Thyroid transcription factor 1 and p63 expression is associated with survival outcome in patients with non‑small cell lung cancer treated with erlotinib

Svatoň, Martin; Fiala, Ondřej; Krákorová, Gabriela; Blažek, Jiří; Hurdalkova, Karolina; Bařinová, Magda; Mukenšnábl, Petr; Pešek, Miloš

doi:10.3892/ol.2020.11663

Cited by 5 publications

(4 citation statements)

References 34 publications

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“…TP63 was identified as a differentially expressed gene (DEG) in cancer cell lines that acquired gefitinib resistance, and CSTA is one of the top 10 hub genes in a network of DEGs for gefitinib resistant cancer cell line [ 32 ]. TP63 expression is associated with survival outcomes in patients with NSCLC, treated with erlotinib [ 33 ]. MDM2 is a novel biomarker and treatment target for NSCLC and confers primary resistance to first-generation EGFR-TKIs induced by MDM2 amplification in NSCLC [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks

et al. 2022

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In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells.

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Section: Resultsmentioning

confidence: 99%

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks

et al. 2022

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“…The TTF1 positivity staining were strongly correlated with the presence of EGFR mutations (p < 0.001) and TTF-1 negativity to be a good predictor of EGFR wt mutations [32]. The results of the Svaton et al study suggested that patients with EGRF wt lung ADs and lack of TTF-1 expression to have significantly lower PFS and OS, and TTF1 expression may be useful predictors of TKIs efficacy in patients with EGFR wt lung ADs [29]. The patients, TTF-1 positive or negative ADs, could benefit from the first-line chemotherapy [30].…”

Section: Villa Et Al Reported That the Most Common Histologic Type Smentioning

confidence: 94%

“…Many authors have studied the association between EGFR mutations and histologic type of Ads and TTF-1 expression, as well as pathologic stage of the disease [27,28,29,30].…”

Section: Other Recurrent Mutations In Exon 18-point Mutations In Posmentioning

confidence: 99%

Association between epidermal growth factor receptor mutation status, clinicopathological characteristics and TTF-1 expression in lung adenocarcinoma: A single center study

et al. 2021

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Introduction/Objective. The presence of EGFR mutations is the best predictor of response for therapy with tyrosine kinase inhibitors. In this study we investigate association between EGFR mutations and clinicopathological characteristics and thyroid transcription factor (TTF-1) expression in lung adenocarcinomas (ADs). Methods. We analyzed 142 surgical samples from patients with histologically confirmed lung ADs from January, 2010. to December, 2015. All tumor tissues were reclassified according to WHO criteria and EGFR mutations detected by Real time PCR. TTF-1 expression was detected by immunohistochemistry in 83 out of 142 cases. The association between EGFR and TTF-1 expression was analyzed using ?2 test or Fisher?s exact test with SPSS software version 20.0. Results. This study included 78 male and 64 women with a median age 61.6 (range, 42-82) years. Acinar (ACN) and solid (SOL) were the most common histological types (47.9% and 38.7% respectively). TTF-1 expression was present in 69 of 83 (83%) ADs. The EGFR mutation was found in 7%, more frequently in women, and patients with smoking history, and acinar type of ADs, whereas it had no association with age and pathological stage and TTF-1 expression. Conclusion. In conclusion, the results of this study demonstrate that the presence of EGFR mutations is associated with some clinical characteristics and histologic type of ADs, but not with TTF-1 expression.

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“…показали, что экспрессия белка p63 -физиологического регулятора фенотипа стволовых клеток в сквамозном эпителии -распространенное явление при ПРЩЖ и АИТ, т.е. экспрессия p63 может выступать в качестве потенциального патобиологического связующего звена между этими двумя заболеваниями [8].…”

Section: иммунологические аспекты папиллярного рака щитовидной железы что нового?unclassified

Immunological aspects of papillary thyroid cancer. What's new?

2021

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Studying of the common links of pathogenesis of endocrine, autoimmune and oncological diseases is the area of interest of researchers from all countries of the world. Comprehension of artificially created mutual influences of molecular-genetic, immune factors that underlie the development and progression of endocrine tumors, primarily thyroid cancer, is important for creation and application of innovative treatment methods in oncoendocrinology.Today, the question of considering autoimmune diseases as a potential cause of oncological processes or on the contrary to consider them as protective conditions in some types of malignant tumors, remains controversial.In particular, autoimmune thyropathies and papillary thyroid cancer is an interesting model for studying these complex relationships. . The purpose of this article is to discuss accumulated experience, review the literature devoted to the study of immunological aspects in the pathogenesis of papillary thyroid cancer, reconsider obtained material and form a conclusion.

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Thyroid transcription factor 1 and p63 expression is associated with survival outcome in patients with non‑small cell lung cancer treated with erlotinib

Cited by 5 publications

References 34 publications

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks

Association between epidermal growth factor receptor mutation status, clinicopathological characteristics and TTF-1 expression in lung adenocarcinoma: A single center study

Immunological aspects of papillary thyroid cancer. What's new?

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