Enhanced phosphorylation of c-Jun by cisplatin treatment as a potential predictive biomarker for cisplatin response in combination with patient-derived tumor organoids.
Yoshiyuki TsukamotoShusaku KurogiTomotaka ShibataKosuke SuzukiYuka HirashitaShoichi FumotoShinji YanoKazuyoshi YanagiharaChisato NakadaFumi MienoKeisuke KinoshitaTakafumi FuchinoKazuhiro MizukamiYoshitake UedaTsuyoshi EtohTomohisa UchidaToshikatsu HanadaMutsuhiro TakekawaTsutomu DaaKuniaki ShiraoShuichi HironakaKazunari MurakamiMasafumi InomataNaoki HijiyaMasatsugu MoriyamaPublished in: Laboratory investigation; a journal of technical methods and pathology (2022)
Despite recent advances in sequencing technology and large-scale drug screenings employing hundreds of cell lines, the predictive accuracy of mutation-based biomarkers is still insufficient as a guide for cancer therapy. Therefore, novel types of diagnostic methods using alternative biomarkers would be highly desirable. We have hypothesized that sensitivity-specific changes in the phosphorylation of signaling molecules could be useful in this respect. Here, with the aim of developing a method for predicting the response of cancers to cisplatin using a combination of specific biomarker(s) and patient-derived tumor organoids (PDOs), we found that cisplatin-sensitive cell lines or PDOs showed enhanced phosphorylation of c-Jun (p-c-Jun) within 24 h after cisplatin treatment. We also compared the responses of 6 PDOs to cisplatin with the therapeutic effect of neoadjuvant chemotherapy (docetaxel/cisplatin/5-fluorouracil) in 6 matched patients. Mechanistically, the c-Jun induction was partly related to TNF signaling induced by cisplatin. Our data suggest that enhanced phosphorylation of c-Jun in response to cisplatin treatment could be a predictive biomarker for the efficacy of cisplatin in selected cancer patients.
Keyphrases
- young adults
- electronic health record
- neoadjuvant chemotherapy
- cancer therapy
- rheumatoid arthritis
- chronic kidney disease
- end stage renal disease
- squamous cell carcinoma
- drug delivery
- machine learning
- emergency department
- newly diagnosed
- lymph node
- risk assessment
- climate change
- deep learning
- artificial intelligence
- replacement therapy
- drug induced
- data analysis