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Michael J. Liptay

Researcher at Rush University Medical Center

Publications -  122
Citations -  10853

Michael J. Liptay is an academic researcher from Rush University Medical Center. The author has contributed to research in topics: Lung cancer & Cancer. The author has an hindex of 34, co-authored 116 publications receiving 8578 citations. Previous affiliations of Michael J. Liptay include NorthShore University HealthSystem & Palacký University, Olomouc.

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Journal ArticleDOI

Comprehensive molecular profiling of lung adenocarcinoma: The cancer genome atlas research network

Eric A. Collisson, +318 more
- 01 Jan 2014 - 
TL;DR: In this paper, the authors report molecular profiling of 230 resected lung adnocarcinomas using messenger RNA, microRNA and DNA sequencing integrated with copy number, methylation and proteomic analyses.
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Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer.

Katherine A Hoadley, +738 more
- 05 Apr 2018 - 
TL;DR: Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, Pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which may inform strategies for future therapeutic development.
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Genomic and Functional Approaches to Understanding Cancer Aneuploidy

Alison M. Taylor, +732 more
- 09 Apr 2018 - 
TL;DR: The genomic and phenotypic correlates of cancer aneuploidy are defined and genome engineering is applied to delete 3p in lung cells, causing decreased proliferation rescued in part by chromosome 3 duplication.
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Wedge resection versus lobectomy for stage I (T1 N0 M0) non-small-cell lung cancer

TL;DR: Wedge resection, done by open thoracotomy or video-assisted techniques, appears to be a viable "compromise" surgical treatment of stage I (T1 N0 M0) non-small-cell lung cancer for patients with cardiopulmonary physiologic impairment.
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STS database risk models: predictors of mortality and major morbidity for lung cancer resection.

TL;DR: The risk-adjustment models created have excellent performance characteristics and identify important predictors of mortality and major morbidity for lung cancer resections for Thoracic surgeons participating in the STS GTDB.