Perthera, Inc., Mclean, VA
R Joseph Bender , David Halverson , Kimberly Mason , Linlin Luo , Jonathan Robert Brody , Lola Rahib , Lynn McCormick Matrisian , Andrew Eugene Hendifar , William Arthur Hoos , Sameh Mikhail , Vincent M. Chung , Vincent J. Picozzi , Corinne Ramos , Craig Heartwell , Katelyn Varieur , Metasebia Aberra , Emanuel Petricoin III, Subha Madhavan , Michael J. Pishvaian
Background: Recent studies have expanded our knowledge of the genomic landscape of PDA. While critical and in some cases, potentially actionable alterations are being identified, limited outcomes data have thus far made it difficult to validate the relevance of these observations. Methods: The Pancreatic Cancer Action Network (PanCAN) and Perthera have facilitated commercial tumor molecular profiling for over 400 PDA pts since 2014 through KYT, and have developed a database of molecular and clinical information useful for data mining of biomarker-survival correlations. The survival significance of biomarkers was assessed using standard statistical methodology including Kaplan-Meier analysis and Cox proportional hazard models. Results: Linked molecular and outcomes data were available for 360 pts, of which 173 had treatment (tx) information available. Pathogenic mutations from targeted NGS, protein expression from IHC, and protein phosphorylation from RPPA were screened for correlations with overall survival (OS) and progression-free survival (PFS) independent of tx received. As shown in the table, mutations in 3 genes were associated with a better OS; while mutations in 8 genes were associated with poorer OS. Only two mutations were correlated with PFS in 1st or 2nd-line tx (BRCA2 and KDM6A, worse PFS). Positive expression of 7 proteins, including TS, TOP1, and RRM1, were associated with reduced OS but were not correlated with PFS. High levels of phospho-ribosomal protein S6 were associated with both poor OS (HR=10.3, p=0.001) and poor PFS (HR=9.6, p=0.006). Conclusions: Multiple biomarkers had significant correlations with OS in PDA, while fewer were correlated with PFS. Growth of this registry database will further validate tx-specific predictive biomarkers for use in pts with multi-omic profiling data.
Marker | Mut | WT | HR | p= |
---|---|---|---|---|
mOS | mOS | |||
BRCA2 | 27.9 | 9.0 | 0.000000012 | 0.001 |
GNAS | 23.9 | 9.0 | 0.3 | 0.027 |
ASXL1 | 32.2 | 8.2 | 0.000000032 | 0.045 |
SMARCA4 | 2.7 | 9.1 | 4.9 | 0.001 |
VEGFA | 2.6 | 9.0 | 3.6 | 0.003 |
SMAD4 | 5.4 | 10.7 | 1.8 | 0.006 |
ZNF217 | 2.6 | 9.1 | 5.8 | 0.007 |
TP53 | 8.2 | 27.9 | 2.0 | 0.009 |
CDKN2B | 6.0 | 11.5 | 1.6 | 0.014 |
CCND3 | 2.6 | 8.5 | 3.0 | 0.024 |
CRKL | 3.9 | 9.0 | 3.3 | 0.034 |
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