Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease.
Michael MasonCarolina SchinkeChristine L P EngFadi TowficFred GruberAndrew P DervanBrian S WhiteAditya PratapaYuanfang GuanHongjie ChenYi CuiBailiang LiThomas YuElias Chaibub NetoKonstantinos MavrommatisMaria OrtizValeriy LyzogubovKamlesh BishtHongyue Y DaiFrank SchmitzErin Flyntnull Dan RozelleSamuel A DanzigerAlexander Ratushnynull nullWilliam S DaltonHartmut GoldschmidtHerve Avet-LoiseauMehmet SamurBoris HayetePieter SonneveldKenneth H ShainNikhil MunshiDaniel AuclairDirk HoseGareth MorganMatthew TrotterDouglas BassettJonathan GokeBrian A WalkerAnjan ThakurtaJustin GuinneyPublished in: Leukemia (2020)
While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19, which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.
Keyphrases
- newly diagnosed
- gene expression
- multiple myeloma
- end stage renal disease
- ejection fraction
- dna methylation
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- poor prognosis
- machine learning
- randomized controlled trial
- patient reported outcomes
- long non coding rna
- deep learning
- small molecule
- patient reported
- quantum dots