The Psychoneurologic Symptom Cluster and Its Association With Breast Cancer Genomic Instability.

OBJECTIVES: To phenotype the psychoneurologic (PN) symptom cluster in individuals with metastatic breast cancer and associate those phenotypes with individual characteristics and cancer genomic variables from circulating tumor DNA. SAMPLE & SETTING: This study included 201 individuals with metastatic breast cancer recruited in western Pennsylvania. METHODS & VARIABLES: A descriptive, cross-sectional design was used. Symptom data were collected via the MD Anderson Symptom Inventory, and cancer genomic data were collected via ultra-low-pass whole-genome sequencing of circulating tumor DNA from participant blood. RESULTS: Three distinct PN symptom phenotypes were described in a population with metastatic breast cancer: mild symptoms, moderate symptoms, and severe mood-related symptoms. Breast cancer TP53 deletion was significantly associated with membership in a moderate to severe symptoms phenotype (p = 0.013). IMPLICATIONS FOR NURSING: Specific cancer genomic changes associated with increased genomic instability may be predictive of PN symptoms. This finding may enable proactive treatment or reveal new therapeutic targets for symptom management.

The future of collaborative precision oncology approaches in sub-Saharan Africa: learnings from around the globe.

As the projected incidence and mortality of cancer in Sub-Saharan Africa (SSA) rises to epidemic proportions, it is imperative that more is done to identify the genomic differences and commonalities between patients of African and European ancestry to fulfil the promise of precision oncology. Here, we summarize the utility of precision oncology approaches, with a focus on comprehensive genomic profiling (CGP) and consolidate examples of national and international consortia that are driving the field forward. We describe the importance of genomic diversity and its relevance in cancer, and propose recommendations, success factors and desired outcomes for precision oncology consortia to adopt in SSA. Through this, we hope to catalyze the initiation of such projects and to contribute to improving cancer patient outcomes in the region.

Early rhombic lip Protogenin+ve stem cells in a human-specific neurovascular niche initiate and maintain group 3 medulloblastoma.

We identify a population of Protogenin-positive (PRTG+ve) MYChigh NESTINlow stem cells in the four-week-old human embryonic hindbrain that subsequently localizes to the ventricular zone of the rhombic lip (RLVZ). Oncogenic transformation of early Prtg+ve rhombic lip stem cells initiates group 3 medulloblastoma (Gr3-MB)-like tumors. PRTG+ve stem cells grow adjacent to a human-specific interposed vascular plexus in the RLVZ, a phenotype that is recapitulated in Gr3-MB but not in other types of medulloblastoma. Co-culture of Gr3-MB with endothelial cells promotes tumor stem cell growth, with the endothelial cells adopting an immature phenotype. Targeting the PRTGhigh compartment of Gr3-MB in vivo using either the diphtheria toxin system or chimeric antigen receptor T cells constitutes effective therapy. Human Gr3-MBs likely arise from early embryonic RLVZ PRTG+ve stem cells inhabiting a specific perivascular niche. Targeting the PRTGhigh compartment and/or the perivascular niche represents an approach to treat children with Gr3-MB.

Network analysis of driver genes in human cancers.

Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences. We present two network-based approaches to identify driver gene patterns among tumor samples. The first approach relies on analysis using the Directed Weighted All Nearest Neighbors (DiWANN) model, which is a variant of sequence similarity network, and the second approach uses bipartite network analysis. A data reduction framework was implemented to extract the minimal relevant information for the sequence similarity network analysis, where a transformed reference sequence is generated for constructing the driver gene network. This data reduction process combined with the efficiency of the DiWANN network model, greatly lowered the computational cost (in terms of execution time and memory usage) of generating the networks enabling us to work at a much larger scale than previously possible. The DiWANN network helped us identify cancer types in which samples were more closely connected to each other suggesting they are less heterogeneous and potentially susceptible to a common drug. The bipartite network analysis provided insight into gene associations and co-occurrences. We identified genes that were broadly mutated in multiple cancer types and mutations exclusive to only a few. Additionally, weighted one-mode gene projections of the bipartite networks revealed a pattern of occurrence of driver genes in different cancers. Our study demonstrates that network-based approaches can be an effective tool in cancer genomics. The analysis identifies co-occurring and exclusive driver genes and mutations for specific cancer types, providing a better understanding of the driver genes that lead to tumor initiation and evolution.

Deep Learning-Based Identification of Tissue of Origin for Carcinomas of Unknown Primary Using MicroRNA Expression: Algorithm Development and Validation.

BACKGROUND: Carcinoma of unknown primary (CUP) is a subset of metastatic cancers in which the primary tissue source of the cancer cells remains unidentified. CUP is the eighth most common malignancy worldwide, accounting for up to 5% of all malignancies. Representing an exceptionally aggressive metastatic cancer, the median survival is approximately 3 to 6 months. The tissue in which cancer arises plays a key role in our understanding of sensitivities to various forms of cell death. Thus, the lack of knowledge on the tissue of origin (TOO) makes it difficult to devise tailored and effective treatments for patients with CUP. Developing quick and clinically implementable methods to identify the TOO of the primary site is crucial in treating patients with CUP. Noncoding RNAs may hold potential for origin identification and provide a robust route to clinical implementation due to their resistance against chemical degradation. OBJECTIVE: This study aims to investigate the potential of microRNAs, a subset of noncoding RNAs, as highly accurate biomarkers for detecting the TOO through data-driven, machine learning approaches for metastatic cancers. METHODS: We used microRNA expression data from The Cancer Genome Atlas data set and assessed various machine learning approaches, from simple classifiers to deep learning approaches. As a test of our classifiers, we evaluated the accuracy on a separate set of 194 primary tumor samples from the Sequence Read Archive. We used permutation feature importance to determine the potential microRNA biomarkers and assessed them with principal component analysis and t-distributed stochastic neighbor embedding visualizations. RESULTS: Our results show that it is possible to design robust classifiers to detect the TOO for metastatic samples on The Cancer Genome Atlas data set, with an accuracy of up to 97% (351/362), which may be used in situations of CUP. Our findings show that deep learning techniques enhance prediction accuracy. We progressed from an initial accuracy prediction of 62.5% (226/362) with decision trees to 93.2% (337/362) with logistic regression, finally achieving 97% (351/362) accuracy using deep learning on metastatic samples. On the Sequence Read Archive validation set, a lower accuracy of 41.2% (77/188) was achieved by the decision tree, while deep learning achieved a higher accuracy of 80.4% (151/188). Notably, our feature importance analysis showed the top 3 most important features for predicting TOO to be microRNA-10b, microRNA-205, and microRNA-196b, which aligns with previous work. CONCLUSIONS: Our findings highlight the potential of using machine learning techniques to devise accurate tests for detecting TOO for CUP. Since microRNAs are carried throughout the body via extracellular vesicles secreted from cells, they may serve as key biomarkers for liquid biopsy due to their presence in blood plasma. Our work serves as a foundation toward developing blood-based cancer detection tests based on the presence of microRNA.

Single-cell signatures identify microenvironment factors in tumors associated with patient outcomes.

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

Modeling metastatic progression from cross-sectional cancer genomics data.

MOTIVATION: Metastasis formation is a hallmark of cancer lethality. Yet, metastases are generally unobservable during their early stages of dissemination and spread to distant organs. Genomic datasets of matched primary tumors and metastases may offer insights into the underpinnings and the dynamics of metastasis formation. RESULTS: We present metMHN, a cancer progression model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer genomics data. The model elucidates the statistical dependencies among genomic events, the formation of metastasis, and the clinical emergence of both primary tumors and their metastatic counterparts. metMHN enables the chronological reconstruction of mutational sequences and facilitates estimation of the timing of metastatic seeding. In a study of nearly 5000 lung adenocarcinomas, metMHN pinpointed TP53 and EGFR as mediators of metastasis formation. Furthermore, the study revealed that post-seeding adaptation is predominantly influenced by frequent copy number alterations. AVAILABILITY AND IMPLEMENTATION: All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN.

Genomic and Immune Landscape Comparison of MET Exon 14 Skipping and MET-Amplified Non-small Cell Lung Cancer.

BACKGROUND: Mutation or amplification of the mesenchymal-epithelial transition (MET) tyrosine kinase receptor causes dysregulation of receptor function and stimulates tumor growth in non-small cell lung cancer (NSCLC) with the most common mutation being MET exon 14 (METex14). We sought to compare the genomic and immune landscape of MET-altered NSCLC with MET wild-type NSCLC. METHODS: 18,047 NSCLC tumors were sequenced with Tempus xT assay. Tumors were categorized based on MET exon 14 (METex14) mutations; low MET amplification defined as a copy number gain (CNG) 6-9, high MET amplification defined as CNG ≥ 10, and MET other type mutations. Immuno-oncology (IO) biomarkers and the frequency of other somatic gene alterations were compared across MET-altered and MET wild-type groups. RESULTS: 276 (1.53%) METex14, 138 (0.76%) high METamp, 63 (0.35%) low METamp, 27 (0.15%) MET other, and 17,543 (97%) MET wild-type were identified. Patients with any MET mutation including METex14 were older, while patients with METex14 were more frequently female and nonsmokers. MET gene expression was highest in METamp tumors. PD-L1 positivity rates were higher in MET-altered groups than MET wild-type. METex14 exhibited the lowest tumor mutational burden (TMB) and lowest neoantigen tumor burden (NTB). METamp exhibited the lowest proportion of CD4 T cells and the highest proportion of NK cells. There were significant differences in co-alterations between METamp and METex14. CONCLUSIONS: METex14 tumors exhibited differences in IO biomarkers and the somatic landscape compared to non-METex14 NSCLC tumors. Variations in immune profiles can affect immunotherapy selection in MET-altered NSCLC and require further exploration.

Significance of Multi-Cancer Genome Profiling Testing for Breast Cancer: A Retrospective Analysis of 3326 Cases from Japan's National Database.

Background: Breast cancer (BC) has the highest morbidity rate and the second-highest mortality rate of all cancers among women. Recently, multi-cancer genome profiling (multi-CGP) tests have become clinically available. In this study, we aimed to clarify the significance of multi-CGP testing of BC by using the large clinical dataset from The Center for Cancer Genomics and Advanced Therapeutics (C-CAT) profiling database in Japan. Materials and Methods: A total of 3744 BC cases were extracted from the C-CAT database, which enrolled 60,250 patients between June 2019 and October 2023. Of the 3744 BC cases, a total of 3326 cases for which the C-CAT included information on ER, PR, and HER2 status were classified into four subtypes, including TNBC, HR+/HER2-, HR+/HER2+, and HR-/HER2+. Comparisons between groups were performed by the χ2 test or Fisher's exact test using EZR. Kaplan-Meier curves were created using the log-rank test. Results: Of all 3326 cases analyzed, 1114 (33.5%) were TNBC cases, HR+/HER2- accounted for 1787 cases (53.7%), HR+/HER2+ for 260 cases (7.8%), and HR-/HER2+ for 165 cases (5.0%). Genetic abnormalities were most frequently detected in TP53 (58.0%), PIK3CA (35.5%), MYC (18.7%), FGF19 (15.5%), and GATA3 (15.1%) across all BCs. The rate of TMB-High was 12.3%, and the rate of MSI-High was 0.3%, in all BC cases. Therapeutic drugs were proposed for patients with mutations in six genes: PIK3CA, ERBB2, PTEN, FGFR1, ESR1, and AKT1. The prognoses of HR+/HER2- cases were significantly (p = 0.044) better in the treated group than in the untreated group. Conclusions: These findings suggest that cancer gene panel testing is useful for HR+/HER2- cases.

Understanding variants of unknown significance: the computational frontier.

The rapid advancement of sequencing technologies has led to the identification of numerous mutations in cancer genomes, many of which are variants of unknown significance (VUS). Computational models are increasingly being used to predict the functional impact of these mutations, in both coding and noncoding regions. Integration of these models with emerging genomic datasets will refine our understanding of mutation effects and guide clinical decision making. Future advancements in modeling protein interactions and transcriptional regulation will further enhance our ability to interpret VUS. Periodic incorporation of these developments into VUS reclassification practice has the potential to significantly improve personalized cancer care.

Cancer mutational signatures identification in clinical assays using neural embedding-based representations.

While mutational signatures provide a plethora of prognostic and therapeutic insights, their application in clinical-setting, targeted gene panels is extremely limited. We develop a mutational representation model (which learns and embeds specific mutation signature connections) that enables prediction of dominant signatures with only a few mutations. We predict the dominant signatures across more than 60,000 tumors with gene panels, delineating their landscape across different cancers. Dominant signature predictions in gene panels are of clinical importance. These included UV, tobacco, and apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC) signatures that are associated with better survival, independently from mutational burden. Further analyses reveal gene and mutation associations with signatures, such as SBS5 with TP53 and APOBEC with FGFR3S249C. In a clinical use case, APOBEC signature is a robust and specific predictor for resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). Our model provides an easy-to-use way to detect signatures in clinical setting assays with many possible clinical implications for an unprecedented number of cancer patients.

Upregulation of GOLPH3 mediated by Bisphenol a promotes colorectal cancer proliferation and migration: evidence based on integrated analysis.

Background: The interaction between environmental endocrine-disrupting chemicals, such as Bisphenol A (BPA), and their influence on cancer progression, particularly regarding the GOLPH3 gene in colorectal cancer, remains unclear. Methods: We performed an integrated analysis of transcriptional profiling, clinical data, and bioinformatics analyses utilizing data from the Comparative Toxicogenomics Database and The Cancer Genome Atlas. The study employed ClueGO, Gene Set Enrichment Analysis, and Gene Set Variation Analysis for functional enrichment analysis, alongside experimental assays to examine the effects of BPA exposure on colorectal cancer cell lines, focusing on GOLPH3 expression and its implications for cancer progression. Results: Our findings demonstrated that BPA exposure significantly promoted the progression of colorectal cancer by upregulating GOLPH3, which in turn enhanced the malignant phenotype of colorectal cancer cells. Comparative analysis revealed elevated GOLPH3 protein levels in cancerous tissues versus normal tissues, with single-cell analysis indicating widespread GOLPH3 presence across various cell types in the cancer microenvironment. GOLPH3 was also associated with multiple carcinogenic pathways, including the G2M checkpoint. Furthermore, our investigation into the colorectal cancer microenvironment and genomic mutation signature underscored the oncogenic potential of GOLPH3, exacerbated by BPA exposure. Conclusion: This study provides novel insights into the complex interactions between BPA exposure and GOLPH3 in the context of colorectal cancer, emphasizing the need for heightened awareness and measures to mitigate BPA exposure risks. Our findings advocate for further research to validate these observations in clinical and epidemiological settings and explore potential therapeutic targets within these pathways.

Sample-Wise and Gene-Wise Comparisons Confirm a Greater Similarity of RNA and Protein Expression Data at the Level of Molecular Pathways and Suggest an Approach for the Data Quality Check in High-Throughput Expression Databases.

Identification of genes and molecular pathways with congruent profiles in the proteomic and transcriptomic datasets may result in the discovery of promising transcriptomic biomarkers that would be more relevant to phenotypic changes. In this study, we conducted comparative analysis of 943 paired RNA and proteomic profiles obtained for the same samples of seven human cancer types from The Cancer Genome Atlas (TCGA) and NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) [two major open human cancer proteomic and transcriptomic databases] that included 15,112 protein-coding genes and 1611 molecular pathways. Overall, our findings demonstrated statistically significant improvement of the congruence between RNA and proteomic profiles when performing analysis at the level of molecular pathways rather than at the level of individual gene products. Transition to the molecular pathway level of data analysis increased the correlation to 0.19-0.57 (Pearson) and 0.14-057 (Spearman), or 2-3-fold for some cancer types. Evaluating the gain of the correlation upon transition to the data analysis the pathway level can be used to refine the omics data by identifying outliers that can be excluded from the comparison of RNA and proteomic profiles. We suggest using sample- and gene-wise correlations for individual genes and molecular pathways as a measure of quality of RNA/protein paired molecular data. We also provide a database of human genes, molecular pathways, and samples related to the correlation between RNA and protein products to facilitate an exploration of new cancer transcriptomic biomarkers and molecular mechanisms at different levels of human gene expression.

Profiling Numerical and Structural Chromosomal Instability in Different Cancer Types.

Many cancers display whole chromosome instability (W-CIN) and structural chromosomal instability (S-CIN), referring to increased rates of acquiring numerically and structurally abnormal chromosome changes. This protocol provides detailed steps to analyze the W-CIN and S-CIN across cancer types, intending to leverage large-scale bulk sequencing and SNP array data complemented with the computational models to gain a better understanding of W-CIN and S-CIN.

Flexible model-based non-negative matrix factorization with application to mutational signatures.

Somatic mutations in cancer can be viewed as a mixture distribution of several mutational signatures, which can be inferred using non-negative matrix factorization (NMF). Mutational signatures have previously been parametrized using either simple mono-nucleotide interaction models or general tri-nucleotide interaction models. We describe a flexible and novel framework for identifying biologically plausible parametrizations of mutational signatures, and in particular for estimating di-nucleotide interaction models. Our novel estimation procedure is based on the expectation-maximization (EM) algorithm and regression in the log-linear quasi-Poisson model. We show that di-nucleotide interaction signatures are statistically stable and sufficiently complex to fit the mutational patterns. Di-nucleotide interaction signatures often strike the right balance between appropriately fitting the data and avoiding over-fitting. They provide a better fit to data and are biologically more plausible than mono-nucleotide interaction signatures, and the parametrization is more stable than the parameter-rich tri-nucleotide interaction signatures. We illustrate our framework in a large simulation study where we compare to state of the art methods, and show results for three data sets of somatic mutation counts from patients with cancer in the breast, Liver and urinary tract.

Gain-Type Aneuploidies Influence the Burden of Selective Long Non-Coding Transcripts in Colorectal Cancer.

Chromosomal instability is a hallmark of colorectal carcinogenesis and produces an accumulation of different forms of aneuploidies or broad copy number aberrations. Colorectal cancer is characterized by gain-type broad copy number aberrations, specifically in Chr20, Chr8q, Chr13 and Chr7, but their roles and mechanisms in cancer progression are not fully understood. It has been suggested that broad copy number gains might contribute to tumor development through the so-called caricature transcriptomic effect. We intend to investigate the impact of broad copy number gains on long non-coding RNAs' expression in colorectal cancer, given their well-known role in oncogenesis. The influence of such chromosomal aberrations on lncRNAs' transcriptome profile was investigated by SNP and transcriptome arrays in our series of colorectal cancer samples and cell lines. The correlation between aneuploidies and transcriptomic profiles led us to obtain a class of Over-UpT lncRNAs, which are transcripts upregulated in CRC and further overexpressed in colon tumors bearing specific chromosomal aberrations. The identified lncRNAs can contribute to a wide interaction network to establish the cancer driving effect of gain-type aneuploidies.

Mutation characteristics of cancer susceptibility genes in Chinese ovarian cancer patients.

Introduction: The association between mutations in susceptibility genes and the occurrence of ovarian cancer has been extensively studied. Previous research has primarily concentrated on genes involved in the homologous recombination repair pathway, particularly BRCA1 and BRCA2. However, a wider range of genes related to the DNA damage response pathways has not been fully explored. Methods: To investigate the mutation characteristics of cancer susceptibility genes in the Chinese ovarian cancer population and the associations between gene mutations and clinical data, this study initially gathered a total of 1171 Chinese ovarian cancer samples and compiled a dataset of germline mutations in 171 genes. Results: In this study, it was determined that MC1R and PRKDC were high-frequency ovarian cancer susceptibility genes in the Chinese population, exhibiting notable distinctions from those in European and American populations; moreover high-frequency mutation genes, such as MC1R: c.359T>C and PRKDC: c.10681T>A, typically had high-frequency mutation sites. Furthermore, we identified c.8187G>T as a characteristic mutation of BRCA2 in the Chinese population, and the CHEK2 mutation was significantly associated with the early onset of ovarian cancer, while the CDH1 and FAM175A mutations were more prevalent in Northeast China. Additionally, Fanconi anemia pathway-related genes were significantly associated with ovarian carcinogenesis. Conclusion: In summary, this research provided fundamental data support for the optimization of ovarian cancer gene screening policies and the determination of treatment, and contributed to the precise intervention and management of patients.

Genomic landscape of comprehensive genomic profiling in patients with malignant solid tumors in Japan.

BACKGROUND: Comprehensive genomic profiling (CGP) can aid the discovery of clinically useful, candidate antitumor agents; however, the variant annotations sometimes differ among the various types of CGP tests as well as the public database. The aim of this study is to clarify the genomic landscape of evaluating detected variants in patients with a malignant solid tumor. METHODS: The present, cross-sectional study used data from 57,084 patients with a malignant solid tumor who underwent CGP at the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) between June 1, 2019 and August 18, 2023. The pathogenicity of the variants was annotated using public databases. RESULTS: As a result of re-annotation of the detected variants, 20.1% were pathogenic and 1.4% were benign. The mean number of pathogenic variants was 4.30 (95% confidence interval: 4.27-4.32) per patient. Of the entire cohort, 5.7% had no pathogenic variant. The co-occurrence of the genes depended on the tumor type. Germline findings were detected in 6.2%, 8.8%, and 15.8% of the patients using a tumor/normal panel, tumor-only panel, and liquid panel, respectively, with the most common gene being BRCA2 followed by TP53 and BRCA1. CONCLUSIONS: The detected variants should be re-annotated because several benign variants or variants of unknown significance were included in the CGP, and the genomic landscape derived from these results will help researchers and physicians interpret the results of CGP tests. The method of extracting presumptive, germline, pathogenic variants from patients using a tumor-only panel or circulating tumor DNA panel requires improvement.

Clinicogenomic characterization of inflammatory breast cancer.

Background: Inflammatory breast cancer (IBC) is a rare and poorly characterized type of breast cancer with an aggressive clinical presentation. The biological mechanisms driving the IBC phenotype are relatively undefined-partially due to a lack of comprehensive, large-scale genomic studies and limited clinical cohorts. Patients and Methods: A retrospective analysis of 2457 patients with metastatic breast cancer who underwent targeted tumor-only DNA-sequencing was performed at Dana-Farber Cancer Institute. Clinicopathologic, single nucleotide variant (SNV), copy number variant (CNV) and tumor mutational burden (TMB) comparisons were made between clinically confirmed IBC cases within a dedicated IBC center versus non-IBC cases. Results: Clinicopathologic differences between IBC and non-IBC cases were consistent with prior reports-including IBC being associated with younger age at diagnosis, higher grade, and enrichment with hormone receptor (HR)-negative and HER2-positive tumors. The most frequent somatic alterations in IBC involved TP53 (72%), ERBB2 (32%), PIK3CA (24%), CCND1 (12%), MYC (9%), FGFR1 (8%) and GATA3 (8%). A multivariate logistic regression analysis revealed a significant enrichment in TP53 SNVs in IBC; particularly in HER2-positive and HR-positive disease which was associated with worse outcomes. Tumor mutational burden (TMB) did not differ substantially between IBC and non-IBC cases and a pathway analysis revealed an enrichment in NOTCH pathway alterations in HER2-positive disease. Conclusion: Taken together, this study provides a comprehensive, clinically informed landscape of somatic alterations in a large cohort of patients with IBC. Our data support higher frequency of TP53 mutations and a potential enrichment in NOTCH pathway activation-but overall; a lack of major genomic differences. These results both reinforce the importance of TP53 alterations in IBC pathogenesis as well as their influence on clinical outcomes; but also suggest additional analyses beyond somatic DNA-level changes are warranted.

Comprehensive genomic profiling of salivary gland carcinoma: Analysis of the Center for Cancer Genomics and Advanced Therapeutics database in Japan.

Comprehensive information on genetic alterations in salivary gland cancer (SGC) is limited. This study aimed to elucidate the genetic and clinical characteristics of patients with SGC using the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database, a Japanese national genomic database. We analyzed data of 776 patients with SGC registered in the C-CAT database between June 1, 2019, and June 30, 2023. Adenoid cystic carcinoma was the most common histologic type, followed by salivary duct carcinoma (SDC) and adenocarcinoma not otherwise specified. Genetic data of 681 patients receiving FoundationOne® CDx were analyzed. We identified specific features of the combination of TP53 and CDKN2A alterations among the histological types. Specific LYN amplification was mainly detected in carcinoma ex pleomorphic adenoma and myoepithelial carcinoma. For SDC, the frequency of ERBB2 and BRAF alterations were higher in cases with metastatic lesions than in those with primary lesions. Although 28.6% patients were offered recommended treatment options, only 6.8% received the recommended treatments. This study highlights the differences in genetic alterations among the histological types of SGC, with comprehensive genomic profiling tests revealing lower drug accessibility. These findings could contribute to the development of personalized treatment for patients with SGC.