Evidence map›Paper›PMID 25533947›Full record

ReviewNature reviews. Clinical oncology2015

Developing biomarker-specific end points in lung cancer clinical trials.

Joel W Neal, Justin F Gainor, Alice T Shaw

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Clinical oncology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

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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

3 authors.

Joel W NealDepartment of Medicine, Division of Oncology, Stanford Cancer Institute and Stanford University School of Medicine, Stanford University, 875 Blake Wilbur Drive, Stanford, CA 94305, USA.
Justin F GainorDivision of Hematology-Oncology, Massachusetts General Hospital Cancer Center, Massachusetts General Hospital, 32 Fruit Street, Boston, MA 02114, USA.
Alice T ShawDivision of Hematology-Oncology, Massachusetts General Hospital Cancer Center, Massachusetts General Hospital, 32 Fruit Street, Boston, MA 02114, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In cancer-drug development, a number of different end points have been used to establish efficacy and support regulatory approval, such as overall survival, progression-free survival (PFS), and radiographic response rate. However, these traditional end points have important limitations. For example, in lung cancer clinical trials, evaluating overall survival end points is a protracted process and these end points are most reliable when crossover to the investigational therapy is not permitted. Furthermore, although radiographic surrogate end points, such as PFS and response rate, generally correlate with clinical benefit in the setting of cytotoxic chemotherapy and molecular targeted therapies, novel immunotherapies might have atypical response kinetics, which confounds radiographic interpretation. In this Review, we discuss the need to develop alternative or surrogate end points for lung cancer clinical trials, and focus on several new biomarkers that could serve as surrogate end points, including functional imaging biomarkers, circulating factors (tumour proteins, DNA, and cells), and pharmacodynamic tumour markers. By enabling the size, duration, and complexity of cancer trials to be reduced, biomarker end points hold the promise to accelerate drug development and improve patient outcomes.

Indexed as

BiomarkersBiomarkers, TumorAntineoplastic AgentsEndpoint DeterminationHumansLung NeoplasmsPrognosisTreatment OutcomeAntineoplastic AgentsBiomarkersBiomarkers, Tumor

Identifiers

What OpenQuestion holds

Textmetadata
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.