Corpus 1,272 assessed · 1,173 scored · 643 reproduced ≥75 · 168 flagged ·∅ 74.1/100
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GSE65185

GEO first seen 2016

RNAseq changes in pre MAPKi treatment and post MAPKi resistance Melanomas

Organism
Homo sapiens
Samples
70
Type
Expression profiling by high...
Submitted
2015-01-22

Melanoma resistance to MAPK- or T cell checkpoint-targeted therapies represents a major clinical challenge, and treatment failures of MAPK-targeted therapies due to acquired resistance often require salvage immunotherapies. We show that genomic analysis of acquired resistance to MAPK inhibitors revealed key driver genes but failedto adequately account for clinical resistance. From a large-scale comparative analysis of temporal transcriptomes from patient-matched tumor biopsies, we discovered hig...

Provenance — who produced it, who reused it

Linked to 17 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.

Deposited / produced by
Willy HugoHubing ShiLu SunMarco PivaChunying SongXiangju KongGatien MoriceauAayoung HongKimberly B DahlmanDouglas B JohnsonJeffrey A SosmanAntoni RibasRoger S Lo
Reused by

11 further papers cite this accession but reuse could not be confirmed.

Deep data QC

metadata only · no data-level QC for this type

Standardized, field-standard QC computed by touching the data — every metric states how it was obtained

No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.

QC cost 22 s compute

measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently