ArticleClinical cancer research : an official journal of the American Association for Cancer Research2024
Novel Approach to Accelerate Lung Cancer Research: Lung-MAP and the Potential of Public-Private Partnerships.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- A Roadmap to Transform Lung Cancer Outcomes: Priorities in Biology, Therapeutic Innovation, Early Detection, Prevention, and Interception.Cancer discovery · 2026Review
- Elevated ctDNA Tumor Fraction Is Associated with Improved Mutation Detection but Worse Overall Survival in Advanced Non-Small Cell Lung Cancer: A Lung-MAP Study.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Article
- Bridging research and practice: enhancing regulatory decisions with pragmatic clinical trials in oncology.ESMO real world data and digital oncology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
The National Cancer Institute recently found that death rates for non-small cell lung cancer (NSCLC) have been reduced by over 6% overall in recent years. This reduction in mortality has been accompanied by an average increase in overall survival and largely credited to the therapeutic advancements for the effective treatment of NSCLC. Numerous molecular alterations have been identified in NSCLC that have enabled the development of new drugs capable of targeting these changes and efficiently kill cancerous cells. New treatments to modulate patients' immune systems have been shown to be effective in stimulating natural immune cells to have an improved anti-cancer effect. While these types of approaches to treat cancer are providing new options for patients, leadership from the Food and Drug Administration (FDA) recognized that the expansion of targeted therapy in NSCLC presented significant promise, but evaluation of the safety and efficacy of these new drugs would be slowed if new models for conducting clinical studies were not identified. Specifically, the FDA recommended that a comprehensive approach be implemented to identify the patients that are the best candidates for these, and other new treatments based upon the molecular characteristics of their tumors, and more efficiently conduct the clinical studies necessary to evaluate the safety and efficacy of new drugs. To address this growing challenge, leading lung cancer experts and stakeholders across academia, government, industry, and patient advocacy came together to design a clinical research approach that could serve as a sustainable infrastructure for new lung cancer treatments called the Lung Cancer Master Protocol.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.