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Clinical utility of tumor next-generational sequencing (NGS) panel testing to inform treatment decisions for patients with advanced solid tumors.

2022· article· en· W4286294385 on OpenAlexaffabout
Lucia Bogdan, Ramy Saleh, Lisa Avery, Samanta Del Rossi, Celeste Yu, Philippe L. Bédard

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkMcGill University Health CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineInternal medicineOncologyPrecision medicineCancerCompanion diagnosticFamily medicinePathology

Abstract

fetched live from OpenAlex

3119 Background: There is limited information about the clinical utility of targeted NGS panel testing to inform decision-making for patients with advanced solid tumors. The Ontario-wide Cancer Targeted Nucleic Acid Evaluation (OCTANE) is an ongoing prospective study that enrolled over 4,500 solid tumor patients for NGS panel testing. We performed a retrospective survey of 21 medical oncologists enrolling OCTANE patients at a single academic institution to evaluate the impact of NGS testing on treatment decisions. Methods: Patients and treating oncologists were identified at the Princess Margaret Cancer Centre between 2016-2021. Tumor-only sequencing was performed using a custom hybridization capture panel of 555 cancer genes (Hi5) or a commercial 161-gene amplicon DNA/RNA panel (Oncomine Comprehensive v3). Oncologists were asked to review testing results for individual patients and complete a survey indicating whether NGS testing impacted treatment decisions. Mutations were defined as actionable based on clinical judgment and compared to classifications provided by OncoKB, an FDA-recognized precision medicine knowledgebase. The primary outcome of this study was rate of treatment change based on mutation results. Patient, test, and physician factors were evaluated for association with treatment changes using univariate analyses and a mixed effects model. Results: Two cohorts were surveyed, the first between 2017-2019 and the second in 2021. Of the 582 surveys sent, 394 (67.7%) were completed. Each physician completed a median of 19 surveys (range, 9-48). We found that 188 (47.7%) patients had a mutation classified as actionable by the oncologist, of whom 134 (71.3%) had ≥1 OncoKB-defined actionable mutation(s). 62/394 (15.7%) patients were matched to a treatment, of whom 37 were enrolled in a clinical trial, 13 received an approved drug, 4 were prescribed off-label therapy and 8 avoided ineffective treatment. 127/188 (67.5%) patients with actionable mutations did not receive treatment due to lack of available therapy, stability on current regimen, clinical deterioration or patient decision. Rate of treatment change was highest for bowel (15/37, 40.5%), breast (14/52, 27.5%), biliary tract (6/22, 27.3%) and lung (4/17, 23.5%) cancers. Treatment decisions were not associated with patient age, gender, physician clinical experience, physician gender, testing experience, OncoKB mutation level or time from biopsy to sequencing. There was no difference in overall survival between patients with matched vs. no matched treatment ( p = 0.55, median survival not reached). Conclusions: OCTANE testing led to a change in drug treatment in 15.7% of patients, supporting the clinical utility of NGS panel testing for patients with advanced solid tumors. Patient, test, and physician characteristics were not significantly associated with treatment change.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.190
GPT teacher head0.438
Teacher spread0.248 · 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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Citations1
Published2022
Admission routes2
Has abstractyes

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