Proteogenomic insights suggest druggable pathways in endometrial carcinoma

Clinical Proteomic Tumor Analysis Consortium

doi:10.1016/j.ccell.2023.07.007

Proteogenomic insights suggest druggable pathways in endometrial carcinoma

Clinical Proteomic Tumor Analysis Consortium

Vagelos College of Physicians and Surgeons

Producción científica › revisión exhaustiva

14 Citas (Scopus)

Resumen

We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.

Idioma original	English
Páginas (desde-hasta)	1586-1605.e15
Publicación	Cancer Cell
Volumen	41
N.º	9
DOI	https://doi.org/10.1016/j.ccell.2023.07.007
Estado	Published - sep. 11 2023

Financiación

Financiadores	Número del financiador
Clinical Proteomic Tumor Analysis Consortium
Janice McNair Foundation
McNair Medical Institute
National Scientific User Facility
National Institutes of Health	S21-167, U24CA210955, U24CA210954, U24CA210979, U24CA210967, U24CA210972, U24CA210986, U24CA210985, U24CA271012, U24CA210993, U24CA271076, U01CA214116, U01CA214114, U01CA214125, 21X164Q, U24CA271114
U.S. Department of Defense	DOD BC220523
U.S. Department of Energy	DE-AC05-76RL01830
National Cancer Institute	T32CA203690, RR160027
Battelle
Cancer Prevention and Research Institute of Texas
Pacific Northwest National Laboratory

ASJC Scopus Subject Areas

Oncology
Cancer Research

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1016/j.ccell.2023.07.007

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@article{b2e3479fc8c649eba469d39060d08795,

title = "Proteogenomic insights suggest druggable pathways in endometrial carcinoma",

abstract = "We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.",

author = "{Clinical Proteomic Tumor Analysis Consortium} and Yongchao Dou and Lizabeth Katsnelson and Gritsenko, {Marina A.} and Yingwei Hu and Boris Reva and Runyu Hong and Wang, {Yi Ting} and Iga Kolodziejczak and Lu, {Rita Jui Hsien} and Tsai, {Chia Feng} and Wen Bu and Wenke Liu and Xiaofang Guo and Eunkyung An and Arend, {Rebecca C.} and Jasmin Bavarva and Lijun Chen and Chu, {Rosalie K.} and Andrzej Czeka{\'n}ski and Teresa Davoli and Demicco, {Elizabeth G.} and Deborah DeLair and Kelly Devereaux and Dhanasekaran, {Saravana M.} and Peter Dottino and Bailee Dover and Fillmore, {Thomas L.} and McKenzie Foxall and Hermann, {Catherine E.} and Tara Hiltke and Galen Hostetter and Marcin J{\c e}dryka and Jewell, {Scott D.} and Isabelle Johnson and Kahn, {Andrea G.} and Ku, {Amy T.} and Chandan Kumar-Sinha and Pawe{\l} Kurzawa and Lazar, {Alexander J.} and Rossana Lazcano and Lei, {Jonathan T.} and Yi Li and Yuxing Liao and Lih, {Tung Shing M.} and Lin, {Tai Tu} and Martignetti, {John A.} and Masand, {Ramya P.} and Rafa{\l} Matkowski and Wilson McKerrow and Jason Wright",

note = "Publisher Copyright: {\textcopyright} 2023",

year = "2023",

month = sep,

day = "11",

doi = "10.1016/j.ccell.2023.07.007",

language = "English",

volume = "41",

pages = "1586--1605.e15",

journal = "Cancer Cell",

issn = "1535-6108",

publisher = "Cell Press",

number = "9",

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T1 - Proteogenomic insights suggest druggable pathways in endometrial carcinoma

AU - Clinical Proteomic Tumor Analysis Consortium

AU - Dou, Yongchao

AU - Katsnelson, Lizabeth

AU - Gritsenko, Marina A.

AU - Hu, Yingwei

AU - Reva, Boris

AU - Hong, Runyu

AU - Wang, Yi Ting

AU - Kolodziejczak, Iga

AU - Lu, Rita Jui Hsien

AU - Tsai, Chia Feng

AU - Bu, Wen

AU - Liu, Wenke

AU - Guo, Xiaofang

AU - An, Eunkyung

AU - Arend, Rebecca C.

AU - Bavarva, Jasmin

AU - Chen, Lijun

AU - Chu, Rosalie K.

AU - Czekański, Andrzej

AU - Davoli, Teresa

AU - Demicco, Elizabeth G.

AU - DeLair, Deborah

AU - Devereaux, Kelly

AU - Dhanasekaran, Saravana M.

AU - Dottino, Peter

AU - Dover, Bailee

AU - Fillmore, Thomas L.

AU - Foxall, McKenzie

AU - Hermann, Catherine E.

AU - Hiltke, Tara

AU - Hostetter, Galen

AU - Jędryka, Marcin

AU - Jewell, Scott D.

AU - Johnson, Isabelle

AU - Kahn, Andrea G.

AU - Ku, Amy T.

AU - Kumar-Sinha, Chandan

AU - Kurzawa, Paweł

AU - Lazar, Alexander J.

AU - Lazcano, Rossana

AU - Lei, Jonathan T.

AU - Li, Yi

AU - Liao, Yuxing

AU - Lih, Tung Shing M.

AU - Lin, Tai Tu

AU - Martignetti, John A.

AU - Masand, Ramya P.

AU - Matkowski, Rafał

AU - McKerrow, Wilson

AU - Wright, Jason

PY - 2023/9/11

Y1 - 2023/9/11

N2 - We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.

AB - We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.

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DO - 10.1016/j.ccell.2023.07.007

M3 - Article

C2 - 37567170

AN - SCOPUS:85169826543

SN - 1535-6108

VL - 41

SP - 1586-1605.e15

JO - Cancer Cell

JF - Cancer Cell

IS - 9

ER -

Proteogenomic insights suggest druggable pathways in endometrial carcinoma

Resumen

Financiación

ASJC Scopus Subject Areas

ODS de las Naciones Unidas

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