Evidence map›Paper›PMID 30617155›Full record

ArticleMolecular & cellular proteomics : MCP2019

Quantitative Mass Spectrometry to Interrogate Proteomic Heterogeneity in Metastatic Lung Adenocarcinoma and Validate a Novel Somatic Mutation CDK12-G879V.

Xu Zhang, Khoa Dang Nguyen, Paul A Rudnick, Nitin Roper, Emily Kawaler, Tapan K Maity, Shivangi Awasthi, Shaojian Gao, Romi Biswas, Abhilash Venugopalan and 3 more

Open access · hybridAbstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.5field-weighted citation impact, top 21% of its field
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

10 citing papers in PubMed, 21 citations in OpenAlex.

  1. Review
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  6. Review
  7. Super-SILAC Quantitative Proteome Profiling of Zebrafish Larvae.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
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  9. Article
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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

13 authors at 2 institutions in 1 country.

Xu ZhangFrom the ‡Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
Khoa Dang NguyenFrom the ‡Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
Paul A Rudnick§Spectragen Informatics LLC, Bainbridge Island, Washington.
Nitin RoperFrom the ‡Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
Emily Kawaler¶Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, New York.
Tapan K MaityFrom the ‡Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
Shivangi AwasthiFrom the ‡Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
Shaojian GaoFrom the ‡Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
Romi BiswasFrom the ‡Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
Abhilash VenugopalanFrom the ‡Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
Constance M CultraroFrom the ‡Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
David Fenyö¶Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, New York.
Udayan GuhaFrom the ‡Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland;. Electronic address: udayan.guha@nih.gov.
Center for Cancer Research · USInstitute for Systems Biology · US

Funding

Proteogenomic Data Analysis for Cancer Systems Biology and Clinical TranslationU24CA210972 · NCI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI DING, LI, FENYO, DAVID · 2016 to 2020
$3.3M
Clinical Protocols in the Cancer Signaling Networks SectionZIABC011672 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI GUHA, UDAYAN · 2016 to 2020
$2.7M
NCI NIH HHS U24 CA210972
6 · The paper itself

Abstract

Lung cancer is the leading cause of cancer death in both men and women. Tumor heterogeneity is an impediment to targeted treatment of all cancers, including lung cancer. Here, we sought to characterize tumor proteome and phosphoproteome changes by longitudinal, prospective collection of tumor tissue from an exceptional responder lung adenocarcinoma patient who survived with metastatic lung adenocarcinoma for over seven years while undergoing HER2-directed therapy in combination with chemotherapy. We employed "Super-SILAC" and TMT labeling strategies to quantify the proteome and phosphoproteome of a lung metastatic site and eight distinct metastatic progressive lymph nodes collected during these seven years, including five lymph nodes procured at autopsy. We identified specific signaling networks enriched in lung compared with the lymph node metastatic sites. We correlated the changes in protein abundance with changes in copy number alteration (CNA) and transcript expression. ERBB2/HER2 protein expression was higher in lung, consistent with a higher degree of

Indexed as

ProteomicsAdenocarcinoma of LungCell Line, TumorCyclin-Dependent KinasesDNA Copy Number VariationsGene Expression Regulation, NeoplasticHumansLymphatic MetastasisMaleMass SpectrometryMiddle AgedMutant ProteinsMutationNeoplasm MetastasisNeoplasm ProteinsPeptidesCDK12 protein, humanCyclin-Dependent KinasesMutant ProteinsNeoplasm ProteinsPeptidesPhosphoproteinsCancer Biology*Database designLung cancerMass SpectrometryMultiple reaction monitoringTumor Heterogeneity

Identifiers

PMID30617155
PMCPMC6442362
OpenAlexW2953149137

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.