Abstract 7382: Trends in next generation sequencing (NGS) testing by social determinants of health for metastatic cancer patients, 2016-2022
Bibliographic record
Abstract
Abstract Introduction: Next Generation Sequencing (NGS) testing is a precision medicine tool that uses genomic information from patient tumors to inform treatment decisions and direct patient access to targeted therapies. Social determinants of health (SDOH) partially explain barriers to healthcare access, which can impact clinical outcomes. Real-world studies examining impact of SDOH on receipt of genomic testing are limited. Objective: To examine annual incidence rates (IR) of NGS testing overall and by SDOH indices in endometrial, lung, and colorectal cancer patients. Methods: This retrospective cohort study included patients diagnosed with advanced and recurrent endometrial cancer (aEC), metastatic non-small cell lung cancer (mNSCLC), or metastatic colorectal cancer (mCRC), from July 2016 to September 2022, in the Optum Research Database. Annual incident rates (per 365, 000 person-days) were calculated for NGS testing and stratified by 5 SDOH domains: social isolation, financial stress, food insecurity, housing insecurity, and transportation difficulty. SDOH measures were categorized as low, medium, or high unmet needs. Full-year NGS testing IRs are reported for trend analysis. Results: The study examined 14, 624 aEC, 33, 608 mNSCLC, and 20, 437 mCRC mostly White patients (69%), with median age (years): 69 aEC, 72 mNSCLC, and 70 mCRC. NGS testing rates varied across tumors during the study follow up-period with the highest overall rates observed in mNSCLC (146.9), followed by CRC (73.8) and aEC (19.3). IRs increased over time for all cancer groups (1.5X higher in 2021 than 2017 for mNSCLC, and 2X higher for aEC and mCRC). Annual NGS testing trends were similar for racial/ethnic groups in aEC and mCRC, but, Black and Hispanic mNSCLC patients had the two lowest testing rates (IR: 139.3 and 134.8 respectively) compared to White (IR: 147.3), and Asian patients (IR: 186.3). In mCRC patients, those with the highest level of unmet need had the lowest IRs for all SDOH domains except food insecurity; a similar pattern was seen in mNSCLC. Before 2019, rates were lower in aEC patients with high financial stress and food insecurity vs. with those with low unmet need; this trend reversed from 2019-2021. Changing testing trends in aEC patients may result from the issuance of a Medicare national coverage determination for NGS testing. Conclusion: NGS testing rates have increased for mCRC, mNCSLC and aEC patients since 2017, however, rates differed by level of SDOH. Targeted interventions focused on increasing NGS testing among patients with more unmet SDOH needs could be impactful. Further investigation into the social factors that influence patient access and treatment decisions may help remove existing barriers to optimal patient care. Citation Format: Clementine Adeyemi, Louise B. Murphy, Tim Bancroft, Kristin Moore, Stephanie Gallagher, Amy Nguyen, Gieira S. Jones. Trends in next generation sequencing (NGS) testing by social determinants of health for metastatic cancer patients, 2016-2022 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7382.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".