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An Ex Vivo Platform for the Prediction of Clinical Response in Multiple Myeloma
Multiple myeloma remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system, Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathema...
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Published in: | Cancer research (Chicago, Ill.) Ill.), 2017-06, Vol.77 (12), p.3336-3351 |
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creator | Silva, Ariosto Silva, Maria C Sudalagunta, Praneeth Distler, Allison Jacobson, Timothy Collins, Aunshka Nguyen, Tuan Song, Jinming Chen, Dung-Tsa Chen, Lu Cubitt, Christopher Baz, Rachid Perez, Lia Rebatchouk, Dmitri Dalton, William Greene, James Gatenby, Robert Gillies, Robert Sontag, Eduardo Meads, Mark B Shain, Kenneth H |
description | Multiple myeloma remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system,
Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathematical models parameterized by an
assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from multiple myeloma patients, allowing us to predict clinical response to up to 31 drugs within 5 days after bone marrow biopsy. From a cohort of 52 multiple myeloma patients, EMMA correctly classified 96% as responders/nonresponders and correctly classified 79% according to International Myeloma Working Group stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson
= 0.5658,
< 0.0001). Preliminary estimates indicate that, among the patients enrolled in this study, 60% were treated with at least one ineffective agent from their therapy combination regimen, whereas 30% would have responded better if treated with another available drug or combination. Two
clinical trials with experimental agents ricolinostat and venetoclax, in a cohort of 19 multiple myeloma patient samples, yielded consistent results with recent phase I/II trials, suggesting that EMMA is a feasible platform for estimating clinical efficacy of drugs and inclusion criteria screening. This unique platform, specifically designed to predict therapeutic response in multiple myeloma patients within a clinically actionable time frame, has shown high predictive accuracy in patients treated with combinations of different classes of drugs. The accuracy, reproducibility, short turnaround time, and high-throughput potential of this platform demonstrate EMMA's promise as a decision support system for therapeutic management of multiple myeloma.
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doi_str_mv | 10.1158/0008-5472.CAN-17-0502 |
format | article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7819642</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1887052757</sourcerecordid><originalsourceid>FETCH-LOGICAL-c439t-6d79ecc77ace413d4f7fb53539eab9fbbdc930ed124ad61fcca5ecf467eae6b53</originalsourceid><addsrcrecordid>eNpdkdFKHTEQhoO06NH6CJZAb3qzNtkkm-xN4XCwVdAqpe1tyGYnNZJNTpNd0bfvLurB9iZDmG9-ZvgQOqHklFKhPhFCVCW4rE83628VlRURpN5DKyqYqiTn4g1a7ZgDdFjK3fwVlIh9dFArTgiXYoWu1xGfPeBf_j7hm2BGl_KA5wePt4BvMvTejj5FnBzeBB-9NQF_h7JNsQD2EV9NYfTbAPjqEUIazDv01plQ4Pi5HqGfX85-bM6ry-uvF5v1ZWU5a8eq6WUL1kppLHDKeu6k6wQTrAXTta7retsyAj2tuekb6qw1AqzjjQQDzUweoc9PudupG6C3EMdsgt5mP5j8qJPx-t9O9Lf6d7rXUtG24fUc8PE5IKc_E5RRD75YCMFESFPRVClJRC2FnNEP_6F3acpxPk_TVjGmWtYslHiibE6lZHC7ZSjRizK96NCLDj0r01TqRdk89_71JbupF0fsL2PPk5Q</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1983389367</pqid></control><display><type>article</type><title>An Ex Vivo Platform for the Prediction of Clinical Response in Multiple Myeloma</title><source>EZB Electronic Journals Library</source><creator>Silva, Ariosto ; Silva, Maria C ; Sudalagunta, Praneeth ; Distler, Allison ; Jacobson, Timothy ; Collins, Aunshka ; Nguyen, Tuan ; Song, Jinming ; Chen, Dung-Tsa ; Chen, Lu ; Cubitt, Christopher ; Baz, Rachid ; Perez, Lia ; Rebatchouk, Dmitri ; Dalton, William ; Greene, James ; Gatenby, Robert ; Gillies, Robert ; Sontag, Eduardo ; Meads, Mark B ; Shain, Kenneth H</creator><creatorcontrib>Silva, Ariosto ; Silva, Maria C ; Sudalagunta, Praneeth ; Distler, Allison ; Jacobson, Timothy ; Collins, Aunshka ; Nguyen, Tuan ; Song, Jinming ; Chen, Dung-Tsa ; Chen, Lu ; Cubitt, Christopher ; Baz, Rachid ; Perez, Lia ; Rebatchouk, Dmitri ; Dalton, William ; Greene, James ; Gatenby, Robert ; Gillies, Robert ; Sontag, Eduardo ; Meads, Mark B ; Shain, Kenneth H</creatorcontrib><description>Multiple myeloma remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system,
Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathematical models parameterized by an
assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from multiple myeloma patients, allowing us to predict clinical response to up to 31 drugs within 5 days after bone marrow biopsy. From a cohort of 52 multiple myeloma patients, EMMA correctly classified 96% as responders/nonresponders and correctly classified 79% according to International Myeloma Working Group stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson
= 0.5658,
< 0.0001). Preliminary estimates indicate that, among the patients enrolled in this study, 60% were treated with at least one ineffective agent from their therapy combination regimen, whereas 30% would have responded better if treated with another available drug or combination. Two
clinical trials with experimental agents ricolinostat and venetoclax, in a cohort of 19 multiple myeloma patient samples, yielded consistent results with recent phase I/II trials, suggesting that EMMA is a feasible platform for estimating clinical efficacy of drugs and inclusion criteria screening. This unique platform, specifically designed to predict therapeutic response in multiple myeloma patients within a clinically actionable time frame, has shown high predictive accuracy in patients treated with combinations of different classes of drugs. The accuracy, reproducibility, short turnaround time, and high-throughput potential of this platform demonstrate EMMA's promise as a decision support system for therapeutic management of multiple myeloma.
.</description><identifier>ISSN: 0008-5472</identifier><identifier>EISSN: 1538-7445</identifier><identifier>DOI: 10.1158/0008-5472.CAN-17-0502</identifier><identifier>PMID: 28400475</identifier><language>eng</language><publisher>United States: American Association for Cancer Research, Inc</publisher><subject>Algorithms ; Antineoplastic Agents - therapeutic use ; Biopsy ; Bone marrow ; Cancer ; Clinical trials ; Decision Support Techniques ; Digital imaging ; Drug screening ; Drugs ; High-Throughput Screening Assays ; Humans ; Image processing ; Mathematical models ; Models, Theoretical ; Multiple myeloma ; Multiple Myeloma - drug therapy ; Patients</subject><ispartof>Cancer research (Chicago, Ill.), 2017-06, Vol.77 (12), p.3336-3351</ispartof><rights>2017 American Association for Cancer Research.</rights><rights>Copyright American Association for Cancer Research, Inc. Jun 15, 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c439t-6d79ecc77ace413d4f7fb53539eab9fbbdc930ed124ad61fcca5ecf467eae6b53</citedby><cites>FETCH-LOGICAL-c439t-6d79ecc77ace413d4f7fb53539eab9fbbdc930ed124ad61fcca5ecf467eae6b53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,777,781,882,27905,27906</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28400475$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Silva, Ariosto</creatorcontrib><creatorcontrib>Silva, Maria C</creatorcontrib><creatorcontrib>Sudalagunta, Praneeth</creatorcontrib><creatorcontrib>Distler, Allison</creatorcontrib><creatorcontrib>Jacobson, Timothy</creatorcontrib><creatorcontrib>Collins, Aunshka</creatorcontrib><creatorcontrib>Nguyen, Tuan</creatorcontrib><creatorcontrib>Song, Jinming</creatorcontrib><creatorcontrib>Chen, Dung-Tsa</creatorcontrib><creatorcontrib>Chen, Lu</creatorcontrib><creatorcontrib>Cubitt, Christopher</creatorcontrib><creatorcontrib>Baz, Rachid</creatorcontrib><creatorcontrib>Perez, Lia</creatorcontrib><creatorcontrib>Rebatchouk, Dmitri</creatorcontrib><creatorcontrib>Dalton, William</creatorcontrib><creatorcontrib>Greene, James</creatorcontrib><creatorcontrib>Gatenby, Robert</creatorcontrib><creatorcontrib>Gillies, Robert</creatorcontrib><creatorcontrib>Sontag, Eduardo</creatorcontrib><creatorcontrib>Meads, Mark B</creatorcontrib><creatorcontrib>Shain, Kenneth H</creatorcontrib><title>An Ex Vivo Platform for the Prediction of Clinical Response in Multiple Myeloma</title><title>Cancer research (Chicago, Ill.)</title><addtitle>Cancer Res</addtitle><description>Multiple myeloma remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system,
Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathematical models parameterized by an
assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from multiple myeloma patients, allowing us to predict clinical response to up to 31 drugs within 5 days after bone marrow biopsy. From a cohort of 52 multiple myeloma patients, EMMA correctly classified 96% as responders/nonresponders and correctly classified 79% according to International Myeloma Working Group stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson
= 0.5658,
< 0.0001). Preliminary estimates indicate that, among the patients enrolled in this study, 60% were treated with at least one ineffective agent from their therapy combination regimen, whereas 30% would have responded better if treated with another available drug or combination. Two
clinical trials with experimental agents ricolinostat and venetoclax, in a cohort of 19 multiple myeloma patient samples, yielded consistent results with recent phase I/II trials, suggesting that EMMA is a feasible platform for estimating clinical efficacy of drugs and inclusion criteria screening. This unique platform, specifically designed to predict therapeutic response in multiple myeloma patients within a clinically actionable time frame, has shown high predictive accuracy in patients treated with combinations of different classes of drugs. The accuracy, reproducibility, short turnaround time, and high-throughput potential of this platform demonstrate EMMA's promise as a decision support system for therapeutic management of multiple myeloma.
.</description><subject>Algorithms</subject><subject>Antineoplastic Agents - therapeutic use</subject><subject>Biopsy</subject><subject>Bone marrow</subject><subject>Cancer</subject><subject>Clinical trials</subject><subject>Decision Support Techniques</subject><subject>Digital imaging</subject><subject>Drug screening</subject><subject>Drugs</subject><subject>High-Throughput Screening Assays</subject><subject>Humans</subject><subject>Image processing</subject><subject>Mathematical models</subject><subject>Models, Theoretical</subject><subject>Multiple myeloma</subject><subject>Multiple Myeloma - drug therapy</subject><subject>Patients</subject><issn>0008-5472</issn><issn>1538-7445</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNpdkdFKHTEQhoO06NH6CJZAb3qzNtkkm-xN4XCwVdAqpe1tyGYnNZJNTpNd0bfvLurB9iZDmG9-ZvgQOqHklFKhPhFCVCW4rE83628VlRURpN5DKyqYqiTn4g1a7ZgDdFjK3fwVlIh9dFArTgiXYoWu1xGfPeBf_j7hm2BGl_KA5wePt4BvMvTejj5FnBzeBB-9NQF_h7JNsQD2EV9NYfTbAPjqEUIazDv01plQ4Pi5HqGfX85-bM6ry-uvF5v1ZWU5a8eq6WUL1kppLHDKeu6k6wQTrAXTta7retsyAj2tuekb6qw1AqzjjQQDzUweoc9PudupG6C3EMdsgt5mP5j8qJPx-t9O9Lf6d7rXUtG24fUc8PE5IKc_E5RRD75YCMFESFPRVClJRC2FnNEP_6F3acpxPk_TVjGmWtYslHiibE6lZHC7ZSjRizK96NCLDj0r01TqRdk89_71JbupF0fsL2PPk5Q</recordid><startdate>20170615</startdate><enddate>20170615</enddate><creator>Silva, Ariosto</creator><creator>Silva, Maria C</creator><creator>Sudalagunta, Praneeth</creator><creator>Distler, Allison</creator><creator>Jacobson, Timothy</creator><creator>Collins, Aunshka</creator><creator>Nguyen, Tuan</creator><creator>Song, Jinming</creator><creator>Chen, Dung-Tsa</creator><creator>Chen, Lu</creator><creator>Cubitt, Christopher</creator><creator>Baz, Rachid</creator><creator>Perez, Lia</creator><creator>Rebatchouk, Dmitri</creator><creator>Dalton, William</creator><creator>Greene, James</creator><creator>Gatenby, Robert</creator><creator>Gillies, Robert</creator><creator>Sontag, Eduardo</creator><creator>Meads, Mark B</creator><creator>Shain, Kenneth H</creator><general>American Association for Cancer Research, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7T5</scope><scope>7TM</scope><scope>7TO</scope><scope>7U9</scope><scope>8FD</scope><scope>FR3</scope><scope>H94</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20170615</creationdate><title>An Ex Vivo Platform for the Prediction of Clinical Response in Multiple Myeloma</title><author>Silva, Ariosto ; 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Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system,
Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathematical models parameterized by an
assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from multiple myeloma patients, allowing us to predict clinical response to up to 31 drugs within 5 days after bone marrow biopsy. From a cohort of 52 multiple myeloma patients, EMMA correctly classified 96% as responders/nonresponders and correctly classified 79% according to International Myeloma Working Group stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson
= 0.5658,
< 0.0001). Preliminary estimates indicate that, among the patients enrolled in this study, 60% were treated with at least one ineffective agent from their therapy combination regimen, whereas 30% would have responded better if treated with another available drug or combination. Two
clinical trials with experimental agents ricolinostat and venetoclax, in a cohort of 19 multiple myeloma patient samples, yielded consistent results with recent phase I/II trials, suggesting that EMMA is a feasible platform for estimating clinical efficacy of drugs and inclusion criteria screening. This unique platform, specifically designed to predict therapeutic response in multiple myeloma patients within a clinically actionable time frame, has shown high predictive accuracy in patients treated with combinations of different classes of drugs. The accuracy, reproducibility, short turnaround time, and high-throughput potential of this platform demonstrate EMMA's promise as a decision support system for therapeutic management of multiple myeloma.
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subjects | Algorithms Antineoplastic Agents - therapeutic use Biopsy Bone marrow Cancer Clinical trials Decision Support Techniques Digital imaging Drug screening Drugs High-Throughput Screening Assays Humans Image processing Mathematical models Models, Theoretical Multiple myeloma Multiple Myeloma - drug therapy Patients |
title | An Ex Vivo Platform for the Prediction of Clinical Response in Multiple Myeloma |
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