Convergent science physical oncology最新文献

Tumor heterogeneity is prevalent in both treatment-naïve and end-stage metastatic castration-resistant prostate cancer (PCa), and may contribute to the broad range of clinical presentation, treatment response, and disease progression. To characterize molecular heterogeneity associated with de novo metastatic PCa, multiplatform single cell profiling was performed using high definition single cell analysis (HD-SCA). HD-SCA enabled morphoproteomic and morphogenomic profiling of single cells from touch preparations of tissue cores (prostate and bone marrow biopsies) as well as liquid samples (peripheral blood and bone marrow aspirate). Morphology, nuclear features, copy number alterations, and protein expression were analyzed. Tumor cells isolated from prostate tissue touch preparation (PTTP) and bone marrow touch preparation (BMTP) as well as metastatic tumor cells (MTCs) isolated from bone marrow aspirate were characterized by morphology and cytokeratin expression. Although peripheral blood was examined, circulating tumor cells were not definitively observed. Targeted proteomics of PTTP, BMTP, and MTCs revealed cell lineage and luminal prostate epithelial differentiation associated with PCa, including co-expression of EpCAM, PSA, and PSMA. Androgen receptor expression was highest in MTCs. Hallmark PCa copy number alterations, including PTEN and ETV6 deletions and NCOA2 amplification, were observed in cells within the primary tumor and bone marrow biopsy samples. Genomic landscape of MTCs revealed to be a mix of both primary and bone metastatic tissue. This multiplatform analysis of single cells reveals several clonal origins of metastatic PCa in a newly diagnosed, untreated patient with polymetastatic disease. This case demonstrates that real-time molecular profiling of cells collected through prostate and bone marrow biopsies is feasible and has the potential to elucidate the origin and evolution of metastatic tumor cells. Altogether, biological and genomic data obtained through longitudinal biopsies can be used to reveal the properties of PCa and can impact clinical management.

Molecular analysis of circulating and disseminated tumor cells (CTCs/DTCs) has great potential as a means for continuous evaluation of prognosis and treatment efficacy in near-real time through minimally invasive liquid biopsies. To realize this potential, however, methods for molecular analysis of these rare cells must be developed and validated. Here, we describe the integration of imaging mass cytometry (IMC) using metal-labeled antibodies as implemented on the Fluidigm Hyperion Imaging System into the workflow of the previously established High Definition Single Cell Analysis (HD-SCA) assay for liquid biopsies, along with methods for image analysis and signal normalization. Using liquid biopsies from a metastatic prostate cancer case, we demonstrate that IMC can extend the reach of CTC characterization to include dozens of protein biomarkers, with the potential to understand a range of biological properties that could affect therapeutic response, metastasis and immune surveillance when coupled with simultaneous phenotyping of thousands of leukocytes.

With increasingly ubiquitous electronic medical record (EMR) implementation accelerated by the adoption of the HITECH Act, there is much interest in the secondary use of collected data to improve outcomes and promote personalized medicine. A plethora of research has emerged using EMRs to investigate clinical research questions and assess variations in both treatments and outcomes. However, whether because of genuine complexities of modeling disease physiology or because of practical problems regarding data capture, data accuracy, and data completeness, the state of current EMR research is challenging and gives rise to concerns regarding study accuracy and reproducibility. This work explores challenges in how different experimental design decisions can influence results using a specific example of breast cancer patients undergoing excision and reconstruction surgeries from EMRs in an academic hospital and the Veterans Health Administration (VHA) We discuss emerging strategies that will mitigate these limitations, including data sharing, application of natural language processing, and improved EMR user design.

Tumor progression modeling offers the potential to predict tumor-spreading behavior to improve prognostic accuracy and guide therapy development. Common simulation methods include continuous reaction-diffusion (RD) approaches that capture mean spatio-temporal tumor spreading behavior and discrete agent-based (AB) approaches which capture individual cell events such as proliferation or migration. The brain cancer glioblastoma (GBM) is especially appropriate for such proliferation-migration modeling approaches because tumor cells seldom metastasize outside of the central nervous system and cells are both highly proliferative and migratory. In glioblastoma research, current RD estimates of proliferation and migration parameters are derived from computed tomography or magnetic resonance images. However, these estimates of glioblastoma cell migration rates, modeled as a diffusion coefficient, are approximately 1-2 orders of magnitude larger than single-cell measurements in animal models of this disease. To identify possible sources for this discrepancy, we evaluated the fundamental RD simulation assumptions that cells are point-like structures that can overlap. To give cells physical size (~10 μm), we used a Brownian dynamics approach that simulates individual single-cell diffusive migration, growth, and proliferation activity via a gridless, off-lattice, AB method where cells can be prohibited from overlapping each other. We found that for realistic single-cell parameter growth and migration rates, a non-overlapping model gives rise to a jammed configuration in the center of the tumor and a biased outward diffusion of cells in the tumor periphery, creating a quasi-ballistic advancing tumor front. The simulations demonstrate that a fast-progressing tumor can result from minimally diffusive cells, but at a rate that is still dependent on single-cell diffusive migration rates. Thus, modeling with the assumption of physically-grounded volume conservation can account for the apparent discrepancy between estimated and measured diffusion of GBM cells and provide a new theoretical framework that naturally links single-cell growth and migration dynamics to tumor-level progression.

We have improved our microfluidic cell culture device that generates an in vitro landscape of stress heterogeneity. We now can do continuous observations of different cancer cell lines and carry out downstream analysis of cell phenotype as a function of position on the stress landscape. We use this technology to probe adaption and evolution dynamics in prostate cancer cell metapopulations under a stress landscape of a chemotherapeutic drug (docetaxel). The utility of this approach is highlighted by analysis of heterogenous prostate cancer cell motility changes as a function of position in the stress landscape. Because the technology presented here is easily adapted to a standard epifluorescence microscope it has the potential for broad application in preclinical drug development and assays of likely drug efficacy.

The potential for local radiation therapy to elicit systemic (abscopal) anti-tumor immune responses has been receiving a significant amount of attention over the last decade. We recently developed a mathematical framework designed to simulate the systemic dissemination of activated T cells among multiple metastatic sites. This framework allowed the identification of non-intuitive patterns of T cell redistribution after localized therapy, and offered suggestions as to the optimal site to irradiate in order to increase the magnitude of an immune-mediated abscopal response. Here, we evaluate the potential for such a framework to provide clinical decision making support to radiation oncologists. Several challenges such as efficient segmentation and delineation of multiple tumor sites on PET/CT scans, validation of model prediction performance, and effective clinical trial design remain to be addressed prior to the incorporation of such a tool in the clinical setting.