Nomogram to Predict Tumor-Infiltrating Lymphocytes in Breast Cancer Patients

Association of Computed Tomography Scan-Assessed Body Composition with Immune and PI3K/AKT Pathway Proteins in Distinct Breast Cancer Tumor Components

This hypothesis-generating study aims to examine the extent to which computed tomography-assessed body composition phenotypes are associated with immune and phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathways in breast tumors. A total of 52 patients with newly diagnosed breast cancer were classified into four body composition types: adequate (lowest two tertiles of total adipose tissue [TAT]) and highest two tertiles of total skeletal muscle [TSM] areas); high adiposity (highest tertile of TAT and highest two tertiles of TSM); low muscle (lowest tertile of TSM and lowest two tertiles of TAT); and high adiposity with low muscle (highest tertile of TAT and lowest tertile of TSM). Immune and PI3K/AKT pathway proteins were profiled in tumor epithelium and the leukocyte-enriched stromal microenvironment using GeoMx (NanoString). Linear mixed models were used to compare log2-transformed protein levels. Compared with the normal type, the low muscle type was associated with higher expression of INPP4B (log2-fold change = 1.14, p = 0.0003, false discovery rate = 0.028). Other significant associations included low muscle type with increased CTLA4 and decreased pan-AKT expression in tumor epithelium, and high adiposity with increased CD3, CD8, CD20, and CD45RO expression in stroma (p < 0.05; false discovery rate > 0.2). With confirmation, body composition can be associated with signaling pathways in distinct components of breast tumors, highlighting the potential utility of body composition in informing tumor biology and therapy efficacies.

Establishment and verification of a prognostic immune cell signature-based model for breast cancer overall survival

Background

Breast cancer (BRCA) is a prevalent and aggressive disease. Despite various treatments being applied, a significant number of patients continue to experience unfavorable prognoses. Accurate prognosis prediction in BRCA is crucial for tailoring individualized treatment plans and improving patient outcomes. Recent studies have highlighted the significance of immune cell infiltration in the tumor microenvironment (TME), but predicting survival remains challenging due to the heterogeneity of BRCA. The aim of this study was thus to produce an immune cell signature-based framework capable of predicting the prognosis of patients with BRCA.

Methods

The GSE169246 dataset was from the Gene Expression Omnibus (GEO) database, comprising single-cell RNA sequencing (scRNA-seq) data from 95 individuals with BRCA. Seurat, principal component analysis (PCA), the unified matrix polynomial approach (UMAP) algorithm, and linear dimensionality reduction were used to determine the heterogeneity of T cells. Overlapping analysis of differentially expressed genes (DEGs), genes associated with prognosis, and T-cell pharmacodynamics-related genes were used to obtain the T-cell core pharmacodynamics-related genes. The dimensionality of the T-cell core pharmacodynamics-related genes was reduced employing the least absolute shrinkage and selection operator (LASSO) Cox regression model and the LASSO model. The prognostic model was built via a Cox analysis of the overall survival (OS) information. The clinical sample included 95 patients with BRCA who underwent surgical treatment from October 2018 to October 2021 at the Second Affiliated Hospital of Qiqihar Medical University. Patients were divided into a good prognosis group and a poor prognosis group based on their prognostic outcomes. The predictive value of tumor characteristics and immune responses was validated through correlation analysis, logistic regression analysis, and receiver operating characteristic (ROC) analysis.

Results

A group of 95 genes was used to establish a prognostic model. In the GEO clinical sample, with a high-risk group demonstrating shorter median survival times (2,447 vs. 6,498 days, P=4.733e-12). Area under the curve (AUC) values of 0.75, 0.75, and 0.72 were obtained for 2-, 4-, and 6-year OS predictions, respectively. Clinical validation found that the 6-year OS of the favorable prognosis group was significantly higher than that of the unfavorable prognosis group (92.06% vs. 65.62%; P=0.005). Poor prognosis was positively correlated with age, tumor size, B-cell level, and CTLA4 level and negatively correlated with tumor stage (T1/T2), lymph node metastasis stage (N0), clinical stage I-II, CD3+T-cell, CD4+T-cell, CD8+T-cell, neutrophil, lymphocyte, natural kill cell, TIGIT expression and OS. The combined model of clinical parameters had an AUC value of 0.898.

Conclusions

This study established a prognostic model that demonstrated excellent predictive value for OS of BRCA. The predictive model developed offers valuable insights into prognosis and treatment planning, emphasizing the importance of tumor characteristics and immune cell infiltration.

Development and validation of a neoadjuvant chemotherapy pathological complete remission model based on Reg IV expression in breast cancer tissues: a clinical retrospective study

OBJECTIVE

To develop and authenticate a neoadjuvant chemotherapy (NACT) pathological complete remission (pCR) model based on the expression of Reg IV within breast cancer tissues with the objective to provide clinical guidance for precise interventions.

METHOD

Data relating to 104 patients undergoing NACT were collected. Variables derived from clinical information and pathological characteristics of patients were screened through logistic regression, random forest, and Xgboost methods to formulate predictive models. The validation and comparative assessment of these models were conducted to identify the optimal model, which was then visualized and tested.

RESULT

Following the screening of variables and the establishment of multiple models based on these variables, comparative analyses were conducted using receiver operating characteristic (ROC) curves, calibration curves, as well as net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Model 2 emerged as the most optimal, incorporating variables such as HER-2, ER, T-stage, Reg IV, and Treatment, among others. The area under the ROC curve (AUC) for Model 2 in the training dataset and test dataset was 0.837 (0.734-0.941) and 0.897 (0.775-1.00), respectively. Decision curve analysis (DCA) and clinical impact curve (CIC) further underscored the potential applications of the model in guiding clinical interventions for patients.

CONCLUSION

The prediction of NACT pCR efficacy based on the expression of Reg IV in breast cancer tissue appears feasible; however, it requires further validation.

Survival analysis and clinicopathological features of patients with stage IA lung adenocarcinoma

Background

With the development of medical technology and change of life habits, early-stage lung adenocarcinoma (LUAD) has become more common. This study aimed to systematically analyzed clinicopathological factors associated to the overall survival (OS) of patients with Stage IA LUAD.

Methods

A total of 5942 Stage IA LUAD patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier methods and log-rank tests were used to compare the differences in OS. A nomogram constructed based on the Cox regression was evaluated by Concordance index (C index), calibration curve, decision curve analysis (DCA) and area under curve (AUC). And 136 patients were recruited from Shandong Province Hospital for external validation.

Results

Cox analysis regression indicated that 12 factors, such as Diagnosis to Treatment Interval (DTI) and Income Level, were independent prognostic factors and were included to establish the nomogram. The C-index of our novel model was 0.702, 0.724 and 0.872 in the training, internal and external validation cohorts, respectively. The 3-year and 5-year survival AUCs and calibration curves showed excellent agreement in each cohort. Some new factors in the SEER database, including DTI and Income Level, were firstly confirmed as independent prognostic factors of Stage IA LUAD patients. The distribution of these factors in the T1a, T1b, and T1c subgroups differed and had different effects on survival.

Conclusion

We summarized 12 factors that affect prognosis and constructed a nomogram to predict OS of Stage IA LUAD patients who underwent operation. For the first time, new SEER database parameters, including DTI and Income Level, were proved to be survival-related.

Mechanism of non-small cell lung cancer cell-derived exosome miR-196b-5p promoting pyroptosis of tumor T cells and tumor cell proliferation by downregulating ING5

In the world, lung cancer is one of the most common malignant cancers and has become the leading cause of death of cancers in China, among which non-small cell lung cancer (NSCLC) accounts for a relatively high proportion, but there is a lack of effective treatment at present. An animal model of NSCLC was established, and BEAS-2b, H1299, Lewis, and T cells were used for subsequent experimental verification. The level of miR-196b-5p was detected by quantitative real-time polymerase chain reaction. Growth inhibitor 5 (ING5), CD9, CD63, HSP70, Caspase-1, NLRP3, and GSDMD-NT were detected by western blot. The level of ING5 was confirmed by immunohistochemistry, the location of miR-196b-5p was analyzed by fluorescence in situ hybridization (FISH), cell viability was investigated by Cell Counting Kit-8 kit, and interleukin (IL)-1β and IL-18 were confirmed by enzyme-linked immunosorbent assay. Cell apoptosis was detected by flow cytometry. In addition, the binding site was verified by dual-luciferase reporter gene experiments. Tumor volume was measured. TUNEL staining was used to detect apoptosis. Flow cytometry was used to measure the levels of CD8 T, CD4 T, and Treg cells in tumors. miR-196-5p was highly expressed in exosomes secreted by tumor cells. miR-196-5p negatively targeted ING5 to promote the growth of tumor cells. Cancer-derived exosomes promote pyroptosis of T cells to further aggravate the development of cancer. Exosome-derived miR-196b-5p promoted pyroptosis of T cells. Exosome-derived miR-196b-5p inhibited the level of ING5 to promote tumor growth and accelerate the process of NSCLC.

PD-L1 and TILs expression and their association with clinicopathological characteristics in Vietnamese women with primary invasive breast cancer

Tumor-infiltrating lymphocytes (TILs) and programmed death ligand 1 (PD-L1) are promising new factors in the prognosis and prediction of breast cancer patients. Our study evaluated the prevalence of expression of TILs on hematoxylin and eosin (H&E) slides, PD-L1 expression on immunohistochemistry, and their association with clinicopathological characteristics in Vietnamese women with invasive breast cancer. This study was conducted on 216 women with primary invasive breast cancer. The evaluation of TILs on the HE slides was based on the International TILs Working Group 2014 recommendation. PD-L1 protein expression was determined using the Combined Positive Score, the number of tumor cells, lymphocytes, and macrophages stained by PD-L1 divided by the total viable tumor cells multiplied by 100. Based on the cutoff of 11%, the prevalence of TILs expression was 35.6%, of which highly expressed TILs (≥50%) accounted for 15.3%. Postmenopausal women and those who had a body mass index of 25 kg/m2 or greater had a higher odds of having TILs expression. However, patients who had the expression of Ki-67, HER-positive molecular subtype, and triple-negative subtype were more likely to have TILs expression. The prevalence of PD-L1 expression was 30.1%. A significantly higher odds of having PD-L1 was found in patients who had a history of benign breast disease, self-detected tumor and had TILs expression. The expression of TILs and PD-L1 is common in Vietnamese women with invasive breast cancer. Because of the importance of these expressions, routine evaluation to find women who had TILs and PD-L1 is needed so that treatment and prognosis can be optimized. Such routine evaluation can be targeted to those who had a high-risk profile found in this study.

Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy

PURPOSE

While a pathologic complete response (pCR) is regarded as a surrogate endpoint for pos-itive outcomes in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NAC), fore-casting the prognosis of non-pCR patients is still an open issue. This study aimed to create and evaluate nomogram models for estimating the likelihood of disease-free survival (DFS) for non-pCR patients.

METHODS

A retrospective analysis of 607 non-pCR BC patients was conducted (2012-2018). After converting continuous variables to categorical variables, variables entering the model were progressively identified by univariate and multivariate Cox regression analyses, and then pre-NAC and post-NAC nomogram models were developed. Regarding their discrimination, ac-curacy, and clinical value, the performance of the models was evaluated by internal and external validation. Two risk assessments were performed for each patient based on two models; patients were separated into different risk groups based on the calculated cut-off values for each model, including low-risk (assessed by the pre-NAC model) to low-risk (assessed by the post-NAC model), high-risk to low-risk, low-risk to high-risk, and high-risk to high-risk groups. The DFS of different groups was assessed using the Kaplan-Meier method.

RESULTS

Both pre-NAC and post-NAC nomogram models were built with clinical nodal (cN) status and estrogen receptor (ER), Ki67, and p53 status (all p < 0.05), showing good discrimination and calibration in both internal and external validation. We also assessed the performance of the two models in four subtypes, with the tri-ple-negative subtype showing the best prediction. Patients in the high-risk to high-risk subgroup have significantly poorer survival rates (p < 0.0001).

CONCLUSION

Two robust and effective nomo-grams were developed to personalize the prediction of DFS in non-pCR BC patients treated with NAC.