Molecular Classification of Lobular Carcinoma of the Breast

The combined signatures of the tumour microenvironment and nucleotide metabolism-related genes provide a prognostic and therapeutic biomarker for gastric cancer

The tumour microenvironment (TME) is vital to tumour development and influences the immunotherapy response. Abnormal nucleotide metabolism (NM) not only promotes tumour cell proliferation but also inhibits immune responses in the TME. Therefore, this study aimed to determine whether the combined signatures of NM and the TME could better predict the prognosis and treatment response in gastric cancer (GC). 97 NM-related genes and 22 TME cells were evaluated in TCGA-STAD samples, and predictive NM and TME characteristics were determined. Subsequent correlation analysis and single-cell data analysis illustrated a link between NM scores and TME cells. Thereafter, NM and TME characteristics were combined to construct an NM-TME classifier. Patients in the NMlow/TMEhigh group exhibited better clinical outcomes and treatment responses, which could be attributed to the differences in immune cell infiltration, immune checkpoint genes, tumour somatic mutations, immunophenoscore, immunotherapy response rate and proteomap. Additionally, the NMhigh/TMElow group benefited more from Imatinib, Midostaurin and Linsitinib, while patients in the NMlow/TMEhigh group benefited more from Paclitaxel, Methotrexate and Camptothecin. Finally, a highly reliable nomogram was developed. In conclusion, the NM-TME classifier demonstrated a pretreatment predictive value for prognosis and therapeutic responses, which may offer novel strategies for strategizing patients with optimal therapies.

Uterine metastasis of lobular breast carcinoma under tamoxifen therapy: A case report

Uterine metastases from breast cancer are uncommon and have rarely been reported in the previous literature. The present report describes the case of a 66-year-old female who developed uterine metastasis 23 years following the primary treatment of invasive breast cancer. Specifically, the patient experienced multiple bone metastases 14 years following primary treatment and had previously been treated with aromatase inhibitors followed by tamoxifen citrate. The patient presented with abnormal genital bleeding and was referred to the Gynecology Department of the Osaka City University Hospital (Osaka, Japan) 23 years following the primary treatment. The results of an endometrial biopsy revealed adenocarcinoma. Initially, it was difficult to differentiate between primary endometrial adenocarcinoma and metastatic adenocarcinoma from breast cancer. The results of pelvic magnetic resonance imaging demonstrated uterine myometrium enlargement and no endometrial thickness. Furthermore, an abdominal total hysterectomy, bilateral salpingo-oophorectomy and a biopsy of the peritoneum were performed. The pathological examination of the resected uterus revealed adenocarcinoma, which proliferated diffusively in the cervical stroma, myometrium, cardinal ligament, bilateral adnexa, omentum and peritoneum. Immunohistochemical results revealed the positive staining of gross cystic disease fluid protein-15, as well as negative staining for CD10 and E-cadherin. Thus, the tumor was diagnosed as metastatic adenocarcinoma from the breast lobular carcinoma. The patient has since been treated with fulvestrant, toremifene citrate and tegafur, and the current patient survival duration is 2 years and 8 months. In conclusion, when patients with breast cancer undergoing hormonal therapy, such as tamoxifen, present with abnormal genital bleeding, future diagnoses should consider both endometrial cancer and uterine metastasis from breast cancer.

Clinicopathological features of and neoadjuvant therapy for human epidermal growth factor receptor 2-positive classic invasive lobular carcinoma

Human epidermal growth factor receptor 2 (HER2)-positive classic invasive lobular carcinoma (ILC) is a rare entity, and its specific pathological characteristics and clinical behavior are unclear. Therefore, we retrospectively investigated the clinicopathological features of HER2-positive classic ILC and described HER2-targeted neoadjuvant chemotherapy outcome. Fourteen HER2-positive classic ILC cases were identified, their clinicopathological data were collected, and their clinical management was investigated. In general, HER2 positivity is associated with higher grade carcinoma in ductal lesions; however, our 14 cases had typical morphologic features of classic ILC. We did not identify unique morphologic features in HER2-positive classic ILC with the clinicopathological parameters examined, including age, menopausal status, histological grade, and hormone receptor status. Four of six patients who received HER2-targeted neoadjuvant chemotherapy had a pathological complete or partial response. Based on our findings, clinicopathological features of classic ILC may not predict HER2 overexpression or amplification. HER2-targeted neoadjuvant chemotherapy was effective against some HER2-positive classic ILCs. Taken together, these results demonstrate that HER2 immunohistochemistry and fluorescence in situ hybridization should be considered for all ILC patients regardless of subtype.

A nomogram for determining the disease-specific survival in invasive lobular carcinoma of the breast: A population study

We aimed to establish and validate a nomogram for predicting the disease-specific survival of invasive lobular carcinoma (ILC) patients.The Surveillance, Epidemiology, and End Results program database was used to identify ILC from 2010 to 2015, in which the data was extracted from 18 registries in the US. Multivariate Cox regression analysis was performed to identify independent prognostic factors and a nomogram was constructed to predict the 3-year and 5-year survival rates of ILC patients based on Cox regression. Predictive values were compared between the new model and the American Joint Committee on Cancer staging system using the concordance index, calibration plots, integrated discrimination improvement, net reclassification improvement, and decision-curve analyses.In total, 4155 patients were identified. After multivariate Cox regression analysis, nomogram was established based on a new model containing the predictive variables of age, the primary tumor site, histology grade, American Joint Committee on Cancer TNM (tumor node metastasis) stages II, III, and IV, breast cancer subtype, therapy modality (surgery and chemotherapy). The concordance index for the training and validation cohorts were higher for the new model (0.781 and 0.832, respectively) than for the old model (0.733 and 0.779). The new model had good performance in the calibration plots. Net reclassification improvement and integrated discrimination improvement were also improved. Finally, decision-curve analyses demonstrated that the nomogram was clinically useful.We have developed a reliable nomogram for determining the prognosis and treatment outcomes of ILC. The new model facilitates the choosing of superior medical examinations and the optimizing of therapeutic regimens with cooperation among oncologists.

Applications of single cell RNA sequencing to research of stem cells

Stem cells (SCs) with their self-renewal and pluripotent differentiation potential, show great promise for therapeutic applications to some refractory diseases such as stroke, Parkinsonism, myocardial infarction, and diabetes. Furthermore, as seed cells in tissue engineering, SCs have been applied widely to tissue and organ regeneration. However, previous studies have shown that SCs are heterogeneous and consist of many cell subpopulations. Owing to this heterogeneity of cell states, gene expression is highly diverse between cells even within a single tissue, making precise identification and analysis of biological properties difficult, which hinders their further research and applications. Therefore, a defined understanding of the heterogeneity is a key to research of SCs. Traditional ensemble-based sequencing approaches, such as microarrays, reflect an average of expression levels across a large population, which overlook unique biological behaviors of individual cells, conceal cell-to-cell variations, and cannot understand the heterogeneity of SCs radically. The development of high throughput single cell RNA sequencing (scRNA-seq) has provided a new research tool in biology, ranging from identification of novel cell types and exploration of cell markers to the analysis of gene expression and predicating developmental trajectories. scRNA-seq has profoundly changed our understanding of a series of biological phenomena. Currently, it has been used in research of SCs in many fields, particularly for the research of heterogeneity and cell subpopulations in early embryonic development. In this review, we focus on the scRNA-seq technique and its applications to research of SCs.

Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time

With the rapid evolution of high-throughput technologies, time series/longitudinal high-throughput experiments have become possible and affordable. However, the development of statistical methods dealing with gene expression profiles across time points has not kept up with the explosion of such data. The feature selection process is of critical importance for longitudinal microarray data. In this study, we proposed aggregating a gene's expression values across time into a single value using the sign average method, thereby degrading a longitudinal feature selection process into a classic one. Regularized logistic regression models with pseudogenes (i.e., the sign average of genes across time as predictors) were then optimized by either the coordinate descent method or the threshold gradient descent regularization method. By applying the proposed methods to simulated data and a traumatic injury dataset, we have demonstrated that the proposed methods, especially for the combination of sign average and threshold gradient descent regularization, outperform other competitive algorithms. To conclude, the proposed methods are highly recommended for studies with the objective of carrying out feature selection for longitudinal gene expression data.

Invasive lobular and ductal breast carcinoma differ in immune response, protein translation efficiency and metabolism

Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer following invasive ductal carcinoma (IDC). ILC differs from IDC in a number of histological and clinical features, such as single strand growth, difficulty in detection, and frequent late recurrences. To understand the molecular pathways involved in the clinical characteristics of ILC, we compared the gene expression profiles of luminal A ILC and luminal A IDC using data from TCGA and utilized samples from METABRIC as a validation data set. Top pathways that were significantly enriched in ILC were related to immune response. ILC exhibited a higher activity of almost all types of immune cells based on cell type-specific signatures compared to IDC. Conversely, pathways that were less enriched in ILC were related to protein translation and metabolism, which we functionally validated in cell lines. The higher immune activity uncovered in our study highlights the currently unexplored potential of a response to immunotherapy in a subset of patients with ILC. Furthermore, the lower rates of protein translation and metabolism - known features of tumor dormancy - may play a role in the late recurrences of ILC and lower detection rate in mammography and PET scanning.