000			02060 am a22003253u 4500
042			_adc
100	1	0	_aWeber, D _eauthor _91268
700	1	0	_aIbn-Salem, J _eauthor _91269
700	1	0	_aSorn, P _eauthor _91270
700	1	0	_aSuchan, M _eauthor _91271
700	1	0	_aHoltsträter, C _eauthor _91272
700	1	0	_aLahrmann, U _eauthor _91273
700	1	0	_aVogler, I _eauthor _91274
700	1	0	_aSchmoldt, K _eauthor _91275
700	1	0	_aLang, F _eauthor _91276
700	1	0	_aSchrörs, B _eauthor _91277
700	1	0	_aLöwer, M _eauthor _91278
700	1	0	_aSahin, U _eauthor _91279
245	0	0	_aAccurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens
260			_c2022-08.
500			_a/pmc/articles/PMC7613288/
500			_a/pubmed/35379963
520			_aCancer associated gene fusions (GF) are a potential source for highly immunogenic neo-antigens, but the lack of computational tools for accurate, sensitive identification of personal GFs has limited their targeting in personalized cancer immunotherapy. Here, we present EasyFuse, a machine learning computational pipeline for detecting cancer-specific GFs in transcriptome data obtained from human cancer samples. We provide an extensive experimental confirmation dataset and demonstrate that EasyFuse predicts personal GFs with high precision and sensitivity, outperforming previously described tools. By testing immunogenicity with autologous blood lymphocytes from patients with cancer, we detected pre-established CD4(+) and CD8(+) T-cell responses for 10 of 21 (48%), and for 1 of 30 (3%) of identified GFs, respectively. The high frequency of T-cell responses detected in cancer patients supports the relevance of individual GFs as neo-antigens that may be targeted in personalized immunotherapies, especially for tumors with low mutation burdens.
540			_a
546			_aen
690			_aArticle
655	7		_aText _2local
786	0		_nNat Biotechnol
856	4	1	_uhttp://dx.doi.org/10.1038/s41587-022-01247-9 _zConnect to this object online.
999			_c1761 _d1761