Literature DB >> 31794076

Targeting fusions for improved outcomes in oncology treatment.

Mina Nikanjam1, Ryosuke Okamura1, Donald A Barkauskas2, Razelle Kurzrock1.   

Abstract

BACKGROUND: Fusions are increasingly pursued as oncology therapeutic targets. The current study evaluated differences in outcomes for fusion versus nonfusion targets.
METHODS: Outcomes were compared for patients with fusions versus those with other alterations for US Food and Drug Administration-approved single agents (from package inserts) and for patients treated at the University of California at San Diego.
RESULTS: A total of 28 drugs approved by the US Food and Drug Administration (6189 patients) were included in the analysis. The median response rate was 68% versus 50% for fusions versus nonfusion matches (odds ratio [OR], 1.67; P < .0001); solid tumor therapies had an OR of 2.07 (P < .0001) and hematologic therapies had an OR of 3.35 (P < .0001) for fusion versus nonfusion targets. The University of California at San Diego analysis included 79 patients in whom fusions were treated of the 2455 patients screened. Patients matched to fusions were found to have a longer median progression-free survival (PFS) (11.6 months; 95% CI, 4.0-35.4 months) compared with those unmatched to fusions (4.9 months; 95% CI, 3.5-8.8 months) (P = .034). Patients with fusions matched to other alterations present in the tumor had a median PFS that was indistinguishable from that of those patients with fusions who were treated with unmatched therapy (4.0 months vs 5.0 months; P = .75).
CONCLUSIONS: Significantly higher response rates and a longer PFS were observed when targeting fusions compared with nonfusions. The observations reported in the current study suggest that fusions are important targets and that additional studies are needed to confirm that optimized therapy may require targeting fusions, even in the presence of other alterations.
© 2019 American Cancer Society.

Entities:  

Keywords:  fusions; matched therapy; molecular alteration; precision medicine

Mesh:

Substances:

Year:  2019        PMID: 31794076      PMCID: PMC7050395          DOI: 10.1002/cncr.32649

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


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8.  Mutational landscape and significance across 12 major cancer types.

Authors:  Cyriac Kandoth; Michael D McLellan; Fabio Vandin; Kai Ye; Beifang Niu; Charles Lu; Mingchao Xie; Qunyuan Zhang; Joshua F McMichael; Matthew A Wyczalkowski; Mark D M Leiserson; Christopher A Miller; John S Welch; Matthew J Walter; Michael C Wendl; Timothy J Ley; Richard K Wilson; Benjamin J Raphael; Li Ding
Journal:  Nature       Date:  2013-10-17       Impact factor: 49.962

9.  The landscape of kinase fusions in cancer.

Authors:  Nicolas Stransky; Ethan Cerami; Stefanie Schalm; Joseph L Kim; Christoph Lengauer
Journal:  Nat Commun       Date:  2014-09-10       Impact factor: 14.919

10.  A systematic analysis of FDA-approved anticancer drugs.

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Review 1.  Overcoming TKI resistance in fusion-driven NSCLC: new generation inhibitors and rationale for combination strategies.

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  1 in total

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