INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCER
在临床联合试验中整合组学和定量成像数据来预测三阴性乳腺癌的治疗反应
基本信息
- 批准号:10020941
- 负责人:
- 金额:$ 63.29万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-09-19 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAlgorithmsAnimalsBiologicalBiological MarkersBiopsyBlood VesselsBreast Cancer PatientBreast Cancer TreatmentCancer BiologyCancer PatientCarboplatinCaringCellularityClinicalClinical TrialsCommunitiesConsensusCoupledDataDevelopmentDiseaseDisease ProgressionEnrollmentEvaluationFundingGenomicsGoalsHealthcareHumanImageImaging technologyImmunotherapyIn complete remissionInformaticsMachine LearningMagnetic Resonance ImagingMalignant NeoplasmsMass Spectrum AnalysisMethodologyMethodsModelingMolecularMolecular TargetMusNeoadjuvant TherapyOnline SystemsPathologicPatient-Focused OutcomesPatientsPhenotypePhysiologicalPrediction of Response to TherapyProteinsRecurrenceRegimenReproducibilityResearchResearch PersonnelResource InformaticsResourcesScienceSignal TransductionSurrogate EndpointTestingTimeTreatment EfficacyTreatment ProtocolsWorkXenograft ModelXenograft procedureanimal dataarmbasebioinformatics resourcecandidate identificationchemotherapycohortcontrast enhanceddata resourcedata sharingdiffusion weighteddocetaxelexomehigh resolution imaginghuman datahuman modelimaging biomarkerimaging modalityimprovedindexinginformatics toolinnovationinterestmRNA Expressionmachine learning algorithmmagnetic resonance imaging biomarkermalignant breast neoplasmmolecular markernovelonline resourceoptimal treatmentsoutcome forecastpersonalized therapeuticpre-clinicalpreclinical trialpredicting responsepredictive markerpredictive modelingprospectivequantitative imagingrepairedresponseresponse biomarkertargeted treatmenttooltranscriptome sequencingtreatment responsetriple-negative invasive breast carcinomatumor
项目摘要
Project Summary
Triple negative breast cancer (TNBC) is a very challenging disease because it is biologically aggressive, there
are no targeted therapies, and, consequently, patients have poor prognosis. Although immunotherapy is
promising for treating many cancers, TNBC lacks specific molecular targets, no predictive biomarkers to
chemotherapy response have yet been identified, and treatment response is difficult to evaluate using current
biomarker assessments. Patient-derived xenograft (PDX) models of TNBC offer the exciting opportunity of
evaluating this disease in terms of molecular features (e.g., genomic copy number, whole exome sequence, and
mRNA expression) to identify candidate “omic” biomarkers that best predict the ultimate response to treatment
and could provide surrogate endpoints to validate novel imaging biomarkers in co-clinical trial human trails.
Moreover, emerging quantitative MRI methods, such as dynamic contrast enhanced magnetic resonance
imaging (DCE-MRI) and diffusion weighted MRI (DW-MRI), contain rich physiological signals in the images for
predicting treatment response, but it is challenging to integrate both animal and human data to reliably predict the
treatment response. A paradigm of “co-clinical trials” is emerging in which new treatments are evaluated in
animals, and the results guide treatments in clinical trials, but there is a paucity of informatics tools and resources
to enable analyses in such animal-to-human work. We believe that an informatics-based methodology that
integrates molecular `omics' and imaging data will propel advances in TNBC by enabling development of
machine learning models to predict the response to therapies. In order to develop research resources that will
encourage consensus on how quantitative imaging methods are optimized to improve the quality of imaging
results for co-clinical trials, we will leverage an ongoing co-clinical trial we are undertaking to pursue the following
specific aims: (1) Identify molecular biomarkers that predict response in TNBC patient-derived xenografts (PDX);
(2) Identify quantitative MRI biomarkers that predict response in TNBC patient-derived xenografts; and (3)
Evaluate our informatics tools in a prospective co-clinical trial. Our proposed research is significant and
innovative because it leverages advances in basic cancer biology, state-of-the-art imaging technologies, and
informatics methods to develop a resource to catalyze discovery in this important disease. Our PDX-based
approach will provide the cancer community with a rational, iterative, combined pre-clinical and clinical
methodology and supporting data resource for making progressively more refined and personalized therapeutic
regimens for TNBC patients. Our methods and tools will likely also generalize to other cancers and could,
therefore, substantially benefit the care of all cancer patients.
项目摘要
三阴性乳腺癌(TNBC)是一种非常具有挑战性的疾病,因为它在生物学上具有侵袭性,
没有靶向治疗,因此,患者预后不良。虽然免疫疗法是
对于治疗许多癌症来说,TNBC缺乏特异性的分子靶点,没有预测性的生物标志物,
化疗反应尚未确定,治疗反应很难使用目前的方法进行评估。
生物标志物评估。TNBC的患者来源的异种移植物(PDX)模型提供了令人兴奋的机会,
根据分子特征(例如,基因组拷贝数,整个外显子组序列,和
mRNA表达)以鉴定最佳预测对治疗的最终响应的候选“组学”生物标志物
并且可以提供替代终点以在共同临床试验人体试验中验证新的成像生物标志物。
此外,新兴的定量MRI方法,如动态对比增强磁共振,
磁共振成像(DCE-MRI)和扩散加权MRI(DW-MRI)在图像中包含丰富的生理信号,
预测治疗反应,但整合动物和人类数据以可靠地预测治疗反应是具有挑战性的。
治疗反应。一种“联合临床试验”的模式正在出现,在这种模式中,
动物,结果指导临床试验中的治疗,但缺乏信息学工具和资源
以便能够分析这种动物到人类的工作。我们认为,一个基于信息的方法,
整合分子“组学”和成像数据将推动TNBC的进步,
机器学习模型来预测对治疗的反应。为了开发研究资源,
鼓励就如何优化定量成像方法以提高成像质量达成共识
联合临床试验的结果,我们将利用我们正在进行的一项正在进行的联合临床试验来实现以下目标
(1)鉴定预测TNBC患者来源的异种移植物(PDX)中的反应的分子生物标志物;
(2)鉴定预测TNBC患者来源的异种移植物中的反应的定量MRI生物标志物;和(3)
在前瞻性联合临床试验中评估我们的信息学工具。我们的研究意义重大,
创新,因为它利用了基础癌症生物学的进步,最先进的成像技术,
信息学方法来开发一种资源,以催化这种重要疾病的发现。我们基于PDX的
该方法将为癌症社区提供合理的,迭代的,结合临床前和临床
方法和支持数据资源,以使治疗逐步更加精细和个性化
TNBC患者的治疗方案。我们的方法和工具也可能推广到其他癌症,
因此,大大有利于所有癌症患者的护理。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Michael T. Lewis其他文献
UBA1 inhibition sensitizes cancer cells to PARP inhibitors
UBA1抑制剂可使癌细胞对聚ADP核糖聚合酶(PARP)抑制剂更敏感 。
- DOI:
10.1016/j.xcrm.2024.101834 - 发表时间:
2024-12-17 - 期刊:
- 影响因子:10.600
- 作者:
Sharad Awasthi;Lacey E. Dobrolecki;Christina Sallas;Xudong Zhang;Yang Li;Sima Khazaei;Sumanta Ghosh;Collene R. Jeter;Jinsong Liu;Gordon B. Mills;Shannon N. Westin;Michael T. Lewis;Weiyi Peng;Anil K. Sood;Timothy A. Yap;S. Stephen Yi;Daniel J. McGrail;Nidhi Sahni - 通讯作者:
Nidhi Sahni
Correction to: In Vivo Modeling of Human Breast Cancer Using Cell Line and Patient-Derived Xenografts
- DOI:
10.1007/s10911-022-09524-8 - 发表时间:
2022-06-01 - 期刊:
- 影响因子:3.600
- 作者:
Eric P. Souto;Lacey E. Dobrolecki;Hugo Villanueva;Andrew G. Sikora;Michael T. Lewis - 通讯作者:
Michael T. Lewis
ZP4: A novel target for CAR-T cell therapy in triple negative breast cancer
ZP4:三阴性乳腺癌中嵌合抗原受体 T 细胞疗法的新靶点
- DOI:
10.1016/j.ymthe.2025.02.029 - 发表时间:
2025-04-02 - 期刊:
- 影响因子:12.000
- 作者:
Lauren K. Somes;Jonathan T. Lei;Xinpei Yi;Diego F. Chamorro;Paul Shafer;Ahmed Z. Gad;Lacey E. Dobrolecki;Emily Madaras;Nabil Ahmed;Michael T. Lewis;Bing Zhang;Valentina Hoyos - 通讯作者:
Valentina Hoyos
Next Stop, the Twilight Zone: Hedgehog Network Regulation of Mammary Gland Development
- DOI:
10.1023/b:jomg.0000037160.24731.35 - 发表时间:
2004-04-01 - 期刊:
- 影响因子:3.600
- 作者:
Michael T. Lewis;Jacqueline M. Veltmaat - 通讯作者:
Jacqueline M. Veltmaat
The Effects of Camera Monitoring on Police Officer Performance in Critical Incident Situations: a MILO Range Simulator Study
摄像机监控对危急事件情况下警务人员表现的影响:MILO 范围模拟器研究
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:1.6
- 作者:
W. Kalkhoff;Joshua W. Pollock;Matthew A. Pfeiffer;Brian A Chopko;P. Palmieri;Michael T. Lewis;Joseph Sidoti;Daniel Burrill;Jon Overton;Graem Sigelmier - 通讯作者:
Graem Sigelmier
Michael T. Lewis的其他文献
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{{ truncateString('Michael T. Lewis', 18)}}的其他基金
INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCER
在临床联合试验中整合组学和定量成像数据来预测三阴性乳腺癌的治疗反应
- 批准号:
10688170 - 财政年份:2019
- 资助金额:
$ 63.29万 - 项目类别:
INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCER
在临床联合试验中整合组学和定量成像数据来预测三阴性乳腺癌的治疗反应
- 批准号:
10241425 - 财政年份:2019
- 资助金额:
$ 63.29万 - 项目类别:
INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCER
在临床联合试验中整合组学和定量成像数据来预测三阴性乳腺癌的治疗反应
- 批准号:
10478972 - 财政年份:2019
- 资助金额:
$ 63.29万 - 项目类别:
Multi-omic, Exposure-informed, Genealogical Approach (mErGE)
多组学、暴露信息、系谱方法 (mErGE)
- 批准号:
10370622 - 财政年份:2017
- 资助金额:
$ 63.29万 - 项目类别:
Research Project 2: Targetin Tumor-Initiating Cell (TIC) Heterogeneity To Overcome Chemotherapy Resistance
研究项目2:靶向肿瘤起始细胞(TIC)异质性以克服化疗耐药性
- 批准号:
10681678 - 财政年份:2017
- 资助金额:
$ 63.29万 - 项目类别:
Targeting mitochondrial dependencies in chemo resistant triple negative breast cancer
针对化疗耐药三阴性乳腺癌的线粒体依赖性
- 批准号:
10581266 - 财政年份:2017
- 资助金额:
$ 63.29万 - 项目类别:
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