Publications

Journal Publications

January 3, 2023
R. M. Brand, D. Pitlor, E. J. Metter, B. Dudley , E. Karloski, A. Zyhowski , R. E. Brand, S. Uttam
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 contextof 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.

June 29, 2022
P. Thota*, J. Nasibli, P. Kumar, M. R. Sanaka, A. Chak, X. Zhang, X. Liu, S. Uttam*, Y. Liu* (* Co-corresponding authors)
Abstract

Background 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. Since these alterations predate the development of dysplasia, we aimed to utilize nanoNAM to identify patients with Barrett’s esophagus (BE) who 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 (controls/non-progressors). The archived formalin-fixed paraffin-embedded (FFPE) tissue blocks collected as part of standard clinical care at index endoscopy were used. The nanoNAM imaging was performed on a 5μm FFPE section and each nucleus was mapped to a three-dimensional (3D) depth-resolved optical path difference (3D-drOPD) nuclear representation, quantifying nanoscale-sensitive alterations in the 3D nuclear architecture of the cell. Using 3D-drOPD representation of each nucleus, twelve patient-level nanoNAM features summarizing the alterations in intrinsic nuclear architecture were computed. 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 non-progressors. Of the twelve nanoNAM features, six – mean-max, mean-mean, mean-median, entropy-median, entropy-entropy, entropy-skewness – showed statistically significant difference between cases and controls. NanoNAM features based prediction model identified progression in independent validation sets, with area under the receiver operating characteristic (auROC) of 80.8% ± 0.35% standard error (s.e.), with an increase to 82.54% ± 0.46% (s.e.), when combined with length of BE segment. Conclusions: NanoNAM can serve as an adjunct to histopathological evaluation of BE patients and aid in risk stratification.‍

               

March 10, 2021
R. M. Brand, B. A. Moore, A. Zyhowski, A. Siegel, S. Uttam, E. J. Metter, J. Engstrom, R. E. Brand, N. Biswas, D. C. Whitcomb, D. G. Binion, M. Schwartz, I. McGowan
Abstract

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.

December 2, 2020
R. Das, K. McGrath, N. Seiser, K. Smith, S. Uttam, R. E. Brand, K. E. Fasanella, A. Khalid, J. S. Chennat, S. Sarkaria, H. Singh, A. Slivka, H. J. Zeh, A. H. Zureikat, M. E. Hogg, K. K. Lee, A. Paniccia, M. C. Ongchin, J. F. Pingpank, B. A. Boone, A. K. Dasyam, N. Bahary, V. C. Gorantla, J. C. Rhee, R. Thomas, S. Ellsworth, M. S. Landau, N. Paul Ohori, P. Henn, S. Shyu, B. K. Theisen, A. D. Singhi
Abstract

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-naive 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-naive 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 ≥ 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.

July 14, 2020
S. Uttam*, A. M. Stern, S. Furman, F. Pullara, D. Spagnolo, L. Nguyen, A. H Gough, C. Sevinsky, F. Ginty, D. L. Taylor, S. C. Chennubhotla* (Co-corresponding authors)
Abstract

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

Conference Presentations

K. Yadav, R. Pawar, A. Singhi, and S. Uttam
Characterizing the Three-Dimensional Nuclear Morphology of Normal Appearing, Immune, and Cancer Cells in Cancer Tumor Microenvironment
2022 Biomedical Engineering Society (BMES) Annual Meeting, Oct 12-15, 2022
October 1, 2022
P. N. Thota, J. Nasibli, P. Kumar, M.R. Sanaka, A. Chak, X. Zhang, X. Liu, S. Uttam, and Y. Liu
Nanoscale nuclear architecture mapping predicts neoplastic progression in Barrett’s esophagus: a proof-of-concept study
in Gastrointest. Endosc.; 95(6) Supplement, AB230-AB231
September 1, 2022
B. Kochetov, P.D. Bell, R. Raphael, B.J. Raymond, B.J. Leibowitz, J. Tong, B. Diergaarde , J. Yu, R.K. Pai, R.E. Schoen, L. Zhang, A. Singhi, and S. Uttam
Unsupervised sub-cellular segmentation of complex tissue and cell samples using highly multiplexed imaging-derived a priori knowledge
Abstract 1930. Cancer Res 15 June 2022; 82 (12_Supplement): 1930
June 1, 2022
B. Kochetov, R. Raphael, and S. Uttam
Unsupervised segmentation of complex tissue using multiplexed imaging-derived a priori knowledge
Biomedical Engineering Society (BMES) 2021 Annual Meeting; Oct 6 - 9, 2021; Orlando, Florida; Abstract 074 - 841 (2021)
October 6, 2021
S. Leng, J. Xu, Y. Liu, and S. Uttam
Demonstration of ability of nanoscale nuclear architecture mapping to study chromatin alteration
Biomedical Engineering Society (BMES) 2021 Annual Meeting; Oct 6 - 9, 2021; Orlando, Florida; Abstract reu-011-3129 (2021).
October 6, 2021
D. Pitlor, R. E. Brand, B. Dudley, E. Karlowski, A. Zyhowski, E. J. Metter, R. M. Brand, and S. Uttam
Coefficient of variation based multiplexed ELISA biomarker selection in HNPCC Syndrome Patients
Biomedical Engineering Society (BMES) 2021 Annual Meeting; Oct 6 - 9, 2021; Orlando, Florida; Abstract reu-007-3147 (2021)
October 6, 2021

Conference Presentations

S. Uttam
Nanoscale nuclear architecture mapping of early carcinogenesis
Paper 12389-32, Quantitative Phase Imaging IX, SPIE Photonics West, San Francisco (January 27 - Feb 1, 2023) [Invited]
February 1, 2023
S. Uttam
Label-free nanoscale nuclear architecture mapping of early carcinogenesis
2023 Biophysics and Quantitative Biology in the AI Era, NSF AI Planning Institute at Carnegie Mellon University (Jan 12-13, 2023)
January 25, 2023
S. Uttam
Overcoming the segmentation barrier in in multiplexed spatial proteomic images
UPMC Hillman Cancer Center, Spatial Omics and Computational Imaging in Human Diseases Symposium, Nov 14, 2022. [Invited]
December 1, 2022
S. Uttam
Cancer systems biology in space and scale
UPMC Hillman Cancer Center, Cancer Biology Program Retreat, Oct 24, 2022 [Invited]
November 20, 2022
B. Raymond, D. J. Hartman, and S. Uttam
Spatial Analysis of Cytotoxic T Lymphocyte Infiltration in Colorectal Tumors for Predicting Stage-independent Relapse and Death
2022 Biomedical Engineering Society (BMES) Annual Meeting, Oct 12-15, 2022
October 1, 2022
C. Newman, B. Kochetov, R. Raphael, L. Zhang, and S. Uttam
Understanding the Cellular Landscape of Preclinical Models of Colorectal Cancer
2022 Biomedical Engineering Society (BMES) Annual Meeting, Oct 12-15, 2022
October 1, 2022
K. Yadav, R. Pawar, A. Singhi, and S. Uttam
Characterizing the Three-Dimensional Nuclear Morphology of Normal Appearing, Immune, and Cancer Cells in Cancer Tumor Microenvironment
2022 Biomedical Engineering Society (BMES) Annual Meeting, Oct 12-15, 2022
October 1, 2022
P. N. Thota, J. Nasibli, P. Kumar, M.R. Sanaka, A. Chak, X. Zhang, X. Liu, S. Uttam, and Y. Liu
Nanoscale nuclear architecture mapping predicts neoplastic progression in Barrett’s esophagus: a proof-of-concept study
in Gastrointest. Endosc.; 95(6) Supplement, AB230-AB231
September 1, 2022
B. Kochetov, P.D. Bell, R. Raphael, B.J. Raymond, B.J. Leibowitz, J. Tong, B. Diergaarde , J. Yu, R.K. Pai, R.E. Schoen, L. Zhang, A. Singhi, and S. Uttam
Unsupervised sub-cellular segmentation of complex tissue and cell samples using highly multiplexed imaging-derived a priori knowledge
Abstract 1930. Cancer Res 15 June 2022; 82 (12_Supplement): 1930
June 1, 2022
B. Kochetov, R. Raphael, and S. Uttam
Unsupervised segmentation of complex tissue using multiplexed imaging-derived a priori knowledge
Biomedical Engineering Society (BMES) 2021 Annual Meeting; Oct 6 - 9, 2021; Orlando, Florida; Abstract 074 - 841 (2021)
October 6, 2021
S. Leng, J. Xu, Y. Liu, and S. Uttam
Demonstration of ability of nanoscale nuclear architecture mapping to study chromatin alteration
Biomedical Engineering Society (BMES) 2021 Annual Meeting; Oct 6 - 9, 2021; Orlando, Florida; Abstract reu-011-3129 (2021).
October 6, 2021
D. Pitlor, R. E. Brand, B. Dudley, E. Karlowski, A. Zyhowski, E. J. Metter, R. M. Brand, and S. Uttam
Coefficient of variation based multiplexed ELISA biomarker selection in HNPCC Syndrome Patients
Biomedical Engineering Society (BMES) 2021 Annual Meeting; Oct 6 - 9, 2021; Orlando, Florida; Abstract reu-007-3147 (2021)
October 6, 2021
S. Uttam
Sampling and scrambling in compressive sensing based spectral domain optical coherence tomography
CLEO Laser Science to Photonic Applications (Optical Society of America, 2020) JTu2F.7.
May 10, 2020
S. Uttam and Y. Liu
Three-dimensional nanoscale nuclear architecture mapping for improved cancer risk stratification
SPIE/OSA European Conferences on Biomedical Optics (ECBO) 2019, 23-27 June 2019, Munich, Germany; Paper 11076-38 (2019). [Invited paper]
June 23, 2019
S. Uttam, A.M. Stern, S. A. Furman, F. Pullara, F. Ginty, D. L. Taylor, S. C. Chennubhotla
Spatial proteomics with hyperplexed fluorescence imaging predicts risk of colorectal cancer recurrence and infers recurrence-specific protein-protein networks
Cancer Res; 79 (13 Supplement): 1642 (2019). [AACR Annual Meeting 2019, March 29-April 3 2019, Atlanta, Georgia.]
March 29, 2019
S. Uttam
Hyperplexed immunofluorescence imaging based on spatial proteomics predicts risk of colorectal cancer recurrence and infers recurrence-specific protein networks
Joint Immunology and Computational and Systems Biology Workshop, Jan 23, 2019, University of Pittsburgh, Pittsburgh, PA, USA (2019
January 23, 2019
R. C. Burgess and S. Uttam
Modeling the impact of chromatin modifications on the DNA damage response in yeast
Find Your Inner Modeler workshop, Aug 16-17, 2018, University of Illinois at Chicago, Chicago, Illinois, USA (2018). [Travel award]
August 16, 2018
F. Pullara, N. Bouhenni, S. Uttam, and S. C. Chennubhotla
Integrative strategies for probing energy landscapes and dynamics of IDPs
Workshop on Intrinsically Disordered Proteins, TSRC 2017, July 11--14, 2017; Telluride, Colorado, USA (2017)
July 11, 2017
S. Uttam, F. Pullara, and S. C. Chennubhotla
Comparative dynamics - An information theoretic perspective
Biomolecular Machines Conference - Protein Flexibility and Allostery, May 18-21, 2017, Banff, Alberta, Canada (2017)
May 17, 2017
S. Uttam
Nanoscale nuclear architecture mapping for cancer-risk stratification and prediction
Computational Pathology Lecture Series, April 14, 2017; Computational Pathology Interest Group and Lecture Series, University of Pittsburgh, Pittsburgh, Pennsylvania, USA (2017). [Invited talk]
April 17, 2017
Y. Liu, S. Uttam, H. V. Pham, and D. J. Hartman
Improved cancer risk stratification and diagnosis via quantitative phase microscopy
SPIE Photonics West (BIOS), Jan 28 -- Feb 2, 2017, San Francisco, USA; Conference: Quantitative Phase Imaging III, Paper 10074-40 (2017).[Invited paper]
February 2, 2017
R. Bista, S. Uttam, D. Hartman, W. Qiu, J. Yu, L. Zhang, R. Brand, and Y. Liu
Investigation of nuclear nano-morphology markers as a novel biomarker for cancer risk assessment using a mouse model
Gastroenterology - San Diego, 2012; 142(5):S-532.
May 1, 2012
S. Uttam, S. Alexandrov, R. Bista, and Y. Liu
Model-based demonstration of spectral tomographic imaging
in Biomedical Optics, OSA Technical Digest (Optical Society of America, 2012), paper BSu3A.61.
February 28, 2012
Y. Liu, S. Alexandrov, S. Uttam, and R. Bista
Probing Cell Nanoscale Structural Properties Using Intrinsic Contrast of Light Scattering
Biophysical Journal, Vol. 102, Issue 3, S1 (2012). [Invited talk ]
February 25, 2012
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