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Record W4320519460 · doi:10.1016/j.lana.2023.100445

Breakthrough SARS-CoV-2 infections among patients with cancer following two and three doses of COVID-19 mRNA vaccines: a retrospective observational study from the COVID-19 and Cancer Consortium

2023· article· en· W4320519460 on OpenAlexaff
Toni K. Choueiri, Chris Labaki, Ziad Bakouny, Chih–Yuan Hsu, Andrew Schmidt, Gilberto Lopes, Clara Hwang, Sunny Singh, Chinmay Jani, Lisa B. Weissmann, Elizabeth A. Griffiths, Susan Halabi, Ulysses Wu, Stephanie Berg, Timothy E. O’Connor, Trisha M. Wise‐Draper, Orestis A. Panagiotou, Elizabeth J. Klein, Monika Joshi, Fares Yared, Míriam Santos Dutra, Na Tosha Gatson, Sibel Blau, Harpreet Singh, Rahul Nanchal, Rana R. McKay, Taylor K. Nonato, Ryann Quinn, Samuel M. Rubinstein, Matthew Puc, Blanche H. Mavromatis, Praveen Vikas, Bryan A. Faller, Howard A. Zaren, Salvatore Del Prete, Karen Russell, Daniel Y. Reuben, Melissa Accordino, Christopher R. Friese, Sanjay Mishra, Donna R. Rivera, Yu Shyr, Dimitrios Farmakiotis, Jeremy L. Warner

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsMcGill University Health Centre
FundersNational Institute on Minority Health and Health DisparitiesNational Center for Advancing Translational SciencesNational Institutes of HealthNational Cancer InstituteGeorgia Clinical and Translational Science AllianceVanderbilt Institute for Clinical and Translational ResearchVanderbilt University
KeywordsMedicineCancerOdds ratioInternal medicineClinical endpointPopulationLogistic regressionVaccinationConfoundingImmunologyClinical trial

Abstract

fetched live from OpenAlex

Background: Breakthrough SARS-CoV-2 infections following vaccination against COVID-19 are of international concern. Patients with cancer have been observed to have worse outcomes associated with COVID-19 during the pandemic. We sought to evaluate the clinical characteristics and outcomes of patients with cancer who developed breakthrough SARS-CoV-2 infections after 2 or 3 doses of mRNA vaccines. Methods: We evaluated the clinical characteristics of patients with cancer who developed breakthrough infections using data from the multi-institutional COVID-19 and Cancer Consortium (CCC19; NCT04354701). Analysis was restricted to patients with laboratory-confirmed SARS-CoV-2 diagnosed in 2021 or 2022, to allow for a contemporary unvaccinated control population; potential differences were evaluated using a multivariable logistic regression model after inverse probability of treatment weighting to adjust for potential baseline confounding variables. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) are reported. The primary endpoint was 30-day mortality, with key secondary endpoints of hospitalization and ICU and/or mechanical ventilation (ICU/MV). Findings: The analysis included 2106 patients, of which 443 and 257 had received 2 or 3 doses of an mRNA vaccine prior to infection, respectively. Hematologic malignancies and recent receipt of systemic anti-neoplastic therapy were more frequent among vaccinated patients. Vaccination was associated with improved outcomes: in the primary analysis, 2 doses (aOR: 0.66, 95%CI: 0.49-0.97) and 3 doses (aOR: 0.32, 95%CI: 0.16-0.61) were associated with decreased 30-day mortality. There were similar findings for the key secondary endpoints of ICU/MV (aOR: 0.60, 95%CI: 0.43-0.83 and 0.37, 95%CI: 0.22-0.61) and hospitalization (aOR: 0.58, 95%CI: 0.45-0.74 and 0.30, 95%CI: 0.21-0.42) for 2 and 3 doses, respectively. Black patients had higher rates of ICU/MV and hospitalization (aOR: 1.42, 95%CI: 1.00-2.01 and 1.42, 95%CI: 1.07-1.87, respectively). Interpretation: Vaccination against COVID-19, especially with additional doses, is a fundamental strategy in the prevention of adverse outcomes including death, among patients with cancer. Trial Registration: This ongoing study is registered on ClinicalTrials.gov (NCT04354701). Funding: Declaration of Interest: TKC reports grants, personal fees and non-financial support from Merck, BMS, Exelixis, Astra Zeneca, Eli Lilly, Eisai, Novartis, GSK, Pfizer, EMD Serono; stocks in Pionyr, Tempest, outside the submitted work; In addition, TKC reports patent: pending International Patent Application No. PCT/US2018/12209, entitled “PBRM1 Biomarkers Predictive of Anti-Immune Checkpoint Response,” filed January 3, 2018, claiming priority to U.S. Provisional Patent Application No. 62/445,094, filed January 11, 2017; pending International Patent Application No.PCT/US2018/058430, entitled “Biomarkers of Clinical Response and Benefit to Immune Checkpoint Inhibitor Therapy,” filed October 31, 2018, claiming priority to U.S. Provisional Patent Application No. 62/581,175, filed November 3, 2017; TKC sits on National Comprehensive Cancer Network kidney panel; CL reports grants from Genentech/ImCore; ZB reports non-financial support from Bristol Myers Squibb, grants from Genentech/ImCore, personal fees from UpToDate, outside the submitted work; ALS reports non-financial support from Astellas and Pfizer outside the submitted work; GdLL reports personal fees from Boehringer Ingelheim, Pfizer, AstraZeneca; grants from AstraZeneca, Merck Sharp & Dohme, EMD Serono, AstraZeneca, Blueprint Medicines, Tesaro, Bavarian Nordic, Novartis, G1 Therapeutics, Adaptimmune, BMS, GSK, Abbvie, Rgenix, Pfizer, Roche, Genentech, Eli Lilly, Janssen; personal fees from Boehringer Ingelheim, Pfizer, E.R. Squibb Sons, LLC, Janssen; all outside the submitted work; CH reports grants from Merck, Bayer, Genentech, AstraZeneca, Bausch Health; Consulting fees from Tempus, Genzyme, EMD Sorono, payment or honoraria from OncLive/MJH Life Sciences, support for attending meetings and/or travel from Merck, participation on a data safety monitoring or advisory board of Henry Ford Cancer Institute, Hoosier Cancer Research Network; Leadership or fiduciary role in Wayne County Medical Society of Southeast Michigan; Stock or stock options in Johnson and Johnson, all outside the submitted work; EAG reports Consulting fees from Alexion Inc, Picnic Health, AbbVie, CTI Biopharma, Genentech Inc., Novartis, Celgene/Bristol Myers-Squibb, Takeda oncology, Taiho Oncology and Research Funding from Genentech Inc, Astex Pharmaceuticals, and BluePrint Medicines, outside the submitted work; SH reports grants/research supports from ASCO TAPUR, Astellas; honoraria or consultation fees from Sanofi, Aveo Oncology, outside the submitted work; SB reports Consulting fees from BMS, Exelexis, Eisai, Pfizer, Myovant, SeaGen; Payment or honoraria from Exelexis, Eisai, BMS; Participation on a Data Safety Monitoring Board or Advisory Board from SeaGen, Pfizer, Myovant; Stock or stock options in Natera; outside the submitted work; MJ reports grants from AstraZeneca, Pfizer; Eisai, personal fees from Seagen, Sanofi, outside the submitted work; NTNG reports personal fees from Novocure, outside the submitted work; RRM reports Advisory board/consultant – Aveo, AstraZeneca, Bayer, Bristol Myers Squib, Calithera, Caris, Dendreon, Exelixis, Janssen, Merck, Myovant, Novartis, Pfizer, Sanofi, Sorrento therapeutics, Pfizer, Tempus, Vividion, unrelated to this work; SMR reports advisory for Roche, Janssen, Sanofi, and EUSA Pharma, unrelated to this work; PV reports institutional research funding from Sanofi; stocks or stock options in Novavax, Biontech; HAZ acknowledges support from Georgia NCORP; CRF reports grants from Merck Foundation, grants from NCCN/Pfizer, grants from National Cancer Institute, other from National Cancer Institute, other from Patient-Centered Outcomes Research Institute, outside the submitted work; SM reports support from National Cancer Institute, and Intl Assoc. for the Study of Lung Cancer during the conduct of the study; and personal fees from National Geographic outside the submitted work; DF reports Grants or contracts from Merck, Viracor, Astellas; Support for attending meetings and/or travel from Viracor; outside the submitted work; JLW reports grants from NIH during the conduct of the study; personal fees from Roche, Westat, Flatiron Health, Melax Tech, IBM Watson Health, ownership of HemOnc.org LLC, grants from AACR; outside the submitted work; C-YH, SRKS, CJ, LBW, UW, TEO'C, TMW-D, OAP, EJK, HS, RN, TKN, RQ, MP, BHM, SADP, KR, BF, DYR, MKA, HS, DRR, YS, SB have nothing to disclose. Ethical Approval: This study was exempt from Institutional Review Board (IRB) review (VUMC IRB#200467) and approved by IRBs at participating sites.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.223
GPT teacher head0.467
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations39
Published2023
Admission routes1
Has abstractyes

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