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Webinar September 28th | Evaluating Immune Checkpoint Biomarkers in Melanoma using HALO and STRATA

Indica Labs / News  / Webinar September 28th | Evaluating Immune Checkpoint Biomarkers in Melanoma using HALO and STRATA

Webinar September 28th | Evaluating Immune Checkpoint Biomarkers in Melanoma using HALO and STRATA

Case Study using HALO™ and STRATA™:
Biomarkers of Anti-tumor Immune Activity Associate with Survival in Melanoma

Wednesday Septmber 28th | 8 AM PDT | 11 AM EDT | 4 PM BST
Presenters
Dr. Tricia Cottrell, Johns Hopkins Medical School 
Vlado Ovtcharov, Indica Labs

PD-L1 expression in the tumor microenvironment can be both prognostic and predictive.  PD-L1 expression is often a marker of an ongoing CD8+ cytotoxic T-cell-mediated anti-tumor immune response, and thus, patients with PD-L1-positive melanomas have improved overall survival relative to those with PD-L1- negative melanomas. PD-L1 expression and high CD8-T-cell density have also been associated with an increased probability of response to PD-1 pathway blockade therapies in melanoma.

In this webinar, Dr. Cottrell and Mr Ovtcharov will describe recent studies using digital image analysis with Indica Labs’ HALOTM and STRATATM platforms to characterize immunohistochemical features of the tumor microenvironment and correlate them with survival for patients with melanoma.

Register Here 


Dr. Cottrell’s Bio

Dr. Tricia Cottrell from Johns Hopkins School of Medicine is a post-doctoral fellow in the lab of Dr. Janis Taube.  Dr. Taube’s laboratory described PD-L1-mediated adaptive immune resistance by melanoma, a finding that has now been extended to other tumor types. She also developed a robust immunohistochemistry assay and interpretation methods for studying PD-L1 as it relates to therapeutic response.  Versions of this assay are now approved by the U.S. Food and Drug Administration for clinical use.  Dr. Cottrell’s work in the Taube lab focuses on the use of digital image analysis platforms for quantitative characterization of biomarkers in the tumor microenvironment for improve prognostic and predictive applications.