Feasibility of Point-of-Care Genomic Profiling in the Diagnosis and Treatment of Cancer of Unknown Primary
Bibliographic record
Abstract
INTRODUCTION: Cancer of unknown primary remains a challenging clinical entity. Despite receiving empiric chemotherapy, median overall survival is approximately 6-12 months. Site-specific therapy based on molecular characterization has been shown to improve outcomes; however, feasibility outside of clinical trials, especially in community centers, is lacking. This study explores the application of rapid next-generation sequencing in defining cancer of unknown primary and to identify therapeutic biomarkers. METHODS: A retrospective chart review was performed by identifying pathological samples designated cancer of unknown primary. Next-generation sequencing testing was based on an automated workflow utilizing the Genexus integrated sequencer, validated for clinical use. Genomic profiling was further integrated within a routine immunohistochemistry service, with results reported directly by anatomic pathologists. RESULTS: Between October 2020 and October 2021, 578 solid tumor samples underwent genomic profiling. Among this cohort, 40 were selected based on an initial diagnosis of cancer of unknown primary. The median (range) age at diagnosis was 70 (42-85) and 23 (57%) were female. Genomic data were used to support a site-specific diagnosis in 6 patients (15%). Median turnaround time was 3 business days (IQR: 1-5). Most common alterations identified were KRAS (35%), CDKN2A (15%), TP53 (15%), and ERBB2 (12%). Actionable molecular targeted therapies were identified in 23 (57%) patients, including alterations in BRAF, CDKN2A, ERBB2, FGFR2, IDH1, and KRAS. Immunotherapy-sensitizing mismatch repair deficiency was identified in 1 patient. CONCLUSION: This study supports the adoption of rapid next-generation sequencing among patients with cancer of unknown primary. We also demonstrate the feasibility of integration of genomic profiling with diagnostic histopathology and immunohistochemistry in a community practice setting. Diagnostic algorithms incorporating genomic profiling to better define cancer of unknown primary should be considered for future study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".