Skip to main content

Chronic COVID-19 infection in an immunosuppressed patient shows changes in lineage over time: a case report



The COVID-19 pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus, emerged in late 2019 and spready globally. Many effects of infection with this pathogen are still unknown, with both chronic and repeated COVID-19 infection producing novel pathologies.

Case presentation

An immunocompromised patient presented with chronic COVID-19 infection. The patient had history of Hodgkin’s lymphoma, treated with chemotherapy and stem cell transplant. During the course of their treatment, eleven respiratory samples from the patient were analyzed by whole-genome sequencing followed by lineage identification. Whole-genome sequencing of the virus present in the patient over time revealed that the patient at various timepoints harboured three different lineages of the virus. The patient was initially infected with the B.1.1.176 lineage before coinfection with BA.1. When the patient was coinfected with both B.1.1.176 and BA.1, the viral populations were found in approximately equal proportions within the patient based on sequencing read abundance. Upon further sampling, the lineage present within the patient during the final two timepoints was found to be BA.2.9. The patient eventually developed respiratory failure and died.


This case study shows an example of the changes that can happen within an immunocompromised patient who is infected with COVID-19 multiple times. Furthermore, this case demonstrates how simultaneous coinfection with two lineages of COVID-19 can lead to unclear lineage assignment by standard methods, which are resolved by further investigation. When analyzing chronic COVID-19 infection and reinfection cases, care must be taken to properly identify the lineages of the virus present.

Key points

  • A patient repeatedly tested positive for COVID-19 over 16 months.

  • Infection progressed from one lineage to coinfection with a second lineage, before clearance of coinfection and reinfection with a third, different lineage.

  • Coinfection was difficult to identify through genomic methods.


Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). As of October 22, 2023, over 771 million cases have been reported worldwide with over 6.9 million deaths as a result of COVID-19 [1]. SARS-CoV-2 primarily enters host cells by binding of its spike (S) protein to human cell-surface angiotensin-converting enzyme 2 (ACE2) receptors [2, 3]. SARS-CoV-2 is a positive-sense, single-stranded RNA virus, with a genome 29–30 kB in size, organized as methyl-capped-5″UTR-ORF1a/b-S-ORF3-E-M-ORF6-ORF7a/b-ORF8-N/ORF9b-ORF9c-ORF10-3’UTR-poly-A-tail [4,5,6]. The S, E, M, and N genes encode key structural proteins found in the mature virion—the Spike, Envelope, Membrane, and Nucleocapsid structures respectively [7]. COVID-19 primarily affects the respiratory tract, and manifests as an acute upper and/or lower respiratory syndrome that can vary in severity [8]. The disease can result in asymptomatic viral shedding, or symptomatic disease associated with fever, cough, fatigue, myalgia, arthralgia, rhinorrhea, sore throat, and conjunctivitis [8,9,10]. However, the disease can also progress to more severe outcomes, including persistent fever, hemoptysis, hypoxia, chest discomfort and/or pain, respiratory failure, and multiorgan failure [9, 10]. Impairment of smell and/or taste is also a common symptom of COVID-19 [11]. Typical, non-chronic, mild and moderate cases of COVID-19 are usually associated with improvement of symptoms about 10 days after onset of symptoms, though in rare cases the infection for persist for a number of weeks, known as chronic or long COVID-19 when symptoms last longer than 3 weeks [12, 13]. While the body of work surrounding comorbidities for COVID-19 infection remains large, relatively less information is available regarding potential comorbidities and risk factors for chronic COVID-19 infection or COVID-19 reinfection (a new COVID-19 infection unrelated to the previous infection) [14, 15], both of which were seen in this case. Changes in lineage (when a patient initially is found to be infected with a certain lineage, and a second, later test identifies infection by a new, distinct lineage) is often indicative of reinfection rather than within-host evolution [15, 16]. There remains a limited number of reports detailing cases of chronic COVID-19 infection and/or repeated infection. We present a chronic infection followed by reinfection, over a 16-month period, in a severely immunocompromised patient.

Case presentation

Initial diagnosis and treatment

A male, in his early fifties, and heavily immunosuppressed with history of Hodgkin’s lymphoma (HL) was initially treated with ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) chemotherapy, and later, GDP (gemcitabine, dexamethasone, and cisplatin) chemotherapy, followed by autologous stem cell transplant (SCT) for relapsed HL one-year post-completion of initial chemotherapy. The patient was maintained on the CD30 antibody–drug conjugate Brentuximab until he was noted to again have HL disease progression, for which he underwent an allogeneic SCT 1.5 years post-autologous transplant. His post-transplant course was complicated by Epstein–Barr Virus (EBV) reactivation and associated post-transplant lymphoproliferative disorder (PTLD), requiring repeated courses of rituximab (twelve doses overall) and graft versus host disease (GVHD) of the skin, gut, and possibly lung, requiring multiple doses of prednisone.

The first episode of COVID-19 infection was one-month post-allogeneic SCT, prior to the diagnosis of PTLD. There were no other microbiological findings in the patient’s lungs. Shortly thereafter, the patient required rituximab for EBV reactivation, followed by recurrent episodes of EBV reactivation and CT-confirmed PTLD, leading to further courses of rituximab. Initial presenting symptoms of COVID-19 were mild upper respiratory tract infection (URTI) symptoms, and Bamlanivimab was received. However, four months after the initial infection, the patient was admitted to hospital with progressive cough and shortness of breath. Upon admission, the patient again tested positive for COVID-19. Treatment included Remdesivir, dexamethasone, and Bamlanivimab with good response. Six months later, the patient developed a progressive chronic cough and was eventually hospitalized (fourteen months after the initial COVID-19 infection) with shortness of breath and new diffuse bilateral lung consolidations. This admission, treatment included sotrovimab along with another course of remdesivir and dexamethasone. Despite initial improvement in respiratory status, the patient developed worsening renal dysfunction and shortness of breath along with progressive lung infiltrates, leading to respiratory failure and ultimately death. Pulmonary issues were multifactorial, including chronic COVID-19 infection, possible lung GVHD, and cardio-renal syndrome. The timeline of COVID-19 lineages, disease symptoms, and treatments received is summarized in Table 1.

Table 1 Approximate times of sampling for various COVID-19 lineages, as well as symptoms and disease status and treatment at these timepoints

Genetic profiling

From March 2021 to June 2022, eleven samples from the patient were amplified for SARS-CoV-2 using the ARTIC V3 or ARTIC V4 protocol as outlined in Nasir et al. 2020 [17] and sequenced using an Illumina NextSeq platform. After sequencing, FASTQ files were analyzed via FastQC [18], barcode and adaptor sequences were removed using Trimmomatic [19], and SPAdes [20] was used to assemble genomes. The resulting genomes were analyzed using the SARS-CoV-2 Illumina GeNome Assembly Line (SIGNAL) pipeline ( After lineage assignment by Phylogenetic Assignment of Named Global Outbreak Lineages (PANGOLIN; within the SIGNAL workflow, mutation profiles and minor variants within samples were determined using breseq [21]. The most prevalent lineages in Ontario at the timepoints the patient was sampled were determined using VirusSeq Public Health of Ontario data ( Canonical sequences for the Alpha, Delta, and Omicron variants of SARS-CoV-2 and the most prevalent lineages at the time of patient samplings were downloaded from the NCBI Virus SARS-CoV-2 Data Hub ( Using these sequences, a maximum-likelihood phylogenetic tree was constructed by first carrying out single-nucleotide polymorphism (SNP) analysis using Parsnp [22] followed by maximum-likelihood phylogenetic tree construction using the RAxML-HPC BlackBox platform with the GTRGAMMA + I substitution model and automatic bootstrapping [23].

Lineages of patient samples, approximate dates of sampling and prevalence rates for patient lineages and most common lineages in Ontario at the time of patient samplings are shown in Table 2, and a phylogenetic tree containing canonical Alpha, Delta, and Omicron samples, the patient samples, and the most prevalent circulating strains over time in Ontario is shown in Fig. 1. Of the first eight sequenced samples, all characteristic mutations of B.1.1.176 were present with the exception of four mutations that were consistently missing in all samples. These missing mutations were L3674 in ORF1a, R203K and G204N in N, and S84LO in ORF8. There were also 10 mutations present in all eight samples that were not characteristic of B.1.1.176. These were C→T at position 241 of the genome in an intergenic region; a 3 bp deletion in ORF1; L642F, P1950L, K2029N, and N2603S in ORF1ab; C→T in the intergenic region between S and ORF3; L95F in ORF3a; a 3 bp change to AAC in N; and G→T in the intergenic region after ORF10. There were also five mutations present in the first four or five samples that were not present in samples six through eight. Present in the first four samples were S6096G in ORF1ab, T307I in S, and N269T in N; present in the first five samples were E484A and Y1155H in S. These mutations decreased in prevalence across time before not being identified in samples five or six. There were also five mutations that were gained across time, not being present in sample 7 and present in nearly 100% of reads in sample 8: a 15 bp deletion in ORF1ab, A5376V in ORF1ab, F490L and S494P in S, and T271I in N.

Table 2 Patient lineages across time and percentages of total lineages in Ontario made up by the lineage found in the patient
Fig. 1
figure 1

Maximum-likelihood phylogenetic tree showing SARS-CoV-2 reference sequence (MN908947.3), an Alpha lineage (B.1.1.7), a Delta lineage (B.1.617.2) and two Omicron lineages (B.1.1.529 and XBB.1.5), as well as the most prevalent lineages in Ontario at the times the patient was sampled (B.1.1.7, AY.74, BA.2, and BA.2.12.1). Bootstrap values are shown at the nodes

The next sample in the series (sample 9) was initially not assigned a lineage; however, breseq analysis revealed an infection that was a mix of B.1.1.176 and BA.1 at proportions of approximately 50% each. Mutations that were present in samples 1–8 but lost in sample 9 are shown in Table 3, while mutations that were first present in sample 9 are shown in Table 4. There were four nonsynonymous mutations present in 100% of reads in samples 1–9: C→T in the intergenic region before ORF1ab (a mutation characteristic of neither lineage), a 6 bp deletion in ORF1ab (characteristic of both B.1.1.176 and BA.1), P4715L in ORF1ab (characteristic of both B.1.1.176 and BA.1), and D614G in S (characteristic of only B.1.1.176). There were 13 nonsynonymous mutations present in samples 1–8 that were present in less than 40% of reads in sample 9 (referred to as lost mutations) and 43 nonsynonymous mutations that were present in greater than 40% of reads in sample 9 after not being present in the first 8 samples (referred to as gained mutations). These are shown in Tables 1 and 2 respectively. Of note for the gained mutations is the mutation S371F in S, which is not characteristic of either B.1.1.176 or BA.1, however the mutation S371L is characteristic of BA.1. There were also 11 nonsynonymous mutations that were not present in sample 1, appeared in some of samples 2–8, and were not present in sample 9, or were present in the intermediate samples, lost, and then reappeared in sample 9. These are shown in Table 5 and are referred to as fleeting mutations.

Table 3 Mutations lost in sample 9
Table 4 Mutations gained in sample 9
Table 5 Fleeting mutations

While sample 9 appeared to be a mixed infection of B.1.1.176 and BA.1, samples 10 and 11 were both assigned the lineage of BA.2.9, with 50 of 56 nonsynonymous mutations characteristic of BA.2.9. The mutations not characteristic of BA.2.9 (Table 6) were A5620S in ORF1ab, a 3 bp change to CTC in ORF6, C→T in the intergenic region between ORF7a and ORF8, A→T in the intergenic region between ORF8 and N, a 3 bp change to AAC in N, and a 26 bp deletion in the intergenic region after ORF10. Yet, samples 9 and 10 were missing 5 characteristic mutations of BA.2.9: L24S in S, D61L in ORF6, S84L in ORF8, and R203K and G204R in N.

Table 6 Mutations found in samples 10 and 11 that are not characteristic of BA.2.9

There were four mutations present in all 11 samples that were identified in 100% of reads: P4715L in ORF1ab and D614G in S (both of which are found in all three of B.1.1.176, BA.1, and BA.2.9), C→T in the intergenic region before ORF1a, and a 3 bp change to AAC in N. The latter two mutations are not characteristic of any of B.1.1.176, BA.1, or BA.2.9.


Several previous studies have identified cancer, and specifically hematologic cancers, as a comorbidity that worsens the health outcomes of those infected with respiratory infections such as COVID-19 [24,25,26]. One case report by Yonal-Hindilerden et al. reported on a patient with Hodgkin’s lymphoma who additionally contracted COVID-19. This patient experienced severe respiratory disease, eventually succumbing to COVID-19 10 days after hospital admission [27]. A second study reported on a patient with Hodgkin’s lymphoma showed reinfection with COVID-19 34 days after clearance of their initial infection [28]. However, neither of these two studies performed whole genome sequencing to assess any lineage changes in the viral infection present in the patients.

One 2021 study found that 0.47% of COVID-19 patients were incidences of reinfection [29]. Of these patients that were reinfected, the majority (67%) were reinfected with a different genomic variant than their original infection [29], as was seen in the present case. The likelihood that the three observed lineages represent within-host evolution is extremely low as B.1.1.176 and BA.1 are evolutionary very distant, with BA.1 and BA.2.9 also being genetically distinct. Another study reported on a chronic SARS-CoV-2 infection lasting over 400 days, with a SARS-CoV-2 mutation rate approximately two-fold higher than the global SARS-CoV-2 evolutionary rate [30]. This study also reported the presence over time of three genetically distinct genotypes within the patient, representing three different viral populations originating from different physical locations within the patient that continually migrated into the nasopharynx [30]. This is contrasted with the present study, where mixed infection only appears in sample 9, where the lineages B.1.1.176 and BA.1 appeared to both be present in the nasopharynx. By the next sample in the series, the lineage BA.2.9 appeared to make up 100% of the viral particles sequenced from the nasopharynx.

Immunocompromised patients are at a higher risk of chronic infection, likely due to their B-cell depleted state [31,32,33], as B-cells play a large role in protective immunity against SARS-CoV-19 [34]. The changes in viral lineage over time may have been associated with a poor health outcome in this patient, as Omicron lineages (BA.1 and BA.2) are associated with higher infectivity and immune escape compared to B.1.1.176 (an Alpha lineage) [35,36,37,38]. Additionally, Omicron lineages are associated with a higher risk of reinfection [39]. Further complicating the progression from an Alpha COVID-19 infection to two latter Omicron infections was the immunocompromised status of the patient, which has been found to be associated with severe clinical outcomes in COVID-19 infections [40]. Cancer patients are classified as a population at high-risk for poor health outcomes in COVID-19 infections due to their immunosuppressive state, with COVID-19 symptoms often more severe in this population [41,42,43]. Again compounding this risk factor in this patient was the fact that they received SCT, another factor which increases the risk of COVID-19 morbidity and mortality [44, 45]. Various studies have shown that due to their immunocompromised state, these patients are at risk for reinfection [46, 47], with one study finding that an immunocompromised patient had a higher viral load than a comparable healthy individual [48].


It has been well characterized that immunocompromised individuals are at a higher risk of developing a chronic SARS-CoV-2 infection [49,50,51,52,53,54]. The present study reports a patient that was initially infected with B.1.1.176, which persisted for fifteen months, before subsequent additional infection with BA.1. When resampled one month later, the patient had apparently cleared the B.1.1.176 and BA.1 infections and had been reinfected again, this time with BA.2.9. This case thus represents incidences of chronic infection, mixed infection, as well as independent COVID-19 reinfection. The results of this case study highlight the need to closely monitor those patients that are both infected with COVID-19 and are in an immunocompromised state. To our knowledge, this case study represents one of the longest chronic COVID infections combined with reinfection in an immunocompromised patient.



Coronavirus disease 2019


Severe acute respiratory syndrome coronavirus 2


Angiotensin converting enzyme 2

S gene:

Spike gene

E gene:

Envelope gene

M gene:

Membrane gene

N gene:

Nucleocapsid gene


Hodgkin’s lymphoma


Doxorubicin, bleomycin, vinblastine, and dacarbazine


Gemcitabine, dexamethasone, and cisplatin


Stem cell transplant


Single-nucleotide polymorphism


Epstein–Barr virus


Post-transplant lymphoproliferative disorder


Graft versus host disease


SARS-CoV-2 Illumina GeNome Assembly Line


Phylogenetic assignment of named global outbreak lineages


  1. World Health Organization. Weekly Epidemiological Update on COVID-19: 27 October 2023.; 2023. Accessed November 21, 2023.

  2. Xu X, Chen P, Wang J, et al. Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission. Sci China Life Sci. 2020;63(3):457–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Yan R, Zhang Y, Li Y, Xia L, Guo Y, Zhou Q. Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science. 2020;367(6485):1444–8.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Chan JFW, Kok KH, Zhu Z, et al. Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan. Emerg Microbes Infect. 2020;9(1):221–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zandi M, Shafaati M, Kalantar-Neyestanaki D, et al. The role of SARS-CoV-2 accessory proteins in immune evasion. Biomed Pharmacother Biomedecine Pharmacother. 2022;156:113889.

    Article  CAS  Google Scholar 

  6. Zandi M. ORF9c and ORF10 as accessory proteins of SARS-CoV-2 in immune evasion. Nat Rev Immunol. 2022;22(5):331.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wu F, Zhao S, Yu B, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020;579(7798):265–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. To KKW, Sridhar S, Chiu KHY, et al. Lessons learned 1 year after SARS-CoV-2 emergence leading to COVID-19 pandemic. Emerg Microbes Infect. 2021;10(1):507–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Docherty AB, Harrison EM, Green CA, et al. Features of 20 133 UK patients in hospital with COVID-19 using the ISARIC WHO clinical characterisation protocol: prospective observational cohort study. BMJ. 2020;369:m1985.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Richardson S, Hirsch JS, Narasimhan M, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA. 2020;323(20):2052–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chung TWH, Sridhar S, Zhang AJ, et al. Olfactory dysfunction in coronavirus disease 2019 patients: observational cohort study and systematic review. Open Forum Infect Dis. 2020;7(6):199.

    Article  CAS  Google Scholar 

  12. Aguilar RB, Hardigan P, Mayi B, et al. Current understanding of COVID-19 clinical course and investigational treatments. Front Med. 2020;7:555301.

    Article  Google Scholar 

  13. Halpin S, O’Connor R, Sivan M. Long COVID and chronic COVID syndromes. J Med Virol. 2021;93(3):1242–3.

    Article  CAS  PubMed  Google Scholar 

  14. Yong SJ. Long COVID or post-COVID-19 syndrome: putative pathophysiology, risk factors, and treatments. Infect Dis Lond Engl. 2021;53(10):737–54.

    Article  CAS  Google Scholar 

  15. Ren X, Zhou J, Guo J, et al. Reinfection in patients with COVID-19: a systematic review. Glob Health Res Policy. 2022;7(1):12.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Quaranta EG, Fusaro A, Giussani E, et al. SARS-CoV-2 intra-host evolution during prolonged infection in an immunocompromised patient. Int J Infect Dis IJID Off Publ Int Soc Infect Dis. 2022;122:444–8.

    Article  CAS  Google Scholar 

  17. Nasir JA, Kozak RA, Aftanas P, et al. A comparison of whole genome sequencing of SARS-CoV-2 using amplicon-based sequencing, random hexamers, and bait capture. Viruses. 2020;12(8):E895.

    Article  CAS  Google Scholar 

  18. Andrews S. FastQC: A Quality Control Tool for High Throughput Sequence Data. Published 2010.

  19. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinforma Oxf Engl. 2014;30(15):2114–20.

    Article  CAS  Google Scholar 

  20. Bankevich A, Nurk S, Antipov D, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol J Comput Mol Cell Biol. 2012;19(5):455–77.

    Article  CAS  Google Scholar 

  21. Deatherage DE, Barrick JE. Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Methods Mol Biol Clifton NJ. 2014;1151:165–88.

    Article  CAS  Google Scholar 

  22. Treangen TJ, Ondov BD, Koren S, Phillippy AM. The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes. Genome Biol. 2014;15(11):524.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinforma Oxf Engl. 2014;30(9):1312–3.

    Article  CAS  Google Scholar 

  24. Liang W, Guan W, Chen R, et al. Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China. Lancet Oncol. 2020;21(3):335–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Martino R, Rámila E, Rabella N, et al. Respiratory virus infections in adults with hematologic malignancies: a prospective study. Clin Infect Dis Off Publ Infect Dis Soc Am. 2003;36(1):1–8.

    Article  Google Scholar 

  26. Hakki M, Rattray RM, Press RD. The clinical impact of coronavirus infection in patients with hematologic malignancies and hematopoietic stem cell transplant recipients. J Clin Virol Off Publ Pan Am Soc Clin Virol. 2015;68:1–5.

    Article  Google Scholar 

  27. Yonal-Hindilerden I, Hindilerden F, Mastanzade M, et al. Case report: Severe COVID-19 pneumonia in a patient with relapsed/refractory Hodgkin’s lymphoma. Front Oncol. 2021;11:601709.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Fakharian A, Ebrahimibagha H, Mirenayat MS, Farahmandi F. COVID-19 reinfection in a patient with Hodgkin lymphoma: a case report. Tanaffos. 2021;20(1):71–4.

    PubMed  PubMed Central  Google Scholar 

  29. Brouqui P, Colson P, Melenotte C, et al. COVID-19 re-infection. Eur J Clin Invest. 2021;51(5):e13537.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chaguza C, Hahn AM, Petrone ME, et al. Accelerated SARS-CoV-2 intrahost evolution leading to distinct genotypes during chronic infection. MedRxiv Prepr Serv Health Sci. 2022.

    Article  Google Scholar 

  31. Dioverti V, Salto-Alejandre S, Haidar G. Immunocompromised patients with protracted COVID-19: a review of “long persisters.” Curr Transpl Rep. 2022;9(4):209–18.

    Article  PubMed  Google Scholar 

  32. Baang JH, Smith C, Mirabelli C, et al. Prolonged severe acute respiratory syndrome coronavirus 2 replication in an immunocompromised patient. J Infect Dis. 2021;223(1):23–7.

    Article  CAS  PubMed  Google Scholar 

  33. Sepulcri C, Dentone C, Mikulska M, et al. The longest persistence of viable SARS-CoV-2 with recurrence of viremia and relapsing symptomatic COVID-19 in an immunocompromised patient-a case study. Open Forum Infect Dis. 2021;8(11):ofab217.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Chen S, Guan F, Candotti F, et al. The role of B cells in COVID-19 infection and vaccination. Front Immunol. 2022;13:988536.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhou Y, Zhi H, Teng Y. The outbreak of SARS-CoV-2 Omicron lineages, immune escape, and vaccine effectivity. J Med Virol. 2023;95(1):e28138.

    Article  CAS  PubMed  Google Scholar 

  36. Dejnirattisai W, Huo J, Zhou D, et al. SARS-CoV-2 Omicron-B1.1.529 leads to widespread escape from neutralizing antibody responses. Cell. 2022;185(3):467-484.e15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zhang L, Li Q, Liang Z, et al. The significant immune escape of pseudotyped SARS-CoV-2 variant Omicron. Emerg Microbes Infect. 2022;11(1):1–5.

    Article  CAS  PubMed  Google Scholar 

  38. Chavda VP, Bezbaruah R, Deka K, Nongrang L, Kalita T. The delta and omicron variants of SARS-CoV-2: what we know so far. Vaccines. 2022;10(11):1926.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Pulliam JRC, van Schalkwyk C, Govender N, et al. Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. Science. 2022.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Bakouny Z, Labaki C, Grover P, et al. Interplay of immunosuppression and immunotherapy among patients with cancer and COVID-19. JAMA Oncol. 2023;9(1):128–34.

    Article  PubMed  Google Scholar 

  41. Liu C, Zhao Y, Okwan-Duodu D, Basho R, Cui X. COVID-19 in cancer patients: risk, clinical features, and management. Cancer Biol Med. 2020;17(3):519–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Zhang L, Zhu F, Xie L, et al. Clinical characteristics of COVID-19-infected cancer patients: a retrospective case study in three hospitals within Wuhan, China. Ann Oncol Off J Eur Soc Med Oncol. 2020;31(7):894–901.

    Article  CAS  Google Scholar 

  43. Dai M, Liu D, Liu M, et al. Patients with cancer appear more vulnerable to SARS-CoV-2: a multicenter study during the COVID-19 outbreak. Cancer Discov. 2020;10(6):783–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Strasfeld L. COVID-19 and HSCT (Hematopoietic stem cell transplant). Best Pract Res Clin Haematol. 2022;35(3): 101399.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Randi BA, Higashino HR, da Silva VP, Xavier EM, Rocha V, Costa SF. COVID-19 in hematopoietic stem-cell transplant recipients: a systematic review and meta-analysis of clinical characteristics and outcomes. Rev Med Virol. 2023;33(6):e2483.

    Article  CAS  PubMed  Google Scholar 

  46. Abolghasemi S, Zolfaghari F, Naeimipoor M, Azhdari Tehrani H, Hakamifard A. COVID-19 reinfection or reactivation in a renal transplant patient. Clin Case Rep. 2021;9(8):e04672.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Côté F, Bestman-Smith J, Gourdeau M, et al. Reinfection with SARS-CoV-2 in a patient undergoing chemotherapy for lymphoma: Case report. J Assoc Med Microbiol Infect Dis Can J Off Assoc Pour Microbiol Medicale Infect Can. 2022;7(3):283–91.

    Article  Google Scholar 

  48. McKittrick JM, Burke TW, Petzold E, et al. SARS-CoV-2 reinfection across a spectrum of immunological states. Health Sci Rep. 2022;5(4):e554.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Avanzato VA, Matson MJ, Seifert SN, et al. Case study: Prolonged infectious SARS-CoV-2 shedding from an asymptomatic immunocompromised individual with cancer. Cell. 2020;183(7):1 1-1912.e9.

    Article  CAS  Google Scholar 

  50. Cele S, Karim F, Lustig G, et al. SARS-CoV-2 prolonged infection during advanced HIV disease evolves extensive immune escape. Cell Host Microbe. 2022;30(2):154-162.e5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Gandhi S, Klein J, Robertson AJ, et al. De novo emergence of a remdesivir resistance mutation during treatment of persistent SARS-CoV-2 infection in an immunocompromised patient: a case report. Nat Commun. 2022;13(1):1547.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kemp SA, Collier DA, Datir RP, et al. SARS-CoV-2 evolution during treatment of chronic infection. Nature. 2021;592(7853):277–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Maponga TG, Jeffries M, Tegally H, et al. Persistent SARS-CoV-2 infection with accumulation of mutations in a patient with poorly controlled HIV infection. Clin Infect Dis Off Publ Infect Dis Soc Am. 2022.

    Article  Google Scholar 

  54. Weigang S, Fuchs J, Zimmer G, et al. Within-host evolution of SARS-CoV-2 in an immunosuppressed COVID-19 patient as a source of immune escape variants. Nat Commun. 2021;12(1):6405.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Canadian COVID-19 Genomics Network. Canadian VirusSeq data portal.

Download references


We thank all St. Joseph’s Healthcare staff who took part in the care of the patient.


This work was supported by Public Health Ontario, by Genome Canada CanCOGeN funding; by McMaster University’s COVID-19 Research Fund; and a David Braley Chair in Computational Biology to AGM. Computational support was provided by the McMaster Service Lab and Repository computing cluster, supplemented by hardware donations and loans from Cisco Systems Canada; Hewlett Packard Enterprise; and Pure Storage.

Author information

Authors and Affiliations



Conceptualization, SJCB; Methodology, LEN, SJCB; Formal Analysis, SJCB; Resources, AGM, MS; Writing Original Draft, SJCB; Writing—Review and Editing, SJCB, LEN, DL, CR, AGM, MS; Supervision, AGM, MS; Funding Acquisition, AGM, MS.

Corresponding author

Correspondence to Andrew G. McArthur.

Ethics declarations

Ethics approval and consent to participate

The need for ethics approval and consent was waived.

Competing interests

The authors have nothing to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baker, S.J.C., Nfonsam, L.E., Leto, D. et al. Chronic COVID-19 infection in an immunosuppressed patient shows changes in lineage over time: a case report. Virol J 21, 8 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: