{Reference Type}: Journal Article
{Title}: Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts.
{Author}: Szadai L;Bartha A;Parada IP;Lakatos AIT;Pál DMP;Lengyel AS;de Almeida NP;Jánosi ÁJ;Nogueira F;Szeitz B;Doma V;Woldmar N;Guedes J;Ujfaludi Z;Pahi ZG;Pankotai T;Kim Y;Győrffy B;Baldetorp B;Welinder C;Szasz AM;Betancourt L;Gil J;Appelqvist R;Kwon HJ;Kárpáti S;Kuras M;Murillo JR;Németh IB;Malm J;Fenyö D;Pawłowski K;Horvatovich P;Wieslander E;Kemény LV;Domont G;Marko-Varga G;Sanchez A;
{Journal}: Front Oncol
{Volume}: 14
{Issue}: 0
{Year}: 2024
{Factor}: 5.738
{DOI}: 10.3389/fonc.2024.1428182
{Abstract}: <B>UNASSIGNED: </B>While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy.<BR><B>UNASSIGNED: </B>Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders.<BR><B>UNASSIGNED: </B>Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort.<BR><B>UNASSIGNED: </B>Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.