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Hyams DM, Bareket-Samish A, Rocha JEB, Diaz-Botero S, Franco S, Gagliato D, Gomez HL, Korbenfeld E, Krygier G, Mattar A, De Pierro AN, Borrego MR, Villarreal C. Selecting postoperative adjuvant systemic therapy for early-stage breast cancer: An updated assessment and systematic review of leading commercially available gene expression assays. J Surg Oncol 2024; 130:166-187. [PMID: 38932668 DOI: 10.1002/jso.27692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 05/05/2024] [Indexed: 06/28/2024]
Abstract
Gene expression assays (GEAs) can guide treatment for early-stage breast cancer. Several large prospective randomized clinical trials, and numerous additional studies, now provide new information for selecting an appropriate GEA. This systematic review builds upon prior reviews, with a focus on five widely commercialized GEAs (Breast Cancer Index®, EndoPredict®, MammaPrint®, Oncotype DX®, and Prosigna®). The comprehensive dataset available provides a contemporary opportunity to assess each GEA's utility as a prognosticator and/or predictor of adjuvant therapy benefit.
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Affiliation(s)
- David M Hyams
- Medical Director, Desert Surgical Oncology, Eisenhower Medical Center, Rancho Mirage, California, USA
| | | | - Juan Enrique Bargallo Rocha
- Breast Cancer Department, Instituto Nacional de Cancerología Mexico and Centro Medico ABC, Mexico City, Mexico
| | - Sebastian Diaz-Botero
- Breast Surgical Oncology Unit, Cancer Center at Clínica Universidad de Navarra, Madrid, Spain
| | - Sandra Franco
- Medical Director, Centro de Tratamiento e Investigación sobre el Cáncer, CTIC, Bogotá, Colombia
| | - Debora Gagliato
- Department of Clinical Oncology, Beneficencia Portuguesa de Sao Paulo, San Paulo, Brazil
| | - Henry L Gomez
- Breast Unit Director, OncoSalud, Clinica Delgado, AUNA, Universidad Ricardo Palma, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Ernesto Korbenfeld
- Department of Oncology, Hospital Británico de Buenos Aires, Buenos Aires, Argentina
| | - Gabriel Krygier
- Department of Oncology, Universitary Hospital de Clínicas, Montevideo, Uruguay
| | - Andre Mattar
- Director of Mastology Center, Centro de Referência da Saúde da Mulher, Hospital da Mulher, São Paulo, Brazil
| | - Aníbal Nuñez De Pierro
- Department of Surgery, Unit of Mastology, Hospital J.A. Fernandez, Buenos Aires City, Argentina
| | - Manuel Ruiz Borrego
- Medical Oncology Service, Hospital Universitario Virgen del Rocío, Seville, Spain
| | - Cynthia Villarreal
- Head, Department of Medical Oncology, Breast Cancer Center, Hospital Zambrano Hellion TecSalud, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico
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Sukocheva OA, Lukina E, Friedemann M, Menschikowski M, Hagelgans A, Aliev G. The crucial role of epigenetic regulation in breast cancer anti-estrogen resistance: Current findings and future perspectives. Semin Cancer Biol 2022; 82:35-59. [PMID: 33301860 DOI: 10.1016/j.semcancer.2020.12.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 10/22/2020] [Accepted: 12/03/2020] [Indexed: 02/07/2023]
Abstract
Breast cancer (BC) cell de-sensitization to Tamoxifen (TAM) or other selective estrogen receptor (ER) modulators (SERM) is a complex process associated with BC heterogeneity and the transformation of ER signalling. The most influential resistance-related mechanisms include modifications in ER expression and gene regulation patterns. During TAM/SERM treatment, epigenetic mechanisms can effectively silence ER expression and facilitate the development of endocrine resistance. ER status is efficiently regulated by specific epigenetic tools including hypermethylation of CpG islands within ER promoters, increased histone deacetylase activity in the ER promoter, and/or translational repression by miRNAs. Over-methylation of the ER α gene (ESR1) promoter by DNA methyltransferases was associated with poor prognosis and indicated the development of resistance. Moreover, BC progression and spreading were marked by transformed chromatin remodelling, post-translational histone modifications, and expression of specific miRNAs and/or long non-coding RNAs. Therefore, targeted inhibition of histone acetyltransferases (e.g. MYST3), deacetylases (e.g. HDAC1), and/or demethylases (e.g. lysine-specific demethylase LSD1) was shown to recover and increase BC sensitivity to anti-estrogens. Indicated as a powerful molecular instrument, the administration of epigenetic drugs can regain ER expression along with the activation of tumour suppressor genes, which can in turn prevent selection of resistant cells and cancer stem cell survival. This review examines recent advances in the epigenetic regulation of endocrine drug resistance and evaluates novel anti-resistance strategies. Underlying molecular mechanisms of epigenetic regulation will be discussed, emphasising the utilization of epigenetic enzymes and their inhibitors to re-program irresponsive BCs.
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Affiliation(s)
- Olga A Sukocheva
- Discipline of Health Sciences, College of Nursing and Health Sciences, Flinders University, Bedford Park, South Australia, 5042, Australia.
| | - Elena Lukina
- Discipline of Biology, College of Sciences, Flinders University, Bedford Park, South Australia, 5042, Australia
| | - Markus Friedemann
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital `Carl Gustav Carus`, Technical University of Dresden, Dresden 01307, Germany
| | - Mario Menschikowski
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital `Carl Gustav Carus`, Technical University of Dresden, Dresden 01307, Germany
| | - Albert Hagelgans
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital `Carl Gustav Carus`, Technical University of Dresden, Dresden 01307, Germany
| | - Gjumrakch Aliev
- Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russia; Institute of Physiologically Active Compounds, Russian Academy of Sciences, Chernogolovka, 142432, Russia; Federal State Budgetary Institution «Research Institute of Human Morphology», 3, Tsyurupy Str., Moscow, 117418, Russian Federation; GALLY International Research Institute, San Antonio, TX, 78229, USA.
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3
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Li Q, Yang H, Wang P, Liu X, Lv K, Ye M. XGBoost-based and tumor-immune characterized gene signature for the prediction of metastatic status in breast cancer. J Transl Med 2022; 20:177. [PMID: 35436939 PMCID: PMC9014628 DOI: 10.1186/s12967-022-03369-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/26/2022] [Indexed: 12/23/2022] Open
Abstract
Background For a long time, breast cancer has been a leading cancer diagnosed in women worldwide, and approximately 90% of cancer-related deaths are caused by metastasis. For this reason, finding new biomarkers related to metastasis is an urgent task to predict the metastatic status of breast cancer and provide new therapeutic targets. Methods In this research, an efficient model of eXtreme Gradient Boosting (XGBoost) optimized by a grid search algorithm is established to realize auxiliary identification of metastatic breast tumors based on gene expression. Estimated by ten-fold cross-validation, the optimized XGBoost classifier can achieve an overall higher mean AUC of 0.82 compared to other classifiers such as DT, SVM, KNN, LR, and RF. Results A novel 6-gene signature (SQSTM1, GDF9, LINC01125, PTGS2, GVINP1, and TMEM64) was selected by feature importance ranking and a series of in vitro experiments were conducted to verify the potential role of each biomarker. In general, the effects of SQSTM in tumor cells are assigned as a risk factor, while the effects of the other 5 genes (GDF9, LINC01125, PTGS2, GVINP1, and TMEM64) in immune cells are assigned as protective factors. Conclusions Our findings will allow for a more accurate prediction of the metastatic status of breast cancer and will benefit the mining of breast cancer metastasis-related biomarkers. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03369-9.
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Sun M, Liu X, Xia L, Chen Y, Kuang L, Gu X, Li T. A nine-lncRNA signature predicts distant relapse-free survival of HER2-negative breast cancer patients receiving taxane and anthracycline-based neoadjuvant chemotherapy. Biochem Pharmacol 2020; 189:114285. [PMID: 33069665 DOI: 10.1016/j.bcp.2020.114285] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 10/13/2020] [Accepted: 10/13/2020] [Indexed: 12/13/2022]
Abstract
Multi-gene prognostic signatures of long non-coding RNAs (lncRNAs) provide new insights into mechanisms of HER2-negative breast cancer development and progression, and predict distant relapse-free survival (DRFS) of patients receiving taxane and anthracycline-based neoadjuvant chemotherapy. The aim of this study was to develop such a multi-lncRNAs signature. Optimal multiple candidate signature lncRNAs associated with DRFS were firstly identified by a univariate Cox proportional hazard regression survival analysis and a robust likelihood-based survival analysis of the GEO dataset GSE25055. A nine-lncRNA prognostic risk score model Risk Score = 0.0289 × EXPLOC100507388 - 0.0814 × EXPLINC00094 - 0.2422 × EXPSMG7-AS1 - 0.2433 × EXPPP14571 + 0.4690 × EXPASAP1-IT1 - 0.2483 × EXPLOC103344931 - 0.2464 × EXPFAM182A + 0.3349 × EXPHCG26 - 0.0216 × EXPLINC00963 was built according to the coefficients of multivariate survival analysis of the association between the candidate lncRNAs and survival. EXPlncRNA was the standardized log2-transformed expression level of the gene. According to this model, higher scores predicted lower survival probability. The area under Receiver operating characteristic (ROC) curve (AUC) was 0.777 to 0.823 from 1- to 7- year survival rate. The model and its individual lncRNAs differentiated survival probability between the higher scores (expression) and the lower scores (expression). The nine-lncRNA signature had the robust prognostic power compared with ER, PR, tumor size (T), lymph node invasion (N), TNM stage, pathologic response, chemosensitivity prediction and PAM50 signature. These results were consistent with those based on the GEO dataset GSE25065. The predictive nomograms integrating both the nine-lncRNA signature classifier and clinical-pathological risk factors were robust in predicting 1-, 3- and 5- year survival probabilities. These results supported that the nine-lncRNA signature was a robust and effective model in predicting DRFS of patients with HER2-negative breast cancer following taxane and anthracycline-based neoadjuvant chemotherapy.
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Affiliation(s)
- Min Sun
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China; Department of Anesthesiology, Institute of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China; Hubei Key Laboratory of Embryonic Stem Cell Research, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China
| | - Xiaoxiao Liu
- Department of Oncology, Xinchang Hospital Affiliated to Wenzhou Medical University, 117 Gushan Middle Road, Xinchang County 312500, Zhejiang Province, China
| | - Lingyun Xia
- Department of Stomatology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China
| | - Yuying Chen
- Department of Anesthesiology, Institute of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China
| | - Li Kuang
- Department of Oncology, Dongfeng General Hospital, Hubei University of Medicine, Shiyan 442000, China
| | - Xinsheng Gu
- College of Basic Medical Sciences, Hubei University of Medicine, Shiyan 442000, China.
| | - Tian Li
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China; School of Basic Medicine, The Fourth Military Medical University, Xi'an 710000, China.
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5
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Brentnall AR, Warren R, Harkness EF, Astley SM, Wiseman J, Fox J, Fox L, Eriksson M, Hall P, Cuzick J, Evans DG, Howell A. Mammographic density change in a cohort of premenopausal women receiving tamoxifen for breast cancer prevention over 5 years. Breast Cancer Res 2020; 22:101. [PMID: 32993747 PMCID: PMC7523310 DOI: 10.1186/s13058-020-01340-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 09/10/2020] [Indexed: 01/13/2023] Open
Abstract
Background A decrease in breast density due to tamoxifen preventive therapy might indicate greater benefit from the drug. It is not known whether mammographic density continues to decline after 1 year of therapy, or whether measures of breast density change are sufficiently stable for personalised recommendations. Methods Mammographic density was measured annually over up to 5 years in premenopausal women with no previous diagnosis of breast cancer but at increased risk of breast cancer attending a family-history clinic in Manchester, UK (baseline 2010-2013). Tamoxifen (20 mg/day) for prevention was prescribed for up to 5 years in one group; the other group did not receive tamoxifen and were matched by age. Fully automatic methods were used on mammograms over the 5-year follow-up: three area-based measures (NN-VAS, Stratus, Densitas) and one volumetric (Volpara). Additionally, percentage breast density at baseline and first follow-up mammograms was measured visually. The size of density declines at the first follow-up mammogram and thereafter was estimated using a linear mixed model adjusted for age and body mass index. The stability of density change at 1 year was assessed by evaluating mean squared error loss from predictions based on individual or mean density change at 1 year. Results Analysis used mammograms from 126 healthy premenopausal women before and as they received tamoxifen for prevention (median age 42 years) and 172 matched controls (median age 41 years), with median 3 years follow-up. There was a strong correlation between percentage density measures used on the same mammogram in both the tamoxifen and no tamoxifen groups (all correlation coeficients > 0.8). Tamoxifen reduced mean breast density in year 1 by approximately 17–25% of the inter-quartile range of four automated percentage density measures at baseline, and from year 2, it decreased further by approximately 2–7% per year. Predicting change at 2 years using individual change at 1 year was approximately 60–300% worse than using mean change at 1year. Conclusions All measures showed a consistent and large average tamoxifen-induced change in density over the first year, and a continued decline thereafter. However, these measures of density change at 1 year were not stable on an individual basis.
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Affiliation(s)
- Adam R Brentnall
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Ruth Warren
- Department of Radiology, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
| | - Elaine F Harkness
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.,Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, M23 9LT, UK
| | - Susan M Astley
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.,Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, M23 9LT, UK.,Manchester Breast Centre, Manchester Cancer Research Centre, University of Manchester, Manchester, M20 4BX, UK
| | - Julia Wiseman
- Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, M23 9LT, UK
| | - Jill Fox
- Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, M23 9LT, UK
| | - Lynne Fox
- Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, M23 9LT, UK
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jack Cuzick
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - D Gareth Evans
- Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, M23 9LT, UK.,Manchester Breast Centre, Manchester Cancer Research Centre, University of Manchester, Manchester, M20 4BX, UK.,NW Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, M13 9WL, UK.,Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Anthony Howell
- Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, M23 9LT, UK. .,Manchester Breast Centre, Manchester Cancer Research Centre, University of Manchester, Manchester, M20 4BX, UK. .,Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK.
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6
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Horimoto Y, Sasahara N, Sasaki R, Hlaing MT, Sakaguchi A, Saeki H, Arakawa A, Himuro T, Saito M. High FOXA1 protein expression might predict late recurrence in patients with estrogen-positive and HER2-negative breast cancer. Breast Cancer Res Treat 2020; 183:41-48. [PMID: 32572714 DOI: 10.1007/s10549-020-05751-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 06/13/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Multi-gene expression assays have been developed with the aim of predicting late recurrence in patients with estrogen receptor (ER)-positive breast cancer. However, establishment of alternative markers based on immunohistochemistry is also important for achieving practical use. Based on our previous study, forkhead box A1 (FOXA1) protein was tested as a potentially useful predictive marker for late recurrence. METHODS 117 patients with ER-positive HER2-negative invasive breast cancer who developed distant metastasis following curative surgery were retrospectively investigated. We also evaluated responsiveness to endocrine therapy according to FOXA1 expression. Furthermore, publicly available mRNA microarray data were analyzed to examine patterns of metastasis according to FOXA1 mRNA expression, employing the Kaplan-Meier plotter. RESULTS High expression of FOXA1 was an independent factor predicting long disease-free survival (DFS), along with small tumor size (p = 0.010 and 0.016, respectively). Discrimination of DFS was improved by combining these two factors, i.e., patients with FOXA1-high small tumors had the longest DFS while those with FOXA1-low large tumors had the shortest DFS. Moreover, we revealed that risk of distant metastasis started to increase after the completion of adjuvant endocrine therapy in patients with FOXA1-high tumors. CONCLUSION Among patients who developed distant metastasis, those with FOXA1-high tumors had significantly longer DFS. We believe our data to raise the possibility of FOXA1 being a useful predictive marker for late recurrence and to provide new insights into the biology of FOXA1-high breast cancers.
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MESH Headings
- Adult
- Aged
- Breast Neoplasms/chemistry
- Breast Neoplasms/genetics
- Breast Neoplasms/metabolism
- Disease-Free Survival
- Estrogens
- Female
- Gene Expression Regulation, Neoplastic
- Hepatocyte Nuclear Factor 3-alpha/biosynthesis
- Hepatocyte Nuclear Factor 3-alpha/genetics
- Hepatocyte Nuclear Factor 3-alpha/physiology
- Humans
- Kaplan-Meier Estimate
- Middle Aged
- Neoplasm Invasiveness
- Neoplasm Proteins/biosynthesis
- Neoplasm Proteins/genetics
- Neoplasm Proteins/physiology
- Neoplasms, Hormone-Dependent/chemistry
- Neoplasms, Hormone-Dependent/genetics
- Neoplasms, Hormone-Dependent/metabolism
- RNA, Messenger/biosynthesis
- RNA, Messenger/genetics
- RNA, Neoplasm/biosynthesis
- RNA, Neoplasm/genetics
- Receptor, ErbB-2/analysis
- Receptors, Estrogen/analysis
- Recurrence
- Time Factors
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Affiliation(s)
- Yoshiya Horimoto
- Department of Breast Oncology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
- Department of Human Pathology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Noriko Sasahara
- Department of Human Pathology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Ritsuko Sasaki
- Department of Breast Oncology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - May Thinzar Hlaing
- Department of Breast Oncology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Asumi Sakaguchi
- Department of Human Pathology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Harumi Saeki
- Department of Human Pathology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Atsushi Arakawa
- Department of Human Pathology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Takanori Himuro
- Department of Breast Oncology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Mitsue Saito
- Department of Breast Oncology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
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Prasad M, Kumar B, Bhat-Nakshatri P, Anjanappa M, Sandusky G, Miller KD, Storniolo AM, Nakshatri H. Dual TGFβ/BMP Pathway Inhibition Enables Expansion and Characterization of Multiple Epithelial Cell Types of the Normal and Cancerous Breast. Mol Cancer Res 2019; 17:1556-1570. [PMID: 30992305 DOI: 10.1158/1541-7786.mcr-19-0165] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 03/18/2019] [Accepted: 04/12/2019] [Indexed: 12/30/2022]
Abstract
Functional modeling of normal breast epithelial hierarchy and stromal-epithelial cell interactions have been difficult due to inability to obtain sufficient stem-progenitor-mature epithelial and stromal cells. Recently reported epithelial reprogramming assay has partially overcome this limitation, but cross-contamination of cells from the feeder layer is a concern. The purpose of this study was to develop a feeder-layer-independent and inexpensive method to propagate multiple cell types from limited tissue resources. Cells obtained after enzymatic digestion of tissues collected at surgery or by core-needle biopsies were plated on tissue culture dishes precoated with laminin-5-rich-conditioned media from the rat bladder tumor cell line 804G and a defined growth media with inhibitors of ROCK, TGFβ, and BMP signaling. Cells were characterized by flow cytometry, mammosphere assay, 3D cultures, and xenograft studies. Cells from the healthy breasts included CD10+/EpCAM- basal/myoepithelial, CD49f+/EpCAM+ luminal progenitor, CD49f-/EpCAM+ mature luminal, CD73+/EpCAM+/CD90- rare endogenous pluripotent somatic stem, CD73+/CD90+/EpCAM-, estrogen receptor alpha-expressing ALCAM (CD166)+/EpCAM+, and ALDFLUOR+ stem/luminal progenitor subpopulations. Epithelial cells were luminal (KRT19+), basal (KRT14+), or dual-positive luminal/basal hybrid cells. While breast cells derived from BRCA1, BRCA2, and PALB2 mutation carriers did not display unique characteristics, cells from women with breast cancer-protective alleles showed enhanced differentiation. Cells could also be propagated from primary tumors and metastasis of breast, ovarian, and pancreatic cancer-neuroendocrine subtype. Xenograft studies confirmed tumorigenic properties of tumor-derived cells. IMPLICATIONS: Our method expands the scope of individualized studies of patient-derived cells and provides resources to model epithelial-stromal interactions under normal and pathologic conditions.
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Affiliation(s)
- Mayuri Prasad
- Department of Surgery, Indiana University School of Medicine, Indianapolis, Indiana
| | - Brijesh Kumar
- Department of Surgery, Indiana University School of Medicine, Indianapolis, Indiana
| | | | - Manjushree Anjanappa
- Department of Surgery, Indiana University School of Medicine, Indianapolis, Indiana
| | - George Sandusky
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana
| | - Kathy D Miller
- Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
| | - Anna Maria Storniolo
- Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
| | - Harikrishna Nakshatri
- Department of Surgery, Indiana University School of Medicine, Indianapolis, Indiana. .,Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana.,Roudebush VA Medical Center, Indianapolis, Indiana
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High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2019. [DOI: 10.3390/make1010021] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time, in recent years there are many new developments that extend this model significantly. Above all, the least absolute shrinkage and selection operator (LASSO) model gained considerable interest. In this paper, we review general regression models with a focus on the LASSO and extensions thereof, including the adaptive LASSO, elastic net, and group LASSO. We discuss the regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models. This makes these modern, computational regression models valuable tools for analyzing high-dimensional problems.
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