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Hillyer GC, Milano N, Bulman WA. Pulmonary nodules and the psychological harm they can cause: A scoping review. Respir Med Res 2024; 86:101121. [PMID: 38964266 DOI: 10.1016/j.resmer.2024.101121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 05/21/2024] [Accepted: 06/13/2024] [Indexed: 07/06/2024]
Abstract
More than 1.6 million pulmonary nodules are diagnosed in the United States each year. Although the majority of nodules are found to be benign, nodule detection and the process of ruling out malignancy can cause patients psychological harm to varying degrees. The present study undertakes a scoping review of the literature investigating pulmonary nodule-related psychological harm as a primary or secondary outcome. Online databases were systematically searched to identify papers published through June 30, 2023, from which 19 publications were reviewed. We examined prevalence by type, measurement, associated factors, and behavioral or clinical consequences. Of the 19 studies reviewed, 11 studies investigated distress, anxiety (n = 6), and anxiety and depression (n = 4). Prevalence of distress was 24.0 %-56.7 %; anxiety 9.9 %-42.1 %, and 14.6 %-27.0 % for depression. A wide range of demographic and social characteristics and clinical factors were associated with nodule-related psychological harm. Outcomes of nodule-related harms included experiencing conflict when deciding about treatment or surveillance, decreased adherence to surveillance, adoption of more aggressive treatment, and lower health-related quality of life. Our scoping review demonstrates that nodule-related psychological harm is common. Findings provide evidence that nodule-related psychological harm can influence clinical decisions and adherence to treatment recommendations. Future research should focus on discerning between nodule-related distress and anxiety; identifying patients at risk; ascertaining the extent of psychological harm on patient behavior and clinical decisions; and developing interventions to assist patients in managing psychological harm for better health-related quality of life and treatment outcomes.
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Affiliation(s)
- Grace C Hillyer
- Mailman School of Public Health at Columbia University, New York, NY, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY USA.
| | - Nicole Milano
- School of Social Work, Rutgers University, New Brunswick, NJ, USA
| | - William A Bulman
- Veracyte Inc., South San Francisco, CA, USA; Columbia University Irving Medical Center, New York, NY, USA
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2
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Lamb CR, Rieger-Christ KM, Reddy C, Huang J, Ding J, Johnson M, Walsh PS, Bulman WA, Lofaro LR, Wahidi MM, Feller-Kopman DJ, Spira A, Kennedy GC, Mazzone PJ. A Nasal Swab Classifier to Evaluate the Probability of Lung Cancer in Patients With Pulmonary Nodules. Chest 2024; 165:1009-1019. [PMID: 38030063 DOI: 10.1016/j.chest.2023.11.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/12/2023] [Accepted: 11/14/2023] [Indexed: 12/01/2023] Open
Abstract
BACKGROUND Accurate assessment of the probability of lung cancer (pCA) is critical in patients with pulmonary nodules (PNs) to help guide decision-making. We sought to validate a clinical-genomic classifier developed using whole-transcriptome sequencing of nasal epithelial cells from patients with a PN ≤ 30 mm who smoke or have previously smoked. RESEARCH QUESTION Can the pCA in individuals with a PN and a history of smoking be predicted by a classifier that uses clinical factors and genomic data from nasal epithelial cells obtained by cytologic brushing? STUDY DESIGN AND METHODS Machine learning was used to train a classifier using genomic and clinical features on 1,120 patients with PNs labeled as benign or malignant established by a final diagnosis or a minimum of 12 months of radiographic surveillance. The classifier was designed to yield low-, intermediate-, and high-risk categories. The classifier was validated in an independent set of 312 patients, including 63 patients with a prior history of cancer (other than lung cancer), comparing the classifier prediction with the known clinical outcome. RESULTS In the primary validation set, sensitivity and specificity for low-risk classification were 96% and 42%, whereas sensitivity and specificity for high-risk classification was 58% and 90%, respectively. Sensitivity was similar across stages of non-small cell lung cancer, independent of subtype. Performance compared favorably with clinical-only risk models. Analysis of 63 patients with prior cancer showed similar performance as did subanalyses of patients with light vs heavy smoking burden and those eligible for lung cancer screening vs those who were not. INTERPRETATION The nasal classifier provides an accurate assessment of pCA in individuals with a PN ≤ 30 mm who smoke or have previously smoked. Classifier-guided decision-making could lead to fewer diagnostic procedures in patients without cancer and more timely treatment in patients with lung cancer.
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Affiliation(s)
- Carla R Lamb
- Department of Pulmonary and Critical Care Medicine, Lahey Hospital and Medical Center, Burlington, MA.
| | - Kimberly M Rieger-Christ
- Department of Pulmonary and Critical Care Medicine, Lahey Hospital and Medical Center, Burlington, MA
| | - Chakravarthy Reddy
- Division of Respiratory, Critical Care, and Occupational Pulmonary Medicine, University of Utah Health Sciences Center, Salt Lake City, UT
| | | | - Jie Ding
- Veracyte, Inc, South San Francisco, CA
| | | | | | | | | | - Momen M Wahidi
- Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University Medical Center, Durham, NC
| | | | - Avrum Spira
- Department of Medicine, Boston University Medical Center, Boston, MA; Johnson & Johnson, Inc, Boston, MA
| | | | - Peter J Mazzone
- Department of Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, OH
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3
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Mohamed E, García Martínez DJ, Hosseini MS, Yoong SQ, Fletcher D, Hart S, Guinn BA. Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis. Carcinogenesis 2024; 45:1-22. [PMID: 38066655 DOI: 10.1093/carcin/bgad091] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/27/2023] [Accepted: 12/05/2023] [Indexed: 02/13/2024] Open
Abstract
Lung cancer (LC) causes few symptoms in the earliest stages, leading to one of the highest mortality rates among cancers. Low-dose computerised tomography (LDCT) is used to screen high-risk individuals, reducing the mortality rate by 20%. However, LDCT results in a high number of false positives and is associated with unnecessary follow-up and cost. Biomarkers with high sensitivities and specificities could assist in the early detection of LC, especially in patients with high-risk features. Carcinoembryonic antigen (CEA), cytokeratin 19 fragments and cancer antigen 125 have been found to be highly expressed during the later stages of LC but have low sensitivity in the earliest stages. We determined the best biomarkers for the early diagnosis of LC, using a systematic review of eight databases. We identified 98 articles that focussed on the identification and assessment of diagnostic biomarkers and achieved a pooled area under curve of 0.85 (95% CI 0.82-0.088), indicating that the diagnostic performance of these biomarkers when combined was excellent. Of the studies, 30 focussed on single/antigen panels, 22 on autoantibodies, 31 on miRNA and RNA panels, and 15 suggested the use of circulating DNA combined with CEA or neuron-specific enolase (NSE) for early LC detection. Verification of blood biomarkers with high sensitivities (Ciz1, exoGCC2, ITGA2B), high specificities (CYFR21-1, antiHE4, OPNV) or both (HSP90α, CEA) along with miR-15b and miR-27b/miR-21 from sputum may improve early LC detection. Further assessment is needed using appropriate sample sizes, control groups that include patients with non-malignant conditions, and standardised cut-off levels for each biomarker.
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Affiliation(s)
- Eithar Mohamed
- Centre for Biomedicine, Hull York Medical School, University of Hull, Kingston-upon-Hull, HU6 7RX, UK
| | - Daniel J García Martínez
- Department of Biotechnology, Pozuelo de Alarcón, University Francisco De Vitoria, Madrid, 28223, Spain
| | - Mohammad-Salar Hosseini
- Research Centre for Evidence-Based Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Si Qi Yoong
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Daniel Fletcher
- Centre for Biomedicine, Hull York Medical School, University of Hull, Kingston-upon-Hull, HU6 7RX, UK
| | - Simon Hart
- Respiratory Medicine, Hull York Medical School, University of Hull, Kingston-upon-Hull, HU6 7RX, UK
| | - Barbara-Ann Guinn
- Centre for Biomedicine, Hull York Medical School, University of Hull, Kingston-upon-Hull, HU6 7RX, UK
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Fawaz A, Ferraresi A, Isidoro C. Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making. J Pers Med 2023; 13:1590. [PMID: 38003905 PMCID: PMC10672164 DOI: 10.3390/jpm13111590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023] Open
Abstract
Cancer is the second major cause of disease-related death worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient's life. Cancer, as a complex and heterogeneous disorder, results from the disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, the histopathology of a biopsy, and imaging (MRI, CT, PET, and US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient's response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient's big data, and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical-clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research.
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Affiliation(s)
| | | | - Ciro Isidoro
- Laboratory of Molecular Pathology, Department of Health Sciences, Università del Piemonte Orientale, 28100 Novara, Italy; (A.F.); (A.F.)
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Parra-Medina R, Pablo Castañeda-González J, Montoya L, Paula Gómez-Gómez M, Clavijo Cabezas D, Plazas Vargas M. Prevalence of oncogenic driver mutations in Hispanics/Latin patients with lung cancer. A systematic review and meta-analysis. Lung Cancer 2023; 185:107378. [PMID: 37729688 DOI: 10.1016/j.lungcan.2023.107378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 09/22/2023]
Abstract
INTRODUCTION The frequency of actionable mutations varies between races, and Hispanic/Latino (H/L) people are a population with different proportions of ancestry. Our purpose was to establish prevalence of actionable mutations in the H/L population with NSCLC. METHODS EMBASE, LILACS, MEDLINE, and Virtual Health Library were searched for studies published up to April 2023 that evaluated the prevalence of ALK, BRAF, EGFR, HER-2, KRAS, MET, NTRK, RET, ROS1 in H/L patients. Meta-analyses were done to determine prevalence using a random effects model. RESULTS Fifty-five articles were included. EGFR and KRAS were the most prevalent genes with high heterogeneity across the countries. The overall mutation frequency for EGFR was 22%. The most frequent mutations in the EGFR gene were del19 (10%) and L858R (7%). The mean of KRAS mutation was a 14% prevalence. KRASG12C was the most frequent mutation with a 7% prevalence in an entire population. The overall frequency of ALK rearrangement was 5%. The mean frequency of ROS-1 rearrangement was 2%, and the frequencies of HER-2, MET, BRAF, RET, NTRK molecular alterations were 4%, 3%, 2%, 2%, and 1% respectively. Almost half of the cases were male, and 65.8% had a history of tobacco exposure. The most common clinical stage was IV. CONCLUSIONS The prevalence of driver mutations such as EGFR and KRAS in LA populations differs from what is reported in Asians and Europeans. In the present article, countries with a high proportion of Amerindian ancestry show a greater prevalence of EGFR in contrast to countries with a high proportion of Caucasians. Lack of information on some countries or studies with a small sample size affects the real prevalence data for the region.
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Affiliation(s)
- Rafael Parra-Medina
- Research Institute, Fundación Universitaria de Ciencias de la Salud - FUCS, Bogotá, Colombia; Department of Pathology, Fundación Universitaria de Ciencias de la Salud - FUCS, Bogotá, Colombia; Department of Pathology, Instituto Nacional de Cancerología, Bogotá, Colombia.
| | - Juan Pablo Castañeda-González
- Research Institute, Fundación Universitaria de Ciencias de la Salud - FUCS, Bogotá, Colombia; Department of Pathology, Fundación Universitaria de Ciencias de la Salud - FUCS, Bogotá, Colombia
| | - Luisa Montoya
- Department of Clinical Epidemiology and Biostatistics, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - María Paula Gómez-Gómez
- Department of Pathology, Fundación Universitaria de Ciencias de la Salud - FUCS, Bogotá, Colombia
| | - Daniel Clavijo Cabezas
- Department of Pathology, Fundación Universitaria de Ciencias de la Salud - FUCS, Bogotá, Colombia
| | - Merideidy Plazas Vargas
- Department of Epidemiology, Fundación Universitaria de Ciencias de la Salud - FUCS, Bogotá, Colombia
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Memarzia A, Saadat S, Asgharzadeh F, Behrouz S, Folkerts G, Boskabady MH. Therapeutic effects of medicinal plants and their constituents on lung cancer, in vitro, in vivo and clinical evidence. J Cell Mol Med 2023; 27:2841-2863. [PMID: 37697969 PMCID: PMC10538270 DOI: 10.1111/jcmm.17936] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 08/07/2023] [Accepted: 08/18/2023] [Indexed: 09/13/2023] Open
Abstract
The most common type of cancer in the world is lung cancer. Traditional treatments have an important role in cancer therapy. In the present review, the most recent findings on the effects of medicinal plants and their constituents or natural products (NP) in treating lung cancer are discussed. Empirical studies until the end of March 2022 were searched using the appropriate keywords through the databases PubMed, Science Direct and Scopus. The extracts and essential oils tested were all shown to effect lung cancer by several mechanisms including decreased tumour weight and volume, cell viability and modulation of cytokine. Some plant constituents increased expression of apoptotic proteins, the proportion of cells in the G2/M phase and subG0/G1 phase, and Cyt c levels. Also, natural products (NP) activate apoptotic pathways in lung cancer cell including p-JNK, Akt/mTOR, PI3/ AKT\ and Bax, Bcl2, but suppressed AXL phosphorylation. Plant-derived substances altered the cell morphology, reduced cell migration and metastasis, oxidative marker production, p-eIF2α and GRP78, IgG, IgM levels and reduced leukocyte counts, LDH, GGT, 5'NT and carcinoembryonic antigen (CEA). Therefore, medicinal plant extracts and their constituents could have promising therapeutic value for lung cancer, especially if used in combination with ordinary anti-cancer drugs.
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Affiliation(s)
- Arghavan Memarzia
- Applied Biomedical Research CenterMashhad University of Medical SciencesMashhadIran
- Department of Physiology, Faculty of MedicineMashhad University of Medical SciencesMashhadIran
| | - Saeideh Saadat
- Applied Biomedical Research CenterMashhad University of Medical SciencesMashhadIran
- Department of Physiology, School of MedicineZahedan University of Medical SciencesZahedanIran
| | - Fereshteh Asgharzadeh
- Department of Physiology, Faculty of MedicineMashhad University of Medical SciencesMashhadIran
| | - Sepide Behrouz
- Department of Animal Science, Faculty of AgricultureUniversity of BirjandBirjandIran
| | - Gert Folkerts
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Faculty of ScienceUtrecht UniversityUtrechtNetherlands
| | - Mohammad Hossein Boskabady
- Applied Biomedical Research CenterMashhad University of Medical SciencesMashhadIran
- Department of Physiology, Faculty of MedicineMashhad University of Medical SciencesMashhadIran
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Paez R, Kammer MN, Tanner NT, Shojaee S, Heideman BE, Peikert T, Balbach ML, Iams WT, Ning B, Lenburg ME, Mallow C, Yarmus L, Fong KM, Deppen S, Grogan EL, Maldonado F. Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules. Chest 2023; 164:1028-1041. [PMID: 37244587 PMCID: PMC10645597 DOI: 10.1016/j.chest.2023.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/16/2023] [Accepted: 05/17/2023] [Indexed: 05/29/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths. Early detection and diagnosis are critical, as survival decreases with advanced stages. Approximately 1.6 million nodules are incidentally detected every year on chest CT scan images in the United States. This number of nodules identified is likely much larger after accounting for screening-detected nodules. Most of these nodules, whether incidentally or screening detected, are benign. Despite this, many patients undergo unnecessary invasive procedures to rule out cancer because our current stratification approaches are suboptimal, particularly for intermediate probability nodules. Thus, noninvasive strategies are urgently needed. Biomarkers have been developed to assist through the continuum of lung cancer care and include blood protein-based biomarkers, liquid biopsies, quantitative imaging analysis (radiomics), exhaled volatile organic compounds, and bronchial or nasal epithelium genomic classifiers, among others. Although many biomarkers have been developed, few have been integrated into clinical practice as they lack clinical utility studies showing improved patient-centered outcomes. Rapid technologic advances and large network collaborative efforts will continue to drive the discovery and validation of many novel biomarkers. Ultimately, however, randomized clinical utility studies showing improved patient outcomes will be required to bring biomarkers into clinical practice.
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Affiliation(s)
- Rafael Paez
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Michael N Kammer
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Nicole T Tanner
- Department of Medicine, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Medical University of South Carolina, Charleston, SC
| | - Samira Shojaee
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Brent E Heideman
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Tobias Peikert
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
| | - Meridith L Balbach
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Wade T Iams
- Department of Medicine, Division of Hematology-Oncology, Vanderbilt University Medical Center, Nashville, TN; Vanderbilt-Ingram Cancer Center, Nashville, TN
| | - Boting Ning
- Department of Medicine, Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA
| | - Marc E Lenburg
- Department of Medicine, Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA
| | - Christopher Mallow
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Miami, Miami, FL
| | - Lonny Yarmus
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD
| | - Kwun M Fong
- University of Queensland Thoracic Research Centre, The Prince Charles Hospital, Brisbane, QLD, Australia
| | - Stephen Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN; Vanderbilt-Ingram Cancer Center, Nashville, TN; Tennessee Valley Healthcare System, Nashville, TN
| | - Eric L Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN; Vanderbilt-Ingram Cancer Center, Nashville, TN; Tennessee Valley Healthcare System, Nashville, TN
| | - Fabien Maldonado
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN.
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Marmor HN, Kammer MN, Deppen SA, Shipe M, Welty VF, Patel K, Godfrey C, Billatos E, Herman JG, Wilson DO, Kussrow AK, Bornhop DJ, Maldonado F, Chen H, Grogan EL. Improving lung cancer diagnosis with cancer, fungal, and imaging biomarkers. J Thorac Cardiovasc Surg 2023; 166:669-678.e4. [PMID: 36792410 PMCID: PMC10287834 DOI: 10.1016/j.jtcvs.2022.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Indeterminate pulmonary nodules (IPNs) represent a significant diagnostic burden in health care. We aimed to compare a combination clinical prediction model (Mayo Clinic model), fungal (histoplasmosis serology), imaging (computed tomography [CT] radiomics), and cancer (high-sensitivity cytokeratin fraction 21; hsCYFRA 21-1) biomarker approach to a validated prediction model in diagnosing lung cancer. METHODS A prospective specimen collection, retrospective blinded evaluation study was performed in 3 independent cohorts with 6- to 30-mm IPNs (n = 281). Serum histoplasmosis immunoglobulin G and immunoglobulin M antibodies and hsCYFRA 21-1 levels were measured and a validated CT radiomic score was calculated. Multivariable logistic regression models were estimated with Mayo Clinic model variables, histoplasmosis antibody levels, CT radiomic score, and hsCYFRA 21-1. Diagnostic performance of the combination model was compared with that of the Mayo Clinic model. Bias-corrected clinical net reclassification index (cNRI) was used to estimate the clinical utility of a combination biomarker approach. RESULTS A total of 281 patients were included (111 from a histoplasmosis-endemic region). The combination biomarker model including the Mayo Clinic model score, histoplasmosis antibody levels, radiomics, and hsCYFRA 21-1 level showed improved diagnostic accuracy for IPNs compared with the Mayo Clinic model alone with an area under the receiver operating characteristics curve of 0.80 (95% CI, 0.76-0.84) versus 0.72 (95% CI, 0.66-0.78). Use of this combination model correctly reclassified intermediate risk IPNs into low- or high-risk category (cNRI benign = 0.11 and cNRI malignant = 0.16). CONCLUSIONS The addition of cancer, fungal, and imaging biomarkers improves the diagnostic accuracy for IPNs. Integrating a combination biomarker approach into the diagnostic algorithm of IPNs might decrease unnecessary invasive testing of benign nodules and reduce time to diagnosis for cancer.
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Affiliation(s)
- Hannah N Marmor
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tenn
| | - Michael N Kammer
- Department of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tenn
| | - Stephen A Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tenn; Section of Thoracic Surgery, Tennessee Valley VA Healthcare System, Nashville, Tenn.
| | - Maren Shipe
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tenn
| | - Valerie F Welty
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tenn
| | - Khushbu Patel
- Department of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tenn
| | - Caroline Godfrey
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tenn
| | - Ehab Billatos
- Section of Pulmonary and Critical Care Medicine, Boston Medical Center, Boston, Mass
| | - James G Herman
- Division of Hematology/Oncology, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - David O Wilson
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | | | | | - Fabien Maldonado
- Department of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tenn
| | - Heidi Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tenn
| | - Eric L Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tenn; Section of Thoracic Surgery, Tennessee Valley VA Healthcare System, Nashville, Tenn
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9
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Kheir F, Uribe JP, Cedeno J, Munera G, Patel H, Abdelghani R, Matta A, Benzaquen S, Villalobos R, Majid A. Impact of an integrated classifier using biomarkers, clinical and imaging factors on clinical decisions making for lung nodules. J Thorac Dis 2023; 15:3557-3567. [PMID: 37559655 PMCID: PMC10407524 DOI: 10.21037/jtd-23-42] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/26/2023] [Indexed: 08/11/2023]
Abstract
BACKGROUND An integrated classifier that utilizes plasma proteomic biomarker along with five clinical and imaging factors was previously shown to be potentially useful in lung nodule evaluation. This study evaluated the impact of the integrated proteomic classifier on management decisions in patients with a pretest probability of cancer (pCA) ≤50% in "real-world" clinical setting. METHODS Retrospective study examining patients with lung nodules who were evaluated using the integrated classifier as compared to standard clinical care during the same period, with at least 1-year follow-up. RESULTS A total of 995 patients were evaluated for lung nodules over 1 year following the implementation of the integrated classifier with 17.3% prevalence of lung cancer. 231 patients met the study eligibility criteria; 102 (44.2%) were tested with the integrated classifier, while 129 (55.8%) did not. The median number of chest imaging studies was 2 [interquartile range (IQR), 1-2] in the integrated classifier arm and 2 [IQR, 1-3] in the non-integrated classifier arm (P=0.09). The median outpatient clinic visit was 2.00 (IQR, 1.00-3.00) in the integrated classifier arm and 2.00 (IQR, 2.00-3.00) in the non-integrated classifier (P=0.004). Fewer invasive procedures were pursued in the integrated classifier arm as compared to non-integrated classifier respectively (26.5% vs. 79.1%, P<0.001). All patients in the integrated classifier arm with post-pCA (likely benign n=39) had designated benign diagnosis at 1-year follow-up. CONCLUSIONS In patients with lung nodules with a pCA ≤50%, use of the integrated classifier was associated with fewer invasive procedures and clinic visits without misclassifying patients with likely benign lung nodules results at 1-year follow-up.
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Affiliation(s)
- Fayez Kheir
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Juan P. Uribe
- Division of Thoracic Surgery and Interventional Pulmonology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Juan Cedeno
- Division of Thoracic Surgery and Interventional Pulmonology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Gustavo Munera
- Division of Thoracic Surgery and Interventional Pulmonology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Harsh Patel
- Division of Pulmonary Diseases, Critical Care and Environmental Medicine, Tulane University Health Sciences Center, New Orleans, LA, USA
| | - Ramsy Abdelghani
- Division of Pulmonary Diseases, Critical Care and Environmental Medicine, Tulane University Health Sciences Center, New Orleans, LA, USA
| | - Atul Matta
- Division of Pulmonary Critical Care and Sleep Medicine, University of Texas Medical Branch, Galveston, TX, USA
| | - Sadia Benzaquen
- Division of Pulmonary Critical Care and Sleep Medicine, Albert Einstein Medical Center, Philadelphia, PA, USA
| | - Regina Villalobos
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Adnan Majid
- Division of Thoracic Surgery and Interventional Pulmonology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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Pritchett MA, Sigal B, Bowling MR, Kurman JS, Pitcher T, Springmeyer SC. Assessing a biomarker's ability to reduce invasive procedures in patients with benign lung nodules: Results from the ORACLE study. PLoS One 2023; 18:e0287409. [PMID: 37432960 DOI: 10.1371/journal.pone.0287409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 06/05/2023] [Indexed: 07/13/2023] Open
Abstract
A blood-based integrated classifier (IC) has been clinically validated to improve accuracy in assessing probability of cancer risk (pCA) for pulmonary nodules (PN). This study evaluated the clinical utility of this biomarker for its ability to reduce invasive procedures in patients with pre-test pCA ≤ 50%. This was a propensity score matching (PSM) cohort study comparing patients in the ORACLE prospective, multicenter, observational registry to control patients treated with usual care. This study enrolled patients meeting the intended use criteria for IC testing: pCA ≤ 50%, age ≥40 years, nodule diameter 8-30 mm, and no history of lung cancer and/or active cancer (except for non-melanomatous skin cancer) within 5 years. The primary aim of this study was to evaluate invasive procedure use on benign PNs of registry patients as compared to control patients. A total of 280 IC tested, and 278 control patients met eligibility and analysis criteria and 197 were in each group after PSM (IC and control groups). Patients in the IC group were 74% less likely to undergo an invasive procedure as compared to the control group (absolute difference 14%, p <0.001) indicating that for every 7 patients tested, one unnecessary invasive procedure was avoided. Invasive procedure reduction corresponded to a reduction in risk classification, with 71 patients (36%) in the IC group classified as low risk (pCA < 5%). The proportion of IC group patients with malignant PNs sent to surveillance were not statistically different than the control group, 7.5% vs 3.5% for the IC vs. control groups, respectively (absolute difference 3.91%, p 0.075). The IC for patients with a newly discovered PN has demonstrated valuable clinical utility in a real-world setting. Use of this biomarker can change physicians' practice and reduce invasive procedures in patients with benign pulmonary nodules. Trial registration: Clinical trial registration: ClinicalTrials.gov NCT03766958.
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Affiliation(s)
- Michael A Pritchett
- Department of Pulmonary Medicine, FirstHealth of the Carolinas & Pinehurst Medical Clinic, Pinehurst, North Carolina, United States of America
| | - Barry Sigal
- Southeastern Research Center, Winston-Salem, North Carolina, United States of America
| | - Mark R Bowling
- Division of Pulmonary, Critical Care, and Sleep Medicine, Brody School of Medicine, Eastern Carolina University, Greenville, North Carolina, United States of America
| | - Jonathan S Kurman
- Division of Critical Care Medicine, Interventional Pulmonology, Pulmonary Disease, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Trevor Pitcher
- Medical Affairs, Biodesix, Inc., Boulder, Colorado, United States of America
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11
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Mastutik G, Rahniayu A, Marhana IA, Kurniasari N, Rahaju AS, Amin M, Trianto HF, Atika. The MAGE A1-A10 Expression associated with Histopathological Findings of Malignant or Non-Malignant Cells in Peripheral Lung Tumors. Asian Pac J Cancer Prev 2023; 24:2329-2335. [PMID: 37505763 PMCID: PMC10676496 DOI: 10.31557/apjcp.2023.24.7.2329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 07/03/2023] [Indexed: 07/29/2023] Open
Abstract
OBJECTIVE The objective was to evaluate the expression of melanoma antigen (MAGE) A from A1 to 10 (A1-10) and the individual MAGE A family in the peripheral lung tumors and to analyze its association with histopathological findings. METHODS A cross-sectional study was conducted on 67 samples of peripheral lung tumor obtained by core biopsies from patients with clinical diagnoses such as lung and mediastinal tumors. The specimens were divided into two, one to perform histopathological diagnosis and the last for mRNA MAGE A examination. A Nested polymerase chain reaction (PCR) was performed using universal primer, MF10/MR10 and MF10/MR12. The collected data were analyzed by appropriate statistical techniques. RESULT The histopathological finding showed 41 (61.2 %) of specimens as malignant cells and 26 (38.8 %) of specimens as non-malignant cells. MAGE A1-10 was expressed at 47 (70.1 %) and MAGE A1-6 was expressed at 25 (37.3 %) of specimens. In a malignant cell, MAGE A1-10 and MAGE A1-6 were expressed at 33 (80.5 %) and 19 (46.3 %), respectively. In non-malignant cells, MAGE A1-10 and MAGE A1-6 were expressed at 14 (53.9 %) and 6 (23.1 %,) respectively. The MAGE A1-10 and MAGE A8 expressions were significantly associated with histopathological findings of malignant or non-malignant cells. The sensitivity, specificity, and diagnostic accuracy of MAGE A1-10 were 80.5 %, 46.2 %, and 67.2 %, respectively; while for MAGE A8 were 41.5 %, 88.5 %, and 59.7 %, respectively. CONCLUSION The MAGE A1-10 expression was the most commonly detected and associated with the histopathological finding. Moreover, it was more sensitive and specific and had higher diagnostic accuracy than others. Therefore, the MAGE A1-10 assay may improve the accuracy of the diagnosis of malignancy in peripheral lung tumors.
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Affiliation(s)
- Gondo Mastutik
- Department of Anatomic Pathology, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia.
| | - Alphania Rahniayu
- Department of Anatomic Pathology, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia.
| | - Isnin Anang Marhana
- Department of Pulmonology, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia.
| | - Nila Kurniasari
- Department of Anatomic Pathology, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia.
| | - Anny Setijo Rahaju
- Department of Anatomic Pathology, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia.
| | - Mochamad Amin
- Institute of Tropical Disease, Universitas Airlangga, Surabaya, Indonesia.
| | - Heru Fajar Trianto
- Department of Anatomic Pathology, Faculty of Medicine, Universitas Tanjungpura, Pontianak, Indonesia.
| | - Atika
- Department of Public Health Sciences-Preventive Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia.
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12
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Wang Z, Xie K, Zhu G, Ma C, Cheng C, Li Y, Xiao X, Li C, Tang J, Wang H, Su Z, Liu D, Zhang W, Huang Y, Tang H, Liu R, Li W. Early detection and stratification of lung cancer aided by a cost-effective assay targeting circulating tumor DNA (ctDNA) methylation. Respir Res 2023; 24:163. [PMID: 37330511 DOI: 10.1186/s12931-023-02449-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 05/12/2023] [Indexed: 06/19/2023] Open
Abstract
BACKGROUND Detection of lung cancer at earlier stage can greatly improve patient survival. We aim to develop, validate, and implement a cost-effective ctDNA-methylation-based plasma test to aid lung cancer early detection. METHODS Case-control studies were designed to select the most relevant markers to lung cancer. Patients with lung cancer or benign lung disease and healthy individuals were recruited from different clinical centers. A multi-locus qPCR assay, LunaCAM, was developed for lung cancer alertness by ctDNA methylation. Two LunaCAM models were built for screening (-S) or diagnostic aid (-D) to favor sensitivity or specificity, respectively. The performance of the models was validated for different intended uses in clinics. RESULTS Profiling DNA methylation on 429 plasma samples including 209 lung cancer, 123 benign diseases and 97 healthy participants identified the top markers that detected lung cancer from benign diseases and healthy with an AUC of 0.85 and 0.95, respectively. The most effective methylation markers were verified individually in 40 tissues and 169 plasma samples to develop LunaCAM assay. Two models corresponding to different intended uses were trained with 513 plasma samples, and validated with an independent collection of 172 plasma samples. In validation, LunaCAM-S model achieved an AUC of 0.90 (95% CI: 0.88-0.94) between lung cancer and healthy individuals, whereas LunaCAM-D model stratified lung cancer from benign pulmonary diseases with an AUC of 0.81 (95% CI: 0.78-0.86). When implemented sequentially in the validation set, LunaCAM-S enables to identify 58 patients of lung cancer (90.6% sensitivity), followed by LunaCAM-D to remove 20 patients with no evidence of cancer (83.3% specificity). LunaCAM-D significantly outperformed the blood test of carcinoembryonic antigen (CEA), and the combined model can further improve the predictive power for lung cancer to an overall AUC of 0.86. CONCLUSIONS We developed two different models by ctDNA methylation assay to sensitively detect early-stage lung cancer or specifically classify lung benign diseases. Implemented at different clinical settings, LunaCAM models has a potential to provide a facile and inexpensive avenue for early screening and diagnostic aids for lung cancer.
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Affiliation(s)
- Zhoufeng Wang
- Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Kehui Xie
- Singlera Genomics (Shanghai) Ltd, Shanghai, China
| | - Guonian Zhu
- Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | | | - Cheng Cheng
- Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yangqian Li
- Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xue Xiao
- Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chengpin Li
- Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jun Tang
- Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hui Wang
- Singlera Genomics (Shanghai) Ltd, Shanghai, China
| | - Zhixi Su
- Singlera Genomics (Shanghai) Ltd, Shanghai, China
| | - Dan Liu
- Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wengeng Zhang
- Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yan Huang
- Health Management Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Huairong Tang
- Health Management Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Rui Liu
- Singlera Genomics (Shanghai) Ltd, Shanghai, China.
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
- Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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13
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Li Y, Jiang G, Wu W, Yang H, Jin Y, Wu M, Liu W, Yang A, Chervova O, Zhang S, Zheng L, Zhang X, Du F, Kanu N, Wu L, Yang F, Wang J, Chen K. Multi-omics integrated circulating cell-free DNA genomic signatures enhanced the diagnostic performance of early-stage lung cancer and postoperative minimal residual disease. EBioMedicine 2023; 91:104553. [PMID: 37027928 PMCID: PMC10102814 DOI: 10.1016/j.ebiom.2023.104553] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND Liquid biopsy is a promising non-invasive alternative for cancer screening and minimal residual disease (MRD) detection, although there are some concerns regarding its clinical applications. We aimed to develop an accurate detection platform based on liquid biopsy for both cancer screening and MRD detection in patients with lung cancer (LC), which is also applicable to clinical use. METHODS We applied a modified whole-genome sequencing (WGS) -based High-performance Infrastructure For MultIomics (HIFI) method for LC screening and postoperative MRD detection by combining the hyper-co-methylated read approach and the circulating single-molecule amplification and resequencing technology (cSMART2.0). FINDINGS For early screening of LC, the LC score model was constructed using the support vector machine, which showed sensitivity (51.8%) at high specificity (96.3%) and achieved an AUC of 0.912 in the validation set prospectively enrolled from multiple centers. The screening model achieved detection efficiency with an AUC of 0.906 in patients with lung adenocarcinoma and outperformed other clinical models in solid nodule cohort. When applied the HIFI model to real social population, a negative predictive value (NPV) of 99.92% was achieved in Chinese population. Additionally, the MRD detection rate improved significantly by combining results from WGS and cSMART2.0, with sensitivity of 73.7% at specificity of 97.3%. INTERPRETATION In conclusion, the HIFI method is promising for diagnosis and postoperative monitoring of LC. FUNDING This study was supported by CAMS Innovation Fund for Medical Sciences, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, Beijing Natural Science Foundation and Peking University People's Hospital.
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14
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Shi M, Han W, Loudig O, Shah CD, Dobkin JB, Keller S, Sadoughi A, Zhu C, Siegel RE, Fernandez MK, DeLaRosa L, Patel D, Desai A, Siddiqui T, Gombar S, Suh Y, Wang T, Hosgood HD, Pradhan K, Ye K, Spivack SD. Initial development and testing of an exhaled microRNA detection strategy for lung cancer case-control discrimination. Sci Rep 2023; 13:6620. [PMID: 37095155 PMCID: PMC10126132 DOI: 10.1038/s41598-023-33698-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 04/18/2023] [Indexed: 04/26/2023] Open
Abstract
For detecting field carcinogenesis non-invasively, early technical development and case-control testing of exhaled breath condensate microRNAs was performed. In design, human lung tissue microRNA-seq discovery was reconciled with TCGA and published tumor-discriminant microRNAs, yielding a panel of 24 upregulated microRNAs. The airway origin of exhaled microRNAs was topographically "fingerprinted", using paired EBC, upper and lower airway donor sample sets. A clinic-based case-control study (166 NSCLC cases, 185 controls) was interrogated with the microRNA panel by qualitative RT-PCR. Data were analyzed by logistic regression (LR), and by random-forest (RF) models. Feasibility testing of exhaled microRNA detection, including optimized whole EBC extraction, and RT and qualitative PCR method evaluation, was performed. For sensitivity in this low template setting, intercalating dye-based URT-PCR was superior to fluorescent probe-based PCR (TaqMan). In application, adjusted logistic regression models identified exhaled miR-21, 33b, 212 as overall case-control discriminant. RF analysis of combined clinical + microRNA models showed modest added discrimination capacity (1.1-2.5%) beyond clinical models alone: all subjects 1.1% (p = 8.7e-04)); former smokers 2.5% (p = 3.6e-05); early stage 1.2% (p = 9.0e-03), yielding combined ROC AUC ranging from 0.74 to 0.83. We conclude that exhaled microRNAs are qualitatively measureable, reflect in part lower airway signatures; and when further refined/quantitated, can potentially help to improve lung cancer risk assessment.
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Affiliation(s)
- Miao Shi
- Pulmonary Medicine, Albert Einstein College of Medicine, Bronx, NY, USA.
| | - Weiguo Han
- Pharmacology and Toxicology, College of Pharmacy, University of Arizona, Tucson, AZ, USA
| | | | - Chirag D Shah
- Pulmonary Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jay B Dobkin
- Pulmonary Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | | | - Ali Sadoughi
- Pulmonary Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Changcheng Zhu
- Pathology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Robert E Siegel
- Pulmonary Medicine, Icahn School of Medicine at Mount Sinai, James J. Peters Veterans Affairs Medical Center, New York, USA
| | | | - Lizett DeLaRosa
- Pulmonary Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | | | | | - Taha Siddiqui
- Pulmonary Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Saurabh Gombar
- Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Yousin Suh
- Reproductive Sciences (in Obstetrics and Gynecology), Columbia University, New York, USA
- Genetics and Development, Columbia University, New York, USA
| | - Tao Wang
- Biostatistics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - H Dean Hosgood
- Epidemiology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kith Pradhan
- Biostatistics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kenny Ye
- Biostatistics, Albert Einstein College of Medicine, Bronx, NY, USA
- Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Simon D Spivack
- Pulmonary Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
- Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- Epidemiology, Albert Einstein College of Medicine, Bronx, NY, USA
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15
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Vachani A, Lam S, Massion PP, Brown JK, Beggs M, Fish AL, Carbonell L, Wang SX, Mazzone PJ. Development and Validation of a Risk Assessment Model for Pulmonary Nodules Using Plasma Proteins and Clinical Factors. Chest 2023; 163:966-976. [PMID: 36368616 PMCID: PMC10258433 DOI: 10.1016/j.chest.2022.10.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 10/22/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Deficiencies in risk assessment for patients with pulmonary nodules (PNs) contribute to unnecessary invasive testing and delays in diagnosis. RESEARCH QUESTION What is the accuracy of a novel PN risk model that includes plasma proteins and clinical factors? How does the accuracy compare with that of an established risk model? STUDY DESIGN AND METHODS Based on technology using magnetic nanosensors, assays were developed with seven plasma proteins. In a training cohort (n = 429), machine learning approaches were used to identify an optimal algorithm that subsequently was evaluated in a validation cohort (n = 489), and its performance was compared with the Mayo Clinic model. RESULTS In the training set, we identified a support vector machine algorithm that included the seven plasma proteins and six clinical factors that demonstrated an area under the receiver operating characteristic curve of 0.87 and met other selection criteria. The resulting risk reclassification model (RRM) was used to recategorize patients with a pretest risk of between 10% and 84%, and its performance was assessed across five risk strata (low, ≤ 10%; moderate, 10%-34%; intermediate, 35%-70%; high, 71%-84%; very high, > 85%). Stratification by the RRM decreased the proportion of intermediate-risk patients from 26.7% to 10.8% (P < .001) and increased the low-risk and high-risk strata from 16.8% to 21.9% (P < .001) and from 3.7% to 12.1% (P < .001), respectively. Among patients classified as low risk by the RRM and Mayo Clinic model, the corresponding true-negative to false-negative ratios were 16.8 and 19.5, respectively. Among patients classified as very high risk by the RRM and Mayo Clinic model, the corresponding true-positive to false-positive ratios were 28.5 and 17.0, respectively. Compared with the Mayo Clinic model, the RRM provided higher specificity at the low-risk threshold and higher sensitivity at the very high-risk threshold. INTERPRETATION The RRM accurately reclassified some patients into low-risk and very high-risk categories, suggesting the potential to improve PN risk assessment.
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Affiliation(s)
- Anil Vachani
- Pulmonary, Allergy, and Critical Care Division, Department of Medicine, University of Pennsylvania, Philadelphia, PA; Corporal Michael J. Crescenz VA Medical Center, Department of Medicine, Philadelphia, PA.
| | - Stephen Lam
- Department of Integrative Oncology, British Columbia Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Pierre P Massion
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University, Nashville, TN
| | - James K Brown
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California, San Francisco, CA; VA Medical Center San Francisco, Department of Medicine, San Francisco, CA
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16
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Kammer MN, Paez R. It Doesn't Smell Like Cancer to Me: The Promise of Exhaled Breath Biomarkers for Lung Cancer Diagnosis. Chest 2023; 163:479-480. [PMID: 36894259 DOI: 10.1016/j.chest.2022.10.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 10/25/2022] [Indexed: 03/09/2023] Open
Affiliation(s)
- Michael N Kammer
- Vanderbilt University Medical Center, Vanderbilt-Ingram Cancer Center, Nashville, TN.
| | - Rafael Paez
- Vanderbilt University Medical Center, Vanderbilt-Ingram Cancer Center, Nashville, TN
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17
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Marmor HN, Zorn JT, Deppen SA, Massion PP, Grogan EL. Biomarkers in Lung Cancer Screening: a Narrative Review. CURRENT CHALLENGES IN THORACIC SURGERY 2023; 5:5. [PMID: 37016707 PMCID: PMC10069480 DOI: 10.21037/ccts-20-171] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Although when used as a lung cancer screening tool low-dose computed tomography (LDCT) has demonstrated a significant reduction in lung cancer related mortality, it is not without pitfalls. The associated high false positive rate, inability to distinguish between benign and malignant nodules, cumulative radiation exposure, and resulting patient anxiety have all demonstrated the need for adjunctive testing in lung cancer screening. Current research focuses on developing liquid biomarkers to complement imaging as non-invasive lung cancer diagnostics. Biomarkers can be useful for both the early detection and diagnosis of disease, thereby decreasing the number of unnecessary radiologic tests performed. Biomarkers can stratify cancer risk to further enrich the screening population and augment existing risk prediction. Finally, biomarkers can be used to distinguish benign from malignant nodules in lung cancer screening. While many biomarkers require further validation studies, several, including autoantibodies and blood protein profiling, are available for clinical use. This paper describes the need for biomarkers as a lung cancer screening tool, both in terms of diagnosis and risk assessment. Additionally, this paper will discuss the goals of biomarker use, describe properties of a good biomarker, and review several of the most promising biomarkers currently being studied including autoantibodies, complement fragments, microRNA, blood proteins, circulating tumor DNA, and DNA methylation. Finally, we will describe future directions in the field of biomarker development.
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Affiliation(s)
- Hannah N. Marmor
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - J. Tyler Zorn
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Stephen A. Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Pierre P. Massion
- Vanderbilt Ingram Cancer Center, Nashville, TN; Department of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Eric L. Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
- Department of Thoracic Surgery, Tennessee Valley VA Healthcare System, Nashville, TN
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18
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Immunogenomic Biomarkers and Validation in Lynch Syndrome. Cells 2023; 12:cells12030491. [PMID: 36766832 PMCID: PMC9914748 DOI: 10.3390/cells12030491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/15/2023] [Accepted: 01/22/2023] [Indexed: 02/05/2023] Open
Abstract
Lynch syndrome (LS) is an inherited disorder in which affected individuals have a significantly higher-than-average risk of developing colorectal and non-colorectal cancers, often before the age of 50 years. In LS, mutations in DNA repair genes lead to a dysfunctional post-replication repair system. As a result, the unrepaired errors in coding regions of the genome produce novel proteins, called neoantigens. Neoantigens are recognised by the immune system as foreign and trigger an immune response. Due to the invasive nature of cancer screening tests, universal cancer screening guidelines unique for LS (primarily colonoscopy) are poorly adhered to by LS variant heterozygotes (LSVH). Currently, it is unclear whether immunogenomic components produced as a result of neoantigen formation can be used as novel biomarkers in LS. We hypothesise that: (i) LSVH produce measurable and dynamic immunogenomic components in blood, and (ii) these quantifiable immunogenomic components correlate with cancer onset and stage. Here, we discuss the feasibility to: (a) identify personalised novel immunogenomic biomarkers and (b) validate these biomarkers in various clinical scenarios in LSVH.
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Wang Q, Gümüş ZH, Colarossi C, Memeo L, Wang X, Kong CY, Boffetta P. SCLC: Epidemiology, Risk Factors, Genetic Susceptibility, Molecular Pathology, Screening, and Early Detection. J Thorac Oncol 2023; 18:31-46. [PMID: 36243387 PMCID: PMC10797993 DOI: 10.1016/j.jtho.2022.10.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 10/03/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022]
Abstract
We review research regarding the epidemiology, risk factors, genetic susceptibility, molecular pathology, and early detection of SCLC, a deadly tumor that accounts for 14% of lung cancers. We first summarize the changing incidences of SCLC globally and in the United States among males and females. We then review the established risk factor (i.e., tobacco smoking) and suspected nonsmoking-related risk factors for SCLC, and emphasize the importance of continued effort in tobacco control worldwide. Review of genetic susceptibility and molecular pathology suggests different molecular pathways in SCLC development compared with other types of lung cancer. Last, we comment on the limited utility of low-dose computed tomography screening in SCLC and on several promising blood-based molecular biomarkers as potential tools in SCLC early detection.
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Affiliation(s)
- Qian Wang
- University Hospitals Seidman Cancer Center, Cleveland, Ohio.
| | - Zeynep H Gümüş
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Center for Thoracic Oncology, Tisch Cancer Center, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Cristina Colarossi
- Pathology Unit, Department of Experimental Oncology, Mediterranean Institute of Oncology, Catania, Italy
| | - Lorenzo Memeo
- Pathology Unit, Department of Experimental Oncology, Mediterranean Institute of Oncology, Catania, Italy
| | - Xintong Wang
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Chung Yin Kong
- Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Paolo Boffetta
- Department of Family, Population & Preventive Medicine, Stony Brook University, Stony Brook, New York; Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
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20
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Lemieux ME, Reveles XT, Rebeles J, Bederka LH, Araujo PR, Sanchez JR, Grayson M, Lai SC, DePalo LR, Habib SA, Hill DG, Lopez K, Patriquin L, Sussman R, Joyce RP, Rebel VI. Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning. Respir Res 2023; 24:23. [PMID: 36681813 PMCID: PMC9862555 DOI: 10.1186/s12931-023-02327-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/12/2023] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Low-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases. METHODS Single cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer. RESULTS Automated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83-0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were < 20 mm (AUC of 0.94; 95% CI 0.89-0.99). Testing of the model on an independent second set of samples showed an AUC of 0.85 (95% CI 0.71-0.98) with an 83% sensitivity, 77% specificity, 95% negative predictive value and 45% positive predictive value. The model is robust to differences in sample processing and disease state. CONCLUSION CyPath Lung correctly classifies samples as cancer or non-cancer with high accuracy, including from participants at different disease stages and with nodules < 20 mm in diameter. This test is intended for use after lung cancer screening to improve early-stage lung cancer diagnosis. Trial registration ClinicalTrials.gov ID: NCT03457415; March 7, 2018.
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Affiliation(s)
| | - Xavier T. Reveles
- bioAffinity Technologies, 22211 W I-10, Suite 1206, San Antonio, TX 78257 USA
| | - Jennifer Rebeles
- bioAffinity Technologies, 22211 W I-10, Suite 1206, San Antonio, TX 78257 USA
| | - Lydia H. Bederka
- bioAffinity Technologies, 22211 W I-10, Suite 1206, San Antonio, TX 78257 USA
| | - Patricia R. Araujo
- bioAffinity Technologies, 22211 W I-10, Suite 1206, San Antonio, TX 78257 USA
| | - Jamila R. Sanchez
- bioAffinity Technologies, 22211 W I-10, Suite 1206, San Antonio, TX 78257 USA
| | - Marcia Grayson
- bioAffinity Technologies, 22211 W I-10, Suite 1206, San Antonio, TX 78257 USA
| | - Shao-Chiang Lai
- bioAffinity Technologies, 22211 W I-10, Suite 1206, San Antonio, TX 78257 USA
| | - Louis R. DePalo
- grid.59734.3c0000 0001 0670 2351Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Sheila A. Habib
- grid.414059.d0000 0004 0617 9080South Texas Veterans Health Care System (STVHCS), Audie L. Murphy Memorial Veterans Hospital, San Antonio, TX USA
| | - David G. Hill
- Waterbury Pulmonary Associates LLC, Waterbury, CT USA
| | - Kathleen Lopez
- grid.477754.2Radiology Associates of Albuquerque, Albuquerque, NM USA
| | - Lara Patriquin
- grid.477754.2Radiology Associates of Albuquerque, Albuquerque, NM USA ,Present Address: Zia Diagnostic Imaging, Albuquerque, NM USA
| | | | | | - Vivienne I. Rebel
- bioAffinity Technologies, 22211 W I-10, Suite 1206, San Antonio, TX 78257 USA
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21
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Tong L, Sun J, Zhang X, Ge D, Yang Y, Zhou J, Wang D, Hu X, Liu H, Bai C. Diagnostic value of tumor associated autoantibody panel in early detection of lung cancer in Chinese population: Protocol for a prospective, observational, and multicenter clinical trial. CLINICAL EHEALTH 2022. [DOI: 10.1016/j.ceh.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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22
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Yu DH, Shafiq M, Batra H, Johnson M, Griscom B, Chamberlin J, Lofaro LR, Huang J, Bulman WA, Kennedy GC, Yarmus LB, Lee HJ, Feller-Kopman D. Comparing modalities for risk assessment in patients with pulmonary lesions and nondiagnostic bronchoscopy for suspected lung cancer. BMC Pulm Med 2022; 22:442. [PMID: 36434574 PMCID: PMC9700899 DOI: 10.1186/s12890-022-02181-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 09/09/2022] [Accepted: 09/28/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Bronchoscopy is commonly utilized for non-surgical sampling of indeterminant pulmonary lesions, but nondiagnostic procedures are common. Accurate assessment of the risk of malignancy is essential for decision making in these patients, yet we lack tools that perform well across this heterogeneous group of patients. We sought to evaluate the accuracy of three previously validated risk models and physician-assessed risk (PAR) in patients with a newly identified lung lesion undergoing bronchoscopy for suspected lung cancer where the result is nondiagnostic. METHODS We performed an analysis of prospective data collected for the Percepta Bronchial Genomic Classifier Multicenter Registry. PAR and three previously validated risk models (Mayo Clinic, Veteran's Affairs, and Brock) were used to determine the probability of lung cancer (low, intermediate, or high) in 375 patients with pulmonary lesions who underwent bronchoscopy for possible lung cancer with nondiagnostic pathology. Results were compared to the actual adjudicated prevalence of malignancy in each pre-test risk group, determined with a minimum of 12 months follow up after bronchoscopy. RESULTS PAR and the risk models performed poorly overall in the assessment of risk in this patient population. PAR most closely matched the observed prevalence of malignancy in patients at 12 months after bronchoscopy, but all modalities had a low area under the curve, and in all clinical models more than half of all the lesions labeled as high risk were truly or likely benign. The studied risk model calculators overestimate the risk of malignancy compared to PAR, particularly in the subset in older patients, irregularly bordered nodules, and masses > 3 cm. Overall, the risk models perform only slightly better when confined to lung nodules < 3 cm in this population. CONCLUSION The currently available tools for the assessment of risk of malignancy perform suboptimally in patients with nondiagnostic findings following a bronchoscopic evaluation for lung cancer. More accurate and objective tools for risk assessment are needed. TRIAL REGISTRATION not applicable.
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Affiliation(s)
- Diana H. Yu
- grid.266102.10000 0001 2297 6811Department of Medicine, Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, University of California, San Francisco, San Francisco, USA CA ,grid.413077.60000 0004 0434 9023UCSF Medical Center, 505 Parnassus Ave, 9414 San Francisco, CA USA
| | - Majid Shafiq
- grid.62560.370000 0004 0378 8294Brigham and Women’s Hospital, Department of Medicine, Division of Pulmonary and Critical Care Medicine, Boston, MA USA
| | - Hitesh Batra
- grid.265892.20000000106344187Department of Medicine, Division of Pulmonary and Critical Care Medicine Birmingham, University of Alabama at Birmingham, Birmingham, AL USA
| | - Marla Johnson
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - Bailey Griscom
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - Janna Chamberlin
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - Lori R. Lofaro
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - Jing Huang
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - William A. Bulman
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - Giulia C. Kennedy
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - Lonny B. Yarmus
- grid.21107.350000 0001 2171 9311Division of Pulmonary and Critical Care Medicine, Section of Interventional Pulmonology, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Hans J. Lee
- grid.21107.350000 0001 2171 9311Division of Pulmonary and Critical Care Medicine, Section of Interventional Pulmonology, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - David Feller-Kopman
- grid.254880.30000 0001 2179 2404Department of Medicine, Division of Pulmonary and Critical Care Medicine, Dartmouth College, Hanover, NH USA
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23
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Raval AA, Benn BS, Benzaquen S, Maouelainin N, Johnson M, Huang J, Lofaro LR, Ansari A, Geurink C, Kennedy GC, Bulman WA, Kurman JS. Reclassification of risk of malignancy with Percepta Genomic Sequencing Classifier following nondiagnostic bronchoscopy. Respir Med 2022; 204:106990. [DOI: 10.1016/j.rmed.2022.106990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/30/2022] [Accepted: 09/11/2022] [Indexed: 10/31/2022]
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24
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Kussrow A, Kammer MN, Massion PP, Webster R, Bornhop DJ. Assay Performance of a Label-Free, Solution-Phase CYFRA 21-1 Determination. ACS OMEGA 2022; 7:31916-31923. [PMID: 36120008 PMCID: PMC9476196 DOI: 10.1021/acsomega.2c02763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
CYFRA 21.1, a cytokeratin fragment of epithelial origin, has long been a valuable blood-based biomarker. As with most biomarkers, the clinical diagnostic value of CYFRA 21.1 is dependent on the quantitative performance of the assay. Looking toward translation, it is shown here that a free-solution assay (FSA) coupled with a compensated interferometric reader (CIR) can be used to provide excellent analytical performance in quantifying CYFRA 21.1 in patient serum samples. This report focuses on the analytical performance of the high-sensitivity (hs)-CYFRA 21.1 assay in the context of quantifying the biomarker in two indeterminate pulmonary nodule (IPN) patient cohorts totaling 179 patients. Each of the ten assay calibrations consisted of 6 concentrations, each run as 7 replicates (e.g., 10 × 6 × 7 data points) and were performed on two different instruments by two different operators. Coefficients of variation (CVs) for the hs-CYFRA 21.1 analytical figures of merit, limit of quantification (LOQ) of ca. 60 pg/mL, B max, initial slope, probe-target binding affinity, and reproducibility of quantifying an unknown were found to range from 2.5 to 8.3%. Our results demonstrate the excellent performance of our FSA-CIR hs-CYFRA 21-1 assay and a proof of concept for potentially redefining the performance characteristics of this existing important candidate biomarker.
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Affiliation(s)
- Amanda
K. Kussrow
- Department
of Chemistry and The Vanderbilt Institute for Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Michael N. Kammer
- Division
of Allergy, Pulmonary and Critical Care Medicine and Vanderbilt-Ingram
Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Pierre P. Massion
- Division
of Allergy, Pulmonary and Critical Care Medicine and Vanderbilt-Ingram
Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Rebekah Webster
- Department
of Chemistry and The Vanderbilt Institute for Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Darryl J. Bornhop
- Department
of Chemistry and The Vanderbilt Institute for Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
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25
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Padinharayil H, Varghese J, John MC, Rajanikant GK, Wilson CM, Al-Yozbaki M, Renu K, Dewanjee S, Sanyal R, Dey A, Mukherjee AG, Wanjari UR, Gopalakrishnan AV, George A. Non-small cell lung carcinoma (NSCLC): Implications on molecular pathology and advances in early diagnostics and therapeutics. Genes Dis 2022. [DOI: 10.1016/j.gendis.2022.07.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022] Open
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26
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Petranovic M, Raoof S, Digumarthy SR, Sharma A, Shepard JAO, Gainor JF, Pandharipande PV. Liquid Biopsy, Diagnostic Imaging, and Future Synergies. J Am Coll Radiol 2022; 19:336-343. [DOI: 10.1016/j.jacr.2021.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 12/16/2022]
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27
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Finamore P, Tanese L, Longo F, De Stefano D, Pedone C, Angelici L, Agabiti N, Cascini S, Davoli M, Zobel BB, Incalzi RA, Crucitti P. The additional value of lung cancer screening program in identifying unrecognized diseases. BMC Pulm Med 2022; 22:48. [PMID: 35101007 PMCID: PMC8802423 DOI: 10.1186/s12890-022-01826-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 12/18/2021] [Indexed: 11/17/2022] Open
Abstract
Background A systematic examination of low-dose CT (LDCT) scan, beside lung nodules, may disclose the presence of undiagnosed diseases, improving the efficacy and the cost/efficacy of these programs. The study was aimed at evaluating the association between LDCT scan findings and non-oncologic and oncologic diseases. Methods The LDCT scan of participants to the “Un Respiro per la vita”® lung cancer screening program were checked and abnormal findings, beside lung nodules, recorded. First admission to the acute care because of cardiovascular (CD), respiratory (RD) and oncological diseases (OD) in the following three years were retrieved. The association of LDCT scan abnormal findings with CD, RD and OD was assessed through univariable and multivariable logistic regression models. Results Mean age of 746 participants was 62 years (SD:5), 62% were male. 11 (1.5%) received a diagnosis of lung cancer. 16.1% participants were admitted to the acute care in the following three years: 8.6% for CD, 4.3% for RD and 5.2% for OD. Valve calcification (OR 2.02, p:0.02) and mucus plugs (OR 3.37, p:0.04) were positively associated with CD, while sub-pleural fibrosis had a protective role (OR 0.47, p:0.01). Lung nodules > 8 mm (OR 5.54, p: < 0.01), tracheal deviation (OR 6.04, p:0.01) and mucus plugs (OR 4.00, p:0.04) were positively associated with OD admissions. Centrilobular emphysema OR for RD admissions was 1.97 (p:0.06). Conclusions The observed association between selected LDCT findings and ensuing CD, RD and OD suggests that the information potential of LCDT goes beyond the screening of lung cancer. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-022-01826-1.
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Affiliation(s)
- Panaiotis Finamore
- Unit of Geriatrics, Department of Medicine and Surgery, Campus Bio-Medico University and Teaching Hospital, Rome, Italy
| | - Luigi Tanese
- Unit of Imaging Center, Department of Medicine and Surgery, Campus Bio-Medico University and Teaching Hospital, Rome, Italy
| | - Filippo Longo
- Unit of Thoracic Surgery, Department of Medicine and Surgery, Campus Bio-Medico University and Teaching Hospital, Rome, Italy.
| | - Domenico De Stefano
- Unit of Imaging Center, Department of Medicine and Surgery, Campus Bio-Medico University and Teaching Hospital, Rome, Italy
| | - Claudio Pedone
- Unit of Geriatrics, Department of Medicine and Surgery, Campus Bio-Medico University and Teaching Hospital, Rome, Italy
| | - Laura Angelici
- Dipartimento di Epidemiologia del Servizio Sanitario Regionale, Regione Lazio, ASL Roma 1, Rome, Italy
| | - Nera Agabiti
- Dipartimento di Epidemiologia del Servizio Sanitario Regionale, Regione Lazio, ASL Roma 1, Rome, Italy
| | - Silvia Cascini
- Dipartimento di Epidemiologia del Servizio Sanitario Regionale, Regione Lazio, ASL Roma 1, Rome, Italy
| | - Marina Davoli
- Dipartimento di Epidemiologia del Servizio Sanitario Regionale, Regione Lazio, ASL Roma 1, Rome, Italy
| | - Bruno Beomonte Zobel
- Unit of Imaging Center, Department of Medicine and Surgery, Campus Bio-Medico University and Teaching Hospital, Rome, Italy
| | - Raffaele Antonelli Incalzi
- Unit of Geriatrics, Department of Medicine and Surgery, Campus Bio-Medico University and Teaching Hospital, Rome, Italy
| | - Pierfilippo Crucitti
- Unit of Thoracic Surgery, Department of Medicine and Surgery, Campus Bio-Medico University and Teaching Hospital, Rome, Italy
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28
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Springmeyer S, Jett J. Letter to Editor: Interpretation and Application of the Likelihood Ratio to Clinical Practice in Thoracic Oncology. J Bronchology Interv Pulmonol 2022; 29:e1-e2. [PMID: 34935671 PMCID: PMC8691369 DOI: 10.1097/lbr.0000000000000804] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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29
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A Cost-Effective and Non-Invasive pfeRNA-Based Test Differentiates Benign and Suspicious Pulmonary Nodules from Malignant Ones. Noncoding RNA 2021; 7:ncrna7040080. [PMID: 34940762 PMCID: PMC8709422 DOI: 10.3390/ncrna7040080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/04/2021] [Accepted: 12/07/2021] [Indexed: 12/19/2022] Open
Abstract
The ability to differentiate between benign, suspicious, and malignant pulmonary nodules is imperative for definitive intervention in patients with early stage lung cancers. Here, we report that plasma protein functional effector sncRNAs (pfeRNAs) serve as non-invasive biomarkers for determining both the existence and the nature of pulmonary nodules in a three-stage study that included the healthy group, patients with benign pulmonary nodules, patients with suspicious nodules, and patients with malignant nodules. Following the standards required for a clinical laboratory improvement amendments (CLIA)-compliant laboratory-developed test (LDT), we identified a pfeRNA classifier containing 8 pfeRNAs in 108 biospecimens from 60 patients by sncRNA deep sequencing, deduced prediction rules using a separate training cohort of 198 plasma specimens, and then applied the prediction rules to another 230 plasma specimens in an independent validation cohort. The pfeRNA classifier could (1) differentiate patients with or without pulmonary nodules with an average sensitivity and specificity of 96.2% and 97.35% and (2) differentiate malignant versus benign pulmonary nodules with an average sensitivity and specificity of 77.1% and 74.25%. Our biomarkers are cost-effective, non-invasive, sensitive, and specific, and the qPCR-based method provides the possibility for automatic testing of robotic applications.
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30
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Wang M, Liu F, Pan Y, Xu R, Li F, Liu A, Yang H, Duan L, Shen L, Wu Q, Liu Y, Liu M, Liu Z, Hu Z, Chen H, Cai H, He Z, Ke Y. Tumor-associated autoantibodies in ESCC screening: Detecting prevalent early-stage malignancy or predicting future cancer risk? EBioMedicine 2021; 73:103674. [PMID: 34753106 PMCID: PMC8586741 DOI: 10.1016/j.ebiom.2021.103674] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/19/2021] [Accepted: 10/19/2021] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND To assess potential roles for tumor-associated autoantibodies (TAAs) in esophageal squamous cell carcinoma (ESCC) screening: detecting early-stage malignancy, and predicting future cancer risk. METHOD Thirteen candidate autoantibodies identified in previous literatures were measured using multiplex serological assays in sera from cases and matched controls nested in two population-level screening cohorts in China. To evaluate the role of TAAs in detecting prevalent esophageal malignant lesions, an identification set (150 cases vs. 560 controls) and an external validation set (34 cases vs. 121 controls) were established with pre-screening sera collected ≤ 12 months prior to screening-related diagnosis. To explore the role of TAAs in predicting future ESCC risk, an exploration set (105 cases vs. 416 controls) with pre-diagnostic sera collected > 12 months before clinical diagnosis was established. Two models, the questionnaire-based model and full model additionally incorporating TAA markers, were constructed. Area under the receiver operating characteristic curve (AUC) and net reclassification improvement (NRI) were calculated to compare the performance of the two models. FINDINGS In the identification set, NY-ESO-1 (OR=2·12, 95% CI=1·02-4·40) and STIP1 (OR=1·83, 95% CI=1·10-3·05) were positively associated with higher risk of esophageal malignancy. Elevated MMP-7 was associated with higher risk of malignancy in females (ORfemale=5·07, 95% CI=1·30-19·71). The estimates in validation set were consistent with these results, but were close to null in exploration set. Integration of selected TAAs improved the performance of questionnaire-based models in detecting prevalent esophageal malignancy (female: AUCfull model=0·745, 95% CI=0·675-0·814, AUCquestionnaire-based model=0·658, 95% CI=0·585-0·732, NRI=0·604, P<0·0001; male: AUCfull model=0·662, 95% CI=0·596-0·728, AUCquestionnaire-based model=0·619, 95% CI=0·548-0·690, NRI=0·357, P=0·0028). This improvement was also seen in validation set, but was not similarly effective in distinguishing long-term incident cases from healthy controls. INTERPRETATION Serological autoantibodies against NY-ESO-1, STIP1, and MMP-7 perform well in detecting early-stage esophageal malignancy, but are less effective in predicting future ESCC risks. FUNDING This work was supported by the National Science & Technology Fundamental Resources Investigation Program of China (2019FY101102), the National Natural Science Foundation of China (82073626), the National Key R&D Program of China (2016YFC0901404), the Beijing-Tianjin-Hebei Basic Research Cooperation Project (J200016), the Digestive Medical Coordinated Development Center of Beijing Hospitals Authority (XXZ0204), and the Natural Science Foundation of Beijing Municipality (7182033).
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Affiliation(s)
- Minmin Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital & Institute, Beijing, P.R. China
| | - Fangfang Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital & Institute, Beijing, P.R. China
| | - Yaqi Pan
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital & Institute, Beijing, P.R. China
| | - Ruiping Xu
- Anyang Cancer Hospital, Anyang, Henan Province, P.R. China
| | - Fenglei Li
- Hua County People's Hospital, Anyang, Henan Province, P.R. China
| | - Anxiang Liu
- Endoscopy center, Anyang Cancer Hospital, Anyang, Henan Province, P.R. China
| | - Haijun Yang
- Department of pathology, Anyang Cancer Hospital, Anyang, Henan Province, P.R. China
| | - Liping Duan
- Department of Gastroenterology, Peking University Third Hospital, Beijing, P.R. China
| | - Lin Shen
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing, P.R. China
| | - Qi Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Endoscopy Center, Peking University Cancer Hospital & Institute, Beijing, P.R. China
| | - Ying Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital & Institute, Beijing, P.R. China
| | - Mengfei Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital & Institute, Beijing, P.R. China
| | - Zhen Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital & Institute, Beijing, P.R. China
| | - Zhe Hu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital & Institute, Beijing, P.R. China
| | - Huanyu Chen
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital & Institute, Beijing, P.R. China
| | - Hong Cai
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital & Institute, Beijing, P.R. China
| | - Zhonghu He
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital & Institute, Beijing, P.R. China.
| | - Yang Ke
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital & Institute, Beijing, P.R. China.
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Garrison GW, Cho JL, Deng JC, Camac E, Oh S, Sundar K, Baptiste JV, Cheng GS, De Cardenas J, Fitzgerald C, Garfield J, Ha NT, Holden VK, O’Corragain O, Patel S, Wayne MT, McSparron JI, Wang T, Çoruh B, Hayes MM, Guzman E, Channick CL. ATS Core Curriculum 2021. Adult Pulmonary Medicine: Thoracic Oncology. ATS Sch 2021; 2:468-483. [PMID: 34667994 PMCID: PMC8518653 DOI: 10.34197/ats-scholar.2021-0032re] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 05/25/2021] [Indexed: 11/18/2022] Open
Abstract
The American Thoracic Society Core Curriculum updates clinicians annually in adult and pediatric pulmonary disease, medical critical care, and sleep medicine at the annual international conference. The 2021 Pulmonary Core Curriculum focuses on lung cancer and include risks and prevention, screening, nodules, therapeutics and associated pulmonary toxicities, and malignant pleural effusions. Although tobacco smoking remains the primary risk factor for developing lung cancer, exposure to other environmental and occupational substances, including asbestos, radon, and burned biomass, contribute to the global burden of disease. Randomized studies have demonstrated that routine screening of high-risk smokers with low-dose chest computed tomography results in detection at an earlier stage and reduction in lung cancer mortality. On the basis of these trials and other lung cancer risk tools, screening recommendations have been developed. When evaluating lung nodules, clinical and radiographic features are used to estimate the probability of cancer. Management guidelines take into account the nodule size and cancer risk estimates to provide recommendations at evaluation. Newer lung cancer therapies, including immune checkpoint inhibitors and molecular therapies, cause pulmonary toxicity more frequently than conventional chemotherapy. Treatment-related toxicity should be suspected in patients receiving these medications who present with respiratory symptoms. Evaluation is aimed at excluding other etiologies, and treatment is based on the severity of symptoms. Malignant pleural effusions can be debilitating. The diagnosis is made by using simple pleural drainage and/or pleural biopsies. Management depends on the clinical scenario and the patient's preferences and includes the use of serial thoracentesis, a tunneled pleural catheter, or pleurodesis.
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Affiliation(s)
- Garth W. Garrison
- Divison of Pulmonary Disease and Critical Care Medicine, Larner College of Medicine, University of Vermont, Burlington, Vermont
| | - Josalyn L. Cho
- Division of Pulmonary, Critical Care and Occupational Medicine, Carver College of Medicine, University of Iowa, Iowa City, Iowa
| | - Jane C. Deng
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Erin Camac
- Department of Thoracic Medicine and Surgery, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania
| | - Scott Oh
- Division of Pulmonary, Critical Care Medicine, Clinical Immunology, and Allergy, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Krishna Sundar
- Division of Respiratory, Critical Care, and Occupational Pulmonary Medicine, University of Utah, Salt Lake City, Utah
| | - Janelle V. Baptiste
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center–Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Guang-Shing Cheng
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington
| | - Jose De Cardenas
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
- Section of Thoracic Surgery, Department of Surgery, School of Medicine, University of Michigan, Ann Arbor, Michigan
| | - Codi Fitzgerald
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington
| | - Jamie Garfield
- Department of Thoracic Medicine and Surgery, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania
| | - Ngoc-Tram Ha
- Division of Pulmonary and Critical Care Medicine, School of Medicine, University of Maryland, Baltimore, Maryland; and
| | - Van K. Holden
- Division of Pulmonary and Critical Care Medicine, School of Medicine, University of Maryland, Baltimore, Maryland; and
| | - Oisin O’Corragain
- Department of Thoracic Medicine and Surgery, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania
| | - Sahil Patel
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center–Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Max T. Wayne
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Jakob I. McSparron
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Tisha Wang
- Division of Pulmonary, Critical Care Medicine, Clinical Immunology, and Allergy, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Başak Çoruh
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington
| | - Margaret M. Hayes
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center–Harvard Medical School, Harvard University, Boston, Massachusetts
| | | | - Colleen L. Channick
- Division of Pulmonary, Critical Care Medicine, Clinical Immunology, and Allergy, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
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32
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Kinsey CM, Billatos E, Mori V, Tonelli B, Cole BF, Duan F, Marques H, de la Bruere I, Onieva J, San José Estépar R, Cleveland A, Idelkope D, Stevenson C, Bates JHT, Aberle D, Spira A, Washko G, San José Estépar R. A simple assessment of lung nodule location for reduction in unnecessary invasive procedures. J Thorac Dis 2021; 13:4207-4216. [PMID: 34422349 PMCID: PMC8339782 DOI: 10.21037/jtd-20-3093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 04/23/2021] [Indexed: 12/05/2022]
Abstract
Background CT screening for lung cancer results in a significant mortality reduction but is complicated by invasive procedures performed for evaluation of the many detected benign nodules. The purpose of this study was to evaluate measures of nodule location within the lung as predictors of malignancy. Methods We analyzed images and data from 3,483 participants in the National Lung Screening Trial (NLST). All nodules (4–20 mm) were characterized by 3D geospatial location using a Cartesian coordinate system and evaluated in logistic regression analysis. Model development and probability cutpoint selection was performed in the NLST testing set. The Geospatial test was then validated in the NLST testing set, and subsequently replicated in a new cohort of 147 participants from The Detection of Early Lung Cancer Among Military Personnel (DECAMP) Consortium. Results The Geospatial Test, consisting of the superior-inferior distance (Z distance), nodule diameter, and radial distance (carina to nodule) performed well in both the NLST validation set (AUC 0.85) and the DECAMP replication cohort (AUC 0.75). A negative Geospatial Test resulted in a less than 2% risk of cancer across all nodule diameters. The Geospatial Test correctly reclassified 19.7% of indeterminate nodules with a diameter over 6mm as benign, while only incorrectly classifying 1% of cancerous nodules as benign. In contrast, the parsimonious Brock Model applied to the same group of nodules correctly reclassified 64.5% of indeterminate nodules as benign but resulted in misclassification of a cancer as benign in 18.2% of the cases. Applying the Geospatial test would result in reducing invasive procedures performed for benign lesions by 11.3% with a low rate of misclassification (1.3%). In contrast, the Brock model applied to the same group of patients results in decreasing invasive procedures for benign lesion by 39.0% but misclassifying 21.1% of cancers as benign. Conclusions Utilizing information about geospatial location within the lung improves risk assessment for indeterminate lung nodules and may reduce unnecessary procedures. Trial Registration NCT00047385, NCT01785342.
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Affiliation(s)
- C Matthew Kinsey
- Division of Pulmonary and Critical Care, University of Vermont Medical Center, Burlington, VT, USA
| | - Ehab Billatos
- Section of Pulmonary and Critical Care Medicine, Department of Medicine, Boston University, Boston, MA, Boston Medical Center, Boston, MA, USA
| | - Vitor Mori
- University of Sao Paolo, Sao Paolo, Brazil
| | | | - Bernard F Cole
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT, USA
| | - Fenghai Duan
- Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA
| | - Helga Marques
- Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA
| | | | - Jorge Onieva
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | | | | | - Dan Idelkope
- Geisel School of Medicine at Dartmouth College, Hanover, NH, USA
| | | | - Jason H T Bates
- Division of Pulmonary and Critical Care, University of Vermont Medical Center, Burlington, VT, USA
| | - Denise Aberle
- David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Avi Spira
- The Pulmonary Unit, Boston Medical Center, Boston, MA, USA
| | - George Washko
- Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, USA
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33
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Mathios D, Johansen JS, Cristiano S, Medina JE, Phallen J, Larsen KR, Bruhm DC, Niknafs N, Ferreira L, Adleff V, Chiao JY, Leal A, Noe M, White JR, Arun AS, Hruban C, Annapragada AV, Jensen SØ, Ørntoft MBW, Madsen AH, Carvalho B, de Wit M, Carey J, Dracopoli NC, Maddala T, Fang KC, Hartman AR, Forde PM, Anagnostou V, Brahmer JR, Fijneman RJA, Nielsen HJ, Meijer GA, Andersen CL, Mellemgaard A, Bojesen SE, Scharpf RB, Velculescu VE. Detection and characterization of lung cancer using cell-free DNA fragmentomes. Nat Commun 2021; 12:5060. [PMID: 34417454 PMCID: PMC8379179 DOI: 10.1038/s41467-021-24994-w] [Citation(s) in RCA: 160] [Impact Index Per Article: 53.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/12/2021] [Indexed: 12/12/2022] Open
Abstract
Non-invasive approaches for cell-free DNA (cfDNA) assessment provide an opportunity for cancer detection and intervention. Here, we use a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation in a prospective study of 365 individuals at risk for lung cancer. We validate the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 lung cancer patients. Combining fragmentation features, clinical risk factors, and CEA levels, followed by CT imaging, detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites distinguished individuals with small cell lung cancer from those with non-small cell lung cancer with high accuracy (AUC = 0.98). A higher fragmentation score represented an independent prognostic indicator of survival. This approach provides a facile avenue for non-invasive detection of lung cancer.
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Affiliation(s)
- Dimitrios Mathios
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Stephen Cristiano
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jamie E Medina
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jillian Phallen
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Klaus R Larsen
- Department of Respiratory Medicine, Infiltrate Unit, Bispebjerg Hospital, Copenhagen, Denmark
| | - Daniel C Bruhm
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Noushin Niknafs
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Leonardo Ferreira
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Vilmos Adleff
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jia Yuee Chiao
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alessandro Leal
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael Noe
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - James R White
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Adith S Arun
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Carolyn Hruban
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Akshaya V Annapragada
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sarah Østrup Jensen
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
| | | | | | - Beatriz Carvalho
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Meike de Wit
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | | | | | | | | | - Patrick M Forde
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Valsamo Anagnostou
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Julie R Brahmer
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Remond J A Fijneman
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Hans Jørgen Nielsen
- Department of Surgical Gastroenterology 360, Hvidovre Hospital, Hvidovre, Denmark
| | - Gerrit A Meijer
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Anders Mellemgaard
- Department of Oncology, Herlev and Gentofte Hospital, Copenhagen, Denmark
| | - Stig E Bojesen
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen, Denmark
| | - Robert B Scharpf
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Victor E Velculescu
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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34
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Liang N, Li B, Jia Z, Wang C, Wu P, Zheng T, Wang Y, Qiu F, Wu Y, Su J, Xu J, Xu F, Chu H, Fang S, Yang X, Wu C, Cao Z, Cao L, Bing Z, Liu H, Li L, Huang C, Qin Y, Cui Y, Han-Zhang H, Xiang J, Liu H, Guo X, Li S, Zhao H, Zhang Z. Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning. Nat Biomed Eng 2021; 5:586-599. [PMID: 34131323 DOI: 10.1038/s41551-021-00746-5] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 05/13/2021] [Indexed: 01/30/2023]
Abstract
The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of tumour-derived signals at dilution factors as low as 1 in 10,000. For a total of 308 patients with surgery-resectable lung cancer and 261 age- and sex-matched non-cancer control individuals recruited from two hospitals, the assay detected 52-81% of the patients at disease stages IA to III with a specificity of 96% (95% confidence interval (CI) 93-98%). In a subgroup of 115 individuals, the assay identified, at 100% specificity (95% CI 91-100%), nearly twice as many patients with cancer as those identified by ultradeep mutation sequencing analysis. The low amounts of ctDNA permitted by machine-learning-aided deep methylation sequencing could provide advantages in cancer screening and the assessment of treatment efficacy.
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Affiliation(s)
- Naixin Liang
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Bingsi Li
- Burning Rock Biotech, Guangzhou, China
| | - Ziqi Jia
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | | | - Pancheng Wu
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Tao Zheng
- Burning Rock Biotech, Guangzhou, China
| | - Yanyu Wang
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Fujun Qiu
- Burning Rock Biotech, Guangzhou, China
| | - Yijun Wu
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Su
- Burning Rock Biotech, Guangzhou, China
| | - Jiayue Xu
- Burning Rock Biotech, Guangzhou, China
| | - Feng Xu
- Burning Rock Biotech, Guangzhou, China
| | | | | | | | - Chengju Wu
- Department of Industrial Engineering & Operations Research, University of California, Berkeley, Berkeley, CA, USA
| | - Zhili Cao
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Lei Cao
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Zhongxing Bing
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Hongsheng Liu
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Li Li
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Cheng Huang
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yingzhi Qin
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yushang Cui
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | | | | | - Hao Liu
- Burning Rock Biotech, Guangzhou, China
| | - Xin Guo
- Department of Industrial Engineering & Operations Research, University of California, Berkeley, Berkeley, CA, USA
| | - Shanqing Li
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China. .,Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
| | - Heng Zhao
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai, China.
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35
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Lin Y, Ma J, Wu M, Zhou H, Lu Y, Cen Y, Yuan Z, Mei Z, Huang Y, Zhou Y. [Cancer Screening Program in Urban Kunming of Yunnan: Evaluation of Lung Cancer Risk Assessment and Screening]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2021; 23:541-546. [PMID: 32702787 PMCID: PMC7406440 DOI: 10.3779/j.issn.1009-3419.2020.101.30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Lung cancer is the most common neoplasmas with a poor prognosis and a low 5-year survival rate. Early screening is an important measure for the prevention and treatment of lung cancer. At present, different countries have issued corresponding lung cancer screening guidelines, but China still lacks guidelines based on Chinese population research. Therefore, the National Cancer Center launched a Multi-center Cancer Screening Program in Urban China. This study analyzed the evaluation of lung cancer risk assessment model and screening effect in urban China of Yunnan, so as to explore the evaluation model of high-risk lung cancer population suitable for China's national conditions and develop lung cancer screening guidelines for Chinese. METHODS A questionnaire survey and lung cancer risk assessment were conducted on 165,337 people in 36 street offices in 4 main urban areas of Kunming, Yunnan Province, using cluster sampling method from January 2015 to December 2019. People with high-risk of lung cancer conducted low-dose computed tomography (LDCT) screening of chest. What's more, all participants were followed up by active or passive follow-up. RESULTS There were 264 patients were diagnosed lung cancer by pathology, and the overall incidence of lung cancer was 0.16% (264/165,337). The high-risk group (0.31%, 116/37,914) was higher than the non-high-risk group (0.12%, 148/127,423), and the difference was statistically significant (P<0.001). The incidence of lung cancer in the high-risk group was higher than the non-high-risk group among the male, female, and lower 50-year-old or more than 50-year-old subgroups, with statistical differences (P<0.001), but there was no statistical difference in the group without LDCT screening (P=0.73). The sensitivity of the lung cancer high-risk population assessment model was 43.94% (116/264) and the specificity was 77.10% (127,275/165,073). The early diagnosis rate of the screening group was 72.97% (54/74), which was significantly higher than that of the non-screening group [28.48% (43/151)]. CONCLUSIONS The lung cancer high-risk population assessment model of National Key Public Health Program: Cancer Screening Program in Urban China can detect high-risk populations and improve the early diagnosis rate of lung cancer effectively.
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Affiliation(s)
- Yanping Lin
- Department of Yunnan Cancer Center, Yunnan Cancer Center/Yunnan Cancer Hospital/The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Jie Ma
- Department of Yunnan Cancer Center, Yunnan Cancer Center/Yunnan Cancer Hospital/The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Meng Wu
- Department of Yunnan Cancer Center, Yunnan Cancer Center/Yunnan Cancer Hospital/The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Hai Zhou
- Department of Yunnan Cancer Center, Yunnan Cancer Center/Yunnan Cancer Hospital/The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Yanni Lu
- Department of Yunnan Cancer Center, Yunnan Cancer Center/Yunnan Cancer Hospital/The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Yongcun Cen
- Department of Yunnan Cancer Center, Yunnan Cancer Center/Yunnan Cancer Hospital/The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Zhongqin Yuan
- Department of Yunnan Cancer Center, Yunnan Cancer Center/Yunnan Cancer Hospital/The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Zechao Mei
- Department of Yunnan Cancer Center, Yunnan Cancer Center/Yunnan Cancer Hospital/The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Yunchao Huang
- Department of Yunnan Cancer Center, Yunnan Cancer Center/Yunnan Cancer Hospital/The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Yongchun Zhou
- Department of Yunnan Cancer Center, Yunnan Cancer Center/Yunnan Cancer Hospital/The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
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36
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Liang W, Chen Z, Li C, Liu J, Tao J, Liu X, Zhao D, Yin W, Chen H, Cheng C, Yu F, Zhang C, Liu L, Tian H, Cai K, Liu X, Wang Z, Xu N, Dong Q, Chen L, Yang Y, Zhi X, Li H, Tu X, Cai X, Jiang Z, Ji H, Mo L, Wang J, Fan JB, He J. Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test. J Clin Invest 2021; 131:145973. [PMID: 33793424 PMCID: PMC8121527 DOI: 10.1172/jci145973] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 03/18/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUNDCurrent clinical management of patients with pulmonary nodules involves either repeated low-dose CT (LDCT)/CT scans or invasive procedures, yet causes significant patient misclassification. An accurate noninvasive test is needed to identify malignant nodules and reduce unnecessary invasive tests.METHODWe developed a diagnostic model based on targeted DNA methylation sequencing of 389 pulmonary nodule patients' plasma samples and then validation in 140 plasma samples independently. We tested the model in different stages and subtypes of pulmonary nodules.RESULTSA 100-feature model was developed and validated for pulmonary nodule diagnosis; the model achieved a receiver operating characteristic curve-AUC (ROC-AUC) of 0.843 on 140 independent validation samples, with an accuracy of 0.800. The performance was well maintained in (a) a 6 to 20 mm size subgroup (n = 100), with a sensitivity of 1.000 and adjusted negative predictive value (NPV) of 1.000 at 10% prevalence; (b) stage I malignancy (n = 90), with a sensitivity of 0.971; (c) different nodule types: solid nodules (n = 78) with a sensitivity of 1.000 and adjusted NPV of 1.000, part-solid nodules (n = 75) with a sensitivity of 0.947 and adjusted NPV of 0.983, and ground-glass nodules (n = 67) with a sensitivity of 0.964 and adjusted NPV of 0.989 at 10% prevalence. This methylation test, called PulmoSeek, outperformed PET-CT and 2 clinical prediction models (Mayo Clinic and Veterans Affairs) in discriminating malignant pulmonary nodules from benign ones.CONCLUSIONThis study suggests that the blood-based DNA methylation model may provide a better test for classifying pulmonary nodules, which could help facilitate the accurate diagnosis of early stage lung cancer from pulmonary nodule patients and guide clinical decisions.FUNDINGThe National Key Research and Development Program of China; Science and Technology Planning Project of Guangdong Province; The National Natural Science Foundation of China National.
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Affiliation(s)
- Wenhua Liang
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Zhiwei Chen
- AnchorDx Medical Co., Guangzhou, China
- AnchorDx Inc., Fremont, California, USA
| | - Caichen Li
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Jun Liu
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | | | - Xin Liu
- AnchorDx Inc., Fremont, California, USA
| | | | - Weiqiang Yin
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Hanzhang Chen
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Chao Cheng
- Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Fenglei Yu
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Chunfang Zhang
- Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Luxu Liu
- Department of Thoracic Surgery, West China Hospital of Sichuan University, Chengdu, China
| | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Kaican Cai
- Department of Thoracic Surgery, Nanfang Hospital of Southern Medical University, Guangzhou, China
| | - Xiang Liu
- Department of Thoracic Surgery, The Second Hospital, University of South China, Hengyang, China
| | - Zheng Wang
- Department of Thoracic Surgery, Shenzhen People’s Hospital, Shenzhen, China
| | - Ning Xu
- Department of Thoracic Surgery, Anhui Chest Hospital, Hefei, China
| | - Qing Dong
- Department of Thoracic Surgery, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Liang Chen
- Department of Thoracic Surgery, Jiangsu Province Hospital, Nanjing, China
| | - Yue Yang
- Department of Thoracic Surgery, Beijing Cancer Hospital, Beijing, China
| | - Xiuyi Zhi
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Hui Li
- AnchorDx Medical Co., Guangzhou, China
| | | | - Xiangrui Cai
- College of Computer Science, Nankai University, Tianjin, China
| | | | - Hua Ji
- College of Computer Science, Nankai University, Tianjin, China
- Laboratory for Foundations of Computer Science, School of Informatics, University of Edinburgh, United Kingdom
| | - Lili Mo
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Jiaxuan Wang
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Jian-Bing Fan
- AnchorDx Medical Co., Guangzhou, China
- Department of Pathology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
| | - Jianxing He
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
- Department of Thoracic Surgery, Nanfang Hospital of Southern Medical University, Guangzhou, China
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37
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Sola Martínez RA, Pastor Hernández JM, Yanes Torrado Ó, Cánovas Díaz M, de Diego Puente T, Vinaixa Crevillent M. Exhaled volatile organic compounds analysis in clinical pediatrics: a systematic review. Pediatr Res 2021; 89:1352-1363. [PMID: 32919397 DOI: 10.1038/s41390-020-01116-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 07/09/2020] [Accepted: 08/04/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND Measured exhaled volatile organic compounds (VOCs) in breath also referred to as exhaled volatilome have been long claimed as a potential source of non-invasive and clinically applicable biomarkers. However, the feasibility of using exhaled volatilome in clinical practice remains to be demonstrated, particularly in pediatrics where the need for improved non-invasive diagnostic and monitoring methods is most urgent. This work presents the first formal evidence-based judgment of the clinical potential of breath volatilome in the pediatric population. METHODS A rigorous systematic review across Web of Science, SCOPUS, and PubMed databases following the PRISMA statement guidelines. A narrative synthesis of the evidence was conducted and QUADAS-2 was used to assess the quality of selected studies. RESULTS Two independent reviewers deemed 22 out of the 229 records initially found to satisfy inclusion criteria. A summary of breath VOCs found to be relevant for several respiratory, infectious, and metabolic pathologies was conducted. In addition, we assessed their associated metabolism coverage through a functional characterization analysis. CONCLUSION Our results indicate that current research remains stagnant in a preclinical exploratory setting. Designing exploratory experiments in compliance with metabolomics practice should drive forward the clinical translation of VOCs breath analysis. IMPACT What is the key message of your article? Metabolomics practice could help to achieve the clinical utility of exhaled volatilome analysis. What does it add to the existing literature? This work is the first systematic review focused on disease status discrimination using analysis of exhaled breath in the pediatric population. A summary of the reported exhaled volatile organic compounds is conducted together with a functional characterization analysis. What is the impact? Having noted challenges preventing the clinical translation, we summary metabolomics practices and the experimental designs that are closer to clinical practice to create a framework to guide future trials.
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Affiliation(s)
- Rosa A Sola Martínez
- Department of Biochemistry and Molecular Biology (B) and Immunology, University of Murcia and Murcian Institute of Biosanitary Research Virgen de la Arrixaca (IMIB), Murcia, Spain
| | - José M Pastor Hernández
- Department of Biochemistry and Molecular Biology (B) and Immunology, University of Murcia and Murcian Institute of Biosanitary Research Virgen de la Arrixaca (IMIB), Murcia, Spain
| | - Óscar Yanes Torrado
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,Institut d'Investigació Sanitària Pere Virgili (IISPV), Metabolomics Platform, Reus, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Manuel Cánovas Díaz
- Department of Biochemistry and Molecular Biology (B) and Immunology, University of Murcia and Murcian Institute of Biosanitary Research Virgen de la Arrixaca (IMIB), Murcia, Spain
| | - Teresa de Diego Puente
- Department of Biochemistry and Molecular Biology (B) and Immunology, University of Murcia and Murcian Institute of Biosanitary Research Virgen de la Arrixaca (IMIB), Murcia, Spain.
| | - María Vinaixa Crevillent
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,Institut d'Investigació Sanitària Pere Virgili (IISPV), Metabolomics Platform, Reus, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
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Smolarz M, Widlak P. Serum Exosomes and Their miRNA Load-A Potential Biomarker of Lung Cancer. Cancers (Basel) 2021; 13:cancers13061373. [PMID: 33803617 PMCID: PMC8002857 DOI: 10.3390/cancers13061373] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 03/14/2021] [Accepted: 03/15/2021] [Indexed: 12/19/2022] Open
Abstract
Early detection of lung cancer in screening programs is a rational way to reduce mortality associated with this malignancy. Low-dose computed tomography, a diagnostic tool used in lung cancer screening, generates a relatively large number of false-positive results, and its complementation with molecular biomarkers would greatly improve the effectiveness of such programs. Several biomarkers of lung cancer based on different components of blood, including miRNA signatures, were proposed. However, only a few of them have been positively validated in the context of early cancer detection yet, which imposes a constant need for new biomarker candidates. An emerging source of cancer biomarkers are exosomes and other types of extracellular vesicles circulating in body fluids. Hence, different molecular components of serum/plasma-derived exosomes were tested and showed different levels in lung cancer patients and healthy individuals. Several studies focused on the miRNA component of these vesicles. Proposed signatures of exosome miRNA had promising diagnostic value, though none of them have yet been clinically validated. These signatures involved a few dozen miRNA species overall, including a few species that recurred in different signatures. It is worth noting that all these miRNA species have cancer-related functions and have been associated with lung cancer progression. Moreover, a few of them, including known oncomirs miR-17, miR-19, miR-21, and miR-221, appeared in multiple miRNA signatures of lung cancer based on both the whole serum/plasma and serum/plasma-derived exosomes.
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Outcomes of clinical utility in amyloid-PET studies: state of art and future perspectives. Eur J Nucl Med Mol Imaging 2021; 48:2157-2168. [PMID: 33594474 PMCID: PMC8175294 DOI: 10.1007/s00259-020-05187-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 12/28/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE To review how outcomes of clinical utility are operationalized in current amyloid-PET validation studies, to prepare for formal assessment of clinical utility of amyloid-PET-based diagnosis. METHODS Systematic review of amyloid-PET research studies published up to April 2020 that included outcomes of clinical utility. We extracted and analyzed (a) outcome categories, (b) their definition, and (c) their methods of assessment. RESULTS Thirty-two studies were eligible. (a) Outcome categories were clinician-centered (found in 25/32 studies, 78%), patient-/caregiver-centered (in 9/32 studies, 28%), and health economics-centered (5/32, 16%). (b) Definition: Outcomes were mainly defined by clinical researchers; only the ABIDE study expressly included stakeholders in group discussions. Clinician-centered outcomes mainly consisted of incremental diagnostic value (25/32, 78%) and change in patient management (17/32, 53%); patient-/caregiver-centered outcomes considered distress after amyloid-pet-based diagnosis disclosure (8/32, 25%), including quantified burden of procedure for patients' outcomes (n = 8) (1/8, 12.5%), impact of disclosure of results (6/8, 75%), and psychological implications of biomarker-based diagnosis (75%); and health economics outcomes focused on costs to achieve a high-confidence etiological diagnosis (5/32, 16%) and impact on quality of life (1/32, 3%). (c) Assessment: all outcome categories were operationalized inconsistently across studies, employing 26 different tools without formal rationale for selection. CONCLUSION Current studies validating amyloid-PET already assessed outcomes for clinical utility, although non-clinician-based outcomes were inconsistent. A wider participation of stakeholders may help produce a more thorough and systematic definition and assessment of outcomes of clinical utility and help collect evidence informing decisions on reimbursement of amyloid-PET.
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Ostrin EJ, Bantis LE, Wilson DO, Patel N, Wang R, Kundnani D, Adams-Haduch J, Dennison JB, Fahrmann JF, Chiu HT, Gazdar A, Feng Z, Yuan JM, Hanash SM. Contribution of a Blood-Based Protein Biomarker Panel to the Classification of Indeterminate Pulmonary Nodules. J Thorac Oncol 2021; 16:228-236. [PMID: 33137463 PMCID: PMC8218328 DOI: 10.1016/j.jtho.2020.09.024] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 09/22/2020] [Accepted: 09/28/2020] [Indexed: 01/05/2023]
Abstract
RATIONALE The workup and longitudinal monitoring for subjects presenting with pulmonary nodules is a pressing clinical problem. A blood-based biomarker panel potentially has utility for identifying subjects at higher risk for harboring a malignant nodule for whom additional workup would be indicated or subjects at reduced risk for whom imaging-based follow-up would be indicated. OBJECTIVES To assess whether a previously described four-protein biomarker panel, reported to improve assessment of lung cancer risk compared with a smoking-based lung cancer risk model, can provide discrimination between benign and malignant indeterminate pulmonary nodules. METHODS A previously validated multiplex enzyme-linked immunoassay was performed on matched case and control samples from each cohort. MEASUREMENTS The biomarker panel was tested in two case-control cohorts of patients presenting with indeterminate pulmonary nodules at the University of Pittsburgh Medical Center and the University of Texas Southwestern. MAIN RESULTS In both cohorts, the biomarker panel resulted in improved prediction of lung cancer risk over a model on the basis of nodule size alone. Of particular note, the addition of the marker panel to nodule size greatly improved sensitivity at a high specificity in both cohorts. CONCLUSIONS A four-marker biomarker panel, previously validated to improve lung cancer risk prediction, was found to also have utility in distinguishing benign from malignant indeterminate pulmonary nodules. Its performance in improving sensitivity at a high specificity indicates potential utility of the marker panel in assessing likelihood of malignancy in otherwise indeterminate nodules.
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Affiliation(s)
- Edwin J. Ostrin
- Department of General Internal Medicine, Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Leonidas E. Bantis
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS
| | - David O. Wilson
- Division of Pulmonary, Allergy and Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA
| | - Nikul Patel
- McCombs Institute for the Early Detection and Treatment of Cancer, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Renwei Wang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA
| | - Deepali Kundnani
- McCombs Institute for the Early Detection and Treatment of Cancer, University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Jennifer B. Dennison
- McCombs Institute for the Early Detection and Treatment of Cancer, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Johannes F. Fahrmann
- McCombs Institute for the Early Detection and Treatment of Cancer, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Hsienchang Thomas Chiu
- Pulmonary and Critical Care Medicine, University of Texas Southwestern Medical Center, Dallas, TX
| | - Adi Gazdar
- Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, TX
| | - Ziding Feng
- Department of Biostatistics, Fred Hutchinson Cancer Center, Seattle, WA
| | - Jian-Min Yuan
- Division of Pulmonary, Allergy and Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Samir M. Hanash
- McCombs Institute for the Early Detection and Treatment of Cancer, University of Texas MD Anderson Cancer Center, Houston, TX
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Rodríguez M, Ajona D, Seijo LM, Sanz J, Valencia K, Corral J, Mesa-Guzmán M, Pío R, Calvo A, Lozano MD, Zulueta JJ, Montuenga LM. Molecular biomarkers in early stage lung cancer. Transl Lung Cancer Res 2021; 10:1165-1185. [PMID: 33718054 PMCID: PMC7947407 DOI: 10.21037/tlcr-20-750] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians’ capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease’s mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.
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Affiliation(s)
- María Rodríguez
- Department of Thoracic Surgery, Clínica Universidad de Navarra, Madrid, Spain
| | - Daniel Ajona
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain
| | - Luis M Seijo
- Department of Pulmonology, Clínica Universidad de Navarra, Madrid, Spain.,Centro de Investigación Biomédica en Red Enfermedades Respiratorias (CIBERES), Madrid, Spain
| | - Julián Sanz
- Department of Pathology, Clínica Universidad de Navarra, Madrid, Spain
| | - Karmele Valencia
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain
| | - Jesús Corral
- Department of Oncology, Clínica Universidad de Navarra, Madrid, Spain
| | - Miguel Mesa-Guzmán
- Department of Thoracic Surgery, Clínica Universidad de Navarra, Pamplona, Spain
| | - Rubén Pío
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain
| | - Alfonso Calvo
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Pathology, Anatomy and Physiology, Schools of Medicine and Sciences, University of Navarra, Pamplona, Spain
| | - María D Lozano
- Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Pathology, Anatomy and Physiology, Schools of Medicine and Sciences, University of Navarra, Pamplona, Spain.,Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Javier J Zulueta
- Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Pulmonology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Luis M Montuenga
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Pathology, Anatomy and Physiology, Schools of Medicine and Sciences, University of Navarra, Pamplona, Spain
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Hopkins RJ, Duan F, Gamble GD, Chiles C, Cavadino A, Billings P, Aberle D, Young RP. Chr15q25 genetic variant (rs16969968) independently confers risk of lung cancer, COPD and smoking intensity in a prospective study of high-risk smokers. Thorax 2021; 76:272-280. [PMID: 33419953 DOI: 10.1136/thoraxjnl-2020-214839] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 10/27/2020] [Accepted: 12/08/2020] [Indexed: 12/12/2022]
Abstract
IMPORTANCE While cholinergic receptor nicotinic alpha 5 (CHRNA5) variants have been linked to lung cancer, chronic obstructive pulmonary disease (COPD) and smoking addiction in case-controls studies, their corelationship is not well understood and requires retesting in a cohort study. OBJECTIVE To re-examine the association between the CHRNA5 variant (rs16969968 AA genotype) and the development of lung cancer, relative to its association with COPD and smoking. METHODS In 9270 Non-Hispanic white subjects from the National Lung Screening Trial, a substudy of high-risk smokers were followed for an average of 6.4 years. We compared CHRNA5 genotype according to baseline smoking exposure, lung function and COPD status. We also compared the lung cancer incidence rate, and used multiple logistic regression and mediation analysis to examine the role of the AA genotype of the CHRNA5 variant in smoking exposure, COPD and lung cancer. RESULTS As previously reported, we found the AA high-risk genotype was associated with lower lung function (p=0.005), greater smoking intensity (p<0.001), the presence of COPD (OR 1.28 (95% CI 1.10 to 1.49) p=0.0015) and the development of lung cancer (HR 1.41, (95% CI 1.03 to 1.93) p=0.03). In a mediation analyses, the AA genotype was independently associated with smoking intensity (OR 1.42 (95% CI 1.25 to 1.60, p<0.0001), COPD (OR 1.25, (95% CI 1.66 to 2.53), p=0.0015) and developing lung cancer (OR 1.37, (95% CI 1.03 to 1.82) p=0.03). CONCLUSION In this large-prospective study, we found the CHRNA5 rs 16 969 968 AA genotype to be independently associated with smoking exposure, COPD and lung cancer (triple whammy effect).
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Affiliation(s)
- Raewyn J Hopkins
- The University of Auckland Faculty of Medical and Health Sciences, Auckland, New Zealand
| | - Fenghai Duan
- Department of Biostatistics and Centre for Statistical Science, Brown University, Providence, Rhode Island, USA
| | - Greg D Gamble
- The University of Auckland Faculty of Medical and Health Sciences, Auckland, New Zealand
| | - Caroline Chiles
- Department of Radiology, Wake Forest Baptist Medical Comprehensive Cancer Center, Winston-Salem, North Carolina, USA
| | - Alana Cavadino
- The University of Auckland Faculty of Medical and Health Sciences, Auckland, New Zealand
| | | | - Denise Aberle
- Department of Radiological Sciences, University of California Los Angeles David Geffen School of Medicine, Los Angeles, California, USA
| | - Robert P Young
- The University of Auckland Faculty of Medical and Health Sciences, Auckland, New Zealand
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Data preprocessing workflow for exhaled breath analysis by GC/MS using open sources. Sci Rep 2020; 10:22008. [PMID: 33319832 PMCID: PMC7738550 DOI: 10.1038/s41598-020-79014-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 12/03/2020] [Indexed: 12/24/2022] Open
Abstract
The noninvasive diagnosis and monitoring of high prevalence diseases such as cardiovascular diseases, cancers and chronic respiratory diseases are currently priority objectives in the area of health. In this regard, the analysis of volatile organic compounds (VOCs) has been identified as a potential noninvasive tool for the diagnosis and surveillance of several diseases. Despite the advantages of this strategy, it is not yet a routine clinical tool. The lack of reproducible protocols for each step of the biomarker discovery phase is an obstacle of the current state. Specifically, this issue is present at the data preprocessing step. Thus, an open source workflow for preprocessing the data obtained by the analysis of exhaled breath samples using gas chromatography coupled with single quadrupole mass spectrometry (GC/MS) is presented in this paper. This workflow is based on the connection of two approaches to transform raw data into a useful matrix for statistical analysis. Moreover, this workflow includes matching compounds from breath samples with a spectral library. Three free packages (xcms, cliqueMS and eRah) written in the language R are used for this purpose. Furthermore, this paper presents a suitable protocol for exhaled breath sample collection from infants under 2 years of age for GC/MS.
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Tanner NT, Springmeyer SC, Porter A, Jett JR, Mazzone P, Vachani A, Silvestri GA. Assessment of Integrated Classifier's Ability to Distinguish Benign From Malignant Lung Nodules: Extended Analyses and 2-Year Follow-Up Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial. Chest 2020; 159:1283-1287. [PMID: 33171158 DOI: 10.1016/j.chest.2020.10.069] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 08/17/2020] [Accepted: 10/15/2020] [Indexed: 11/29/2022] Open
Affiliation(s)
- Nichole T Tanner
- Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson Veterans Affairs Medical Center, Charleston, SC; Department of Medicine, Thoracic Oncology Research Group, Medical University of South Carolina, Charleston, SC.
| | | | - Alex Porter
- Department of Clinical Operations and Medical Affairs, AllerGenis, Hatfield, PA
| | | | | | | | - Gerard A Silvestri
- Department of Medicine, Thoracic Oncology Research Group, Medical University of South Carolina, Charleston, SC
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Lung cancer screening by nodule volume in Lung-RADS v1.1: negative baseline CT yields potential for increased screening interval. Eur Radiol 2020; 31:1956-1968. [PMID: 32997182 PMCID: PMC7979670 DOI: 10.1007/s00330-020-07275-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 07/21/2020] [Accepted: 09/09/2020] [Indexed: 12/17/2022]
Abstract
Objectives The 2019 Lung CT Screening Reporting & Data System version 1.1 (Lung-RADS v1.1) introduced volumetric categories for nodule management. The aims of this study were to report the distribution of Lung-RADS v1.1 volumetric categories and to analyse lung cancer (LC) outcomes within 3 years for exploring personalized algorithm for lung cancer screening (LCS). Methods Subjects from the Multicentric Italian Lung Detection (MILD) trial were retrospectively selected by National Lung Screening Trial (NLST) criteria. Baseline characteristics included selected pre-test metrics and nodule characterization according to the volume-based categories of Lung-RADS v1.1. Nodule volume was obtained by segmentation with dedicated semi-automatic software. Primary outcome was diagnosis of LC, tested by univariate and multivariable models. Secondary outcome was stage of LC. Increased interval algorithms were simulated for testing rate of delayed diagnosis (RDD) and reduction of low-dose computed tomography (LDCT) burden. Results In 1248 NLST-eligible subjects, LC frequency was 1.2% at 1 year, 1.8% at 2 years and 2.6% at 3 years. Nodule volume in Lung-RADS v1.1 was a strong predictor of LC: positive LDCT showed an odds ratio (OR) of 75.60 at 1 year (p < 0.0001), and indeterminate LDCT showed an OR of 9.16 at 2 years (p = 0.0068) and an OR of 6.35 at 3 years (p = 0.0042). In the first 2 years after negative LDCT, 100% of resected LC was stage I. The simulations of low-frequency screening showed a RDD of 13.6–21.9% and a potential reduction of LDCT burden of 25.5–41%. Conclusions Nodule volume by semi-automatic software allowed stratification of LC risk across Lung-RADS v1.1 categories. Personalized screening algorithm by increased interval seems feasible in 80% of NLST eligible. Key Points • Using semi-automatic segmentation of nodule volume, Lung-RADS v1.1 selected 10.8% of subjects with positive CT and 96.87 relative risk of lung cancer at 1 year, compared to negative CT. • Negative low-dose CT by Lung-RADS v1.1 was found in 80.6% of NLST eligible and yielded 40 times lower relative risk of lung cancer at 2 years, compared to positive low-dose CT; annual screening could be preference sensitive in this group. • Semi-automatic segmentation of nodule volume and increased screening interval by volumetric Lung-RADS v1.1 could retrospectively suggest a 25.5–41% reduction of LDCT burden, at the cost of 13.6–21.9% rate of delayed diagnosis. Electronic supplementary material The online version of this article (10.1007/s00330-020-07275-w) contains supplementary material, which is available to authorized users.
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Marsh TL, Janes H, Pepe MS. Statistical inference for net benefit measures in biomarker validation studies. Biometrics 2020; 76:843-852. [PMID: 31732971 PMCID: PMC7228830 DOI: 10.1111/biom.13190] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Revised: 10/21/2019] [Accepted: 10/29/2019] [Indexed: 12/21/2022]
Abstract
Referral strategies based on risk scores and medical tests are commonly proposed. Direct assessment of their clinical utility requires implementing the strategy and is not possible in the early phases of biomarker research. Prior to late-phase studies, net benefit measures can be used to assess the potential clinical impact of a proposed strategy. Validation studies, in which the biomarker defines a prespecified referral strategy, are a gold standard approach to evaluating biomarker potential. Uncertainty, quantified by a confidence interval, is important to consider when deciding whether a biomarker warrants an impact study, does not demonstrate clinical potential, or that more data are needed. We establish distribution theory for empirical estimators of net benefit and propose empirical estimators of variance. The primary results are for the most commonly employed estimators of net benefit: from cohort and unmatched case-control samples, and for point estimates and net benefit curves. Novel estimators of net benefit under stratified two-phase and categorically matched case-control sampling are proposed and distribution theory developed. Results for common variants of net benefit and for estimation from right-censored outcomes are also presented. We motivate and demonstrate the methodology with examples from lung cancer research and highlight its application to study design.
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Affiliation(s)
- Tracey L. Marsh
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A
| | - Holly Janes
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A
| | - Margaret S. Pepe
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A
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Gasparri R, Sedda G, Spaggiari L. Biomarkers in Early Diagnosis and Early Stage Lung Cancer: The Clinician's Point of View. J Clin Med 2020; 9:E1790. [PMID: 32526831 PMCID: PMC7355900 DOI: 10.3390/jcm9061790] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 06/05/2020] [Indexed: 12/19/2022] Open
Abstract
Starting from the work of Ulivi and colleagues, we aim to summarize the research area of biomarkers for early diagnosis and early stage lung cancer.
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Affiliation(s)
- Roberto Gasparri
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Via Ripamonti, 435, 20141 Milan, Italy; (G.S.); (L.S.)
| | - Giulia Sedda
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Via Ripamonti, 435, 20141 Milan, Italy; (G.S.); (L.S.)
| | - Lorenzo Spaggiari
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Via Ripamonti, 435, 20141 Milan, Italy; (G.S.); (L.S.)
- Department of Oncology and Hemato-oncology, University of Milan, 20122 Milan, Italy
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48
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Fox AH, Tanner NT. Approaches to lung nodule risk assessment: clinician intuition versus prediction models. J Thorac Dis 2020; 12:3296-3302. [PMID: 32642253 PMCID: PMC7330782 DOI: 10.21037/jtd.2020.03.68] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Pulmonary nodules are increasingly identified on imaging exams performed for a number of clinical presentations and can pose a diagnostic problem for clinicians. Guideline-directed management algorithms are structured on nodule pre-test probability of malignancy. The risk of malignancy can be clinician-assigned or calculated utilizing validated risk prediction calculators. Once pre-test probability of cancer is estimated, nodule management options range from a conservative approach with serial imaging to more invasive measures including biopsy procedures or surgical resection. Here we review pulmonary nodule management with a focus on methods for assigning malignancy risk and highlight novel ways currently under active research to improve nodule risk assessment and management.
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Affiliation(s)
- Adam H Fox
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Nichole T Tanner
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Medical University of South Carolina, Charleston, SC, USA.,Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson Veterans Affairs Hospital, Charleston, SC, USA
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Wang W, Zhuang R, Ma H, Fang L, Wang Z, Lv W, Hu J. The diagnostic value of a seven-autoantibody panel and a nomogram with a scoring table for predicting the risk of non-small-cell lung cancer. Cancer Sci 2020; 111:1699-1710. [PMID: 32108977 PMCID: PMC7226194 DOI: 10.1111/cas.14371] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 02/20/2020] [Accepted: 02/22/2020] [Indexed: 12/17/2022] Open
Abstract
The early detection of non-small-cell lung cancer (NSCLC) remains a common concern. The aim of our study was to validate the diagnostic value of a seven-autoantibody (7-AAB) panel compared with radiological diagnosis for NSCLC. We constructed a nomogram and a scoring table based on the 7-AAB panel's result to predict the risk of NSCLC. We prospectively enrolled 268 patients who presented with radiological lesions and underwent both the 7-AAB panel test and pathological diagnosis by surgical resection. A comparison between the 7-AAB panel and radiological diagnosis was performed. A nomogram and a scoring table based on the 7-AAB panel's result to predict the risk of NSCLC were constructed and internally validated. The 7-AAB panel test had a specificity of 90.2% and a positive predictive value (PPV) of 92.7%, which were significantly higher than those of the radiological diagnosis. The 7-AAB panel also showed a preferable sensitivity in patients with early-stage disease. Seven factors, including the 7-AAB panel results, were integrated into the nomogram. For more convenient application, we formulated a scoring table based on the nomogram. The area under the receiver operating characteristic curve was 0.840 and 0.860 in the training group and validation group, respectively, which was higher than that using the 7-AAB panel or radiological diagnosis alone. This study reveals that our 7-AAB panel has clinical value in the diagnosis of NSCLC. The utility of our nomogram and the scoring table indicated that they have the potential to assist clinicians in avoiding unnecessary treatment or needless follow-up.
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Affiliation(s)
- Weidong Wang
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
| | - Runzhou Zhuang
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
| | - Honghai Ma
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
| | - Likui Fang
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
| | - Zhitian Wang
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
| | - Wang Lv
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
| | - Jian Hu
- Department of Thoracic SurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhouChina
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50
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Campos-Carrillo A, Weitzel JN, Sahoo P, Rockne R, Mokhnatkin JV, Murtaza M, Gray SW, Goetz L, Goel A, Schork N, Slavin TP. Circulating tumor DNA as an early cancer detection tool. Pharmacol Ther 2020; 207:107458. [PMID: 31863816 PMCID: PMC6957244 DOI: 10.1016/j.pharmthera.2019.107458] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 12/12/2019] [Indexed: 02/07/2023]
Abstract
Circulating tumor DNA holds substantial promise as an early detection biomarker, particularly for cancers that do not have currently accepted screening methodologies, such as ovarian, pancreatic, and gastric cancers. Many features intrinsic to ctDNA analysis may be leveraged to enhance its use as an early cancer detection biomarker: including ctDNA fragment lengths, DNA copy number variations, and associated patient phenotypic information. Furthermore, ctDNA testing may be synergistically used with other multi-omic biomarkers to enhance early detection. For instance, assays may incorporate early detection proteins (i.e., CA-125), epigenetic markers, circulating tumor RNA, nucleosomes, exosomes, and associated immune markers. Many companies are currently competing to develop a marketable early cancer detection test that leverages ctDNA. Although some hurdles (like early stage disease assay accuracy, high implementation costs, confounding from clonal hematopoiesis, and lack of clinical utility studies) need to be addressed before integration into healthcare, ctDNA assays hold substantial potential as an early cancer screening test.
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Affiliation(s)
| | | | - Prativa Sahoo
- City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Russell Rockne
- City of Hope National Medical Center, Duarte, CA 91010, USA
| | | | - Muhammed Murtaza
- Translational Genomics Research Institute, Phoenix, AZ 85004, USA
| | - Stacy W Gray
- City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Laura Goetz
- City of Hope National Medical Center, Duarte, CA 91010, USA; Translational Genomics Research Institute, Phoenix, AZ 85004, USA
| | - Ajay Goel
- City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Nicholas Schork
- City of Hope National Medical Center, Duarte, CA 91010, USA; Translational Genomics Research Institute, Phoenix, AZ 85004, USA
| | - Thomas P Slavin
- City of Hope National Medical Center, Duarte, CA 91010, USA.
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