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.

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.

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.

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.

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.

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.

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.

Tomographic imaging via spectral encoding of spatial frequency.

Three-dimensional optical tomographic imaging plays an important role in biomedical research and clinical applications. We introduce spectral tomographic imaging (STI) via spectral encoding of spatial frequency principle that not only has the capability for visualizing the three-dimensional object at sub-micron resolution but also providing spatially-resolved quantitative characterization of its structure with nanoscale accuracy for any volume of interest within the object. The theoretical basis and the proof-of-concept numerical simulations are presented to demonstrate the feasibility of spectral tomographic imaging.

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.

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.

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.

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.

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.

Nuclear nano-morphology markers of histologically normal cells detect the "field effect" of breast cancer.

Accurate detection of breast malignancy from histologically normal cells ("field effect") has significant clinical implications in a broad base of breast cancer management, such as high-risk lesion management, personalized risk assessment, breast tumor recurrence, and tumor margin management. More accurate and clinically applicable tools to detect markers characteristic of breast cancer "field effect" that are able to guide the clinical management are urgently needed. We have recently developed a novel optical microscope, spatial-domain low-coherence quantitative phase microscopy, which extracts the nanoscale structural characteristics of cell nuclei (i.e., nuclear nano-morphology markers), using standard histology slides. In this proof-of-concept study, we present the use of these highly sensitive nuclear nano-morphology markers to identify breast malignancy from histologically normal cells. We investigated the nano-morphology markers from 154 patients with a broad spectrum of breast pathology entities, including normal breast tissue, non-proliferative benign lesions, proliferative lesions (without and with atypia), "malignant-adjacent" normal tissue, and invasive carcinoma. Our results show that the nuclear nano-morphology markers of "malignant-adjacent" normal tissue can detect the presence of invasive breast carcinoma with high accuracy and do not reflect normal aging. Further, we found that a progressive change in nuclear nano-morphology markers that parallel breast cancer risk, suggesting its potential use for risk stratification. These novel nano-morphology markers that detect breast cancerous changes from nanoscale structural characteristics of histologically normal cells could potentially benefit the diagnosis, risk assessment, prognosis, prevention, and treatment of breast cancer.

Real-time quantitative visualization of 3D structural information.

We demonstrate a novel approach for the real time visualization and quantification of the 3D spatial frequencies in an image domain. Our approach is based on the spectral encoding of spatial frequency principle and permits the formation of an image as a color map in which spatially separated spectral wavelengths correspond to the dominant 3D spatial frequencies of the object. We demonstrate that our approach can visualize and analyze the dominant axial internal structure for each image point in real time and with nanoscale sensitivity to structural changes. Computer modeling and experimental results of instantaneous color visualization and quantification of 3D structures of a model system and biological samples are presented.

Spectral contrast imaging microscopy.

We introduce a new technique, spectral contrast imaging microscopy (SCIM), for super-resolution microscopic imaging. Based on a novel contrast mechanism that encodes each local spatial frequency with a corresponding optical wavelength, SCIM provides a real-time high-resolution spectral contrast microscopic image with superior contrast. We show that two microscopic objects, separated by a distance smaller than the diffraction limit of the optical system, can be spatially resolved in the SCIM image as different colors. Results with numerical simulation and experiments using a high-resolution United States Air Force target are presented. The ability of SCIM for imaging biological cells is also demonstrated.

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.

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.

Optically multiplexed imaging with superposition space tracking.

We describe a novel method to track targets in a large field of view. This method simultaneously images multiple, encoded sub-fields of view onto a common focal plane. Sub-field encoding enables target tracking by creating a unique connection between target characteristics in superposition space and the target's true position in real space. This is accomplished without reconstructing a conventional image of the large field of view. Potential encoding schemes include spatial shift, rotation, and magnification. We discuss each of these encoding schemes, but the main emphasis of the paper and all examples are based on one-dimensional spatial shift encoding. System performance is evaluated in terms of two criteria: average decoding time and probability of decoding error. We study these performance criteria as a function of resolution in the encoding scheme and signal-to-noise ratio. Finally, we include simulation and experimental results demonstrating our novel tracking method.