Review of the Tumor Microenvironment in Basal and Squamous Cell Carcinoma

Chiang, Elizabeth; Stafford, Haleigh; Buell, Jane; Ramesh, Uma; Amit, Moran; Nagarajan, Priyadharsini; Migden, Michael R.; Yaniv, Dan

doi:10.3390/cancers15092453

Cited by 4 publications

(13 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other solid tumors in which TAMs have a crucial role in mediating tumor progression and immune escape are neuroblastoma (NB), bladder cancer, renal carcinoma, gastric cancer (GC), basal cell carcinoma (BCC), oral squamous cell carcinoma (OSCC), prostate cancer (PCa), nasopharyngeal carcinoma (NPC), multiple myeloma (MM), sarcoma, pancreatic cancer (PDA) and lung cancer [148][149][150][151][152][153][154][155][156][157][158][159][160][161][162][163]. In addition, it is known that in these tumors TAMs correlate with worse prognosis and drug resistance [164].…”

Section: Other Solid Tumorsmentioning

confidence: 99%

See 1 more Smart Citation

NF-κB: Governing Macrophages in Cancer

Cornice,

Verzella,

Arboretto

et al. 2024

Genes

View full text Add to dashboard Cite

Tumor-associated macrophages (TAMs) are the major component of the tumor microenvironment (TME), where they sustain tumor progression and or-tumor immunity. Due to their plasticity, macrophages can exhibit anti- or pro-tumor functions through the expression of different gene sets leading to distinct macrophage phenotypes: M1-like or pro-inflammatory and M2-like or anti-inflammatory. NF-κB transcription factors are central regulators of TAMs in cancers, where they often drive macrophage polarization toward an M2-like phenotype. Therefore, the NF-κB pathway is an attractive therapeutic target for cancer immunotherapy in a wide range of human tumors. Hence, targeting NF-κB pathway in the myeloid compartment is a potential clinical strategy to overcome microenvironment-induced immunosuppression and increase anti-tumor immunity. In this review, we discuss the role of NF-κB as a key driver of macrophage functions in tumors as well as the principal strategies to overcome tumor immunosuppression by targeting the NF-κB pathway.

show abstract

Section: Other Solid Tumorsmentioning

confidence: 99%

“…BCC is the most common skin cancer with a complicated pathogenesis [160]. In aggressive (micronodular) BCC, increased tumor invasion and angiogenesis correlates with the number of TAMs [171].…”

Section: Other Solid Tumorsmentioning

confidence: 99%

NF-κB: Governing Macrophages in Cancer

Cornice,

Verzella,

Arboretto

et al. 2024

Genes

View full text Add to dashboard Cite

show abstract

“…These differences including the neoplastic epithelial cells, the tumor microenvironment (TME) and their cross talk are thought to play a critical role in cancer development and its consequent prognosis. 6 Therefore, characterization of the tumor cytology such as nuclear pleomorphism and the surrounding TME may provide subtle evidence to recognize and even stratify BCCs more exactly. However, during the practice of diagnosing BCC, it is difficult for pathologists to quantify the minor difference of nuclei features of basaloid cell tumors and describe their TME signatures.…”

Section: Introductionmentioning

confidence: 99%

Differentiation and risk stratification of basal cell carcinoma with deep learning on histopathologic images and measuring nuclei and tumor microenvironment features

Lan,

Guo,

Wang

et al. 2024

Skin Research and Technology

View full text Add to dashboard Cite

BackgroundNuclear pleomorphism and tumor microenvironment (TME) play a critical role in cancer development and progression. Identifying most predictive nuclei and TME features of basal cell carcinoma (BCC) may provide insights into which characteristics pathologists can use to distinguish and stratify this entity.ObjectivesTo develop an automated workflow based on nuclei and TME features from basaloid cell tumor regions to differentiate BCC from trichoepithelioma (TE) and stratify BCC into high‐risk (HR) and low‐risk (LR) subtypes, and to identify the nuclear and TME characteristics profile of different basaloid cell tumors.MethodsThe deep learning systems were trained on 161 H&E ‐stained sections which contained 51 sections of HR‐BCC, 50 sections of LR‐BCC and 60 sections of TE from one institution (D1), and externally and independently validated on D2 (46 sections) and D3 (76 sections), from 2015 to 2022. 60%, 20% and 20% of D1 data were randomly splitted for training, validation and testing, respectively. The framework comprised four stages: tumor regions identification by multi‐head self‐attention (MSA) U‐Net, nuclei segmentation by HoVer‐Net, quantitative feature by handcrafted extraction, and differentiation and risk stratification classifier construction. Pixel accuracy, precision, recall, dice score, intersection over union (IoU) and area under the curve (AUC) were used to evaluate the performance of tumor segmentation model and classifiers.ResultsMSA‐U‐Net model detected tumor regions with 0.910 precision, 0.869 recall, 0.889 dice score and 0.800 IoU. The differentiation classifier achieved 0.977 ± 0.0159, 0.955 ± 0.0181, 0.885 ± 0.0237 AUC in D1, D2 and D3, respectively. The most discriminative features between BCC and TE contained Homogeneity, Elongation, T‐T_meanEdgeLength, T‐T_Nsubgraph, S‐T_HarmonicCentrality, S‐S_Degrees. The risk stratification model can well predict HR‐BCC and LR‐BCC with 0.920 ± 0.0579, 0.839 ± 0.0176, 0.825 ± 0.0153 AUC in D1, D2 and D3, respectively. The most discriminative features between HR‐BCC and LR‐BCC comprised IntensityMin, Solidity, T‐T_minEdgeLength, T‐T_Coreness, T‐T_Degrees, T‐T_Betweenness, S‐T_Degrees.ConclusionsThis framework hold potential for future use as a second opinion helping inform diagnosis of BCC, and identify nuclei and TME features related with malignancy and tumor risk stratification.

show abstract

“…It is generally accepted that the prognoses for skin cancers other than melanoma are less harmful [8]. SCCa and BCCa are the most occurring types after melanoma, but these diseases are less aggressive than melanoma [9]. Dermoscopy is a non-surgical technique employed by doctors to identify suspicious skin lesions [10].…”

Section: Introductionmentioning

confidence: 99%

SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

Naeem,

Anees,

Khalil

et al. 2024

Mathematics

View full text Add to dashboard Cite

The medical sciences are facing a major problem with the auto-detection of disease due to the fast growth in population density. Intelligent systems assist medical professionals in early disease detection and also help to provide consistent treatment that reduces the mortality rate. Skin cancer is considered to be the deadliest and most severe kind of cancer. Medical professionals utilize dermoscopy images to make a manual diagnosis of skin cancer. This method is labor-intensive and time-consuming and demands a considerable level of expertise. Automated detection methods are necessary for the early detection of skin cancer. The occurrence of hair and air bubbles in dermoscopic images affects the diagnosis of skin cancer. This research aims to classify eight different types of skin cancer, namely actinic keratosis (AKs), dermatofibroma (DFa), melanoma (MELa), basal cell carcinoma (BCCa), squamous cell carcinoma (SCCa), melanocytic nevus (MNi), vascular lesion (VASn), and benign keratosis (BKs). In this study, we propose SNC_Net, which integrates features derived from dermoscopic images through deep learning (DL) models and handcrafted (HC) feature extraction methods with the aim of improving the performance of the classifier. A convolutional neural network (CNN) is employed for classification. Dermoscopy images from the publicly accessible ISIC 2019 dataset for skin cancer detection is utilized to train and validate the model. The performance of the proposed model is compared with four baseline models, namely EfficientNetB0 (B1), MobileNetV2 (B2), DenseNet-121 (B3), and ResNet-101 (B4), and six state-of-the-art (SOTA) classifiers. With an accuracy of 97.81%, a precision of 98.31%, a recall of 97.89%, and an F1 score of 98.10%, the proposed model outperformed the SOTA classifiers as well as the four baseline models. Moreover, an Ablation study is also performed on the proposed method to validate its performance. The proposed method therefore assists dermatologists and other medical professionals in early skin cancer detection.

show abstract

Review of the Tumor Microenvironment in Basal and Squamous Cell Carcinoma

Cited by 4 publications

References 62 publications

NF-κB: Governing Macrophages in Cancer

NF-κB: Governing Macrophages in Cancer

Differentiation and risk stratification of basal cell carcinoma with deep learning on histopathologic images and measuring nuclei and tumor microenvironment features

SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

Contact Info

Product

Resources

About