Evidence map›Paper›PMID 41698347›Full record

ArticleJournal of the National Comprehensive Cancer Network : JNCCN2026

A Taxonomy for Assessing Real-World Targeted Cancer Therapy Options in the Context of Broad Genomic Profiling.

Xiao Wang, Jessica B Long, John Rothen, Sida Huang, Pamela R Soulos, Sarah B Goldberg, Timothy J Robinson, Shuangge Ma, Ronac Mamtani, Carolyn J Presley and 4 more

Abstract read
In one paragraph

Article in Journal of the National Comprehensive Cancer Network : JNCCN, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Xiao Wang1Section of Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.
Jessica B Long2Yale Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center, New Haven, CT.
John Rothen2Yale Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center, New Haven, CT.
Sida Huang4Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT.
Pamela R Soulos2Yale Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center, New Haven, CT.
Sarah B Goldberg1Section of Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.
Timothy J Robinson5Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT.
Shuangge Ma6Department of Biostatistics, Yale School of Public Health, New Haven, CT.
Ronac Mamtani7Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.
Carolyn J Presley8Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, OH.
Shi-Yi Wang2Yale Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center, New Haven, CT.
Natalia Kunst2Yale Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center, New Haven, CT.
Cary P Gross2Yale Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center, New Haven, CT.
Michaela A Dinan2Yale Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center, New Haven, CT.

Funding

Yale Cancer Center Advanced Training Program for Physician-Scientists (YCC-ATPP)T32CA233414 · NCI · YALE UNIVERSITY · PI Eric P. Winer · 2019 to 2026
$3.2M
Broad Genomic Profiling in patients with advanced lung cancer: empirically assessing adoption, clinical utility, and the value of additional evidence in an evolving landscape of cancer careR01CA280359 · NCI · YALE UNIVERSITY · PI DINAN, MICHAELA ANN, GROSS, CARY P. · 2023 to 2025
$2.0M
NCI NIH HHS R01 CA280359NCI NIH HHS T32 CA233414
6 · The paper itself

Abstract

backgroundBroad genomic profiling (BGP) is increasingly used to determine eligibility of molecularly targeted cancer therapies. Understanding the population-level impact of BGP on treatment selection requires a clinically oriented framework guiding interpretation of the guideline-directed actionability of BGP results.

methodsWe developed the Yale Molecular Alteration Taxonomy for Real-World Individualized Treatments (Y-MATRIX) to classify BGP results in relation to contemporaneous FDA approvals and NCCN Guidelines. We then applied our schema to patients with advanced non-small cell lung cancer (aNSCLC) and assessed temporal changes in the distribution of alteration categories using a large, nationwide database of patients with aNSCLC diagnosed from 2017 to 2023.

resultsY-MATRIX classifies molecular testing results as categories A (first-line option), B (later-line option), C (off-label, guideline-concordant option), D (off-label, guideline-discordant option), E (no actionable alterations), or X (no alterations detected). In a sample of 8,717 patients with aNSCLC (median age, 69 years; 49.2% female), the proportion of patients in category A/B increased from 15.8% in 2017 to 33.6% in 2023, driven predominantly by an increase in later-line therapies (category B, 0% to 15.2%). Patients in category C/D increased from 21.2% to 39.3%, whereas patients in category E/X decreased markedly from 63.1% to 27.1%. Nonsmoking status, younger age, and nonsquamous histology were associated with having category A alterations.

conclusionsY-MATRIX provided a comprehensive taxonomy of BGP results by clinical actionability and demonstrates the increasing actionability of BGP results as the aNSCLC treatment landscape evolved over time. There was an increase in patients with category C and D findings, representing a diverse set of clinical circumstances of unclear actionability with opportunities for both innovative and potentially inappropriate targeted therapy use. The Y-MATRIX schema can be applied to facilitate research on all cancers, including the study of alterations across strata of targetable potential and the clinical utility of molecular profiling in precision oncology over time.

Indexed as

Biomarkers, TumorCarcinoma, Non-Small-Cell LungGenomicsLung NeoplasmsMolecular Targeted TherapyPrecision MedicineAgedFemaleGene Expression ProfilingHumansMaleMiddle AgedBiomarkers, Tumor

Identifiers

PMID41698347
PMCPMC13112471

What OpenQuestion holds

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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.