Strategies to improve the sensitivity and ranking ability of neoantigen prediction methods: Report on the results of the Tumor nEoantigen SeLection Alliance (TESLA) Meeting Abstract

  

Authors:	Wells, D. K.; Dang, K.; Hubbard-Lucey, V. M.; Sheehan, K. C.; Lamb, A.; Ward, J. P.; Sidney, J.; Blazquez, A. B.; Rech, A. J.; Zaretsky, J.; Comin-Anduix, B.; Ng, A. H.; Chour, W.; Yu, T. V.; Rizvi, H.; Chen, J.; Manning, P.; Merghoub, T.; Guinney, J.; Kolom, A.; Selinsky, C.; Ribas, A.; Hellmann, M. D.; Schumacher, T. N.; Hacohen, N.; Kvistborg, P.; Sette, A.; Heath, J. R.; Bhardwaj, N.; Ramsdell, F.; Schreiber, R. D.; Defranoux, N. A.; and TESLA Consortium
Abstract Title:	Strategies to improve the sensitivity and ranking ability of neoantigen prediction methods: Report on the results of the Tumor nEoantigen SeLection Alliance (TESLA)
Meeting Title:	111th Annual Meeting of the American Association for Cancer Research (AACR)
Journal Title:	Cancer Research
Volume:	80
Issue:	16 Suppl.
Meeting Dates:	2020 Apr 27-28/Jun 22-24
Meeting Location:	Philadelphia, PA
ISSN:	0008-5472
Publisher:	American Association for Cancer Research
Date Published:	2020-08-01
Language:	English
ACCESSION:	WOS:000590059301036
DOI:	10.1158/1538-7445.Am2020-3210
PROVIDER:	wos
Notes:	Meeting Abstract: 3210 -- Due to COVID-19 pandemic, the conference was held virtually -- Source: Wos

https://synapse.mskcc.org/synapse/works/has_related_data_chk/179076