{"doi":"10.1200/po.23.00349","title":"Cellular Mass Response to Therapy Correlates With Clinical Response for a Range of Malignancies","abstract":"<jats:sec><jats:title>PURPOSE</jats:title><jats:p> Cancer patients with advanced-stage disease have poor prognosis, typically having limited options for efficacious treatment, and genomics-based therapy guidance continues to benefit only a fraction of patients. Next-generation ex vivo approaches, such as cell mass-based response testing (MRT), offer an alternative precision medicine approach for a broader population of patients with cancer, but validation of clinical feasibility and potential impact remain necessary. </jats:p></jats:sec><jats:sec><jats:title>MATERIALS AND METHODS</jats:title><jats:p> We evaluated the clinical feasibility and accuracy of using live-cell MRT to predict patient drug sensitivity. Using a unified measurement workflow with a 48-hour result turnaround time, samples were subjected to MRT after treatment with a panel of drugs in vitro. After completion of therapeutic course, clinical response data were correlated with MRT-based predictions of outcome. Specimens were collected from 104 patients with solid (n = 69) and hematologic (n = 35) malignancies, using tissue formats including needle biopsies, malignant fluids, bone marrow aspirates, and blood samples. Of the 81 (78%) specimens qualified for MRT, 41 (51%) patients receiving physician-selected therapies had treatments matched to MRT. </jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p> MRT demonstrated high concordance with clinical responses with an odds ratio (OR) of 14.80 ( P = .0003 [95% CI, 2.83 to 102.9]). This performance held for both solid and hematologic malignances with ORs of 20.67 ( P = .0128 [95% CI, 1.45 to 1,375.57]) and 8.20 ( P = .045 [95% CI, 0.77 to 133.56]), respectively. Overall, these results had a predictive accuracy of 80% ( P = .0026 [95% CI, 65 to 91]). </jats:p></jats:sec><jats:sec><jats:title>CONCLUSION</jats:title><jats:p> MRT showed highly significant correlation with clinical response to therapy. Routine clinical use is technically feasible and broadly applicable to a wide range of samples and malignancy types, supporting the need for future validation studies. </jats:p></jats:sec>","journal":"JCO Precision Oncology","year":2024,"id":643350,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1331682,"name":"Robert J. Kimmerling","orcid":null,"position":1,"is_corresponding":false},{"id":1331684,"name":"Selim Olcum","orcid":null,"position":2,"is_corresponding":false},{"id":948824,"name":"Madeleine Vacha","orcid":null,"position":3,"is_corresponding":false},{"id":948825,"name":"Rachel LaBella","orcid":null,"position":4,"is_corresponding":false},{"id":948344,"name":"Anthony Minnah","orcid":"0000-0001-8934-1554","position":5,"is_corresponding":false},{"id":627236,"name":"Katelin Katsis","orcid":null,"position":6,"is_corresponding":false},{"id":948827,"name":"Juanita Fujii","orcid":null,"position":7,"is_corresponding":false},{"id":948828,"name":"Zayna Shaheen","orcid":null,"position":8,"is_corresponding":false},{"id":948829,"name":"Srividya Sundaresan","orcid":null,"position":9,"is_corresponding":false},{"id":1673870,"name":"Joseph Criscitiello","orcid":null,"position":10,"is_corresponding":false},{"id":1673871,"name":"Ruben Niesvizky","orcid":null,"position":11,"is_corresponding":false},{"id":267975,"name":"Noopur Raje","orcid":"0000-0003-3066-1275","position":12,"is_corresponding":false},{"id":644062,"name":"Andrew R. Branagan","orcid":"0000-0002-3868-9267","position":13,"is_corresponding":false},{"id":1239399,"name":"Amrita Krishnan","orcid":"0000-0002-8484-175X","position":14,"is_corresponding":false},{"id":25424,"name":"Sundar Jagannath","orcid":"0000-0003-2934-6518","position":15,"is_corresponding":false},{"id":50182,"name":"Samir Parekh","orcid":"0000-0001-9694-8469","position":16,"is_corresponding":false},{"id":481403,"name":"Adam S. Sperling","orcid":"0000-0002-9369-4413","position":17,"is_corresponding":false},{"id":574843,"name":"Cara A. Rosenbaum","orcid":"0000-0002-3576-9106","position":18,"is_corresponding":false},{"id":228854,"name":"Nikhil C. Munshi","orcid":"0000-0002-7344-9795","position":19,"is_corresponding":false},{"id":565017,"name":"Marlise R. Luskin","orcid":"0000-0002-5781-4529","position":20,"is_corresponding":false},{"id":948832,"name":"Anobel Tamrazi","orcid":null,"position":21,"is_corresponding":false},{"id":948346,"name":"Clifford A. Reid","orcid":"0000-0002-9438-9349","position":22,"is_corresponding":false},{"id":948342,"name":"Mark M. Stevens","orcid":"0000-0001-9207-4979","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Cellular Mass Response to Therapy Correlates With Clinical Response for a Range of Malignancies","abstract":"<jats:sec><jats:title>PURPOSE</jats:title><jats:p> Cancer patients with advanced-stage disease have poor prognosis, typically having limited options for efficacious treatment, and genomics-based therapy guidance continues to benefit only a fraction of patients. Next-generation ex vivo approaches, such as cell mass-based response testing (MRT), offer an alternative precision medicine approach for a broader population of patients with cancer, but validation of clinical feasibility and potential impact remain necessary. </jats:p></jats:sec><jats:sec><jats:title>MATERIALS AND METHODS</jats:title><jats:p> We evaluated the clinical feasibility and accuracy of using live-cell MRT to predict patient drug sensitivity. Using a unified measurement workflow with a 48-hour result turnaround time, samples were subjected to MRT after treatment with a panel of drugs in vitro. After completion of therapeutic course, clinical response data were correlated with MRT-based predictions of outcome. Specimens were collected from 104 patients with solid (n = 69) and hematologic (n = 35) malignancies, using tissue formats including needle biopsies, malignant fluids, bone marrow aspirates, and blood samples. Of the 81 (78%) specimens qualified for MRT, 41 (51%) patients receiving physician-selected therapies had treatments matched to MRT. </jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p> MRT demonstrated high concordance with clinical responses with an odds ratio (OR) of 14.80 ( P = .0003 [95% CI, 2.83 to 102.9]). This performance held for both solid and hematologic malignances with ORs of 20.67 ( P = .0128 [95% CI, 1.45 to 1,375.57]) and 8.20 ( P = .045 [95% CI, 0.77 to 133.56]), respectively. Overall, these results had a predictive accuracy of 80% ( P = .0026 [95% CI, 65 to 91]). </jats:p></jats:sec><jats:sec><jats:title>CONCLUSION</jats:title><jats:p> MRT showed highly significant correlation with clinical response to therapy. Routine clinical use is technically feasible and broadly applicable to a wide range of samples and malignancy types, supporting the need for future validation studies. </jats:p></jats:sec>","is_dataset_classified":null,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38237098","pmcid":"PMC10805426","openalex_id":"https://openalex.org/W4390968158","authors":[],"funders":[{"funder_name":"NCI NIH HHS","grant_id":"K08 CA252174","title":null},{"funder_name":"National Science Foundation","grant_id":"2026060","title":"SBIR Phase II:  Developing suspended microchannel resonators as a platform for personalized medicine in cancer"}],"total_grants":2,"fwci":1.0722,"citation_percentile":0.73880234,"influential_citations":0,"citation_trend":[{"year":2024,"count":2},{"year":2025,"count":2}],"oa_status":"hybrid","license":"cc-by-nc-nd","oa_locations":[{"url":"https://doi.org/10.1200/po.23.00349","host_type":"journal"},{"url":"https://doi.org/10.1200/po.23.00349","host_type":"publisher"},{"url":"https://ascopubs.org/doi/pdfdirect/10.1200/PO.23.00349","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38237098","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10805426","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10805426/pdf/po-8-e2300349.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC10805426","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC10805426?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.1200/PO.23.00349","host_type":""}],"fields_of_study":["MRI in cancer diagnosis","Radiomics and Machine Learning in Medical Imaging","Cancer Genomics and Diagnostics","0301 basic medicine","03 medical and health sciences","0302 clinical medicine","Humans","Neoplasms","Hematologic Neoplasms"],"mesh_terms":["Humans","Neoplasms","Hematologic Neoplasms"],"keywords":["Medicine","Internal medicine","Concordance","Clinical trial","Cancer","Oncology","Population","Neoplasms","Hematologic Neoplasms","Humans","ORIGINAL REPORTS"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-08T15:54:45.804576Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}