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  • 1
    In: British Journal of Haematology, Wiley, Vol. 200, No. 3 ( 2023-02)
    Type of Medium: Online Resource
    ISSN: 0007-1048 , 1365-2141
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 1475751-5
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  • 2
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2014
    In:  Bulletin of Volcanology Vol. 76, No. 11 ( 2014-11)
    In: Bulletin of Volcanology, Springer Science and Business Media LLC, Vol. 76, No. 11 ( 2014-11)
    Type of Medium: Online Resource
    ISSN: 0258-8900 , 1432-0819
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2014
    detail.hit.zdb_id: 635594-8
    detail.hit.zdb_id: 1458483-9
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  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 2151-2151
    Abstract: Background: Precision cancer medicine aims to identify the right drug for the right patient, enriching for patients more likely to respond to a particular treatment. This paradigm is gaining importance during the early clinical lifecycle of a new potential drug to improve patient-centric trial designs, drive clinical success and eventually increase approval rates - enlarging the therapeutic arsenal available for oncology patients. To optimize the chance of success with our A2AR-selective antagonist, EXS-21546 (546; NCT04727138, discovered in collaboration with Evotec), we have identified an adenosine-induced immunosuppression biomarker signature (adenosine burden score or ABS) for clinical trial patient selection that also correlates with checkpoint inhibitor (CI) response prediction in ex vivo primary models. Here we present transcriptional and functional data mapping adenosine burden at the single cell level, and investigate subsequent modulation through antagonism of A2AR with 546, combination effects with CI, to prioritize patients for 546+CI therapy. Methods: By leveraging disease-relevant primary human tissues together with matched single cell and bulk transcriptomics, we assess adenosine-induced anticancer immune suppression and show initial biological confirmation of patient selection methodology and combination therapy effects with a translatable high content imaging platform (Kornauth et al 2021 & Snidjer et al 2017). Results: The ABS detects adenosine rich microenvironments with greater specificity and sensitivity than other published signatures. Validating the ABS in TGCA, we found the ABS anti-correlates with a validated predictor of anti-PD-1 therapy success (TIS, Damotte et al 2019), unraveling that high-adenosine/ABS cases are among patients least likely to respond to immunotherapy (low TIS). A2AR antagonism with 546 demonstrated a reduction of the adenosine burden, and restored the CI response potential as addressed by the ABS and TIS, respectively. Further immune reactivation was seen with antagonism of adenosine signaling by ‘546/CI combination ex vivo in primary tissues pre-selected with our ABS signature. Conclusions: Combining deep learning of single cell functional and multi-omics profiling data of disease relevant primary model systems, we model the association of the immune response potential to A2AR antagonism in cancer to define a biomarker signature to predict patients likely to benefit from A2AR antagonism and CI. This will be confirmed and validated retrospectively in an ongoing clinical study of 546 in two cancer indications. Citation Format: Isabella Alt, Robert Sehlke, Anna Lobley, Claudia Baumgaertler, Maja Stulic, Klaus Hackner, Lucia Dzurillova, Edgar Petru, Laudia Hadjari, Judith Lafleur, Josef Singer, Nikolaus Krall, Jozef Šufliarsky, Lukas Hefler, Thorsten Füreder, Christina Taubert, Andrew Payne, Christophe Boudesco, Gregory Ian Vladimer. Identification of transcript adenosine fingerprint to enrich for A2AR and PD-1 inhibition responders [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 2151.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 4
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 5233-5233
    Abstract: Introduction: A vast majority of investigational anticancer drugs found to be active in preclinical development later do not show the desired effect in the clinic (Wong et al., Biostatistics 2019). This suggests that currently used preclinical models do not fully recapitulate the complexity of the human disease. The study of drug activity in primary human tissue samples, by contrast, could provide a more immediate picture of the activity of a molecule's effect in a patient. Factors that have so far hampered the use of primary tissue samples for drug discovery and development include access in sufficient quantity as well as robust analytical methods. We hypothesised that malignant pleural effusions and ascites (MPAs) of solid tumour patients are a promising model system to study drug activity in a preclinical setting. They are easily accessible in large quantities and contain cancer cells as well as major recruited immune populations. The latter could render them interesting model systems for studying I/O drug activity. Following previous successes in studying drug action in primary human tissues of haematological cancer patients with automated microscopy (Snijder et al 2017, Lancet Haem, NCT03096821) we here describe advances in using high content imaging and deep learning-based image analysis to study drug action in MPAs of solid tumour patients. Methods: MPAs from patients with metastatic breast, pancreatic, lung and ovarian cancer (at least n=10 of each) were collected under appropriate ethics approval. The response of EpCam+/CD45− and CD45+ cells against small molecule drugs was evaluated using high content microscopy. Drug response was quantified with single cell resolution using regional convolutional neural networks (R-CNNs) comprising an object detection and a single cell classification stage. EpCam+ and live/dead cell classification accuracies on the validation set were & gt;94%. Results: MPAs contain both cancer cells (range: 1-37%) and recruited myeloid and lymphoid immune populations with varying activation states (e.g. CD69+/PD-1+ CD8+ T-cells). Ex vivo drug responses from each patient sample were measured and combined to form an overall map of drug susceptibility across the patient population, pan-indication. Sensitivity mirrors drug approvals and also reveals drugs with potential off label use. Conclusions: Single-cell phenotypic analysis of MPAs enables the study of anticancer drug action in a setting that is one step closer to the clinic than cell line or outgrown organoid models of solid tumor. While initial response patterns can be observed that mirror current approvals, further biological and clinical validation must occur to understand in how far these data can be used for drug discovery and translational research purposes. Application as a functional tool for selecting salvage therapies for late-stage solid tumor patients (“functional prevision medicine”) can also be envisioned. Citation Format: Robert Sehlke, Christina Taubert, Isabella Alt, Florian Rohrer, Elisabeth Fuchs, Bojan Vilagos, Nikolaus Krall, Minichsdorfer Christoph, Michael Schumacher, Dominik Maurer, Klaus Hackner, Judith Lafleur, Lukas Hefler, Thorsten Fureder, Gregory Ian Vladimer. Towards patient-centric drug discovery: Analyzing drug action in malignant pleural effusions and ascites using high content imaging and deep learning [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5233.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 5
    Online Resource
    Online Resource
    Elsevier BV ; 2016
    In:  Journal of Volcanology and Geothermal Research Vol. 327 ( 2016-11), p. 330-348
    In: Journal of Volcanology and Geothermal Research, Elsevier BV, Vol. 327 ( 2016-11), p. 330-348
    Type of Medium: Online Resource
    ISSN: 0377-0273
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2016
    detail.hit.zdb_id: 196065-9
    detail.hit.zdb_id: 1494881-3
    SSG: 16,13
    SSG: 13
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  • 6
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 38, No. 15_suppl ( 2020-05-20), p. e21633-e21633
    Abstract: e21633 Background: Many anticancer drugs found to be active in preclinical development later do not show desired effect clinically. This suggests that currently used preclinical models do not fully recapitulate the complexity of the disease. The study of drug activity in primary samples could provide a more immediate picture of a molecule’s activity in a patient. Factors that have so far hampered the use of primary tissue samples for drug discovery and development include access in sufficient quantity as well as robust analytical methods. We hypothesised that malignant pleural effusions and ascites (MPAs) of solid tumour patients are a promising model system to study preclinical drug activity. MPAs are easily accessible and contain cancer cells as well as recruited immune cells. Following previous successes in studying drug action in primary tissues of patients with haematological malignancies with automated microscopy (Snijder et al 2017, Lanc Haem, NCT03096821) we describe advances in using high content imaging and deep learning-based image analysis to study drug action in MPAs of solid tumour patients. Methods: MPAs from patients with metastatic breast, pancreatic and ovarian cancer (at least n = 10 of each) were collected. The response of EpCam+/CD45- and CD45+ cells against small molecule drugs was evaluated using high content microscopy. Drug response was quantified at single cell resolution using regional convolutional neural networks (R-CNNs) comprising object detection and single cell classification. Results: MPAs contain both cancer cells and recruited myeloid and lymphoid immune cells with varying activation. Ex vivo drug responses from each patient sample were measured and the EC50 of each molecule determined by curve fitting. Sensitivity mirrored drug approvals for some indications, and also revealed drugs with potential off label use. On target and off-target response curves, along with integrative scores are used to visualize the effects. Conclusions: Single-cell phenotypic analysis of MPAs enables the study of anticancer drug action in a setting that is one step closer to the clinic than cell line or outgrown organoid models of solid tumor. While initial response patterns can be observed that mirror current approvals, further biological and clinical validation must occur to understand in how far these data can be used for drug discovery and translational research purposes.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
    detail.hit.zdb_id: 2005181-5
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  • 7
    In: eLife, eLife Sciences Publications, Ltd, Vol. 10 ( 2021-04-21)
    Abstract: Reduced activity of the insulin/IGF signalling network increases health during ageing in multiple species. Diverse and tissue-specific mechanisms drive the health improvement. Here, we performed tissue-specific transcriptional and proteomic profiling of long-lived Drosophila dilp2-3,5 mutants, and identified tissue-specific regulation of 〉 3600 transcripts and 〉 3700 proteins. Most expression changes were regulated post-transcriptionally in the fat body, and only in mutants infected with the endosymbiotic bacteria, Wolbachia pipientis , which increases their lifespan. Bioinformatic analysis identified reduced co-translational ER targeting of secreted and membrane-associated proteins and increased DNA damage/repair response proteins. Accordingly, age-related DNA damage and genome instability were lower in fat body of the mutant, and overexpression of a minichromosome maintenance protein subunit extended lifespan. Proteins involved in carbohydrate metabolism showed altered expression in the mutant intestine, and gut-specific overexpression of a lysosomal mannosidase increased autophagy, gut homeostasis, and lifespan. These processes are candidates for combatting ageing-related decline in other organisms.
    Type of Medium: Online Resource
    ISSN: 2050-084X
    Language: English
    Publisher: eLife Sciences Publications, Ltd
    Publication Date: 2021
    detail.hit.zdb_id: 2687154-3
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  • 8
    In: The EMBO Journal, EMBO, Vol. 40, No. 8 ( 2021-04-15)
    Type of Medium: Online Resource
    ISSN: 0261-4189 , 1460-2075
    RVK:
    Language: English
    Publisher: EMBO
    Publication Date: 2021
    detail.hit.zdb_id: 1467419-1
    detail.hit.zdb_id: 586044-1
    SSG: 12
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  • 9
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 1893-1893
    Abstract: Background: High unmet need of ovarian cancer (OC) suggests the discovery of new targeted therapeutics is crucial to improve patient prognosis. Unlike artificial model systems such as cell lines, primary cancer samples recapitulate the complexity of the original microenvironment consisting of cancer cells as well as stromal and immune cells; this is especially important when evaluating IO targets and signalling pathways. Supported by our previous success predicting therapy for late stage haematological cancer patients in the EXALT-I trial using AI-supported functional single cell quantification of drug action (Kornauth et. al. 2021) we set out to systematically reveal novel targets and pathways in OC using small molecule drugs (SMDs) as tools. Single cell phenotypic screening of OC MPAs (malignant pleural effusion and ascites) was enabled by the quantification of drug effects using an end-to-end scalable deep learning driven image analysis tool chain. This custom state-of-the-art AI software is critical to enable robust primary cell screening given the diversity of cells within each sample. This revealed anaplastic lymphoma kinase (ALK), as well as structurally related targets and pathway associated proteins, as being potential novel targets in a subset of OC patient samples. There is sparse literature evidence for therapeutic utilisation of the ALK pathway in OC, and the diversity of responses indicates a further novel patient selection method. Methods: MPAs from OC patients (n = 20) were collected and the sensitivity of the cancer cells to 85 SMDs was evaluated using high content microscopy. Individual cells were segmented and classified using convolutional neural networks and drug responses were estimated from the resulting cell counts. The integration of these results with whole exome and RNA sequencing guided target and pathway prioritisation. Results: Screening for novel sensitivities using SMDs as tools uncovered inhibitors of ALK and related targets as having strong cancer cell cytotoxic effects, recapitulated in solid tumour biopsies. Transcriptomic profiling revealed pathway correlations to ALK inhibitor sensitivity, however non-annotated polypharmacological effects of each drug cannot yet be excluded. Conclusions: Quantifying SMD sensitivity in a disease relevant model system identified ALK as a promising and overlooked target in OC, providing an upstream and potentially more specific target to the recently suggested MEK, PI3K and STAT3 (Papp et. al. 2018, Izar et al. 2020). While further work to confirm the target is required, this study supports a notion of patient-centric drug development using disease relevant models and deep learning. Our work introduces a novel patient-centric tool to advance understanding of the OC target landscape and provides a resource for the development of novel therapeutic approaches. Citation Format: Irene Gutierrez-Perez, Joost Van Ham, Valentin Aranha, Rin Okumura, Elisabeth Waltenberger, Isabella Alt, Claudia Baumgaertler, Maja Stulic, Edgar Petru, Christoph Minichsdorfer, Lukas Hefler, Judith Lafleur, Nikolaus Krall, Thorsten Füreder, Gregory Ian Vladimer, Robert Sehlke, Bojan Vilagos. Deep learning supported high content analysis of primary patient samples identifies ALK inhibition as a novel mechanism of action in a subset of ovarian cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1893.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 10
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 4956-4956
    Abstract: Background: There is a critical necessity to reveal novel and tractable targets for anti-cancer treatments in indications with high unmet medical need, such as high grade serous ovarian cancer (HGSOC). However, standard process for target discovery using models such as outgrown cell lines and well-averaged readouts has yielded a less than 5% approval rate for drugs entering trials (Thomas et al. 2016 Bio.org). Here, we describe patient-centric target discovery through the use of disease relevant primary OC samples and single cell functional characterization using a platform with proven hemonc translatability (Kornauth et al. 2021, Snijder et al. 2017). We integrate data from our functional drug testing platform under multiple drug perturbations with matching genomic and transcriptomic data to reveal associations with novel downstream regulators of sensitivity. Methods: Sensitivity of the cancer cell compartment in primary malignant ascites samples (n = 20; 75% HGSOC) to 85 small molecule drugs, was evaluated using a proprietary and translatable deep learning-driven single cell imaging platform (Vladimer et al. 2017). Cancer cell sensitivity from the drugs was combined with WES, bulk-RNAseq and drug induced changes in phosphoproteome, and single cell RNAseq transcriptome to identify perturbed targets and pathways. Results: Here we describe a family of TKIs including ALKi that induce cytotoxicity of cancer cells in primary samples, not previously captured in publicly available cell line drug sensitivity screening data (Iorio et al. 2016). We report novel sensitivity of OC driven by non-canonical targets of ceritinib such as FAK1 or IGF1R, mediated by the downstream signaling hub YBX1 (Kuenzi et al. 2017), involved in NFB pathway regulation (Motolani et al. 2021). Indeed, transcriptomic scRNA analysis upon ceritinib treatment of primary OC cells revealed rapid perturbation of numerous NFB pathway members, alongside YBX1 inactivation. Conclusions: Combining functional endpoints and single cell-based differential expression analysis of primary OC samples, we have identified the NFB pathway and the regulator YBX1 as a promising novel sensitivity for HGSOC treatment development. These and several other important targetable nodes identified, sit outside the recently suggested JAK/STAT pathway (Izar et al. 2020), thereby demonstrating a pipeline towards novel drug target and pathway discovery driven by patient-centric, disease relevant models of high-need indications. Citation Format: Irene Gutierrez-Perez, Bekir Ergüner, Pisanu Buphamalai, Joost Van Ham, Paul Heinz, Valentin Aranha, Rin Okumura, Elisabeth Waltenberger, Isabella Alt, Claudia Baumgaertler, Maja Stulic, Edgar Petru, Christoph Minichsdorfer, Judith Lafleur, Lukas Hefler, Laudia Hadjari, Lucia Dzurillova, Jozef Sufliarsky, Nikolaus Krall, Thorsten Füreder, Gregory Ian Vladimer, Bojan Vilagos, Robert Sehlke. Discovering novel targetable pathways by combining functional and multi-omic data from primary ovarian cancer samples. [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 4956.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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