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      Identification of CDC25 as a Common Therapeutic Target for Triple-Negative Breast Cancer

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          SUMMARY

          CDK4/6 inhibitors are effective against cancer cells expressing the tumor suppressor RB1, but not RB1-deficient cells, posing the challenge of how to target RB1 loss. In triple-negative breast cancer (TNBC), RB1 and PTEN are frequently inactivated together with TP53. We performed kinome/phosphatase inhibitor screens on primary mouse Rb/p53-, Pten/p53-, and human RB1/PTEN/TP53-deficient TNBC cell lines and identified CDC25 phosphatase as a common target. Pharmacological or genetic inhibition of CDC25 suppressed growth of RB1-deficient TNBC cells that are resistant to combined CDK4/6 plus CDK2 inhibition. Minimal cooperation was observed in vitro between CDC25 antagonists and CDK1, CDK2, or CDK4/6 inhibitors, but strong synergy with WEE1 inhibition was apparent. In accordance with increased PI3K signaling following long-term CDC25 inhibition, CDC25 and PI3K inhibitors effectively synergized to suppress TNBC growth both in vitro and in xenotransplantation models. These results provide a rationale for the development of CDC25-based therapies for diverse RB1/PTEN/TP53-deficient and -proficient TNBCs.

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          In Brief

          Liu et al. report that inhibition of the protein phosphatase CDC25 kills diverse triple-negative breast cancer (TNBC) cells. Moreover, CDC25 antagonists cooperate with other drugs, such as PI3K inhibitors, to efficiently suppress growth of human TNBC engrafted into mice.

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          Comprehensive molecular portraits of human breast tumors

          Summary We analyzed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, mRNA arrays, microRNA sequencing and reverse phase protein arrays. Our ability to integrate information across platforms provided key insights into previously-defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at > 10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the Luminal A subtype. We identified two novel protein expression-defined subgroups, possibly contributed by stromal/microenvironmental elements, and integrated analyses identified specific signaling pathways dominant in each molecular subtype including a HER2/p-HER2/HER1/p-HER1 signature within the HER2-Enriched expression subtype. Comparison of Basal-like breast tumors with high-grade Serous Ovarian tumors showed many molecular commonalities, suggesting a related etiology and similar therapeutic opportunities. The biologic finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biologic subtypes of breast cancer.
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            The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups

            The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ~40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA–RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the ‘CNA-devoid’ subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.
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              Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors.

              A generalized method for analyzing the effects of multiple drugs and for determining summation, synergism and antagonism has been proposed. The derived, generalized equations are based on kinetic principles. The method is relatively simple and is not limited by whether the dose-effect relationships are hyperbolic or sigmoidal, whether the effects of the drugs are mutually exclusive or nonexclusive, whether the ligand interactions are competitive, noncompetitive or uncompetitive, whether the drugs are agonists or antagonists, or the number of drugs involved. The equations for the two most widely used methods for analyzing synergism, antagonism and summation of effects of multiple drugs, the isobologram and fractional product concepts, have been derived and been shown to have limitations in their applications. These two methods cannot be used indiscriminately. The equations underlying these two methods can be derived from a more generalized equation previously developed by us (59). It can be shown that the isobologram is valid only for drugs whose effects are mutually exclusive, whereas the fractional product method is valid only for mutually nonexclusive drugs which have hyperbolic dose-effect curves. Furthermore, in the isobol method, it is laborious to find proper combinations of drugs that would produce an iso-effective curve, and the fractional product method tends to give indication of synergism, since it underestimates the summation of the effect of mutually nonexclusive drugs that have sigmoidal dose-effect curves. The method described herein is devoid of these deficiencies and limitations. The simplified experimental design proposed for multiple drug-effect analysis has the following advantages: It provides a simple diagnostic plot (i.e., the median-effect plot) for evaluating the applicability of the data, and provides parameters that can be directly used to obtain a general equation for the dose-effect relation; the analysis which involves logarithmic conversion and linear regression can be readily carried out with a simple programmable electronic calculator and does not require special graph paper or tables; and the simplicity of the equation allows flexibility of application and the use of a minimum number of data points. This method has been used to analyze experimental data obtained from enzymatic, cellular and animal systems.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                1 August 2022
                03 April 2018
                07 August 2022
                : 23
                : 1
                : 112-126
                Affiliations
                [1 ]Toronto General Research Institute - University Health Network, 67 College Street, Toronto, ON, Canada M5G 2M1
                [2 ]Department of Agriculture, Food, and Environmental Sciences, University of Perugia, Perugia, Italy
                [3 ]Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada
                [4 ]Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, Toronto, ON, Canada
                [5 ]The Donnelly Centre, University of Toronto, Toronto, ON, Canada
                [6 ]The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
                [7 ]Network Biology Collaborative Centre, SMART Laboratory for High-Throughput Screening Programs, Mount Sinai Hospital, Toronto, ON, Canada
                [8 ]The Key Laboratory of Chemistry for Natural Products of Guizhou Province and Chinese Academic of Sciences, Guiyang, Guizhou 550014, China
                [9 ]State Key Laboratory for Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang, Guizhou 550025, China
                [10 ]Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
                [11 ]Program in Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada
                [12 ]Department of Medicine, University of Toronto, Toronto, ON, Canada
                [13 ]These authors contributed equally
                [14 ]Lead Contact
                Author notes

                AUTHOR CONTRIBUTIONS

                Conceptualization, J.C.L. and E.Z.; Experimentations, J.C.L., L.G., M.S., I.V., R.J., E.A.R., Y.J., and Z.J.; Bioinformatics Analysis, J.C.L., D.-Y.W., and G.P.; Supervision, J.R.W., G.D.B., C.A.P., A.D., and E.Z.; Funding Acquisition, J.R.W., S.E.E., C.A.P., G.D.B., A.D., and E.Z.; Writing – Original Draft, J.C.L., J.R.W., and E.Z.; Writing – Review & Editing, J.R.W., M.S., I.V., J.Z., S.E.E., Y.B.-D., G.D.B., A.D., and E.Z. All authors reviewed and edited the final manuscript.

                Article
                NIHMS1826944
                10.1016/j.celrep.2018.03.039
                9357459
                29617654
                9860adfb-3cca-4f8c-ab91-c1e04b95d304

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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