Clinicopathological Features, Tumor Mutational Burden, and Tumour-Infiltrating Lymphocyte Interplay in ERBB2-Mutated Breast Cancer: In Silico Analysis

A Proposal for a Modified Evaluation System of Tumor-Infiltrating Lymphocytes Using HE-Stained Sections in Breast Cancer

Tumor-infiltrating lymphocyte (TIL) scoring in tumor specimens has gained increasing attention in determining patients who are likely to benefit from immunotherapies. However, the histological evaluation methods of TILs in breast cancer remain limited. This study aimed to assess four components of lymphocytic reaction and overall lymphocytic score (L-score) used in colorectal cancer, investigate its association with clinicopathological factors, and examine the effect of TILs on postoperative mortality using 231 invasive breast cancers without neoadjuvant chemotherapy. Besides L-score, increasing modified L-score lacking peritumoral lymphocytic reaction was significantly associated with aggressive breast cancer phenotypes, including larger invasive size, higher tumor stage, higher Ki-67 labeling index, triple negative and HER2-enriched subtypes, and higher Nottingham histological grade. Importantly, modified L-score status but not L-score or TIL-Working Group (WG) score status was positively correlated with the disease-specific survival rate of the overall patients as well as the patients with luminal type or histological Grade III breast cancers. These results indicated that the modified L-score is a favorable method to comprehensively assess lymphocytic reaction to predict prognosis among patients with breast cancer, even compared with the currently used TIL-WG method, which may possess their potential integration into clinical practice.

Computational molecular insights into ibrutinib as a potent inhibitor of HER2-L755S mutant in breast cancer: gene expression studies, virtual screening, docking, and molecular dynamics analysis

Background

The proposed study integrates several advanced computational techniques to unravel the molecular mechanisms underlying breast cancer progression and drug resistance.

Methods

We investigated HER2-L755S mutation through a multi-step approach, including gene expression analysis, molecular docking, and molecular dynamics simulations.

Results and Discussion

By conducting a network-based analysis of gene expression data from breast cancer samples, key hub genes such as MYC, EGFR, CDKN2A, ERBB2, CDK1, E2F1, TOP2A, MDM2, TGFB1, and FOXM1 were identified, all of which are critical in tumor growth and metastasis. The study mainly focuses on the ERBB2 gene, which encodes the HER2 protein, and its common mutation HER2-L755S, associated with breast cancer and resistance to the drug lapatinib. The HER2-L755S mutation contributes to both tumorigenesis and therapeutic failure. To address this, alternative therapeutic strategies were investigated using combinatorial computational approaches. The stability and flexibility of the HER2-L755S mutation were evaluated through comparative molecular dynamics simulations over 1000 ns using Gromacs in the unbound (Apo) state. Virtual screening with Schrodinger Glide identified ibrutinib as a promising alternative to lapatinib for targeting the HER2-L755S mutant. Detailed docking and molecular dynamics simulations in the bound (Holo) state demonstrated that the HER2-L755S-ibrutinib complex exhibited higher binding affinity and lower binding energy, indicating more stable interactions compared to other complexes. MM-PBSA analysis revealed that the HER2-L755S-ibrutinib complex had more negative binding energy than the HER2-L755S-afatinib, HER2-L755S-lapatinib, and HER2-L755S-neratinib complexes, suggesting that ibrutinib forms the most stable complex with favorable binding interactions.

Conclusion

These results provide in-depth atomic-level insights into the binding mechanisms of these inhibitors, highlighting ibrutinib as a potentially effective inhibitor for the clinical treatment of breast cancer.

Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review

Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E-stained whole-slide images (WSIs) that can be used to develop deep learning algorithms. We systematically searched 9 scientific literature databases and 9 research data repositories and found 17 publicly available datasets containing 10 385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled 2 lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata.

Insights into E-Cadherin Impairment in CDH1-Unaltered Invasive Lobular Carcinoma: A Comprehensive Bioinformatic Study

Invasive lobular carcinoma exhibits unique morphological features frequently associated with alterations in CDH1. Although some studies have identified abnormalities in adhesion factors other than E-cadherin, the molecular mechanisms underlying E-cadherin abnormalities in CDH1-unaltered invasive lobular carcinoma remain poorly understood. In this study, we investigated the molecular underpinnings of E-cadherin dysregulation in invasive lobular carcinoma in the absence of CDH1 gene alterations, using comprehensive bioinformatic analyses. We conducted a comparative study of CDH1-mutated and non-mutated invasive lobular carcinoma and evaluated the differences in mRNA levels, reverse-phase protein array, methylation, and miRNAs. We observed that invasive lobular carcinoma cases without CDH1 alterations exhibited a significantly higher incidence of the Claudin-low subtype (p < 0.01). The results of the reverse-phase protein array indicate no significant difference in E-cadherin expression between CDH1-mutated and non-mutated cases. Therefore, abnormalities in E-cadherin production also exist in CDH1 non-mutated invasive lobular carcinoma. Considering that there are no differences in mRNA levels and methylation status, post-translational modifications are the most plausible explanation for the same. Hence, future studies should focus on elucidating the mechanism underlying E-cadherin inactivation via post-translational modifications in CDH1 non-mutated invasive lobular carcinoma.

MCM2 is involved in subtyping and tamoxifen resistance of ERα-positive breast cancer by acting as the downstream factor of ERα

Tamoxifen (TAM) resistance is finally developed in over 40% of patients with estrogen receptor α-positive breast cancer (ERα+ -BC), documenting that discovering new molecular subtype is needed to confer perception to the heterogeneity of ERα+ -BC. We obtained representative gene sets subtyping ERα+ -BC using gene set variation analysis (GSVA), non-negative matrix factorization (NMF), and COX regression methods on the basis of METABRIC, TCGA, and GEO databases. Furthermore, the risk score of ERα+ -BC subtyping was established using least absolute shrinkage and selection operator (LASSO) regression on the basis of genes in the representative gene sets, thereby generating the two subtypes of ERα+ -BC. We further found that minichromosome maintenance complex component 2 (MCM2) functioned as the hub gene subtyping ERα+ -BC using GO, KEGG, and MCODE. MCM2 expression was capable for specifically predicting 1-year overall survival (OS) of ERα+ -BC and correlated with T stage, AJCC stage, and tamoxifen (TAM) sensitivity of ERα+ -BC. The downregulation of MCM2 expression inhibited proliferation, migration, and invasion of TAM-resistant cells and promoted G0/G1 arrest. Altogether, tamoxifen resistance entails that MCM2 is a hub gene subtyping ERα+ -BC, providing a novel dimension for discovering a potential target of TAM-resistant BC.

ERBB2-Mutant Gastrointestinal Tumors Represent Heterogeneous Molecular Biology, Particularly in Microsatellite Instability, Tumor Mutation Burden, and Co-Mutated Genes: An In Silico Study

During recent years, activating mutations in ERBB2 have been reported in solid tumors of various organs, and clinical trials targeting ERBB2-mutant tumors have been conducted. However, no effective treatment has been established for gastrointestinal tumors targeting ERBB2 mutations. ERBB2-mutant tumors have a higher tumor mutation burden (TMB) and microsatellite instability (MSI) than ERBB2 non-mutant tumors, but not all ERBB2-mutant tumors are TMB- and MSI-high. Thus, a more detailed classification of ERBB2-mutant tumors based on the underlying molecular mechanisms is required. Herein, we classified ERBB2 mutations into three groups-group 1: both ERBB2 mutations and amplifications; group 2: ERBB2 mutations annotated as putative driver mutations but without amplifications; group 3: ERBB2 mutations annotated as non-driver mutations (passenger mutations or unknown significance) and those that were not amplified in gastrointestinal tumors. Esophageal adenocarcinoma, gastric cancer, and colorectal cancer presented significantly higher MSI and TMB in the ERBB2-mutant group than in the ERBB2-wild-type group. The proportions of TMB- and MSI-high tumors and frequency of co-mutated downstream genes differed among the groups. We identified TMB- and MSI-high groups; this classification is considered important for guiding the selection of drugs for ERBB2-mutant tumors with downstream genetic mutations.

Immunogenic cell death-related gene landscape predicts the overall survival and immune infiltration status of ovarian cancer

Background: Ovarian cancer (OC) is the most troubling malignant tumor of the female reproductive system. It has a low early diagnosis rate and a high tumor recurrence rate after treatment. Immunogenic cell death (ICD) is a unique form of regulated cell death that can activate the adaptive immune system through the release of DAMPs and cytokines in immunocompromised hosts and establish long-term immunologic memory. Therefore, this study aims to explore the prognostic value and underlying mechanisms of ICD-related genes in OC on the basis of characteristics. Methods: The gene expression profiles and related clinical information of OC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. ICD-related genes were collected from the Genecards database. ICD-related prognostic genes were obtained by intersecting ICD-related genes with the OC prognostic-related genes that were analyzed in the TCGA database. Functional enrichment, genetic mutation, and immune infiltration correlation analyses were further performed to identify underlying mechanisms. Subsequently, we developed a TCGA cohort-based prognostic risk model that included a nine-gene signature through univariate and multivariate Cox regression and LASSO regression analyses. Meanwhile, external validation was performed on two sets of GEO cohorts and the TCGA training cohort for three other common tumors in women. In addition, a nomogram was established by integrating clinicopathological features and ICD-related gene signature to predict survival probability. Finally, functional enrichment and immune infiltration analyses were performed on the two risk subgroups. Results: By utilizing nine genes (ERBB2, RB1, CCR7, CD38, IFNB1, ANXA2, CXCL9, SLC9A1, and SLAMF7), we constructed an ICD-related prognostic signature. Subsequently, patients were subdivided into high- and low-risk subgroups in accordance with the median value of the risk score. In multivariate Cox regression analyses, risk score was an independent prognostic factor (hazard ratio = 2.783; p < 0.01). In the TCGA training cohort and the two GEO validation cohorts, patients with high-risk scores had worse prognosis than those with low-risk scores (p < 0.05). The time-dependent receiver operating characteristic curve further validated the prognostic power of the gene signature. Finally, gene set enrichment analysis indicated that multiple oncological pathways were significantly enriched in the high-risk subgroup. By contrast, the low-risk subgroup was strongly related to the immune-related signaling pathways. Immune infiltration analysis further illustrated that most immune cells showed higher levels of infiltration in the low-risk subgroup than in the high-risk subgroup. Conclusion: We constructed a novel ICD-related gene model for forecasting the prognosis and immune infiltration status of patients with OC. In the future, new ICD-related genes may provide novel potential targets for the therapeutic intervention of OC.