Tissue dynamics spectroscopic imaging: functional imaging of heterogeneous cancer tissue.
ABSTRACT: SIGNIFICANCE:Tumor heterogeneity poses a challenge for the chemotherapeutic treatment of cancer. Tissue dynamics spectroscopy captures dynamic contrast and can capture the response of living tissue to applied therapeutics, but the current analysis averages over the complicated spatial response of living biopsy samples. AIM:To develop tissue dynamics spectroscopic imaging (TDSI) to map the heterogeneous spatial response of tumor tissue to anticancer drugs. APPROACH:TDSI is applied to tumor spheroids grown from cell lines and to ex vivo living esophageal biopsy samples. Doppler fluctuation spectroscopy is performed on a voxel basis to extract spatial maps of biodynamic biomarkers. Functional images and bivariate spatial maps are produced using a bivariate color merge to represent the spatial distribution of pairs of signed drug-response biodynamic biomarkers. RESULTS:We have mapped the spatial variability of drug responses within biopsies and have tracked sample-to-sample variability. Sample heterogeneity observed in the biodynamic maps is associated with histological heterogeneity observed using inverted selective-plane illumination microscopy. CONCLUSION:We have demonstrated the utility of TDSI as a functional imaging method to measure tumor heterogeneity and its potential for use in drug-response profiling.
Project description:Development of an assay to predict response to chemotherapy has remained an elusive goal in cancer research. We report a phenotypic chemosensitivity assay for epithelial ovarian cancer based on Doppler spectroscopy of infrared light scattered from intracellular motions in living three-dimensional tumor biopsy tissue measured in vitro. The study analyzed biospecimens from 20 human patients with epithelial ovarian cancer. Matched primary and metastatic tumor tissues were collected for 3 patients, and an additional 3 patients provided only metastatic tissues. Doppler fluctuation spectra were obtained using full-field optical coherence tomography through off-axis digital holography. Frequencies in the range from 10 mHz to 10 Hz are sensitive to changes in intracellular dynamics caused by platinum-based chemotherapy. Metastatic tumor tissues were found to display a biodynamic phenotype that was similar to primary tissue from patients who had poor clinical outcomes. The biodynamic phenotypic profile correctly classified 90% [88-91% c.i.] of the patients when the metastatic samples were characterized as having a chemoresistant phenotype. This work suggests that Doppler profiling of tissue response to chemotherapy has the potential to predict patient clinical outcomes based on primary, but not metastatic, tumor tissue.
Project description:Three-dimensional (3D) tissue cultures are replacing conventional two-dimensional (2D) cultures for applications in cancer drug development. However, direct comparisons of in vitro 3D models relative to in vivo models derived from the same cell lines have not been reported because of the lack of sensitive optical probes that can extract high-content information from deep inside living tissue. Here we report the use of biodynamic imaging (BDI) to measure response to platinum in 3D living tissue. BDI combines low-coherence digital holography with intracellular Doppler spectroscopy to study tumor drug response. Human ovarian cancer cell lines were grown either in vitro as 3D multicellular monoculture spheroids or as xenografts in nude mice. Fragments of xenografts grown in vivo in nude mice from a platinum-sensitive human ovarian cell line showed rapid and dramatic signatures of induced cell death when exposed to platinum ex vivo, while the corresponding 3D multicellular spheroids grown in vitro showed negligible response. The differences in drug response between in vivo and in vitro growth have important implications for predicting chemotherapeutic response using tumor biopsies from patients or patient-derived xenografts.
Project description:Tumor heterogeneity can manifest itself by sub-populations of cells having distinct phenotypic profiles expressed as diverse molecular, morphological and spatial distributions. This inherent heterogeneity poses challenges in terms of diagnosis, prognosis and efficient treatment. Consequently, tools and techniques are being developed to properly characterize and quantify tumor heterogeneity. Multiplexed immunofluorescence (MxIF) is one such technology that offers molecular insight into both inter-individual and intratumor heterogeneity. It enables the quantification of both the concentration and spatial distribution of 60+ proteins across a tissue section. Upon bioimage processing, protein expression data can be generated for each cell from a tissue field of view.The Multi-Omics Heterogeneity Analysis (MOHA) tool was developed to compute tissue heterogeneity metrics from MxIF spatially resolved tissue imaging data. This technique computes the molecular state of each cell in a sample based on a pathway or gene set. Spatial states are then computed based on the spatial arrangements of the cells as distinguished by their respective molecular states. MOHA computes tissue heterogeneity metrics from the distributions of these molecular and spatially defined states. A colorectal cancer cohort of approximately 700 subjects with MxIF data is presented to demonstrate the MOHA methodology. Within this dataset, statistically significant correlations were found between the intratumor AKT pathway state diversity and cancer stage and histological tumor grade. Furthermore, intratumor spatial diversity metrics were found to correlate with cancer recurrence.MOHA provides a simple and robust approach to characterize molecular and spatial heterogeneity of tissues. Research projects that generate spatially resolved tissue imaging data can take full advantage of this useful technique. The MOHA algorithm is implemented as a freely available R script (see supplementary information).
Project description:Spatial mapping of genomic data to tissue context in a high-throughput and high-resolution manner has been challenging due to technical limitations. Here, we describe PHLI-seq, a novel approach that enables high-throughput isolation and genome-wide sequence analysis of single cells or small numbers of cells to construct genomic maps within cancer tissue in relation to the images or phenotypes of the cells. By applying PHLI-seq, we reveal the heterogeneity of breast cancer tissues at a high resolution and map the genomic landscape of the cells to their corresponding spatial locations and phenotypes in the 3D tumor mass.
Project description:PURPOSE:Delineation of lesion boundaries from volumetric MRSI metabolite ratio maps using a method that accounts for the spatial response function of the acquisition and variable spectral quality and is robust to signal heterogeneity within the lesion. METHODS:A novel method for lesion segmentation, termed convolution difference, has been developed that is robust to signal heterogeneity within the lesion and to differences in the spatial response function. Procedures are described for processing metabolite ratio maps and to exclude regions of inadequate spectral quality. This method was evaluated using computer simulations, and the results were compared with an iterative thresholding technique that determines an optimal amplitude threshold, and with the use of a fixed amplitude threshold. These methods were evaluated for segmentation of volumetric MRSI studies of gliomas using maps of the choline to N-acetylaspartate ratio, and a qualitative comparison of lesion volumes carried out. RESULTS:Simulation studies indicated improved performance for the convolution difference method when applied to ratio maps. Variations in tumor volume were observed for the in vivo studies between the convolution difference and the iterative thresholding methods; however, visual analysis indicates that both showed improved accuracy in comparison to using a fixed amplitude threshold. CONCLUSION:This study reinforces previous reports indicating that the use of fixed threshold values for segmentation of maps with broad spatial response functions can result in errors in lesion volume definition. A novel segmentation method, termed the convolution difference, has been introduced and demonstrated to be robust for segmentation of volumetric MRSI metabolite data.
Project description:Recent developments in optical tissue clearing and microscopic imaging have advanced three-dimensional (3D) visualization of intact tissues and organs at high resolution. However, to expand applications to oncology, critical limitations of current methods must be addressed. Here we describe transparent tissue tomography (T3) as a tool for rapid, three-dimensional, multiplexed immunofluorescent tumor imaging. Cutting tumors into sub-millimeter macrosections enables simple and rapid immunofluorescence staining, optical clearing, and confocal microscope imaging. Registering and fusing macrosection images yields high resolution 3D maps of multiple tumor microenvironment components and biomarkers throughout a tumor. The 3D maps can be quantitatively evaluated by automated image analysis. As an application of T3, 3D mapping and analysis revealed a heterogeneous distribution of programmed death-ligand 1 (PD-L1) in Her2 transgenic mouse mammary tumors, with high expression limited to tumor cells at the periphery and to CD31+ vascular endothelium in the core. Also, strong spatial correlation between CD45+ immune cell distribution and PD-L1 expression was revealed by T3 analysis of the whole tumors. Our results demonstrate that a tomographic approach offers simple and rapid access to high-resolution three-dimensional maps of the tumor immune microenvironment, offering a new tool to examine tumor heterogeneity.
Project description:Intratumoral heterogeneity has been revealed in primary liver carcinoma (PLC). However, spatial heterogeneity of tumor-infiltrating lymphocytes (TILs), which reflects one dimension of a tumor's spatial heterogeneity, and the relationship between TIL diversity, local immune response and mutation burden remain unexplored in PLC. Therefore, we performed immune repertoire sequencing, gene expression profiling analysis and whole-exome sequencing in parallel on five regions of each tumor and on matched adjacent normal tissues and peripheral blood from five PLC patients. A significantly higher cumulative frequency of the top 250 most abundant TIL clones was observed in tumors than in peripheral blood. Besides, overlap rates of T cell receptor (TCR) repertoire for intratumor comparisons, significant higher than those for tumor-adjacent normal tissue comparisons and tumor-blood comparisons, which provide evidence for antigen-driven clonal expansion in PLC. Analysis of the percentage of ubiquitous TCR sequences, regional frequencies of each clone and TIL diversity suggested TIL clones varying between distinct regions of the same tumor, which indicated weak TCR repertoire similarity within a single tumor. Furthermore, correlation analysis revealed that TIL diversity significantly correlated with the expression of immune response genes rather than the mutation load. We conclude that intratumoural T-cell clones are spatially heterogeneous, which can lead to underestimate the immune profile of PLC from a single biopsy sample and may present challenge to adoptive cell therapy using autologous TILs. TIL diversity provides a reasonable explanation for the degree of immune response, implied TIL diversity can serve as a surrogate marker to monitor the effect of immunotherapy.
Project description:We describe an algorithm to calculate an index that characterizes spatial differences in broadband near-infrared [(NIR), 650-1000 nm] absorption spectra of tumor-containing breast tissue. Patient-specific tumor spatial heterogeneities are visualized through a heterogeneity spectrum function (HS). HS is a biomarker that can be attributed to different molecular distributions within the tumor. To classify lesion heterogeneities, we built a heterogeneity index (HI) derived from the HS by weighing the HS in specific NIR absorption bands. It is shown that neoadjuvant chemotherapy (NAC) response is potentially related to the tumor heterogeneity. Therefore, we correlate the heterogeneity index obtained prior to treatment with the final response to NAC. From a pilot study of 15 cancer patients treated with NAC, pathological complete responders (pCR) were separated from non-pCR according to their HI (-44 ± 12 and 43 ± 17, p = 3 × 10(-8), respectively). We conclude that the HS function is a biomarker that can be used to visualize spatial heterogeneities in lesions, and the baseline HI prior to therapy correlates with chemotherapy pathological response.
Project description:To eliminate the variable of tumor heterogeneity from a novel in vivo model of tumor angiogenesis.We developed a method to navigate tumor neovasculature in a living tissue microenvironment, enabling relocation of a cell- or microregion-of-interest, for serial in vivo imaging. Orthotopic melanoma was grown, in immunocompetent Tie2GFP mice. Intravital multiphoton fluorescence and confocal reflectance imaging was performed, on a custom microscope with motorized stage and coordinate navigation software. A point within a Tie2GFP+ microvessel was selected for relocation. Custom software predicted target coordinates based upon reference points (tissue-embedded polystyrene beads) and baseline target coordinates. Mice were removed from the stage to make previously-obtained target coordinates invalid in subsequent imaging.Coordinate predictions always relocated target points, in vivo, to within 10-200 microm (within a single 40x field-of-view). The model system provided a virtual living histology of tumor neovascularization and microenvironment, with subcellular spatial resolution and hemodynamic information.The navigation procedure, termed in vivo microcartography, permits control of tissue heterogeneity, as a variable. Tie2 may be the best reporter gene identified, to-date, for intravital microscopy of tumor angiogenesis. This novel model system should strengthen intravital microscopy in its historical role as a vital tool in oncology, angiogenesis research, and angiotherapeutic drug development.
Project description:Biodynamic imaging (BDI) is a novel phenotypic cancer profiling technology which optically characterizes changes in subcellular motion within living tumor tissue samples in response to ex vivo treatment with cancer chemotherapy drugs. The purpose of this preliminary study was to assess the ability of ex vivo BDI to predict in vivo clinical response to chemotherapy in ten dogs with naturally-occurring non-Hodgkin's lymphomas. Pre-treatment tumor biopsy samples were obtained from all dogs and treated ex vivo with doxorubicin (10 ?M). BDI measured six dynamic biomarkers of subcellular motion from all biopsy samples at baseline and at regular intervals for 9 h following drug application. All dogs subsequently received doxorubicin to treat their lymphomas. Best overall response to and progression-free survival time following chemotherapy were recorded for all dogs. Receiver operating characteristic (ROC) curves were used to determine accuracy and identify possible cut-off values for the BDI-measured biomarkers which could accurately predict those dogs' cancers that would and would not respond to doxorubicin chemotherapy. One biomarker (designated 'MEM') showed 100% discriminative capability for predicting clinical response to doxorubicin (area under the ROC curve = 1.00, 95% CI 0.692-1.000), while other biomarkers also showed promising predictive capability. These preliminary findings suggest that ex vivo BDI can accurately predict treatment outcome following doxorubicin chemotherapy in a spontaneous animal cancer model, and is worthy of further investigation as a technology for personalized cancer medicine.