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Article ; Online: Perspective on quantitative phase imaging to improve precision cancer medicine.

Liu, Yang / Uttam, Shikhar

2024 Volume 29, Issue Suppl 2, Page(s) S22705

Abstract: 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 ... ...

Abstract	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 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.
MeSH term(s)	Humans ; Quantitative Phase Imaging ; Artificial Intelligence ; Neoplasms/diagnostic imaging
Language	English
Publishing date	2024-03-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	1309154-2
ISSN	1560-2281 ; 1083-3668
ISSN (online)	1560-2281
ISSN	1083-3668
DOI	10.1117/1.JBO.29.S2.S22705
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Article ; Online: PARP2 promotes Break Induced Replication-mediated telomere fragility in response to replication stress.

Muoio, Daniela / Laspata, Natalie / Dannenberg, Rachel L / Curry, Caroline / Darkoa-Larbi, Simone / Hedglin, Mark / Uttam, Shikhar / Fouquerel, Elise

Nature communications

2024 Volume 15, Issue 1, Page(s) 2857

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Poly (ADP-Ribose) Polymerase-1/genetics ; Poly (ADP-Ribose) Polymerase-1/metabolism ; DNA Repair ; DNA/metabolism ; DNA Damage ; DNA Helicases/genetics ; DNA Helicases/metabolism ; Telomere/genetics ; Telomere/metabolism
Chemical Substances	Poly (ADP-Ribose) Polymerase-1 (EC 2.4.2.30) ; DNA (9007-49-2) ; DNA Helicases (EC 3.6.4.-)
Language	English
Publishing date	2024-04-02
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-024-47222-7
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Article ; Online: rxCOV is a quantitative metric for assessing immunoassay analyte fidelity.

Brand, Rhonda M / Pitlor, Danielle / Metter, E Jeffrey / Dudley, Beth / Karloski, Eve / Zyhowski, Ashley / Brand, Randall E / Uttam, Shikhar

Scientific reports

2023 Volume 13, Issue 1, Page(s) 88

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Immunoassay/methods ; Signal-To-Noise Ratio
Language	English
Publishing date	2023-01-03
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-27309-1
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Article: Fourier phase based depth-resolved nanoscale nuclear architecture mapping for cancer detection

Uttam, Shikhar / Liu, Yang

Methods. 2018 Mar. 01, v. 136

2018

Abstract: 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 ... ...

Abstract	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.
Keywords	absorbance ; at-risk population ; carcinogenesis ; cell nucleus ; colon ; image analysis ; instrumentation ; neoplasms ; patients ; refractive index ; screening ; tomography
Language	English
Dates of publication	2018-0301
Size	p. 134-151.
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	1066584-5
ISSN	1095-9130 ; 1046-2023
ISSN (online)	1095-9130
ISSN	1046-2023
DOI	10.1016/j.ymeth.2017.10.011
Database	NAL-Catalogue (AGRICOLA)

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Zs.A 3239: Show issues

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Article ; Online: Fourier phase based depth-resolved nanoscale nuclear architecture mapping for cancer detection.

Uttam, Shikhar / Liu, Yang

Methods (San Diego, Calif.)

2017 Volume 136, Page(s) 134–151

Abstract	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.
MeSH term(s)	Cell Nucleus/ultrastructure ; Fourier Analysis ; Humans ; Image Interpretation, Computer-Assisted/methods ; Neoplasms/diagnosis ; Neoplasms/pathology ; Tomography, Optical Coherence/methods
Language	English
Publishing date	2017-11-07
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	1066584-5
ISSN	1095-9130 ; 1046-2023
ISSN (online)	1095-9130
ISSN	1046-2023
DOI	10.1016/j.ymeth.2017.10.011
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Article ; Online: In situ

Furman, Samantha A / Stern, Andrew M / Uttam, Shikhar / Taylor, D Lansing / Pullara, Filippo / Chennubhotla, S Chakra

Cell reports methods

2021 Volume 1, Issue 5

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Humans ; Artificial Intelligence ; Ecosystem ; Algorithms ; Biomarkers ; Colorectal Neoplasms/genetics
Chemical Substances	Biomarkers
Language	English
Publishing date	2021-09-15
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ISSN	2667-2375
ISSN (online)	2667-2375
DOI	10.1016/j.crmeth.2021.100072
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Article: UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples.

Kochetov, Bogdan / Bell, Phoenix / Garcia, Paulo S / Shalaby, Akram S / Raphael, Rebecca / Raymond, Benjamin / Leibowitz, Brian J / Schoedel, Karen / Brand, Rhonda M / Brand, Randall E / Yu, Jian / Zhang, Lin / Diergaarde, Brenda / Schoen, Robert E / Singhi, Aatur / Uttam, Shikhar

bioRxiv : the preprint server for biology

2024

Abstract: 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 ... ...

Abstract	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. However, limited work has been done in developing such generalist methods within the label-free unsupervised context. 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 morphology 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 exemplar biological scenario. Within the UNSEG framework, we also introduce a new perturbed watershed algorithm capable of stably and automatically segmenting a cluster of cell nuclei into individual cell nuclei that increases the accuracy of classical watershed. Perturbed watershed can also be used as a standalone algorithm that researchers can incorporate within their supervised or unsupervised learning approaches to extend classical watershed, particularly in the multiplexed imaging context. Finally, as part of developing UNSEG, we have generated a high-quality annotated gastrointestinal tissue (GIT) dataset, which we anticipate will be useful for the broader research community. We demonstrate the efficacy of UNSEG on the GIT dataset, publicly available datasets, and on a range of practical scenarios. In these contexts, we also discuss the possibility of bias inherent in quantification of segmentation accuracy based on F1 score. 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.
Language	English
Publishing date	2024-04-23
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.11.13.566842
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Article ; Online: Prediction of neoplastic progression in Barrett's esophagus using nanoscale nuclear architecture mapping: a pilot study.

Thota, Prashanthi N / Nasibli, Jalil / Kumar, Prabhat / Sanaka, Madhusudhan R / Chak, Amitabh / Zhang, Xuefeng / Liu, Xiuli / Uttam, Shikhar / Liu, Yang

Gastrointestinal endoscopy

2022 Volume 95, Issue 6, Page(s) 1239–1246

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Adenocarcinoma ; Barrett Esophagus/pathology ; Case-Control Studies ; Disease Progression ; Esophageal Neoplasms/pathology ; Formaldehyde ; Humans ; Hyperplasia ; Pilot Projects ; Precancerous Conditions/pathology ; Risk Assessment
Chemical Substances	Formaldehyde (1HG84L3525)
Language	English
Publishing date	2022-01-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	391583-9
ISSN	1097-6779 ; 0016-5107
ISSN (online)	1097-6779
ISSN	0016-5107
DOI	10.1016/j.gie.2022.01.007
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Zs.B 283: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular ab Jg. 2022: Lesesaal (EG)
Zs.MO 519: Show issues

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Article: 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.

Brand, Rhonda M / Dudley, Beth / Karloski, Eve / Zyhowski, Ashley / Raphael, Rebecca / Pitlor, Danielle / Metter, E Jeffrey / Pai, Reet / Lee, Kenneth / Brand, Randall E / Uttam, Shikhar

medRxiv : the preprint server for health sciences

2023

Abstract: 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 ... ...

Abstract	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 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.
Language	English
Publishing date	2023-03-06
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.03.03.23286594
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Article: 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.

Brand, Rhonda M / Dudley, Beth / Karloski, Eve / Zyhowski, Ashley / Raphael, Rebecca / Pitlor, Danielle / Metter, E Jeffrey / Pai, Reet / Lee, Kenneth / Brand, Randall E / Uttam, Shikhar

Frontiers in oncology

2023 Volume 13, Page(s) 1174831

Abstract	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 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.
Language	English
Publishing date	2023-08-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2023.1174831
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