Evidence map›Paper›PMID 35227895›Full record

ArticleMolecular & cellular proteomics : MCP2022

Proteogenomic Analysis of Breast Cancer Transcriptomic and Proteomic Data, Using De Novo Transcript Assembly: Genome-Wide Identification of Novel Peptides and Clinical Implications.

P S Hari, Lavanya Balakrishnan, Chaithanya Kotyada, Arivusudar Everad John, Shivani Tiwary, Nameeta Shah, Ravi Sirdeshmukh

Open access · goldAbstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.1field-weighted citation impact, top 24% 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

6 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Omics-Based Investigations of Breast Cancer.Molecules (Basel, Switzerland) · 2023
    Review
  5. Article
  6. Review
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

7 authors at 3 institutions in 2 countries.

P S HariMazumdar Shaw Center for Translational Research, Narayana Health, Bangalore, India.
Lavanya BalakrishnanMazumdar Shaw Center for Translational Research, Narayana Health, Bangalore, India.
Chaithanya KotyadaMazumdar Shaw Center for Translational Research, Narayana Health, Bangalore, India.
Arivusudar Everad JohnMazumdar Shaw Center for Translational Research, Narayana Health, Bangalore, India.
Shivani TiwarySimulation and Modeling Sciences, Pfizer Pharma GmBH, Berlin, Germany.
Nameeta ShahMazumdar Shaw Center for Translational Research, Narayana Health, Bangalore, India. Electronic address: nameeta.shah@gmail.com.
Ravi SirdeshmukhMazumdar Shaw Center for Translational Research, Narayana Health, Bangalore, India; Institute of Bioinformatics, International Tech Park, Bangalore, India; Health Sciences, Manipal Academy of Higher Education, Manipal, India. Electronic address: ravisirdeshmukh@gmail.com.
Narayana Health · INManipal Academy of Higher Education · INPfizer (Germany) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We have carried out proteogenomic analysis of the breast cancer transcriptomic and proteomic data, available at The Clinical Proteomic Tumor Analysis Consortium resource, to identify novel peptides arising from alternatively spliced events as well as other noncanonical expressions. We used a pipeline that consisted of de novo transcript assembly, six frame-translated custom database, and a combination of search engines to identify novel peptides. A portfolio of 4,387 novel peptide sequences initially identified was further screened through PepQuery validation tool (Clinical Proteomic Tumor Analysis Consortium), which yielded 1,558 novel peptides. We considered the dataset of 1,558 validated through PepQuery to understand their functional and clinical significance, leaving the rest to be further verified using other validation tools and approaches. The novel peptides mapped to the known gene sequences as well as to genomic regions yet undefined for translation, 580 novel peptides mapped to known protein-coding genes, 147 to non-protein-coding genes, and 831 belonged to novel translational sequences. The novel peptides belonging to protein-coding genes represented alternatively spliced events or 5' or 3' extensions, whereas others represented translation from pseudogenes, long noncoding RNAs, or novel peptides originating from uncharacterized protein-coding sequences-mostly from the intronic regions of known genes. Seventy-six of the 580 protein-coding genes were associated with cancer hallmark genes, which included key oncogenes, transcription factors, kinases, and cell surface receptors. Survival association analysis of the 76 novel peptide sequences revealed 10 of them to be significant, and we present a panel of six novel peptides, whose high expression was found to be strongly associated with poor survival of patients with human epidermal growth factor receptor 2-enriched subtype. Our analysis represents a landscape of novel peptides of different types that may be expressed in breast cancer tissues, whereas their presence in full-length functional proteins needs further investigations.

Indexed as

Breast NeoplasmsProteogenomicsFemaleHumansPeptidesProteomicsTranscriptomePeptidesalternative splicingbreast cancerCPTACde novo transcript assemblyproteogenomics

Identifiers

PMID35227895
PMCPMC9020135
OpenAlexW4214738062

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.