MétaCan
Menu
← Back to cohort

COVID-19 vaccine humoral response and treatment effect in patients with hematologic malignancy.

2022· article· en· W4286299150 on OpenAlexaff
Alex Hillyer, Jordan Spradbrow, Michael J. Knauer, Jenny Kim, Anthony Quint, Selay Lam, Husam Abdoh, Danny Dawd, Joy Mangel, Kang Howson‐Jan, Anargyros Xenocostas, Uday Deotare, Lalit Saini, Alejandro Lazo‐Langner, Cheryl Foster, Martha Louzada, Jenny Ho, Ian Chin‐Yee, Shawn Li, Chai W. Phua

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineInternal medicineTiterVaccinationGastroenterologyImmunologyHematologic malignancySerologyAntibody titerAntibody

Abstract

fetched live from OpenAlex

e18780 Background: Patients with hematologic malignancies have a lower vaccine response and higher rates for SARS CoV-2 morbidity and mortality. We present preliminary data focusing on humoral vaccine responses and correlates with disease subtype and treatment exposure. Methods: We analyzed data from 332 patients with a hematologic malignancy from May 1, 2021 – Jan 31, 2022 who received SARS-CoV-2 vaccination and performed a prospective cohort serologic study with the Elecsys® Anti-SARS-CoV-2-S test. Patients received homologous or heterologous vaccine combination of BNT162b2, mRNA1273, ChAdOx1 nCoV-19, and/or Ad26.COV2.S. Blood samples were obtained before any vaccination, 2-6 weeks after the second vaccine (2V), before third vaccine (3V), and 2-6 weeks after 3V. Results: The median age was 67 years (range 18-91years) with 41.9% female. At 2V, 11.5% and at 3V, 23.8% received heterologous vaccines. Treatment status at first vaccine dose significantly affected peak 2V antibody response (p < 0.05). Seropositive rate and median antibody titer after 2V for previously untreated patients were higher compared to patients on active therapy or had previously been treated. Treatment naïve (n = 60; seropositivity 85.1%; median titer 1306 U/mL; [Q1-Q3:11.4- > 2499]); first-line (1L) active therapy (n = 127;65.4%; 41.25 U/mL; [ < 0.8-592.5]); second-line and beyond (2L+) active therapy (n = 56; 60.7%; 2.6U/mL; [ < 0.8-154]); previous treatment with 1L (n = 66;64.8%; 118 U/mL; [ < 0.8- > 2499]); previous treatment with 2L+ (n = 23; 59.1%; 4U/mL; [ < 0.8-229.5]). Of 61 patients that were seronegative at 2V, 17 (27.9%) seroconverted after 3V. Anti-CD20 monoclonal antibody (mAb) containing therapy as the most recent treatment from 2V had the greatest impact on humoral response. Exposure to anti-CD20 mAb based regimens or as monotherapy revealed low antibody responses (n = 84; seropositivity 22.6%; median titer < 0.8 U/mL; Q1-Q3 [ < 0.8- < 0.8]). On analysis of indolent B-cell Non-Hodgkin Lymphomas whereby antiCD-20 mAb are often incorporated, treatment proximity to 2V impacted responses: < 3 months (n = 33; 22%; < 0.8 U/mL; [ < 0.8- < 0.8]) vs. 12-24 months (n = 4; 60%; 228 U/mL; [ < 0.8-232]). In contrast, tyrosine kinase inhibitor (n = 38; 100%; 858 U/mL; [221- > 2499]), proteosome inhibitor monotherapy (n = 4;100%; median titer 1520 U/mL; [462- > 2499]) were among the subgroups with the highest numerical responses, however, the addition of corticosteroids impacted vaccine response as seen in proteosome inhibitor with corticosteroids (n = 7; 85.7%; 6.6 U/mL; [1.8-115.2]). Conclusions: The humoral response from our single institution cohort identifies diminished responses depending on treatment status and the type of treatment including the proximity of treatment exposure to receipt of vaccination. Furthermore, vaccine boosters can induce antibody responses in patients who were previously seronegative.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.442
Teacher spread0.372 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicCancer Immunotherapy and Biomarkers→French-language works237,207→