JCB J Circ Biomark 2022; 11: 24-27ISSN 1849-4544 | DOI: 10.33393/jcb.2022.2337ORIGINAL RESEARCH ARTICLE Journal of Circulating Biomarkers - ISSN 1849-4544 - www.aboutscience.eu/jcb © 2022 The Authors. This article is published by AboutScience and licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Commercial use is not permitted and is subject to Publisher’s permissions. Full information is available at www.aboutscience.eu syndrome in patients with severe SARS-CoV-2. This may cause harmful tissue damage, multiple organ failure and hypercoagulability, and is associated with poor clinical out- comes (1). Conversely it is known that people with immune deficiency have an increase in mortality when admitted to hospital with Covid-19 (2). A range of serum autoantibodies, such as nucleolar antinuclear antibodies (ANAs), antineutro- phil cytoplasmic antibody (ANCA), anti-cyclic citrullinated peptide, and antiphospholipid autoantibodies, have already been detected in severe SARS-CoV-2 patients and linked to disease severity, reflecting immune system dysregulation in patients with severe SARS-CoV-2 lung disease (3-5). It is not yet clear, however, whether patients who exhibit such robust immune response to SARS-CoV-2 have higher background levels of antibody and autoantibody responsiveness when compared to patients who develop mild disease (6), and for how long the level of autoantibodies persist. One form of antibody response to the development of abnormal cell surface characteristics is tumour-associated autoantibodies. These proteins are produced early in tumorigenesis, being measurable up to 5 years before the development of clinical symptoms (7). They represent biologically amplified markers, Assessment of background levels of autoantibodies as a prognostic marker for severe SARS-CoV-2 infection Frank M. Sullivan1, Agnes Tello1,2, Petra Rauchhaus3, Virginia Hernandez Santiago1, Fergus Daly1 1Division of Population and Behavioural Sciences, St Andrews University Medical School, St Andrews - United Kingdom 2Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh - United Kingdom 3Tayside Clinical Trials Unit, University of Dundee, Dundee - United Kingdom ABSTRACT Background: Patients with more severe forms of SARS-CoV-2 exhibit activation of immunological cascades. Participants (current or ex-smokers with at least 20 years pack history) in a trial (Early Diagnosis of Lung Cancer, Scotland [ECLS]) of autoantibody detection to predict lung cancer risk had seven autoantibodies measured 5 years before the pandemic. This study compared the response to Covid infection in study participants who tested positive and negative to antibodies to tumour-associated antigens: p53, NY-ESO-1, CAGE, GBU4-5, HuD, MAGE A4 and SOX2. Methods: Autoantibody data from the ECLS study was deterministically linked to the EAVE II database, a national, real-time prospective cohort using Scotland’s health data infrastructure, to describe the epidemiology of SARS- CoV-2 infection, patterns of healthcare use and outcomes. The strength of associations was explored using a network algorithm for exact contingency table significance testing by permutation. Results: There were no significant differences discerned between SARS-CoV-2 test results and EarlyCDT-Lung test results (p = 0.734). An additional analysis of intensive care unit (ICU) admissions detected no significant differences between those who tested positive and negative. Subgroup analyses showed no difference in COVID-19 positivity or death rates amongst those diagnosed with chronic obstructive pulmonary disease (COPD) with positive and negative EarlyCDT results. Conclusions: This hypothesis-generating study demonstrated no clinically valuable or statistically significant asso- ciations between EarlyCDT positivity in 2013-15 and the likelihood of SARS-CoV-2 positivity in 2020, ICU admis- sion or death in all participants (current or ex-smokers with at least 20 years pack history) or in those with COPD or lung cancer. Keywords: COVID-19, Current or ex-smokers, Lung cancer, Mortality prediction, Serum biomarkers Received: September 7, 2021 Accepted: April 20, 2022 Published online: May 3, 2022 Corresponding author: Frank M. Sullivan Division of Population and Behavioural Sciences St Andrews University Medical School St Andrews - United Kingdom fms20@st-andrews.ac.uk Introduction Patients infected with Covid-19 show a range of immune responses, from weaker immune responses in asymptom- atic individuals, to symptomatic patients showing a varying degree of immune dysregulation. These may be manifested by increased levels of interleukins, C-reactive protein and D-dimer, along with lymphopenia, monocytosis and neutro- philia. Extremely high levels of proinflammatory cytokines can lead to a cytokine storm and macrophage activation https://doi.org/10.33393/jcb.2022.2337 https://creativecommons.org/licenses/by-nc/4.0/legalcode mailto:fms20@st-andrews.ac.uk Sullivan et al. J Circ Biomark 2022; 11: 25 © 2022 The Authors. Published by AboutScience - www.aboutscience.eu increasing the detectable signal for the corresponding level of antigen (8). They persist in the circulation with half-lives of typically up to 30 days (9). The EarlyCDT-Lung test is an enzyme-linked immuno- sorbent assay (ELISA) that measures seven autoantibodies, each with individual specificity for the following tumour- associated antigens (TAAs): p53, NY-ESO-1, CAGE, GBU4-5, HuD, MAGE A4 and SOX2 (10). A sample is positive if at least one autoantibody is elevated above a predetermined cut- off (11). The test has been developed throughout the pre- clinical, clinical assay validation and retrospective biomarker development pathway stages. In cohort studies, it has dem- onstrated a specificity of 91% and sensitivity of 41%. The Early Diagnosis of Lung Cancer Scotland (ECLS) study was a phase IV biomarker trial using EarlyCDT-Lung followed by imaging in 12,208 smokers and ex-smokers aged 50-75 at risk of developing lung cancer recruited from General Practices in Scotland (12,13). A total of 6,088 participants in the inter- vention arm received the EarlyCDT-Lung test at the baseline visit and 598 (9.8%) had a positive autoantibody result. In the 2-year analysis of the ECLS trial, EarlyCDT-Lung was shown to reduce late stage presentations of lung cancer. We have investigated whether the production of autoan- tibodies in response to cell surface abnormalities in cancer, as measured by the baseline EarlyCDT-Lung test in the ECLS trial, was associated with more severe disease in at-risk par- ticipants (current and former smokers) who then developed a SARS-CoV-2 infection 5-6 years later. Methods Participants aged 50-75 who were current or ex-smokers with at least 20 years pack history were recruited to ECLS between December 2013 and April 2015, and all baseline assessments of plasma antibody levels occurred during this time (14). SARS-CoV-2 status and outcome data for ECLS par- ticipants during 2020 were obtained from the EAVE II data- base, which is a national, real-time prospective cohort using Scotland’s health data infrastructure, to describe the epide- miology of SARS-CoV-2 infection, patterns of healthcare use and outcomes (15,16). Data from both sources was linked using Scotland’s Community Health Index (CHI) number at the University of Dundee’s Health Informatics Centre (HIC) (17,18). The strength of associations was explored using a network algorithm for exact contingency table significance testing by permutation. This approach is appropriate for the sparseness of the data here, where an approximate chi-squared analysis would provide severely discrepant outputs. (For 2 × 2 con- tingency tables, the network algorithm reduces identically to Fisher’s exact test.) Results There were no significant differences discerned between SARS-CoV-2 test results and EarlyCDT-Lung test results (posi- tive/negative) (p = 0.734); or likewise between SARS-CoV-2 test results and EarlyCDT-Lung test results (positive/negative/ control) (p = 0.779); or finally between SARS-CoV-2 test results and Treatment (tested/not tested) (p = 0.587). An additional analysis of intensive care unit (ICU) admissions detected no significant differences between those who tested positive and negative. There was no difference in COVID-19 positivity or death rates amongst those diagnosed with lung cancer with posi- tive and negative EarlyCDT-Lung test results (Tab. I). Table I - SARS-CoV-2 test results by EarlyCDT-Lung test result Result of SARS- COV-2 test Positive Negative Control N % N % N % Positive 9 6.7 86 7.8 84 7.0 Negative 126 93.3 1021 92.2 1110 93.0 Total 135 100 1107 100 1194 100 Patient deceased No 131 97.0 1072 96.8 1155 96.7 Yes 4 3.0 35 3.2 39 3.3 Total 135 100 1107 100 1194 100 In Table II, nil significance was found. Table II - Outcomes in at-risk participants (current and former smokers) with lung cancer EarlyCDT-Lung test result Stage Test- positive Test- negative Not tested Total N % N % N % N % Stage 3 0 (0.0) 1 (14.3) 4 (44.4) 5 (27.8) Stage 4 0 (0.0) 1 (14.3) 0 (0.0) 1 (5.6) Other 2 (0.0) 5 (71.4) 5 (55.6) 12 (66.7) Total 2 (100) 7 (100) 9 (100) 18 (100) Covid result N % N % N % N % Test-positive 0 (0.0) 1 (14.3) 1 (11.1) 2 (11.1) Test-negative 2 (100.0) 6 (85.7) 8 (88.9) 16 (88.9) Total 2 (100) 7 (100) 9 (100) 18 (100) OR** = 0.00 (0.00, 66.5) p = 1.0 Hospitalized* N % N % N % N % No 0 (0.0) 4 (57.1) 3 (33.3) 7 (38.9) Yes 2 (100.0) 3 (42.9) 6 (66.7) 11 (61.1) Total 2 (100) 7 (100) 9 (100) 18 (100) OR** = 0.00 (0.00, 4.20) p = 0.44 Death* N % N % N % N % No 2 (100.0) 6 (85.7) 9 (100.0) 17 (94.4) Yes 0 (0.0) 1 (14.3) 0 (0.0) 1 (5.6) Total 2 (100) 7 (100) 9 (100) 18 (100) OR** = 9999 (0.015, 9999) p = 1.0 *Event within 28 days of a Covid test. **Odds ratio (Test-positive vs Test-negative). No association between previous autoantibody expression and development of SARS-CoV-226 © 2022 The Authors. Journal of Circulating Biomarkers - ISSN 1849-4544 - www.aboutscience.eu/jcb Table III shows no difference in COVID-19 positivity or death rates amongst those diagnosed with chronic obstruc- tive pulmonary disease (COPD) with positive and negative EarlyCDT results. Table III - Outcomes in at-risk participants (current and former smokers) with COPD EarlyCDT-Lung test result Covid result Test- positive Test- negative Not tested Total N % N % N % N % Positive 1 (6.6) 3 (2.6) 9 (8.3) 13 (5.4) Negative 15 (93.8) 113 (97.4) 100 (91.7) 228 (94.6) Total 16 (100) 116 (100) 109 (100) 241 (100) OR** = 2.51 (0.0913, 24.13) p = 0.407 Hospitalized* N % N % N % N % No 9 (56.3) 68 (58.6) 58 (53.2) 135 (56.0) Yes 7 (43.8) 48 (41.4) 51 (46.8) 106 (44.0) Total 16 (100) 116 (100) 109 (100) 241 (100) OR** = 0.908 (0.312, 2.858) p = 1.0 Death* N % N % N % N % No 16 (100.0) 110 (94.8) 105 (96.3) 231 (95.9) Yes 0 (0.0) 6 (5.2) 4 (3.7) 10 (4.1) Total 16 (100) 116 (100) 109 (100) 241 (100) OR** = 9999 (0.176, 9999) p = 1.0 *Event within 28 days of a Covid test. **Odds ratio (Test-positive vs Test-negative). Discussion and conclusions No clinically valuable or statistically significant associa- tions between EarlyCDT-Lung positivity in 2013-15 and the likelihood of SARS-CoV-2 positivity in 2020, ICU admission or death were found. This was true for the entire study cohort and in subgroup analyses of at-risk participants (current and former smokers) with lung cancer and COPD. This is in contra- distinction to those exhibiting the nucleolar immunofluores- cence pattern where a significant association with interstitial lung SARS-CoV-2 disease has been demonstrated (19). Strengths of the study include the community-based sampling of the ECLS cohort, large numbers of the cohort who had a Covid test validated by laboratory and outcome assessment. Weaknesses include the time which had elapsed between the initial trial and the onset of the pandemic, as well as small numbers of study subjects who were in the sub- group analyses. Some studies have shown that some routine clinical labo- ratory tests, such as lymphocyte count, lactate dehydroge- nase and D-dimer are known to be affected in patients with COVID-19 (20), with lymphopenia, raised lactate dehydro- genase and elevated D-dimer being associated with worse disease severity and outcomes (21-23). Other studies have shown significant differences in inflammatory markers amongst patients who required ICU admission compared to patients who have not, and markers of infection and inflam- mation such as C-reactive protein, procalcitonin, and fer- ritin which are, as expected, correlated with severe disease (24-27). This hypothesis-generating study did not find a clear asso- ciation between the expression of tumour-associated anti- bodies in the ECLS cohort of at-risk participants (all current and former smokers) and the development of SARS-CoV-2 infection and its complications 5 years later. Author disclosures This project was funded by The Lung Foundation. The funder had no role in the design, conduct or analysis of the study. The authors have no other financial conflicts to disclose. The authors confirm that all appropriate ethical guidelines for the use of human subjects have been followed and ethics committee review has been obtained. The authors confirm that all necessary patient/participant consent or assent has been obtained, and the appropriate institutional forms have been archived. A version of the article appears as a preprint on ResearchSquare (DOI: 10.21203/rs.3.rs-842075/v1) Institutional approval was provided by the University of St Andrews. References 1. Jamal M, Bangash HI, Habiba M, et al. Immune dysregulation and system pathology in COVID-19. Virulence. 2021;12(1): 918-936. CrossRef PubMed 2. Geretti AM, Stockdale AJ, Kelly SH, et al. Outcomes of COVID-19 related hospitalization among people with HIV in the ISARIC WHO Clinical Characterization Protocol (UK): a prospective observational study. Clin Infect Dis. 2021 Oct 23:ciaa1605. CrossRef PubMed 3. Chang SH, Minn D, Kim YK. Autoantibodies in moderate and critical cases of COVID-19. Clin Transl Sci. 2021;14(5):1625- 1626. CrossRef PubMed 4. Chauvineau-Grenier A, Bastard P, Servajean A, et al. Auto- antibodies neutralizing type I interferons in 20% of COVID-19 deaths in a French hospital. J Clin Immunol. 2022 Apr;42(3): 459-470. CrossRef PubMed 5. Pascolini S, Vannini A, Deleonardi G, et al. COVID-19 and immu- nological dysregulation: can autoantibodies be useful? Clin Transl Sci. 2021;14(2):502-508. CrossRef PubMed 6. Widjaja G, Turki Jalil A, Sulaiman Rahman H, et al. Humoral immune mechanisms involved in protective and pathological immunity during COVID-19. Hum Immunol. 2021 Jul 1:S0198- 8859(21)00174-9. CrossRef PubMed 7. Zhong L, Coe SP, Stromberg AJ, Khattar NH, Jett JR, Hirschowitz EA. Profiling tumor-associated antibodies for early detection. CrossRef PubMed 8. Chu GCW, Lazare K, Sullivan F. Serum and blood based bio- markers for lung cancer screening: a systematic review. BMC Cancer. 2018;18(1):181. CrossRef PubMed 9. Anderson KS, LaBaer J. The sentinel within: exploiting the immune system for cancer biomarkers. J Proteome Res. 2005; 4(4):1123-1133. CrossRef PubMed 10. Lam S, Boyle P, Healey GF, et al. EarlyCDT-Lung: an immunobio- marker test as an aid to early detection of lung cancer. Cancer Prev Res (Phila). 2011;4(7):1126-1134. CrossRef PubMed 11. Murray A, Chapman CJ, Healey G, et al. Technical validation of an autoantibody test for lung cancer. Ann Oncol. 2010;21(8): 1687-1693. CrossRef PubMed https://doi.org/10.21203/rs.3.rs-842075/v1 https://doi.org/10.1080/21505594.2021.1898790 https://pubmed.ncbi.nlm.nih.gov/33757410/ https://doi.org/10.1093/cid/ciaa1605 https://pubmed.ncbi.nlm.nih.gov/33095853/ https://doi.org/10.1111/cts.13036 https://www.ncbi.nlm.nih.gov/pubmed/33934534 https://doi.org/10.1007/s10875-021-01203-3 https://pubmed.ncbi.nlm.nih.gov/35083626/ https://doi.org/10.1111/cts.12908 https://www.ncbi.nlm.nih.gov/pubmed/32989903 https://doi.org/10.1016/j.humimm.2021.06.011 https://pubmed.ncbi.nlm.nih.gov/34229864/ https://doi.org/10.1016/S1556-0864(15)30352-X https://www.ncbi.nlm.nih.gov/pubmed/17409910 https://doi.org/10.1186/s12885-018-4024-3 https://www.ncbi.nlm.nih.gov/pubmed/29439651 https://doi.org/10.1021/pr0500814 https://www.ncbi.nlm.nih.gov/pubmed/16083262 https://doi.org/10.1158/1940-6207.CAPR-10-0328 https://www.ncbi.nlm.nih.gov/pubmed/21733826 https://doi.org/10.1093/annonc/mdp606 https://www.ncbi.nlm.nih.gov/pubmed/20124350 Sullivan et al. J Circ Biomark 2022; 11: 27 © 2022 The Authors. Published by AboutScience - www.aboutscience.eu 12. Pepe MS, Etzioni R, Feng Z, et al. Phases of biomarker devel- opment for early detection of cancer. J Natl Cancer Inst. 2001 Jul;93(14):1054-1061. CrossRef PubMed 13. Sullivan FM, Mair FS, Anderson W, et al; Early Diagnosis of Lung Cancer Scotland (ECLS) Team. Earlier diagnosis of lung cancer in a randomised trial of an autoantibody blood test followed by imaging. Eur Respir J. 2020;57:2000670. CrossRef PubMed 14. Sullivan FM, Farmer E, Mair FS, et al. Detection in blood of auto- antibodies to tumour antigens as a case-finding method in lung cancer using the EarlyCDT®-Lung Test (ECLS): study protocol for a randomized controlled trial. BMC Cancer. 2017;17(1):187. CrossRef PubMed 15. Kendrick S, Clarke J. The Scottish record linkage system. Health Bull (Edinb). 1993;51(2):72-79. PubMed 16. Mulholland RH, Vasileiou E, Simpson CR, et al. Cohort pro- file: early pandemic evaluation and enhanced surveillance of COVID-19 (EAVE II) database. Int J Epidemiol. 2021;50(4):1064- 1065. CrossRef PubMed 17. National Health Service (NHS). Community Health Index (CHI). Online (last accessed August 2021). 18. Data Service, Data Team Linkage Options, University of Dundee Online (last accessed April 2022). 19. Muratori P, Lenzi M, Muratori L, Granito A. Antinuclear anti- bodies in COVID 19. Clin Transl Sci. 2021;14(5):1627-1628. CrossRef PubMed 20. Qi X, Liu C, Jiang Z, et al. Multicenter analysis of clinical char- acteristics and outcomes in patients with COVID-19 who develop liver injury. J Hepatol. 2020;73(2):455-458. CrossRef PubMed 21. Lee J, Park SS, Kim TY, Lee DG, Kim DW. Lymphopenia as a bio- logical predictor of outcomes in COVID-19 patients: a nation- wide cohort study. Cancers (Basel). 2021;13(3):471. CrossRef PubMed 22. Martha JW, Wibowo A, Pranata R. Prognostic value of elevated lactate dehydrogenase in patients with COVID-19: a systematic review and meta-analysis. Postgrad Med J. 2021 Jan 15:post- gradmedj-2020-139542. CrossRef PubMed 23. Poudel A, Poudel Y, Adhikari A, et al. D-dimer as a biomarker for assessment of COVID-19 prognosis: d-dimer levels on admis- sion and its role in predicting disease outcome in hospitalized patients with COVID-19. PLoS One. 2021;16(8):e0256744. CrossRef PubMed 24. Shen B, Yi X, Sun Y, et al. Proteomic and metabolomic char- acterization of COVID-19 patient sera. Cell. 2020;182(1):59-72. e15. CrossRef PubMed 25. Yitbarek GY, Walle Ayehu G, Asnakew S, et al. The role of C-reactive protein in predicting the severity of COVID-19 dis- ease: a systematic review. SAGE Open Med. 2021;9:205031212 11050755. CrossRef PubMed 26. Tong-Minh K, van der Does Y, Engelen S, et al. High procalcito- nin levels associated with increased intensive care unit admis- sion and mortality in patients with a COVID-19 infection in the emergency department. BMC Infect Dis. 2022;22(1):165. CrossRef PubMed 27. Kaushal K, Kaur H, Sarma P, et al. Serum ferritin as a predictive biomarker in COVID-19. A systematic review, meta-analysis and meta-regression analysis. J Crit Care. 2022 Feb;67:172-181. CrossRef PubMed https://doi.org/10.1093/jnci/93.14.1054 https://pubmed.ncbi.nlm.nih.gov/11459866/ https://doi.org/10.1183/13993003.00670-2020 https://www.ncbi.nlm.nih.gov/pubmed/32732334 https://doi.org/10.1186/s12885-017-3175-y https://www.ncbi.nlm.nih.gov/pubmed/28284200 https://www.ncbi.nlm.nih.gov/pubmed/8514493 https://doi.org/10.1093/ije/dyab028 https://www.ncbi.nlm.nih.gov/pubmed/34089614 https://datadictionary.nhs.uk/attributes/community_health_index_number.html https://www.dundee.ac.uk/hic/datalinkageservice/ https://doi.org/10.1111/cts.13026 https://www.ncbi.nlm.nih.gov/pubmed/33932091 https://doi.org/10.1016/j.jhep.2020.04.010 https://www.ncbi.nlm.nih.gov/pubmed/32305291 https://doi.org/10.3390/cancers13030471 https://pubmed.ncbi.nlm.nih.gov/33530509/ https://doi.org/10.1136/postgradmedj-2020-139542 https://pubmed.ncbi.nlm.nih.gov/33452143/ https://doi.org/10.1371/journal.pone.0256744 https://www.ncbi.nlm.nih.gov/pubmed/34437642 https://doi.org/10.1016/j.cell.2020.05.032 https://www.ncbi.nlm.nih.gov/pubmed/32492406 https://doi.org/10.1177/20503121211050755 https://www.ncbi.nlm.nih.gov/pubmed/34659766 https://doi.org/10.1186/s12879-022-07144-5 https://www.ncbi.nlm.nih.gov/pubmed/35189826 https://doi.org/10.1016/j.jcrc.2021.09.023 https://pubmed.ncbi.nlm.nih.gov/34808527/