1
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Agbana S, McIlroy M. Extra-nuclear and cytoplasmic steroid receptor signalling in hormone dependent cancers. J Steroid Biochem Mol Biol 2024; 243:106559. [PMID: 38823459 DOI: 10.1016/j.jsbmb.2024.106559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/28/2024] [Accepted: 05/30/2024] [Indexed: 06/03/2024]
Abstract
Steroid hormone receptors are key mediators in the execution of hormone action through a combination of genomic and non-genomic action. Since their isolation and characterisation in the early 20th Century much of our understanding of the biological actions of steroid hormones are underpinned by their activated receptor activity. Over the past two decades there has been an acceleration of more omics-based research which has resulted in a major uptick in our comprehension of genomic steroid action. However, it is well understood that steroid hormones can induce very rapid signalling events in tandem with their genomic actions wherein they exert their influence through alterations in gene expression. Thus the totality of genomic and non-genomic steroid action occurs in a simultaneous and reciprocal manner and a greater appreciation of whole cell action is required to fully evaluate steroid hormone activity in vivo. In this mini-review we outline the most recent developments in non-genomic steroid action and cytoplasmic steroid hormone receptor biology in endocrine-related cancers with a focus on the 3-keto steroid receptors, in particular the androgen receptor.
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Affiliation(s)
- Stephanie Agbana
- Androgens in Health and Disease research group, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Department of Surgery, RCSI University of Medicine and Health Sciences, Ireland
| | - Marie McIlroy
- Androgens in Health and Disease research group, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Department of Surgery, RCSI University of Medicine and Health Sciences, Ireland.
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2
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Huang L, Wang Y, He Y, Huang D, Wen T, Han Z. Association Between COVID-19 and Neurological Diseases: Evidence from Large-Scale Mendelian Randomization Analysis and Single-Cell RNA Sequencing Analysis. Mol Neurobiol 2024:10.1007/s12035-024-03975-2. [PMID: 38300446 DOI: 10.1007/s12035-024-03975-2] [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: 10/13/2023] [Accepted: 01/18/2024] [Indexed: 02/02/2024]
Abstract
Observational studies have suggested that SARS-CoV-2 infection increases the risk of neurological diseases, but it remains unclear whether the association is causal. The present study aims to evaluate the causal relationships between SARS-CoV-2 infections and neurological diseases and analyzes the potential routes of SARS-CoV-2 entry at the cellular level. We performed Mendelian randomization (MR) analysis with CAUSE method to investigate causal relationship of SARS-CoV-2 infections with neurological diseases. Then, we conducted single-cell RNA sequencing (scRNA-seq) analysis to obtain evidence of potential neuroinvasion routes by measuring SARS-CoV-2 receptor expression in specific cell subtypes. Fast gene set enrichment analysis (fGSEA) was further performed to assess the pathogenesis of related diseases. The results showed that the COVID-19 is causally associated with manic (delta_elpd, - 0.1300, Z-score: - 2.4; P = 0.0082) and epilepsy (delta_elpd: - 2.20, Z-score: - 1.80; P = 0.038). However, no significant effects were observed for COVID-19 on other traits. Moreover, there are 23 cell subtypes identified through the scRNA-seq transcriptomics data of epilepsy, and SARS-CoV-2 receptor TTYH2 was found to be specifically expressed in oligodendrocyte and astrocyte cell subtypes. Furthermore, fGSEA analysis showed that the cell subtypes with receptor-specific expression was related to methylation of lysine 27 on histone H3 (H3K27ME3), neuronal system, aging brain, neurogenesis, and neuron projection. In summary, this study shows causal links between SARS-CoV-2 infections and neurological disorders such as epilepsy and manic, supported by MR and scRNA-seq analysis. These results should be considered in further studies and public health measures on COVID-19 and neurological diseases.
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Affiliation(s)
- Lin Huang
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Yongheng Wang
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China
- International Research Laboratory of Reproduction & Development, Chongqing Medical University, Chongqing, China
| | - Yijie He
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Dongyu Huang
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Tong Wen
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Zhijie Han
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
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3
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Joyce R, Pascual R, Heitink L, Capaldo BD, Vaillant F, Christie M, Tsai M, Surgenor E, Anttila CJA, Rajasekhar P, Jackling FC, Trussart M, Milevskiy MJG, Song X, Li M, Teh CE, Gray DHD, Smyth GK, Chen Y, Lindeman GJ, Visvader JE. Identification of aberrant luminal progenitors and mTORC1 as a potential breast cancer prevention target in BRCA2 mutation carriers. Nat Cell Biol 2024; 26:138-152. [PMID: 38216737 DOI: 10.1038/s41556-023-01315-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 11/15/2023] [Indexed: 01/14/2024]
Abstract
Inheritance of a BRCA2 pathogenic variant conveys a substantial life-time risk of breast cancer. Identification of the cell(s)-of-origin of BRCA2-mutant breast cancer and targetable perturbations that contribute to transformation remains an unmet need for these individuals who frequently undergo prophylactic mastectomy. Using preneoplastic specimens from age-matched, premenopausal females, here we show broad dysregulation across the luminal compartment in BRCA2mut/+ tissue, including expansion of aberrant ERBB3lo luminal progenitor and mature cells, and the presence of atypical oestrogen receptor (ER)-positive lesions. Transcriptional profiling and functional assays revealed perturbed proteostasis and translation in ERBB3lo progenitors in BRCA2mut/+ breast tissue, independent of ageing. Similar molecular perturbations marked tumours bearing BRCA2-truncating mutations. ERBB3lo progenitors could generate both ER+ and ER- cells, potentially serving as cells-of-origin for ER-positive or triple-negative cancers. Short-term treatment with an mTORC1 inhibitor substantially curtailed tumorigenesis in a preclinical model of BRCA2-deficient breast cancer, thus uncovering a potential prevention strategy for BRCA2 mutation carriers.
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Affiliation(s)
- Rachel Joyce
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Rosa Pascual
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Luuk Heitink
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Bianca D Capaldo
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - François Vaillant
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Michael Christie
- Department of Anatomical Pathology, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Minhsuang Tsai
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Elliot Surgenor
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Casey J A Anttila
- Advanced Technology and Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Pradeep Rajasekhar
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
- Advanced Technology and Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Felicity C Jackling
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Marie Trussart
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Michael J G Milevskiy
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Xiaoyu Song
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Mengbo Li
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Charis E Teh
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
- Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Daniel H D Gray
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
- Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Gordon K Smyth
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Yunshun Chen
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Geoffrey J Lindeman
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.
- Parkville Familial Cancer Centre and Department of Medical Oncology, The Royal Melbourne Hospital and Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.
| | - Jane E Visvader
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia.
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4
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Qing T, Karn T, Rozenblit M, Foldi J, Marczyk M, Shan NL, Blenman K, Holtrich U, Kalinsky K, Meric-Bernstam F, Pusztai L. Molecular differences between younger versus older ER-positive and HER2-negative breast cancers. NPJ Breast Cancer 2022; 8:119. [PMID: 36344517 PMCID: PMC9640562 DOI: 10.1038/s41523-022-00492-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 10/26/2022] [Indexed: 11/09/2022] Open
Abstract
The RxPONDER and TAILORx trials demonstrated benefit from adjuvant chemotherapy in patients age ≤ 50 with node-positive breast cancer and Recurrence Score (RS) 0-26, and in node-negative disease with RS 16-25, respectively, but no benefit in older women with the same clinical features. We analyzed transcriptomic and genomic data of ER+/HER2- breast cancers with in silico RS < 26 from TCGA (n = 530), two microarray cohorts (A: n = 865; B: n = 609), the METABRIC (n = 867), and the SCAN-B (n = 1636) datasets. There was no difference in proliferation-related gene expression between age groups. Older patients had higher mutation burden and more frequent ESR1 copy number gain, but lower frequency of GATA3 mutations. Younger patients had higher rate of ESR1 copy number loss. In all datasets, younger patients had significantly lower mRNA expression of ESR1 and ER-associated genes, and higher expression of immune-related genes. The ER- and immune-related gene signatures showed negative correlation and defined three subpopulations in younger women: immune-high/ER-low, immune-intermediate/ER-intermediate, and immune-low/ER-intermediate. We hypothesize that in immune-high cancers, the cytotoxic effect of chemotherapy may drive the benefit, whereas in immune-low/ER-intermediate cancers chemotherapy induced ovarian suppression may play important role.
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Affiliation(s)
- Tao Qing
- Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA
| | - Thomas Karn
- Department of Gynecology and Obstetrics, Goethe-University Frankfurt, Frankfurt, Germany
| | - Mariya Rozenblit
- Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA
| | - Julia Foldi
- Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA
| | - Michal Marczyk
- Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Naing Lin Shan
- Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA
| | - Kim Blenman
- Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA
| | - Uwe Holtrich
- Department of Gynecology and Obstetrics, Goethe-University Frankfurt, Frankfurt, Germany
| | - Kevin Kalinsky
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Funda Meric-Bernstam
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lajos Pusztai
- Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA.
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5
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Chatsirisupachai K, Lagger C, de Magalhães JP. Age-associated differences in the cancer molecular landscape. Trends Cancer 2022; 8:962-971. [PMID: 35811230 DOI: 10.1016/j.trecan.2022.06.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/11/2022] [Accepted: 06/13/2022] [Indexed: 12/24/2022]
Abstract
Cancer is an age-related disease, as incidence and mortality for most types of cancer increase with age. However, how molecular alterations in tumors differ among patients of different ages remains poorly understood. Recent studies have shed light on the age-associated molecular landscapes in cancer. Here, we summarize the main findings of these current studies, highlighting major differences in the genomic, transcriptomic, epigenetic, and immunological landscapes between cancer in younger and older patients. Importantly, some cancer driver genes are mutated more frequently in younger or older patients. We discuss the potential roles of aging-related processes in shaping these age-related differences in cancer. We further emphasize the remaining unsolved questions that could provide important insights that will have implications in personalized medicine.
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Affiliation(s)
- Kasit Chatsirisupachai
- Integrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK.
| | - Cyril Lagger
- Integrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK
| | - João Pedro de Magalhães
- Integrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK.
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6
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Kumar R, Abreu C, Toi M, Saini S, Casimiro S, Arora A, Paul AM, Velaga R, Rameshwar P, Lipton A, Gupta S, Costa L. Oncobiology and treatment of breast cancer in young women. Cancer Metastasis Rev 2022; 41:749-770. [PMID: 35488982 DOI: 10.1007/s10555-022-10034-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 04/14/2022] [Indexed: 12/20/2022]
Abstract
Female breast cancer emerged as the leading cancer type in terms of incidence globally in 2020. Although mortality due to breast cancer has improved during the past three decades in many countries, this trend has reversed in women less than 40 years since the past decade. From the biological standpoint, there is consensus among experts regarding the clinically relevant definition of breast cancer in young women (BCYW), with an age cut-off of 40 years. The idea that breast cancer is an aging disease has apparently broken in the case of BCYW due to the young onset and an overall poor outcome of BCYW patients. In general, younger patients exhibit a worse prognosis than older pre- and postmenopausal patients due to the aggressive nature of cancer subtypes, a high percentage of cases with advanced stages at diagnosis, and a high risk of relapse and death in younger patients. Because of clinically and biologically unique features of BCYW, it is suspected to represent a distinct biologic entity. It is unclear why BCYW is more aggressive and has an inferior prognosis with factors that contribute to increased incidence. However, unique developmental features, adiposity and immune components of the mammary gland, hormonal interplay and crosstalk with growth factors, and a host of intrinsic and extrinsic risk factors and cellular regulatory interactions are considered to be the major contributing factors. In the present article, we discuss the status of BCYW oncobiology, therapeutic interventions and considerations, current limitations in fully understanding the basis and underlying cause(s) of BCYW, understudied areas of BCYW research, and postulated advances in the coming years for the field.
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Affiliation(s)
- Rakesh Kumar
- Cancer Research Institute, Himalayan Institute of Medical Sciences, Swami Rama Himalayan University, Dehradun, India. .,Cancer Research Program, Rajiv Gandhi Centre for Biotechnology, Trivandrum, India. .,Department of Medicine, Division of Hematology and Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA. .,Department of Human and Molecular Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, USA.
| | - Catarina Abreu
- Department of Medical Oncology, Hospital de Santa Maria- Centro Hospitalar Universitário Lisboa Norte, Lisbon, Portugal
| | - Masakazu Toi
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Sunil Saini
- Cancer Research Institute, Himalayan Institute of Medical Sciences, Swami Rama Himalayan University, Dehradun, India
| | - Sandra Casimiro
- Instituto de Medicina Molecular-João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal
| | - Anshika Arora
- Cancer Research Institute, Himalayan Institute of Medical Sciences, Swami Rama Himalayan University, Dehradun, India
| | - Aswathy Mary Paul
- Cancer Research Program, Rajiv Gandhi Centre for Biotechnology, Trivandrum, India
| | - Ravi Velaga
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Pranela Rameshwar
- Department of Medicine, Division of Hematology and Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Allan Lipton
- Hematology-Oncology, Department of Medicine, Penn State University School of Medicine, Hershey, PA, USA
| | - Sudeep Gupta
- Department of Medical Oncology, Tata Memorial Centre and Homi Bhabha National Institute, Mumbai, India
| | - Luis Costa
- Department of Medical Oncology, Hospital de Santa Maria- Centro Hospitalar Universitário Lisboa Norte, Lisbon, Portugal.,Instituto de Medicina Molecular-João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal
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7
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Miyano M, Sayaman RW, Shalabi SF, Senapati P, Lopez JC, Angarola BL, Hinz S, Zirbes A, Anczukow O, Yee LD, Sedrak MS, Stampfer MR, Seewaldt VL, LaBarge MA. Breast-Specific Molecular Clocks Comprised of ELF5 Expression and Promoter Methylation Identify Individuals Susceptible to Cancer Initiation. Cancer Prev Res (Phila) 2021; 14:779-794. [PMID: 34140348 PMCID: PMC8338914 DOI: 10.1158/1940-6207.capr-20-0635] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 04/29/2021] [Accepted: 06/07/2021] [Indexed: 01/09/2023]
Abstract
A robust breast cancer prevention strategy requires risk assessment biomarkers for early detection. We show that expression of ELF5, a transcription factor critical for normal mammary development, is downregulated in mammary luminal epithelia with age. DNA methylation of the ELF5 promoter is negatively correlated with expression in an age-dependent manner. Both ELF5 methylation and gene expression were used to build biological clocks to estimate chronological ages of mammary epithelia. ELF5 clock-based estimates of biological age in luminal epithelia from average-risk women were within three years of chronological age. Biological ages of breast epithelia from BRCA1 or BRCA2 mutation carriers, who were high risk for developing breast cancer, suggested they were accelerated by two decades relative to chronological age. The ELF5 DNA methylation clock had better performance at predicting biological age in luminal epithelial cells as compared with two other epigenetic clocks based on whole tissues. We propose that the changes in ELF5 expression or ELF5-proximal DNA methylation in luminal epithelia are emergent properties of at-risk breast tissue and constitute breast-specific biological clocks. PREVENTION RELEVANCE: ELF5 expression or DNA methylation level at the ELF5 promoter region can be used as breast-specific biological clocks to identify women at higher than average risk of breast cancer.
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Affiliation(s)
- Masaru Miyano
- Department of Population Sciences, Beckman Research Institute at City of Hope, Duarte, California
| | - Rosalyn W Sayaman
- Department of Population Sciences, Beckman Research Institute at City of Hope, Duarte, California
- Department of Laboratory Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California
| | - Sundus F Shalabi
- Department of Population Sciences, Beckman Research Institute at City of Hope, Duarte, California
- Irell and Manella Graduate School of Biological Sciences, City of Hope, Duarte, California
| | - Parijat Senapati
- Department of Diabetes Complications and Metabolism, Beckman Research Institute at City of Hope, Duarte, California
| | - Jennifer C Lopez
- Department of Population Sciences, Beckman Research Institute at City of Hope, Duarte, California
| | | | - Stefan Hinz
- Department of Population Sciences, Beckman Research Institute at City of Hope, Duarte, California
| | - Arrianna Zirbes
- Department of Population Sciences, Beckman Research Institute at City of Hope, Duarte, California
- Irell and Manella Graduate School of Biological Sciences, City of Hope, Duarte, California
| | - Olga Anczukow
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | - Lisa D Yee
- Department of Surgery, City of Hope National Medical Center, Duarte, California
| | - Mina S Sedrak
- Center for Cancer and Aging, City of Hope, Duarte, California
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, California
| | - Martha R Stampfer
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California
| | - Victoria L Seewaldt
- Department of Population Sciences, Beckman Research Institute at City of Hope, Duarte, California
| | - Mark A LaBarge
- Department of Population Sciences, Beckman Research Institute at City of Hope, Duarte, California.
- Center for Cancer and Aging, City of Hope, Duarte, California
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California
- Center for Cancer Biomarkers, University of Bergen, Bergen, Norway
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8
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Bleach R, Madden SF, Hawley J, Charmsaz S, Selli C, Sheehan KM, Young LS, Sims AH, Souček P, Hill AD, McIlroy M. Steroid Ligands, the Forgotten Triggers of Nuclear Receptor Action; Implications for Acquired Resistance to Endocrine Therapy. Clin Cancer Res 2021; 27:3980-3989. [PMID: 34016642 PMCID: PMC9401529 DOI: 10.1158/1078-0432.ccr-20-4135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 01/22/2021] [Accepted: 05/18/2021] [Indexed: 01/07/2023]
Abstract
PURPOSE There is strong epidemiologic evidence indicating that estrogens may not be the sole steroid drivers of breast cancer. We hypothesize that abundant adrenal androgenic steroid precursors, acting via the androgen receptor (AR), promote an endocrine-resistant breast cancer phenotype. EXPERIMENTAL DESIGN AR was evaluated in a primary breast cancer tissue microarray (n = 844). Androstenedione (4AD) levels were evaluated in serum samples (n = 42) from hormone receptor-positive, postmenopausal breast cancer. Levels of androgens, progesterone, and estradiol were quantified using LC/MS-MS in serum from age- and grade-matched recurrent and nonrecurrent patients (n = 6) before and after aromatase inhibitor (AI) therapy (>12 months). AR and estrogen receptor (ER) signaling pathway activities were analyzed in two independent AI-treated cohorts. RESULTS AR protein expression was associated with favorable progression-free survival in the total population (Wilcoxon, P < 0.001). Pretherapy serum samples from breast cancer patients showed decreasing levels of 4AD with age only in the nonrecurrent group (P < 0.05). LC/MS-MS analysis of an AI-sensitive and AI-resistant cohort demonstrated the ability to detect altered levels of steroids in serum of patients before and after AI therapy. Transcriptional analysis showed an increased ratio of AR:ER signaling pathway activities in patients failing AI therapy (t test P < 0.05); furthermore, 4AD mediated gene changes associated with acquired AI resistance. CONCLUSIONS This study highlights the importance of examining the therapeutic consequences of the steroid microenvironment and demonstrable receptor activation using indicative gene expression signatures.
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Affiliation(s)
- Rachel Bleach
- Endocrine Oncology Research, Department of Surgery, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Stephen F Madden
- Data Science Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - James Hawley
- Department of Biochemistry, Manchester University, NHS Foundation Trust, London, United Kingdom
| | - Sara Charmsaz
- Endocrine Oncology Research, Department of Surgery, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Cigdem Selli
- Applied Bioinformatics of Cancer, Institute of Genetics and Cancer, University of Edinburgh Cancer Research Centre, Edinburgh, United Kingdom
| | | | - Leonie S Young
- Endocrine Oncology Research, Department of Surgery, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Andrew H Sims
- Applied Bioinformatics of Cancer, Institute of Genetics and Cancer, University of Edinburgh Cancer Research Centre, Edinburgh, United Kingdom
| | - Pavel Souček
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czech Republic
- Toxicogenomics Unit, National Institute of Public Health, Prague, Czech Republic
| | - Arnold D Hill
- Endocrine Oncology Research, Department of Surgery, RCSI University of Medicine and Health Sciences, Dublin, Ireland
- Department of Surgery, Beaumont Hospital, Dublin, Ireland
| | - Marie McIlroy
- Endocrine Oncology Research, Department of Surgery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
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9
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A primer on applying AI synergistically with domain expertise to oncology. Biochim Biophys Acta Rev Cancer 2021; 1876:188548. [PMID: 33901609 DOI: 10.1016/j.bbcan.2021.188548] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/13/2021] [Accepted: 04/15/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND The concurrent growth of large-scale oncology data alongside the computational methods with which to analyze and model it has created a promising environment for revolutionizing cancer diagnosis, treatment, prevention, and drug discovery. Computational methods applied to large datasets have accelerated the drug discovery process by reducing bottlenecks and widening the search space beyond what is experimentally tractable. As the research community gains understanding of the myriad genetic underpinnings of cancer via sequencing, imaging, screens, and more that are ingested, transformed, and modeled by top open-source machine learning and artificial intelligence tools readily available, the next big drug candidate might seem merely an "Enter" key away. Of course, the reality is more convoluted, but still promising. SCOPE OF REVIEW We present methods to approach the process of building an AI model, with strong emphasis on the aspects of model development we believe to be crucial to success but that are not commonly discussed: diligence in posing questions, identifying suitable datasets and curating them, and collaborating closely with biology and oncology experts while designing and evaluating the model. Digital pathology, Electronic Health Records, and other data types outside of high-throughput molecular data are reviewed well by others and outside of the scope of this review. This review emphasizes the importance of considering the limitations of the datasets, computational methods, and our minds when designing AI models. For example, datasets can be biased towards areas of research interest, funding, and particular patient populations. Neural networks may learn representations and correlations within the data that are grounded not in biological phenomena, but statistical anomalies erroneously extracted from the training data. Researchers may mis-interpret or over-interpret the output, or design and evaluate the training process such that the resultant model generalizes poorly. Fortunately, awareness of the strengths and limitations of applying data analytics and AI to drug discovery enables us to leverage them carefully and insightfully while maximizing their utility. These applications when performed in close collaboration with domain experts, together with continuous critical evaluation, generation of new data to minimize known blind spots as they are found, and rigorous experimental validation, increases the success rate of the study. We will discuss applications including AI-assisted target identification, drug repurposing, patient stratification, and gene prioritization. MAJOR CONCLUSIONS Data analytics and AI have demonstrated capabilities to revolutionize cancer research, prevention, and treatment by maximizing our understanding and use of the expanding panoply of experimental data. However, to separate promise from true utility, computational tools must be carefully designed, critically evaluated, and constantly improved. Once that is achieved, a human-computer hybrid discovery process will outperform one driven by each alone. GENERAL SIGNIFICANCE This review highlights the challenges and promise of synergizing predictive AI models with human expertise towards greater understanding of cancer.
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Chatsirisupachai K, Lesluyes T, Paraoan L, Van Loo P, de Magalhães JP. An integrative analysis of the age-associated multi-omic landscape across cancers. Nat Commun 2021; 12:2345. [PMID: 33879792 PMCID: PMC8058097 DOI: 10.1038/s41467-021-22560-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 03/17/2021] [Indexed: 02/07/2023] Open
Abstract
Age is the most important risk factor for cancer, as cancer incidence and mortality increase with age. However, how molecular alterations in tumours differ among patients of different age remains largely unexplored. Here, using data from The Cancer Genome Atlas, we comprehensively characterise genomic, transcriptomic and epigenetic alterations in relation to patients' age across cancer types. We show that tumours from older patients present an overall increase in genomic instability, somatic copy-number alterations (SCNAs) and somatic mutations. Age-associated SCNAs and mutations are identified in several cancer-driver genes across different cancer types. The largest age-related genomic differences are found in gliomas and endometrial cancer. We identify age-related global transcriptomic changes and demonstrate that these genes are in part regulated by age-associated DNA methylation changes. This study provides a comprehensive, multi-omics view of age-associated alterations in cancer and underscores age as an important factor to consider in cancer research and clinical practice.
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Affiliation(s)
- Kasit Chatsirisupachai
- Integrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | | | - Luminita Paraoan
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | | | - João Pedro de Magalhães
- Integrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK.
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Abstract
In recent biomedical studies, multidimensional profiling, which collects proteomics as well as other types of omics data on the same subjects, is getting increasingly popular. Proteomics, transcriptomics, genomics, epigenomics, and other types of data contain overlapping as well as independent information, which suggests the possibility of integrating multiple types of data to generate more reliable findings/models with better classification/prediction performance. In this chapter, a selective review is conducted on recent data integration techniques for both unsupervised and supervised analysis. The main objective is to provide the "big picture" of data integration that involves proteomics data and discuss the "intuition" beneath the recently developed approaches without invoking too many mathematical details. Potential pitfalls and possible directions for future developments are also discussed.
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Affiliation(s)
- Mengyun Wu
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Yu Jiang
- School of Public Health, University of Memphis, Memphis, TN, USA
| | - Shuangge Ma
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, USA.
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