Perspective on quantitative phase imaging to improve precision cancer medicine.

Significance: Quantitative phase imaging (QPI) offers a label-free approach to non-invasively characterize cellular processes by exploiting their refractive index based intrinsic contrast. QPI captures this contrast by translating refractive index associated phase shifts into intensity-based quantifiable data with nanoscale sensitivity. It holds significant potential for advancing precision cancer medicine by providing quantitative characterization of the biophysical properties of cells and tissue in their natural states. Aim: This perspective aims to discuss the potential of QPI to increase our understanding of cancer development and its response to therapeutics. It also explores new developments in QPI methods towards advancing personalized cancer therapy and early detection. Approach: We begin by detailing the technical advancements of QPI, examining its implementations across transmission and reflection geometries and phase retrieval methods, both interferometric and non-interferometric. The focus then shifts to QPI's applications in cancer research, including dynamic cell mass imaging for drug response assessment, cancer risk stratification, and in-vivo tissue imaging. Results: QPI has emerged as a crucial tool in precision cancer medicine, offering insights into tumor biology and treatment efficacy. Its sensitivity to detecting nanoscale changes holds promise for enhancing cancer diagnostics, risk assessment, and prognostication. The future of QPI is envisioned in its integration with artificial intelligence, morpho-dynamics, and spatial biology, broadening its impact in cancer research. Conclusions: QPI presents significant potential in advancing precision cancer medicine and redefining our approach to cancer diagnosis, monitoring, and treatment. Future directions include harnessing high-throughput dynamic imaging, 3D QPI for realistic tumor models, and combining artificial intelligence with multi-omics data to extend QPI's capabilities. As a result, QPI stands at the forefront of cancer research and clinical application in cancer care.

PARP2 promotes Break Induced Replication-mediated telomere fragility in response to replication stress.

PARP2 is a DNA-dependent ADP-ribosyl transferase (ARTs) enzyme with Poly(ADP-ribosyl)ation activity that is triggered by DNA breaks. It plays a role in the Base Excision Repair pathway, where it has overlapping functions with PARP1. However, additional roles for PARP2 have emerged in the response of cells to replication stress. In this study, we demonstrate that PARP2 promotes replication stress-induced telomere fragility and prevents telomere loss following chronic induction of oxidative DNA lesions and BLM helicase depletion. Telomere fragility results from the activity of the break-induced replication pathway (BIR). During this process, PARP2 promotes DNA end resection, strand invasion and BIR-dependent mitotic DNA synthesis by orchestrating POLD3 recruitment and activity. Our study has identified a role for PARP2 in the response to replication stress. This finding may lead to the development of therapeutic approaches that target DNA-dependent ART enzymes, particularly in cancer cells with high levels of replication stress.

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples.

Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within the supervised learning paradigm, deep learning based segmentation methods demonstrating human level performance have emerged. Here we present an unsupervised segmentation (UNSEG) method that achieves deep learning level performance without requiring any training data. UNSEG leverages a Bayesian-like framework and the specificity of nucleus and cell membrane markers to construct an a posteriori probability estimate of each pixel belonging to the nucleus, cell membrane, or background. It uses this estimate to segment each cell into its nuclear and cell-membrane compartments. We show that UNSEG is more internally consistent and better at generalizing to the complexity of tissue samples than current deep learning methods. This allows UNSEG to unambiguously identify the cytoplasmic compartment of a cell, which we employ to demonstrate its use in an example biological scenario. Within the UNSEG framework, we also introduce a new perturbed watershed algorithm capable of stably and accurately segmenting a cell nuclei cluster into individual cell nuclei. Perturbed watershed can also be used as a standalone algorithm that researchers can incorporate within their supervised or unsupervised learning approaches to replace classical watershed. Finally, as part of developing UNSEG, we have generated a high-quality annotated gastrointestinal tissue dataset, which we anticipate will be useful for the broader research community. Segmentation, despite its long antecedents, remains a challenging problem, particularly in the context of tissue samples. UNSEG, an easy-to-use algorithm, provides an unsupervised approach to overcome this bottleneck, and as we discuss, can help improve deep learning based segmentation methods by providing a bridge between unsupervised and supervised learning paradigms.

Immune microenvironment profiling of normal appearing colorectal mucosa biopsied over repeat patient visits reproducibly separates lynch syndrome patients based on their history of colon cancer.

Introduction: Lynch syndrome (LS) is the most common hereditary cause of colorectal cancer (CRC), increasing lifetime risk of CRC by up to 70%. Despite this higher lifetime risk, disease penetrance in LS patients is highly variable and most LS patients undergoing CRC surveillance will not develop CRC. Therefore, biomarkers that can correctly and consistently predict CRC risk in LS patients are needed to both optimize LS patient surveillance and help identify better prevention strategies that reduce risk of CRC development in the subset of high-risk LS patients. Methods: Normal-appearing colorectal tissue biopsies were obtained during repeat surveillance colonoscopies of LS patients with and without a history of CRC, healthy controls (HC), and patients with a history of sporadic CRC. Biopsies were cultured in an ex-vivo explant system and their supernatants were assayed via multiplexed ELISA to profile the local immune signaling microenvironment. High quality cytokines were identified using the rxCOV fidelity metric. These cytokines were used to perform elastic-net penalized logistic regression-based biomarker selection by computing a new measure - overall selection probability - that quantifies the ability of each marker to discriminate between patient cohorts being compared. Results: Our study demonstrated that cytokine based local immune microenvironment profiling was reproducible over repeat visits and sensitive to patient LS-status and CRC history. Furthermore, we identified sets of cytokines whose differential expression was predictive of LS-status in patients when compared to sporadic CRC patients and in identifying those LS patients with or without a history of CRC. Enrichment analysis based on these biomarkers revealed an LS and CRC status dependent constitutive inflammatory state of the normal appearing colonic mucosa. Discussion: This prospective pilot study demonstrated that immune profiling of normal appearing colonic mucosa discriminates LS patients with a prior history of CRC from those without it, as well as patients with a history of sporadic CRC from HC. Importantly, it suggests the existence of immune signatures specific to LS-status and CRC history. We anticipate that our findings have the potential to assess CRC risk in individuals with LS and help in preemptively mitigating it by optimizing surveillance and identifying candidate prevention targets. Further studies are required to validate our findings in an independent cohort of LS patients over multiple visits.

Immuno-PET Imaging of CD69 Visualizes T-Cell Activation and Predicts Survival Following Immunotherapy in Murine Glioblastoma.

Glioblastoma (GBM) is the most common and malignant primary brain tumor in adults. Immunotherapy may be promising for the treatment of some patients with GBM; however, there is a need for noninvasive neuroimaging techniques to predict immunotherapeutic responses. The effectiveness of most immunotherapeutic strategies requires T-cell activation. Therefore, we aimed to evaluate an early marker of T-cell activation, CD69, for its use as an imaging biomarker of response to immunotherapy for GBM. Herein, we performed CD69 immunostaining on human and mouse T cells following in vitro activation and post immune checkpoint inhibitors (ICI) in an orthotopic syngeneic mouse glioma model. CD69 expression on tumor-infiltrating leukocytes was assessed using single-cell RNA sequencing (scRNA-seq) data from patients with recurrent GBM receiving ICI. Radiolabeled CD69 Ab PET/CT imaging (CD69 immuno-PET) was performed on GBM-bearing mice longitudinally to quantify CD69 and its association with survival following immunotherapy. We show CD69 expression is upregulated upon T-cell activation and on tumor-infiltrating lymphocytes (TIL) in response to immunotherapy. Similarly, scRNA-seq data demonstrated elevated CD69 on TILs from patients with ICI-treated recurrent GBM as compared with TILs from control cohorts. CD69 immuno-PET studies showed a significantly higher tracer uptake in the tumors of ICI-treated mice compared with controls. Importantly, we observed a positive correlation between survival and CD69 immuno-PET signals in immunotherapy-treated animals and established a trajectory of T-cell activation by virtue of CD69-immuno-PET measurements. Our study supports the potential use of CD69 immuno-PET as an immunotherapy response assessment imaging tool for patients with GBM. Significance: Immunotherapy may hold promise for the treatment of some patients with GBM. There is a need to assess therapy responsiveness to allow the continuation of effective treatment in responders and to avoid ineffective treatment with potential adverse effects in the nonresponders. We demonstrate that noninvasive PET/CT imaging of CD69 may allow early detection of immunotherapy responsiveness in patients with GBM.

Immune Microenvironment Profiling of Normal Appearing Colorectal Mucosa Biopsied Over Repeat Patient Visits Reproduciably Separates Lynch Syndrome Patients Based on Their History of Colon Cancer.

Introduction: Lynch syndrome (LS) is the most common hereditary cause of colorectal cancer (CRC), increasing lifetime risk of CRC by up to 70%. Despite this higher lifetime risk, disease penetrance in LS patients is highly variable and most LS patients undergoing CRC surveillance will not develop CRC. Therefore, biomarkers that can correctly and consistently predict CRC risk in LS patients are needed to both optimize LS patient surveillance and help identify better prevention strategies that reduce risk of CRC development in the subset of high-risk LS patients. Methods: Normal-appearing colorectal tissue biopsies were obtained during repeat surveillance colonoscopies of LS patients with and without a history of CRC, healthy controls (HC), and patients with a history of sporadic CRC. Biopsies were cultured in an ex-vivo explant system and their supernatants were assayed via multiplexed ELISA to profile the local immune signaling microenvironment. High quality cytokine signatures were identified using rx COV fidelity metric. These signatures were used to perform biomarker selection by computing their selection probability based on penalized logistic regression. Results: Our study demonstrated that cytokine based local immune microenvironment profiling was reproducible over repeat visits and sensitive to patient LS-status and CRC history. Furthermore, we identified sets of biomarkers whose differential expression was predictive of LS-status in patients when compared to sporadic CRC patients and in identifying those LS patients with or without a history of CRC. Enrichment analysis based on these biomarkers revealed an LS and CRC status dependent constitutive inflammatory state of the normal appearing colonic mucosa. Discussion: This prospective pilot study demonstrated that immune profiling of normal appearing colonic mucosa discriminates LS patients with a prior history of CRC from those without it, as well as patients with a history of sporadic CRC from HC. Importantly, it suggests existence of immune signatures specific to LS-status and CRC history. We anticipate that our findings have the potential to assess CRC risk in individuals with LS and help in preemptively mitigating it by optimizing surveillance and identifying candidate prevention targets. Further studies are required to validate our findings in an independent cohort of LS patients over multiple visits.

rxCOV is a quantitative metric for assessing immunoassay analyte fidelity.

Immunoassay based bioanalytical measurements are widely used in a variety of biomedical research and clinical settings. In these settings they are assumed to faithfully represent the experimental conditions being tested and the sample groups being compared. Although significant technical advances have been made in improving sensitivity and quality of the measurements, currently no metrics exist that objectively quantify the fidelity of the measured analytes with respect to noise associated with the specific assay. Here we introduce ratio of cross-coefficient-of-variation (rxCOV), a fidelity metric for objectively assessing immunoassay analyte measurement quality when comparing its differential expression between different sample groups or experimental conditions. We derive the metric from first principles and establish its feasibility and applicability using simulated and experimental data. We show that rxCOV assesses fidelity independent of statistical significance, and importantly, identifies when latter is meaningful. We also discuss its importance in the context of averaging experimental replicates for increasing signal to noise ratio. Finally, we demonstrate its application in a Lynch Syndrome case study. We conclude by discussing its applicability to multiplexed immunoassays, other biosensing assays, and to paired and unpaired data. We anticipate rxCOV to be adopted as a simple and easy-to-use fidelity metric for performing robust and reproducible biomedical research.

Prediction of neoplastic progression in Barrett's esophagus using nanoscale nuclear architecture mapping: a pilot study.

BACKGROUND AND AIMS: Nanoscale nuclear architecture mapping (nanoNAM), an optical coherence tomography-derived approach, is capable of detecting with nanoscale sensitivity structural alterations in the chromatin of epithelial cell nuclei at risk for malignant transformation. Because these alterations predate the development of dysplasia, we aimed to use nanoNAM to identify patients with Barrett's esophagus (BE) who might progress to high-grade dysplasia (HGD) or esophageal adenocarcinoma (EAC). METHODS: This is a nested case-control study of 46 BE patients, of which 21 progressed to HGD/EAC over 3.7 ± 2.37 years (cases/progressors) and 25 patients who did not progress over 6.3 ± 3.1 years (control subjects/nonprogressors). The archived formalin-fixed paraffin-embedded tissue blocks collected as part of standard clinical care at the index endoscopy were used. nanoNAM imaging was performed on a 5-µm formalin-fixed paraffin-embedded section, and each nucleus was mapped to a 3-dimensional (3D) depth-resolved optical path difference (drOPD) nuclear representation, quantifying nanoscale-sensitive alterations in the 3D nuclear architecture of the cell. Using 3D-drOPD representation of each nucleus, we computed 12 patient-level nanoNAM features summarizing the alterations in intrinsic nuclear architecture. A risk prediction model was built incorporating nanoNAM features and clinical features. RESULTS: A statistically significant differential shift was observed in the drOPD cumulative distributions between progressors and nonprogressors. Of the 12 nanoNAM features, 6 (mean-maximum, mean-mean, mean-median, entropy-median, entropy-entropy, entropy-skewness) showed a statistically significant difference between cases and control subjects. NanoNAM features based prediction model identified progression in independent validation sets, with an area under the receiver operating characteristic curve of 80.8% ± .35% (mean ± standard error), with an increase to 82.54% ± .46% when combined with length of the BE segment. CONCLUSIONS: NanoNAM can serve as an adjunct to histopathologic evaluation of BE patients and aid in risk stratification.

Tumor Size Differences Between Preoperative Endoscopic Ultrasound and Postoperative Pathology for Neoadjuvant-Treated Pancreatic Ductal Adenocarcinoma Predict Patient Outcome.

BACKGROUND & AIMS: The assessment of therapeutic response after neoadjuvant treatment and pancreatectomy for pancreatic ductal adenocarcinoma (PDAC) has been an ongoing challenge. Several limitations have been encountered when employing current grading systems for residual tumor. Considering endoscopic ultrasound (EUS) represents a sensitive imaging technique for PDAC, differences in tumor size between preoperative EUS and postoperative pathology after neoadjuvant therapy were hypothesized to represent an improved marker of treatment response. METHODS: For 340 treatment-naïve and 365 neoadjuvant-treated PDACs, EUS and pathologic findings were analyzed and correlated with patient overall survival (OS). A separate group of 200 neoadjuvant-treated PDACs served as a validation cohort for further analysis. RESULTS: Among treatment-naïve PDACs, there was a moderate concordance between EUS imaging and postoperative pathology for tumor size (r = 0.726, P < .001) and AJCC 8th edition T-stage (r = 0.586, P < .001). In the setting of neoadjuvant therapy, a decrease in T-stage correlated with improved 3-year OS rates (50% vs 31%, P < .001). Through recursive partitioning, a cutoff of ≥47% tumor size reduction was also found to be associated with improved OS (67% vs 32%, P < .001). Improved OS using a ≥47% threshold was validated using a separate cohort of neoadjuvant-treated PDACs (72% vs 36%, P < .001). By multivariate analysis, a reduction in tumor size by ≥47% was an independent prognostic factor for improved OS (P = .007). CONCLUSIONS: The difference in tumor size between preoperative EUS imaging and postoperative pathology among neoadjuvant-treated PDAC patients is an important prognostic indicator and may guide subsequent chemotherapeutic management.

In situ functional cell phenotyping reveals microdomain networks in colorectal cancer recurrence.

Tumors are dynamic ecosystems comprising localized niches (microdomains), possessing distinct compositions and spatial configurations of cancer and non-cancer cell populations. Microdomain-specific network signaling coevolves with a continuum of cell states and functional plasticity associated with disease progression and therapeutic responses. We present LEAPH, an unsupervised machine learning algorithm for identifying cell phenotypes, which applies recursive steps of probabilistic clustering and spatial regularization to derive functional phenotypes (FPs) along a continuum. Combining LEAPH with pointwise mutual information and network biology analyses enables the discovery of outcome-associated microdomains visualized as distinct spatial configurations of heterogeneous FPs. Utilization of an immunofluorescence-based (51 biomarkers) image dataset of colorectal carcinoma primary tumors (n = 213) revealed microdomain-specific network dysregulation supporting cancer stem cell maintenance and immunosuppression that associated selectively with the recurrence phenotype. LEAPH enables an explainable artificial intelligence platform providing insights into pathophysiological mechanisms and novel drug targets to inform personalized therapeutic strategies.

Tofacitinib inhibits inflammatory cytokines from ulcerative colitis and healthy mucosal explants and is associated with pSTAT1/3 reduction in T-cells.

Poor translatability of animal disease models has hampered the development of new inflammatory bowel disorder (IBD) therapeutics. We describe a preclinical, ex vivo system using freshly obtained and well-characterized human colorectal tissue from patients with ulcerative colitis (UC) and healthy control (HC) participants to test potential therapeutics for efficacy and target engagement, using the JAK/STAT inhibitor tofacitinib (TOFA) as a model therapeutic. Colorectal biopsies from HC participants and patients with UC were cultured and stimulated with multiple mitogens ± TOFA. Soluble biomarkers were detected using a 29-analyte multiplex ELISA. Target engagement in CD3+CD4+ and CD3+CD8+ T-cells was determined by flow cytometry in peripheral blood mononuclear cells (PBMCs) and isolated mucosal mononuclear cells (MMCs) following the activation of STAT1/3 phosphorylation. Data were analyzed using linear mixed-effects modeling, t test, and analysis of variance. Biomarker selection was performed using penalized and Bayesian logistic regression modeling, with results visualized using uniform manifold approximation and projection. Under baseline conditions, 27 of 29 biomarkers from patients with UC were increased versus HC participants. Explant stimulation increased biomarker release magnitude, expanding the dynamic range for efficacy and target engagement studies. Logistic regression analyses identified the most representative UC baseline and stimulated biomarkers. TOFA inhibited biomarkers dependent on JAK/STAT signaling. STAT1/3 phosphorylation in T-cells revealed compartmental differences between PBMCs and MMCs. Immunogen stimulation increases biomarker release in similar patterns for HC participants and patients with UC, while enhancing the dynamic range for pharmacological effects. This work demonstrates the power of ex vivo human colorectal tissue as preclinical tools for evaluating target engagement and downstream effects of new IBD therapeutic agents.NEW & NOTEWORTHY Using colorectal biopsy material from healthy volunteers and patients with clinically defined IBD supports translational research by informing the evaluation of therapeutic efficacy and target engagement for the development of new therapeutic entities. Combining experimental readouts from intact and dissociated tissue enhances our understanding of the tissue-resident immune system that contribute to disease pathology. Bayesian logistic regression modeling is an effective tool for predicting ex vivo explant biomarker release patterns.

Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks.

An unmet clinical need in solid tumor cancers is the ability to harness the intrinsic spatial information in primary tumors that can be exploited to optimize prognostics, diagnostics and therapeutic strategies for precision medicine. Here, we develop a transformational spatial analytics computational and systems biology platform (SpAn) that predicts clinical outcomes and captures emergent spatial biology that can potentially inform therapeutic strategies. We apply SpAn to primary tumor tissue samples from a cohort of 432 chemo-naïve colorectal cancer (CRC) patients iteratively labeled with a highly multiplexed (hyperplexed) panel of 55 fluorescently tagged antibodies. We show that SpAn predicts the 5-year risk of CRC recurrence with a mean AUROC of 88.5% (SE of 0.1%), significantly better than current state-of-the-art methods. Additionally, SpAn infers the emergent network biology of tumor microenvironment spatial domains revealing a spatially-mediated role of CRC consensus molecular subtype features with the potential to inform precision medicine.

Three-Dimensional Nanoscale Nuclear Architecture Mapping of Rectal Biopsies Detects Colorectal Neoplasia in Patients with Inflammatory Bowel Disease.

Patients with inflammatory bowel disease (IBD) colitis are at an increased risk of developing colorectal cancer and are currently recommended to undergo extensive annual or biennial colonoscopy, a costly and invasive procedure. Most surveillance colonoscopies are negative with no existing objective measures for assessing their risk of developing cancer. We have recently developed a less invasive, cost-effective and objective method to assess cancer risk by detecting the presence of colonic neoplasia via 3-dimensional (3D) nanoscale nuclear architecture mapping (nanoNAM) of normal-appearing rectal biopsies. To establish its translational relevance, we prospectively recruited 103 patients with IBD colitis undergoing surveillance colonoscopy and measured submicroscopic alterations in aberrant intrinsic nuclear architecture of epithelial cells from normal-appearing rectal biopsies with nanoNAM. The results were correlated with the histologic diagnoses from all random biopsies obtained during initial and follow-up colonoscopy within 3 years. Using nanoNAM-based structural characterization as input features into a soft margin-based ν-SVM risk classifier, we show that nanoNAM detects colonic neoplasia with AUC of 0.87 ± 0.04, sensitivity of 0.81 ± 0.09, and specificity of 0.82 ± 0.07 in the independent validation set. In addition, projecting nanoNAM features onto a 2-sphere reveals patients with low-risk and high-risk IBD colitis existing on separate hemispheres. Finally, we show that this ability to assess cancer risk translates to clinically-relevant estimation of individual-patient likelihood of being truly at risk. We demonstrate the potential of nanoNAM to identify patients with IBD at higher risk of developing cancer from normal-appearing rectum tissue, which may aid clinicians in patients with personalized IBD colitis surveillance.

Targeted and Persistent 8-Oxoguanine Base Damage at Telomeres Promotes Telomere Loss and Crisis.

Telomeres are essential for genome stability. Oxidative stress caused by excess reactive oxygen species (ROS) accelerates telomere shortening. Although telomeres are hypersensitive to ROS-mediated 8-oxoguanine (8-oxoG) formation, the biological effect of this common lesion at telomeres is poorly understood because ROS have pleiotropic effects. Here we developed a chemoptogenetic tool that selectively produces 8-oxoG only at telomeres. Acute telomeric 8-oxoG formation increased telomere fragility in cells lacking OGG1, the enzyme that removes 8-oxoG, but did not compromise cell survival. However, chronic telomeric 8-oxoG induction over time shortens telomeres and impairs cell growth. Accumulation of telomeric 8-oxoG in chronically exposed OGG1-deficient cells triggers replication stress, as evidenced by mitotic DNA synthesis at telomeres, and significantly increases telomere losses. These losses generate chromosome fusions, leading to chromatin bridges and micronucleus formation upon cell division. By confining base damage to the telomeres, we show that telomeric 8-oxoG accumulation directly drives telomere crisis.

Super-Resolution Imaging of Higher-Order Chromatin Structures at Different Epigenomic States in Single Mammalian Cells.

Histone modifications influence higher-order chromatin structures at individual epigenomic states and chromatin environments to regulate gene expression. However, genome-wide higher-order chromatin structures shaped by different histone modifications remain poorly characterized. With stochastic optical reconstruction microscopy (STORM), we characterized the higher-order chromatin structures at their epigenomic states, categorized into three major types in interphase: histone acetylation marks form spatially segregated nanoclusters, active histone methylation marks form spatially dispersed larger nanodomains, and repressive histone methylation marks form condensed large aggregates. These distinct structural characteristics are also observed in mitotic chromosomes. Furthermore, active histone marks coincide with less compact chromatin and exhibit a higher degree of co-localization with other active marks and RNA polymerase II (RNAP II), while repressive marks coincide with densely packed chromatin and spatially distant from repressive marks and active RNAP II. Taken together, super-resolution imaging reveals three distinct chromatin structures at various epigenomic states, which may be spatially coordinated to impact transcription.

Fourier phase based depth-resolved nanoscale nuclear architecture mapping for cancer detection.

Quantitative phase imaging (QPI) modality has been widely adopted in a variety of applications ranging from identifying photomask defects in lithography to characterizing cell structure and tissue morphology in cancer. Traditional QPI utilizes the electromagnetic phase of transmitted light to measure, with nanometer scale sensitivity, alterations in the optical thickness of a sample of interest. In our work, the QPI paradigm is generalized to study depth-resolved properties of phase objects with slowly varying refractive index without a strong interface by utilizing the Fourier phase associated with Fourier-domain optical coherence tomography (FD-OCT). Specifically, based on computing the Fourier phase of light back-scattered by cell nuclei, we have developed nanoscale nuclear architecture mapping (nanoNAM) method that quantifies, with nanoscale sensitivity, (a) the depth-resolved alterations in mean nuclear optical density, and (b) depth-resolved localized heterogeneity in optical density of the cell nuclei. We have used nanoNAM to detect malignant transformation in colon carcinogenesis, even in tissue that appears histologically normal according to pathologists, thereby showing its potential as a pathology aid in cases where pathology examination remains inconclusive, and for screening patient populations at risk of developing cancer. In this paper, we integrate all aspects of nanoNAM, from principle through instrumentation and analysis, to show that nanoNAM is a promising, low-cost, and label-free method for identifying pathologically indeterminate pre-cancerous and cancerous cells. Importantly, it can seamlessly integrate into the clinical pipeline by utilizing clinically prepared formalin-fixed, paraffin-embedded tissue sections.

Early Prediction of Cancer Progression by Depth-Resolved Nanoscale Mapping of Nuclear Architecture from Unstained Tissue Specimens.

Early cancer detection currently relies on screening the entire at-risk population, as with colonoscopy and mammography. Therefore, frequent, invasive surveillance of patients at risk for developing cancer carries financial, physical, and emotional burdens because clinicians lack tools to accurately predict which patients will actually progress into malignancy. Here, we present a new method to predict cancer progression risk via nanoscale nuclear architecture mapping (nanoNAM) of unstained tissue sections based on the intrinsic density alteration of nuclear structure rather than the amount of stain uptake. We demonstrate that nanoNAM detects a gradual increase in the density alteration of nuclear architecture during malignant transformation in animal models of colon carcinogenesis and in human patients with ulcerative colitis, even in tissue that appears histologically normal according to pathologists. We evaluated the ability of nanoNAM to predict "future" cancer progression in patients with ulcerative colitis who did and did not develop colon cancer up to 13 years after their initial colonoscopy. NanoNAM of the initial biopsies correctly classified 12 of 15 patients who eventually developed colon cancer and 15 of 18 who did not, with an overall accuracy of 85%. Taken together, our findings demonstrate great potential for nanoNAM in predicting cancer progression risk and suggest that further validation in a multicenter study with larger cohorts may eventually advance this method to become a routine clinical test.

Fourier phase in Fourier-domain optical coherence tomography.

Phase of an electromagnetic wave propagating through a sample-of-interest is well understood in the context of quantitative phase imaging in transmission-mode microscopy. In the past decade, Fourier-domain optical coherence tomography has been used to extend quantitative phase imaging to the reflection-mode. Unlike transmission-mode electromagnetic phase, however, the origin and characteristics of reflection-mode Fourier phase are poorly understood, especially in samples with a slowly varying refractive index. In this paper, the general theory of Fourier phase from first principles is presented, and it is shown that Fourier phase is a joint estimate of subresolution offset and mean spatial frequency of the coherence-gated sample refractive index. It is also shown that both spectral-domain phase microscopy and depth-resolved spatial-domain low-coherence quantitative phase microscopy are special cases of this general theory. Analytical expressions are provided for both, and simulations are presented to explain and support the theoretical results. These results are further used to show how Fourier phase allows the estimation of an axial mean spatial frequency profile of the sample, along with depth-resolved characterization of localized optical density change and sample heterogeneity. Finally, a Fourier phase-based explanation of Doppler optical coherence tomography is also provided.

Assessment of nuclear nanomorphology marker to improve the detection of malignancy from bile duct biopsy specimens.

OBJECTIVES: The accurate diagnosis of malignancy from small bile duct biopsy specimens is often challenging. This proof-of-concept study assessed the feasibility of a novel optical technology, spatial-domain low-coherence quantitative phase microscopy (SL-QPM), that assesses nanoscale structural alterations in epithelial nuclei for improving the diagnosis of malignancy in bile duct biopsy specimens. METHODS: The SL-QPM analysis was performed on standard histology specimens of bile duct biopsy specimens from 45 patients. We analyzed normal cells with benign follow-up, histologically normal cells with pancreaticobiliary malignancy, and malignant epithelial cells. RESULTS: The SL-QPM-derived nuclear nanomorphology marker can not only distinguish benign and malignant epithelial cells but can also detect features of malignancy in those cells normal by light microscopy with a discriminatory accuracy of 0.90. When combining pathology with SL-QPM, the sensitivity is improved to 88.5% from 65.4% of conventional pathology, while maintaining 100% specificity. CONCLUSIONS: SL-QPM-derived nuclear nanomorphology markers represent a novel approach for detecting malignancy from histologically normal-appearing epithelial cells, with potential as an adjunctive test in patients with negative or inconclusive pathologic diagnosis on bile duct biopsy specimens.

Investigation of nanoscale structural alterations of cell nucleus as an early sign of cancer.

BACKGROUND: The cell and tissue structural properties assessed with a conventional bright-field light microscope play a key role in cancer diagnosis, but they sometimes have limited accuracy in detecting early-stage cancers or predicting future risk of cancer progression for individual patients (i.e., prognosis) if no frank cancer is found. The recent development in optical microscopy techniques now permit the nanoscale structural imaging and quantitative structural analysis of tissue and cells, which offers a new opportunity to investigate the structural properties of cell and tissue below 200 - 250 nm as an early sign of carcinogenesis, prior to the presence of microscale morphological abnormalities. Identification of nanoscale structural signatures is significant for earlier and more accurate cancer detection and prognosis. RESULTS: Our group has recently developed two simple spectral-domain optical microscopy techniques for assessing 3D nanoscale structural alterations - spectral-encoding of spatial frequency microscopy and spatial-domain low-coherence quantitative phase microscopy. These two techniques use the scattered light from biological cells and tissue and share a common experimental approach of assessing the Fourier space by various wavelengths to quantify the 3D structural information of the scattering object at the nanoscale sensitivity with a simple reflectance-mode light microscopy setup without the need for high-NA optics. This review paper discusses the physical principles and validation of these two techniques to interrogate nanoscale structural properties, as well as the use of these methods to probe nanoscale nuclear architectural alterations during carcinogenesis in cancer cell lines and well-annotated human tissue during carcinogenesis. CONCLUSIONS: The analysis of nanoscale structural characteristics has shown promise in detecting cancer before the microscopically visible changes become evident and proof-of-concept studies have shown its feasibility as an earlier or more sensitive marker for cancer detection or diagnosis. Further biophysical investigation of specific 3D nanoscale structural characteristics in carcinogenesis, especially with well-annotated human cells and tissue, is much needed in cancer research.