup (GCIG) sixth Ovarian Cancer Conference on Clinical Research was held virtually in October, 2021, following published consensus guidelines. The goal of the consensus meeting was to achieve harmonisation on the design elements of upcoming trials in ovarian cancer, to select important questions for future study, and to identify unmet needs. All 33 GCIG member groups participated in the development, refinement, and adoption of 20 statements within four topic groups on clinical research in ovarian cancer including first line treatment, recurrent disease, disease subgroups, and future trials. Unanimous consensus was obtained for 14 of 20 statements, with greater than 90% concordance in the remaining six statements. The high acceptance rate following active deliberation among the GCIG groups confirmed that a consensus process could be applied in a virtual setting. Together with detailed categorisation of unmet needs, these consensus statements will promote the harmonisation of international clinical research in ovarian cancer.

We offer a model and evidence that private debtholders play a key role in setting the endogenous asset value threshold below which corporations declare bankruptcy. The model, in the spirit of Black and Cox (1976), implies that the recovery rate at emergence from bankruptcy on all of the firm's debt taken together is increasing in the pre-bankruptcy share of private debt in all debt. Empirical evidence supports this and other implications of the model. Indeed, debt composition has a more economically material empirical influence on recovery than all other variables we try taken together.

There are two serous ovarian cancer histotypes, low and high grade (LGSOC and HGSOC), which are distinct clinical and biological entities. LGSOC is a rare histotype with a relatively stable genome, while HGSC is more common and genomically unstable. Somewhat surprisingly LGSOC expresses high levels of the stimulator of the interferon genes (STING). The STING pathway recognizes cytoplasmic double-stranded DNA and mounts innate cellular immunity through interferon-beta type I production. Our objective is to investigate the aberrant STING signaling in LGSOC and test the effectiveness of oncolytic viruses against LGSOC. We used immunohistochemistry on tissue microarrays (TMAs) to assess STING protein expression in different ovarian cancer histotypes. Whole proteome analysis was applied to identify differentially expressed proteins in LGSOC and HGSOC patient samples (both n=9). Further, a semi-targeted proteomics approach was used to evaluate the expression levels of the STING pathway-related proteins in LGSOC, HGSOC, and LGSOC precursor tumors (each subtype, n=20). To evaluate the key transcription, phosphorylation, and translocation events in STING signaling, we treated LGSOC cell lines with an agonist (dsDNA90) and performed qPCR, immunoblotting, and immunofluorescence experiments, respectively. We tested the viability of the LGSOC cell lines in response to Vaccinia Virus (VV), and Vesicular Stomatitis Virus (VSV) based oncolytic vectors with or without immunostimulatory transgenes. Our results show that STING protein levels were consistently higher in LGSOC TMAs relative to other histotypes. Proteomics analysis showed that the half of the 16 most differentially expressed proteins were the effectors of STING signaling with unexpectedly lower expression in LGSOC, suggesting that despite the robust levels of STING in LGSOC tumors, the pathway is not fully active. Attenuated STING translocation and expression of IFNB1 and other cytokines in LGSOC cell lines confirm the aberrancy in the STING pathway. Semi-targeted proteomics revealed the considerable overexpression of STIM1 in LGSOC patient tumors, which has previously been shown to sequester STING in the endoplasmic reticulum. The treatment with VV and VSV oncolytic viruses significantly reduced the proliferation of LGSOC cell lines. In summary, we find attenuated STING signaling in LGSOC, possibly due to overexpression of STIM1 preventing STING translocation. Although oncolytic viruses show promising results in LGSOC cell lines, more research is needed to determine the optimal treatment strategy, testing oncolytic viruses expressing various transgenes and combination therapies. Citation Format: Almira Zhantuyakova, Dawn Cochrane, Gian Negri, Sandra E. Spencer Miko, Taha Azad, Jutta Huvila, Marta Llaurado Fernandez, Mark Carey, Gregg B. Morin, John Bell, David Huntsman. Deciphering aberrant STING pathway and exploring oncolytic viruses therapy in low grade serous ovarian carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6417.

The species-area relationship (SAR) is widely applied in ecology. Mathematically, it is usually expressed as either a semi-log or power-law relationship, with the former being introduced by Gleason and the latter by Arrhenius. We here resolve the dispute about which form of the SAR to prefer by introducing a novel model that smoothly transforms between the Gleason semi-log (GSL) and Arrhenius power law (APL) forms. The model introduced has the form of lnq (S) = a + z ln A, with lnq being a generalized logarithmic function, which is a linear map (y = x) for q = 0 and a logarithmic map (y = ln x) for q = 1 and q can take any intermediate value between 0 and 1. We applied this model to 100 datasets (mostly islands), linking species richness to island area. The APL was the preferred model in 68% of head-to-head comparisons with the GSL. Both models were supported in 40% of cases. In just under half (44%) of the cases, an intermediate model best explained the data. The results demonstrate the utility of a simple intermediate SAR model. Visualizing the profile of the range of model fits for all q ∈ [0, 1] (a q chart) allows us to gain extra insight into SARs not yielded by head-to-head comparisons of GSL and APL. The mathematics related to the generalized logarithmic function introduced here should have applications to other areas of ecological modelling.

Aim: The General Dynamic Model of island biogeography (GDM) explains patterns of species richness on volcanic hotspot archipelagos with respect to island age (T) and area (A), most commonly employing what we here call semi‐log ATT² and LogATT² models. These can be considered direct extensions of semi‐log and power law species‐area relationships (SARs), with a hump‐shaped function of T modelling drivers of biodiversity over and above area per se. We compare these and related models and investigate their conceptual and mathematical connections. Location: The Azores, Galapagos, Hawaii, Marquesas, Society Islands, Cape Verde Islands, Canary Islands and Aeolian archipelagos. Taxa: Vascular plants, birds, bryophytes, arthropods and molluscs. Methods: We gathered species richness data from databases and published sources, resulting in 34 datasets for various taxa. We used the Akaike information criterion corrected for small sample size (AICc) to compare the relative performance of models. We analysed the data at archipelago level and across archipelagos by using mixed effect models. We also calculated the relative time of expected peak diversity (Tpeak) from parameter estimates. Results: In almost three‐fourth of the cases the best model overall was one that contained only A or T rather than both predictors. There was greater variance between taxa than between archipelagos. Better dispersed taxa tended to reach peak diversity earlier. The most generally applicable of the models including T were the LogATT² and LogTT² models. These have important, previously unrecognized mathematical properties. Main Conclusions: SARs and species–time relationships should always be considered as potentially more parsimonious options. Considering the LogATT² model as a modification of the power law S = cAz, where a lognormal function in T replaces the constant, c allows the construction of an extended framework for the GDM with incorporation of additional predictors such as measures of environmental heterogeneity and application beyond hotspot archipelagos. Refereed/Peer-reviewed