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Jiao F, Yu C, Wheat A, Chen L, Lih TSM, Zhang H, Huang L. DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples. J Proteome Res 2024; 23:3269-3279. [PMID: 38334954 PMCID: PMC11296914 DOI: 10.1021/acs.jproteome.3c00832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Protein-protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks.
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Affiliation(s)
- Fenglong Jiao
- Department of Physiology and Biophysics, University of California, Irvine, CA 92697
| | - Clinton Yu
- Department of Physiology and Biophysics, University of California, Irvine, CA 92697
| | - Andrew Wheat
- Department of Physiology and Biophysics, University of California, Irvine, CA 92697
| | - Lijun Chen
- Department of Pathology and Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21231
| | - Tung-Shing Mamie Lih
- Department of Pathology and Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21231
| | - Hui Zhang
- Department of Pathology and Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21231
| | - Lan Huang
- Department of Physiology and Biophysics, University of California, Irvine, CA 92697
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de Leeuw C, Werme J, Savage JE, Peyrot WJ, Posthuma D. On the interpretation of transcriptome-wide association studies. PLoS Genet 2023; 19:e1010921. [PMID: 37676898 PMCID: PMC10508613 DOI: 10.1371/journal.pgen.1010921] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/19/2023] [Accepted: 08/15/2023] [Indexed: 09/09/2023] Open
Abstract
Transcriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results from TWAS analyses are often interpreted as indicating a genetic relationship between gene expression and a phenotype, but this interpretation is not consistent with the null hypothesis that is evaluated in the traditional TWAS framework. In this study we provide a mathematical outline of this TWAS framework, and elucidate what interpretations are warranted given the null hypothesis it actually tests. We then use both simulations and real data analysis to assess the implications of misinterpreting TWAS results as indicative of a genetic relationship between gene expression and the phenotype. Our simulation results show considerably inflated type 1 error rates for TWAS when interpreted this way, with 41% of significant TWAS associations detected in the real data analysis found to have insufficient statistical evidence to infer such a relationship. This demonstrates that in current implementations, TWAS cannot reliably be used to investigate genetic relationships between gene expression and a phenotype, but that local genetic correlation analysis can serve as a potential alternative.
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Affiliation(s)
- Christiaan de Leeuw
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands
| | - Josefin Werme
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands
| | - Jeanne E. Savage
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands
| | - Wouter J. Peyrot
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychology and Psychiatry, section Complex Trait Genetics, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, The Netherlands
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Fiorica PN, Sheng H, Zhu Q, Roh JM, Laurent CA, Ergas IJ, Delmerico J, Kwan ML, Kushi LH, Ambrosone CB, Yao S. A Mendelian Randomization Analysis of 55 Genetically Predicted Metabolic Traits with Breast Cancer Survival Outcomes in the Pathways Study. CANCER RESEARCH COMMUNICATIONS 2023; 3:1104-1112. [PMID: 37377609 PMCID: PMC10286812 DOI: 10.1158/2767-9764.crc-23-0047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 04/05/2023] [Accepted: 05/30/2023] [Indexed: 06/29/2023]
Abstract
Previous studies suggest associations of metabolic syndromes with breast cancer prognosis, yet the evidence is mixed. In recent years, the maturation of genome-wide association study findings has led to the development of polygenic scores (PGS) for many common traits, making it feasible to use Mendelian randomization to examine associations between metabolic traits and breast cancer outcomes. In the Pathways Study of 3,902 patients and a median follow-up time of 10.5 years, we adapted a Mendelian randomization approach to calculate PGS for 55 metabolic traits and tested their associations with seven survival outcomes. Multivariable Cox proportional hazards models were used to derive HRs and 95% confidence intervals (CI) with adjustment for covariates. The highest tertile (T3) of PGS for cardiovascular disease was associated with shorter overall survival (HR = 1.34, 95% CI = 1.11-1.61) and second primary cancer-free survival (HR = 1.31, 95% CI = 1.12-1.53). PGS for hypertension (T3) was associated with shorter overall survival (HR = 1.20, 95% CI = 1.00-1.43), second primary cancer-free survival (HR = 1.24, 95% CI = 1.06-1.45), invasive disease-free survival (HR = 1.18, 95% CI = 1.01-1.38), and disease-free survival (HR = 1.21, 95% CI = 1.04-1.39). PGS for serum cystatin C levels (T3) was associated with longer disease-free survival (HR = 0.82, 95% CI = 0.71-0.95), breast event-free survival (HR = 0.74, 95% CI = 0.61-0.91), and breast cancer-specific survival (HR = 0.72, 95% CI = 0.54-0.95). The above associations were significant at a nominal P < 0.05 level but not after correcting for multiple testing (Bonferroni P < 0.0009). Our analyses revealed notable associations of PGS for cardiovascular disease, hypertension, and cystatin C levels with breast cancer survival outcomes. These findings implicate metabolic traits in breast cancer prognosis. Significance To our knowledge, this is the largest study of PGS for metabolic traits with breast cancer prognosis. The findings revealed significant associations of PGS for cardiovascular disease, hypertension, and cystatin C levels with several breast cancer survival outcomes. These findings implicate an underappreciated role of metabolic traits in breast cancer prognosis that would warrant further exploration.
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Affiliation(s)
- Peter N. Fiorica
- Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Haiyang Sheng
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Qianqian Zhu
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Janise M. Roh
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Cecile A. Laurent
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Isaac J. Ergas
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Jennifer Delmerico
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Marilyn L. Kwan
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Lawrence H. Kushi
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Christine B. Ambrosone
- Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York
| | - Song Yao
- Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, New York
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Shieh Y, Roger J, Yau C, Wolf DM, Hirst GL, Swigart LB, Huntsman S, Hu D, Nierenberg JL, Middha P, Heise RS, Shi Y, Kachuri L, Zhu Q, Yao S, Ambrosone CB, Kwan ML, Caan BJ, Witte JS, Kushi LH, 't Veer LV, Esserman LJ, Ziv E. Development and testing of a polygenic risk score for breast cancer aggressiveness. NPJ Precis Oncol 2023; 7:42. [PMID: 37188791 PMCID: PMC10185660 DOI: 10.1038/s41698-023-00382-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 04/28/2023] [Indexed: 05/17/2023] Open
Abstract
Aggressive breast cancers portend a poor prognosis, but current polygenic risk scores (PRSs) for breast cancer do not reliably predict aggressive cancers. Aggressiveness can be effectively recapitulated using tumor gene expression profiling. Thus, we sought to develop a PRS for the risk of recurrence score weighted on proliferation (ROR-P), an established prognostic signature. Using 2363 breast cancers with tumor gene expression data and single nucleotide polymorphism (SNP) genotypes, we examined the associations between ROR-P and known breast cancer susceptibility SNPs using linear regression models. We constructed PRSs based on varying p-value thresholds and selected the optimal PRS based on model r2 in 5-fold cross-validation. We then used Cox proportional hazards regression to test the ROR-P PRS's association with breast cancer-specific survival in two independent cohorts totaling 10,196 breast cancers and 785 events. In meta-analysis of these cohorts, higher ROR-P PRS was associated with worse survival, HR per SD = 1.13 (95% CI 1.06-1.21, p = 4.0 × 10-4). The ROR-P PRS had a similar magnitude of effect on survival as a comparator PRS for estrogen receptor (ER)-negative versus positive cancer risk (PRSER-/ER+). Furthermore, its effect was minimally attenuated when adjusted for PRSER-/ER+, suggesting that the ROR-P PRS provides additional prognostic information beyond ER status. In summary, we used integrated analysis of germline SNP and tumor gene expression data to construct a PRS associated with aggressive tumor biology and worse survival. These findings could potentially enhance risk stratification for breast cancer screening and prevention.
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Affiliation(s)
- Yiwey Shieh
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
| | - Jacquelyn Roger
- PhD Program in Biological and Medical Informatics, University of California, San Francisco, San Francisco, CA, USA
| | - Christina Yau
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Denise M Wolf
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Gillian L Hirst
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Lamorna Brown Swigart
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Scott Huntsman
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Donglei Hu
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jovia L Nierenberg
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Pooja Middha
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Rachel S Heise
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yushu Shi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Qianqian Zhu
- Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Song Yao
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Christine B Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Marilyn L Kwan
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Bette J Caan
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Lawrence H Kushi
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Laura van 't Veer
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Laura J Esserman
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Elad Ziv
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
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5
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Willman M, Willman J, Lucke-Wold B. Endocrine resistant breast cancer: brain metastasis. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2022; 3:240-251. [PMID: 35505937 PMCID: PMC9060566 DOI: 10.37349/etat.2022.00081] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 03/09/2022] [Indexed: 02/05/2023] Open
Abstract
Endocrine resistant breast cancer metastasis continues to serve as a significant clinical challenge with high morbidity and mortality for patients. As the number of breast cancer cases continues to rise, the rate of brain metastasis has also increased. For single lesions or a large symptomatic lesion with other smaller lesions, surgical resection is a viable option in non-eloquent regions. Stereotactic radiosurgery is a great option for post-operative therapy or for 10 or fewer small lesions (< 3 cm in size). Whole-brain radiation can be used sparingly for large tumor burdens but should encompass hippocampus sparing techniques. Chemotherapy options have remained relatively limited due to decreased permeability of the blood-brain barrier. Emerging monoclonal antibody treatments have offered initial promise, especially for endocrine resistant breast cancer metastasis.
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Affiliation(s)
- Matthew Willman
- Department of Neurosurgery, University of Florida, Gainesville, FL 32610-0265, USA
| | - Jonathan Willman
- Department of Neurosurgery, University of Florida, Gainesville, FL 32610-0265, USA
| | - Brandon Lucke-Wold
- Department of Neurosurgery, University of Florida, Gainesville, FL 32610-0265, USA
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