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Record W2741738423 · doi:10.1158/1538-7445.am2017-4267

Abstract 4267: Association of polygenic risk scores and family history with the risk of chronic lymphocytic leukemia (CLL)

2017· article· en· W2741738423 on OpenAlexaff
Geffen Kleinstern, Sílvia de Sanjosé, Nicola J. Camp, Claire M. Vajdic, Timothy G. Call, Mark Liebow, Dennis P. Robinson, Neil E. Kay, Julie L. Cunningham, Yolanda Benavente, Alain Monnereau, John Spinelli, James R. Cerhan, Susan L. Slager

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsSpinal Cord Injury BCBC Cancer Agency
Fundersnot available
KeywordsOdds ratioChronic lymphocytic leukemiaSingle-nucleotide polymorphismConfidence intervalMedicineFamily historyInternal medicineCase-control studyLogistic regressionSNPOncologyLeukemiaGeneticsBiologyGenotypeGene

Abstract

fetched live from OpenAlex

Abstract Background Thirty-four single nucleotide polymorphisms (SNPs) are associated with CLL risk to-date. Moreover, family history (FH) of hematological malignancy has been consistently found to be associated with CLL, with an 8.5-fold increased risk of CLL among first-degree relatives. However, there has not been an evaluation of the interactive effects among genetic factors and FH with CLL risk. Methods We pooled data from 8 CLL case-control studies within the InterLymph Consortium (1499 CLL cases and 2601 controls). We computed a polygenic risk score (PRS), a weighted average of the number of risk alleles across the 34 SNPs, with the weights being the log of the previously reported odds ratio (OR) for each SNP. We categorized the PRS by quintiles using the cutoff points based on the distribution of all InterLymph controls (N=8228). Self-reported FH data was available for 60% of cases and 73% of controls. FH was defined as any hematological malignancy in one or more first-degree relative. Logistic regression was used to estimate ORs and 95% confidence intervals (CIs) adjusted for age, sex, socioeconomic status and study. Results The median age at diagnosis of CLL was 63 years and median age of consent was 60 years in the controls. 67% were male in CLL cases and 57% in controls. As expected, FH was associated with CLL risk (OR= 2.14, CI= 1.60-2.86). The median PRS in the cases was 0.40 and in the controls was -0.36 with the frequency of CLL cases in the upper PRS quintile as 48% while in the lowest quintile only 6%. The PRS was strongly associated with CLL risk (OR= 2.90, CI= 2.35-3.56 for upper versus middle quintile). When jointly modeling FH with PRS, a significant interaction was observed (P=0.03). When stratifying by FH, the upper quintile of the PRS had an 11.8-fold (CI= 3.97-34.8) increased risk relative to those in the middle quintile in the FH+ strata, while a 3.11-fold (CI= 2.35-4.10) increased risk was observed in the FH- strata. Conclusions Our data suggest that the PRS has a strong association with CLL risk and this association varies with FH status. Among those with FH-, the risk of CLL was 3-fold for those with many inherited variants while the CLL risk was much higher than that in those with a FH+. Studies are needed to see whether this PRS stratifies risk among those with monoclonal B-cell lymphocytosis, the CLL precursor condition that affects 5-7% of the general population. Citation Format: Geffen Kleinstern, Silvia de Sanjosé, Nicola Camp, Claire M. Vajdic, Timothy G. Call, Mark Liebow, Dennis Robinson, Neil E. Kay, Julie Cunningham, Yolanda Benavente, Alain Monnereau, John Spinelli, James R. Cerhan, Susan L. Slager. Association of polygenic risk scores and family history with the risk of chronic lymphocytic leukemia (CLL) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4267. doi:10.1158/1538-7445.AM2017-4267

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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)0.001
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.351
Teacher spread0.306 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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