Activation of FGFR2 Signaling Suppresses BRCA1 and Drives Triple-Negative Mammary Tumorigenesis That is Sensitive to Immunotherapy

Elucidating the Role of Wildtype and Variant FGFR2 Structural Dynamics in (Dys)Function and Disorder

The fibroblast growth factor receptor 2 (FGFR2) gene is one of the most extensively studied genes with many known mutations implicated in several human disorders, including oncogenic ones. Most FGFR2 disease-associated gene mutations are missense mutations that result in constitutive activation of the FGFR2 protein and downstream molecular pathways. Many tertiary structures of the FGFR2 kinase domain are publicly available in the wildtype and mutated forms and in the inactive and activated state of the receptor. The current literature suggests a molecular brake inhibiting the ATP-binding A loop from adopting the activated state. Mutations relieve this brake, triggering allosteric changes between active and inactive states. However, the existing analysis relies on static structures and fails to account for the intrinsic structural dynamics. In this study, we utilize experimentally resolved structures of the FGFR2 tyrosine kinase domain and machine learning to capture the intrinsic structural dynamics, correlate it with functional regions and disease types, and enrich it with predicted structures of variants with currently no experimentally resolved structures. Our findings demonstrate the value of machine learning-enabled characterizations of structure dynamics in revealing the impact of mutations on (dys)function and disorder in FGFR2.

Artificial tumor matrices and bioengineered tools for tumoroid generation

The tumor microenvironment (TME) is critical for tumor growth and metastasis. The TME contains cancer-associated cells, tumor matrix, and tumor secretory factors. The fabrication of artificial tumors, so-called tumoroids, is of great significance for the understanding of tumorigenesis and clinical cancer therapy. The assembly of multiple tumor cells and matrix components through interdisciplinary techniques is necessary for the preparation of various tumoroids. This article discusses current methods for constructing tumoroids (tumor tissue slices and tumor cell co-culture) for pre-clinical use. This article focuses on the artificial matrix materials (natural and synthetic materials) and biofabrication techniques (cell assembly, bioengineered tools, bioprinting, and microfluidic devices) used in tumoroids. This article also points out the shortcomings of current tumoroids and potential solutions. This article aims to promotes the next-generation tumoroids and the potential of them in basic research and clinical application.

Genome-Wide Association Study of the Reproductive Traits of the Dazu Black Goat (Capra hircus) Using Whole-Genome Resequencing

Reproductive traits are the basic economic traits of goats and important indicators in goat breeding. In this study, Dazu black goats (DBGs; n = 150), an important Chinese local goat breed with excellent reproductive performance, were used to screen for important variation loci and genes of reproductive traits. Through genome-wide association studies (GWAS), 18 SNPs were found to be associated with kidding traits (average litter size, average litter size in the first three parity, and average litter size in the first six parity), and 10 SNPs were associated with udder traits (udder depth, teat diameter, teat length, and supernumerary teat). After gene annotation of the associated SNPs and in combination with relevant references, the candidate genes, namely ATP1A1, LRRC4C, SPCS2, XRRA1, CELF4, NTM, TMEM45B, ATE1, and FGFR2, were associated with udder traits, while the ENSCHIG00000017110, SLC9A8, GLRB, GRIA2, GASK1B, and ENSCHIG00000026285 genes were associated with litter size. These SNPs and candidate genes can provide useful biological information for improvement of the reproductive traits of goats.

Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation

Drug combination therapy is a highly effective approach for enhancing the therapeutic efficacy of anti-cancer drugs and overcoming drug resistance. However, the innumerable possible drug combinations make it impractical to screen all synergistic drug pairs. Moreover, biological insights into synergistic drug pairs are still lacking. To address this challenge, we systematically analyzed drug combination datasets curated from multiple databases to identify drug pairs more likely to show synergy. We classified drug pairs based on their MoA and discovered that 110 MoA pairs were significantly enriched in synergy in at least one type of cancer. To improve the accuracy of predicting synergistic effects of drug pairs, we developed a suite of machine learning models that achieve better predictive performance. Unlike most previous methods that were rarely validated by wet-lab experiments, our models were validated using two-dimensional cell lines and three-dimensional tumor slice culture (3D-TSC) models, implying their practical utility. Our prediction and validation results indicated that the combination of the RTK inhibitors Lapatinib and Pazopanib exhibited a strong therapeutic effect in breast cancer by blocking the downstream PI3K/AKT/mTOR signaling pathway. Furthermore, we incorporated molecular features to identify potential biomarkers for synergistic drug pairs, and almost all potential biomarkers found connections between drug targets and corresponding molecular features using protein-protein interaction network. Overall, this study provides valuable insights to complement and guide rational efforts to develop drug combination treatments.

Machine learning-assisted analysis of epithelial mesenchymal transition pathway for prognostic stratification and immune infiltration assessment in ovarian cancer

Background

Ovarian cancer is the most lethal gynaecological malignancy, and serous ovarian cancer (SOC) is one of the more important pathological subtypes. Previous studies have reported a significant association of epithelial tomesenchymal transition (EMT) with invasive metastasis and immune modulation of SOC, however, there is a lack of prognostic and immune infiltration biomarkers reported for SOC based on EMT.

Methods

Gene expression data for ovarian cancer and corresponding patient clinical data were collected from the TCGA database and the GEO database, and cell type annotation and spatial expression analysis were performed on single cell sequencing data from the GEO database. To understand the cell type distribution of EMT-related genes in SOC single-cell data and the enrichment relationships of biological pathways and tumour functions. In addition, GO functional annotation analysis and KEGG pathway enrichment analysis were performed on mRNAs predominantly expressed with EMT to predict the biological function of EMT in ovarian cancer. The major differential genes of EMT were screened to construct a prognostic risk prediction model for SOC patients. Data from 173 SOC patient samples obtained from the GSE53963 database were used to validate the prognostic risk prediction model for ovarian cancer. Here we also analysed the direct association between SOC immune infiltration and immune cell modulation and EMT risk score. and calculate drug sensitivity scores in the GDSC database.In addition, we assessed the specific relationship between GAS1 gene and SOC cell lines.

Results

Single cell transcriptome analysis in the GEO database annotated the major cell types of SOC samples, including: T cell, Myeloid, Epithelial cell, Fibroblast, Endothelial cell, and Bcell. cellchat revealed several cell type interactions that were shown to be associated with EMT-mediated SOC invasion and metastasis. A prognostic stratification model for SOC was constructed based on EMT-related differential genes, and the Kapan-Meier test showed that this biomarker had significant prognostic stratification value for several independent SOC databases. The EMT risk score has good stratification and identification properties for drug sensitivity in the GDSC database.

Conclusions

This study constructed a prognostic stratification biomarker based on EMT-related risk genes for immune infiltration mechanisms and drug sensitivity analysis studies in SOC. This lays the foundation for in-depth clinical studies on the role of EMT in immune regulation and related pathway alterations in SOC. It is also hoped to provide effective potential solutions for early diagnosis and clinical treatment of ovarian cancer.

Luminal-Type Invasive Carcinoma in Association With Microglandular Adenosis/Atypical Microglandular Adenosis: A Case Report and Molecular Comparison

Microglandular adenosis (MGA) is a proliferative breast lesion composed of small, uniform glands lacking a myoepithelial cell layer while still invested by the basement membrane. The glands percolate haphazardly through the breast parenchyma rather than maintaining a lobular architecture, typical of other forms of adenosis.MGA is a benign lesion though atypical forms have been well described, often in close association with carcinoma. MGA, atypical MGA (AMGA), and the vast majority of MGA-associated carcinomas (MGACA) are negative for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor 2 (HER2) by immunohistochemistry. In light of these findings and early molecular studies, MGA is hypothesized to represent a clonal process and nonobligate precursor of basal-type breast carcinomas. We present the case of a 58-year-old woman and the first published molecular comparison of a luminal-type invasive ductal carcinoma with its associated MGA/AMGA. Analysis of small nucleotide variants (SNVs) revealed that 63% of the SNVs identified in the MGA were present in the AMGA while only 10% of them were present in the MGACA, suggesting a direct relationship between MGA and AMGA but not MGA and MGACA.

Dissecting the roles and clinical potential of YY1 in the tumor microenvironment

Yin-Yang 1 (YY1) is a member of the GLI-Kruppel family of zinc finger proteins and plays a vital dual biological role in cancer as an oncogene or a tumor suppressor during tumorigenesis and tumor progression. The tumor microenvironment (TME) is identified as the “soil” of tumor that has a critical role in both tumor growth and metastasis. Many studies have found that YY1 is closely related to the remodeling and regulation of the TME. Herein, we reviewed the expression pattern of YY1 in tumors and summarized the function and mechanism of YY1 in regulating tumor angiogenesis, immune and metabolism. In addition, we discussed the potential value of YY1 in tumor diagnosis and treatment and provided a novel molecular strategy for the clinical diagnosis and treatment of tumors.

Targeted thorium-227 conjugates as treatment options in oncology

Targeted alpha therapy (TAT) is a promising approach for addressing unmet needs in oncology. Inherent properties make α-emitting radionuclides well suited to cancer therapy, including high linear energy transfer (LET), penetration range of 2-10 cell layers, induction of complex double-stranded DNA breaks, and immune-stimulatory effects. Several alpha radionuclides, including radium-223 (²²³Ra), actinium-225 (²²⁵Ac), and thorium-227 (²²⁷Th), have been investigated. Conjugation of tumor targeting modalities, such as antibodies and small molecules, with a chelator moiety and subsequent radiolabeling with α-emitters enables specific delivery of cytotoxic payloads to different tumor types. ²²³Ra dichloride, approved for the treatment of patients with metastatic castration-resistant prostate cancer (mCRPC) with bone-metastatic disease and no visceral metastasis, is the only approved and commercialized alpha therapy. However, ²²³Ra dichloride cannot currently be complexed to targeting moieties. In contrast to ²²³Ra, ²²⁷Th may be readily chelated, which allows radiolabeling of tumor targeting moieties to produce targeted thorium conjugates (TTCs), facilitating delivery to a broad range of tumors. TTCs have shown promise in pre-clinical studies across a range of tumor-cell expressing antigens. A clinical study in hematological malignancy targeting CD22 has demonstrated early signs of activity. Furthermore, pre-clinical studies show additive or synergistic effects when TTCs are combined with established anti-cancer therapies, for example androgen receptor inhibitors (ARI), DNA damage response inhibitors such as poly (ADP)-ribose polymerase inhibitors or ataxia telangiectasia and Rad3-related kinase inhibitors, as well as immune checkpoint inhibitors.

Transcriptomic Analysis of Subtype-Specific Tyrosine Kinases as Triple Negative Breast Cancer Biomarkers

Triple negative breast cancer (TNBC) shows impediment to the development of targeted therapies due to the absence of specific molecular targets. The high heterogeneity across TNBC subtypes, which can be classified to be at least four subtypes, including two basal-like (BL1, BL2), a mesenchymal (M), and a luminal androgen receptor (LAR) subtype, limits the response to cancer therapies. Despite many attempts to identify TNBC biomarkers, there are currently no effective targeted therapies against this malignancy. In this study, thus, we identified the potential tyrosine kinase (TK) genes that are uniquely expressed in each TNBC subtype, since TKs have been typically used as drug targets. Differentially expressed TK genes were analyzed from The Cancer Genome Atlas (TCGA) database and were confirmed with the other datasets of both TNBC patients and cell lines. The results revealed that each TNBC subtype expressed distinct TK genes that were specific to the TNBC subtype. The identified subtype-specific TK genes of BL1, BL2, M, and LAR are LYN, CSF1R, FGRF2, and SRMS, respectively. These findings could serve as a potential biomarker of specific TNBC subtypes, which could lead to an effective treatment for TNBC patients.

SB Digestor: a tailored driver gene identification tool for dissecting heterogeneous Sleeping Beauty transposon-induced tumors

Sleeping Beauty (SB) insertional mutagenesis has been widely used for genome-wide functional screening in mouse models of human cancers, however, intertumor heterogeneity can be a major obstacle in identifying common insertion sites (CISs). Although previous algorithms have been successful in defining some CISs, they also miss CISs in certain situations. A major common characteristic of these previous methods is that they do not take tumor heterogeneity into account. However, intertumoral heterogeneity directly influences the sequence read number for different tumor samples and then affects CIS identification. To precisely detect and define cancer driver genes, we developed SB Digestor, a computational algorithm that overcomes biological heterogeneity to identify more potential driver genes. Specifically, we define the relationship between the sequenced read number and putative gene number to deduce the depth cutoff for each tumor, which can reduce tumor complexity and precisely reflect intertumoral heterogeneity. Using this new tool, we re-analyzed our previously published SB-based screening dataset and identified many additional potent drivers involved in Brca1-related tumorigenesis, including Arhgap42, Tcf12, and Fgfr2. SB Digestor not only greatly enhances our ability to identify and prioritize cancer drivers from SB tumors but also substantially deepens our understanding of the intrinsic genetic basis of cancer.

成纤维细胞生长因子受体2在肾透明细胞癌中的表达及意义

OBJECTIVE

To investigate the expression of fibroblast growth factor receptor 2 (FGFR2) in clear cell renal cell carcinoma (ccRCC; or kidney renal clear cell carcinoma, KIRC), to analyze the relationship between the expression of FGFR2 and the clinical pathological features and prognosis of ccRCC, to study the relationship between the expression of FGFR2 and other molecules, and to explore its role in the development of ccRCC.

METHODS

Gene expressional and clinical information of ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus(GEO) database. Next, the data were transformed and collated. In the study, 104 clinical ccRCC samples and corresponding paracancerous normal tissue samples were collected from Department of Urology, Peking University First Hospital. Immunohistochemistry (IHC) was performed and the staining results were scored, so as to compare the expression of FGFR2 in ccRCC and paracancerous normal tissues. Besides, quantify real-time polymerase chain reaction (qRT-PCR) was used to detect the mRNA expression level of FGFR2 in normal renal epithelial cell lines (293) and ccRCC cell lines (786-O, 769-P, OSRC-2, Caki-1, ACHN, and A498). In addition, the relationship between FGFR2 expression and clinical pathological characteristics (including TNM staging and pathological grading) and survival prognosis in ccRCC patients was further analyzed. Furthermore, the relationship between FGFR2 expression and B cells, T cells, natural killer (NK) cells and neutrophil infiltration in the ccRCC patients was analyzed, and the Biological General Repository for Interactionh Datasets (BioGRID) was used to builds protein-protein interaction (PPI) networks to study molecules that interacted with the FGFR2 protein.

RESULTS

In the TCGA database, the expression of FGFR2 was down-regulated in ccRCC tissue samples compared with normal tissue samples, and the expression in the GEO database also showed this differences. Furthermore, FGFR2 expression was downregulated in ccRCC clinical samples and ccRCC cell lines, compared with corresponding paracancerous normal tissue or normal renal epithelial cell lines. In addition, FGFR2 high expression was associated with earlier, lower-level ccRCC and was associated with a better prognosis in the patients with ccRCC. Moreover, FGFR2 expression was not significantly related to B cells, T cells, NK cells and neutrophil infiltration, and the PPI network showed that FGFR2 protein interacted with certain molecules.

CONCLUSION

Our work sheds light on the potential role of FGFR2 in the development of ccRCC, suggesting that FGFR2 may serve as a prognostic marker and potential therapeutic target for patients with ccRCC.

FGFR2-BRD4 Axis Regulates Transcriptional Networks of Histone 3 Modification and Synergy Between Its Inhibitors and PD-1/PD-L1 in a TNBC Mouse Model

Epigenetic reprogramming is an independent mode of gene expression that often involves changes in the transcription and chromatin structure due to tumor initiation and development. In this study, we developed a specifically modified peptide array and searched for a recognized epigenetic reader. Our results demonstrated that BRD4 is not only an acetylation reader but of propionylation as well. We also studied the quantitative binding affinities between modified peptides and epigenetic regulators by isothermal titration calorimetry (ITC). Furthermore, we introduced the Fgfr2-S252W transgenic mouse model to confirm that this acetylation is associated with the activation of c-Myc and drives tumor formation. Targeted disruption of BRD4 in Fgfr2-S252W mouse tumor cells also confirmed that BRD4 is a key regulator of histone 3 acetylation. Finally, we developed a tumor slice culture system and demonstrated the synergy between immune checkpoint blockade and targeted therapy in triple-negative breast cancer (TNBC). These data extend our understanding of epigenetic reprogramming and epigenetics-based therapies.