Pancreatic cancer prognosis is predicted by an ATAC-array technology for assessing chromatin accessibility

被引:0
|
作者
S. Dhara
S. Chhangawala
H. Chintalapudi
G. Askan
V. Aveson
A. L. Massa
L. Zhang
D. Torres
A. P. Makohon-Moore
N. Lecomte
J. P. Melchor
J. Bermeo
A. Cardenas
S. Sinha
D. Glassman
R. Nicolle
R. Moffitt
K. H. Yu
S. Leppanen
S. Laderman
B. Curry
J. Gui
V. P. Balachandran
C. Iacobuzio-Donahue
R. Chandwani
C. S. Leslie
S. D. Leach
机构
[1] Dartmouth Geisel School of Medicine and Norris Cotton Cancer Center,
[2] Weill Cornell Graduate School of Medical Sciences,undefined
[3] Computational Biology Program,undefined
[4] Memorial Sloan Kettering Cancer Center,undefined
[5] David M. Rubenstein Center for Pancreatic Cancer Research,undefined
[6] Memorial Sloan Kettering Cancer Center,undefined
[7] Weill Cornell Medicine,undefined
[8] Programme Cartes d’Identité des Tumeurs,undefined
[9] Ligue Nationale Contre Le Cancer,undefined
[10] Stony Brook University,undefined
[11] Agilent Technologies Inc.,undefined
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Unlike other malignancies, therapeutic options in pancreatic ductal adenocarcinoma (PDAC) are largely limited to cytotoxic chemotherapy without the benefit of molecular markers predicting response. Here we report tumor-cell-intrinsic chromatin accessibility patterns of treatment-naïve surgically resected PDAC tumors that were subsequently treated with (Gem)/Abraxane adjuvant chemotherapy. By ATAC-seq analyses of EpCAM+ PDAC malignant epithelial cells sorted from 54 freshly resected human tumors, we show here the discovery of a signature of 1092 chromatin loci displaying differential accessibility between patients with disease free survival (DFS) < 1 year and patients with DFS > 1 year. Analyzing transcription factor (TF) binding motifs within these loci, we identify two TFs (ZKSCAN1 and HNF1b) displaying differential nuclear localization between patients with short vs. long DFS. We further develop a chromatin accessibility microarray methodology termed “ATAC-array”, an easy-to-use platform obviating the time and cost of next generation sequencing. Applying this methodology to the original ATAC-seq libraries as well as independent libraries generated from patient-derived organoids, we validate ATAC-array technology in both the original ATAC-seq cohort as well as in an independent validation cohort. We conclude that PDAC prognosis can be predicted by ATAC-array, which represents a low-cost, clinically feasible technology for assessing chromatin accessibility profiles.
引用
收藏
相关论文
共 17 条
  • [1] Pancreatic cancer prognosis is predicted by an ATAC-array technology for assessing chromatin accessibility
    Dhara, S.
    Chhangawala, S.
    Chintalapudi, H.
    Askan, G.
    Aveson, V
    Massa, A. L.
    Zhang, L.
    Torres, D.
    Makohon-Moore, A. P.
    Lecomte, N.
    Melchor, J. P.
    Bermeo, J.
    Cardenas, A.
    Sinha, S.
    Glassman, D.
    Nicolle, R.
    Moffitt, R.
    Yu, K. H.
    Leppanen, S.
    Laderman, S.
    Curry, B.
    Gui, J.
    Balachandran, V. P.
    Iacobuzio-Donahue, C.
    Chandwani, R.
    Leslie, C. S.
    Leach, S. D.
    NATURE COMMUNICATIONS, 2021, 12 (01)
  • [2] Pancreatic cancer prognosis is predicted by chromatin accessibility microarray
    Dhara, Surajit
    Chhangawala, Sagar
    Chintalapudi, Himanshu
    Massa, Alexandra L.
    Aveson, Victoria
    Askan, Gokce
    Zhang, Liguo
    Nicolle, Remy
    Makohon-Moore, Alvin P.
    Sinha, Smrita
    Gui, Jiang
    Moffitt, Richard
    Yu, Kenneth H.
    Balachandran, Vinod
    Chandwani, Rohit
    Leslie, Christina
    Leach, Steven D.
    CANCER RESEARCH, 2020, 80 (16)
  • [3] Predicted prognosis of pancreatic cancer patients by machine learning
    Higashi, M.
    Yokoyama, S.
    Tanimoto, A.
    VIRCHOWS ARCHIV, 2020, 477 : S198 - S198
  • [4] Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
    Yokoyama, Seiya
    Hamada, Taiji
    Higashi, Michiyo
    Matsuo, Kei
    Maemura, Kosei
    Kurahara, Hiroshi
    Horinouchi, Michiko
    Hiraki, Tsubasa
    Sugimoto, Tomoyuki
    Akahane, Toshiaki
    Yonezawa, Suguru
    Kornmann, Marko
    Batra, Surinder K.
    Hollingsworth, Michael A.
    Tanimoto, Akihide
    CLINICAL CANCER RESEARCH, 2020, 26 (10) : 2411 - 2421
  • [5] Identifying cancer vulnerabilities associated with changes in chromatin accessibility by simultaneously profiling hundreds of cancer cell lines with ATAC-seq
    Borck, Patricia
    Wienand, Kirsty
    Duarte, Fabiana
    Qin, Alvin
    Zhang, Ruochi
    Babu, Juliana
    Zhang, Simone
    Maffa, Samuel
    Horlbeck, Max
    Shrestha, Rojesh
    Dempster, Joshua
    Roth, Jennifer
    Campbell, Catarina
    Buenrostro, Jason
    Vazquez, Francisca
    CANCER RESEARCH, 2024, 84 (07)
  • [6] Assessing regulatory network rewiring in cancer patients using chromatin accessibility profiles
    Forbes, Andre
    Xu, Duo
    Khurana, Ekta K.
    CANCER RESEARCH, 2020, 80 (16)
  • [7] Serum ferritin predicted prognosis in patients with locally advanced pancreatic cancer
    Wang, Si-Liang
    Cao, Shuo
    Wu, Rong
    Chi, Feng
    Tang, Mei-Yue
    Jin, Xue-Ying
    Chen, Xiao-Dong
    FUTURE ONCOLOGY, 2015, 11 (21) : 2905 - 2910
  • [8] Predicted Prognosis of Pancreatic Cancer Patients by Machine Learning-Letter
    Kernbach, Julius M.
    Staartjes, Victor E.
    CLINICAL CANCER RESEARCH, 2020, 26 (14) : 3891 - 3891
  • [9] Using Chromatin Accessibility to Delineate Therapeutic Subtypes in Pancreatic Cancer Patient-Derived Cell Lines
    Brunton, Holly
    Garner, Ian M.
    Bailey, Ulla-Maja
    Upstill-Goddard, Rosie
    Bailey, Peter J.
    STAR PROTOCOLS, 2020, 1 (02):
  • [10] Integrated profiling of human pancreatic cancer organoids reveals chromatin accessibility features associated with drug sensitivity
    Xiaohan Shi
    Yunguang Li
    Qiuyue Yuan
    Shijie Tang
    Shiwei Guo
    Yehan Zhang
    Juan He
    Xiaoyu Zhang
    Ming Han
    Zhuang Liu
    Yiqin Zhu
    Suizhi Gao
    Huan Wang
    Xiongfei Xu
    Kailian Zheng
    Wei Jing
    Luonan Chen
    Yong Wang
    Gang Jin
    Dong Gao
    Nature Communications, 13