a 2022

Identification of molecular heterogeneity in pancreatic ductal adenocarcinoma by multivariate profiling of unfolded protein response

SOCHOROVÁ, Dana, Lumír KUNOVSKÝ, Michal EID, Viktorie GABRIELOVÁ, Lukáš PEČINKA et. al.

Základní údaje

Originální název

Identification of molecular heterogeneity in pancreatic ductal adenocarcinoma by multivariate profiling of unfolded protein response

Autoři

SOCHOROVÁ, Dana, Lumír KUNOVSKÝ, Michal EID, Viktorie GABRIELOVÁ, Lukáš PEČINKA, Lukáš MORÁŇ, Peter STAŇO, Volodymyr POROKH, K. SOUCEK, Z. KAHOUNOVA, Josef HAVEL, Petr VAŇHARA a Zdeněk KALA

Vydání

54th meeting of the European Pancreatic Club, 2022

Další údaje

Jazyk

angličtina

Typ výsledku

Konferenční abstrakt

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Impakt faktor

Impact factor: 3.600

Organizační jednotka

Lékařská fakulta

ISSN

UT WoS

999

Příznaky

Mezinárodní význam
Změněno: 29. 1. 2023 20:01, doc. RNDr. Petr Vaňhara, Ph.D.

Anotace

V originále

Introduction: Ductal adenocarcinoma of the pancreas (PDAC) accounts for nearly 90% of pancreatic tumors. The prognosis is poor due to both delayed diagnosis and the complicated molecular pathway involved in development, progression and metastasis of PDAC. Markers enabling identification of early stages and their distinguishing from too advanced cases remain a challenge. Understanding the role of endoplasmatic reticulum (ER) stress response, which plays a key microenvironmental role, would enable personalized-medicine approach by identifying aggressive and treatment-resistant PDAC varieties. Purpose: To identify the relation between heterogeneity in PDAC and the ER stress response, we analyzed 1) the proteosynthetic stress response of ex-vivo cultured cells by revealing the unfolded protein response (UPR) status, 2) alterations in spectral profiles corresponding to metabolome, lipidome and low proteome of intact PDAC cells. Integrated data were used as inputs for sophisticated biostatistics and machine learning. Materials and methods: Primary cancer cell lines were established from explanted, histopathologically-validated PDAC tumors. Cells were analyzed for canonical and noncanonical UPR regulators by immunoblotting and immunofluorescence microscopy. For mass spectrometry, whole (intact) cells were used as described previously [1]. Statistical analysis was performed in R environment. Results: We revealed distinct UPR and spectral profiles of patient-specific PDAC cancer cell lines, documenting the intrinsic variability in the cohort of patient-derived samples. Global analyses of mass spectra based on pattern recognition and spectral fingerprinting provided clear discrimination of pancreatic cancer types with distinct histopathology. Conclusions: We proved the applicability of combined molecular and mass spectrometry-based approach in identifying the heterogeneity in PDAC and demonstrated that unique molecular and metabolic profiles of patient-specific PDAC cells can provide an unbiased tool for revealing PDAC heterogeneity with clinical implications.

Návaznosti

MUNI/A/1330/2021, interní kód MU
Název: Nové přístupy ve výzkumu, diagnostice a terapii hematologických malignit IX (Akronym: VýDiTeHeMa IX)
Investor: Masarykova univerzita, Nové přístupy ve výzkumu, diagnostice a terapii hematologických malignit IX
MUNI/A/1398/2021, interní kód MU
Název: Zdroje pro tkáňové inženýrství 12 (Akronym: TissueEng 12)
Investor: Masarykova univerzita, Zdroje pro tkáňové inženýrství 12