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Snijder B, Vladimer GI, Krall N, Miura K, Schmolke AS, Kornauth C, Lopez de la Fuente O, Choi HS, van der Kouwe E, Gültekin S, Kazianka L, Bigenzahn JW, Hoermann G, Prutsch N, Merkel O, Ringler A, Sabler M, Jeryczynski G, Mayerhoefer ME, Simonitsch-Klupp I, Ocko K, Felberbauer F, Müllauer L, Prager GW, Korkmaz B, Kenner L, Sperr WR, Kralovics R, Gisslinger H, Valent P, Kubicek S, Jäger U, Staber PB, Superti-Furga G. Image-based ex-vivo drug screening for patients with aggressive haematological malignancies: interim results from a single-arm, open-label, pilot study. LANCET HAEMATOLOGY 2017; 4:e595-e606. [PMID: 29153976 PMCID: PMC5719985 DOI: 10.1016/s2352-3026(17)30208-9] [Citation(s) in RCA: 123] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Revised: 10/13/2017] [Accepted: 10/16/2017] [Indexed: 02/07/2023]
Abstract
Background Patients with refractory or relapsed haematological malignancies have few treatment options and short survival times. Identification of effective therapies with genomic-based precision medicine is hampered by intratumour heterogeneity and incomplete understanding of the contribution of various mutations within specific cancer phenotypes. Ex-vivo drug-response profiling in patient biopsies might aid effective treatment identification; however, proof of its clinical utility is limited. Methods We investigated the feasibility and clinical impact of multiparametric, single-cell, drug-response profiling in patient biopsies by immunofluorescence, automated microscopy, and image analysis, an approach we call pharmacoscopy. First, the ability of pharmacoscopy to separate responders from non-responders was evaluated retrospectively for a cohort of 20 newly diagnosed and previously untreated patients with acute myeloid leukaemia. Next, 48 patients with aggressive haematological malignancies were prospectively evaluated for pharmacoscopy-guided treatment, of whom 17 could receive the treatment. The primary endpoint was progression-free survival in pharmacoscopy-treated patients, as compared with their own progression-free survival for the most recent regimen on which they had progressive disease. This trial is ongoing and registered with ClinicalTrials.gov, number NCT03096821. Findings Pharmacoscopy retrospectively predicted the clinical response of 20 acute myeloid leukaemia patients to initial therapy with 88·1% accuracy. In this interim analysis, 15 (88%) of 17 patients receiving pharmacoscopy-guided treatment had an overall response compared with four (24%) of 17 patients with their most recent regimen (odds ratio 24·38 [95% CI 3·99–125·4], p=0·0013). 12 (71%) of 17 patients had a progression-free survival ratio of 1·3 or higher, and median progression-free survival increased by four times, from 5·7 (95% CI 4·1–12·1) weeks to 22·6 (7·4–34·0) weeks (hazard ratio 3·14 [95% CI 1·37–7·22], p=0·0075). Interpretation Routine clinical integration of pharmacoscopy for treatment selection is technically feasible, and led to improved treatment of patients with aggressive refractory haematological malignancies in an initial patient cohort, warranting further investigation. Funding Austrian Academy of Sciences; European Research Council; Austrian Science Fund; Austrian Federal Ministry of Science, Research and Economy; National Foundation for Research, Technology and Development; Anniversary Fund of the Austrian National Bank; MPN Research Foundation; European Molecular Biology Organization; and Swiss National Science Foundation.
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Affiliation(s)
- Berend Snijder
- CeMM Research Center for Molecular Medicine, Vienna, Austria; Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Gregory I Vladimer
- CeMM Research Center for Molecular Medicine, Vienna, Austria; Allcyte, Vienna, Austria
| | - Nikolaus Krall
- CeMM Research Center for Molecular Medicine, Vienna, Austria
| | - Katsuhiro Miura
- Department of Internal Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Ann-Sofie Schmolke
- Department of Internal Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Christoph Kornauth
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | | | - Hye-Soo Choi
- Department of Internal Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Emiel van der Kouwe
- Department of Internal Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Sinan Gültekin
- Department of Internal Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Lukas Kazianka
- Department of Internal Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | | | - Gregor Hoermann
- Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Nicole Prutsch
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | - Olaf Merkel
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | - Anna Ringler
- CeMM Research Center for Molecular Medicine, Vienna, Austria; Christian Doppler Laboratory for Chemical Epigenetics and Anti-Infectives, Vienna, Austria
| | - Monika Sabler
- CeMM Research Center for Molecular Medicine, Vienna, Austria
| | - Georg Jeryczynski
- Department of Internal Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Marius E Mayerhoefer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Katharina Ocko
- Pharmacy Department, Vienna General Hospital, Vienna, Austria
| | - Franz Felberbauer
- Division of General Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria
| | - Leonhard Müllauer
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | - Gerald W Prager
- Department of Internal Medicine I, Division of Oncology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Belgin Korkmaz
- Division of General Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria
| | - Lukas Kenner
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria; Ludwig Boltzmann Institute for Cancer Research, Vienna, Austria; Unit of Laboratory Animal Pathology, University of Veterinary Medicine, Vienna, Austria
| | - Wolfgang R Sperr
- Department of Internal Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria; Ludwig Boltzmann Institute for Cancer Research, Vienna, Austria
| | | | - Heinz Gisslinger
- Department of Internal Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Peter Valent
- Department of Internal Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria; Ludwig Boltzmann Cluster Oncology, Medical University of Vienna, Vienna, Austria
| | - Stefan Kubicek
- CeMM Research Center for Molecular Medicine, Vienna, Austria; Christian Doppler Laboratory for Chemical Epigenetics and Anti-Infectives, Vienna, Austria
| | - Ulrich Jäger
- Department of Internal Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Philipp B Staber
- Department of Internal Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Giulio Superti-Furga
- CeMM Research Center for Molecular Medicine, Vienna, Austria; Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria.
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Senft D, Leiserson MDM, Ruppin E, Ronai ZA. Precision Oncology: The Road Ahead. Trends Mol Med 2017; 23:874-898. [PMID: 28887051 PMCID: PMC5718207 DOI: 10.1016/j.molmed.2017.08.003] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 08/06/2017] [Accepted: 08/08/2017] [Indexed: 02/06/2023]
Abstract
Current efforts in precision oncology largely focus on the benefit of genomics-guided therapy. Yet, advances in sequencing techniques provide an unprecedented view of the complex genetic and nongenetic heterogeneity within individual tumors. Herein, we outline the benefits of integrating genomic and transcriptomic analyses for advanced precision oncology. We summarize relevant computational approaches to detect novel drivers and genetic vulnerabilities, suitable for therapeutic exploration. Clinically relevant platforms to functionally test predicted drugs/drug combinations for individual patients are reviewed. Finally, we highlight the technological advances in single cell analysis of tumor specimens. These may ultimately lead to the development of next-generation cancer drugs, capable of tackling the hurdles imposed by genetic and phenotypic heterogeneity on current anticancer therapies.
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Affiliation(s)
- Daniela Senft
- Tumor Initiation and Maintenance Program, NCI designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Mark D M Leiserson
- Microsoft Research New England, Cambridge, MA 02142, USA; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
| | - Eytan Ruppin
- School of Computer Sciences and Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
| | - Ze'ev A Ronai
- Tumor Initiation and Maintenance Program, NCI designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA; Technion Integrated Cancer Center, Faculty of Medicine, Technion, Israel Institute of Technology, Haifa, 31096, Israel.
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Dhandapani M, Goldman A. Preclinical Cancer Models and Biomarkers for Drug Development: New Technologies and Emerging Tools. ACTA ACUST UNITED AC 2017; 8. [PMID: 29285415 PMCID: PMC5743226 DOI: 10.4172/2155-9929.1000356] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Background and purpose Predicting the efficacy of anticancer therapy is the holy grail of drug development and treatment selection in the clinic. To achieve this goal, scientists require pre-clinical models that can reliably screen anticancer agents with robust clinical correlation. However, there is increasing challenge to develop models that can accurately capture the diversity of the tumor ecosystem, and therefore reliably predict how tumors respond or resistant to treatment. Indeed, tumors are made up of a heterogeneous landscape comprising malignant cells, normal and abnormal stroma, immune cells, and dynamic microenvironment containing chemokines, cytokines and growth factors. In this mini-review we present a focused, brief perspective on emerging preclinical models for anticancer therapy that attempt to address the challenge posed by tumor heterogeneity, highlighting biomarkers of response and resistance. Recent findings Starting from 2-dimensional and 3-dimensional in-vitro models, we discuss how organoid co-cultures have led to accelerated efforts in anti-cancer drug screening, and advanced our fundamental understanding for mechanisms of action using high-throughput platforms that interrogate various biomarkers of ‘clinical’ efficacy. Then, mentioning the limitations that exist, we focus on in-vivo and human explant technologies and models, which build-in intrinsic tumor heterogeneity using the native microenvironment as a scaffold. Importantly, we will address how these models can be harnessed to understand cancer immunotherapy, an emerging therapeutic strategy that seeks to recalibrate the body’s own immune system to fight cancer. Conclusion Over the past several decades, numerous model systems have emerged to address the exploding market of drug development for cancer. While all of the present models have contributed critical information about tumor biology, each one carries limitations. Harnessing pre-clinical models that incorporate cell heterogeneity is beginning to address some of the underlying challenges associated with predicting clinical efficacy of novel anticancer agents.
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Affiliation(s)
- Muthu Dhandapani
- Integrative Immuno-Oncology Center, Mitra Biotech Woburn, MA 01801, USA
| | - Aaron Goldman
- Integrative Immuno-Oncology Center, Mitra Biotech Woburn, MA 01801, USA.,Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.,Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
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