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Abstract B025: Universal Germline Testing in Young-Onset GI Malignancies: Expanding Access for Underserved Patients

2025· article· en· W4417208876 on OpenAlexaboutno aff
Kaysia Ludford, Ysaith Orellana Ascencio, Hilary Ma, Yi-Qian Nancy You

Bibliographic record

VenueClinical Cancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsnot available
Fundersnot available
KeywordsGermlinePsychosocialGenetic testingCancerHereditary CancerLynch syndromeColorectal cancerGermline mutationIncidence (geometry)

Abstract

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Abstract Purpose: Historically, indigent populations have faced significant disparities in access to germline testing for various gastrointestinal (GI) cancers. With the rising incidence of GI cancers among adults under 50, coupled with evidence that pathogenic germline variants (PGVs) in young-onset (YO) cancers can be present in over 20% of cases, there is a critical need to address barriers to genetic testing in underserved populations. This study aimed to assess the feasibility and impact of a multilevel intervention designed to optimize the implementation of universal germline testing (UGT) in a county hospital system serving a predominantly low-income population. Methods: Between March 2024 and September 2025, patients aged 50 years and younger, diagnosed with GI cancers, and receiving care at Lyndon B. Johnson Hospital in Houston, Texas, were recruited for the study. Participants were universally offered germline testing with a 48-gene multiplex panel covering major hereditary cancer predisposition syndromes, along with pre-recorded video education and standardized pretest counseling. Pre- and post-test surveys were administered to assess patient-level impacts on genetic knowledge, psychosocial factors, and stressors related to UGT. The primary outcomes included the proportion of patients with YO GI cancers who completed germline testing, compared to historical controls (previously reported completion rate: 49%), and the feasibility of implementing this universal approach. Results: Of the 64 eligible patients, 41 (64%) enrolled in the pilot study and completed germline testing. The majority of participants had colorectal cancer (48%), followed by gastro-esophageal cancers (12%). The median age at diagnosis was 42 years (range: 24-50). Spanish was the primary language spoken by 68% of participants, and 40% were diagnosed with stage IV cancer. Pathogenic germline variants (PGVs) were identified in 16% of participants, while variants of uncertain significance (VUS) were found in 42%. PGVs included BRCA2, FAP, MLH1, and ATM. Patient level impacts of the intervention on genetic knowledge, psychosocial factors, and stressors related to UGT will be reported. Conclusion: The rate of PGVs among this underserved population was notably high, with approximately 1 in 6 patients diagnosed with GI cancers at or under the age of 50 carrying a germline mutation linked to cancer. The universal approach to germline testing proved to be feasible and resulted in a higher completion rate compared to historical controls, who were offered targeted testing. This pilot demonstrates strong interest in UGT among underserved cancer patients and suggests that improving the framework for genetic testing in this context is a valuable investment for enhancing clinical outcomes and equitable access to genetic insights. Citation Format: Kaysia Ludford, Ysaith Orellana Ascencio, Hilary Ma, Yi-Qian Nancy You. Universal Germline Testing in Young-Onset GI Malignancies: Expanding Access for Underserved Patients [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr B025.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.407
GPT teacher head0.566
Teacher spread0.160 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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