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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: 13] [Impact Index Per Article: 6.5] [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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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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Hijazo-Pechero S, Alay A, Marín R, Vilariño N, Muñoz-Pinedo C, Villanueva A, Santamaría D, Nadal E, Solé X. Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer. Cancers (Basel) 2021; 13:4734. [PMID: 34638221 PMCID: PMC8507534 DOI: 10.3390/cancers13194734] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 09/13/2021] [Indexed: 01/20/2023] Open
Abstract
Recent technological advances and the application of high-throughput mutation and transcriptome analyses have improved our understanding of cancer diseases, including non-small cell lung cancer. For instance, genomic profiling has allowed the identification of mutational events which can be treated with specific agents. However, detection of DNA alterations does not fully recapitulate the complexity of the disease and it does not allow selection of patients that benefit from chemo- or immunotherapy. In this context, transcriptional profiling has emerged as a promising tool for patient stratification and treatment guidance. For instance, transcriptional profiling has proven to be especially useful in the context of acquired resistance to targeted therapies and patients lacking targetable genomic alterations. Moreover, the comprehensive characterization of the expression level of the different pathways and genes involved in tumor progression is likely to better predict clinical benefit from different treatments than single biomarkers such as PD-L1 or tumor mutational burden in the case of immunotherapy. However, intrinsic technical and analytical limitations have hindered the use of these expression signatures in the clinical setting. In this review, we will focus on the data reported on molecular classification of non-small cell lung cancer and discuss the potential of transcriptional profiling as a predictor of survival and as a patient stratification tool to further personalize treatments.
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Affiliation(s)
- Sara Hijazo-Pechero
- Unit of Bioinformatics for Precision Oncology, Catalan Institute of Oncology (ICO), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (S.H.-P.); (A.A.); (R.M.)
- Preclinical and Experimental Research in Thoracic Tumors (PrETT), Molecular Mechanisms and Experimental Therapy in Oncology Program (Oncobell), Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (N.V.); (C.M.-P.)
| | - Ania Alay
- Unit of Bioinformatics for Precision Oncology, Catalan Institute of Oncology (ICO), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (S.H.-P.); (A.A.); (R.M.)
- Preclinical and Experimental Research in Thoracic Tumors (PrETT), Molecular Mechanisms and Experimental Therapy in Oncology Program (Oncobell), Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (N.V.); (C.M.-P.)
| | - Raúl Marín
- Unit of Bioinformatics for Precision Oncology, Catalan Institute of Oncology (ICO), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (S.H.-P.); (A.A.); (R.M.)
- Preclinical and Experimental Research in Thoracic Tumors (PrETT), Molecular Mechanisms and Experimental Therapy in Oncology Program (Oncobell), Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (N.V.); (C.M.-P.)
| | - Noelia Vilariño
- Preclinical and Experimental Research in Thoracic Tumors (PrETT), Molecular Mechanisms and Experimental Therapy in Oncology Program (Oncobell), Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (N.V.); (C.M.-P.)
- Thoracic Oncology Unit, Department of Medical Oncology, Catalan Institute of Oncology (ICO), L’Hospitalet de Llobregat, 08908 Barcelona, Spain
- Neuro-Oncology Unit, Hospital Universitari de Bellvitge-ICO L’Hospitalet (IDIBELL), 08908 Barcelona, Spain
| | - Cristina Muñoz-Pinedo
- Preclinical and Experimental Research in Thoracic Tumors (PrETT), Molecular Mechanisms and Experimental Therapy in Oncology Program (Oncobell), Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (N.V.); (C.M.-P.)
| | - Alberto Villanueva
- Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain;
| | - David Santamaría
- INSERM U1218, ACTION Laboratory, Institut Européen de Chimie et Biologie (IECB), Université de Bordeaux, F-33607 Pessac, France;
| | - Ernest Nadal
- Preclinical and Experimental Research in Thoracic Tumors (PrETT), Molecular Mechanisms and Experimental Therapy in Oncology Program (Oncobell), Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (N.V.); (C.M.-P.)
- Thoracic Oncology Unit, Department of Medical Oncology, Catalan Institute of Oncology (ICO), L’Hospitalet de Llobregat, 08908 Barcelona, Spain
| | - Xavier Solé
- Unit of Bioinformatics for Precision Oncology, Catalan Institute of Oncology (ICO), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (S.H.-P.); (A.A.); (R.M.)
- Preclinical and Experimental Research in Thoracic Tumors (PrETT), Molecular Mechanisms and Experimental Therapy in Oncology Program (Oncobell), Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (N.V.); (C.M.-P.)
- CIBER (Consorcio de Investigación Biomédica en Red) Epidemiologia y Salud Pública (CIBERESP), 28029 Madrid, Spain
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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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5
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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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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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Ruiz-Cordero R, Ma J, Khanna A, Lyons G, Rinsurongkawong W, Bassett R, Guo M, Routbort MJ, Zhang J, Skoulidis F, Heymach J, Roarty EB, Tang Z, Medeiros LJ, Patel KP, Luthra R, Roy-Chowdhuri S. Simplified molecular classification of lung adenocarcinomas based on EGFR, KRAS, and TP53 mutations. BMC Cancer 2020; 20:83. [PMID: 32005111 PMCID: PMC6995064 DOI: 10.1186/s12885-020-6579-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 01/24/2020] [Indexed: 01/25/2023] Open
Abstract
Background Gene expression profiling has consistently identified three molecular subtypes of lung adenocarcinoma that have prognostic implications. To facilitate stratification of patients with this disease into similar molecular subtypes, we developed and validated a simple, mutually exclusive classification. Methods Mutational status of EGFR, KRAS, and TP53 was used to define seven mutually exclusive molecular subtypes. A development cohort of 283 cytology specimens of lung adenocarcinoma was used to evaluate the associations between the proposed classification and clinicopathologic variables including demographic characteristics, smoking history, fluorescence in situ hybridization and molecular results. For validation and prognostic assessment, 63 of the 283 cytology specimens with available survival data were combined with a separate cohort of 428 surgical pathology specimens of lung adenocarcinoma. Results The proposed classification yielded significant associations between these molecular subtypes and clinical and prognostic features. We found better overall survival in patients who underwent surgery and had tumors enriched for EGFR mutations. Worse overall survival was associated with older age, stage IV disease, and tumors with co-mutations in KRAS and TP53. Interestingly, neither chemotherapy nor radiation therapy showed benefit to overall survival. Conclusions The mutational status of EGFR, KRAS, and TP53 can be used to easily classify lung adenocarcinoma patients into seven subtypes that show a relationship with prognosis, especially in patients who underwent surgery, and these subtypes are similar to classifications based on more complex genomic methods reported previously.
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Affiliation(s)
- Roberto Ruiz-Cordero
- Department of Pathology, University of California San Francisco, 1825 4th Street, Room L2181A, San Francisco, CA, 94158, USA.
| | - Junsheng Ma
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Abha Khanna
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Genevieve Lyons
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Waree Rinsurongkawong
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Roland Bassett
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ming Guo
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mark J Routbort
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jianjun Zhang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ferdinandos Skoulidis
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John Heymach
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Emily B Roarty
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zhenya Tang
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - L Jeffrey Medeiros
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Keyur P Patel
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rajyalakshmi Luthra
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sinchita Roy-Chowdhuri
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Martin F, Talikka M, Ivanov NV, Haziza C, Hoeng J, Peitsch MC. A Meta-Analysis of the Performance of a Blood-Based Exposure Response Gene Signature Across Clinical Studies on the Tobacco Heating System 2.2 (THS 2.2). Front Pharmacol 2019; 10:198. [PMID: 30971916 PMCID: PMC6444181 DOI: 10.3389/fphar.2019.00198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 02/18/2019] [Indexed: 11/28/2022] Open
Abstract
As part of emerging tobacco harm reduction strategies, modified risk tobacco products (MRTP) are being developed to offer alternatives that have the potential to reduce the individual risk and population harm compared with smoking cigarettes for adult smokers who want to continue using tobacco and nicotine products. MRTPs are defined as any tobacco products that are distributed for use to reduce harm or the risk of tobacco-related disease associated with commercially marketed tobacco products. One such candidate MRTP is the Tobacco Heating System (THS) 2.2, which does not burn tobacco but instead heats it, thus producing significantly reduced levels of harmful and potentially harmful constituents compared with cigarettes. The clinical assessment of candidate MRTPs requires the development of exposure-response markers to distinguish current smokers from either nonsmokers or former smokers with high specificity and sensitivity. Toward this end, a whole blood-derived gene signature was previously developed and reported. Four randomized, controlled, open-label, three-arm parallel group reduced exposure clinical studies have been conducted with subjects randomized to three arms: switching from cigarettes to THS 2.2, continuous use of cigarettes, or smoking abstinence. These clinical studies had an investigational period of 5 days in confinement, which was followed by an 85-day ambulatory period in two studies. Here we tested the previously developed blood-derived signature on the samples derived from those clinical studies. We showed that in all four studies, the signature scores were reduced consistently in subjects who either stopped smoking or switched to THS 2.2 compared with subjects who continued smoking cigarettes.
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Affiliation(s)
- Florian Martin
- Philip Morris International Research and Development, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Marja Talikka
- Philip Morris International Research and Development, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Nikolai V Ivanov
- Philip Morris International Research and Development, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Christelle Haziza
- Philip Morris International Research and Development, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Julia Hoeng
- Philip Morris International Research and Development, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Manuel C Peitsch
- Philip Morris International Research and Development, Philip Morris Products S.A., Neuchâtel, Switzerland
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Wang H, Shen L, Geng J, Wu Y, Xiao H, Zhang F, Si H. Prognostic value of cancer antigen -125 for lung adenocarcinoma patients with brain metastasis: A random survival forest prognostic model. Sci Rep 2018; 8:5670. [PMID: 29618796 PMCID: PMC5884842 DOI: 10.1038/s41598-018-23946-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 03/20/2018] [Indexed: 01/09/2023] Open
Abstract
Using random survival forest, this study was intended to evaluate the prognostic value of serum markers for lung adenocarcinoma patients with brain metastasis (BM), and tried to integrate them into a prognostic model. During 2010 to 2015, the patients were retrieved from two medical centers. Besides the Cox proportional hazards regression, the random survival forest (RSF) were also used to develop prognostic model from the group A (n = 142). In RSF of the group A, the factors, whose minimal depth were greater than the depth threshold or had a negative variable importance (VIMP), were firstly excluded. Subsequently, C-index and Akaike information criterion (AIC) were used to guide us finding models with higher prognostic ability and lower overfitting possibility. These RSF models, together with the Cox, modified-RPA and lung-GPA index were validated and compared, especially in the group B (CAMS, n = 53). Our data indicated that the KSE125 model (KPS, smoking, EGFR-20 (exon 18, 19 and 21) and Ca125) was the best in survival prediction, and performed well in internal and external validation. In conclusions, for lung adenocarcinoma patients with brain metastasis, a validated prognostic nomogram (KPS, smoking, EGFR-20 and Ca125) can more accurately predict 1-year and 2-year survival of the patients.
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Affiliation(s)
- Hao Wang
- Department of Radiotherapy, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, 230022, China
| | - Liuhai Shen
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, 230022, China
| | - Jianhua Geng
- Department of Nuclear Medicine, National Cancer Center/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yitian Wu
- Department of Nuclear Medicine, National Cancer Center/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Huan Xiao
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, 230022, China
| | - Fan Zhang
- Department of Radiotherapy, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, 230022, China
| | - Hongwei Si
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, 230022, China.
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Karlsson A, Brunnström H, Micke P, Veerla S, Mattsson J, La Fleur L, Botling J, Jönsson M, Reuterswärd C, Planck M, Staaf J. Gene Expression Profiling of Large Cell Lung Cancer Links Transcriptional Phenotypes to the New Histological WHO 2015 Classification. J Thorac Oncol 2017; 12:1257-1267. [PMID: 28535939 DOI: 10.1016/j.jtho.2017.05.008] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 04/26/2017] [Accepted: 05/12/2017] [Indexed: 12/25/2022]
Abstract
INTRODUCTION Large cell lung cancer (LCLC) and large cell neuroendocrine carcinoma (LCNEC) constitute a small proportion of NSCLC. The WHO 2015 classification guidelines changed the definition of the debated histological subtype LCLC to be based on immunomarkers for adenocarcinoma and squamous cancer. We sought to determine whether these new guidelines also translate into the transcriptional landscape of lung cancer, and LCLC specifically. METHODS Gene expression profiling was performed by using Illumina V4 HT12 microarrays (Illumina, San Diego, CA) on samples from 159 cases (comprising all histological subtypes, including 10 classified as LCLC WHO 2015 and 14 classified as LCNEC according to the WHO 2015 guidelines), with complimentary mutational and immunohistochemical data. Derived transcriptional phenotypes were validated in 199 independent tumors, including six WHO 2015 LCLCs and five LCNECs. RESULTS Unsupervised analysis of gene expression data identified a phenotype comprising 90% of WHO 2015 LCLC tumors, with characteristics of poorly differentiated proliferative cancer, a 90% tumor protein p53 gene (TP53) mutation rate, and lack of well-known NSCLC oncogene driver alterations. Validation in independent data confirmed aggregation of WHO 2015 LCLCs in the specific phenotype. For LCNEC tumors, the unsupervised gene expression analysis suggested two different transcriptional patterns corresponding to a proposed genetic division of LCNEC tumors into SCLC-like and NSCLC-like cancer on the basis of TP53 and retinoblastoma 1 gene (RB1) alteration patterns. CONCLUSIONS Refined classification of LCLC has implications for diagnosis, prognostics, and therapy decisions. Our molecular analyses support the WHO 2015 classification of LCLC and LCNEC tumors, which herein follow different tumorigenic paths and can accordingly be stratified into different transcriptional subgroups, thus linking diagnostic immunohistochemical staining-driven classification with the transcriptional landscape of lung cancer.
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Affiliation(s)
- Anna Karlsson
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Hans Brunnström
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden; Department of Pathology, Regional Laboratories Region Skåne, Lund, Sweden
| | - Patrick Micke
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Srinivas Veerla
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Johanna Mattsson
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Linnea La Fleur
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Johan Botling
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Mats Jönsson
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Christel Reuterswärd
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Maria Planck
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, 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, Lund, Sweden.
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