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Veerla S, Hohmann L, Nacer DF, Vallon-Christersson J, Staaf J. Perturbation and stability of PAM50 subtyping in population-based primary invasive breast cancer. NPJ Breast Cancer 2023; 9:83. [PMID: 37857634 PMCID: PMC10587090 DOI: 10.1038/s41523-023-00589-0] [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: 04/18/2023] [Accepted: 09/29/2023] [Indexed: 10/21/2023] Open
Abstract
PAM50 gene expression subtypes represent a cornerstone in the molecular classification of breast cancer and are included in risk prediction models to guide therapy. We aimed to illustrate the impact of included genes and biological processes on subtyping while considering a tumor's underlying clinical subgroup defined by ER, PR, and HER2 status. To do this we used a population-representative and clinically annotated early-stage breast tumor cohort of 6233 samples profiled by RNA sequencing and applied a perturbation strategy of excluding co-expressed genes (gene sets). We demonstrate how PAM50 nearest-centroid classification depends on biological processes present across, but also within, ER/PR/HER2 subgroups and PAM50 subtypes themselves. Our analysis highlights several key aspects of PAM50 classification. Firstly, we demonstrate the tight connection between a tumor's nearest and second-nearest PAM50 centroid. Additionally, we show that the second-best subtype is associated with overall survival in ER-positive, HER2-negative, and node-negative disease. We also note that ERBB2 expression has little impact on PAM50 classification in HER2-positive disease regardless of ER status and that the Basal subtype is highly stable in contrast to the Normal subtype. Improved consciousness of the commonly used PAM50 subtyping scheme will aid in our understanding and interpretation of breast tumors that have seemingly conflicting PAM50 classification when compared to clinical biomarkers. Finally, our study adds further support in challenging the common misconception that PAM50 subtypes are distinct classes by illustrating that PAM50 subtypes in tumors represent a continuum with prognostic implications.
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Affiliation(s)
- Srinivas Veerla
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Lennart Hohmann
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Deborah F Nacer
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | | | - Johan Staaf
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden.
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden.
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2
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[Research Progress of Treatment for NSCLC in Young Patients]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2022; 25:888-894. [PMID: 36617475 PMCID: PMC9845094 DOI: 10.3779/j.issn.1009-3419.2022.102.48] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Non-small cell lung cancer (NSCLC) young patients (≤45 years old), despite their low prevalence, have unique clinical and pathological features. Its morbidity has been on the rise in recent years. With the concept of individualized lung cancer treatment, related researches are gradually gaining attention. In addition, the treatment response and prognosis in NSCLC young patients are different from older patients, so the study of NSCLC young patients is of great clinical significance. This article reviews the clinical manifestations, treatment and prognosis of NSCLC young patients.
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3
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Proteogenomic analysis of lung adenocarcinoma reveals tumor heterogeneity, survival determinants, and therapeutically relevant pathways. Cell Rep Med 2022; 3:100819. [PMID: 36384096 PMCID: PMC9729884 DOI: 10.1016/j.xcrm.2022.100819] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/09/2022] [Accepted: 10/18/2022] [Indexed: 11/17/2022]
Abstract
We present a deep proteogenomic profiling study of 87 lung adenocarcinoma (LUAD) tumors from the United States, integrating whole-genome sequencing, transcriptome sequencing, proteomics and phosphoproteomics by mass spectrometry, and reverse-phase protein arrays. We identify three subtypes from somatic genome signature analysis, including a transition-high subtype enriched with never smokers, a transversion-high subtype enriched with current smokers, and a structurally altered subtype enriched with former smokers, TP53 alterations, and genome-wide structural alterations. We show that within-tumor correlations of RNA and protein expression associate with tumor purity and immune cell profiles. We detect and independently validate expression signatures of RNA and protein that predict patient survival. Additionally, among co-measured genes, we found that protein expression is more often associated with patient survival than RNA. Finally, integrative analysis characterizes three expression subtypes with divergent mutations, proteomic regulatory networks, and therapeutic vulnerabilities. This proteogenomic characterization provides a foundation for molecularly informed medicine in LUAD.
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4
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Staaf J, Häkkinen J, Hegardt C, Saal LH, Kimbung S, Hedenfalk I, Lien T, Sørlie T, Naume B, Russnes H, Marcone R, Ayyanan A, Brisken C, Malterling RR, Asking B, Olofsson H, Lindman H, Bendahl PO, Ehinger A, Larsson C, Loman N, Rydén L, Malmberg M, Borg Å, Vallon-Christersson J. RNA sequencing-based single sample predictors of molecular subtype and risk of recurrence for clinical assessment of early-stage breast cancer. NPJ Breast Cancer 2022; 8:94. [PMID: 35974007 PMCID: PMC9381586 DOI: 10.1038/s41523-022-00465-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 07/20/2022] [Indexed: 11/09/2022] Open
Abstract
Multigene assays for molecular subtypes and biomarkers can aid management of early invasive breast cancer. Using RNA-sequencing we aimed to develop single-sample predictor (SSP) models for clinical markers, subtypes, and risk of recurrence (ROR). A cohort of 7743 patients was divided into training and test set. We trained SSPs for subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for biomarkers from histopathology. Classifications were compared with Prosigna in two external cohorts (ABiM, n = 100 and OSLO2-EMIT0, n = 103). Prognostic value was assessed using distant recurrence-free interval. Agreement between SSP and NC for PAM50 (five subtypes) was high (85%, Kappa = 0.78) for Subtype (four subtypes) very high (90%, Kappa = 0.84) and for ROR risk category high (84%, Kappa = 0.75, weighted Kappa = 0.90). Prognostic value was assessed as equivalent and clinically relevant. Agreement with histopathology was very high or high for receptor status, while moderate for Ki67 status and poor for Nottingham histological grade. SSP and Prosigna concordance was high for subtype (OSLO-EMIT0 83%, Kappa = 0.73 and ABiM 80%, Kappa = 0.72) and moderate and high for ROR risk category (68 and 84%, Kappa = 0.50 and 0.70, weighted Kappa = 0.70 and 0.78). Pooled concordance for emulated treatment recommendation dichotomized for chemotherapy was high (85%, Kappa = 0.66). Retrospective evaluation suggested that SSP application could change chemotherapy recommendations for up to 17% of postmenopausal ER+/HER2-/N0 patients with balanced escalation and de-escalation. Results suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level and that SSP models can be derived to closely match clinical tests.
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Affiliation(s)
- Johan Staaf
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE 22381, Lund, Sweden.
| | - Jari Häkkinen
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE 22381, Lund, Sweden
| | - Cecilia Hegardt
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE 22381, Lund, Sweden
| | - Lao H Saal
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE 22381, Lund, Sweden
| | - Siker Kimbung
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE 22381, Lund, Sweden
| | - Ingrid Hedenfalk
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE 22381, Lund, Sweden
| | - Tonje Lien
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, POB 4953 Nydalen N-0424, Oslo, Norway.,Department of Pathology, Oslo University Hospital, POB 4953 Nydalen N-0424, Oslo, Norway
| | - Therese Sørlie
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, POB 4953 Nydalen N-0424, Oslo, Norway.,Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Bjørn Naume
- Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.,Department of Oncology, Division of Cancer Medicine, Oslo University Hospital, POB 4953 Nydalen N-0424, Oslo, Norway
| | - Hege Russnes
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, POB 4953 Nydalen N-0424, Oslo, Norway.,Department of Pathology, Oslo University Hospital, POB 4953 Nydalen N-0424, Oslo, Norway
| | - Rachel Marcone
- ISREC-Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, CH-1005, Lausanne, Switzerland
| | - Ayyakkannu Ayyanan
- ISREC-Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland
| | - Cathrin Brisken
- ISREC-Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland
| | | | - Bengt Asking
- Department of Surgery, Region Jönköping County, Jönköping, Sweden
| | - Helena Olofsson
- Department of Clinical Pathology, Akademiska Hospital, Uppsala, Sweden.,Department of Pathology, Centre for Clinical Research of Uppsala University, Vastmanland´s Hospital Västerås, Västerås, Sweden
| | - Henrik Lindman
- Department of Immunology, Genetics and Pathology, Uppsala University Hospital, Uppsala, Sweden
| | - Pär-Ola Bendahl
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE 22381, Lund, Sweden
| | - Anna Ehinger
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE 22381, Lund, Sweden.,Department of Genetics and Pathology, Laboratory Medicine, Region Skåne, Lund, Sweden
| | - Christer Larsson
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Niklas Loman
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE 22381, Lund, Sweden.,Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Lisa Rydén
- Division of Surgery, Department of Clinical Sciences, Lund University, Lund, Sweden.,Department of Surgery and Gastroenterology, Skåne University Hospital Malmö, Malmö, Sweden
| | - Martin Malmberg
- Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Åke Borg
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE 22381, Lund, Sweden
| | - Johan Vallon-Christersson
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE 22381, Lund, Sweden.
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Karlström J, Aine M, Staaf J, Veerla S. SRIQ clustering: A fusion of Random Forest, QT clustering, and KNN concepts. Comput Struct Biotechnol J 2022; 20:1567-1579. [PMID: 35465158 PMCID: PMC9010551 DOI: 10.1016/j.csbj.2022.03.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/21/2022] [Accepted: 03/31/2022] [Indexed: 12/01/2022] Open
Abstract
Gene expression profiling together with unsupervised analysis methods, typically clustering methods, has been used extensively in cancer research to unravel, e.g., new molecular subtypes that hold promise of disease refinement that may ultimately benefit patients. However, many of the commonly used methods require a prespecified number of clusters to extract and frequently require some type of feature pre-selection, e.g. variance filtering. This introduces subjectivity to the process of cluster discovery and the definition of putative novel tumor subtypes. Here, we introduce SRIQ, a novel unsupervised clustering method that could circumvent some of the issues in commonly used unsupervised analysis methods. SRIQ incorporates concepts from random forest machine learning as well as quality threshold- and k-nearest neighbor clustering. It is implemented as a Java and Python pipeline including data pre-processing, differential expression analysis, and pathway analysis. Using 434 lung adenocarcinomas profiled by RNA sequencing, we demonstrate the technical reproducibility of SRIQ and benchmark its performance compared to the commonly used consensus clustering method. Based on differential gene expression analysis and auxiliary molecular data we show that SRIQ can define new tumor subsets that appear biologically relevant and consistent compared and that these new subgroups seem to refine existing transcriptional subtypes that were defined using consensus clustering. Together, this provides support that SRIQ may be a useful new tool for unsupervised analysis of gene expression data from human malignancies.
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6
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Cirenajwis H, Lauss M, Planck M, Vallon-Christersson J, Staaf J. Performance of gene expression-based single sample predictors for assessment of clinicopathological subgroups and molecular subtypes in cancers: a case comparison study in non-small cell lung cancer. Brief Bioinform 2021; 21:729-740. [PMID: 30721923 PMCID: PMC7299291 DOI: 10.1093/bib/bbz008] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 12/04/2018] [Accepted: 01/07/2019] [Indexed: 12/14/2022] Open
Abstract
The development of multigene classifiers for cancer prognosis, treatment prediction, molecular subtypes or clinicopathological groups has been a cornerstone in transcriptomic analyses of human malignancies for nearly two decades. However, many reported classifiers are critically limited by different preprocessing needs like normalization and data centering. In response, a new breed of classifiers, single sample predictors (SSPs), has emerged. SSPs classify samples in an N-of-1 fashion, relying on, e.g. gene rules comparing expression values within a sample. To date, several methods have been reported, but there is a lack of head-to-head performance comparison for typical cancer classification problems, representing an unmet methodological need in cancer bioinformatics. To resolve this need, we performed an evaluation of two SSPs [k-top-scoring pair classifier (kTSP) and absolute intrinsic molecular subtyping (AIMS)] for two case examples of different magnitude of difficulty in non-small cell lung cancer: gene expression–based classification of (i) tumor histology and (ii) molecular subtype. Through the analysis of ~2000 lung cancer samples for each case example (n = 1918 and n = 2106, respectively), we compared the performance of the methods for different sample compositions, training data set sizes, gene expression platforms and gene rule selections. Three main conclusions are drawn from the comparisons: both methods are platform independent, they select largely overlapping gene rules associated with actual underlying tumor biology and, for large training data sets, they behave interchangeably performance-wise. While SSPs like AIMS and kTSP offer new possibilities to move gene expression signatures/predictors closer to a clinical context, they are still importantly limited by the difficultness of the classification problem at hand.
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Affiliation(s)
- Helena Cirenajwis
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden
| | - Martin Lauss
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden
| | - Maria Planck
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden
| | - Johan Vallon-Christersson
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden
| | - Johan Staaf
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden
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7
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Nacer DF, Liljedahl H, Karlsson A, Lindgren D, Staaf J. Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor. Brief Bioinform 2021; 22:6272790. [PMID: 33971670 PMCID: PMC8574611 DOI: 10.1093/bib/bbab154] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/17/2021] [Accepted: 04/02/2021] [Indexed: 12/24/2022] Open
Abstract
Gene-expression profiling can be used to classify human tumors into molecular subtypes or risk groups, representing potential future clinical tools for treatment prediction and prognostication. However, it is less well-known how prognostic gene signatures derived in one malignancy perform in a pan-cancer context. In this study, a gene-rule-based single sample predictor (SSP) called classifier for lung adenocarcinoma molecular subtypes (CLAMS) associated with proliferation was tested in almost 15 000 samples from 32 cancer types to classify samples into better or worse prognosis. Of the 14 malignancies that presented both CLAMS classes in sufficient numbers, survival outcomes were significantly different for breast, brain, kidney and liver cancer. Patients with samples classified as better prognosis by CLAMS were generally of lower tumor grade and disease stage, and had improved prognosis according to other type-specific classifications (e.g. PAM50 for breast cancer). In all, 99.1% of non-lung cancer cases classified as better outcome by CLAMS were comprised within the range of proliferation scores of lung adenocarcinoma cases with a predicted better prognosis by CLAMS. This finding demonstrates the potential of tuning SSPs to identify specific levels of for instance tumor proliferation or other transcriptional programs through predictor training. Together, pan-cancer studies such as this may take us one step closer to understanding how gene-expression-based SSPs act, which gene-expression programs might be important in different malignancies, and how to derive tools useful for prognostication that are efficient across organs.
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Yu H, Zhang Z. ALKBH5-mediated m6A demethylation of lncRNA RMRP plays an oncogenic role in lung adenocarcinoma. Mamm Genome 2021; 32:195-203. [PMID: 33934179 DOI: 10.1007/s00335-021-09872-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 04/19/2021] [Indexed: 11/26/2022]
Abstract
Lung adenocarcinomas are more common in non-smoking males and females. In this study, we investigated the function of long non-coding RNA RMRP in lung adenocarcinoma and further explore the regulatory role of ALKBH5 in lncRNA methylation. The results showed lncRNA RMRP expression was significantly enhanced in lung adenocarcinoma tissues, and is positively correlated with poor prognosis. RMRP knockdown in lung adenocarcinoma cell lines suppressed cell proliferation, migration and invasion, and promoted cell apoptosis. In addition, ALKBH5 upregulated RMRP expression via demethylation, and ALKBH5 knockdown inhibited the tumorigenesis of lung adenocarcinoma in vitro and vivo. Given these clear patterns, suppressing RMRP through ALKBH5 manipulation may represent a promising therapeutic target for lung adenocarcinoma.
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Affiliation(s)
- Hui Yu
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, 138 Yi xue yuan Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Yi xue yuan Road, Shanghai, 200032, People's Republic of China
| | - Zhe Zhang
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, 138 Yi xue yuan Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Yi xue yuan Road, Shanghai, 200032, People's Republic of China.
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9
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Lebovitz C, Wretham N, Osooly M, Milne K, Dash T, Thornton S, Tessier-Cloutier B, Sathiyaseelan P, Bortnik S, Go NE, Halvorsen E, Cederberg RA, Chow N, Dos Santos N, Bennewith KL, Nelson BH, Bally MB, Lam WL, Gorski SM. Loss of Parkinson's susceptibility gene LRRK2 promotes carcinogen-induced lung tumorigenesis. Sci Rep 2021; 11:2097. [PMID: 33483550 PMCID: PMC7822882 DOI: 10.1038/s41598-021-81639-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 01/07/2021] [Indexed: 12/13/2022] Open
Abstract
Pathological links between neurodegenerative disease and cancer are emerging. LRRK2 overactivity contributes to Parkinson’s disease, whereas our previous analyses of public cancer patient data revealed that decreased LRRK2 expression is associated with lung adenocarcinoma (LUAD). The clinical and functional relevance of LRRK2 repression in LUAD is unknown. Here, we investigated associations between LRRK2 expression and clinicopathological variables in LUAD patient data and asked whether LRRK2 knockout promotes murine lung tumorigenesis. In patients, reduced LRRK2 was significantly associated with ongoing smoking and worse survival, as well as signatures of less differentiated LUAD, altered surfactant metabolism and immunosuppression. We identified shared transcriptional signals between LRRK2-low LUAD and postnatal alveolarization in mice, suggesting aberrant activation of a developmental program of alveolar growth and differentiation in these tumors. In a carcinogen-induced murine lung cancer model, multiplex IHC confirmed that LRRK2 was expressed in alveolar type II (AT2) cells, a main LUAD cell-of-origin, while its loss perturbed AT2 cell morphology. LRRK2 knockout in this model significantly increased tumor initiation and size, demonstrating that loss of LRRK2, a key Parkinson’s gene, promotes lung tumorigenesis.
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Affiliation(s)
- Chandra Lebovitz
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada
| | - Nicole Wretham
- Department of Experimental Therapeutics, BC Cancer, Vancouver, BC, V5Z 1L3, Canada
| | - Maryam Osooly
- Department of Experimental Therapeutics, BC Cancer, Vancouver, BC, V5Z 1L3, Canada
| | - Katy Milne
- Deeley Research Centre, BC Cancer, Victoria, BC, V8R 6V5, Canada
| | - Tia Dash
- Deeley Research Centre, BC Cancer, Victoria, BC, V8R 6V5, Canada.,Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, V8P 5C2, Canada
| | - Shelby Thornton
- Deeley Research Centre, BC Cancer, Victoria, BC, V8R 6V5, Canada.,Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, V8P 5C2, Canada
| | - Basile Tessier-Cloutier
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Paalini Sathiyaseelan
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada.,Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
| | - Svetlana Bortnik
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada
| | - Nancy Erro Go
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada
| | - Elizabeth Halvorsen
- Department of Integrative Oncology, BC Cancer, Vancouver, BC, V5Z 1L3, Canada
| | - Rachel A Cederberg
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.,Department of Integrative Oncology, BC Cancer, Vancouver, BC, V5Z 1L3, Canada
| | - Norman Chow
- Department of Experimental Therapeutics, BC Cancer, Vancouver, BC, V5Z 1L3, Canada
| | - Nancy Dos Santos
- Department of Experimental Therapeutics, BC Cancer, Vancouver, BC, V5Z 1L3, Canada
| | - Kevin L Bennewith
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.,Department of Integrative Oncology, BC Cancer, Vancouver, BC, V5Z 1L3, Canada
| | - Brad H Nelson
- Deeley Research Centre, BC Cancer, Victoria, BC, V8R 6V5, Canada.,Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, V8P 5C2, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Marcel B Bally
- Department of Experimental Therapeutics, BC Cancer, Vancouver, BC, V5Z 1L3, Canada.,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Wan L Lam
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.,Department of Integrative Oncology, BC Cancer, Vancouver, BC, V5Z 1L3, Canada
| | - Sharon M Gorski
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada. .,Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.
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10
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Halvorsen AR, Haugen MH, Öjlert ÅK, Lund-Iversen M, Jørgensen L, Solberg S, Mælandsmo GM, Brustugun OT, Helland Å. Protein Kinase C Isozymes Associated With Relapse Free Survival in Non-Small Cell Lung Cancer Patients. Front Oncol 2020; 10:590755. [PMID: 33324562 PMCID: PMC7725872 DOI: 10.3389/fonc.2020.590755] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 10/22/2020] [Indexed: 12/16/2022] Open
Abstract
INTRODUCTION Protein expression is deregulated in cancer, and the proteomic changes observed in lung cancer may be a consequence of mutations in essential genes. The purpose of this study was to identify protein expression associated with prognosis in lung cancers stratified by smoking status, molecular subtypes, and EGFR-, TP53-, and KRAS-mutations. METHODS We performed profiling of 295 cancer-relevant phosphorylated and non-phosphorylated proteins, using reverse phase protein arrays. Biopsies from 80 patients with operable lung adenocarcinomas were analyzed for protein expression and association with relapse free survival (RFS) were studied. RESULTS Spearman's rank correlation analysis identified 46 proteins with significant association to RFS (p<0.05). High expression of protein kinase C (PKC)-α and the phosporylated state of PKC-α, PKC-β, and PKC-δ, showed the strongest positive correlation to RFS, especially in the wild type samples. This was confirmed in gene expression data from 172 samples. Based on protein expression, unsupervised hierarchical clustering separated the samples into four subclusters enriched with the molecular subtypes terminal respiratory unit (TRU), proximal proliferative (PP), and proximal inflammatory (PI) (p=0.0001). Subcluster 2 contained a smaller cluster (2a) enriched with samples of the subtype PP, low expression of the PKC isozymes, and associated with poor RFS (p=0.003) compared to the other samples. Low expression of the PKC isozymes in the subtype PP and a reduced relapse free survival was confirmed with The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) samples. CONCLUSION This study identified different proteins associated with RFS depending on molecular subtype, smoking- and mutational-status, with PKC-α, PKC-β, and PKC-δ showing the strongest correlation.
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Affiliation(s)
- Ann Rita Halvorsen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
- Department of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Mads Haugland Haugen
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
| | - Åsa Kristina Öjlert
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
| | - Marius Lund-Iversen
- Department of Pathology, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
| | - Lars Jørgensen
- Department of Cardiothoracic Surgery, Oslo University Hospital-Rikshospitalet, Oslo, Norway
| | - Steinar Solberg
- Department of Cardiothoracic Surgery, Oslo University Hospital-Rikshospitalet, Oslo, Norway
| | - Gunhild M. Mælandsmo
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
- Faculty of Health Sciences, Institute of Medical Biology, UiT-Arctic University of Norway, Tromso, Norway
| | - Odd Terje Brustugun
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
- Section of Oncology, Drammen Hospital, Vestre Viken Hospital Trust, Drammen, Norway
| | - Åslaug Helland
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
- Department of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Oncology, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
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11
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Liljedahl H, Karlsson A, Oskarsdottir GN, Salomonsson A, Brunnström H, Erlingsdottir G, Jönsson M, Isaksson S, Arbajian E, Ortiz-Villalón C, Hussein A, Bergman B, Vikström A, Monsef N, Branden E, Koyi H, de Petris L, Patthey A, Behndig AF, Johansson M, Planck M, Staaf J. A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis. Int J Cancer 2020; 148:238-251. [PMID: 32745259 PMCID: PMC7689824 DOI: 10.1002/ijc.33242] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 07/03/2020] [Accepted: 07/07/2020] [Indexed: 12/14/2022]
Abstract
Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expression-based AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminal-respiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample predictor (SSP) for molecular subtype assignment and risk stratification that could function in a clinical setting. Two-class (TRU/nonTRU=SSP2) and three-class (TRU/PP/PI=SSP3) SSPs using the AIMS algorithm were trained in 1655 ACs (n = 9659 genes) from public repositories vs TCGA centroid subtypes. Validation and survival analysis were performed in 977 patients using overall survival (OS) and distant metastasis-free survival (DMFS) as endpoints. In the validation cohort, SSP2 and SSP3 showed accuracies of 0.85 and 0.81, respectively. SSPs captured relevant biology previously associated with the TCGA subtypes and were associated with prognosis. In survival analysis, OS and DMFS for cases discordantly classified between TCGA and SSP2 favored the SSP2 classification. In resected Stage I patients, SSP2 identified TRU-cases with better OS (hazard ratio [HR] = 0.30; 95% confidence interval [CI] = 0.18-0.49) and DMFS (TRU HR = 0.52; 95% CI = 0.33-0.83) independent of age, Stage IA/IB and gender. SSP2 was transformed into a NanoString nCounter assay and tested in 44 Stage I patients using RNA from formalin-fixed tissue, providing prognostic stratification (relapse-free interval, HR = 3.2; 95% CI = 1.2-8.8). In conclusion, gene expression-based SSPs can provide molecular subtype and independent prognostic information in early-stage lung ACs. SSPs may overcome critical limitations in the applicability of gene signatures in lung cancer.
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Affiliation(s)
- Helena Liljedahl
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden
| | - Anna Karlsson
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden
| | - Gudrun N Oskarsdottir
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden.,Department of Respiratory Medicine and Allergology, Skåne University Hospital, Lund, Sweden
| | - Annette Salomonsson
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden
| | - Hans Brunnström
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden.,Department of Pathology, Laboratory Medicine Region Skåne, Lund, Sweden
| | - Gigja Erlingsdottir
- Department of Pathology, Landspitali University Hospital, Reykjavik, Iceland.,Department of Laboratory Medicine, Department of Pathology, Skåne University Hospital, Malmö, Sweden
| | - Mats Jönsson
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden
| | - Sofi Isaksson
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden
| | - Elsa Arbajian
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden
| | | | - Aziz Hussein
- Department of Pathology and Cytology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Bengt Bergman
- Department of Respiratory Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Anders Vikström
- Department of Pulmonary Medicine, University Hospital Linköping, Linköping, Sweden
| | - Nastaran Monsef
- Department of Pathology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Eva Branden
- Respiratory Medicine Unit, Department of Medicine Solna and CMM, Karolinska Institute and Karolinska University Hospital Solna, Stockholm, Sweden.,Centre for Research and Development, Uppsala University/Region Gävleborg, Gävle, Sweden
| | - Hirsh Koyi
- Respiratory Medicine Unit, Department of Medicine Solna and CMM, Karolinska Institute and Karolinska University Hospital Solna, Stockholm, Sweden.,Centre for Research and Development, Uppsala University/Region Gävleborg, Gävle, Sweden
| | - Luigi de Petris
- Thoracic Oncology Unit, Karolinska University Hospital and Department Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Annika Patthey
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
| | - Annelie F Behndig
- Department of Public Health and Clinical Medicine, Division of Medicine, Umeå University, Umeå, Sweden
| | - Mikael Johansson
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Maria Planck
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden.,Department of Respiratory Medicine and Allergology, Skåne University Hospital, Lund, Sweden
| | - Johan Staaf
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden
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12
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Kong Q, Xiang Z, Wu Y, Gu Y, Guo J, Geng F. Analysis of the susceptibility of lung cancer patients to SARS-CoV-2 infection. Mol Cancer 2020; 19:80. [PMID: 32345328 PMCID: PMC7186321 DOI: 10.1186/s12943-020-01209-2] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 04/23/2020] [Indexed: 12/22/2022] Open
Abstract
Recent studies have reported that COVID-19 patients with lung cancer have a higher risk of severe events than patients without cancer. In this study, we investigated the gene expression of angiotensin I-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) with prognosis in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Lung cancer patients in each age stage, subtype, and pathological stage are susceptible to SARS-CoV-2 infection, except for the primitive subtype of LUSC. LUAD patients are more susceptible to SARS-CoV-2 infection than LUSC patients. The findings are unanimous on tissue expression in gene and protein levels.
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Affiliation(s)
- Qi Kong
- Institute of Laboratory Animal Sciences, Chinese Academy of Medical Sciences (CAMS) and Comparative Medicine Center, Peking Union Medical College (PUMC), Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health; Beijing Key Laboratory for Animal Models of Emerging and Reemerging Infectious Diseases, 5 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, P.R. China.
| | - Zhiguang Xiang
- Institute of Laboratory Animal Sciences, Chinese Academy of Medical Sciences (CAMS) and Comparative Medicine Center, Peking Union Medical College (PUMC), Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health; Beijing Key Laboratory for Animal Models of Emerging and Reemerging Infectious Diseases, 5 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, P.R. China
| | - Yue Wu
- Institute of Laboratory Animal Sciences, Chinese Academy of Medical Sciences (CAMS) and Comparative Medicine Center, Peking Union Medical College (PUMC), Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health; Beijing Key Laboratory for Animal Models of Emerging and Reemerging Infectious Diseases, 5 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, P.R. China
| | - Yu Gu
- Institute of Laboratory Animal Sciences, Chinese Academy of Medical Sciences (CAMS) and Comparative Medicine Center, Peking Union Medical College (PUMC), Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health; Beijing Key Laboratory for Animal Models of Emerging and Reemerging Infectious Diseases, 5 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, P.R. China
| | - Jianguo Guo
- Institute of Laboratory Animal Sciences, Chinese Academy of Medical Sciences (CAMS) and Comparative Medicine Center, Peking Union Medical College (PUMC), Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health; Beijing Key Laboratory for Animal Models of Emerging and Reemerging Infectious Diseases, 5 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, P.R. China
| | - Fei Geng
- W Booth School of Engineering Practice and Technology, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 0A3, Canada
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13
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Zhao L, Lee VHF, Ng MK, Yan H, Bijlsma MF. Molecular subtyping of cancer: current status and moving toward clinical applications. Brief Bioinform 2020; 20:572-584. [PMID: 29659698 DOI: 10.1093/bib/bby026] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 03/01/2018] [Indexed: 12/14/2022] Open
Abstract
Cancer is a collection of genetic diseases, with large phenotypic differences and genetic heterogeneity between different types of cancers and even within the same cancer type. Recent advances in genome-wide profiling provide an opportunity to investigate global molecular changes during the development and progression of cancer. Meanwhile, numerous statistical and machine learning algorithms have been designed for the processing and interpretation of high-throughput molecular data. Molecular subtyping studies have allowed the allocation of cancer into homogeneous groups that are considered to harbor similar molecular and clinical characteristics. Furthermore, this has helped researchers to identify both actionable targets for drug design as well as biomarkers for response prediction. In this review, we introduce five frequently applied techniques for generating molecular data, which are microarray, RNA sequencing, quantitative polymerase chain reaction, NanoString and tissue microarray. Commonly used molecular data for cancer subtyping and clinical applications are discussed. Next, we summarize a workflow for molecular subtyping of cancer, including data preprocessing, cluster analysis, supervised classification and subtype characterizations. Finally, we identify and describe four major challenges in the molecular subtyping of cancer that may preclude clinical implementation. We suggest that standardized methods should be established to help identify intrinsic subgroup signatures and build robust classifiers that pave the way toward stratified treatment of cancer patients.
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Affiliation(s)
- Lan Zhao
- Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong
| | - Victor H F Lee
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Michael K Ng
- Centre for Mathematical Imaging and Vision and Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Hong Yan
- Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong
| | - Maarten F Bijlsma
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam and Academic Medical Center, Amsterdam, The Netherlands
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14
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Halvorsen AR, Ragle Aure M, Õjlert ÅK, Brustugun OT, Solberg S, Nebdal D, Helland Å. Identification of microRNAs involved in pathways which characterize the expression subtypes of NSCLC. Mol Oncol 2019; 13:2604-2615. [PMID: 31505091 PMCID: PMC6887593 DOI: 10.1002/1878-0261.12571] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 08/28/2019] [Accepted: 09/06/2019] [Indexed: 12/17/2022] Open
Abstract
Dysregulation of microRNAs is a common mechanism in the development of lung cancer, but the relationship between microRNAs and expression subtypes in non‐small‐cell lung cancer (NSCLC) is poorly explored. Here, we analyzed microRNA expression from 241 NSCLC samples and correlated this with the expression subtypes of adenocarcinomas (AD) and squamous cell carcinomas (SCC) to identify microRNAs specific for each subtype. Gene set variation analysis and the hallmark gene set were utilized to calculate gene set scores specific for each sample, and these were further correlated with the expression of the subtype‐specific microRNAs. In ADs, we identified nine aberrantly regulated microRNAs in the terminal respiratory unit (TRU), three in the proximal inflammatory (PI), and nine in the proximal proliferative subtype (PP). In SCCs, 1, 5, 5, and 9 microRNAs were significantly dysregulated in the basal, primitive, classical, and secretory subtypes, respectively. The subtype‐specific microRNAs were highly correlated to specific gene sets, and a distinct pattern of biological processes with high immune activity for the AD PI and SCC secretory subtypes, and upregulation of cell cycle‐related processes in AD PP, SCC primitive, and SCC classical subtypes were found. Several in silico predicted targets within the gene sets were identified for the subtype‐specific microRNAs, underpinning the findings. The results were significantly validated in the LUAD (n = 492) and LUSC (n = 380) TCGA dataset (False discovery rates‐corrected P‐value < 0.05). Our study provides novel insight into how expression subtypes determined with discrete biological processes may be regulated by subtype‐specific microRNAs. These results may have importance for the development of combinatory therapeutic strategies for lung cancer patients.
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Affiliation(s)
- Ann Rita Halvorsen
- Department of Cancer Genetics, Institute for Cancer Research, OUS Radiumhospitalet, Oslo, Norway.,Institute for Clinical Medicine, University of Oslo, Norway
| | - Miriam Ragle Aure
- Department of Cancer Genetics, Institute for Cancer Research, OUS Radiumhospitalet, Oslo, Norway
| | - Åsa Kristina Õjlert
- Department of Cancer Genetics, Institute for Cancer Research, OUS Radiumhospitalet, Oslo, Norway
| | - Odd Terje Brustugun
- Department of Cancer Genetics, Institute for Cancer Research, OUS Radiumhospitalet, Oslo, Norway
| | - Steinar Solberg
- Department of Cardiothoracic Surgery, Oslo University Hospital-Rikshospitalet, Norway
| | - Daniel Nebdal
- Department of Cancer Genetics, Institute for Cancer Research, OUS Radiumhospitalet, Oslo, Norway
| | - Åslaug Helland
- Department of Cancer Genetics, Institute for Cancer Research, OUS Radiumhospitalet, Oslo, Norway.,Institute for Clinical Medicine, University of Oslo, Norway
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15
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Ramón Y Cajal S, Hümmer S, Peg V, Guiu XM, De Torres I, Castellvi J, Martinez-Saez E, Hernandez-Losa J. Integrating clinical, molecular, proteomic and histopathological data within the tissue context: tissunomics. Histopathology 2019; 75:4-19. [PMID: 30667539 PMCID: PMC6851567 DOI: 10.1111/his.13828] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 01/19/2019] [Indexed: 12/14/2022]
Abstract
Malignant tumours show a marked degree of morphological, molecular and proteomic heterogeneity. This variability is closely related to microenvironmental factors and the location of the tumour. The activation of genetic alterations is very tissue‐dependent and only few tumours have distinct genetic alterations. Importantly, the activation state of proteins and signaling factors is heterogeneous in the primary tumour and in metastases and recurrences. The molecular diagnosis based only on genetic alterations can lead to treatments with unpredictable responses, depending on the tumour location, such as the tumour response in melanomas versus colon carcinomas with BRAF mutations. Therefore, we understand that the correct evaluation of tumours requires a system that integrates both morphological, molecular and protein information in a clinical and pathological context, where intratumoral heterogeneity can be assessed. Thus, we propose the term ‘tissunomics’, where the diagnosis will be contextualised in each tumour based on the complementation of the pathological, molecular, protein expression, environmental cells and clinical data.
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Affiliation(s)
- Santiago Ramón Y Cajal
- Translational Molecular Pathology, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Department of Pathology, Vall d'Hebron University Hospital, Barcelona, Spain.,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Barcelona, Spain
| | - Stefan Hümmer
- Translational Molecular Pathology, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Barcelona, Spain
| | - Vicente Peg
- Department of Pathology, Vall d'Hebron University Hospital, Barcelona, Spain.,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Barcelona, Spain
| | - Xavier M Guiu
- Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Barcelona, Spain.,Department of Pathology, Bellvitge University Hospital, Barcelona, Spain
| | - Inés De Torres
- Department of Pathology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Josep Castellvi
- Department of Pathology, Vall d'Hebron University Hospital, Barcelona, Spain.,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Barcelona, Spain
| | - Elena Martinez-Saez
- Department of Pathology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Javier Hernandez-Losa
- Translational Molecular Pathology, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Department of Pathology, Vall d'Hebron University Hospital, Barcelona, Spain.,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Barcelona, Spain
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16
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Multiregion gene expression profiling reveals heterogeneity in molecular subtypes and immunotherapy response signatures in lung cancer. Mod Pathol 2018; 31:947-955. [PMID: 29410488 DOI: 10.1038/s41379-018-0029-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Revised: 12/07/2017] [Accepted: 12/10/2017] [Indexed: 12/14/2022]
Abstract
Intra-tumor heterogeneity may be present at all molecular levels. Genomic intra-tumor heterogeneity at the exome level has been reported in many cancer types, but comprehensive gene expression intra-tumor heterogeneity has not been well studied. Here, we delineated the gene expression intra-tumor heterogeneity by exploring gene expression profiles of 35 tumor regions from 10 non-small cell lung cancer tumors (three or four regions/tumor), including adenocarcinoma, squamous cell carcinoma, large-cell carcinoma, and pleomorphic carcinoma of the lung. Using Affymetrix Gene 1.0 ST arrays, we generated the gene expression data for every sample. Inter-tumor heterogeneity was generally higher than intra-tumor heterogeneity, but some tumors showed a substantial level of intra-tumor heterogeneity. The analysis of various clinically relevant gene expression signatures including molecular subtype, epithelial-to-mesenchymal transition, and anti-PD-1 resistance signatures also revealed heterogeneity between different regions of the same tumor. The gene expression intra-tumor heterogeneity we observed was associated with heterogeneous tumor microenvironments represented by stromal and immune cells infiltrated. Our data suggest that RNA-based prognostic or predictive molecular tests should be carefully conducted in consideration of the gene expression intra-tumor heterogeneity.
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17
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Wang WW, Zhou XL, Song YJ, Yu CH, Zhu WG, Tong YS. Combination of long noncoding RNA MALAT1 and carcinoembryonic antigen for the diagnosis of malignant pleural effusion caused by lung cancer. Onco Targets Ther 2018; 11:2333-2344. [PMID: 29731641 PMCID: PMC5923246 DOI: 10.2147/ott.s157551] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Purpose Long noncoding RNAs (lncRNAs) are present in body fluids, but their potential as tumor biomarkers has never been investigated in malignant pleural effusion (MPE) caused by lung cancer. The aim of this study was to assess the clinical significance of lncRNAs in pleural effusion, which could potentially serve as diagnostic and predictive markers for lung cancer-associated MPE (LC-MPE). Patients and methods RNAs from pleural effusion were extracted in 217 cases of LC-MPE and 132 cases of benign pleural effusion (BPE). Thirty-one lung cancer-associated lncRNAs were measured using quantitative real-time polymerase chain reaction (qRT-PCR). The level of carcinoembryonic antigen (CEA) was also determined. The receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were established to evaluate the sensitivity and specificity of the identified lncRNAs and other biomarkers. The correlations between baseline pleural effusion lncRNAs expression and response to chemotherapy were also analyzed. Results Three lncRNAs (MALAT1, H19, and CUDR) were found to have potential as diagnostic markers in LC-MPE. The AUCs for MALAT1, H19, CUDR, and CEA were 0.891, 0.783, 0.824, and 0.826, respectively. Using a logistic model, the combination of MALAT1 and CEA (AUC, 0.924) provided higher sensitivity and accuracy in predicting LC-MPE than CEA (AUC, 0.826) alone. Moreover, baseline MALAT1 expression in pleural fluid was inversely correlated with chemotherapy response in patients with LC-MPE. Conclusion Pleural effusion lncRNAs were effective in differentiating LC-MPE from BPE. The combination of MALAT1 and CEA was more effective for LC-MPE diagnosis.
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Affiliation(s)
- Wan-Wei Wang
- Department of Radiation Oncology, Huai'an First People's Hospital, Nanjing Medical University, Huai'an, Jiangsu, China
| | - Xi-Lei Zhou
- Department of Radiation Oncology, Huai'an First People's Hospital, Nanjing Medical University, Huai'an, Jiangsu, China
| | - Ying-Jian Song
- Department of Respiratory Medicine, Huai'an First People's Hospital, Nanjing Medical University, Huai'an, Jiangsu, China
| | - Chang-Hua Yu
- Department of Radiation Oncology, Huai'an First People's Hospital, Nanjing Medical University, Huai'an, Jiangsu, China
| | - Wei-Guo Zhu
- Department of Radiation Oncology, Huai'an First People's Hospital, Nanjing Medical University, Huai'an, Jiangsu, China
| | - Yu-Suo Tong
- Department of Radiation Oncology, Huai'an First People's Hospital, Nanjing Medical University, Huai'an, Jiangsu, China
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18
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Ringnér M, Staaf J. Consensus of gene expression phenotypes and prognostic risk predictors in primary lung adenocarcinoma. Oncotarget 2018; 7:52957-52973. [PMID: 27437773 PMCID: PMC5288161 DOI: 10.18632/oncotarget.10641] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Accepted: 06/13/2016] [Indexed: 11/25/2022] Open
Abstract
Transcriptional profiling of lung adenocarcinomas has identified numerous gene expression phenotype (GEP) and risk prediction (RP) signatures associated with patient outcome. However, classification agreement between signatures, underlying transcriptional programs, and independent signature validation are less studied. We classified 2395 transcriptional adenocarcinoma profiles, assembled from 17 public cohorts, using 11 GEP and seven RP signatures, finding that 16 signatures were associated with patient survival in the total cohort and in multiple individual cohorts. For significant signatures, total cohort hazard ratios were ~2 in univariate analyses (mean=1.95, range=1.4-2.6). Strong classification agreement between signatures was observed, especially for predicted low-risk patients by adenocarcinoma-derived signatures. Expression of proliferation-related genes correlated strongly with GEP subtype classifications and RP scores, driving the gene signature association with prognosis. A three-group consensus definition of samples across 10 GEP classifiers demonstrated aggregation of samples with specific smoking patterns, gender, and EGFR/KRAS mutations, while survival differences were only significant when patients were divided into low- or high-risk. In summary, our study demonstrates a consensus between GEPs and RPs in lung adenocarcinoma through a common underlying transcriptional program. This consensus generalizes reported problems with current signatures in a clinical context, stressing development of new adenocarcinoma-specific single sample predictors for clinical use.
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Affiliation(s)
- Markus Ringnér
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE 22381, Lund, Sweden
| | - Johan Staaf
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE 22381, Lund, Sweden
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19
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Qi L, Li T, Shi G, Wang J, Li X, Zhang S, Chen L, Qin Y, Gu Y, Zhao W, Guo Z. An individualized gene expression signature for prediction of lung adenocarcinoma metastases. Mol Oncol 2017; 11:1630-1645. [PMID: 28922552 PMCID: PMC5663997 DOI: 10.1002/1878-0261.12137] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Revised: 09/01/2017] [Accepted: 09/06/2017] [Indexed: 12/17/2022] Open
Abstract
Our laboratory previously reported an individual‐level signature consisting of nine gene pairs, named 9‐GPS. This signature was developed by training on microarray expression data and validated using three independent integrated microarray data sets, with samples of stage I non‐small‐cell lung cancer after complete surgical resection. In this study, we first validated the cross‐platform robustness of 9‐GPS by demonstrating that 9‐GPS could significantly stratify the overall survival of 213 stage I lung adenocarcinoma (LUAD) patients detected with RNA‐sequencing platform in The Cancer Genome Atlas (TCGA; log‐rank P = 0.0318, C‐index = 0.55). Applying 9‐GPS to all the 423 stage I‐IV LUAD samples in TCGA, the predicted high‐risk samples were significantly enriched with clinically diagnosed metastatic samples (Fisher's exact test, P = 0.0015). We further modified the voting rule of 9‐GPS and found that the modified 9‐GPS had a better performance in predicting metastasis states (Fisher's exact test, P < 0.0001). With the aid of the modified 9‐GPS for reclassifying the metastasis states of patients with LUAD, the reclassified metastatic samples presented clearer transcriptional and genomic characteristics compared to the reclassified nonmetastatic samples. Finally, regulator network analysis identified TP53 and IRF1 with frequent genomic aberrations in the reclassified metastatic samples, indicating their key roles in driving tumor metastasis. In conclusion, 9‐GPS is a robust signature for identifying early‐stage LUAD patients with potential occult metastasis. This occult metastasis prediction was associated with clear transcriptional and genomic characteristics as well as the clinical diagnoses.
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Affiliation(s)
- Lishuang Qi
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Tianhao Li
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Gengen Shi
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Jiasheng Wang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Xin Li
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Sainan Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Libin Chen
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Yuan Qin
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Yunyan Gu
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Wenyuan Zhao
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Zheng Guo
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
- Department of BioinformaticsKey Laboratory of Ministry of Education for Gastrointestinal CancerSchool of Basic Medical SciencesFujian Medical UniversityFuzhouChina
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20
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Green S, Dawe DE, Banerji S. Immune Signatures of Non-Small Cell Lung Cancer. J Thorac Oncol 2017; 12:913-915. [PMID: 28532560 DOI: 10.1016/j.jtho.2017.04.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 04/12/2017] [Indexed: 12/24/2022]
Affiliation(s)
- Susan Green
- Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada and Department of Medical Oncology, CancerCare Manitoba, Winnipeg, Canada
| | - David E Dawe
- Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada and Department of Medical Oncology, CancerCare Manitoba, Winnipeg, Canada
| | - Shantanu Banerji
- Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada and Department of Medical Oncology, CancerCare Manitoba, Winnipeg, Canada.
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21
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Lin K, Xu T, He BS, Pan YQ, Sun HL, Peng HX, Hu XX, Wang SK. MicroRNA expression profiles predict progression and clinical outcome in lung adenocarcinoma. Onco Targets Ther 2016; 9:5679-5692. [PMID: 27695346 PMCID: PMC5029843 DOI: 10.2147/ott.s111241] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Lung cancer is one of the leading causes of cancer death worldwide. Accumulating evidence has indicated that microRNAs (miRNAs) can be proposed as promising diagnostic and prognostic markers for various cancers. The current study analyzed the miRNA expression profiles of 418 lung adenocarcinoma (LUAD) cases obtained from The Cancer Genome Atlas dataset, with the aim to investigate the relationship of miRNAs with progression and prognosis of LUAD. A total of 185 miRNAs were found to be differentially expressed between LUAD tumor tissues and adjacent normal tissues. Among them, 13, 10, 0, and 10 miRNAs were discovered to be associated with pathologic T, N, M, and Stage, respectively. Interestingly, mir-200 family (mir-200a, mir-200b, and mir-429) was shown to play a critical role in the progression of LUAD. In the multivariate Cox regression analysis, mir-1468 (P=0.009), mir-212 (P=0.026), mir-3653 (P=0.012), and mir-31 (P=0.002) were significantly correlated with recurrence-free survival. With regard to overall survival, mir-551b (P=0.011), mir-3653 (P=0.016), and mir-31 (P=0.001) were proven as independent prognostic markers. In summary, this study identified the cancer-specific miRNAs that may predict the progression and prognosis of LUAD.
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Affiliation(s)
- Kang Lin
- Central Laboratory, Nanjing First Hospital, Nanjing Medical University
| | - Tao Xu
- Central Laboratory, Nanjing First Hospital, Nanjing Medical University
| | - Bang-Shun He
- Central Laboratory, Nanjing First Hospital, Nanjing Medical University
| | - Yu-Qin Pan
- Central Laboratory, Nanjing First Hospital, Nanjing Medical University
| | - Hui-Ling Sun
- Central Laboratory, Nanjing First Hospital, Nanjing Medical University
| | - Hong-Xin Peng
- Medical School, Southeast University, Nanjing, Jiangsu, People's Republic of China
| | - Xiu-Xiu Hu
- Medical School, Southeast University, Nanjing, Jiangsu, People's Republic of China
| | - Shu-Kui Wang
- Central Laboratory, Nanjing First Hospital, Nanjing Medical University
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