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Demographic disparities in genomic data and clinical trials for multiple myeloma.

2022· article· en· W4281661063 on OpenAlexaboutno aff
Nidhi Aggarwal, Pankaj Ahluwalia, Ravindra Kolhe, Jörge E. Cortes

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIncidence (geometry)DemographyInternal medicineOncologyEthnic group

Abstract

fetched live from OpenAlex

8043 Background: There are considerable outcome disparities among patients with multiple myeloma (MM). African-Americans have higher risk of developing MM, earlier age at diagnosis, and higher mortality compared to Whites. While outcomes have multifactorial socioeconomic etiologies, race and ethnicity (R&E) correlate with genomic ancestry, and any role of genetics should be explored. This requires equitable representation in biological databases like The Cancer Genome Atlas (TCGA). Here, we characterize disparities in MM cases of TCGA and clinical trials (CT). Methods: MM incidence was obtained from North American Association of Central Cancer Registries (NAACCR) for R&E, sex and age ( < 50, 50-64, 65+ years). Race includes White, Black, and Asian, along with Hispanic ethnicity, as TCGA MM is limited to these groups. Genes with oncogenic potential and > 5% mutation were stratified by R&E and age. TCGA MM cases are from USA, Canada, Italy, and Spain. On ClinicalTrials.gov, completed MM CT limited to these countries were identified. Student’s t-test and chi-square test were used to analyze disparities and gene mutations. Kaplan-Meier curve was generated to evaluate survival. Results: TCGA MM representation is in Table, calculated as (demographic TCGA cases divided by total TCGA cases) divided by (demographic incidence divided by total incidence). Race was not reported in 20% of TCGA, 4% of CT, and 3% of NAACCR. Of cases reporting R&E, Hispanics are underrepresented by 26% in TCGA and 47% in CT relative to incidence, Blacks by 22% in TCGA and 41% in CT, and Asians by 40% in TCGA and 10% in CT. Whites are overrepresented, by 8% in TCGA and 7% in CT. More men are in TCGA than women, in all R&E. Less than 25% of patients age < 50 relative to incidence are in TCGA, in all R&E. BCL7A is mutated more in Blacks age < 50 (n = 20, 50%) than Whites (n = 39, 10%) (p < 0.01). FAT4 is mutated in Whites age 65+ (n = 31, 10%) but not Blacks. In all ages, KRAS is mutated more in Blacks than Whites (31-35% vs. 20-25%, p < 0.05). Black patients have lower survival than Whites (p < 0.02). In patients age 65+, KRAS mutation is associated with 10% lower 4.5-year survival. Conclusions: Substantial disparities in Black, Hispanic, women, and age representation exist for MM cases in TCGA and CT. This stratification by R&E and age offers new insight on BCL7A, FAT4, and KRAS mutation in MM. Mutation status is associated with survival in older patients. Equitable demographic representation should be pursued to improve quality of available data and access to medical resources for all populations. [Table: see text]

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)0.016
metaresearch head score (Gemma)0.079
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 score0.016
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.079
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.007
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.271
GPT teacher head0.504
Teacher spread0.233 · 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
Published2022
Admission routes1
Has abstractyes

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