COVID-19 Induces Prolonged Immunological Exhaustion Leading To Relapse Of Hematological Malignancies Except In Hematopoietic Cell Transplant Recipients

COVID-19 Induces Prolonged Immunological Exhaustion Leading To Relapse Of Hematological Malignancies Except In Hematopoietic Cell Transplant Recipients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article COVID-19 Induces Prolonged Immunological Exhaustion Leading To Relapse Of Hematological Malignancies Except In Hematopoietic Cell Transplant Recipients Suparno Chakrabarti, Snigdha Banerjee, Mahak Agarwal, Gitali Bhagawati, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5452369/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We studied the impact of COVID-19 on relapse in patients with hematological malignancies who had achieved complete remission (CR) and were either treatment-free or maintained on uninterrupted therapy over a 24-month period. Among 144 patients fulfilling the inclusion criteria, the overall relapse rate was 30.9%, with a significantly higher incidence in COVID-19-positive patients (60.9%) compared to COVID-19-negative individuals (25.2%, HR- p = 0.0001). Stratification by disease risk index (DRI) revealed a pronounced effect of COVID-19 on relapse in the DRI-high cohort (64.3% vs. 20.1%, p = 0.0001). Hematopoietic cell transplantation (HCT) appeared protective, with relapse rates of 22% in HCT recipients and 36.9% in the non-HCT cohort (p = 0.06), with no impact of COVID-19 on relapse in patients undergoing HCT. However, the effect of COVID-19 on relapse was predominantly observed in the non-HCT group (92.3% vs. 27.8%, HR-8.9, p < 0.001). Immune exhaustion markers, including PD-1 on T cells and NKG2A on NK cells, were significantly upregulated in COVID-19-exposed patients, particularly in those who relapsed, compared to recipients of allogeneic HCT with and without exposure to SARS-CoV2. These findings suggest COVID-19-induced immune dysregulation may facilitate relapse, particularly in non-HCT patients, warranting further exploration of the immunobiological mechanisms responsible for this phenomenon and possible immune-targeted interventions in this context. COVID-19 Relapse Cancer Immune exhaustion PD-1 NKG2A Figures Figure 1 Figure 2 Figure 3 Introduction Explosion of SARS-CoV2 infection across the globe in the form of a pandemic in early 2020 perturbed the ongoing treatment of every single disease but affected the field of oncology more than any other[ 1 ]. The uncertainty regarding the impact of cytotoxic and immunosuppressive treatment for cancer on the outcome of COVID-19 led to an initial freeze on more intensive approaches to treatment in such conditions[2; 3]. This was enforced with the tacit acceptance of the fact that such an approach might be counterproductive as far as the outcome of the cancer is concerned, particularly in the case of blood cancers[4; 5]. While the overwhelming concern regarding the impact of immune-disruptive treatment on COVID-19 was understandable, the impact of SARS-CoV2 itself on the outcome of cancer was barely known[ 6 ]. Despite some reports of remission of various cancers following COVID-19 and some speculation regarding its direct impact on cancer progression, the issue remains largely unexplored[7; 8; 9]. In this regard, we carried out a single center observational study over two years on the impact of COVID-19 on disease progression in patients with hematological malignancies who had achieved a complete remission and were on no treatment or those whose treatment was not interrupted due to extraneous considerations. In addition, exhaustion status of T and NK cell subsets were studied to understand how COVID-19 might be influencing relapse of the underlying hematological malignancy. Results Patient characteristics 144 patients were included in this study. Patient and disease characteristics are detailed in Table 1 . The median age of the cohort was 50 years (range, 10–70). Acute leukemia, myelodysplastic syndrome, lymphoproliferative disorders and myeloma accounted for 43%, 39% and 18% respectively. All patients had achieved a CR following the first line therapy and were on no active therapy when enrolled in the study. The only exception was those with myeloma, who had achieved at least a VGPR (n = 12) and were on oral maintenance therapy with lenalidomide or thalidomide. Based on DRI scoring, 58%, 35% and 7% of the patients had a high-risk or very-high risk (DRI-high) intermediate risk and low-risk disease (DRI-non-high) respectively. Of the 84 patients in the DRI-high cohort, 45 underwent an allogeneic hematopoietic cell transplantation (HCT), 45 from family donors (HLA-haploidentical- 41; HLA-matched-4) and 6 underwent an autologous HCT. Minimal residual disease was not considered for analysis as all patients with acute leukemia or MDS were MRD positive at the time of HCT. Table 1 Patient Characteristics COVID-19 negative (N = 121) COVID-19 positive (N = 23) p-value Age (median, range) years 51 (10–70) 46 (12–65) 0.3 Disease Type AML/ ALL/MDS Myeloma Lymphoma 22/24/5 22 48 6/3/2 4 8 0.9 Disease Risk Index Low Intermediate High/Very High 8 44 69 3 5 15 1.0 Gender (Male/Female) 73/48 15/8 0.3 HCT Allogeneic (HFD/MFD) Autologous 41 37 34/3 4 10 8 7/1 2 0.9 Relapse 31 14 0.0001 Abbreviations : AML- Acute Myeloid Leukemia; ALL-Acute Lymphoblastic Leukemia; HCT - Hematopoietic Cell Transplantation; HFD-Haploidentical Family Donor; MDS- Myelodysplastic Syndrome; MFD- Matched Family Donor COVID-19 and Relapse The incidence of COVID-19 was 18.9% (n = 23; 95% CI, 15.3–22.5). Moderate to Severe COVID-19 was documented in six of them (26%). The median duration of illness was 18 days (12–28). The overall incidence of relapse was 30.9% (45 patients). This was 60.9% in those with COVID-19, compared to 25.2% in those without (Fig. 1 A, HR-3.6[1.9–6.8], p = 0.0001). Relapse was not influenced by severity of COVID-19. There were no deaths directly related to COVID-19. Relapse tended to occur sooner in patients with COVID-19 (median 127 [22–280] days vs 259 [28–680] days, p = 0.001). Patients were stratified as DRI-high (includes high and very-high DRI) or DRI-non-high (includes low and intermediate DRI). The effect of COVID-19 on relapse was more striking in DRI-high group (64.3% vs 20.1%, p = 0.0001, Fig. 1 B), but tended to be more in DRI-non-high group as well (55.6% vs 32.5%, p = 0.06, Fig. 1 C). There was no influence of disease type on relapse with respect to COVID-19. COVID-19 and Relapse in relation to HCT. There was no difference in the incidence of COVID-19 between HCT and non-HCT patients (10/51 vs 13/93, p = 0.5). The incidence of relapse was 22% in the HCT cohort, compared to 36.9% in the non-HCT cohort (p = 0.06). There was no difference in relapse in the HCT group, stratified by COVID-19 (20.6% vs 21.0%, p = 1.0, Fig. 1 D). However, in the non-HCT group, incidence of relapse was 27.8% (22/80) in those without COVID-19, compared to 92.3% (12/13) in the COVID-19 positive group (HR-8.9, 95% CI-4.2-18.9, p = 0.0001, Fig. 1 E). There was no relation to DRI status or the disease-type on relapse incidence in the HCT group. On multivariate analysis, COVID-19 was the only risk factor for relapse (HR-4.8, 95% CI-2.4-9.5; p = 0.0001). On the other hand, HCT was associated with a protective effect against relapse with COVID-19 in the model (HR-0.37, 95% CI-0.2-0.7; p = 0.007). Survival The overall survival in patients with COVID-19 at 2 years was 82.6%, compared to 94.2% in those without COVID-19 (p = 0.05, Fig. 2 A). There was no difference in survival in the HCT cohort stratified by SARS-CoV2 exposure (Fig. 2 B). However, in the non-HCT cohort, OS was significantly inferior in those with COVID-19 (69.2% VS 93.8%, P = 0.003, Fig. 2 C). Immune exhaustion following COVID-19 and Relapse Longitudinal evaluation for PD1 expression on CD3 + T cells and NKG2A expression on NK cells was carried out in 16 unselected patients from COVID-19 exposed cohort at 30–45 days and 60–90 days post-infection; 8 patients each from HCT and non-HCT groups. This was also carried out during the same period in 10 COVID-19 non-exposed patients who received an allogeneic HCT, at 30 and 60 days post-HCT. Patients who relapsed within 60 days of either diagnosis of COVID-19 or the HCT procedure were not included in the analysis. Upregulation of exhaustion receptors on T and NK cells was associated with Relapse Relapse was documented in 8 of these 26 patients at a median of 86 days (range, 68–152). PD1 (CD279) was significantly upregulated in CD3 + T cells in those with relapse at day 30 (65.3 ± 17.5 vs 22.8 ± 17.2 p = 0.0001, Fig. 3 A) and day 60 (54.8 ± 12.8 vs 31 ± 12.8, p = 0.001, Fig. 3 A). A similar trend was noted in NK cells as well (Fig. 3 A), with regard to expression of the inhibitory NKG2A receptor at day 30 (74.3 ± 14.5 vs 57.5 ± 15.4 p = 0.01) and day 60 (66 ± 16.5 vs 46 ± 16.9, p = 0.01). Exploring the reason for higher relapse in non-HCT cohort in relation to Immune exhaustion PD1 While analyzing the propensity for higher incidence of relapse in COVID-19 exposed non-HCT cohort, it was noted that PD1 expression was significantly upregulated in in CD3 + T cells in COVID-19 + non-HCT cohort at day 30 (60.6 ± 11.2% vs 39.8 ± 12.1% in COVID-19 + HCT cohort, p = 0.01). PD1 was persistently upregulated in the non-HCT cohort at day + 60, compared to the COVID-19 + HCT cohort, where it showed a reduction in expression of PD1(Fig. 3 B, p = 0.002). On the other hand, the COVID-19 negative HCT cohort showed a lower expression of exhaustion markers on both subsets of T cells at both day + 30 and + 60. The expression of PD1 was significantly lower, compared to COVID-19 exposed patients from both HCT and non-HCT cohorts at the day 30 assessment. This trend was sustained when compared with COVID-19 + non-HCT cohort at day 60 (p < 0.001), but not for COVID-19 + HCT cohort (Fig. 3 B, p = 0.2). NKG2A The median NKG2A expression at day 30 for COVID-19 negative HCT cohort was 49.6%, compared to 64.4% (p = 0.03) and 80.5% (p < 0.0001) in COVID-19 positive HCT and non-HCT cohorts respectively (Fig. 3 C). NKG2A expression reduced at day 60 in both HCT cohorts, 38% in COVID-19 negative (p = 0.02) and 46.1% in COVID-19 positive (p = 0.004) cohorts. However, high NKG2A expression was sustained at a median of 79.5% in the non-HCT cohort at 60 days. Thus, the significant difference in NKG2A expression was sustained between HCT and non-HCT groups (Fig. 3 C). Discussion In the early days of the pandemic there were both planned and unplanned disruptions in definitive treatment for cancers, which included a delay in surgery, adjuvant chemotherapy and radiotherapy for solid tumours. This was often due to the healthcare system being overwhelmed by the sudden surge of an unknown illness and also due to the uncertainty regarding the adverse impact of chemo-radiotherapy on the outcome of COVID-19, which could have been acquired during or following such treatment[ 10 ]. At the same time, a few others speculated about the immunological impact of SARS-CoV2 infection on cancer growth and progression. However, most of it remained speculative and harped on the impact of inflammation on cancer, without any definite clinical or preclinical data[ 7 ]. Our group had been actively pursuing the role of adaptive NK cells on the acquisition and progression of COVID-19[11; 12; 13; 14]. In that process, data on all patients with hematological malignancies, who were on active follow-up during this period, were diligently collected, while the work on ANK cells in both COVID-19 and haploidentical HCT were being pursued[ 15 ]. The data collected between 2020 and 2022 were retrospectively analysed with the aim of identifying any temporal correlation between occurrence of COVID-19 in patients who have achieved a CR for hematological malignancies. Very strict criteria were enforced to exclude any patient whose treatment was interrupted beyond 4 weeks during this period, as it could be a major confounding factor while elucidating any effect of this virus on the incidence of relapse. The rather strong correlation between COVID-19 and disease relapse across all major types of hematological malignancy was indeed a revelation as no study has addressed this issue until now. No predilection could be ascertained based on disease type or the risk-status. This indeed could be due to the small sample size. In addition, exclusion of mortality due to COVID-19 probably helped in having one less confounding factor when attributing a cause-effect relation to COVID-19 and relapse. Two further observations raise the possibility of COVID-19 impacting the progression of diseases otherwise in clinical CR. First of them is the compelling documentation of both T cell and NK cell exhaustion in COVID-19 patients and the second is a protective effect of an allogeneic HCT in offering protection against sustained immune exhaustion. The ongoing immunological study was not primarily aimed at the outset to understand the mechanistic pathways as to how SARS-CoV2 might be influencing recurrence of the disease, but the limited data did provide some serendipitous understanding in this regard. Exhaustion of T cells have been studied recently in patients with long-Covid-19 and sustained exhaustion has been implicated in protracted illness, which is noted in a subgroup of infected patients[16; 17; 18]. We did observe sustained exhaustion in the terms of expression of PD-1 on T cells and NKG2A on NK cells, in those who were subsequently diagnosed with disease relapse. Even though the effect of COVID-19 was evident on immune exhaustion in recipients of allogeneic HCT as well, this effect was significantly less dramatic as well as less sustained than those who were HCT-naïve. This brings to fore the possible impact of COVID-19 on an immune system which sustained the rigor of protracted anticancer therapy versus a rejuvenated immune system which is derived from a healthy allogenic graft. The HCT protocol currently employed in our institution has been shown to have a salutary effect on reducing relapse incidence, which was mediated by ANK cells along with lack of immune exhaustion[ 15 ]. The biggest limitation of the study is of course its retrospective nature and a small sample size. One might argue that the immunological correlates might have been a function of selectivity and caution must be exercised in extrapolation of these findings. Could the use of a wider array of exhaustion markers such as TIM3, LAG3, or TIGIT, have helped unravel the correlation any better? The answer is probably in the affirmative[ 17 ]. The same might be said of the selection of NKG2A as negative regulator of NK cell cytotoxicity, which was derived as a part of an ongoing study. However, it is worth noting that unlike the exhaustion molecules on T cells which are upregulated in response to persistent state of activation or antigen presentation, NKG2A expression on NK cells as an inhibitory receptor is not only constitutive but also an ancestrally preserved response across species[ 19 ]. In addition, the antileukemia effect of adaptive NK cells is contingent upon downregulation of NKG2A, as the receptor affinity for NKG2A is several times higher than the activating receptor NKG2C[ 20 ]. COVID-19 has been shown to upregulate NKG2A on NK cells and HLA-E, the cognate ligand on lung epithelial cells, leading to functional exhaustion of NK cells[21; 22]. While our group has demonstrated the adverse implications of upregulation of NKG2A on disease relapse and vice versa[15; 23], T cell exhaustion has been shown to be a dominant pathway for immune escape of leukemia cells and subsequent relapse[ 24 ]. With the above considerations, there might be a case for anti-PD1 therapy for patients at high risk of relapse following COVID-19. While there is no approved therapy for targeting NKG2A upregulated NK cells, our studies on administration of the heat-killed Mycobacterium w (Sepsivac, Cadilla, India) as prophylaxis for COVID-19 resulted in down regulation of NKG2A + NK cells[ 12 ]. Retrospective studies which are observational in nature might not provide the perfect answer, yet, might point towards the right direction. Thus, the strong correlation between COVID-19 and relapse witnessed in relation to sustained immune exhaustion in non-HCT cohort vis-à-vis a protective role of allogeneic HCT, might prompt more studies with a deeper and better understanding of this phenomenon. This might even provide a window for therapeutic intervention to prevent immune escape of the malignancy in those at high risk for relapse following COVID-19. Patient & Methods Ethical considerations Institutional Review Board (IRB) of the Dharamshila Narayana Superspeciality Hospital and Research Centre, New Delhi approved the study (DNSH-EC-270622) according to the 2013 Helsinki Declaration. Written informed consent was obtained from all study participants. Study participant’s names and other Health Insurance Portability and Accountability Act (HIPAA) identifiers were removed from all sections of the manuscript, including supplementary information Study design This was a prospective observational longitudinal study which included all patients in the age group between 10–70 years, who responded to the treatment of their primary hematological cancer during the period of March 2020 and December 2021 and were in active follow up from 1-Mar-2020 to 31-Mar-2022, with a minimum follow-up of 6 months after achievement of complete remission. Data collection, compilation and analysis were carried out after the completion of the observation period. Only those with morphological, anatomical or metabolic for acute leukemia and lymphomas and at least a very good partial response for myeloma were included in the study. Any patient with interruption of definitive therapy for more than 4 weeks were excluded from the study (2). Relapse or disease progression was diagnosed for individual disease entities based on standard criteria. Those who succumbed to COVID-19 within 30 days of the diagnosis or relapsed were excluded from the study. Diagnosis of COVID‑19 samples All patients included in the study had nasopharyngeal swab evaluated for SARS-CoV-2 by reverse transcriptase-polymerase chain reaction (RT-PCR), on development of symptoms suggestive of COVID-19 or following unprotected contact with an individual with COVID19, either at home or hospital. COVID-19 was diagnosed, and its severity was graded as per established criteria[ 12 ]. All Covid tests were done by Truenat real-time RT-PCR test as described earlier[ 12 ]. Immune Response: T Cell and NK Cell Exhaustion As part of a simultaneous ongoing project, immunological parameters related to T and NK cell subsets were sequentially monitored by flow cytometry in patients undergoing allogeneic HCT. During the study period, these evaluations were extended to a cohort of non-HCT patients developing COVID-19. Details of the procedure have been described previously[11; 12; 15]. The following antibodies were used for phenotypic analysis: CD3(APC-H7, SK-7) CD16 (PE-Cy7, B73.1), CD56 (APC R700, NCAM16.2), CD57 (BV605, NK-1), NKG2A (PE-Cy7, Z199), CD4 (APC-H7), CD8 (Per-CP Cy), CD45RA (FITC), CD45RO (BV605), CD279 (PD-1, BV605) from BD Biosciences, (San Jose, CA) and NKG2C (PE, REA205) from Miltenyi Biotec, Germany. Flow Cytometry was performed using 10 colour flow cytometry (BD FACS Lyrics) and the flow cytometry data was analyzed using FlowJo software (v10.6.2, FlowJo). Statistical Analysis Binary variables were compared between the groups using chi-square test. The continuous variables were analyzed using independent sample t-test considering Levenes test for equality of variances and non-parametric tests (Mann-Whitney U test). Probabilities of survival were estimated using the Kaplan-Meier product-limit method. The cumulative incidence rates of relapse were computed censuring competing risks using Fine and Gray method ( https://cran.rproject.org/web/packages/cmprsk/index.html ). Multivariate analysis was carried out using Cox regression analysis. An outcome was determined to be significantly different if the observed P value was < 0.05. All analyses were performed using statistical software IBM SPSS Statistics Version 24.0 (Armonk, USA). Statistical divergences from flow cytometry-based data were analysed by the GraphPad Prism software. Declarations Authorship: SC, SB, SRJ and NS designed the study. SB and MA collected the data. SC, SRJ, SB and MA analysed the data. NS and GB reviewed the data. All the authors drafted and reviewed the manuscript. Conflict of Interest: The authors declare no competing financial interests. Data Availability: All data generated or analysed during this study are included in this published article [and its supplementary information files].. References Hanna TP, Evans GA, Booth CM (2020) Cancer, COVID-19 and the precautionary principle: prioritizing treatment during a global pandemic. Nat Rev Clin Oncol Jee J, Foote MB, Lumish M, Stonestrom AJ, Wills B, Narendra V, Avutu V, Murciano-Goroff YR, Chan JE, Derkach A, Philip J, Belenkaya R, Kerpelev M, Maloy M, Watson A, Fong C, Janjigian Y, Diaz LA Jr., Bolton KL (2020) Pessin, Chemotherapy and COVID-19 Outcomes in Patients With Cancer. J Clin Oncol 38:3538–3546 Fedele P, Sanna V, Fancellu A, Marino A, Calvani N, Cinieri S (2021) De-escalating cancer treatments during COVID 19 pandemic: Is metronomic chemotherapy a reasonable option? 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Blood 117:4394–4396 Bortolotti D, Gentili V, Rizzo S, Rotola A, Rizzo R (2020) SARS-CoV-2 Spike 1 Protein Controls Natural Killer Cell Activation via the HLA-E/NKG2A Pathway. Cells 9 Antonioli L, Fornai M, Pellegrini C, Blandizzi C (2020) NKG2A and COVID-19: another brick in the wall. Cellular & Molecular Immunology Jaiswal SR, Bhakuni P, Bhagawati G, Aiyer HM, Soni M, Sharma N, Jaiswal RR, Chakrabarti A, Chakrabarti S (2021) CTLA4Ig-primed donor lymphocyte infusions following haploidentical transplantation improve outcome with a distinct pattern of early immune reconstitution as compared to conventional donor lymphocyte infusions in advanced hematological malignancies. Bone Marrow Transpl 56:185–194 Zeiser R, Vago L (2019) Mechanisms of immune escape after allogeneic hematopoietic cell transplantation. Blood 133:1290–1297 Authorship SC SB, SRJ and NS designed the study. SB and MA collected the data. SC, SRJ, SB and MA analysed the data. NS and GB reviewed the data. All the authors drafted and reviewed the manuscript Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5452369","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":381016268,"identity":"64daa6e8-1ef1-4e7c-b77b-1d95509e5678","order_by":0,"name":"Suparno Chakrabarti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBAC9gYgwQNGjA+QhAwscGrhOQDWYgDUwmyAJGQgQVALA0KLRAKYxK2FvcfswZuaPzL80s2MjytqDufzSz6/uuFHgQQDf3t3AlYtPGfMDeccM+CRnHOY2fDMscOWM2fnlN3sATpM4szZDdi02EvkmEnzsBnwGNzIPybZ2HDYwOB2TtoNHqAWA4lcrFp4wFr+gbQks4G12N88k3bzDyEtvG1IWgwk2I/dxmsLz7Eyybl9xjySM5KZDRuOpRtInMlhuy1jIMGDyy887M3bJN58k7Pnl0hmfNhQY23A33782c03f2zk+Nt7sWrBagw4gniIVQ4C7A9IUT0KRsEoGAXDHwAAxONZlcgSUw0AAAAASUVORK5CYII=","orcid":"","institution":"Dharamshila Narayana Superspeciality Hospital and Research Centre","correspondingAuthor":true,"prefix":"","firstName":"Suparno","middleName":"","lastName":"Chakrabarti","suffix":""},{"id":381016269,"identity":"42bf5553-2671-4fdd-a0ea-e9edd5bf58ad","order_by":1,"name":"Snigdha Banerjee","email":"","orcid":"","institution":"Jamia Hamdard","correspondingAuthor":false,"prefix":"","firstName":"Snigdha","middleName":"","lastName":"Banerjee","suffix":""},{"id":381016270,"identity":"ace57970-29da-4b0d-97fb-63192561afd9","order_by":2,"name":"Mahak Agarwal","email":"","orcid":"","institution":"Dharamshila Narayana Superspeciality Hospital and Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Mahak","middleName":"","lastName":"Agarwal","suffix":""},{"id":381016271,"identity":"851caa80-2543-4ffb-a6e6-1c90c7f9279d","order_by":3,"name":"Gitali Bhagawati","email":"","orcid":"","institution":"Dharamshila Narayana Superspeciality Hospital and Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Gitali","middleName":"","lastName":"Bhagawati","suffix":""},{"id":381016272,"identity":"23bdcfdf-bef3-410c-9ab3-2365745a22f0","order_by":4,"name":"Nilanjan Saha","email":"","orcid":"","institution":"Jamia Hamdard","correspondingAuthor":false,"prefix":"","firstName":"Nilanjan","middleName":"","lastName":"Saha","suffix":""},{"id":381016273,"identity":"eb01e887-dde0-40cb-aae2-148b2c888d8f","order_by":5,"name":"Sarita Rani Jaiswal","email":"","orcid":"","institution":"Dharamshila Narayana Superspeciality Hospital and Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Sarita","middleName":"Rani","lastName":"Jaiswal","suffix":""}],"badges":[],"createdAt":"2024-11-14 08:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5452369/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5452369/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69830000,"identity":"76bf4e4f-3cc1-4f18-89be-4deaadcbee61","added_by":"auto","created_at":"2024-11-25 15:29:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":220727,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative Incidence of Relapse Stratified by COVID-19 Status: The cumulative incidence of relapse among hematological malignancy patients (n=144) were compared between COVID-19 positive and negative groups. (\u003cstrong\u003eA\u003c/strong\u003e) Overall relapse rates (n=45): 60.1% (COVID-19+) vs 25.2% (COVID-19-), p=0.0001. \u003cstrong\u003e(B\u003c/strong\u003e) DRI-high group (n=84): 64.3% (COVID-19+) vs 21.5% (COVID-19-), p=0.0001. (\u003cstrong\u003eC\u003c/strong\u003e) DRI-non-high group (n=60): 55.6% (COVID-19+) vs 32.5% (COVID-19-), p=0.06. (\u003cstrong\u003eD\u003c/strong\u003e) HCT group (n=51): 21.0% (COVID-19+) vs 20.6% (COVID-19-), p=0.9. (\u003cstrong\u003eE\u003c/strong\u003e) Non-HCT group (n=93): 92.3% (COVID-19+) vs 27.8% (COVID-19-), p=0.0001. *Statistically significant at the 0.05 level.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5452369/v1/b70e6d0e9866a60d339e3787.png"},{"id":69829998,"identity":"d800e8a3-ae0b-472f-8f54-cc7552824d2b","added_by":"auto","created_at":"2024-11-25 15:29:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":117266,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival stratified by COVID-19 infection and HCT status: \u003cstrong\u003eA\u003c/strong\u003e) The two-year overall survival rate was 82.6% among patients with COVID-19 (n=121), compared to 94.2% among those without COVID-19 (n=23) (p=0.05). \u003cstrong\u003eB)\u003c/strong\u003e In the HCT cohort (n=51): survival rates in patients with COVID-19 (100%) and those without COVID-19 (95.1%) (p=0.5). \u003cstrong\u003eC) \u003c/strong\u003eIn the non-HCT cohort (n=93), patients with COVID-19 experienced significantly lower overall survival (69.2% vs. 93.8%, p=0.003). *Statistically significant at the 0.05 level.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5452369/v1/5ba7b6ec41143c175dcd31df.png"},{"id":69829999,"identity":"48a7cf41-2e0a-4421-8df2-421ee3f67a0d","added_by":"auto","created_at":"2024-11-25 15:29:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38887,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) Longitudinal evaluation of exhaustion markers (PD1 on T cells and NKG2A on NK cells) in patients with relapse (n=8) (red triangles) and without relapse (n=18) (green circles) on day 30 and day 60 post-COVID-19 or post-HCT. (\u003cstrong\u003eB\u003c/strong\u003e) PD-1 expression on CD3+ T cells and (\u003cstrong\u003eC\u003c/strong\u003e) NKG2A expression on NK cells at days 30 and 60 post-COVID-19 or post-HCT. Three cohorts were analyzed: COVID-19\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e non-HCT (grey triangles), COVID-19\u003csup\u003e+\u003c/sup\u003e HCT (green squares), and COVID-19\u003csup\u003e-\u003c/sup\u003e (red circles). Each symbol represents an individual patient, with horizontal lines indicating mean values. Statistical significance: ns = not significant, *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5452369/v1/fedbeaf238cacdbb4c167ab0.png"},{"id":69830799,"identity":"dae6c455-1232-432e-aea8-b152b22095d3","added_by":"auto","created_at":"2024-11-25 15:37:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":793966,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5452369/v1/8f706f3d-9e67-476f-9eb3-17e02d1ce486.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"COVID-19 Induces Prolonged Immunological Exhaustion Leading To Relapse Of Hematological Malignancies Except In Hematopoietic Cell Transplant Recipients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eExplosion of SARS-CoV2 infection across the globe in the form of a pandemic in early 2020 perturbed the ongoing treatment of every single disease but affected the field of oncology more than any other[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The uncertainty regarding the impact of cytotoxic and immunosuppressive treatment for cancer on the outcome of COVID-19 led to an initial freeze on more intensive approaches to treatment in such conditions[2; 3]. This was enforced with the tacit acceptance of the fact that such an approach might be counterproductive as far as the outcome of the cancer is concerned, particularly in the case of blood cancers[4; 5]. While the overwhelming concern regarding the impact of immune-disruptive treatment on COVID-19 was understandable, the impact of SARS-CoV2 itself on the outcome of cancer was barely known[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite some reports of remission of various cancers following COVID-19 and some speculation regarding its direct impact on cancer progression, the issue remains largely unexplored[7; 8; 9].\u003c/p\u003e \u003cp\u003eIn this regard, we carried out a single center observational study over two years on the impact of COVID-19 on disease progression in patients with hematological malignancies who had achieved a complete remission and were on no treatment or those whose treatment was not interrupted due to extraneous considerations. In addition, exhaustion status of T and NK cell subsets were studied to understand how COVID-19 might be influencing relapse of the underlying hematological malignancy.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003ePatient characteristics\u003c/h2\u003e\n \u003cp\u003e144 patients were included in this study. Patient and disease characteristics are detailed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age of the cohort was 50 years (range, 10\u0026ndash;70). Acute leukemia, myelodysplastic syndrome, lymphoproliferative disorders and myeloma accounted for 43%, 39% and 18% respectively. All patients had achieved a CR following the first line therapy and were on no active therapy when enrolled in the study. The only exception was those with myeloma, who had achieved at least a VGPR (n\u0026thinsp;=\u0026thinsp;12) and were on oral maintenance therapy with lenalidomide or thalidomide. Based on DRI scoring, 58%, 35% and 7% of the patients had a high-risk or very-high risk (DRI-high) intermediate risk and low-risk disease (DRI-non-high) respectively. Of the 84 patients in the DRI-high cohort, 45 underwent an allogeneic hematopoietic cell transplantation (HCT), 45 from family donors (HLA-haploidentical- 41; HLA-matched-4) and 6 underwent an autologous HCT. Minimal residual disease was not considered for analysis as all patients with acute leukemia or MDS were MRD positive at the time of HCT.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOVID-19 negative (N\u0026thinsp;=\u0026thinsp;121)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOVID-19 positive (N\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (median, range) years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (10\u0026ndash;70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (12\u0026ndash;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease Type\u003c/p\u003e\n \u003cp\u003eAML/ ALL/MDS\u003c/p\u003e\n \u003cp\u003eMyeloma\u003c/p\u003e\n \u003cp\u003eLymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22/24/5\u003c/p\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6/3/2\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease Risk Index\u003c/p\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003cp\u003eHigh/Very High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Male/Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73/48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15/8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCT\u003c/p\u003e\n \u003cp\u003eAllogeneic (HFD/MFD)\u003c/p\u003e\n \u003cp\u003eAutologous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003cp\u003e34/3\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e7/1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelapse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: AML- Acute Myeloid Leukemia; ALL-Acute Lymphoblastic Leukemia; HCT - Hematopoietic Cell Transplantation; HFD-Haploidentical Family Donor; MDS- Myelodysplastic Syndrome; MFD- Matched Family Donor\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eCOVID-19 and Relapse\u003c/h3\u003e\n\u003cp\u003eThe incidence of COVID-19 was 18.9% (n\u0026thinsp;=\u0026thinsp;23; 95% CI, 15.3\u0026ndash;22.5). Moderate to Severe COVID-19 was documented in six of them (26%). The median duration of illness was 18 days (12\u0026ndash;28). The overall incidence of relapse was 30.9% (45 patients). This was 60.9% in those with COVID-19, compared to 25.2% in those without (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA, HR-3.6[1.9\u0026ndash;6.8], p\u0026thinsp;=\u0026thinsp;0.0001). Relapse was not influenced by severity of COVID-19. There were no deaths directly related to COVID-19.\u003c/p\u003e\n\u003cp\u003eRelapse tended to occur sooner in patients with COVID-19 (median 127 [22\u0026ndash;280] days vs 259 [28\u0026ndash;680] days, p\u0026thinsp;=\u0026thinsp;0.001). Patients were stratified as DRI-high (includes high and very-high DRI) or DRI-non-high (includes low and intermediate DRI). The effect of COVID-19 on relapse was more striking in DRI-high group (64.3% vs 20.1%, p\u0026thinsp;=\u0026thinsp;0.0001, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB), but tended to be more in DRI-non-high group as well (55.6% vs 32.5%, p\u0026thinsp;=\u0026thinsp;0.06, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). There was no influence of disease type on relapse with respect to COVID-19.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOVID-19 and Relapse in relation to HCT.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no difference in the incidence of COVID-19 between HCT and non-HCT patients (10/51 vs 13/93, p\u0026thinsp;=\u0026thinsp;0.5). The incidence of relapse was 22% in the HCT cohort, compared to 36.9% in the non-HCT cohort (p\u0026thinsp;=\u0026thinsp;0.06). There was no difference in relapse in the HCT group, stratified by COVID-19 (20.6% vs 21.0%, p\u0026thinsp;=\u0026thinsp;1.0, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). However, in the non-HCT group, incidence of relapse was 27.8% (22/80) in those without COVID-19, compared to 92.3% (12/13) in the COVID-19 positive group (HR-8.9, 95% CI-4.2-18.9, p\u0026thinsp;=\u0026thinsp;0.0001, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). There was no relation to DRI status or the disease-type on relapse incidence in the HCT group.\u003c/p\u003e\n\u003cp\u003eOn multivariate analysis, COVID-19 was the only risk factor for relapse (HR-4.8, 95% CI-2.4-9.5; p\u0026thinsp;=\u0026thinsp;0.0001). On the other hand, HCT was associated with a protective effect against relapse with COVID-19 in the model (HR-0.37, 95% CI-0.2-0.7; p\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e\n\u003ch3\u003eSurvival\u003c/h3\u003e\n\u003cp\u003eThe overall survival in patients with COVID-19 at 2 years was 82.6%, compared to 94.2% in those without COVID-19 (p\u0026thinsp;=\u0026thinsp;0.05, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). There was no difference in survival in the HCT cohort stratified by SARS-CoV2 exposure (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). However, in the non-HCT cohort, OS was significantly inferior in those with COVID-19 (69.2% VS 93.8%, P\u0026thinsp;=\u0026thinsp;0.003, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\n\u003ch3\u003eImmune exhaustion following COVID-19 and Relapse\u003c/h3\u003e\n\u003cp\u003eLongitudinal evaluation for PD1 expression on CD3\u0026thinsp;+\u0026thinsp;T cells and NKG2A expression on NK cells was carried out in 16 unselected patients from COVID-19 exposed cohort at 30\u0026ndash;45 days and 60\u0026ndash;90 days post-infection; 8 patients each from HCT and non-HCT groups. This was also carried out during the same period in 10 COVID-19 non-exposed patients who received an allogeneic HCT, at 30 and 60 days post-HCT. Patients who relapsed within 60 days of either diagnosis of COVID-19 or the HCT procedure were not included in the analysis.\u003c/p\u003e\n\u003ch3\u003eUpregulation of exhaustion receptors on T and NK cells was associated with Relapse\u003c/h3\u003e\n\u003cp\u003eRelapse was documented in 8 of these 26 patients at a median of 86 days (range, 68\u0026ndash;152). PD1 (CD279) was significantly upregulated in CD3\u0026thinsp;+\u0026thinsp;T cells in those with relapse at day 30 (65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5 vs 22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2 p\u0026thinsp;=\u0026thinsp;0.0001, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA) and day 60 (54.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8 vs 31\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8, p\u0026thinsp;=\u0026thinsp;0.001, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). A similar trend was noted in NK cells as well (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA), with regard to expression of the inhibitory NKG2A receptor at day 30 (74.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5 vs 57.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4 p\u0026thinsp;=\u0026thinsp;0.01) and day 60 (66\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5 vs 46\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9, p\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eExploring the reason for higher relapse in non-HCT cohort in relation to Immune exhaustion\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003ePD1\u003c/h2\u003e\n \u003cp\u003eWhile analyzing the propensity for higher incidence of relapse in COVID-19 exposed non-HCT cohort, it was noted that PD1 expression was significantly upregulated in in CD3\u0026thinsp;+\u0026thinsp;T cells in COVID-19\u0026thinsp;+\u0026thinsp;non-HCT cohort at day 30 (60.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2% vs 39.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1% in COVID-19\u0026thinsp;+\u0026thinsp;HCT cohort, p\u0026thinsp;=\u0026thinsp;0.01). PD1 was persistently upregulated in the non-HCT cohort at day\u0026thinsp;+\u0026thinsp;60, compared to the COVID-19\u0026thinsp;+\u0026thinsp;HCT cohort, where it showed a reduction in expression of PD1(Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\n \u003cp\u003eOn the other hand, the COVID-19 negative HCT cohort showed a lower expression of exhaustion markers on both subsets of T cells at both day\u0026thinsp;+\u0026thinsp;30 and +\u0026thinsp;60. The expression of PD1 was significantly lower, compared to COVID-19 exposed patients from both HCT and non-HCT cohorts at the day 30 assessment. This trend was sustained when compared with COVID-19\u0026thinsp;+\u0026thinsp;non-HCT cohort at day 60 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not for COVID-19\u0026thinsp;+\u0026thinsp;HCT cohort (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, p\u0026thinsp;=\u0026thinsp;0.2).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eNKG2A\u003c/h3\u003e\n\u003cp\u003eThe median NKG2A expression at day 30 for COVID-19 negative HCT cohort was 49.6%, compared to 64.4% (p\u0026thinsp;=\u0026thinsp;0.03) and 80.5% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in COVID-19 positive HCT and non-HCT cohorts respectively (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). NKG2A expression reduced at day 60 in both HCT cohorts, 38% in COVID-19 negative (p\u0026thinsp;=\u0026thinsp;0.02) and 46.1% in COVID-19 positive (p\u0026thinsp;=\u0026thinsp;0.004) cohorts. However, high NKG2A expression was sustained at a median of 79.5% in the non-HCT cohort at 60 days. Thus, the significant difference in NKG2A expression was sustained between HCT and non-HCT groups (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the early days of the pandemic there were both planned and unplanned disruptions in definitive treatment for cancers, which included a delay in surgery, adjuvant chemotherapy and radiotherapy for solid tumours. This was often due to the healthcare system being overwhelmed by the sudden surge of an unknown illness and also due to the uncertainty regarding the adverse impact of chemo-radiotherapy on the outcome of COVID-19, which could have been acquired during or following such treatment[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. At the same time, a few others speculated about the immunological impact of SARS-CoV2 infection on cancer growth and progression. However, most of it remained speculative and harped on the impact of inflammation on cancer, without any definite clinical or preclinical data[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur group had been actively pursuing the role of adaptive NK cells on the acquisition and progression of COVID-19[11; 12; 13; 14]. In that process, data on all patients with hematological malignancies, who were on active follow-up during this period, were diligently collected, while the work on ANK cells in both COVID-19 and haploidentical HCT were being pursued[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The data collected between 2020 and 2022 were retrospectively analysed with the aim of identifying any temporal correlation between occurrence of COVID-19 in patients who have achieved a CR for hematological malignancies. Very strict criteria were enforced to exclude any patient whose treatment was interrupted beyond 4 weeks during this period, as it could be a major confounding factor while elucidating any effect of this virus on the incidence of relapse. The rather strong correlation between COVID-19 and disease relapse across all major types of hematological malignancy was indeed a revelation as no study has addressed this issue until now. No predilection could be ascertained based on disease type or the risk-status. This indeed could be due to the small sample size. In addition, exclusion of mortality due to COVID-19 probably helped in having one less confounding factor when attributing a cause-effect relation to COVID-19 and relapse.\u003c/p\u003e \u003cp\u003eTwo further observations raise the possibility of COVID-19 impacting the progression of diseases otherwise in clinical CR. First of them is the compelling documentation of both T cell and NK cell exhaustion in COVID-19 patients and the second is a protective effect of an allogeneic HCT in offering protection against sustained immune exhaustion. The ongoing immunological study was not primarily aimed at the outset to understand the mechanistic pathways as to how SARS-CoV2 might be influencing recurrence of the disease, but the limited data did provide some serendipitous understanding in this regard.\u003c/p\u003e \u003cp\u003eExhaustion of T cells have been studied recently in patients with long-Covid-19 and sustained exhaustion has been implicated in protracted illness, which is noted in a subgroup of infected patients[16; 17; 18]. We did observe sustained exhaustion in the terms of expression of PD-1 on T cells and NKG2A on NK cells, in those who were subsequently diagnosed with disease relapse. Even though the effect of COVID-19 was evident on immune exhaustion in recipients of allogeneic HCT as well, this effect was significantly less dramatic as well as less sustained than those who were HCT-na\u0026iuml;ve. This brings to fore the possible impact of COVID-19 on an immune system which sustained the rigor of protracted anticancer therapy versus a rejuvenated immune system which is derived from a healthy allogenic graft. The HCT protocol currently employed in our institution has been shown to have a salutary effect on reducing relapse incidence, which was mediated by ANK cells along with lack of immune exhaustion[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe biggest limitation of the study is of course its retrospective nature and a small sample size. One might argue that the immunological correlates might have been a function of selectivity and caution must be exercised in extrapolation of these findings. Could the use of a wider array of exhaustion markers such as TIM3, LAG3, or TIGIT, have helped unravel the correlation any better? The answer is probably in the affirmative[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The same might be said of the selection of NKG2A as negative regulator of NK cell cytotoxicity, which was derived as a part of an ongoing study. However, it is worth noting that unlike the exhaustion molecules on T cells which are upregulated in response to persistent state of activation or antigen presentation, NKG2A expression on NK cells as an inhibitory receptor is not only constitutive but also an ancestrally preserved response across species[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition, the antileukemia effect of adaptive NK cells is contingent upon downregulation of NKG2A, as the receptor affinity for NKG2A is several times higher than the activating receptor NKG2C[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. COVID-19 has been shown to upregulate NKG2A on NK cells and HLA-E, the cognate ligand on lung epithelial cells, leading to functional exhaustion of NK cells[21; 22]. While our group has demonstrated the adverse implications of upregulation of NKG2A on disease relapse and vice versa[15; 23], T cell exhaustion has been shown to be a dominant pathway for immune escape of leukemia cells and subsequent relapse[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. With the above considerations, there might be a case for anti-PD1 therapy for patients at high risk of relapse following COVID-19. While there is no approved therapy for targeting NKG2A upregulated NK cells, our studies on administration of the heat-killed \u003cem\u003eMycobacterium w\u003c/em\u003e (Sepsivac, Cadilla, India) as prophylaxis for COVID-19 resulted in down regulation of NKG2A\u0026thinsp;+\u0026thinsp;NK cells[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRetrospective studies which are observational in nature might not provide the perfect answer, yet, might point towards the right direction. Thus, the strong correlation between COVID-19 and relapse witnessed in relation to sustained immune exhaustion in non-HCT cohort vis-\u0026agrave;-vis a protective role of allogeneic HCT, might prompt more studies with a deeper and better understanding of this phenomenon. This might even provide a window for therapeutic intervention to prevent immune escape of the malignancy in those at high risk for relapse following COVID-19.\u003c/p\u003e "},{"header":"Patient \u0026 Methods ","content":"\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003e Institutional Review Board (IRB) of the Dharamshila Narayana Superspeciality Hospital and Research Centre, New Delhi approved the study (DNSH-EC-270622) according to the 2013 Helsinki Declaration. Written informed consent was obtained from all study participants. Study participant\u0026rsquo;s names and other Health Insurance Portability and Accountability Act (HIPAA) identifiers were removed from all sections of the manuscript, including supplementary information\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis was a prospective observational longitudinal study which included all patients in the age group between 10\u0026ndash;70 years, who responded to the treatment of their primary hematological cancer during the period of March 2020 and December 2021 and were in active follow up from 1-Mar-2020 to 31-Mar-2022, with a minimum follow-up of 6 months after achievement of complete remission. Data collection, compilation and analysis were carried out after the completion of the observation period. Only those with morphological, anatomical or metabolic for acute leukemia and lymphomas and at least a very good partial response for myeloma were included in the study. Any patient with interruption of definitive therapy for more than 4 weeks were excluded from the study (2). Relapse or disease progression was diagnosed for individual disease entities based on standard criteria. Those who succumbed to COVID-19 within 30 days of the diagnosis or relapsed were excluded from the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDiagnosis of COVID‑19 samples\u003c/h2\u003e \u003cp\u003eAll patients included in the study had nasopharyngeal swab evaluated for SARS-CoV-2 by reverse transcriptase-polymerase chain reaction (RT-PCR), on development of symptoms suggestive of COVID-19 or following unprotected contact with an individual with COVID19, either at home or hospital. COVID-19 was diagnosed, and its severity was graded as per established criteria[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. All Covid tests were done by Truenat real-time RT-PCR test as described earlier[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImmune Response: T Cell and NK Cell Exhaustion\u003c/h2\u003e \u003cp\u003eAs part of a simultaneous ongoing project, immunological parameters related to T and NK cell subsets were sequentially monitored by flow cytometry in patients undergoing allogeneic HCT. During the study period, these evaluations were extended to a cohort of non-HCT patients developing COVID-19. Details of the procedure have been described previously[11; 12; 15]. The following antibodies were used for phenotypic analysis: CD3(APC-H7, SK-7) CD16 (PE-Cy7, B73.1), CD56 (APC R700, NCAM16.2), CD57 (BV605, NK-1), NKG2A (PE-Cy7, Z199), CD4 (APC-H7), CD8 (Per-CP Cy), CD45RA (FITC), CD45RO (BV605), CD279 (PD-1, BV605) from BD Biosciences, (San Jose, CA) and NKG2C (PE, REA205) from Miltenyi Biotec, Germany. Flow Cytometry was performed using 10 colour flow cytometry (BD FACS Lyrics) and the flow cytometry data was analyzed using FlowJo software (v10.6.2, FlowJo).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eBinary variables were compared between the groups using chi-square test. The continuous variables were analyzed using independent sample t-test considering Levenes test for equality of variances and non-parametric tests (Mann-Whitney U test). Probabilities of survival were estimated using the Kaplan-Meier product-limit method. The cumulative incidence rates of relapse were computed censuring competing risks using Fine and Gray method (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.rproject.org/web/packages/cmprsk/index.html\u003c/span\u003e\u003cspan address=\"https://cran.rproject.org/web/packages/cmprsk/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Multivariate analysis was carried out using Cox regression analysis. An outcome was determined to be significantly different if the observed P value was \u0026lt;\u0026thinsp;0.05. All analyses were performed using statistical software IBM SPSS Statistics Version 24.0 (Armonk, USA). Statistical divergences from flow cytometry-based data were analysed by the GraphPad Prism software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship:\u003c/strong\u003e SC, SB, SRJ and NS designed the study. SB and MA collected the data. SC, SRJ, SB and MA analysed the data. NS and GB reviewed the data. All the authors drafted and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e The authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files]..\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHanna TP, Evans GA, Booth CM (2020) Cancer, COVID-19 and the precautionary principle: prioritizing treatment during a global pandemic. Nat Rev Clin Oncol\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJee J, Foote MB, Lumish M, Stonestrom AJ, Wills B, Narendra V, Avutu V, Murciano-Goroff YR, Chan JE, Derkach A, Philip J, Belenkaya R, Kerpelev M, Maloy M, Watson A, Fong C, Janjigian Y, Diaz LA Jr., Bolton KL (2020) Pessin, Chemotherapy and COVID-19 Outcomes in Patients With Cancer. J Clin Oncol 38:3538\u0026ndash;3546\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFedele P, Sanna V, Fancellu A, Marino A, Calvani N, Cinieri S (2021) De-escalating cancer treatments during COVID 19 pandemic: Is metronomic chemotherapy a reasonable option? Crit Rev Oncol Hematol 157:103148\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNunez-Torron C, Garcia-Gutierrez V, Tenorio-Nunez MC, Moreno-Jimenez G, Lopez-Jimenez FJ (2021) Herrera-Puente, Poor outcome in patients with acute leukemia on intensive chemotherapy and COVID-19. 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Int Arch Allergy Immunol 185:489\u0026ndash;502\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin K, Peluso MJ, Luo X, Thomas R, Shin MG, Neidleman J, Andrew A, Young KC, Ma T, Hoh R, Anglin K, Huang B, Argueta U, Lopez M, Valdivieso D, Asare K, Deveau TM, Munter SE, Ibrahim R, Standker L, Lu S, Goldberg SA, Lee SA, Lynch KL, Kelly JD, Martin JN, Munch J, Deeks SG, Henrich TJ, Roan NR (2024) Long COVID manifests with T cell dysregulation, inflammation and an uncoordinated adaptive immune response to SARS-CoV-2. Nat Immunol 25:218\u0026ndash;225\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorowitz A, Djaoud Z, Nemat-Gorgani N, Blokhuis J, Hilton HG, Beziat V, Malmberg KJ, Norman PJ, Guethlein LA, Parham P (2016) Class I HLA haplotypes form two schools that educate NK cells in different ways. Sci Immunol 1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeziat V, Hervier B, Achour A, Boutolleau D, Marfain-Koka A, Vieillard V (2011) Human NKG2A overrides NKG2C effector functions to prevent autoreactivity of NK cells. Blood 117:4394\u0026ndash;4396\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBortolotti D, Gentili V, Rizzo S, Rotola A, Rizzo R (2020) SARS-CoV-2 Spike 1 Protein Controls Natural Killer Cell Activation via the HLA-E/NKG2A Pathway. Cells 9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntonioli L, Fornai M, Pellegrini C, Blandizzi C (2020) NKG2A and COVID-19: another brick in the wall. Cellular \u0026amp; Molecular Immunology\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaiswal SR, Bhakuni P, Bhagawati G, Aiyer HM, Soni M, Sharma N, Jaiswal RR, Chakrabarti A, Chakrabarti S (2021) CTLA4Ig-primed donor lymphocyte infusions following haploidentical transplantation improve outcome with a distinct pattern of early immune reconstitution as compared to conventional donor lymphocyte infusions in advanced hematological malignancies. Bone Marrow Transpl 56:185\u0026ndash;194\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeiser R, Vago L (2019) Mechanisms of immune escape after allogeneic hematopoietic cell transplantation. Blood 133:1290\u0026ndash;1297\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuthorship SC SB, SRJ and NS designed the study. SB and MA collected the data. SC, SRJ, SB and MA analysed the data. NS and GB reviewed the data. All the authors drafted and reviewed the manuscript\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Relapse, Cancer, Immune exhaustion, PD-1, NKG2A","lastPublishedDoi":"10.21203/rs.3.rs-5452369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5452369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe studied the impact of COVID-19 on relapse in patients with hematological malignancies who had achieved complete remission (CR) and were either treatment-free or maintained on uninterrupted therapy over a 24-month period. Among 144 patients fulfilling the inclusion criteria, the overall relapse rate was 30.9%, with a significantly higher incidence in COVID-19-positive patients (60.9%) compared to COVID-19-negative individuals (25.2%, HR- p\u0026thinsp;=\u0026thinsp;0.0001). Stratification by disease risk index (DRI) revealed a pronounced effect of COVID-19 on relapse in the DRI-high cohort (64.3% vs. 20.1%, p\u0026thinsp;=\u0026thinsp;0.0001). Hematopoietic cell transplantation (HCT) appeared protective, with relapse rates of 22% in HCT recipients and 36.9% in the non-HCT cohort (p\u0026thinsp;=\u0026thinsp;0.06), with no impact of COVID-19 on relapse in patients undergoing HCT. However, the effect of COVID-19 on relapse was predominantly observed in the non-HCT group (92.3% vs. 27.8%, HR-8.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Immune exhaustion markers, including PD-1 on T cells and NKG2A on NK cells, were significantly upregulated in COVID-19-exposed patients, particularly in those who relapsed, compared to recipients of allogeneic HCT with and without exposure to SARS-CoV2. These findings suggest COVID-19-induced immune dysregulation may facilitate relapse, particularly in non-HCT patients, warranting further exploration of the immunobiological mechanisms responsible for this phenomenon and possible immune-targeted interventions in this context.\u003c/p\u003e","manuscriptTitle":"COVID-19 Induces Prolonged Immunological Exhaustion Leading To Relapse Of Hematological Malignancies Except In Hematopoietic Cell Transplant Recipients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 15:28:56","doi":"10.21203/rs.3.rs-5452369/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"25a4aa4d-7728-4d43-a658-3500720e5714","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T15:28:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-25 15:28:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5452369","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5452369","identity":"rs-5452369","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}