Multiparametric Analysis of Longitudinal Quantitative MRI Data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer

Syed, Anum; Whisenant, Jennifer G.; Barnes, Stephanie L.; Sorace, Anna G.; Yankeelov, Thomas E.

doi:10.3390/cancers12061682

Cited by 34 publications

(49 citation statements)

References 82 publications

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“…In previous studies, higher doses of trastuzumab (such as 10 mg/kg) were used to mimic clinical doses and were observed to improve tumor oxygenation [ 18 , 23 , 25 , 26 , 29 ]; however, other studies have reported using up to 35 mg/kg to show sustained response to trastuzumab [ 26 , 30 ]. We previously observed that treating an in vivo model of HER2+ breast cancer with 10 mg/kg trastuzumab prior to doxorubicin significantly improved tumor vascular delivery and sensitized the tumor to chemotherapy [ 23 ], therefore significantly altering sustained tumor burden long after therapy was stopped.…”

Section: Discussionmentioning

confidence: 99%

Modulation of the Tumor Microenvironment with Trastuzumab Enables Radiosensitization in HER2+ Breast Cancer

Song

Mansur

Yun

et al. 2022

Cancers

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DNA damage repair and tumor hypoxia contribute to intratumoral cellular and molecular heterogeneity and affect radiation response. The goal of this study is to investigate anti-HER2-induced radiosensitization of the tumor microenvironment to enhance fractionated radiotherapy in models of HER2+ breast cancer. This is monitored through in vitro and in vivo studies of phosphorylated γ-H2AX, [18F]-fluoromisonidazole (FMISO)-PET, and transcriptomic analysis. In vitro, HER2+ breast cancer cell lines were treated with trastuzumab prior to radiation and DNA double-strand breaks (DSB) were quantified. In vivo, HER2+ human cell line or patient-derived xenograft models were treated with trastuzumab, fractionated radiation, or a combination and monitored longitudinally with [18F]-FMISO-PET. In vitro DSB analysis revealed that trastuzumab administered prior to fractionated radiation increased DSB. In vivo, trastuzumab prior to fractionated radiation significantly reduced hypoxia, as detected through decreased [18F]-FMISO SUV, synergistically improving long-term tumor response. Significant changes in IL-2, IFN-gamma, and THBS-4 were observed in combination-treated tumors. Trastuzumab prior to fractionated radiation synergistically increases radiotherapy in vitro and in vivo in HER2+ breast cancer which is independent of anti-HER2 response alone. Modulation of the tumor microenvironment, through increased tumor oxygenation and decreased DNA damage response, can be translated to other cancers with first-line radiation therapy.

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Section: Discussionmentioning

confidence: 99%

Modulation of the Tumor Microenvironment with Trastuzumab Enables Radiosensitization in HER2+ Breast Cancer

Song

Mansur

Yun

et al. 2022

Cancers

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“…The spatial variations may change during tumour growth [ 45 ] or with treatment. Imaging-based biomarkers have the potential to evaluate intra-tumoural heterogeneity and its relationship to tumour growth and response to therapy [ 26 , 48 ]. MRI is a useful modality for evaluating spatial and temporal variations in alterations in the biologic characteristics of tumours that may include changes in apoptosis, cellular proliferation, cellular invasion, and angiogenesis [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

“…The validity of different DCE-MRI models to assess tumour haemodynamic heterogeneity has not been studied in detail. A recent study identified physiological tumour habitats from DCE-MRI data using parameters derived from Tofts model and evaluated their alterations in response to therapy in preclinical breast cancer models [ 26 ]. Although

, K ep can be estimated using the commonly used general kinetic or Tofts (GKM) or its extended version (ETM).…”

Section: Introductionmentioning

confidence: 99%

Assessing Tumour Haemodynamic Heterogeneity and Response to Choline Kinase Inhibition Using Clustered Dynamic Contrast Enhanced MRI Parameters in Rodent Models of Glioblastoma

Bhaduri

Lesbats

Sharkey

et al. 2022

Cancers

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To investigate the utility of DCE-MRI derived pharmacokinetic parameters in evaluating tumour haemodynamic heterogeneity and treatment response in rodent models of glioblastoma, imaging was performed on intracranial F98 and GL261 glioblastoma bearing rodents. Clustering of the DCE-MRI-based parametric maps (using Tofts, extended Tofts, shutter speed, two-compartment, and the second generation shutter speed models) was performed using a hierarchical clustering algorithm, resulting in areas with poor fit (reflecting necrosis), low, medium, and high valued pixels representing parameters , Ktrans , ve, Kep, vp, τi and Fp. There was a significant increase in the number of necrotic pixels with increasing tumour volume and a significant correlation between ve and tumour volume suggesting increased extracellular volume in larger tumours. In terms of therapeutic response in F98 rat GBMs, a sustained decrease in permeability and perfusion and a reduced cell density was observed during treatment with JAS239 based on Ktrans , Fp and ve as compared to control animals. No significant differences in these parameters were found for the GL261 tumour, indicating that this model may be less sensitive to JAS239 treatment regarding changes in vascular parameters. This study demonstrates that region-based clustered pharmacokinetic parameters derived from DCE-MRI may be useful in assessing tumour haemodynamic heterogeneity with the potential for assessing therapeutic response.

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“…On the other hand, physiological tumor subregions or habitats such as hypoxia and hypermetabolic activity can provide more meaningful biological information, and they do not necessarily follow this simple geometric paradigm 22 , 23 . One caveat is that these physiological tumor subregions are likely cancer type-specific and imaging modality-dependent, and reliable identification of these subregions requires sophisticated algorithms, such as habitat imaging 24 , 25 .…”

Section: Discussionmentioning

confidence: 99%

Radiological tumour classification across imaging modality and histology

Gensheimer

et al. 2021

Nat Mach Intell

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Radiomics refers to the high-throughput extraction of quantitative features from radiological scans and is widely used to search for imaging biomarkers for prediction of clinical outcomes. Current radiomic signatures suffer from limited reproducibility and generalizability, because most features are dependent on imaging modality and tumor histology, making them sensitive to variations in scan protocol. Here, we propose novel radiological features that are specially designed to ensure compatibility across diverse tissues and imaging contrast. These features provide systematic characterization of tumor morphology and spatial heterogeneity. In an international multi-institution study of 1,682 patients, we discover and validate four unifying imaging subtypes across three malignancies and two major imaging modalities. These tumor subtypes demonstrate distinct molecular characteristics and prognoses after conventional therapies. In advanced lung cancer treated with immunotherapy, one subtype is associated with improved survival and increased tumor-infiltrating lymphocytes compared with the others. Deep learning enables automatic tumor segmentation and reproducible subtype identification, which can facilitate practical implementation. The unifying radiological tumor classification may inform prognosis and treatment response for precision medicine.

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Multiparametric Analysis of Longitudinal Quantitative MRI Data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer

Cited by 34 publications

References 82 publications

Modulation of the Tumor Microenvironment with Trastuzumab Enables Radiosensitization in HER2+ Breast Cancer

Modulation of the Tumor Microenvironment with Trastuzumab Enables Radiosensitization in HER2+ Breast Cancer

Assessing Tumour Haemodynamic Heterogeneity and Response to Choline Kinase Inhibition Using Clustered Dynamic Contrast Enhanced MRI Parameters in Rodent Models of Glioblastoma

Radiological tumour classification across imaging modality and histology

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