TY - JOUR
T1 - Clinically relevant inflammatory breast cancer patient-derived xenograft–derived ex vivo model for evaluation of tumor-specific therapies
AU - Eckhardt, Bedrich
AU - Gagliardi, Maria
AU - Iles, La Kesla
AU - Evans, Kurt
AU - Ivan, Cristina
AU - Liu, Xiuping
AU - Liu, Chang-Gong
AU - Souza, Glauco
AU - Uppore Kukkillaya, Arvind Rao
AU - Meric-Bernstam, Funda
AU - Ueno, Naoto T.
AU - Bartholomeusz, Geoffrey
N1 - Funding Information:
This work was supported by an Avon Foundation grant 02-2015-067 (https://www. avonfoundation.org/programs/breast-cancer/) to Geoffrey A. Bartholomeusz, the Morgan Welch Inflammatory Breast Cancer Research Program and Clinic (https://www.mdanderson.org/research/ departments-labs-institutes/programs-centers/ inflammatory-breast-cancer-research-program. html) to Geoffrey A. Bartholomeusz, Cancer Prevention and Research Institute of Texas grants RP110532 and RP150578 (www.cprit.state.tx.us) to Arvind Rao, an American Cancer Society grant RSG-16-005-01 (Cancer.org) to Arvind Rao, and a Cancer Center Support Grant P30CA016672 from the National Cancer Institute, which supports the Bioinformatics Shared Resource to Arvind Rao. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Stephanie Deming for her editing assistance and Sandra Bishnoi, an IBC patient advocate, who through her courage battling IBC is an inspiration to us all.
Publisher Copyright:
© 2018 Eckhardt et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2018/5
Y1 - 2018/5
N2 - Inflammatory breast cancer (IBC) is a rare and aggressive presentation of invasive breast cancer with a 62% to 68% 5-year survival rate. It is the most lethal form of breast cancer, and early recognition and treatment is important for patient survival. Like non-inflammatory breast cancer, IBC comprises multiple subtypes, with the triple-negative subtype being overrepresented. Although the current multimodality treatment regime of anthracycline- and tax-ane-based neoadjuvant therapy, surgery, and radiotherapy has improved the outcome of patients with triple-negative IBC, overall survival continues to be worse than in patients with non-inflammatory locally advanced breast cancer. Translation of new therapies into the clinics to successfully treat IBC has been poor, in part because of the lack of in vitro preclinical models that can accurately predict the response of the original tumor to therapy. We report the generation of a preclinical IBC patient-derived xenograft (PDX)-derived ex vivo (PDXEx) model and show that it closely replicates the tissue architecture of the original PDX tumor harvested from mice. The gene expression profile of our IBC PDXEx model had a high degree of correlation to that of the original tumor. This suggests that the process of generating the PDXEx model did not significantly alter the molecular signature of the original tumor. We demonstrate a high degree of similarity in drug response profile between a PDX mouse model and our PDXEx model generated from the same original PDX tumor tissue and treated with the same panel of drugs, indicating that our PDXEx model had high predictive value in identifying effective tumor-specific therapies. Finally, we used our PDXEx model as a platform for a robotic-based high-throughput drug screen of a 386-drug anti-cancer compound library. The top candidates identified from this drug screen all demonstrated greater therapeutic efficacy than the standard-of-care drugs used in the clinic to treat triple-negative IBC, doxorubicin and paclitaxel. Our PDXEx model is simple, and we are confident that it can be incorporated into a PDX mouse system for use as a first-pass screening platform. This will permit the identification of effective tumor-specific therapies with high predictive value in a resource-, time-, and cost-efficient manner.
AB - Inflammatory breast cancer (IBC) is a rare and aggressive presentation of invasive breast cancer with a 62% to 68% 5-year survival rate. It is the most lethal form of breast cancer, and early recognition and treatment is important for patient survival. Like non-inflammatory breast cancer, IBC comprises multiple subtypes, with the triple-negative subtype being overrepresented. Although the current multimodality treatment regime of anthracycline- and tax-ane-based neoadjuvant therapy, surgery, and radiotherapy has improved the outcome of patients with triple-negative IBC, overall survival continues to be worse than in patients with non-inflammatory locally advanced breast cancer. Translation of new therapies into the clinics to successfully treat IBC has been poor, in part because of the lack of in vitro preclinical models that can accurately predict the response of the original tumor to therapy. We report the generation of a preclinical IBC patient-derived xenograft (PDX)-derived ex vivo (PDXEx) model and show that it closely replicates the tissue architecture of the original PDX tumor harvested from mice. The gene expression profile of our IBC PDXEx model had a high degree of correlation to that of the original tumor. This suggests that the process of generating the PDXEx model did not significantly alter the molecular signature of the original tumor. We demonstrate a high degree of similarity in drug response profile between a PDX mouse model and our PDXEx model generated from the same original PDX tumor tissue and treated with the same panel of drugs, indicating that our PDXEx model had high predictive value in identifying effective tumor-specific therapies. Finally, we used our PDXEx model as a platform for a robotic-based high-throughput drug screen of a 386-drug anti-cancer compound library. The top candidates identified from this drug screen all demonstrated greater therapeutic efficacy than the standard-of-care drugs used in the clinic to treat triple-negative IBC, doxorubicin and paclitaxel. Our PDXEx model is simple, and we are confident that it can be incorporated into a PDX mouse system for use as a first-pass screening platform. This will permit the identification of effective tumor-specific therapies with high predictive value in a resource-, time-, and cost-efficient manner.
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U2 - 10.1371/journal.pone.0195932
DO - 10.1371/journal.pone.0195932
M3 - Article
C2 - 29768500
AN - SCOPUS:85047219627
SN - 1932-6203
VL - 13
JO - PloS one
JF - PloS one
IS - 5
M1 - e0195932
ER -