Evidence map›Paper›PMID 36961477›Full record

ArticleThe oncologist2023

Supporting Biomarker-Driven Therapies in Oncology: A Genomic Testing Cost Calculator.

Albrecht Stenzinger, Brian Cuffel, Noman Paracha, Eric Vail, Jesus Garcia-Foncillas, Clifford Goodman, Ulrik Lassen, Gilles Vassal, Sean D Sullivan

Abstract read
In one paragraph

Article in The oncologist, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Review
  10. Observational
  11. Article
  12. Next step of molecular pathology: next-generation sequencing in cytology.Journal of pathology and translational medicine · 2024
    Review
  13. Article
  14. Article
  15. Review
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Albrecht StenzingerInstitute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.
Brian CuffelBayer Pharmaceuticals, Basel, Switzerland.
Noman ParachaBayer Pharmaceuticals, Basel, Switzerland.
Eric VailDepartment of Pathology and Laboratory Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA.
Jesus Garcia-FoncillasUniversity Cancer Institute and the Department of Oncology, University Hospital Fundacion Jimenez Diaz, Madrid, Spain.
Clifford GoodmanThe Lewin Group, Inc., Falls Church, VA, USA.
Ulrik LassenDepartment of Oncology, Copenhagen University Hospital, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
Gilles VassalGustave Roussy Comprehensive Cancer Center, Villejuif, France.
Sean D SullivanCHOICE Institute, Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdoption of high-throughput, gene panel-based, next-generation sequencing (NGS) into routine cancer care is widely supported, but hampered by concerns about cost. To inform policies regarding genomic testing strategies, we propose a simple metric, cost per correctly identified patient (CCIP), that compares sequential single-gene testing (SGT) vs. multiplex NGS in different tumor types. MATERIALS AND

methodsA genomic testing cost calculator was developed based on clinically actionable genomic alterations identified in the European Society for Medical Oncology Scale for Clinical Actionability of molecular Targets. Using sensitivity/specificity data for SGTs (immunohistochemistry, polymerase chain reaction, and fluorescence in situ hybridization) and NGS and marker prevalence, the number needed to predict metric was monetarized to estimate CCIP.

resultsAt base case, CCIP was lower with NGS than sequential SGT for advanced/metastatic non-squamous non-small cell lung cancer (NSCLC), breast, colorectal, gastric cancers, and cholangiocarcinoma. CCIP with NGS was also favorable for squamous NSCLC, pancreatic, and hepatic cancers, but with overlapping confidence intervals. CCIP favored SGT for prostate cancer. Alternate scenarios using different price estimates for each test showed similar trends, but with incremental changes in the magnitude of difference between NGS and SGT, depending on price estimates for each test.

conclusionsThe cost to correctly identify clinically actionable genomic alterations was lower for NGS than sequential SGT in most cancer types evaluated. Decreasing price estimates for NGS and the rapid expansion of targeted therapies and accompanying biomarkers are anticipated to further support NGS as a preferred diagnostic standard for precision oncology.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsBiomarkersGenetic TestingHigh-Throughput Nucleotide SequencingHumansIn Situ Hybridization, FluorescenceMaleMedical OncologyMutationPrecision MedicineBiomarkersbiomarkercalculatornext-generation sequencingprecision oncology

Identifiers

PMID36961477
PMCPMC10166172

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.