The majority of cancers are diagnosed using excised biopsy specimens. These are graded, using a gold-standard histopathology protocol based on haemotoxylin and eosin ('H + E') chemical staining. However the grading is done by eye and if the same biopsy is graded by different practitioners, they typically only agree similar to 70% of the time. The resulting overtreatment problem constitutes a massive unmet need worldwide. Our new "Digistain" technology, uses mid-infrared imaging to map the fractional concentration of nucleic acids, i.e. the nuclear-to-cytoplasmic chemical ratio (NCR) across an unstained biopsy section. It allows a quantitative `Digistain index' (DI) score, corresponding to the NCR, to be reproducibly extracted from an objective physical measurement of a cancer. Our objective here is to evaluate its potential for aiding cancer diagnosis for the first time. We correlate the DI scores with H + E grades in a double-blind clinical pilot trial. Two adjacent slices were taken from 75 breast cancer FFPE blocks; one was graded with the standard H + E protocol, and also used to define a 'region of interest' (RoI). Digistain was then used to acquire a DI value averaged over the corresponding RoI on the other (unstained) slice and the results were statistically analysed. We find the DI score correlates significantly (p = 0.0007) with tumor grade in a way that promises to significantly reduce the inherent subjectivity and variability in biopsy grading. The NCR is elevated by increased mitotic activity because cells divide when they are younger and, on average, become smaller as the disease progresses. Also, extra DNA and RNA is generated as the nuclear transcription machinery goes awry and nuclear pleomorphism occurs. Both effects make the NCR a recognized biomarker for a wide range of tumors, so we expect Digistain will find application in a very wide range of cancers.
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.
The question of the existence of cancer is inadequately answered by invoking somatic mutations or the disruptions of cellular and tissue control mechanisms. As such uniformly random events alone cannot account for the almost inevitable occurrence of an extremely complex process such as cancer. In the different epistemic realm, an ultimate explanation of cancer is that cancer is a reversion of a cell to an ancestral pre-Metazoan state, i.e. a cellular form of atavism. Several studies have suggested that genes involved in cancer have evolved at particular evolutionary time linked to the unicellular-multicellular transition. Here we used a refined phylostratigraphic analysis of evolutionary ages of the known genes/pathways associated with cancer and the genes differentially expressed between normal and cancer tissue as well as between embryonic and mature (differentiated) cells. We found that cancer-specific transcriptomes and cancer-related pathways were enriched for genes that evolved in the pre-Metazoan era and depleted of genes that evolved in the post-Metazoan era. By contrast an opposite relation was found for cell maturation: the age distribution frequency of the genes expressed in differentiated epithelial cells were enriched for post-Metazoan genes and depleted of pre-Metazoan ones. These findings support the atavism theory that cancer cells manifest the reactivation of an ancient ancestral state featuring unicellular modalities. Thus our bioinformatics analyses suggest that not only does oncogenesis recapitulate ontogenesis, and ontogenesis recapitulates phylogenesis, but also oncogenesis recapitulates phylogenesis. This more encompassing perspective may offer a natural organizing framework for genetic alterations in cancers and point to new treatment options that target the genes controlling the atavism transition. One Sentence Summary Tracing cancer gene evolutionary ages revealed that cancer reverts to a pre-existing early Metazoan state.
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.
A rapid increase in the volume and availability of electronic medical record (EMR) data and its potential to positively impact patient care, led to a collaboration between the Department of Veterans Affairs (VA) and the National Cancer Institute (NCI). The result of the collaboration, the big data scientist training enhancement program (BD-STEP) was launched in 2015 with the objective of enabling a transition from the traditional healthcare model of observe and treat to a desired future state of predict and prevent. The BD-STEP Program brings together expertise of clinicians, researchers and data scientists to use EMR data to directly impact patient decision-making. This program represents a first time collaboration in VA of the Office of Academic Affiliations, Employee Education System, and the Office of Research and Development with the intended outcome of bridging the gap between the administrative, clinical, and research worlds to achieving a common goal-to train the next generation of data scientists to understand data in the context of health care delivery. As an interdisciplinary program, BD-STEP will provide data scientists trained to use EMR data to: (1) accelerate learning (2) inform healthcare systems administrators and; (3) empower clinicians to translate findings to improve patient care. This review will provide an overview of the intersection of precision medicine, data science, and patient engagement, and share a vision for how BD-STEP will use a team science approach to deliver personalized care for patients to reduce cost and improve outcomes.
The growing global burden of hepatocellular carcinoma, the poor response to chemotherapies such as sorafenib, and the inoperable status of most patients when they present clinically have led, over the last three decades, to development and application of loco-regional therapies such as ablation and embolization. Unfortunately, incomplete treatment and local recurrence are all too common with these methods. In this report, we describe a fundamentally new strategy, an image-guided embolization method employing a targeted chemical reaction to affect local biology. We demonstrate feasibility in a simple model system using an acid chloride as the electrophile in an inert carrier solvent. The reagent solution is delivered through a small catheter in the target artery. Once released, the acid chloride reacts vigorously with any water or available functional groups present such as hydroxyl or amino groups in the tissue and simultaneously generates an acidic local environment. We call this new method thermoembolization due to the exotherm that is observed in the tissues as captured by both thermocouple and infrared measurements. The in-situ reaction of a small volume of the electrophile delivered intra-arterially causes highly localized endovascular ablation in our model system. The ratio of coagulated tissue volume to injected material was consistently in the range of 40:1 which compares very favorably against the 1:1 ratio found in chemical ablation using direct, intratumoral ethanol injection. The largest increase in temperature observed was 24.1 degrees C, meaning that the thermal energy alone could be enough to coagulate tissues. The acid that is released at the same locale further enhances the denaturation observed. Taken together, these findings underscore the potential of this new approach for treating malignancies in a nonsurgical way.
Parotid glands are treated clinically as though the distribution of functional burden were homogeneous. Radiotherapy treatments are planned using whole parotid mean dose to predict risk of salivary dysfunction. Recent progress has identified specific parotid non-homogeneities by demonstrating the existence of regional, bath-and-shower, and dose-volume effects. In this work, parotid regional effects and their impact on salivary function are quantified using a non-parametric (model-free) approach. Regional effects have implications for clinical sparing practices. Radiotherapy planning contours, dose profiles, and late clinical outcomes from a single cohort consisting of N = 332 patients was used. Pre-radiotherapy and one year post-radiotherapy whole mouth stimulated saliva were collected for assessment of salivary dysfunction. Organ-at-risk parotid glands were segmented into 2, 3, 4,18, and 96 equal-volume sub-segments. Sub-segment relative importance was derived from mean dose regressors using random forests and conditional inference trees. Regressor multicollinearity, cohort homogeneity, and overfitting were addressed. Linear and exponential whole parotid mean dose models were also implemented for comparison purposes. Exclusion of caudal-anterior sub-segments negatively impacted prediction the most. The most important sub-segments had importances 2.4x (on average over all segmentation methods) or >4x (at the finest level of segmentation) that of an equivalent sub-segment in a theoretical homogeneous parotid. In contrast, the least important sub-segments held virtually no importance for prediction. Both random forests and conditional inference trees outperformed parametric (model-based) techniques. Both improved prediction as segmentation was refined. Radiation dose to caudal-anterior aspects of the parotid are the strongest predictors of radiotherapy-induced late stimulated whole mouth saliva, and are thus the most clinically-relevant regions for controlling dysfunction. Cranial and posterior aspects are less important. Shifting dose from regions of high importance to low importance may therefore improve patient outcomes.
Determining the ownership of a patient ’ s personal genomic data is important because it impacts how data is governed and shared, which has both clinical and research implications for precision oncology. The 21st Century Cures Act enacted in December 2016 defined the ownership of clinical genomic data, but the governance of research-grade genomic data remains a hotly contested topic. The many stakeholders often have competing perspectives about ownership of raw and processed genomic data derived in research settings and how to weigh risks versus benefits of sharing this data with study participants. A growing number of research studies, policy recommendations, and ethics reviews have not been enough to influence changes in practice. Most genomic research is conducted in academia, which is guided by Institutional Review Board-approved protocols to protect study participants. The current standard is to limit the return of research-grade data to study participants, and give data ownership solely to the researchers or the research institution, since this data is not vetted for clinical purposes and is meant for research use only. However, these practices conflict not only with recommendations from peer-reviewed literature on best practices for addressing research study participants ’ needs but might indeed run counter to legal and ethical guidelines about data ownership. For example, patient-participants faced with poorly understood or incurable diseases such as certain cancers want, and could potentially benefit from, having access to their personal genomic data in this rapidly evolving field. This commentary highlights the gap between the status quo as approved by the IRB and the literature suggesting that study participants should be given access to their personal genomic data. There is an opportunity to facilitate a more effective and ethical way to collect genomic data for research use across institutions.
Oral squamous cell carcinomas (OSCC) are the sixth most common cancer and the diagnosis is often belated for a curative treatment. The reliable and early differentiation between healthy and diseased cells is the main aim of this study in order to improve the quality of the treatment and to understand tumour pathogenesis. Here, the optical stretcher is used to analyse mechanical properties of cells and their potential to serve as a marker for malignancy. Stretching experiments revealed for the first time that cells of primary OSCCs were deformed by 2.9% rendering them softer than cells of healthy mucosa which were deformed only by 1.9%. Furthermore, the relaxation behaviour of the cells revealed that these malignant cells exhibit a faster contraction than their benign counterparts. This suggests that deformability as well as relaxation behaviour can be used as distinct parameters to evaluate emerging differences between these benign and malignant cells. Since many studies in cancer research are performed with cancer cell lines rather than primary cells, we have compared the deformability and relaxation of both types, showing that long time culturing leads to softening of cells. The higher degree of deformability and relaxation behaviour can enable cancer cells to traverse tissue emphasizing that changes in cell architecture may be a potential precondition for malignant transformation. Respecting the fact that even short culture times have an essential effect on the significance of the results, the use of primary cells for further research is recommended. The distinction between malignant and benign cells would enable an early confirmation of cancer diagnoses by testing cell samples of suspect oral lesions.
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.
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.
Lung cancer is often classified by the presence of oncogenic drivers, such as epidermal growth factor receptor (EGFR), rather than patterns of anatomical distribution. While metastatic spread may seem a random and unpredictable process, we explored the possibility of using its quantifiable nature as a measure of describing and comparing different subsets of disease. We constructed a database of 664 non-small cell lung cancer (NSCLC) patients treated at the University of Southern California Norris Comprehensive Cancer Center and the Los Angeles County Medical Center. Markov mathematical modeling was employed to assess metastatic sites in a spatiotemporal manner through every time point in progression of disease. Our findings identified a preferential pattern of primary lung disease progressing through lung metastases to the brain amongst EGFR mutated (EGFRm) NSCLC patients, with exon 19 deletions or exon 21 L858R mutations, as compared to EGFR wild type (EGFRwt). The brain was classified as an anatomic "sponge", with a higher ratio of incoming to outgoing spread, for EGFRm NSCLC. Bone metastases were more commonly identified in EGFRwt patients. Our study supports a link between the anatomical and molecular characterization of lung metastatic cancer. Improved understanding of the differential biology that drives discordant patterns of anatomic spread, based on genotype specific profiling, has the potential to improve personalized oncologic care.
Cellular motility and invasion in connective tissue is a basic and fundamental process during normal physiological tissue developments and assemblies, prevention of inflammation after tissue injury through wound healing and malignant progression of cancer such as metastasis. Cell invasion usually requires cell adhesion via cell-matrix receptors to the extracellular matrix which are coupled to the cell's actomyosin cytoskeleton. In many tumors, matrix and hence tissue mechanics are altered and increased tissue stiffness is associated with increased malignancy and metastasis. Moreover, cellular mechanical properties are altered in invasive cancer cells compared to less invasive cancer cells. Here, we have studied the invasion of human breast cancer cells into loose and dense 3D engineered matrices consisting of a collagen type I fiber network and determined the cellular mechanical properties such as stiffness. As expected, the cellular stiffness correlates with the invasiveness of the cancer cells in loose and dense 3D matrices. We hypothesized that the matrix and cellular mechanical properties regulate the motility (invasiveness) of cancer cells in 3D engineered matrices, which has been shown to be to be regulated by the actomyosin cytoskeleton and is in 3D constrictions possibly also regulated by the small Rho GTPase Rac1's activity as demonstrated for cellular motility on 2D substrates. Pharmacological interventions indicate that in 3D matrices, invasive cancer cells behave similarly as non-invasive cancer cells, when treated with inhibitors of the small Rho GTPase Rac1 or the actomyosin-contractility. Matrix fiber displacement analysis in 3D engineered matrices revealed that the invasive MDA-MB-231 cancer cells generated significantly higher and long-raged matrix fiber displacements by contraction of the matrix environment than non-invasive MCF-7 and MCF-10A breast cancer cells, which seems to be the main prerequisite for their increased invasion through the 3D extracellular matrix environment. In addition, a decrease in adhesion-dependent and adhesion-independent cellular stiffness of invasive MDA-MB-231 cancer cells after addition of the actin polymerization inhibitor Latrunculin A leads to reduced 3D invasiveness and significantly reduced matrix fiber displacements. Besides cellular mechanical properties, a 18-fold increase in matrix stiffness increased the invasiveness of all three cell types suggesting that matrix stiffness may have an invasion-enhancing effect. Finally, our findings demonstrate that mechanical properties of breast cancer cells and the 3D matrix environment facilitate 3D matrix invasion through increased actomyosin-dependent cellular stiffness and transmission of contractile force in dense 3D engineered collagen matrices.
Abstract Intermittent treatment schedules have been proposed to improve the tolerance of drugs for cancer chemoprevention. However, determining a maximum tolerated dose, and the extent of the improvement, has been challenging experimentally and clinically. In order to determine the quantitative advantage of intermittent pulse treatment schedules for the chemoprevention of colon cancer we have used a computer model of human colon crypts calibrated with measurements of human biopsy specimens. In simulations, crypts were treated with an agent that increases the probability that cells, both normal and mutant, would be removed at the top of the crypt. Sulindac, which increases apoptosis at the lumen surface, is such an agent. The effect of intermittent pulse drug treatment schedules were compared with constant drug treatment schedules. Crypts treated with intermittent pulse schedules have three times the maximum tolerated dose than crypts treated with constant schedules, and have a 10 year delay in the appearance of adenomas. Intermittent treatment schedules have previously been proposed for chemoprevention. Here computer simulations have quantified the effect on human colon crypts of intermittent treatment schedules and constant treatment schedules of a chemotherapeutic drug. Intermittent pulses have an advantage, they allow an increased maximum tolerated dose, and result in an increased chemoprevention by delay.
Despite many years of effort from cancer biologists and clinical oncologists, pancreatic ductal adenocarcinoma (PDAC) remains a thoroughly recalcitrant disease, resisting both conventional forms of cancer treatment and radical innovative therapies. Perhaps driving the lack of success in PDAC is the underappreciation of the primary hallmark of the tumour: a fibrotic extracellular matrix (ECM) that composes a majority of the highly rigid solid carcinoma. In recent years we have come to understand that the homeostasis of ECM mechanics is pivotal for the homeostasis of cells and the progression or initiation of a malignant phenotype. This can be understood by way of mechanobiology, a field that attempts to understand how physical forces like ECM stiffness alter cell behaviour. In this review, we provide an overview of our understanding of PDAC from this perspective and the recent advances in biophysics and engineering that allow for new tools with which to investigate the mechanobiology of PDAC.
Abstract Surgery, radiation therapy and chemotherapy are the primary modes of therapeutic intervention in current clinical practice. The price often paid for effective treatment is the development of a secondary malignancy several decades after successful treatment of the primary tumor, as a result of the mutagenic effects of the initial cancer treatments on normal cells. In this work, we employ a biologically motivated mathematical model to estimate the radiation and chemotherapy-induced relative risks of thyroid malignancies in four childhood cancer study survivors (CCSS) data sets. A sensitivity analysis is performed on various chemotherapy treatment variables to evaluate their impact on second cancer risks. Furthermore, the predictions of radiation and chemotherapy-induced relative risks of secondary thyroid malignancies using the mathematical model are compared against four clinical datasets from the CCSS cohort. Moreover, the extracted average value of growth rate of premalignant cells is 0.8175 (d −1 ) and the extracted chemotherapy-induced mutation rate is of the order of 10 −10 (per unit of chemotherapeutic dose). In addition, our model predictions of sequential therapy induced carcinogenic risks are in line with the clinical data in secondary thyroid cancers. Our in silico risk predictions can provide insight into the impact of therapy sequencing on secondary cancer risks, while at the same time eliminating the primary tumor. These findings might potentially guide clinicians in developing optimal treatment regimens that minimize secondary cancer risks.
Abstract Initiation and development of cancer are usually accompanied by alterations in the cellular mechanical properties such as its stiffness and viscosity. Understanding the viscoelasticity of cancer cells can provide a better insight into the mechanics of the metastasis of cancer cells. Here, we use atomic force microscopy to compare the viscoelasticity of mammary epithelial cells with different metastatic potentials in their adherent and suspended states. We measure cell elasticity through the spatial mapping of Young’s modulus using the force-indentation technique and cell viscosity using stress relaxation. The viscoelastic properties of cancer cells are associated with their malignancy and intrinsic cytoskeletal structures. Our results suggest that the Young’s modulus of adherent cells inversely correlates to their malignancy and that the F-actin arrangement and intensity support the mechanical phenotype. For suspended cells, we observe that they exhibit lower elasticity than adhered cells due to the distribution of actin filaments at the cell cortex as well as reduced polymerization. Our viscosity results suggest that in both adhered and suspended cases, normal breast epithelial cells exhibit higher viscosity than that of cancer cells. Actin distribution and higher nucleus to cytoplasmic ratio in cancer cells are observed to be the two main factors in determining cell viscosity.
Despite recent advances in developing HER-family targeted drugs, clinical trials have shown poor results. Tumour evolution takes place overtime, frequently leading to aberrant new signalling cascades that disrupt the efficacy of targeted therapies and ultimately cause patients to develop resistance against initially effective drugs. To predict outcome and stratify treatment there is an imperative need to develop a systems understanding of concentration- independent parameters that could be monitored in trials and report on tumour evolution. Amongst the circulating tumour markers, exosomes offer a suitable platform for the longitudinal monitoring of protein network signalling in the form of a liquid-biopsy by imaging receptor dimerisation status. Here, we illustrate the biomarker utility of monitoring oncogenic receptor signal rewiring using exosomal FRET/FLIM to aid the prediction of clinical outcome and patient treatment stratification.
