Subclassification Of Tumors Research Articles

Diagnostic accuracy and clinical impact of renal biopsy cytology.

The role of fine needle biopsy cytology in the workup of renal mass lesions remains controversial. With advances in imaging technology and clinical management for renal masses, a critical reevaluation of the role of renal biopsy is needed. This study was designed to provide a comprehensive evaluation of the performance and clinical impact of fine needle biopsy in patients with renal masses. A 5-year retrospective study of ultrasound or computer tomography (CT)-guided fine needle biopsies of renal masses diagnosed via cytopathology was conducted. Overall diagnostic rate, sensitivity, and diagnostic accuracy were calculated. Further analysis of the impact of fine needle biopsy cytology on clinical management was performed. A total of 227 cases of fine-needle aspiration and/or biopsy (FNA/B) of renal masses were identified, including 76 with subsequent nephrectomies. Complications were rare (<1%). The diagnostic rate and sensitivity of FNA/B were 83.3% and 89.5%, respectively. Diagnostic accuracy was 98.7% at the major categorical level and 94.7% at the tumor subtype level. Subsequent clinical actions were associated with a definitive cytologic diagnosis of malignancy/neoplasia (p < .05) and were affected by tumor subtype (p < .05). This study demonstrates that FNA/B of renal masses is a safe and reliable minimally invasive diagnostic tool with excellent accuracy in confirmation of malignancy and subclassification of tumors. Diagnoses made on FNA/B play a key role in guiding a personalized clinical treatment plan.

Immunohistochemistry Markers in Ovarian and Fallopian Tube Neoplasms: a Comprehensive Review.

Immunohistochemistry (IHC) has emerged as a crucial tool in diagnosing and managing ovarian cancer, offering invaluable insights into tumor biology and guiding therapeutic decisions. The intricate histopathological landscape of ovarian cancer presents challenges in accurate diagnosis and classification. IHC offers a complementary approach, aiding in the characterization of tumor subtypes, prognostication, and prediction of treatment response. By targeting specific biomarkers, IHC enables the identification of diverse histological features and molecular alterations associated with ovarian malignancies. The integration of IHC into routine diagnostic workflows enhances diagnostic accuracy, aids in the subclassification of ovarian tumors, and facilitates personalized treatment strategies. Emphasis is placed on the judicious selection of antibody panels tailored to specific clinical scenarios, ensuring optimal utilization of resources and minimizing diagnostic pitfalls. Overall, this review underscores the pivotal role of IHC in refining the diagnosis, prognostication, and management of ovarian cancer, highlighting its significance in the era of precision medicine. By leveraging the molecular insights provided by IHC, clinicians and pathologists can optimize patient care and improve outcomes in ovarian cancer management.

Streamlined Intraoperative Brain Tumor Classification and Molecular Subtyping in Stereotactic Biopsies Using Stimulated Raman Histology and Deep Learning.

Recent artificial intelligence algorithms aided intraoperative decision-making via stimulated Raman histology (SRH) during craniotomy. This study assesses deep learning algorithms for rapid intraoperative diagnosis from SRH images in small stereotactic-guided brain biopsies. It defines a minimum tissue sample size threshold to ensure diagnostic accuracy. A prospective single-center study examined 121 SRH images from 84 patients with unclear intracranial lesions undergoing stereotactic brain biopsy. Unprocessed, label-free samples were imaged using a portable fiber laser Raman scattering microscope. Three deep learning models were tested to (i) identify tumorous/nontumorous tissue as qualitative biopsy control; (ii) subclassify into high-grade glioma (central nervous system World Health Organization grade 4), diffuse low-grade glioma (central nervous system World Health Organization grades 2-3), metastases, lymphoma, or gliosis; and (iii) molecularly subtype IDH and 1p/19q statuses of adult-type diffuse gliomas. Model predictions were evaluated against frozen section analysis and final neuropathologic diagnoses. The first model identified tumorous/nontumorous tissue with 91.7% accuracy. Sample size on slides impacted accuracy in brain tumor subclassification (81.6%, κ = 0.72 frozen section; 73.9%, κ = 0.61 second model), with SRH images being smaller than hematoxylin and eosin images (4.1 ± 2.5 mm2 vs. 16.7 ± 8.2 mm2, P < 0.001). SRH images with more than 140 high-quality patches and a mean squeezed sample of 5.26 mm2 yielded 89.5% accuracy in subclassification and 93.9% in molecular subtyping of adult-type diffuse gliomas. Artificial intelligence-based SRH image analysis is non-inferior to frozen section analysis in detecting and subclassifying brain tumors during small stereotactic-guided biopsies once a critical squeezed sample size is reached. Beyond frozen section analysis, it enables valid molecular glioma subtyping, allowing faster treatment decisions in the future; however, refinement is needed for long-term application.

Abstract IA012: Generation and Mining of a Pediatric-focused Cancer Cell Line Atlas to Define Druggable Genetic Interactions in Childhood Malignancies

Abstract Pediatric solid and central nervous system tumors are the primary cause of cancer-related fatalities in children. Discovering novel targeted therapies requires utilizing pediatric cancer models that accurately mirror the patient's illness. However, the creation and evaluation of these models have significantly trailed adult cancer research, emphasizing the pressing demand for pediatric-centric cell line repositories. Here, we establish a centralized collection of over 450 childhood cancer cell lines. We subjected over 250 of these cell lines to comprehensive multi-omics analyses (including DNA sequencing, RNA sequencing, and DNA methylation analysis), while concurrently conducting pharmacological screenings and genetic CRISPR-Cas9 loss-of-function assays to unveil pediatric-specific treatment avenues and biomarkers. Machine learning approaches were then applied to uncover genotype-phenotype relationships and synthetic lethal interactions. Our endeavor sheds light on the specific vulnerabilities of pathways in molecularly characterized pediatric tumor subclasses and reveals clinically relevant therapeutic opportunities linked with biomarkers. We offer access to the cell line data and resources through an open-access portal. Citation Format: Ron Firestein. Generation and Mining of a Pediatric-focused Cancer Cell Line Atlas to Define Druggable Genetic Interactions in Childhood Malignancies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr IA012.

Abstract 3497: Glioma Immune Microenvironment Composition Calculator (GIMiCC): A method of estimating the proportions of eighteen key cell types from human brain tissue assayed using Illumina DNA methylation microarray technology

Abstract Precise detection of both tumor subclass and immune composition is critical in the pursuit of personalized immunotherapeutic treatment strategies for glioma. DNA methylation (DNAm) biomarkers are promising on both fronts. DNAm-based biomarkers for molecular classification have been developed and integrated into World Health Organization (WHO) diagnostic criteria. However, prediction of the tumor microenvironment is not as well defined. To fill this gap, we present GIMiCC, an approach for cellular deconvolution of DNAm microarray data for four WHO 2021 tumor subclasses: glioblastoma, oligodendroglioma, grade 2/3 astrocytoma and grade 4 astrocytoma. Using data from 17 isolated cell types, we describe the derivation of the deconvolution libraries and test the method on independent datasets from the Cancer Genome Atlas (TCGA) and the DKFZ molecular neuropathology classifier (PMID: 29539639). We utilize GIMiCC to illustrate that DNAm-based glioma classification is unlikely to be biased by compositional variation. In addition, we utilize GIMiCC to identify composition independent DNAm alterations that are associated with high immune infiltration. Our future work aims to optimize GIMiCC and advance the clinical evaluation of glioma with this new tool. Citation Format: Steven C. Pike, John K. Wiencke, Ze Zhang, Annette M. Molinaro, Devin C. Koestler, Brock C. Christensen, Karl T. Kelsey, Lucas A. Salas. Glioma Immune Microenvironment Composition Calculator (GIMiCC): A method of estimating the proportions of eighteen key cell types from human brain tissue assayed using Illumina DNA methylation microarray technology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3497.

Abstract 1767: Molecular subclassification of gastrointestinal stromal tumors by genomic backgrounds

Abstract Gastrointestinal stromal tumors (GISTs) are the most common gastrointestinal mesenchymal tumors. While about 10% of GISTs are known to be developed from the KIT or PDGFRA gain-of-function mutations, the rest, which is the majority of GISTs, is so-called as wild-type GISTs. There are attempts to make subclassification of wild-type GISTs in syndromic or non-syndromic group as promising for understanding of heterogeneity or treatment strategy. Genomic states of succinate dehydrogenase B (SDHB) and neurofibromatosis type 1 (NF1) have been raised as candidates for subclassification of wild-type GISTs. In this presentation, we will discuss our recent three cases of wild-type GISTs, carrying germline pathogenic variants in SDHB or NF1. Two cases with SDHB germline pathogenic variants showed stomach GISTs with good clinical course, the maintained performance status and long survival nevertheless of the recurrence. The other case with an NF1 germline pathogenic variant showed multiple GISTs in small intestine which was controlled surgically and the late onset of other clinical symptoms of NF1. These cases might clinically suggest potential prognostic and predictive markers in genomic alterations of SDHB or NF1 for the cases with wild-type GISTs. Citation Format: Hideki Yamamoto, Eiji Nakada, Seiji Kawano, Fumino Kato, Chika Fukano, Mashu Futagawa, Yusaku Urakawa, Risa Ohsumi, Sayaka Ueno, Hiroyuki Yanai, Akira Hirasawa. Molecular subclassification of gastrointestinal stromal tumors by genomic backgrounds [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1767.

A signature of enhanced proliferation associated with response and survival to anti-PD-L1 therapy in early-stage non-small cell lung cancer

In early-stage non-small cell lung cancer, the combination of neoadjuvant anti-PD-L1 and subablative stereotactic body radiation therapy (SBRT) is associated with higher rates of major pathologic response compared to anti-PD-L1 alone. Here, we identify a 140-gene set, enriched in genes characteristic of highly proliferating cells, associated with response to the dual therapy. Analysis of on-treatment transcriptome data indicate roles for T and B cells in response. The 140-gene set is associated with disease-free survival when applied to the combined trial arms. This 140-gene set identifies a subclass of tumors in all 7 of The Cancer Genome Atlas tumor types examined. Worse survival is associated with the 140-gene signature in 5 of these tumor types. Collectively, our data support that this 140-gene set, discovered in association with response to combined anti-PD-L1 and SBRT, identifies a clinically aggressive subclass of solid tumors that may be more likely to respond to immunotherapies.

SerpinB3/4 Expression Is Associated with Poor Prognosis in Patients with Cholangiocarcinoma.

Cholangiocarcinoma (CCA), the second most common primary liver tumor, is associated with a dismal outcome, and useful prognostic markers are not currently available in clinical practice. SerpinB3, a serine protease inhibitor, was recently found to play a relevant role in malignant transformation in different cancers. The aim of the present study was to determine the expression of SerpinB3/4 in tissue and serum samples of patients with CCA in relation to clinical outcomes. SerpinB3/4 was assessed in the tissue microarrays (TMAs) of 123 surgically resected CCAs. ELISA assays were carried out in 188 patients with CCA to detect the free and IgM-linked forms of SerpinB3/4. Overall survival was analyzed in relation to SerpinB3/4 expression, and Cox models were used to identify the variables associated with survival. High levels of SerpinB3/4 (TMA score 2+/3+) were detected in 15 tumors (12.2%), characterized by a more advanced TNM stage (III/IV: 64.3% vs. 31.3%; p = 0.031) and lower overall patient survival, independently of CCA subclass (intrahepatic CCA: median 1.1 (0.8-Not Estimable, NE) vs. 2.4 (1.8-3.4) years; p = 0.0007; extrahepatic CCA: median 0.8 (0.2-NE) vs. 2.2 (1.5-5.4) years; p = 0.011). Vascular invasion (p = 0.027) and SerpinB3/4 scores (p = 0.0016) were independently associated with mortality in multivariate analysis. Patients who had detectable free or IgM-linked SerpinB3/4 in their serum showed poorer survival (1 vs. 2.4 years, p = 0.015, for free SerpinB3/4, and 1 vs. 2.6 years, p = 0.0026, for SerpinB3/4-IgM). In conclusion, high levels of SerpinB3/4 in tissue and serum in CCA are associated with poor outcomes after surgery, regardless of tumor subclass.

Ultra-fast deep-learned CNS tumour classification during surgery

Central nervous system tumours represent one of the most lethal cancer types, particularly among children1. Primary treatment includes neurosurgical resection of the tumour, in which a delicate balance must be struck between maximizing the extent of resection and minimizing risk of neurological damage and comorbidity2,3. However, surgeons have limited knowledge of the precise tumour type prior to surgery. Current standard practice relies on preoperative imaging and intraoperative histological analysis, but these are not always conclusive and occasionally wrong. Using rapid nanopore sequencing, a sparse methylation profile can be obtained during surgery4. Here we developed Sturgeon, a patient-agnostic transfer-learned neural network, to enable molecular subclassification of central nervous system tumours based on such sparse profiles. Sturgeon delivered an accurate diagnosis within 40 minutes after starting sequencing in 45 out of 50 retrospectively sequenced samples (abstaining from diagnosis of the other 5 samples). Furthermore, we demonstrated its applicability in real time during 25 surgeries, achieving a diagnostic turnaround time of less than 90 min. Of these, 18 (72%) diagnoses were correct and 7 did not reach the required confidence threshold. We conclude that machine-learned diagnosis based on low-cost intraoperative sequencing can assist neurosurgical decision-making, potentially preventing neurological comorbidity and avoiding additional surgeries.

Management of neurofibromatosis type 1 associated tumors of central and peripheral nervous system.

In recent years emerging evidence suggests that some tumor types, extremely rare in general population and understudied, can be observed in NF1 and neoplasms related with this condition harbor peculiar genetic and epigenetic features. The aim of this review is to summarize recent advances that, delving into the tumor complexity, have identified new diagnostic tools and potential tumor subtype that may have been associated with clinical implications. The available data confirmed the presence of peculiar molecular signatures in those tumors, different from those observed in sporadic neoplasms and suggest that a specific reference to NF1 associated neoplasms would deserve to be mentioned in tumor WHO classification. Comprehensive multiomic analysis shows that the histologic assessment does not always match the methylation group assignment and facilitates tumor subclassification into categories predictive of clinical behavior. The non-invasive assessment of tumor genetic profiles by the analysis of plasma ctDNA is representative of tumor features, may help differential diagnosis and may identify malignant transformation, sparing the patient from repeated biopsies. A better knowledge of NF1 associated tumors at the molecular level may suggest changes in the clinical management of the disease and open new frontiers of personalized treatment.

Noncanonical HPV carcinogenesis drives radiosensitization of head and neck tumors

We analyzed transcriptional data from 104 HPV+ (Human papillomavirus) HNSCC (head and neck squamous cell carcinoma) tumors together with two publicly available sources to identify highly robust transcriptional programs (modules) which could be detected consistently despite heterogeneous sequencing and quantification methodologies. Among 22 modules identified, we found a single module that naturally subclassifies HPV+ HNSCC tumors based on a bimodal pattern of gene expression, clusters all atypical features of HPV+ HNSCC biology into a single subclass, and predicts patient outcome in four independent cohorts. The subclass-defining gene set was strongly correlated with Nuclear factor kappa B (NF-κB) target expression. Tumors with high expression of this NF-κB module were rarely associated with activating PIK3CA alterations or viral integration, and also expressed higher levels of HPHPV E2 and had decreased APOBEC mutagenesis. Alternatively, they harbored inactivating alterations of key regulators of NF-κB, TNF receptor associated factor 3 (TRAF3), and cylindromatosis (CYLD), as well as retinoblastoma protein (RB1). HPV+ HNSCC cells in culture with experimental depletion of TRAF3 or CYLD displayed increased expression of the subclass-defining genes, as well as robust radio-sensitization, thus recapitulating both the tumor transcriptional state and improved treatment response observed in patient data. Across all gene sets investigated, methylation to expression correlations were the strongest for the subclass-defining, NF-κB-related genes. Increased tumor-infiltrating CD4+ T cells and increased Estrogen receptors alpha (ERα) expression were identified in NF-κB active tumors. Based on the relatively high rates of cure in HPV+ HNSCC, deintensification of therapy to reduce treatment-related morbidity is being studied at many institutions. Tumor subclassification based on oncogenic subtypes may help guide the selection of therapeutic intensity or modality for patients with HPV+ HNSCC.

Distinct genetic and phenotypic characteristics of not otherwise specified (NOS) melanoma.

e21571 Background: We previously reported on the biological relevance of melanoma histologic subtypes (Lattanzi et al, JNCI 2019). However, a substantial percentage of primary melanoma is histologically subtyped as NOS. The impact of this designation on clinical outcome is untested but casually believed not to have an impact considering the increasing reliance of molecular subclassification of melanoma tumor that might eliminate the need for histologic characterization. We also reported our data that supported a role for melanoma germline prognostic factors independent of known prognostic criteria (Chat et al, Front Oncol 2023). In this study, we sought to determine if NOS represents phenotypically and genetically distinct subtype or its clinical behavior is determined by AJCC criteria. Methods: We studied primary melanoma patients prospectively accrued and followed up using an IRB approved protocol at NYULH. We tested the association between NOS and baseline characteristics (e.g. thickness, ulceration, stage, Mitotic Index, TIL) and its association with recurrence free survival (RFS), and overall survival (OS) using Kaplan-Meier and multivariable Cox regression analyses. We also performed Principal Component Analysis (PCA) to test the clustering of NOS compared to other subtypes. We then examined the association between germline genetic variants and the development of NOS melanoma. Analyzing imputed SNP data generated using global screening array (GSA) from blood DNA we compared NOS (cases) with other histologic melanomas (controls) controlling for clinical and demographic variables. Results: NOS were identified in 550/2,891 (19%) of patients (median follow up of 4.44 years). PCA revealed clustering of NOS melanoma and superficial spreading melanoma (SSM) with similar favorable prognostic criteria (thinner, less ulcerated, earlier stage) compared to other subtypes (P &lt; 0.001) However, NOS patients had a significantly shorter RFS compared to SSM (median RFS 8.83 years compared to 17.5 years, respectively; HR = 1.79 (95% CI: 1.37-2.33), P &lt; 0.001 in multivariate analysis Genetic analysis of 919 cases (NOS = 127 versus all others n = 792) revealed a cluster of four genetic variants defining a linkage disequilibrium (LD) block mapping in the region of annotated microRNA 924 (LD-MIR924; OR = 1.844, p = 2.72E-06, 95% CI = 1.40-2.43), associating with ~1.8-fold increased risk of developing NOS compared to other melanoma subtypes. Conclusions: Our data suggest NOS melanoma might be a distinct genetic subtype that require further analysis considering the significant association with worse clinical outcome. The genetic variants associated with the risk of developing NOS melanoma point to MIR924, a microRNA previously linked to cancer progression. Our findings support the integration of studying patients’ genetics in addition to tumor somatic mutations to better understand the heterogeneous behavior of melanoma.

Clinical Exome Gene Panel Analysis of a Cohort of Urothelial Bladder Cancer Patients from Sri Lanka.

Bladder cancer has a high rate of recurrence and high mortality rates in those who progress to muscle invasive disease. Biomarkers and molecular sub classification of tumours beyond standard histopathology has been proposed to address therapeutic dilemmas. The Cancer Genome Atlas project and other studies have contributed to the enhanced knowledge base of the mutational landscape of urothelial bladder cancer. Once again, these are mostly from Caucasian and Chinese patients, with data from the rest of Asia and Sri Lanka being sparse. The objective of this study was to assess the genomic variations of a cohort of urothelial bladder cancer patients in Sri Lanka. The molecular genetic study was conducted on formalin fixed paraffin embedded tumour samples of 24 patients, prospectively enrolled from 2013 to 2017. The samples were sequenced and variant distribution performed based on a 70-gene panel. Total number of filtered mutations in the 24 patients was 10453. Median mutations per patient were 450 (range 22-987). The predominant mutational change was C>T and G>A. The top 5 mutated genes in our cohort were SYNE1, SYNE2, KMT2C, LRP2, and ANK2. The genes were clustered into 3 groups dependent on the number of mutations per patient per gene. The genes of cluster 1 and 2 mapped to Chromatin modifying enzymes and Generic Transcription Pathway. The chromatin remodelling pathway accounted for the largest proportion (22%) of mutations. Clinical exome sequencing utilising a gene panel yielded a high mutation rate in our patients. The predominant mutational change was C>T and G>A. Three clusters of genes were identified. SYNE1 was the gene with the most mutations. The mutations comprised predominantly of genes of the chromatin remodelling pathway.

Primary and secondary angiosarcomas of the liver: a multi-institutional study of 32 cases

Angiosarcomas involving the liver can be hepatic primary or metastasis from another anatomic site, which have not been systematically compared. We analyzed a series of liver biopsy or resection specimens carrying a diagnosis of angiosarcoma collected between 2005 and 2022 at 3 tertiary medical centers. The cohort included 32 patients (20M and 12F) with a median age of 64 years. Nineteen were primary hepatic angiosarcoma (PHA) and 13 metastatic angiosarcoma to liver (MA). Males were predominant in PHA (15/19, 78%) compared to MA (5/13, 38%, P=.025). There was no age difference between the 2 groups. Five cases had background hepatic cirrhosis, which more likely harbored PHA (4/5, 80%). Multifocality and multiorgan involvement were common in both groups. Tumor size was significantly larger in PHA than in MA (10.4 versus 4.7cm, P<.01). Histologically, there were no differences in terms of tumor morphology (spindled versus epithelioid) and growth patterns (vasoformative versus solid) between the 2 groups. Immunohistochemically, all tumor cells were positive for CD31 (28/28, 100%) and ERG (18/18, 100%). Molecular analysis in 5 cases demonstrated different mutation profiles involving different genes, including MTOR, PIK3CA, ARID1A, CDKN2A, PTEN, TP53, ATRX, KDR/VEGFR2, and so forth. On follow-up, 30patients (93%) died of disease, with a median survival of 114 days. Univariate and multivariate analyses revealed PHA and epithelioid morphology to be associated with worse survival (P<.05), while treatment was associated with better survival (P<.001). Our results confirmed that angiosarcoma, particularly PHA, is extremely aggressive. Epithelioid morphology is an adverse prognosticator and may be used for tumor subclassification.

Abstract 6586: Updates to UALCAN, a comprehensive cancer proteogenomic data analysis platform for discovery research

Abstract Background: Recent advances in high throughput technologies and initiation of multiple consortium driven projects have led to generation of high volume of molecular data related to thousands of cancer patients. These high throughput data are extremely useful for novel biomarker identification, therapeutic target identification in tumor subclasses and allows numerous hypothesis driven cancer research projects. However, the sheer volume of data and diverse data formats presents tremendous challenge for researchers with no programming experience to access and analyze comprehensively. These factors prompted us to develop UALCAN (University of ALabama CANcer portal, Yes! You All Can) for comprehensive cancer data analysis in a user-friendly manner. UALCAN enables cancer researchers/clinicians in tumor subgroup based gene expression, protein expression and survival analyses. UALCAN is extremely popular among cancer researchers round the world with well over a “MILLION” site visits and cited over 3400 times in research articles. UALCAN integrates The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), as well as gene expression data from Children Brain Tumor Tissue Consortium (CBTTC). Users can also analyze non-coding RNA (miRNA and lncRNA) expression data from The Cancer Genome Atlas (TCGA) and the epigenetic data though the promoter DNA methylation analysis form the TCGA data. Results: UALCAN now enables user to study expression profile of ~10000 proteins in 14 CPTAC cancers and expression pattern and survival impact of 20,500 protein-coding, ~17,000 lncRNA and 1800 miRNA in 33 TCGA cancers. It also facilitates analysis of gene expression pattern in pediatric brain cancer. User-friendly graphical user interface of UALCAN facilitates a) analyzing protein/non-coding RNA expression in tumor subgroups based on race, stage, age and molecular types; b) identifying top differentially expressed lncRNA/miRNA in specific cancer subtype; c) listing potential gene targets of non-coding RNA; d) performing pan-cancer analysis and e) determining impact of gene expression on patient’s overall survival. Conclusions: UALCAN is freely available at http://ualcan.path.uab.edu (Google: UALCAN) This dynamic data portal provides constant updates that empower cancer researchers in studying gene and protein level expression and identifying tumor subgroup specific non-coding RNA biomarkers. UALCAN serves a one-stop platform for access, study, analyze and leverage large cancer datasets. Citation Format: Darshan Shimoga Chandrashekar, Sooryanarayana Varambally, Santhosh Kumar Karthikeyan, Praveen Kumar Korla, Henalben Patel, Mohammad Athar, Upender Manne, George J. Netto, Ahmedur Rahman Shovon, Sidharth Kumar, Zhaohui S. Qin, Chad J. Crieghton. Updates to UALCAN, a comprehensive cancer proteogenomic data analysis platform for discovery research [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 6586.

Abstract 6535: Identification of coding and non-coding genes associated with survival outcomes in uterine cancer

Abstract Endometrial (or uterine) cancer is the most commonly diagnosed cancer in the female reproductive system and it disproportionately affects Black women, who are twice as likely to die from the disease than White women. In this study, we focused on the molecular characterization of copy-number high (CN-high) molecular subclassification of uterine cancer tumors, which includes most serous and serous-like endometroid tumors. These poorly characterized subcategories account for less than 10% of all uterine cancers but are responsible for over 80% of the mortality in uterine cancers and occur more commonly in Black women.We leveraged publicly available Pan-Cancer Atlas data to perform differential gene expression analyses between control and cancer samples in the Black and White CN-high groups with the aim of identifying relevant genes that could play a role in the aggressiveness of this cancer. We then performed survival analysis using the genes of interest using Kaplan-Meier estimators to ascertain the gene-specific survival outcomes.In the significantly upregulated subset of genes, we found that the high expression of the candidate gene was associated with higher survival in both White patients (p=0.046), and Black patients (p=0.021). Additionally, the expression levels of one of the noncoding genes were associated with differential survival between the groups, with low expression being associated with higher survival in White patients (p=0.0045), and high expression associated with higher survival in Black patients (p=0.018). This differential association of survival was also observed in one of the most downregulated genes in both groups, with low expression predicting higher survival in white patients (p=0.004), and Black patient survival is associated with high expression (p=0.0082). We also identified novel non-coding genes that predicted different survival outcomes in both groups, with high expression predicting better survival in Black patients and low expression predicting better survival in White patients (p=0.048 and p=0.039, respectively). These results showcase the identification of both coding and noncoding potential genomic targets that are associated with clinical outcomes in the aggressive CN-high molecular subtype of uterine cancer. These targets merit further characterization and experimental validation to elucidate their molecular roles in the progression of endometrial cancer. Acknowledgments S.S.G. is a CPRIT scholar in cancer research and is supported by a First-time Faculty Recruitment Award from the Cancer Prevention and Research Institute of Texas (CPRIT; RR170020). S.S.G. is also supported by a) the Lizanell and Colbert Coldwell Foundation, b) The Edward N. and Margaret G. Marsh Foundation, and c) the American Cancer Society. Citation Format: Enrique I. Ramos, Axel M. Hidalgo, Ken Y. Lin, Shrikanth S. Gadad. Identification of coding and non-coding genes associated with survival outcomes in uterine cancer [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 6535.

Immunocytochemical detection by a fully automated immunostainer for assisting subclassification of pulmonary tumors on ThinPrep slides in real-life clinical samples: A single-institution experience.

This study aims to investigate the feasibility and reliability of ThinPrep slides in detecting the subclassification of lung cancer and develop a process for immunocytochemistry (ICC) with optimized staining steps of an automated immunostainer. Cytomorphology and ancillary ICC by automated immunostainer on ThinPrep slides were performed to subclassify 271 cytology cases of pulmonary tumor, which were stained with 2 or more of the following antibodies: p40, p63, thyroid transcription factor-1 (TTF-1), Napsin A, synaptophysin (Syn), and CD56. The accuracy of cytological subtyping was improved from 67.2% to 92.7% (p < .0001) after ICC. The accuracy of cytomorphology combined with ICC results for lung squamous-cell carcinoma (LUSC), lung adenocarcinomas (LUAD), and small cell carcinoma (SCLC) was 89.5% (51 of 57), 97.8% (90 of 92), and 98.8% (85 of 86), respectively. The sensitivity and specificity of 6 antibodies were as follows: p63 (91.2%, 90.4%) and p40 (84.2%, 95.1%) for LUSC, TTF-1(95.6%, 64.6%) and Napsin A (89.7%, 96.7%) for LUAD and Syn (90.7%, 60.0%) and CD56 (97.7%, 50.0%) for SCLC, respectively. P40 expression on ThinPrep slides had the highest agreement (κ=0.881) with immunohistochemistry (IHC) results, followed by p63 (κ=0.873), Napsin A (κ=0.795), TTF-1 (κ=0.713), CD56 (κ=0.576), and Syn (κ=0.491). The result of ancillary ICC on ThinPrep slides by fully automated immunostainer was in good agreement with the gold standard in pulmonary tumors subtype and immunoreactivity, objectively achieving accurate subtyping in cytology.

A Novel Molecular Classification Method for Glioblastoma Based on Tumor Cell Differentiation Trajectories.

The latest 2021 WHO classification redefines glioblastoma (GBM) as the hierarchical reporting standard by eliminating glioblastoma, IDH-mutant and only retaining the tumor entity of "glioblastoma, IDH-wild type." Knowing that subclassification of tumors based on molecular features is supposed to facilitate the therapeutic choice and increase the response rate in cancer patients, it is necessary to carry out molecular classification of the newly defined GBM. Although differentiation trajectory inference based on single-cell sequencing (scRNA-seq) data holds great promise for identifying cell heterogeneity, it has not been used in the study of GBM molecular classification. Single-cell transcriptome sequencing data from 10 GBM samples were used to identify molecular classification based on differentiation trajectories. The expressions of identified features were validated by public bulk RNA-sequencing data. Clinical feasibility of the classification system was examined in tissue samples by immunohistochemical (IHC) staining and immunofluorescence, and their clinical significance was investigated in public cohorts and clinical samples with complete clinical follow-up information. By analyzing scRNA-seq data of 10 GBM samples, four differentiation trajectories from the glioblastoma stem cell-like (GSCL) cluster were identified, based on which malignant cells were classified into five characteristic subclusters. Each cluster exhibited different potential drug sensitivities, pathways, functions, and transcriptional modules. The classification model was further examined in TCGA and CGGA datasets. According to the different abundance of five characteristic cell clusters, the patients were classified into five groups which we named Ac-G, Class-G, Neo-G, Opc-G, and Undiff-G groups. It was found that the Undiff-G group exhibited the worst overall survival (OS) in both TCGA and CGGA cohorts. In addition, the classification model was verified by IHC staining in 137 GBM samples to further clarify the difference in OS between the five groups. Furthermore, the novel biomarkers of glioblastoma stem cells (GSCs) were also described. In summary, we identified five classifications of GBM and found that they exhibited distinct drug sensitivities and different prognoses, suggesting that the new grouping system may be able to provide important prognostic information and have certain guiding significance for the treatment of GBM, and identified the GSCL cluster in GBM tissues and described its characteristic program, which may help develop new potential therapeutic targets for GSCs in GBM.

Transcriptomic Deconvolution of Neuroendocrine Neoplasms Predicts Clinically Relevant Characteristics.

Pancreatic neuroendocrine neoplasms (panNENs) are a rare yet diverse type of neoplasia whose precise clinical-pathological classification is frequently challenging. Since incorrect classifications can affect treatment decisions, additional tools which support the diagnosis, such as machine learning (ML) techniques, are critically needed but generally unavailable due to the scarcity of suitable ML training data for rare panNENs. Here, we demonstrate that a multi-step ML framework predicts clinically relevant panNEN characteristics while being exclusively trained on widely available data of a healthy origin. The approach classifies panNENs by deconvolving their transcriptomes into cell type proportions based on shared gene expression profiles with healthy pancreatic cell types. The deconvolution results were found to provide a prognostic value with respect to the prediction of the overall patient survival time, neoplastic grading, and carcinoma versus tumor subclassification. The performance with which a proliferation rate agnostic deconvolution ML model could predict the clinical characteristics was found to be comparable to that of a comparative baseline model trained on the proliferation rate-informed MKI67 levels. The approach is novel in that it complements established proliferation rate-oriented classification schemes whose results can be reproduced and further refined by differentiating between identically graded subgroups. By including non-endocrine cell types, the deconvolution approach furthermore provides an in silico quantification of panNEN dedifferentiation, optimizing it for challenging clinical classification tasks in more aggressive panNEN subtypes.

A novel molecular classification method for osteosarcoma based on tumor cell differentiation trajectories

Subclassification of tumors based on molecular features may facilitate therapeutic choice and increase the response rate of cancer patients. However, the highly complex cell origin involved in osteosarcoma (OS) limits the utility of traditional bulk RNA sequencing for OS subclassification. Single-cell RNA sequencing (scRNA-seq) holds great promise for identifying cell heterogeneity. However, this technique has rarely been used in the study of tumor subclassification. By analyzing scRNA-seq data for six conventional OS and nine cancellous bone (CB) samples, we identified 29 clusters in OS and CB samples and discovered three differentiation trajectories from the cancer stem cell (CSC)-like subset, which allowed us to classify OS samples into three groups. The classification model was further examined using the TARGET dataset. Each subgroup of OS had different prognoses and possible drug sensitivities, and OS cells in the three differentiation branches showed distinct interactions with other clusters in the OS microenvironment. In addition, we verified the classification model through IHC staining in 138 OS samples, revealing a worse prognosis for Group B patients. Furthermore, we describe the novel transcriptional program of CSCs and highlight the activation of EZH2 in CSCs of OS. These findings provide a novel subclassification method based on scRNA-seq and shed new light on the molecular features of CSCs in OS and may serve as valuable references for precision treatment for and therapeutic development in OS.