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Diffusion model comparison identifies distinct tumor sub-regions and tracks treatment response.

ABSTRACT: PURPOSE:MRI biomarkers of tumor response to treatment are typically obtained from parameters derived from a model applied to pre-treatment and post-treatment data. However, as tumors are spatially and temporally heterogeneous, different models may be necessary in different tumor regions, and model suitability may change over time. This work evaluates how the suitability of two diffusion-weighted (DW) MRI models varies spatially within tumors at the voxel level and in response to radiotherapy, potentially allowing inference of qualitatively different tumor microenvironments. METHODS:DW-MRI data were acquired in CT26 subcutaneous allografts before and after radiotherapy. Restricted and time-independent diffusion models were compared, with regions well-described by the former hypothesized to reflect cellular tissue, and those well-described by the latter expected to reflect necrosis or oedema. Technical and biological validation of the percentage of tissue described by the restricted diffusion microstructural model (termed %MM) was performed through simulations and histological comparison. RESULTS:Spatial and radiotherapy-related variation in model suitability was observed. %MM decreased from a mean of 64% at baseline to 44% 6 days post-radiotherapy in the treated group. %MM correlated negatively with the percentage of necrosis from histology, but overestimated it due to noise. Within MM regions, microstructural parameters were sensitive to radiotherapy-induced changes. CONCLUSIONS:There is spatial and radiotherapy-related variation in different models' suitability for describing diffusion in tumor tissue, suggesting the presence of different and changing tumor sub-regions. The biological and technical validation of the proposed %MM cancer imaging biomarker suggests it correlates with, but overestimates, the percentage of necrosis.

SUBMITTER: McHugh DJ

PROVIDER: S-EPMC7317874 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Diffusion model comparison identifies distinct tumor sub-regions and tracks treatment response.

McHugh Damien J DJ Lipowska-Bhalla Grazyna G Babur Muhammad M Watson Yvonne Y Peset Isabel I Mistry Hitesh B HB Hubbard Cristinacce Penny L PL Naish Josephine H JH Honeychurch Jamie J Williams Kaye J KJ O'Connor James P B JPB Parker Geoffrey J M GJM

Magnetic resonance in medicine 20200214 3

<h4>Purpose</h4>MRI biomarkers of tumor response to treatment are typically obtained from parameters derived from a model applied to pre-treatment and post-treatment data. However, as tumors are spatially and temporally heterogeneous, different models may be necessary in different tumor regions, and model suitability may change over time. This work evaluates how the suitability of two diffusion-weighted (DW) MRI models varies spatially within tumors at the voxel level and in response to radiothe ...[more]

PMID: 32057115

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Project description:IntroductionBreast cancer heterogeneity occurs as a consequence of the dysregulation of numerous oncogenic pathways as well as many non-genetic factors, including tumor microenvironmental stresses such as hypoxia, lactic acidosis, and glucose deprivation. Although the importance of these non-genetic factors is well recognized, it is not clear how to integrate these factors within the genetic framework of cancer as the next logical step in understanding tumor heterogeneity.MethodsWe report here the development of a series of gene expression signatures to measure the influences of microenvironmental stresses. The pathway activities of hypoxia, lactic acidosis, acidosis and glucose deprivation were investigated in a collection of 1,143 breast tumors, which have been separated into 17 breast tumor subgroups defined by their distinct patterns of oncogenic pathways. A validation dataset comprised of 547 breast tumors was also used to confirm the major findings, and representative breast cancer cell lines were utilized to validate in silico results and mechanistic studies.ResultsThrough the integrative pathway analysis of microenvironmental stresses and oncogenic events in breast tumors, we identified many known and novel correlations between these two sources of tumor heterogeneity. Focusing on differences between two human epidermal growth factor receptor 2 (HER2)-related subgroups, previously identified based on patterns of oncogenic pathway activity, we determined that these subgroups differ with regards to tumor microenvironmental signatures, including hypoxia. We further demonstrate that each of these subgroups have features consistent with basal and luminal breast tumors including patterns of oncogenic signaling pathways, expression of subtype specific genes, and cellular mechanisms that regulate the hypoxia response. Importantly, we also demonstrate that the correlated pattern of hypoxia-related gene expression and basal-associated gene expression are consistent across HER2-related tumors whether we analyze the tumors as a function of our pathway-based classification scheme, using the intrinsic gene list (ERBB2+), or based on HER2 IHC status. Our results demonstrate a cell lineage-specific phenomenon in which basal-like tumors, HER2-related tumors with high hypoxia, as well as normal basal epithelial cells express increased mRNA levels of HIF-1α compared to luminal types and silencing of HIF-1α results in decreased expression of hypoxia-induced genes.ConclusionsThis study demonstrates differences in microenvironmental conditions in HER2-related subgroups defined by distinct oncogenic pathway activities, and provides a mechanistic explanation for differences in the observed hypoxia response between these subgroups. Collectively, these data demonstrate the potential of a pathway-based classification strategy as a framework to integrate genetic and non-genetic factors to investigate the basis of tumor heterogeneity.

Dataset Information

Diffusion model comparison identifies distinct tumor sub-regions and tracks treatment response.

Publications

Diffusion model comparison identifies distinct tumor sub-regions and tracks treatment response.

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