Subtype Of Squamous Cell Carcinoma Research Articles

Proportion of CD44+ Subset of Tumour Cells in Single Cell Suspension Prepared from FFPET Sections Directly Correlates with Histological Subtyping of Head and Neck Squamous Cell Carcinoma

CD44 expression in tumours imparts potential to progress, metastasize, recurrence, and resistance against antineoplastic therapy. In this study, we sought to describe the variation in the immuno-expression and numeration of MDR1+ and CD44+ potential cancer stem cells in different histological grades and subtypes of head and neck squamous cell carcinoma (HNSCC). Flow-cytometric analysis was performed on single cell suspension prepared from formalin fixed paraffin embedded tissue (FFPET) sections of HNSCC using anti-CD44 and anti-MDR1/ABCB-1 primary monoclonal antibodies. Immunohistochemical (IHC) staining was also carried out using both of these antibodies on HNSCC tissue sections mounted on super frosted glass slides. On immunohistochemical analysis, the mean IRS for CD44 and MDR1 were 8.6364 ±3.02114 and 1.5909 ±1.27674 respectively. When mean immune-expression scores of CD44 antibody and MDR1/ABCB-1 were compared with histological grades and subtypes of HNSCC, the relationship was found to be statistically insignificant. Interestingly, a strong statistical difference (p = 0.000) was observed when the mean score of subset of dysplastic squamous epithelial cells with characteristics of cell stemness (CD326+CD44+) was compared among different histological subtypes of HNSCC using flowcytometric analysis. While no statistically significant association was observed when the mean score for subset of dysplastic cells with potential of drug resistance (CD44+MDR1+) was compared among different histological subtypes of HNSCC. Although potential cancer stem cell marker CD44 and the multidrug resistance maker ABCB-1/MDR1 co-expressed in HNSCC but the proportion of CD326+CD44+ subset of tumour cells (potential cancer stem cells/CSCs) significantly correlates with least aggressive to more aggressive tumour subtypes.

Mapping Mitochondrial Diversity in NSCLC.

A recent study shows that mitochondrial networks in the two main subtypes of non-small cell lung cancer, adenocarcinoma and squamous cell carcinoma, are structurally and functionally distinct. This difference could impact therapeutic targeting of cancer metabolism, a complex field that has been slow to bear fruit.

Abstract 2049: Clinically and biologically distinct molecular subtypes of lung squamous cell carcinoma (LUSC)

Abstract Background: Lung cancer is the leading cause of cancer death worldwide, with LUSC accounting for a third of cases. Stage I and II LUSC is typically treated with resection, and decisions related to adjuvant chemotherapy are related to recurrence risk, which is a function of stage. However, disease stage does not fully capture tumor biology, which may be a more important determinant of recurrence risk. Our objective was to identify biologically significant molecular subgroups of LUSC that more accurately reflect recurrence risk. Methods: Transcriptomic data for resected stage I/II LUSC were obtained from The Cancer Genome Atlas (TCGA). A training set consisting of patients who underwent resection without adjuvant chemotherapy or radiotherapy was used for discovery (N=161). A proprietary machine learning algorithm (HighLifeR™) was used to identify the genes most associated with disease-free survival (DFS). Molecular subgroups were identified by unsupervised clustering of prognostic genes. Functional differences between molecular subgroups were identified by gene set enrichment analysis (GSEA) and Ingenuity Pathway Analysis (IPA). Results: HighLifeR™ identified 60 highly prognostic genes. Two molecular subgroups were identified with significantly different median DFS: a high-risk group with 59.300 months and hazard ratio of 11.551, and a low-risk group that did not reach 50% DFS (log-rank p = 1.96 x 10-5). These subgroups did not differ in age, sex, race, smoking status, or overall stage (p > 0.05). Importantly, univariate and multivariate Cox regression analysis showed that the molecular subgroups outperformed clinical stage in predicting DFS. The molecular subgroups were biologically distinct. On GSEA, the high-risk group was significantly enriched in genes involved in mitotic spindle assembly (NES=1.94, p<0.001); TGFα signaling and PI3K/AKT/mTOR signaling were modestly enriched. IPA pathway analysis demonstrated significant enrichment of numerous pathways linked to neuronal growth and signaling (eg: CREB signaling, S100 signaling, pathways in myelination and synaptogenesis), as well as TGFα signaling and AMPK signaling (all p<0.001). Conclusions: Our prognostic transcriptomic signature identified two biologically distinct molecular subgroups of LUSC. Molecular subgroup classification was more predictive of DFS in stage I and II LUSC than any other clinical or pathological variable. If further validation confirms this, then this biomarker may form the basis of a diagnostic test that helps inform which patients could be considered for adjuvant chemotherapy. Citation Format: Ashar Siddiqui, Cynthia Stretch, Farshad Farshidfar, Oliver F. Bathe. Clinically and biologically distinct molecular subtypes of lung squamous cell carcinoma (LUSC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2049.

Abstract 4627: Immune profiling of immunotherapy and adjuvant chemotherapy pretreatment NSCLC tissues by CODEX

Abstract Non-small cell lung cancer (NSCLC), including adenocarcinoma and squamous cell carcinoma subtypes, is a leading cause of cancer deaths worldwide. Treatment of NSCLC has advanced from chemotherapy modalities to the use of immunotherapy, namely immune checkpoint inhibitors (ICIs) which enhance the adaptive immune response against tumour cells. Thus, while the immune cell composition of tumours and its influence on treatment outcomes is poorly understood, it likely holds the key to effective, personalized treatment regimens. Here we profiled an adjuvant chemotherapy (n=61) as well as a second line immunotherapy (n=42) NSCLC cohort by the Phenocycler CODEX technology (highplex spatial proteomics) to investigate the association between immune composition and patient outcome. We applied a panel of 38 markers to delineate naïve, memory, cytotoxic and hyperactivated T cell states, as well as B cells, Tregs and myeloid lineage innate immune cell types. Our study sought to understand the heterogeneity of tumour-immune composition across patients and investigate the spatial neighbourhoods and clusters that these cells inhabit within TMA cores. We used Phenoplex™ software for tissue segmentation (into classes for tumor, stroma, artifacts, blood vessels, etc.), cellular segmentation, and cellular phenotyping based on thresholds for each marker, and then performed spatial analyses (distances, interactions, neighborhoods) using SpatialMap1 to identify cellular motifs associated with clinical phenotypes. Our study has identified spatial features associated with immune contexture linked to therapy outcome in both chemotherapy and immunotherapy modalities. Taken together, our study demonstrates the utility of spatial proteomics to identify cellular features associated with outcome to therapy in lung cancer. 1) Trevino A, Ivison G, Hamel S, Chiou A (2022). SpatialMap: Analysis of Spatial Biology Data. R package version 0.4.57. Citation Format: James Monkman, Afshin Moradi, Connor O'Leary, Zhenqin Wu, Steven Hamel, David Mason, Fabian Schneider, James Robert Mansfield, Aaron Meyer, Ken O'Byrne, Arutha Kulasinghe. Immune profiling of immunotherapy and adjuvant chemotherapy pretreatment NSCLC tissues by CODEX. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4627.

MSMFN: An Ultrasound Based Multi-Step Modality Fusion Network for Identifying the Histologic Subtypes of Metastatic Cervical Lymphadenopathy.

Identifying squamous cell carcinoma and adenocarcinoma subtypes of metastatic cervical lymphadenopathy (CLA) is critical for localizing the primary lesion and initiating timely therapy. B-mode ultrasound (BUS), color Doppler flow imaging (CDFI), ultrasound elastography (UE) and dynamic contrast-enhanced ultrasound provide effective tools for identification but synthesis of modality information is a challenge for clinicians. Therefore, based on deep learning, rationally fusing these modalities with clinical information to personalize the classification of metastatic CLA requires new explorations. In this paper, we propose Multi-step Modality Fusion Network (MSMFN) for multi-modal ultrasound fusion to identify histological subtypes of metastatic CLA. MSMFN can mine the unique features of each modality and fuse them in a hierarchical three-step process. Specifically, first, under the guidance of high-level BUS semantic feature maps, information in CDFI and UE is extracted by modality interaction, and the static imaging feature vector is obtained. Then, a self-supervised feature orthogonalization loss is introduced to help learn modality heterogeneity features while maintaining maximal task-consistent category distinguishability of modalities. Finally, six encoded clinical information are utilized to avoid prediction bias and improve prediction ability further. Our three-fold cross-validation experiments demonstrate that our method surpasses clinicians and other multi-modal fusion methods with an accuracy of 80.06%, a true-positive rate of 81.81%, and a true-negative rate of 80.00%. Our network provides a multi-modal ultrasound fusion framework that considers prior clinical knowledge and modality-specific characteristics. Our code will be available at: https://github.com/RichardSunnyMeng/MSMFN.

Genes transcriptional activity features in different histological subtypes of tongue squamous cell carcinoma

Introduction. Over the past decade, tongue cancer has maintained a leading position in the overall structure of the incidence of head and neck malignant tumors. Squamous cell carcinoma of the tongue is an aggressive form and has a clinically unpredictable prognosis. Currently, there are several histological subtypes of this disease. And the search for new prognostic factors that could reflect the actual state of tumor progression and give an objective prognosis of disease development is an important research area in molecular oncology. Such factors may be certain transcriptomic characteristics of tumors, which determine the features of pathogenesis in each specific case.Aim. To research genes transcriptional activity features in various histological subtypes of tongue squamous cell carcinoma using bioinformatic and molecular approaches.Materials and methods. The stage of screening bioinformatics analysis was performed using an interactive web server for analyzing data on messenger RNA expression of 9736 tumors and 8587 normal samples from the The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects using a standard processing pipeline (GEPIA). The main (validation) stage of the study was performed on 300 patients with locally advanced malignant tumors of the tongue. The quantitative real-time polymerase chain reaction method was used to determine the values of the relative expression of genes identified at the stage of bioinformatic analysis.Results. Bioinformatic analysis identified 1488 genes that increase expression and 589 genes that decrease expression in tongue squamous cell carcinoma. Of these 2077 genes, 23 genetic loci were selected that most strongly alter expression in tumor tissue relative to normal tissue of the tongue. Of these, when validated by polymerase chain reaction, only 14 changed their transcriptional profile in tumor tissue relative to normal: MMP1, MMP11, CA9, PTHLH, MMP9, LAMC2, MMP3, ANXA1, MT-ND6, CRNN, MAL, TGM3, IL1RN and CLU. The analysis of polymerase chain reaction data revealed significant heterogeneity in a number of biological samples studied. Cluster analysis made it possible to divide the total sample of 300 patients into 3 groups differing in gene expression: cluster 1 (n = 90), cluster 2 (n = 101) and cluster 3 (n = 109), corresponding to the basaloid, acantholytic and usual histological subtypes. Thus, the study made it possible to identify a number of molecular markers of tongue squamous cell carcinoma (MMP1, MMP11, CA9, PTHLH, MMP9, LAMC2, MMP3, ANXA1, MT-ND6, CRNN, MAL, TGM3, IL1RN and CLU), as well as to reveal the transcriptional features of various histological subtypes of this disease.

Development and validation of a nomogram model for lung cancer based on radiomics artificial intelligence score and clinical blood test data.

Artificial intelligence (AI) discrimination models using single radioactive variables in recognition algorithms of lung nodules cannot predict lung cancer accurately. Hence, we developed a clinical model that combines AI with blood test variables to predict lung cancer. Between 2018 and 2021, 584 individuals (358 patients with lung cancer and 226 individuals with lung nodules other than cancer as control) were enrolled prospectively. Machine learning algorithms including lasso regression and random forest (RF) were used to select variables from blood test data, Logistic regression analysis was used to reconfirm the features to build the nomogram model. The predictive performance was assessed by performing the receiver operating characteristic (ROC) curve analysis as well as calibration, clinical decision and impact curves. A cohort of 48 patients was used to independently validate the model. The subgroup application was analyzed by pathological diagnosis. A total of 584 patients were enrolled (358 lung cancers, 61.30%,226 patients for the control group) to establish the model. The integrated model identified eight potential factors including carcinoembryonic antigen (CEA), AI score, Pro-Gastrin Releasing Peptide (ProGRP), cytokeratin 19 fragment antigen21-1(CYFRA211), squamous cell carcinoma antigen(SCC), indirect bilirubin(IBIL), activated partial thromboplastin time(APTT) and age. The area under the curve (AUC) of the nomogram was 0.907 (95% CI, 0.881-0.929). The decision and clinical impact curves showed good predictive accuracy of the model. An AUC of 0.844 (95% CI, 0.710 - 0.932) was obtained for the external validation group. The nomogram model integrating AI and clinical data can accurately predict lung cancer, especially for the squamous cell carcinoma subtype.

Recent Advances in the Management of Small Cell Carcinoma of the Uterine Cervix.

Small cell neuroendocrine carcinoma of the cervix (SCNEC) is a rare disease characterized by a higher incidence of lymphatic invasion, metastasis, and recurrence compared to the squamous cell carcinoma and adenocarcinoma subtypes. Furthermore, it is prone to early distant metastasis and has a poor prognosis. Chemotherapy has an important role in the management of cervical SCNEC. The effective treatment schemes for early-stage SCNEC are local treatment with radical surgery and systemic chemotherapy. However no standard treatment regimen exists because of a rare disease. We reviewed previous reports to determine whether etoposide/platinum, which is used for histopathologically similar small cell carcinoma of the lung, is an appropriate initial chemotherapy regimen for SCNEC of the cervix. In this review the Cochrane library sources, ClinicalTrials.gov, Web of Silence, PubMed and search engine of Google scholar were searched for all interventional studies, reviews, case reports and meta-analyses published in 1997-2021. Etoposide/platinum (EP) is the most commonly used regimen and paclitaxel/carboplatin is the second most common, used as a part of multimodality therapy for SCNEC of the cervix in most studies. Cisplatin/vincristine/bleomycin, cisplatin/irinotecan, cisplatin/ifosfamide/etoposide were also reported in concurrence with EP; however no clinical trials are dedicated to SCNEC. Etoposide and platinum tend to have a better prognosis compared to other regimens used for other subtypes of cervical cancer. For recurrent cervical SCNEC, treatment options for patients are very limited. The application of molecular testing for targeted mutations may suggest potential future therapies that may be useful in this disease.

Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?

The aim of this study was to investigate the usefulness of radiomics in the absence of well-defined standard guidelines. Specifically, we extracted radiomics features from multicenter computed tomography (CT) images to differentiate between the four histopathological subtypes of non-small-cell lung carcinoma (NSCLC). In addition, the results that varied with the radiomics model were compared. We investigated the presence of the batch effects and the impact of feature harmonization on the models' performance. Moreover, the question on how the training dataset composition influenced the selected feature subsets and, consequently, the model's performance was also investigated. Therefore, through combining data from the two publicly available datasets, this study involves a total of 152 squamous cell carcinoma (SCC), 106 large cell carcinoma (LCC), 150 adenocarcinoma (ADC), and 58 no other specified (NOS). Through the matRadiomics tool, which is an example of Image Biomarker Standardization Initiative (IBSI) compliant software, 1781 radiomics features were extracted from each of the malignant lesions that were identified in CT images. After batch analysis and feature harmonization, which were based on the ComBat tool and were integrated in matRadiomics, the datasets (the harmonized and the non-harmonized) were given as an input to a machine learning modeling pipeline. The following steps were articulated: (i) training-set/test-set splitting (80/20); (ii) a Kruskal-Wallis analysis and LASSO linear regression for the feature selection; (iii) model training; (iv) a model validation and hyperparameter optimization; and (v) model testing. Model optimization consisted of a 5-fold cross-validated Bayesian optimization, repeated ten times (inner loop). The whole pipeline was repeated 10 times (outer loop) with six different machine learning classification algorithms. Moreover, the stability of the feature selection was evaluated. Results showed that the batch effects were present even if the voxels were resampled to an isotropic form and whether feature harmonization correctly removed them, even though the models' performances decreased. Moreover, the results showed that a low accuracy (61.41%) was reached when differentiating between the four subtypes, even though a high average area under curve (AUC) was reached (0.831). Further, a NOS subtype was classified as almost completely correct (true positive rate ~90%). The accuracy increased (77.25%) when only the SCC and ADC subtypes were considered, as well as when a high AUC (0.821) was obtained-although harmonization decreased the accuracy to 58%. Moreover, the features that contributed the most to models' performance were those extracted from wavelet decomposed and Laplacian of Gaussian (LoG) filtered images and they belonged to the texture feature class.. In conclusion, we showed that our multicenter data were affected by batch effects, that they could significantly alter the models' performance, and that feature harmonization correctly removed them. Although wavelet features seemed to be the most informative features, an absolute subset could not be identified since it changed depending on the training/testing splitting. Moreover, performance was influenced by the chosen dataset and by the machine learning methods, which could reach a high accuracy in binary classification tasks, but could underperform in multiclass problems. It is, therefore, essential that the scientific community propose a more systematic radiomics approach, focusing on multicenter studies, with clear and solid guidelines to facilitate the translation of radiomics to clinical practice.

Human papilomaviru-related oropharyngeal squamous cell carcinoma and radiomics: A new era?

The increase of the incidence of human papillomavirus dependent oropharyngeal squamous cell carcinoma is alarming, although we have greatly progressed in the classification and staging of this disease. We now know that human papillomavirus related oropharyngeal squamous cell carcinoma is a sub-type of head and neck squamous cell carcinoma with favourable prognosis and good response to therapy that needs a proper system of classification and staging. Thus, in routine practice it is essential to test patients for the presence of human papillomavirus. The most popular technique to assess human papillomavirus status is immunohistochemistry on biopsy samples with p16, which is an excellent surrogate for high-risk human papillomavirus infection. Another highly sensitive and specific tissue-based technique for the detection of human papillomavirus is RNAscope In situ hybridization that has a prohibitive cost, limiting its use in routine practice. Radiomics is an artificial intelligence based non-invasive method of computational analysis of computed tomography, magnetic resonance imaging, positron emission tomography, and ultrasound images. In this review, we summarise the last findings of radiomics applied to human papillomavirus associated oropharyngeal squamous cell carcinoma. A growing body of evidence suggest that radiomics is able to characterise and detect early relapse after treatment, and enable development of tailored therapy of human papillomavirus positive oropharyngeal squamous cell carcinoma.

Tumor-Stroma Ratio in Basaloid and Conventional Laryngeal Squamous Cell Carcinoma: Prognostic Significance and Concordance in Paired Biopsies and Surgical Samples

Basaloid squamous cell carcinoma (BSCC) is a subtype of squamous cell carcinoma (SCC) associated with a poor prognosis. Tumor-stroma ratio (TSR) has been introduced as a prognostic feature in many solid tumors. TSR was investigated in a series of laryngeal BSCCs and compared with a group of stage-matched conventional SCCs (cSCCs), in both preoperative and surgical specimens, with the intent of ascertaining the more aggressive behavior of BSCC and verifying the presence of stromal-related causes. A series of 14 consecutive laryngeal BSCCs and a control group of 28 stage-matched conventional cSCCs were analyzed. A higher nodal metastasis presence was found in BSCCs (57.1% vs. 28.6%). The recurrence rate was 33.5% and 63.6% in the cSCC and BSCC groups; disease-free survival (DFS) was higher, though not significantly, in patients with cSCC. TSR, large cell nests, and tumor budding showed a moderate to very good agreement, and stroma type a good to very good agreement between biopsies and surgical specimens in the cSCC group. In the BSCC group, agreement was poor to very good for TSR and stroma type, and good to very good for large cell nests and tumor budding. Age was the only feature significant in predicting recurrence in the BSCC group (p = 0.0235). In cSCC, TSR low/stroma rich cases, when evaluated on biopsies or surgical specimens, were associated with lower DFS (p = 0.0036; p = 0.0041, respectively). Laryngeal BSCCs showed a lower DFS than cSCCs, even if statistical significance was not reached. TSR, evaluated in laryngeal biopsies and excised tumors, was prognostic in terms of DFS in cSCC but not in BSCC cases.

Enhancer RNA IRS2e is essential for IRS2 expression and the oncogenic properties in oral squamous cell carcinoma.

The crucial roles of enhancer RNAs (eRNAs) in the regulation of gene expression in human diseases have drawn wider and wider attention in recent years. However, the specific expression profile and function of eRNAs are still rarely discussed in oral squamous cell carcinoma (OSCC), the most common subtype of head and neck squamous cell carcinoma (HNSC). In this study, we aimed to investigate the expression and function of an uncharacterized eRNA, insulin receptor substrate 2 enhancer RNA (IRS2e), in OSCC. We found that IRS2e was overexpressed in HNSC and its overexpression was positively correlated with a poor prognosis. The downregulation of IRS2e by short hairpin RNA significantly inhibited cell growth and induced cellular apoptosis and cell-cycle arrest in OSCC cells. Furthermore, the ablation of IRS2e inhibited tumor growth in vivo. Mechanically, IRS2e is essential for the expression of insulin receptor substrate 2 (IRS2), an oncogene nearby IRS2e in chromosome 13. Altogether, our study demonstrated that IRS2e is a novel oncogenic eRNA required for oncogene IRS2 expression in OSCC.

Programmed death ligand-1 expression and its association with the degree of differentiation and the presence of necrosis in non-small cell lung carcinoma

The mechanisms underlying the expression of programmed death ligand-1 (PD-L1) in non-small cell lung carcinoma (NSCLC) are not yet fully clarified. In this study, surgical resections of 730 lung cancer patients with diagnosed NSCLC were analyzed. Results of PD-L1 immunohistochemistry (using clone 22C3) were correlated with clinicopathological variables including the degree of tumor differentiation and the presence of confluent areas of coagulative necrosis. PD-L1 immunohistochemistry was analyzed in tumor cells, whereas PD-L1 positivity was defined as membranous staining in ≥1 of tumor cells. A significantly higher proportion of PD-L1 positive cases was noted in poorly differentiated (grade 3) adenocarcinomas compared to better differentiated (grade 1 and grade 2) subtypes (63.8% vs. 28.7%; p<0.001). Contrary to this, better differentiated (keratinizing) and less differentiated (non-keratinizing) squamous cell carcinoma subtypes were found to have a similar proportion of PD-L1 positive cases (51.4% vs. 55.8%; p=0.570). High levels of PD-L1 expression significantly correlated with the presence of necrosis in NSCLC: seventy-nine of 109 NSCLC cases with the presence of necrosis were PD-L1 positive compared to 256 out of 621 NSCLC without necrosis (72.5% vs. 41.2%; p<0.001). High PD-L1 expression was not positively correlated with age, gender, and advanced T stage but a significant association between PD-L1 positivity and higher N stage was observed (p<0.001) in NSCLC patients. In conclusion, the proportion of PD-L1 positive cases is higher only in poorly differentiated NSCLC of the adenocarcinoma type. A significantly higher overall rate of PD-L1 positive cases was noted in NSCLC with the presence of necrosis. Further investigation is suggested to elucidate the intricated interconnections between the plethora of hypoxic biomarkers and immunological factors in different types and subtypes of NSCLC.

A novel pyroptosis scoring model was associated with the prognosis and immune microenvironment of esophageal squamous cell carcinoma.

The phenotype of pyroptosis has been extensively studied in a variety of tumors, but the relationship between pyroptosis and esophageal squamous cell carcinoma (ESCC) remains unclear. Here, 22 pyroptosis genes were downloaded from the website of Gene Set Enrichment Analysis (GSEA), 79 esophageal squamous cell carcinoma samples and GSE53625 containing 179 pairs of esophageal squamous cell carcinoma samples were collected from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), respectively. Then, pyroptosis subtypes of esophageal squamous cell carcinoma were obtained by cluster analysis according to the expression difference of pyroptosis genes, and a pyroptosis scoring model was constructed by the pyroptosis-related genes screened from different pyroptosis subtypes. Time-dependent receiver operator characteristic (timeROC) curves and the area under the curve (AUC) values were used to evaluate the prognostic predictive accuracy of the pyroptosis scoring model. Kaplan-Meier method with log-rank test were conducted to analyze the impact of the pyroptosis scoring model on overall survival (OS) of patients with esophageal squamous cell carcinoma. Nomogram models and calibration curves were used to further confirm the effect of the pyroptosis scoring model on prognosis. Meanwhile, CIBERSORTx and ESTIMATE algorithm were applied to calculate the influence of the pyroptosis scoring model on esophageal squamous cell carcinoma immune microenvironment. Our findings revealed that the pyroptosis scoring model established by the pyroptosis-related genes was associated with the prognosis and immune microenvironment of esophageal squamous cell carcinoma, which can be used as a biomarker to predict the prognosis and act as a potential target for the treatment of esophageal squamous cell carcinoma.

Squamous Cell Carcinoma Arising in Lesions of Discoid Lupus Erythematosus in a Patient with Overlap Syndrome: A Chronic Rare Complication.

Squamous Cell Carcinoma Arising in Lesions of Discoid Lupus Erythematosus in a Patient with Overlap Syndrome: A Chronic Rare Complication.

Metaplastic Breast Carcinoma (MBC), Primary Squamous Cell Carcinoma Subtype in A 49-Year-Old Woman: A Case Report

Introduction: Metaplastic Breast Carcinoma (MBC) is quite a challenging case because metaplastic breast cancer is one of the rarest subtypes of invasive breast cancer. It is reported that MBC occurs only 0.2 to 1% throughout the world. The metaplastic changes can be squamous cells or other mesenchymal cell types. Clinically, MBC presents as a large palpable mass and may be associated with rapid growth. The size of MBC tends to be larger compared with other types of invasive breast cancer ranging from 1 to more than 10 cm. Although there are several main categories of MBC, some carcinoma can be difficult to classify due to their unusual histologic patterns. This case report study is to provide a clinicopathological overview and approach to MBC.Case Presentation: We reported a 49-year-old woman who suffered from a breast mass that rapidly grew for less than one year. The microscopic findings showed squamous cell carcinoma. While molecular studies revealed triple negative results for hormone receptors although Human Epidermal Growth Factor Receptor 2 (HER2) overexpression was unusual (&lt; 5%). Then, we confirmed with chromogen in situ hybridization (CISH) and there was no gen amplification for HER2. Microscopically, we found ductal carcinoma in situ and this finding supported breast origin.Conclusions: Metaplastic carcinoma did not have any specific and distinctive signs clinically. Metaplastic carcinoma can be monophasic (with only a metaplastic component) or biphasic with two or more components. As treatment options, our patient received conventional chemotherapy. Metaplastic breast cancer is reported to have a lower response rate to conventional adjuvant chemotherapy and worse clinical outcome after chemotherapy than other forms of triple-negative breast cancer.

Integrated molecular characterization of esophageal basaloid squamous cell carcinoma: a subtype with distinct RNA expression pattern and immune characteristics, but no specific genetic mutations.

Esophageal basaloid squamous cell carcinoma (bSCC) is a subtype of squamous cell carcinoma (SCC) with a different behavior and poor prognosis. Exploring bSCC's molecular characteristics and treatment strategies are of great clinical significance. We performed multi-omics analysis of paired bSCC and common SCC (cSCC) using whole exome sequencing and a NanoString nCounter gene expression panel. Immunohistochemistry was used for verification of candidate biomarkers. Different treatment response was analyzed on both patients receiving neoadjuvant treatment and late-stage patients. The common genetically-clonal origin of bSCC and cSCC was confirmed. No significant differences between their genetic alterations or mutation spectra were observed. Mutation signature 15 (associated with defective DNA damage repair) was less prominent, and tumor mutational burden (TMB) was lower in bSCC. bSCC with an RNA expression pattern resembling cSCC had a better survival than other bSCCs. Moreover, bSCC showed significant upregulation of expression of genes associated with angiogenesis response, basement membranes, and epithelial-mesenchymal transition, and downregulation of KRT14 (squamous differentiation) and CCL21 (associated with immune response). Immunohistochemistry for SFRP1 was shown to be highly sensitive and specific for bSCC diagnosis (p < 0.001). In addition, bSCC receiving neoadjuvant immuno-chemotherapy had a worse pathological response than bSCC receiving neoadjuvant chemotherapy (but without statistical significance), even in bSCC positive for PD-L1. Our results demonstrated the molecular characteristics of esophageal bSCC as a subtype with a distinct RNA expression pattern and immune characteristics, but no specific genetic mutations. We provided a useful biomarker, SFRP1, for diagnosis. After outcome analysis for six bSCCs with neoadjuvant immunotherapy treatment and four late-stage bSCCs with immunotherapy, we found that immunotherapy may not be an effective treatment option for most bSCCs. This may also provide a clue for the same subtypes of lung and head and neck cancer. Our study highlighted the heterogeneity among bSCC patients, and might explain the conflicting results of bSCC outcomes in existing studies. © 2022 The Pathological Society of Great Britain and Ireland.

Deep-Learning Algorithm and Concomitant Biomarker Identification for NSCLC Prediction Using Multi-Omics Data Integration.

Early diagnosis of lung cancer to increase the survival rate, which is currently at a low range of mid-30%, remains a critical need. Despite this, multi-omics data have rarely been applied to non-small-cell lung cancer (NSCLC) diagnosis. We developed a multi-omics data-affinitive artificial intelligence algorithm based on the graph convolutional network that integrates mRNA expression, DNA methylation, and DNA sequencing data. This NSCLC prediction model achieved a 93.7% macro F1-score, indicating that values for false positives and negatives were substantially low, which is desirable for accurate classification. Gene ontology enrichment and pathway analysis of features revealed that two major subtypes of NSCLC, lung adenocarcinoma and lung squamous cell carcinoma, have both specific and common GO biological processes. Numerous biomarkers (i.e., microRNA, long non-coding RNA, differentially methylated regions) were newly identified, whereas some biomarkers were consistent with previous findings in NSCLC (e.g., SPRR1B). Thus, using multi-omics data integration, we developed a promising cancer prediction algorithm.

A narrative review on machine learning in diagnosis and prognosis prediction for tongue squamous cell carcinoma.

Tongue squamous cell carcinoma (TSCC) is the most common subtype of oral cavity squamous cell carcinoma (OCSCC), and it also has the worst prognosis. It is crucial to find an effective way to solve the challenges in diagnosis and prognosis prediction for TSCC. Machine learning (ML) has been widely used in medical research and has shown good performance. It can be used for feature extraction, feature selection, model construction, etc. Radiomics and deep learning (DL), the new components of ML, have also been utilized to explore the relationship between image features and diseases. The current study aimed to highlight the importance of ML as a potential method for addressing the challenges in diagnosis and prognosis prediction of TSCC by reviewing studies on ML in TSCC. The studies on ML in TSCC in PubMed, Scopus, Web of Science, and China National Knowledge Infrastructure published between the dates of inception of these databases and April 30, 2022, were reviewed. ML (including radiomics and DL) which was used in diagnosis and prognosis prediction for TSCC, has shown promising performance. Despite its limitations, ML is still a potential approach that can help to deal with the challenges in diagnosis and prognosis prediction for TSCC. Nevertheless, more efforts are needed to enhance the usefulness of ML in this field.

Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung.

Based on pretherapy dual-energy computed tomography (DECT) images, we developed and validated a nomogram combined with clinical parameters and radiomic features to predict the pathologic subtypes of non-small cell lung cancer (NSCLC) - adenocarcinoma (ADC) and squamous cell carcinoma (SCC). A total of 129 pathologically confirmed NSCLC patients treated at the Second Affiliated Hospital of Nanchang University from October 2017 to October 2021 were retrospectively analyzed. Patients were randomly divided in a ratio of 7:3 (n=90) into training and validation cohorts (n=39). Patients' pretherapy clinical parameters were recorded. Radiomics features of the primary lesion were extracted from two sets of monoenergetic images (40 keV and 100 keV) in arterial phases (AP) and venous phases (VP). Features were selected successively through the intra-class correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was then performed to establish predictive models. The prediction performance between models was evaluated and compared using the receiver operating characteristic (ROC) curve, DeLong test, and Akaike information criterion (AIC). A nomogram was developed based on the model with the best predictive performance to evaluate its calibration and clinical utility. A total of 87 ADC and 42 SCC patients were enrolled in this study. Among the five constructed models, the integrative model (AUC: Model 4 = 0.92, Model 5 = 0.93) combining clinical parameters and radiomic features had a higher AUC than the individual clinical models or radiomic models (AUC: Model 1 = 0.84, Model 2 = 0.79, Model 3 = 0.84). The combined clinical-venous phase radiomics model had the best predictive performance, goodness of fit, and parsimony; the area under the ROC curve (AUC) of the training and validation cohorts was 0.93 and 0.90, respectively, and the AIC value was 60.16. Then, this model was visualized as a nomogram. The calibration curves demonstrated it's good calibration, and decision curve analysis (DCA) proved its clinical utility. The combined clinical-radiomics model based on pretherapy DECT showed good performance in distinguishing ADC and SCC of the lung. The nomogram constructed based on the best-performing combined clinical-venous phase radiomics model provides a relatively accurate, convenient and noninvasive method for predicting the pathological subtypes of ADC and SCC in NSCLC.