Thymosin beta 15A (TMSB15A) is a predictor of chemotherapy response in triple-negative breast cancer

Assessment of novel prognostic biomarkers to predict pathological complete response in patients with non-metastatic triple-negative breast cancer using a window of opportunity design

Background

Triple-negative breast cancer (TNBC) includes approximately 20% of all breast cancer and is characterized by its aggressive nature, high recurrence rates, and visceral metastasis. Pathological complete response (pCR) is an established surrogate endpoint for survival. The window of opportunity studies provide valuable information on the disease biology prior to definitive treatment.

Objectives

To study the association of dynamic change in pathological, imagining, and genomic biomarkers that can prognosticate pCR. The study aims to develop a composite prognostic score.

Design

Clinical, interventional, and prognostic biomarker study using the novel window of opportunity design.

Methods

The study aims to enroll 80 treatment-naïve, pathologically confirmed TNBC patients, administering a single dose of paclitaxel and carboplatin during the window period before neoadjuvant chemotherapy (NACT). Tumor tissue will be obtained through a tru-cut biopsy, and positron emission tomography and computed tomography scans will be performed for each patient at two time points aiming to evaluate biomarker alterations. This will be followed by the administration of standard dose-dense NACT containing anthracyclines and taxanes, with the study culminating in surgery to assess pCR.

Results

The study would develop a composite prognostic risk score derived from the dynamic change in the Ki-67, tumor-infiltrating lymphocytes, Standardized Uptake Value (SUV max), Standardized Uptake Value for lean body mass (SUL max), and gene expression level pre- and post-intervention during the window period prior to the start of definitive treatment. This outcome will aid in categorizing the disease biology into risk categories.

Trial registration

The current study is approved by the Institutional Ethics Committee [Ethics: Protocol. no. JIP/IEC/2020/019]. This study was registered with ClinicalTrials.gov [CTRI Registration: CTRI/2022/06/043109].

Conclusion

The validated biomarker score will help to personalize NACT protocols in patients in TNBC planned for definitive treatment.

Construction of an immune-related gene signature for overall survival prediction and immune infiltration in gastric cancer

BACKGROUND

Treatment options for patients with gastric cancer (GC) continue to improve, but the overall prognosis is poor. The use of PD-1 inhibitors has also brought benefits to patients with advanced GC and has gradually become the new standard treatment option at present, and there is an urgent need to identify valuable biomarkers to classify patients with different characteristics into subgroups.

AIM

To determined the effects of differentially expressed immune-related genes (DEIRGs) on the development, prognosis, tumor microenvironment (TME), and treatment response among GC patients with the expectation of providing new biomarkers for personalized treatment of GC populations.

METHODS

Gene expression data and clinical pathologic information were downloaded from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were searched from ImmPort. DEIRGs were extracted from the intersection of the differentially-expressed genes (DEGs) and IRGs lists. The enrichment pathways of key genes were obtained by analyzing the Kyoto Encyclopedia of Genes and Genomes (KEGGs) and Gene Ontology (GO) databases. To identify genes associated with prognosis, a tumor risk score model based on DEIRGs was constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression. The tumor risk score was divided into high- and low-risk groups. The entire cohort was randomly divided into a 2:1 training cohort and a test cohort for internal validation to assess the feasibility of the risk model. The infiltration of immune cells was obtained using 'CIBERSORT,' and the infiltration of immune subgroups in high- and low-risk groups was analyzed. The GC immune score data were obtained and the difference in immune scores between the two groups was analyzed.

RESULTS

We collected 412 GC and 36 adjacent tissue samples, and identified 3627 DEGs and 1311 IRGs. A total of 482 DEIRGs were obtained. GO analysis showed that DEIRGs were mainly distributed in immunoglobulin complexes, receptor ligand activity, and signaling receptor activators. KEGG pathway analysis showed that the top three DEIRGs enrichment types were cytokine-cytokine receptors, neuroactive ligand receptor interactions, and viral protein interactions. We ultimately obtained an immune-related signature based on 10 genes, including 9 risk genes (LCN1, LEAP2, TMSB15A mRNA, DEFB126, PI15, IGHD3-16, IGLV3-22, CGB5, and GLP2R) and 1 protective gene (LGR6). Kaplan-Meier survival analysis, receiver operating characteristic curve analysis, and risk curves confirmed that the risk model had good predictive ability. Multivariate COX analysis showed that age, stage, and risk score were independent prognostic factors for patients with GC. Meanwhile, patients in the low-risk group had higher tumor mutation burden and immunophenotype, which can be used to predict the immune checkpoint inhibitor response. Both cytotoxic T lymphocyte antigen4+ and programmed death 1+ patients with lower risk scores were more sensitive to immunotherapy.

CONCLUSION

In this study a new prognostic model consisting of 10 DEIRGs was constructed based on the TME. By providing risk factor analysis and prognostic information, our risk model can provide new directions for immunotherapy in GC patients.

Isoform switching leads to downregulation of cytokine producing genes in estrogen receptor positive breast cancer

Objective: Estrogen receptor breast cancer (BC) is characterized by the expression of estrogen receptors. It is the most common cancer among women, with an incidence rate of 2.26 million cases worldwide. The aim of this study was to identify differentially expressed genes and isoform switching between estrogen receptor positive and triple negative BC samples. Methods: The data were collected from ArrayExpress, followed by preprocessing and subsequent mapping from HISAT2. Read quantification was performed by StringTie, and then R package ballgown was used to perform differential expression analysis. Functional enrichment analysis was conducted using Enrichr, and then immune genes were shortlisted based on the ScType marker database. Isoform switch analysis was also performed using the IsoformSwitchAnalyzeR package. Results: A total of 9,771 differentially expressed genes were identified, of which 86 were upregulated and 117 were downregulated. Six genes were identified as mainly associated with estrogen receptor positive BC, while a novel set of ten genes were found which have not previously been reported in estrogen receptor positive BC. Furthermore, alternative splicing and subsequent isoform usage in the immune system related genes were determined. Conclusion: This study identified the differential usage of isoforms in the immune system related genes in cancer cells that suggest immunosuppression due to the dysregulation of CXCR chemokine receptor binding, iron ion binding, and cytokine activity.

Triple-Negative Breast Cancer and Predictive Markers of Response to Neoadjuvant Chemotherapy: A Systematic Review

Around 40-50% of all triple-negative breast cancer (TNBC) patients achieve a pathological complete response (pCR) after treatment with neoadjuvant chemotherapy (NAC). The identification of biomarkers predicting the response to NAC could be helpful for personalized treatment. This systematic review provides an overview of putative biomarkers at baseline that are predictive for a pCR following NAC. Embase, Medline and Web of Science were searched for articles published between January 2010 and August 2022. The articles had to meet the following criteria: patients with primary invasive TNBC without distant metastases and patients must have received NAC. In total, 2045 articles were screened by two reviewers resulting in the inclusion of 92 articles. Overall, the most frequently reported biomarkers associated with a pCR were a high expression of Ki-67, an expression of PD-L1 and the abundance of tumor-infiltrating lymphocytes, particularly CD8+ T cells, and corresponding immune gene signatures. In addition, our review reveals proteomic, genomic and transcriptomic markers that relate to cancer cells, the tumor microenvironment and the peripheral blood, which also affect chemo-sensitivity. We conclude that a prediction model based on a combination of tumor and immune markers is likely to better stratify TNBC patients with respect to NAC response.

Integrated single-cell and bulk RNA sequencing analysis identifies a cancer-associated fibroblast-related gene signature for predicting survival and therapy in gastric cancer

As the dominant component of the tumor microenvironment, cancer-associated fibroblasts (CAFs), play a vital role in tumor progression. An increasing number of studies have confirmed that CAFs are involved in almost every aspect of tumors including tumorigenesis, metabolism, invasion, metastasis and drug resistance, and CAFs provide an attractive therapeutic target. This study aimed to explore the feature genes of CAFs for potential therapeutic targets and reliable prediction of prognosis in patients with gastric cancer (GC). Bioinformatic analysis was utilized to identify the feature genes of CAFs in GC by performing an integrated analysis of single-cell and transcriptome RNA sequencing using R software. Based on these feature genes, a CAF-related gene signature was constructed for prognostic prediction by LASSO. Simultaneously, survival analysis and nomogram were performed to validate the prognostic predictive value of this gene signature, and qRT-PCR and immunohistochemical staining verified the expression of the feature genes of CAFs. In addition, small molecular drugs for gene therapy of CAF-related gene signatures in GC patients were identified using the connectivity map (CMAP) database. A combination of nine CAF-related genes was constructed to characterize the prognosis of GC, and the prognostic potential and differential expression of the gene signature were initially validated. Additionally, three small molecular drugs were deduced to have anticancer properties on GC progression. By integrating single-cell and bulk RNA sequencing analyses, a novel gene signature of CAFs was constructed. The results provide a positive impact on future research and clinical studies involving CAFs for GC.

Identifying biomarkers of differential chemotherapy response in TNBC patient-derived xenografts with a CTD/WGCNA approach

Although systemic chemotherapy remains the standard of care for TNBC, even combination chemotherapy is often ineffective. The identification of biomarkers for differential chemotherapy response would allow for the selection of responsive patients, thus maximizing efficacy and minimizing toxicities. Here, we leverage TNBC PDXs to identify biomarkers of response. To demonstrate their ability to function as a preclinical cohort, PDXs were characterized using DNA sequencing, transcriptomics, and proteomics to show consistency with clinical samples. We then developed a network-based approach (CTD/WGCNA) to identify biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (c, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). CTD/WGCNA multigene biomarkers are predictive in PDX datasets (RNAseq and Affymetrix) for both taxane- (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating cross-expression platform and cross-drug class robustness. These biomarkers were also predictive in clinical datasets, thus demonstrating translational potential.

Thymic function affects breast cancer development and metastasis by regulating expression of thymus secretions PTMα and Tβ15b1

Breast cancer is currently one of the most common malignant tumors in women. Our previous research found that thymic dysfunction has a certain relationship with the occurrence and development of breast cancer. In order to explore whether the functional status of thymus is related to the development and metastasis of breast cancer, we use BALB/c wild type mice (BALB wt), BALB/c nude mice (BALB nu), BALB wt mice implanted with 4T1 cells (wt 4T1), BALB nu with 4T1 (nu 4T1), D-galactose treatment wt 4T1 mice (D-Gal), Thymalfasin treatment wt 4T1 mice (Tα1), Cyclophosphamide treatment wt 4T1 mice (CTX), Doxorubicin treatment wt 4T1 mice (Dox) in the research. As a result, nu 4T1, D-Gal and DOX had earlier lung metastases. Gene chip results showed that PTMα and Tβ15b1 were the most up-regulated and down-regulated genes in thymosin-related genes, respectively. Overexpression or silencing of PTMα and Tβ15b1 genes did not affect the proliferation of 4T1 cells. PTMα gene silenced, cell migration and invasion ability enhanced, while PTMα gene overexpression, the cell invasion ability weaken. In vivo, PTMα gene overexpression promotes tumor growth and lung metastasis in the early stage, but has no significant effect in the later stage. Tβ15b1 overexpression also promotes tumor growth in the early stage, but suppresses in the later stage. Tβ15b1 gene silencing inhibits tumor lung metastasis. Thus, our findings demonstrated that thymic function affects breast cancer development and metastasis by regulating expression of thymus secretions PTMα and Tβ15b1. Our study provided new directions for breast cancer therapy.

LncRNA Profile Study Reveals a Three-LncRNA Signature Associated With the Pathological Complete Response Following Neoadjuvant Chemotherapy in Breast Cancer

Background

The purpose of this study is to develop an effective but concise long non-coding RNA (lncRNA) expression signature that can predict response to neoadjuvant chemotherapy for breast cancer (BC) patients.

Methods

lncRNA expression profiling in 1102 BC patients from Gene Expression Omnibus datasets was analyzed using lncRNA-mining approach. The association between lncRNA signature and pathological complete response (pCR) was analyzed using logistic regression model in the training set (GSE25066, n = 488). Validation was performed in independent testing datasets, GSE20194, GSE20271, GSE22093, and GSE23988 (n = 614). Bonferroni method was employed for multiple testing corrections. Cell proliferation assay and Western blot assay were performed to evaluate the cell viability and protein expression level, respectively.

Results

Three lncRNAs (AK291479, U79293, and BC032585) have been identified to be significantly associated with pCR in the training dataset (Bonferroni p-value < 0.05). Expression signature with these lncRNAs was predictive of pCR in the training (AUC = 0.74) and testing (AUC = 0.72) datasets. Weighted gene co-expression network analysis and gene functional annotation suggest that the three lncRNAs were involved in cell cycle process. To confirm the functional significance of the identified lncRNAs, BC032585 was selectively silenced using RNA interference. Knockdown of BC032585 lncRNA significantly promoted cell resistance to multiple anticancer-drugs through upregulating MDR1 expression in breast cancer cells.

Conclusion

These results suggest that lncRNAs such as BC032585 might be involved in chemotherapeutic response in breast cancer patients, and the three-lncRNA signature identified in the present study may serve as a useful biomarker for the selection of responsive breast cancer patients in neoadjuvant chemotherapy.

Neuroevolution as a tool for microarray gene expression pattern identification in cancer research

Microarrays are still one of the major techniques employed to study cancer biology. However, the identification of expression patterns from microarray datasets is still a significant challenge to overcome. In this work, a new approach using Neuroevolution, a machine learning field that combines neural networks and evolutionary computation, provides aid in this challenge by simultaneously classifying microarray data and selecting the subset of more relevant genes. The main algorithm, FS-NEAT, was adapted by the addition of new structural operators designed for this high dimensional data. In addition, a rigorous filtering and preprocessing protocol was employed to select quality microarray datasets for the proposed method, selecting 13 datasets from three different cancer types. The results show that Neuroevolution was able to successfully classify microarray samples when compared with other methods in the literature, while also finding subsets of genes that can be generalized for other algorithms and carry relevant biological information. This approach detected 177 genes, and 82 were validated as already being associated to their respective cancer types and 44 were associated to other types of cancer, becoming potential targets to be explored as cancer biomarkers. Five long non-coding RNAs were also detected, from which four don't have described functions yet. The expression patterns found are intrinsically related to extracellular matrix, exosomes and cell proliferation. The results obtained in this work could aid in unraveling the molecular mechanisms underlying the tumoral process and describe new potential targets to be explored in future works.

Robust identification of target genes and outliers in triple-negative breast cancer data

Correct classification of breast cancer subtypes is of high importance as it directly affects the therapeutic options. We focus on triple-negative breast cancer which has the worst prognosis among breast cancer types. Using cutting edge methods from the field of robust statistics, we analyze Breast Invasive Carcinoma transcriptomic data publicly available from The Cancer Genome Atlas data portal. Our analysis identifies statistical outliers that may correspond to misdiagnosed patients. Furthermore, it is illustrated that classical statistical methods may fail to identify outliers due to their heavy influence, prompting the need for robust statistics. Using robust sparse logistic regression we obtain 36 relevant genes, of which ca. 60% have been previously reported as biologically relevant to triple-negative breast cancer, reinforcing the validity of the method. The remaining 14 genes identified are new potential biomarkers for triple-negative breast cancer. Out of these, JAM3, SFT2D2, and PAPSS1 were previously associated to breast tumors or other types of cancer. The relevance of these genes is confirmed by the new DetectDeviatingCells outlier detection technique. A comparison of gene networks on the selected genes showed significant differences between triple-negative breast cancer and non-triple-negative breast cancer data. The individual role of FOXA1 in triple-negative breast cancer and non-triple-negative breast cancer, and the strong FOXA1-AGR2 connection in triple-negative breast cancer stand out. The goal of our paper is to contribute to the breast cancer/triple-negative breast cancer understanding and management. At the same time it demonstrates that robust regression and outlier detection constitute key strategies to cope with high-dimensional clinical data such as omics data.

Prognostic Impact of Circulating Tumor Cells for Breast Cancer Patients Treated in the Neoadjuvant "Geparquattro" Trial

Purpose: This study aimed to evaluate the prognostic impact of circulating tumor cells (CTC) detected in patients with operable or locally advanced breast cancer before and after neoadjuvant therapy (NT) within the clinical trial GeparQuattro.Experimental Design: Data on CTCs enumerated with the CellSearch system were available for 213 and 207 patients before and after NT, respectively. Associations of CTCs with disease-free survival (DFS) and overall survival (OS) were analyzed by nonparametric Kaplan-Meier estimates and parametric Cox regression.Results: After a median follow-up of 67.1 months, the detection of ≥1 CTC/7.5 mL and ≥2 CTCs/7.5 mL before NT was associated with reduced DFS (P = 0.031 and P < 0.0001, respectively) and OS (P = 0.0057 and P < 0.0001, respectively), whereas CTCs detected after NT did not correlate with DFS or OS. In parametric univariate and multivariate Cox models, ≥1 CTC/7.5 mL, ≥2 CTCs/7.5 mL, and absolute CTC numbers before NT revealed to be independent prognostic parameters of DFS and OS. CTC-negative patients with pathologic complete response (pCR) exhibited the best prognosis, whereas those with CTCs and less tumor response were at high risk of tumor relapse. In HER2 (ERBB2)-positive and triple-negative patients, ≥2 CTCs/7.5 mL detected before NT also were significantly associated with worse DFS and OS.Conclusions: Detection of CTCs before NT is an independent prognostic factor of impaired clinical outcome, and combined with pCR, it could be helpful to stratify breast cancer patients for therapeutic interventions. Clin Cancer Res; 23(18); 5384-93. ©2017 AACR.

[Thymosin beta 10 prompted the VEGF-C expression in lung cancer cell]

背景与目的

我们前期的研究发现胸腺素β10（thymosin β10, Tβ10）在肺癌中过表达并与肺癌的分期、分化及淋巴结转移呈正相关。本研究旨在探讨外源人重组蛋白Tβ10在肺癌细胞系中促进血管内皮生长因子（vascular endothelial growth factor, VEGF）-C表达情况及其调控机制。

方法

采用RT-PCR法检测不同肺癌细胞系加入外源Tβ10或Tβ10加AKT特异性抑制剂LY294002后VEGF-C mRNA水平的变化；采用Western blot法检测不同肺癌细胞系加入Tβ10或Tβ10加LY294002后VEGF-C及P-AKT蛋白的变化。

结果

在肺癌细胞系SPC-A-1中加入Tβ10可以促进VEGF-C mRNA及蛋白的表达水平，同时促进AKT的磷酸化。在肺癌细胞系A549和LK2中加入Tβ10同样可以促进VEGF-C mRNA及蛋白的表达（P < 0.05），并且这种促进作用可以被LY294002所抑制（P < 0.05）。

结论

人重组蛋白Tβ10肺癌通过激活AKT的磷酸化促进VEGF-C的表达。

Triple-negative breast cancer: bridging the gap from cancer genomics to predictive biomarkers

Triple-negative breast cancer (TNBC) represents a challenge clinically due to a lack of response to hormonal and HER2-targeted agents coupled with an aggressive disease course. As the biology of this breast cancer subtype is better understood, it is clear that TNBC is a heterogeneous disease and one targeted therapy is unlikely to be active in all patients. Biomarkers predictive of response to treatment are thus of great importance in TNBC. This review outlines studies evaluating biomarkers predictive of response to neoadjuvant chemotherapy and to targeted therapies in the advanced setting. The development of validated biomarkers in conjunction with novel targeted therapies represents an opportunity to improve patient outcomes in TNBC.

Neoadjuvant chemotherapy induces expression levels of breast cancer resistance protein that predict disease-free survival in breast cancer

Three main xenobiotic efflux pumps have been implicated in modulating breast cancer chemotherapy responses. These are P-glycoprotein (Pgp), Multidrug Resistance-associated Protein 1 (MRP1), and Breast Cancer Resistance Protein (BCRP). We investigated expression of these proteins in breast cancers before and after neoadjuvant chemotherapy (NAC) to determine whether their levels define response to NAC or subsequent survival. Formalin-fixed paraffin-embedded tissues were collected representing matched pairs of core biopsy (pre-NAC) and surgical specimen (post-NAC) from 45 patients with invasive ductal carcinomas. NAC regimes were anthracyclines +/− taxanes. Immunohistochemistry was performed for Pgp, MRP1 and BCRP and expression was quantified objectively using computer-aided scoring. Pgp and MRP1 were significantly up-regulated after exposure to NAC (Wilcoxon signed-rank p = 0.0024 and p<0.0001), while BCRP showed more variation in response to NAC, with frequent up- (59% of cases) and down-regulation (41%) contributing to a lack of significant difference overall. Pre-NAC expression of all markers, and post-NAC expression of Pgp and MRP1 did not correlate with NAC response or with disease-free survival (DFS). Post-NAC expression of BCRP did not correlate with NAC response, but correlated significantly with DFS (Log rank p = 0.007), with longer DFS in patients with low post-NAC BCRP expression. In multivariate Cox regression analyses, post-NAC BCRP expression levels proved to predict DFS independently of standard prognostic factors, with high expression associated with a hazard ratio of 4.04 (95% confidence interval 1.3–12.2; p = 0.013). We conclude that NAC-induced expression levels of BCRP predict survival after NAC for breast cancer, while Pgp and MRP1 expression have little predictive value.