Article in Cancer discovery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing 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.
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
19 authors.
Mengxiong WangDivision of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.ORCID 0000-0002-8290-151X
Kathryn T Bieging-RolettDivision of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.ORCID 0009-0002-2601-0031
Alyssa M KaiserDivision of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.ORCID 0009-0005-2947-8547
Colleen A BradyDivision of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.ORCID 0009-0002-1674-3370
John H LockhartDepartment of Molecular Oncology, H. Lee Moffitt Cancer Center, Tampa, Florida.ORCID 0000-0002-2727-6969
Sofia FerreiraDivision of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.ORCID 0000-0001-9535-2161
Kha T NguyenDivision of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.ORCID 0000-0001-9191-8754
Arati RajeevanDivision of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.ORCID 0000-0002-8699-5816
Simone A EvansDepartment of Genetics, Stanford University School of Medicine, Stanford, California.ORCID 0000-0003-4975-8747
Tianyu ZhaoDivision of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.ORCID 0000-0002-1120-7272
Nitin RajDivision of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.ORCID 0000-0001-7678-3855
Arielle ElkriefDepartment of Pathology and Laboratory Medicine, and Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0003-1981-1535
Sam E TischfieldMarie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0002-5717-3856
Marc LadanyiDepartment of Pathology and Laboratory Medicine, and Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0001-9055-7213
Michael G OzawaDepartment of Pathology, Stanford University School of Medicine, Stanford, California.ORCID 0009-0009-8768-3812
Nam Q BuiDivision of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, California.ORCID 0000-0001-6896-4349
Christopher T ChenDivision of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, California.ORCID 0000-0001-8962-3527
Elsa R FloresDepartment of Molecular Oncology, H. Lee Moffitt Cancer Center, Tampa, Florida.ORCID 0000-0002-6173-2403
Laura D AttardiDivision of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.ORCID 0000-0003-1782-9045
Funding
X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
Integrative approaches to elucidate p53 transcriptional networks during carcinogenesisR35CA197591 · NCI · STANFORD UNIVERSITY · PI LAURA D ATTARDI · 2015 to 2026
$11.6M
National Cancer Institute, Cairo University (NCI) CA197591NCI NIH HHS P30 CA008748NCI NIH HHS R35 CA197591Tobacco-Related Disease Research Program (TRDRP) T31FT1610
6 · The paper itself
Abstract
Pharmacologic restoration of p53 tumor suppressor function is a conceptually appealing therapeutic strategy for the many deadly cancers with compromised p53 activity, including lung adenocarcinoma. However, the p53 pathway has remained undruggable, partly because of insufficient understanding of how to drive effective therapeutic responses without toxicity. In this study, we use mouse and human models to deconstruct the transcriptional programs and sequelae underlying robust therapeutic responses in lung adenocarcinoma. We show that p53 drives potent tumor regression by direct Tsc2 transactivation, leading to mTORC1 inhibition and Transcription factor EB (TFEB) nuclear accumulation, which in turn triggers lysosomal gene expression programs, autophagy, and cellular senescence. Senescent lung adenocarcinoma cells secrete factors to recruit macrophages, precipitating cancer cell phagocytosis and tumor regression. Collectively, our analyses reveal a surprisingly complex cascade of events underlying a p53 therapeutic response in lung adenocarcinoma and illuminate targetable nodes for p53 combination therapies, thus establishing a critical framework for optimizing p53-based therapeutics. SIGNIFICANCE: Cancer therapies based on targeting the p53 pathway remain elusive. To address this gap, we unravel the detailed sequence of events governing p53-induced tumor regression in lung adenocarcinoma. These analyses reveal a TSC2-mTORC1-TFEB axis underlying p53-driven senescence and tumor regression, which suggests new strategies to perfect p53-based combination therapies for lung adenocarcinoma.
Indexed as
Adenocarcinoma of LungBasic Helix-Loop-Helix Leucine Zipper Transcription FactorsLung NeoplasmsTuberous Sclerosis Complex 2 ProteinTumor Suppressor Protein p53AnimalsCell Line, TumorCellular SenescenceHumansMechanistic Target of Rapamycin Complex 1MiceBasic Helix-Loop-Helix Leucine Zipper Transcription FactorsMechanistic Target of Rapamycin Complex 1TFEB protein, humanTP53 protein, humanTSC2 protein, humanTsc2 protein, mouseTuberous Sclerosis Complex 2 ProteinTumor Suppressor Protein p53
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 Socratic graph.