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  • American Association for Cancer Research (AACR)  (10)
  • 1
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), ( 2024-03-11)
    Abstract: Background: BCMA-CARTs improve results obtained with conventional therapy in the treatment of relapsed/refractory multiple myeloma. However, the high demand and expensive costs associated with CART therapy might prove unsustainable for health systems. Academic CARTs could potentially overcome these issues. Moreover, response biomarkers and resistance mechanisms need to be identified and addressed to improve efficacy and patient selection. Here, we present clinical and ancillary results of the 60 patients treated with the academic BCMA-CART, ARI0002h, in the CARTBCMA-HCB-01 trial. Methods: We collected apheresis, final product, peripheral blood and bone marrow samples before and after infusion. We assessed BCMA, T-cell subsets, CART kinetics and antibodies, B-cell aplasia, cytokines, and measurable residual disease by next generation flow cytometry, and correlated these to clinical outcomes. Results: At cutoff date March 17th 2023, with a median follow-up of 23.1 months (95%CI 9.2-37.1), overall response rate in the first 3 months was 95% (95%CI 89.5-100); cytokine release syndrome (CRS) was observed in 90% of patients (5% grades≥3) and grade 1 immune effector cell-associated neurotoxicity syndrome was reported in 2 patients (3%). Median progression-free survival was 15.8 months (95%CI 11.5-22.4). Surface BCMA was not predictive of response or survival, but soluble BCMA correlated with worse clinical outcomes and CRS severity. Activation marker HLA-DR in the apheresis was associated with longer progression-free survival and increased exhaustion markers correlated with poorer outcomes. ARI0002h kinetics and loss of B-cell aplasia were not predictive of relapse. Conclusion: Despite deep and sustained responses achieved with ARI0002h, we identified several biomarkers that correlate with poor outcomes.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2024
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  • 2
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2018
    In:  Cancer Research Vol. 78, No. 13_Supplement ( 2018-07-01), p. 3886-3886
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 13_Supplement ( 2018-07-01), p. 3886-3886
    Abstract: This abstract has been withheld from publication due to its inclusion in the AACR Annual Meeting 2018 Official Press Program. It will be posted online following its presentation. Citation Format: Jonathan R. Dry, Michael P. Menden, Krishna Bulusu, Justin Guinney, Julio Saez-Rodriguez. A large cancer pharmacogenomics combination screen powering crowd-sourced advancement of computational drug synergy predictions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3886.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
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    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 3 ( 2018-02-01), p. 769-780
    Abstract: Transcriptional dysregulation induced by aberrant transcription factors (TF) is a key feature of cancer, but its global influence on drug sensitivity has not been examined. Here, we infer the transcriptional activity of 127 TFs through analysis of RNA-seq gene expression data newly generated for 448 cancer cell lines, combined with publicly available datasets to survey a total of 1,056 cancer cell lines and 9,250 primary tumors. Predicted TF activities are supported by their agreement with independent shRNA essentiality profiles and homozygous gene deletions, and recapitulate mutant-specific mechanisms of transcriptional dysregulation in cancer. By analyzing cell line responses to 265 compounds, we uncovered numerous TFs whose activity interacts with anticancer drugs. Importantly, combining existing pharmacogenomic markers with TF activities often improves the stratification of cell lines in response to drug treatment. Our results, which can be queried freely at dorothea.opentargets.io, offer a broad foundation for discovering opportunities to refine personalized cancer therapies. Significance: Systematic analysis of transcriptional dysregulation in cancer cell lines and patient tumor specimens offers a publicly searchable foundation to discover new opportunities to refine personalized cancer therapies. Cancer Res; 78(3); 769–80. ©2017 AACR.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
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    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 4
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 26, No. 13_Supplement ( 2020-07-01), p. B76-B76
    Abstract: Cancer-associated fibroblasts (CAFs) play fundamental roles in cancer and are emerging as therapeutic target in tumors with extensive stromal regions and in those for which there are limited targeted therapies against the cancer cells, such as ovarian cancer. A unique feature of the CAFs is their ability to secrete abundant collagen-rich extracellular matrix (ECM) that promotes the desmoplastic reaction that accompanies tumor progression and drives tumor growth and metastasis. Altered tumor metabolism is a hallmark of cancer, and understanding whether and how metabolic pathways support protumorigenic and proinvasive CAF functions may identify ways to target these cells to effectively target tumors. Using global phosphoproteomics, we have found that the activity of the pyruvate dehydrogenase complex (PDC), which is the rate-limiting enzyme for the entry of glycolysis-derived metabolites into the TCA cycle by converting pyruvate into acetyl-CoA, is strongly increased in patient-derived CAFs compared to their normal fibroblast counterpart. Consistently, the expression of pyruvate dehydrogenase kinase (PDK), which phosphorylates and inhibits PDC activity, is downregulated in CAFs and in the stroma of tumor patient samples. We found that PDC activity in CAFs leads to increased acetyl-CoA production. Surprisingly, 13C-glucose tracing experiments showed that CAFs do not channel acetyl-CoA into the TCA cycle. Instead, CAFs use acetyl-CoA to activate an epigenetic switch triggered by acetylation of H3K27. H3K27 acetylation is a known marker of gene expression activation. In CAFs, it triggered the expression of several collagen genes. Interestingly, also the expression of enzymes of the proline synthesis pathway was induced following H3K27 acetylation. Collagens have an unusually high content of proline residues, and we show that enhanced proline synthesis is necessary to support the production of collagen-rich ECM in CAFs. Targeting the PDK/PDC pathway or H3K27 acetylation or the proline synthesis pathway was sufficient to inhibit collagen synthesis in CAFs in in vitro experiments. Targeting proline synthesis in the stroma was sufficient to reduce tumor growth in vivo. Our work provides a first evidence that metabolism and epigenetics are tightly intertwined in regulating CAF functions and that targeting the PDK/PDC pathway or the proline synthesis pathway in the stroma could halt the development of a desmoplastic reaction and tumor progression. Citation Format: Emily Kay, Lisa Neilson, Claudia Boldrini, Juan Hernandez-Fernaud, Enio Gjerga, David Sumpton, Sandeep Dhayade, Grace McGregor, Grigorios Koulouras, Jurre Kamphorst, Karen Blyth, Julio Saez-Rodriguez, Gillian Mackay, Sara Zanivan. Pyruvate dehydrogenase: A key to epigenetic regulation in CAFs [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B76.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
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  • 5
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2012
    In:  Cancer Research Vol. 72, No. 8_Supplement ( 2012-04-15), p. 49-49
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 72, No. 8_Supplement ( 2012-04-15), p. 49-49
    Abstract: In a recent publication (Kulbe et al Cancer Res 2011 epub ahead of print), we have shown how key pathways in cancer-related inflammation and Notch signaling are part of an autocrine malignant cell network in high-grade serous ovarian cancer, HGSOC. This network, that we have named the TNF network, has paracrine actions on angiogenesis, the stromal signature and the immune cell infiltrate in HGSOC. We have now used a systems biology approach, combining data from phospho-proteomic mass spectrometry and gene expression array analysis, to define the best therapeutic targets within the network and to identify drugs that may synergise with cytokine and chemokine inhibitors. First, we established a hierarchy of kinases involved in the TNF network and analyzed the constitutively active kinases in one of the high TNF network cell lines. Of 45 constitutively active kinases, 33 of these kinases showed direct interactions with each other. Next, we mapped gene expression microarray data onto the Connectivity Map of drugs in order to identify compounds having an effect on transcription similar to that of the TNF network. Among the identified candidate drugs were luteolin, apigenin and resveratrol. One of the known targets of this class of drugs is the protein kinase Casein kinase II (CSNK2A1), a kinase activated in association with the TNF network. In conclusion, we have identified kinases, particularly CK2, associated with the TNF network that may play a central role in sustaining the cytokine network and/or mediating its effects in ovarian cancer. We believe our findings have implications for our understanding of ovarian cancer biology and the development of new and more effective treatments for this disease. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 49. doi:1538-7445.AM2012-49
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2012
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    detail.hit.zdb_id: 410466-3
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  • 6
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2018
    In:  Molecular Cancer Therapeutics Vol. 17, No. 1_Supplement ( 2018-01-01), p. B076-B076
    In: Molecular Cancer Therapeutics, American Association for Cancer Research (AACR), Vol. 17, No. 1_Supplement ( 2018-01-01), p. B076-B076
    Abstract: Selecting the right drug combination for the right patient is a complex problem. Some success has been seen from preclinical screening and computational approaches combining molecular, drug and tumor properties to predict combination benefit within the tumor cell. However the context specificity and heterogeneity of drug resistance is often overlooked, leading to inappropriate assumptions of statistical power and resulting in poor translatability. Knowledge-aware approaches will be needed to augment pre-clinical statistical discovery but a huge variety of techniques exist, each offering different strengths. To test this world of methods, AstraZeneca partnered with DREAM, Sage Bionetworks and the Sanger Institute. We released over 11,000 test results from drug combinations in cancer cell lines for an incentivized crowd-sourcing challenge to develop computational models predicting synergistic drug combinations and biomarkers determining response. This became the highest participated of any DREAM challenge, with & gt;700 participants from diverse geographies and knowledge/skill specialties collectively contributing & gt;50,000 hours to the problem. Over 70 models were submitted, testing most machine learning algorithms alongside knowledge-driven approaches. The best models out-performed the state of the art, consistently predicting at an equivalent level to biological replication. Network techniques to select molecular markers most relevant to drug target biology improved prediction the most. Amongst results that could be validated in independent data, ERBB2/EGFR/EP300 variants were predictive for combinations inhibiting AKT+EGFR/ERBB, KRAS for BRAF+MAP2K, AR for AKT/SGK+MAP2K, and ESR1 for ESR1+AKT/PI3K. Alongside revealing interesting biology in our data and providing predictive models, the challenge has given clear direction to our future investment in computational methods for combination pharmacogenomics. Citation Format: Jonathan R. Dry, Michael Menden, Julio Saez Rodriguez, Justin Guinney. A DREAM-world of approaches for predicting combination synergies in cancer [abstract] . In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr B076.
    Type of Medium: Online Resource
    ISSN: 1535-7163 , 1538-8514
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
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  • 7
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2011
    In:  Cancer Research Vol. 71, No. 16 ( 2011-08-15), p. 5400-5411
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 71, No. 16 ( 2011-08-15), p. 5400-5411
    Abstract: Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of “omic” data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here, we combine network analysis and functional experimentation by using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and 4 hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type, and these families clustered topologically into normal and diseased sets. Cancer Res; 71(16); 5400–11. ©2011 AACR.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2011
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  • 8
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 77, No. 12 ( 2017-06-15), p. 3364-3375
    Abstract: Genomic features are used as biomarkers of sensitivity to kinase inhibitors used widely to treat human cancer, but effective patient stratification based on these principles remains limited in impact. Insofar as kinase inhibitors interfere with signaling dynamics, and, in turn, signaling dynamics affects inhibitor responses, we investigated associations in this study between cell-specific dynamic signaling pathways and drug sensitivity. Specifically, we measured 14 phosphoproteins under 43 different perturbed conditions (combinations of 5 stimuli and 7 inhibitors) in 14 colorectal cancer cell lines, building cell line–specific dynamic logic models of underlying signaling networks. Model parameters representing pathway dynamics were used as features to predict sensitivity to a panel of 27 drugs. Specific parameters of signaling dynamics correlated strongly with drug sensitivity for 14 of the drugs, 9 of which had no genomic biomarker. Following one of these associations, we validated a drug combination predicted to overcome resistance to MEK inhibitors by coblockade of GSK3, which was not found based on associations with genomic data. These results suggest that to better understand the cancer resistance and move toward personalized medicine, it is essential to consider signaling network dynamics that cannot be inferred from static genotypes. Cancer Res; 77(12); 3364–75. ©2017 AACR.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2017
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 9
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2017
    In:  Clinical Cancer Research Vol. 23, No. 1_Supplement ( 2017-01-01), p. A44-A44
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 23, No. 1_Supplement ( 2017-01-01), p. A44-A44
    Abstract: Systematic studies of cancer genomes are providing unprecedented insights into the molecular nature of human cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here we report how cancer-driving alterations identified in 11,289 tumors from 29 tissues (integrating mutations, copy-number alterations, methylation and gene expression) correlate with response to 265 compounds profiled in 1,001 human cancer cell-lines. We find that cell-lines faithfully recapitulate oncogenic aberrations identified in tumors, and that many of these associate with drug sensitivity or resistance. Logic-based modeling uncovers combinations of aberrations that specifically sensitize to drugs, while machine-learning demonstrates the redundancy of different molecular data types in predicting drug response. Our comprehensive analysis and associated datasets are rich resources to identify novel therapeutic options for selected cancer sub-populations. Citation Format: Francesco Iorio, Theo Knijnenburg, Daniel vis, Graham Bignell, Michael Menden, Lodewyk Wessels, Julio Saez-Rodriguez, Ultan McDermott, Mathew Garnett. A landscape of pharmacogenomic interactions in cancer. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Targeting the Vulnerabilities of Cancer; May 16-19, 2016; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(1_Suppl):Abstract nr A44.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2017
    detail.hit.zdb_id: 1225457-5
    detail.hit.zdb_id: 2036787-9
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  • 10
    In: Molecular Cancer Research, American Association for Cancer Research (AACR), Vol. 19, No. 11 ( 2021-11-01), p. 1840-1853
    Abstract: Lymphangioleiomyomatosis (LAM) is a rare, low-grade metastasizing disease characterized by cystic lung destruction. LAM can exhibit extensive heterogeneity at the molecular, cellular, and tissue levels. However, the molecular similarities and differences among LAM cells and tissue, and their connection to cancer features are not fully understood. By integrating complementary gene and protein LAM signatures, and single-cell and bulk tissue transcriptome profiles, we show sources of disease heterogeneity, and how they correspond to cancer molecular portraits. Subsets of LAM diseased cells differ with respect to gene expression profiles related to hormones, metabolism, proliferation, and stemness. Phenotypic diseased cell differences are identified by evaluating lumican (LUM) proteoglycan and YB1 transcription factor expression in LAM lung lesions. The RUNX1 and IRF1 transcription factors are predicted to regulate LAM cell signatures, and both regulators are expressed in LAM lung lesions, with differences between spindle-like and epithelioid LAM cells. The cancer single-cell transcriptome profiles most similar to those of LAM cells include a breast cancer mesenchymal cell model and lines derived from pleural mesotheliomas. Heterogeneity is also found in LAM lung tissue, where it is mainly determined by immune system factors. Variable expression of the multifunctional innate immunity protein LCN2 is linked to disease heterogeneity. This protein is found to be more abundant in blood plasma from LAM patients than from healthy women. Implications: This study identifies LAM molecular and cellular features, master regulators, cancer similarities, and potential causes of disease heterogeneity.
    Type of Medium: Online Resource
    ISSN: 1541-7786 , 1557-3125
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
    detail.hit.zdb_id: 2097884-4
    SSG: 12
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