Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Immunotherapy of cancer

5380 - Immune Checkpoint Inhibitor (ICPI) Efficacy and Resistance Detected by Comprehensive Genomic Profiling (CGP) in Non-Small Cell Lung Cancer (NSCLC)

Date

11 Sep 2017

Session

Immunotherapy of cancer

Presenters

Jeffrey Ross

Citation

Annals of Oncology (2017) 28 (suppl_5): v403-v427. 10.1093/annonc/mdx376

Authors

J.S. Ross1, M.E. Goldberg2, L.A. Albacker2, L.M. Gay3, V. Agarwala4, J.A. Elvin3, J. Vergilio3, J. Suh3, S. Ramkissoon3, E. Severson3, S. Daniel3, S.M. Ali5, A.B. Schrock5, G.M. Frampton2, D. Fabrizio6, V.A. Miller7, G. Singal8, A. Abernethy9, P.J. Stephens2

Author affiliations

  • 1 Pathology, Albany Medical Center, 12208 - Albany/US
  • 2 Cancer Genomics, Foundation Medicine, 02141 - Cambridge/US
  • 3 Pathology, Foundation Medicine, 02141 - Cambridge/US
  • 4 Product, Flatiron Health, Inc., New York/US
  • 5 Clinical Development, Foundation Medicine, MA 02141 - Cambridge/US
  • 6 Cancer Immunotherapy, Foundation Medicine Inc., 02141 - Cambridge/US
  • 7 Medical Group, Foundation Medicine, MA 02141 - Cambridge/US
  • 8 Data Strategy, Foundation Medicine, 02141 - Cambridge/US
  • 9 Medical Group, Flatiron Health, Inc., New York/US
More

Resources

Abstract 5380

Background

The prediction of outcome to ICPI in advanced NSCLC is of great clinical interest. We considered CGP, PD-L1 IHC, and real world data to investigate potential biomarkers for ICPI response.

Methods

CGP and IHC was performed on 1,619 FFPE NSCLC samples in the FoundationCORE database (FMI). The SP142 antibody was used to capture PD-L1 tumor expression (PD-L1 TE) for these 1,619 samples. NSCLC patients (n = 2139) in the Flatiron Health Analytic Database with FoundationOne testing CGP results and real world IHC results for PD-L1 TE were analyzed separately (FMI-FIH). CGP used ≥50 ng of DNA and a hybrid-capture, adaptor ligation-based assay (median coverage depth >600X). TMB (mut/Mb) was determined on 1.1 Mb of sequenced DNA.

Results

PD-L1 IHC TE correlated weakly with TMB (FMI samples) (Spearman’s ρ 0.085, p = 6.16e-4); mean TMB was 10.9 mut/Mb, median 8.1 mut/Mb and 14.5% had high TMB (≥20 mut/Mb). From FMI-FIH, high TMB but not PD-L1 status predicted longer mean duration on therapy (DOT) (p = 0.001). Analysis of the FMI and FMI-FIH datasets revealed relationships between GA, PD-L1 TE, TMB, and mean DOT. Inactivating STK11 GA were seen in 12.1% of FMI-FIH and 15.1% of FMI samples, most often adenocarcinomas (aCa). STK11 GA correlated with high TMB/low PD-L1 (FMI; p = 0.0014) and preliminary analyses suggest correlation with negative ICPI treatment outcome. Several genes were commonly co-altered with STK11 (FMI): KRAS (54.5%), TP53 (43%), CDKN2A (27.5%), CDKN2B (20.1%), KEAP1 (18.9%), and MYC (13.5%). BRAF GA, most often short variants (SV) in aCa, were associated with prolonged DOT on ICPI regardless of TMB score (FMI-FIH; p = 0.0073). MET SV also predicted prolonged DOT on ICPI, but insufficient events prevented calculation of statistical significance (FMI-FIH). Analysis of the TCGA lung aCa dataset revealed MET SV (2.8%) linked with immune activation gene expression profiles (p 

Conclusions

Although TMB powerfully predicts ICPI outcome independent of tumor cell PD-L1 expression, considering GA in STK11, BRAF or MET may significantly increase the precision and improve outcomes when using genomics with IHC to guide to ICPI selection.

Clinical trial identification

Legal entity responsible for the study

Foundation Medicine, Inc.

Funding

Foundation Medicine, Inc., Flatiron Health

Disclosure

J.S. Ross, M.E. Goldberg, L.A. Albacker, L.M. Gay, J.A. Elvin, J-A. Vergilio, J. Suh, S. Ramkissoon, E. Severson, S. Daniel, S.M. Ali, A.B. Schrock, G.M. Frampton, D. Fabrizio, V.A. Miller, G. Singal, P.J. Stephens: Employee of and stockholder in Foundation Medicine, Inc. V. Agarwala: Employee of Flatiron Health. A. Abernethy: Employee of Flatiron Health.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.