Evidence map›Paper›PMID 34552204›Full record

ArticleCommunications biology2021

Pan-cancer proteogenomic investigations identify post-transcriptional kinase targets.

Abdulkadir Elmas, Serena Tharakan, Suraj Jaladanki, Matthew D Galsky, Tao Liu, Kuan-Lin Huang

Abstract read
In one paragraph

Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
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

6 authors.

Abdulkadir ElmasCenter for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.ORCID 0000-0002-7999-5770
Serena TharakanCenter for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Suraj JaladankiCenter for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.ORCID 0000-0003-0406-2973
Matthew D GalskyDivision of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Tao LiuBiological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID 0000-0001-9529-6550
Kuan-Lin HuangCenter for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. kuan-lin.huang@mssm.edu.ORCID 0000-0002-5537-5817

Funding

THE TISCH CANCER INSTITUTE - CANCER CENTER SUPPORT GRANTP30CA196521 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Deanna L Benson · 2015 to 2026
$35.4M
PNNL Proteome Characterization CenterU24CA210955 · NCI · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI LIU, TAO, SMITH, RICHARD D · 2016 to 2020
$5.3M
Integrative Approaches for Identifying Causal Gene-Cell Type Pairs of Complex DiseaseR35GM138113 · NIGMS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Kuan-lin Huang · 2020 to 2026
$3.0M
NCI NIH HHS P30 CA196521NCI NIH HHS U24 CA210955NIGMS NIH HHS R35 GM138113
6 · The paper itself

Abstract

Identifying genomic alterations of cancer proteins has guided the development of targeted therapies, but proteomic analyses are required to validate and reveal new treatment opportunities. Herein, we develop a new algorithm, OPPTI, to discover overexpressed kinase proteins across 10 cancer types using global mass spectrometry proteomics data of 1,071 cases. OPPTI outperforms existing methods by leveraging multiple co-expressed markers to identify targets overexpressed in a subset of tumors. OPPTI-identified overexpression of ERBB2 and EGFR proteins correlates with genomic amplifications, while CDK4/6, PDK1, and MET protein overexpression frequently occur without corresponding DNA- and RNA-level alterations. Analyzing CRISPR screen data, we confirm expression-driven dependencies of multiple currently-druggable and new target kinases whose expressions are validated by immunochemistry. Identified kinases are further associated with up-regulated phosphorylation levels of corresponding signaling pathways. Collectively, our results reveal protein-level aberrations-sometimes not observed by genomics-represent cancer vulnerabilities that may be targeted in precision oncology.

Indexed as

Gene Expression Regulation, NeoplasticUp-RegulationAdultAgedAlgorithmsChildFemaleHumansMaleMiddle AgedNeoplasmsPhosphorylationProtein KinasesProteogenomicsSignal TransductionProtein Kinases

Identifiers

PMID34552204
PMCPMC8458405

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.