Correlation of PD-L1 immunohistochemical expression with microsatellite instability and p53 status in endometrial carcinoma

Arafa, Mohammad; Shebl, Abdelhadi M.; Salama, Amany A.; Elzahaf, Eman; Ashamallah, Sylvia A.; Foda, Abd Al-Rahman Mohammad; Awad, AzmyAbd El-Hameed; Shalaby, Asem

doi:10.1016/j.eurox.2022.100172

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…The strong association between p53 positivity and PD-L1 staining found in this study is in line with reports describing a similar link in other tumor entities such as for example triple negative breast cancer [ 52 ], endometrial cancer [ 53 ], hepatocellular carcinoma [ 54 ], and oral squamous cell carcinomas [ 55 ]. p53 is known to play an important role in DNA damage pathways, which also belong to the relevant mechanisms inducing up-regulation of PD-L1 expression (summarized in [ 56 ]).…”

Section: Discussionsupporting

confidence: 92%

PD-L1 expression in tumor and inflammatory cells is associated with favorable tumor features and favorable prognosis in muscle-invasive urothelial carcinoma of the bladder not treated by immune checkpoint inhibitors

Plage,

Furlano,

Hofbauer

et al. 2024

BMC Urol

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Background A high level of PD-L1 expression is the most relevant predictive parameter for response to immune checkpoint inhibitor (CPI) therapy in urinary bladder cancer. Existing data on the relationship between PD-L1 expression and the natural course of disease are controversial and sparse. Methods To expand our understanding of the relationship between PD-L1 expression and parameters of cancer aggressiveness, PD-L1 was analyzed on tissue microarrays containing 2710 urothelial bladder carcinomas including 512 patients with follow-up data who underwent radical cystectomy and follow-up therapies in the pre-immune checkpoint inhibitor therapy era. Results Tumor cell positivity in ≥10% of cells were seen in 513 (20%) and an immune cell positivity occurred in 872 (34%) of 2566 interpretable cancers. PD-L1 positivity in tumor cells increased from pTaG2 low grade (0.9% positive) to pTaG3 high grade (4.1%; p = 0.0255) and was even higher in muscle-invasive (pT2–4) carcinomas (29.3%; p < 0.0001). However, within pT2–4 carcinomas, PD-L1 positivity was linked to low pT stage (p = 0.0028), pN0 (p < 0.0001), L0 status (p = 0.0005), and a better prognosis within 512 patients with cystectomy who never received CPIs (p = 0.0073 for tumor cells and p = 0.0086 for inflammatory cells). PD-L1 staining in inflammatory cells was significantly linked to PD-L1 staining in tumor cells (p < 0.0001) and both were linked to a positive p53 immunostaining (p < 0.0001). Conclusion It cannot be fully excluded that the strong statistical link between PD-L1 status and favorable histological tumor features as well as better prognosis could influence the outcome of studies evaluating CPIs in muscle-invasive urothelial carcinoma.

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Section: Discussionsupporting

confidence: 92%

PD-L1 expression in tumor and inflammatory cells is associated with favorable tumor features and favorable prognosis in muscle-invasive urothelial carcinoma of the bladder not treated by immune checkpoint inhibitors

Plage,

Furlano,

Hofbauer

et al. 2024

BMC Urol

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A prediction model based on deep learning and radiomics features of DWI for the assessment of microsatellite instability in endometrial cancer

Wang,

Song,

Zhang

et al. 2024

Cancer Medicine

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BackgroundTo explore the efficacy of a prediction model based on diffusion‐weighted imaging (DWI) features extracted from deep learning (DL) and radiomics combined with clinical parameters and apparent diffusion coefficient (ADC) values to identify microsatellite instability (MSI) in endometrial cancer (EC).MethodsThis study included a cohort of 116 patients with EC, who were subsequently divided into training (n = 81) and test (n = 35) sets. From DWI, conventional radiomics features and convolutional neural network‐based DL features were extracted. Random forest (RF) and logistic regression were adopted as classifiers. DL features, radiomics features, clinical variables, ADC values, and their combinations were applied to establish DL, radiomics, clinical, ADC, and combined models, respectively. The predictive performance was evaluated through the area under the receiver operating characteristic curve (AUC), total integrated discrimination index (IDI), net reclassification index (NRI), calibration curves, and decision curve analysis (DCA).ResultsThe optimal predictive model, based on an RF classifier, comprised four DL features, three radiomics features, two clinical variables, and an ADC value. In the training and test sets, this model exhibited AUC values of 0.989 (95% CI: 0.935–1.000) and 0.885 (95% CI: 0.731–0.967), respectively, demonstrating different degrees of improvement compared with the clinical, DL, radiomics, and ADC models (AUC‐training = 0.671, 0.873, 0.833, and 0.814, AUC‐test = 0.685, 0.783, 0.708, and 0.713, respectively). The NRI and IDI analyses revealed that the combined model resulted in improved risk reclassification of the MSI status compared to the clinical, radiomics, DL, and ADC models. The calibration curves and DCA indicated good consistency and clinical utility of this model, respectively.ConclusionsThe predictive model based on DWI features extracted from DL and radiomics combined with clinical parameters and ADC values could effectively assess the MSI status in EC.

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Features of the immunosuppressive tumor microenvironment in endometrial cancer based on molecular subtype

Zhang,

Wang,

2023

Front. Oncol.

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Endometrial cancer (EC) is one of the three most prevalent gynecological tumors affecting women and is the most prevalent gynecological malignancy in the developed world. Its incidence is rapidly increasing worldwide, mostly affecting postmenopausal women, whereas recently its prevalence has increased in younger people. EC is an immune gene disease and many studies have shown that the tumor-immunosuppressive microenvironment plays an important role in cancer progression. In recent years, findings regarding the immunosuppressive tumor microenvironment (ITME) of EC have included immune evasion mechanisms and immunotherapy, which are mostly immune checkpoint inhibitors (ICI) for EC. Recently studies on the ITME of different molecular types of EC have found that different molecular types may have different ITME. With the research on the immune microenvironment of EC, a new immunophenotype classification based on the immune microenvironment has been carried out in recent years. However, the impact of the ITME on EC remains unclear, and the immunophenotype of EC remains limited to the research stage. Our review describes recent findings regarding the ITME features of different EC molecular types. The advent of immunotherapy has brought hope for improved efficacy and prognosis in patients with advanced or recurrent EC. The efficacy and safety of ICIs combination therapy remains the focus of future research.

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Correlation of PD-L1 immunohistochemical expression with microsatellite instability and p53 status in endometrial carcinoma

Cited by 3 publications

References 31 publications

PD-L1 expression in tumor and inflammatory cells is associated with favorable tumor features and favorable prognosis in muscle-invasive urothelial carcinoma of the bladder not treated by immune checkpoint inhibitors

PD-L1 expression in tumor and inflammatory cells is associated with favorable tumor features and favorable prognosis in muscle-invasive urothelial carcinoma of the bladder not treated by immune checkpoint inhibitors

A prediction model based on deep learning and radiomics features of DWI for the assessment of microsatellite instability in endometrial cancer

Features of the immunosuppressive tumor microenvironment in endometrial cancer based on molecular subtype

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