Clonal Hematopoiesis and COVID-19 Severity in Cancer Patients
Bibliographic record
Abstract
Background: Advanced age, medical co-morbidities and a pro-inflammatory immunologic profile are associated with severe COVID-19. Acquired somatic mutations in hematopoietic stem cells (clonal hematopoiesis or CH) are common in elderly individuals. Previous studies have shown CH is associated with alterations in immunologic repertoire and function. Here, we investigate whether CH predisposes to severe COVID-19 or other infections. Methods: We performed a retrospective cohort study of 39,291 adult cancer patients with non-hematologic malignancies who had their tumor and blood sequenced using a panel of 468 known cancer driver genes. We identified individuals who tested positive for SARS-CoV-2 between March 8, 2020 and June 9, 2020. We determined the association between CH and severe COVID-19 (hypoxic event, hospitalization or death) using multivariable logistic regression models. We used an established aggregation schema for ICD-CM codes to identify distinct infection subtypes. The relationship between CH and risk of incident infections was determined using multivariable Cox regression. Results: In the study cohort, 436 patients were positive for SARS-CoV-2 and 165 developed severe COVID-19. COVID-19 patients with CH were more likely to develop severe disease compared to non-severe (OR=1.8; 95% CI: 1.1-2.9; p=0.01). CH was also more common among patients with severe COVID-19 compared to COVID-19 negative individuals (OR=1.6, 95% CI: 1.2-2.4, p=0.01). In 62,891 person-years of follow-up, 4,059 individuals developed an infection. CH was associated with risk of sepsis (HR=1.13; 95% CI 1.01-1.25, p=0.04) and bacterial enteritis (HR=1.8, 95% CI 1.1-2.8, p=0.01). Conclusions: CH is associated with severe COVID-19 and an increased risk of other infections in cancer patients. Disclosures Bolton: GRAIL: Research Funding. Jee:MDSeq Inc.: Patents & Royalties. Papaemmanuil:Celgene: Consultancy, Honoraria, Research Funding; Prime Oncology: Consultancy, Honoraria; Illumina: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; MSKCC: Patents & Royalties; Kyowa Hakko Kirin: Consultancy, Honoraria; Isabl: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees. Berger:Illumina: Research Funding; Grail: Research Funding; Roche: Consultancy. Levine:Prelude Therapeutics: Research Funding; Qiagen: Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; Loxo: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Imago: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Isoplexis: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria, Research Funding; Lilly: Consultancy, Honoraria; Janssen: Consultancy; Astellas: Consultancy; Morphosys: Consultancy; Novartis: Consultancy; Amgen: Honoraria; Gilead: Honoraria. Zehir:Illumina: Honoraria; Memorial Sloan Kettering Cancer Center: Current Employment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".