Evidence map›Paper›PMID 36928816›Full record

ArticleNature medicine2023

A longitudinal circulating tumor DNA-based model associated with survival in metastatic non-small-cell lung cancer.

Zoe June F Assaf, Wei Zou, Alexander D Fine, Mark A Socinski, Amanda Young, Doron Lipson, Jonathan F Freidin, Mark Kennedy, Eliana Polisecki, Makoto Nishio and 9 more

Abstract read
In one paragraph

Article in Nature medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
103citing papers in PubMed, 3 pooled it
–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

103 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  10. Applications of circulating tumor DNA in unresectable and advanced non-small cell lung cancer immunotherapy: from stratification to therapeutic guidance.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Review
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  14. Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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43 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Zoe June F AssafGenentech Inc., South San Francisco, CA, USA. assaf.zoe-june@gene.com.
Wei ZouGenentech Inc., South San Francisco, CA, USA.ORCID http://orcid.org/0000-0002-3639-9821
Alexander D FineFoundation Medicine Inc., Cambridge, MA, USA.
Mark A SocinskiAdventHealth Cancer Institute, Orlando, FL, USA.
Amanda YoungFoundation Medicine Inc., Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-2199-1873
Doron LipsonFoundation Medicine Inc., Cambridge, MA, USA.
Jonathan F FreidinFoundation Medicine Inc., Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-1821-8932
Mark KennedyFoundation Medicine Inc., Cambridge, MA, USA.
Eliana PoliseckiFoundation Medicine Inc., Cambridge, MA, USA.
Makoto NishioThe Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan.
David FabrizioFoundation Medicine Inc., Cambridge, MA, USA.
Geoffrey R OxnardFoundation Medicine Inc., Cambridge, MA, USA.
Craig CummingsGenentech Inc., South San Francisco, CA, USA.
Anja RodeF. Hoffman-La Roche AG, Basel, Switzerland.
Martin ReckLungenClinic Grosshansdorf, Airway Research Center North, German Center for Lung Research, Grosshansdorf, Germany.
Namrata S PatilGenentech Inc., South San Francisco, CA, USA.
Mark LeeGenentech Inc., South San Francisco, CA, USA.
David S ShamesGenentech Inc., South San Francisco, CA, USA. shames.david@gene.com.ORCID http://orcid.org/0000-0002-5900-1177
Katja SchulzeGenentech Inc., South San Francisco, CA, USA. schulze.katja.ks2@gene.com.ORCID http://orcid.org/0000-0003-3704-5651

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the great challenges in therapeutic oncology is determining who might achieve survival benefits from a particular therapy. Studies on longitudinal circulating tumor DNA (ctDNA) dynamics for the prediction of survival have generally been small or nonrandomized. We assessed ctDNA across 5 time points in 466 non-small-cell lung cancer (NSCLC) patients from the randomized phase 3 IMpower150 study comparing chemotherapy-immune checkpoint inhibitor (chemo-ICI) combinations and used machine learning to jointly model multiple ctDNA metrics to predict overall survival (OS). ctDNA assessments through cycle 3 day 1 of treatment enabled risk stratification of patients with stable disease (hazard ratio (HR) = 3.2 (2.0-5.3), P < 0.001; median 7.1 versus 22.3 months for high- versus low-intermediate risk) and with partial response (HR = 3.3 (1.7-6.4), P < 0.001; median 8.8 versus 28.6 months). The model also identified high-risk patients in an external validation cohort from the randomized phase 3 OAK study of ICI versus chemo in NSCLC (OS HR = 3.73 (1.83-7.60), P = 0.00012). Simulations of clinical trial scenarios employing our ctDNA model suggested that early ctDNA testing outperforms early radiographic imaging for predicting trial outcomes. Overall, measuring ctDNA dynamics during treatment can improve patient risk stratification and may allow early differentiation between competing therapies during clinical trials.

Indexed as

Carcinoma, Non-Small-Cell LungCirculating Tumor DNALung NeoplasmsBiomarkers, TumorHumansBiomarkers, TumorCirculating Tumor DNA

Identifiers

PMID36928816
PMCPMC10115641

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.