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Whiteaker JR, Zhao L, Schoenherr RM, Huang D, Lundeen RA, Voytovich U, Kennedy JJ, Ivey RG, Lin C, Murillo OD, Lorentzen TD, Colantonio S, Caceres TW, Roberts RR, Knotts JG, Reading JJ, Perry CD, Richardson CW, Garcia-Buntley SS, Bocik W, Hewitt SM, Chowdhury S, Vandermeer J, Smith SD, Gopal AK, Ramchurren N, Fling SP, Wang P, Paulovich AG. A multiplexed assay for quantifying immunomodulatory proteins supports correlative studies in immunotherapy clinical trials. Front Oncol 2023; 13:1168710. [PMID: 37205196 PMCID: PMC10185886 DOI: 10.3389/fonc.2023.1168710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/10/2023] [Indexed: 05/21/2023] Open
Abstract
Introduction Immunotherapy is an effective treatment for a subset of cancer patients, and expanding the benefits of immunotherapy to all cancer patients will require predictive biomarkers of response and immune-related adverse events (irAEs). To support correlative studies in immunotherapy clinical trials, we are developing highly validated assays for quantifying immunomodulatory proteins in human biospecimens. Methods Here, we developed a panel of novel monoclonal antibodies and incorporated them into a novel, multiplexed, immuno-multiple reaction monitoring mass spectrometry (MRM-MS)-based proteomic assay targeting 49 proteotypic peptides representing 43 immunomodulatory proteins. Results and discussion The multiplex assay was validated in human tissue and plasma matrices, where the linearity of quantification was >3 orders of magnitude with median interday CVs of 8.7% (tissue) and 10.1% (plasma). Proof-of-principle demonstration of the assay was conducted in plasma samples collected in clinical trials from lymphoma patients receiving an immune checkpoint inhibitor. We provide the assays and novel monoclonal antibodies as a publicly available resource for the biomedical community.
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Affiliation(s)
- Jeffrey R. Whiteaker
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Lei Zhao
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Regine M. Schoenherr
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Dongqing Huang
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Rachel A. Lundeen
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Ulianna Voytovich
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Jacob J. Kennedy
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Richard G. Ivey
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Chenwei Lin
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Oscar D. Murillo
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Travis D. Lorentzen
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Simona Colantonio
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Tessa W. Caceres
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Rhonda R. Roberts
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Joseph G. Knotts
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Joshua J. Reading
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Candice D. Perry
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Christopher W. Richardson
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Sandra S. Garcia-Buntley
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - William Bocik
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Stephen M. Hewitt
- Experimental Pathology Laboratory, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD, United States
| | - Shrabanti Chowdhury
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Jackie Vandermeer
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- Division of Medical Oncology, Department of Internal Medicine, University of Washington, Seattle, WA, United States
| | - Stephen D. Smith
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- Division of Medical Oncology, Department of Internal Medicine, University of Washington, Seattle, WA, United States
| | - Ajay K. Gopal
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- Division of Medical Oncology, Department of Internal Medicine, University of Washington, Seattle, WA, United States
| | - Nirasha Ramchurren
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Steven P. Fling
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Amanda G. Paulovich
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- *Correspondence: Amanda G. Paulovich,
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Matvienko OA, Alekel DL, Bhupathiraju SN, Hofmann H, Ritland LM, Reddy MB, Van Loan MD, Perry CD. Androidal fat dominates in predicting cardiometabolic risk in postmenopausal women. Cardiol Res Pract 2010; 2011:904878. [PMID: 21197412 PMCID: PMC3010706 DOI: 10.4061/2011/904878] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2010] [Revised: 11/04/2010] [Accepted: 11/24/2010] [Indexed: 11/20/2022] Open
Abstract
We hypothesized that soy isoflavones would attenuate the anticipated increase in androidal fat mass in postmenopausal women during the 36-month treatment, and thereby favorably modify the circulating cardiometabolic risk factors: triacylglycerol, LDL-C, HDL-C, glucose, insulin, uric acid, C-reactive protein, fibrinogen, and homocysteine. We collected data on 224 healthy postmenopausal women at risk for osteoporosis (45.8–65 y, median BMI 24.5) who consumed placebo or soy isoflavones (80 or 120 mg/d) for 36 months and used longitudinal analysis to examine the contribution of isoflavone treatment, androidal fat mass, other biologic factors, and dietary quality to cardiometabolic outcomes. Except for homocysteine, each cardiometabolic outcome model was significant (overall P-values from ≤.0001 to .0028). Androidal fat mass was typically the strongest covariate in each model. Isoflavone treatment did not influence any of the outcomes. Thus, androidal fat mass, but not isoflavonetreatment, is likely to alter the cardiometabolic profile in healthy postmenopausal women.
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Affiliation(s)
- O A Matvienko
- School of Health, Physical Education and Leisure Services, 133 Wellness/Recreation Center, University of Northern Iowa, Cedar Falls, IA 50614-0241, USA
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Gertz ER, Silverman NE, Wise KS, Hanson KB, Alekel DL, Stewart JW, Perry CD, Bhupathiraju SN, Kohut ML, Van Loan MD. Contribution of serum inflammatory markers to changes in bone mineral content and density in postmenopausal women: a 1-year investigation. J Clin Densitom 2010; 13:277-82. [PMID: 20605499 PMCID: PMC2912969 DOI: 10.1016/j.jocd.2010.04.003] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2009] [Revised: 02/17/2010] [Accepted: 04/14/2010] [Indexed: 10/19/2022]
Abstract
Bone formation and resorption are influenced by inflammatory processes. We examined the relationships among inflammatory markers and bone mineral content (BMC) and density (BMD) and determined the contribution of inflammatory markers to 1-yr changes in BMC and BMD in healthy postmenopausal women. This analysis included 242 women at baseline from our parent Soy Isoflavones for Reducing Bone Loss project who were randomly assigned to 1 of 3 treatment groups: placebo, 80 mg/d soy isoflavones, or 120 mg/d soy isoflavones. BMD and BMC from the lumbar spine (LS), total proximal femur (hip), and whole body were measured by dual energy X-ray absorptiometry and the 4% distal tibia by peripheral quantitative computed tomography. Serum inflammatory markers (C-reactive protein, interleukin [IL]-1 beta, IL-6, tumor necrosis factor-alpha [TNF-alpha], and white blood cell count [WBC]) were measured at baseline, 6, and 12 mo. Because of attrition or missing values, data analysis at 12 mo includes only 235 women. Significant associations among IL-6, TNF-alpha, and WBC were observed with percent change in LS, hip, and whole body BMC and BMD. Multiple regression analysis indicated that in combination inflammatory markers accounted for 1.1-6.1% of the variance to the observed 12-mo changes in BMC and BMD. Our results suggest that modifying inflammatory markers, even in healthy postmenopausal women, may possibly reduce bone loss.
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Affiliation(s)
- E R Gertz
- US Department of Agriculture, ARS, Western Human Nutrition Research Center, Davis, CA, USA
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Abstract
This study was designed to determine whether variability in bone mineral content (BMC) at the lumbar vertebrae (L2-4), radius shaft (RS), femoral neck, and distal radius can significantly contribute to the variability observed in body density (Db) among 89 females (age = 25.1 +/- 5.3 yr) of varying activity levels and menstrual status. Theoretical differences in Db were calculated at +/- 1 and +/- 2 standard deviations of BMC (SDBMC) for the population as well as for the subgroups: eumenorrheic inactive controls (C), recreational runners (RR), collegiate runners (CR), body builders (BB), swimmers (S), and amenorrheic runners (AR). Multiple regression to predict Db yielded significant coefficients (b) for BMC at L2-4 (b = 0.0190, P less than 0.001) and RS (b = 0.0425, P less than 0.01) when added separately to the sum of four skinfolds (subscapula, abdomen, thigh, calf). The differences in % BFHW at +/- 1 and +/- 2 SDBMC for the sample mean for RSBMC were +/- 1.0% and +/- 2.0%, respectively. Variability in L2-4 contributed differences of +/- 1.3% and +/- 2.6% at +/- 1 and +/- 2 SDBMC. The subgroup % BFHW differences (due to L2-4 and RS combined) ranged from an average overestimation of 1.3% for the AR to an average underestimation of 1.4% for the BB. Estimated mean errors for remaining groups were less than or equal to 0.5%. Individual differences ranged from a 3.3% underestimation (BB) to a 3.0% overestimation (AR).(ABSTRACT TRUNCATED AT 250 WORDS)
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Affiliation(s)
- J C Bunt
- Department of Exercise and Sport Sciences, University of Arizona, Tucson
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