Evidence map›Paper›PMID 40415485›Full record

ArticleMolecular oncology2025

Landscape of BRAF transcript variants in human cancer.

Maurizio S Podda, Danilo Tatoni, Gianluca Mattei, Alberto Magi, Romina D'Aurizio, Laura Poliseno

Abstract read
In one paragraph

Article in Molecular oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Maurizio S PoddaInstitute of Clinical Physiology (IFC), CNR, Pisa, Italy.
Danilo TatoniUniversity of Siena, Italy.
Gianluca MatteiDepartment of Information Engineering, University of Florence, Italy.
Alberto MagiDepartment of Information Engineering, University of Florence, Italy.
Romina D'AurizioCTGLab, Institute of Informatics and Telematics (IIT), CNR, Pisa, Italy.
Laura PolisenoInstitute of Clinical Physiology (IFC), CNR, Pisa, Italy.ORCID 0000-0001-6557-955X

Funding

AIRC IG25694European Union - Next Generation EU 20227NHWH2European Union - Next Generation EU ECS00000017Fondazione Pisa #311/22ISPRO-Istituto per lo Studio, la Prevenzione e la Rete OncologicaRegione Toscana CRCScreen
6 · The paper itself

Abstract

The BRAFV600E mutant kinase is widely studied as a cancer driver and therapeutic target. Here, we investigated how the annotation of the BRAF-reference (ref) and BRAF-X1 variants has evolved in public databases and addressed challenges posed by their discrimination and quantification from short-read sequencing. We built IsoWorm, a bioinformatic pipeline tailored to discriminate and quantify BRAF variants, and employed it to analyze > 600 cancer cell lines and > 1000 cancer tissue samples. Using FLIBase, we reanalyzed TCGA data from > 9000 cancer tissue samples. We consistently found that BRAF-X1 (now BRAF-204) is very abundant in human cancer and its expression is 1.5-75 times greater than that of BRAF-ref (now BRAF-220). Crucially, we identified KIRP-kidney renal papillary cell carcinoma as a cancer subtype in which a high BRAF-204/BRAF-220 ratio is an independent prognostic factor of poor outcome. Our in silico analyses establish BRAF as a mix of two protein-coding transcript variants, with BRAF-204 being more highly expressed than BRAF-220. These findings prompt us to undertake the systematic benchmarking of BRAF-204 against BRAF-220 in terms of molecular mechanisms, biological activities, druggability, and clinical relevance.

Indexed as

Genetic VariationNeoplasmsProto-Oncogene Proteins B-rafCell Line, TumorComputational BiologyGene Expression Regulation, NeoplasticHumansMutationRNA, MessengerBRAF protein, humanProto-Oncogene Proteins B-rafRNA, MessengerBRAF‐204BRAF‐220cancerKIRPlong readsRNA sequencingshort readstranscript variants

Identifiers

PMID40415485
PMCPMC12420348

What OpenQuestion holds

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Registered trials

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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.