Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer

Genomic Insights Into Early Relapsed Breast Cancer: Prognostic Challenges and Mutation Landscape

Purpose

Early relapsed breast cancer, characterized by recurrence within two years post-surgery, often results from drug resistance and rapid progression. The clinicopathological, prognostic and molecular features of these patients still await exploration.

Methods

In this study, 43 patients with early relapsed breast cancer were included as well as 42 advanced breast cancer patients who experienced a recurrence after two years since surgery as the control group. Clinicopathological factors and prognosis were compared among the two groups, and tumor tissue from 27 available early relapsed patients was subjected to genetic sequencing.

Results

Compared with the control group, early relapsed group exhibited more aggressive malignant biological characteristics, shorter median overall survival (27.8 vs 49.8 months, P=0.005) and lower objective response rate for the first line treatment (42.90% vs 86.8%, P<0.001). Genetic sequencing of 27 early relapsed breast cancer demonstrated with TP53 (52%), PIK3CA (22%), and MLL3 (19%) as the top three frequently mutated genes, suggesting potential therapeutic targets for personalized treatment strategies.

Conclusion

Early relapsed breast cancer patients demonstrated poor prognosis and treatment response, indicating a reagent need of effective treatment combination for disease control. Genetic sequencing may identify potential therapeutic targets, providing new therapeutic opportunities for such patients. These findings underline the urgent need for personalized therapeutic strategies informed by genetic profiling to improve outcomes for early-relapsed breast cancer patients.

Importance of circulating tumor DNA analysis at diagnosis in early triple-negative breast cancer patients

BACKGROUND

Circulating tumor DNA (ctDNA) enables non-invasive evaluation and is considered a promising tool for diagnosis, treatment selection, risk stratification, and disease monitoring. However, while the utility of ctDNA has been demonstrated in advanced-stage cancers, its detection in early breast cancer (EBC) remains limited. This study investigated the characteristics of EBC patients associated with higher ctDNA detectability.

METHODS

A total of 101 patients with EBC were enrolled. Formalin-fixed paraffin-embedded samples (FFPEs) were obtained from biopsy tissue, and plasma samples were collected before and after neoadjuvant chemotherapy (NAC). Forty-seven breast cancer-related genes were analyzed using next-generation sequencing. The diagnostic performance of ctDNA was evaluated, and logistic regression analyses were conducted to assess the impact of clinical and molecular factors on ctDNA status.

RESULTS

The most frequently identified gene was TP53 (FFPE, 66.7%; ctDNA, 46.4%), followed by PIK3CA (FFPE, 36.2%; ctDNA, 17.4%). The diagnostic performance of the three most common genes showed a sensitivity range of 11.1-58.7%, specificity of 78.3-100%, and diagnostic accuracy of 65.2-78.3%. The triple-negative breast cancer (TNBC) subtype exhibited the strongest association with ctDNA detection (odds ratio [OR] 209.50, p = 0.005) in multivariate analysis. Also, those who exhibited ctDNA clearance after NAC had a higher pathological complete response rate compared to those without clearance (38.5% vs. 11.1%, p = 0.238).

CONCLUSIONS

Our study highlights that ctDNA analysis can complement genetic testing from a single tissue biopsy in breast cancer patients. Furthermore, ctDNA analysis may be particularly important in patients with TNBC.

Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine

Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.

HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data

MOTIVATION

Tissue context and molecular profiling are commonly used measures in understanding normal development and disease pathology. In recent years, the development of spatial molecular profiling technologies (e.g. spatial resolved transcriptomics) has enabled the exploration of quantitative links between tissue morphology and gene expression. However, these technologies remain expensive and time-consuming, with subsequent analyses necessitating high-throughput pathological annotations. On the other hand, existing computational tools are limited to predicting only a few dozen to several hundred genes, and the majority of the methods are designed for bulk RNA-seq.

RESULTS

In this context, we propose HE2Gene, the first multi-task learning-based method capable of predicting tens of thousands of spot-level gene expressions along with pathological annotations from H&E-stained images. Experimental results demonstrate that HE2Gene is comparable to state-of-the-art methods and generalizes well on an external dataset without the need for re-training. Moreover, HE2Gene preserves the annotated spatial domains and has the potential to identify biomarkers. This capability facilitates cancer diagnosis and broadens its applicability to investigate gene-disease associations.

AVAILABILITY AND IMPLEMENTATION

The source code and data information has been deposited at https://github.com/Microbiods/HE2Gene.

Identification of Subtypes in Triple-negative Breast Cancer Based on Shared Genes Between Immunity and Cancer Stemness

The correlation between triple-negative breast cancer (TNBC) and genes related to immunity and cancer stemness, particularly shared genes, remains unclear. This study aimed to investigate the correlation of immunity and cancer stemness with the molecular subtyping and survival rates in TNBC using bioinformatics approaches. Differential gene analysis was conducted to identify TNBC-associated differentially expressed genes (DEGs). Cancer stem cell (CSC)-related genes were obtained using weighted gene coexpression network analysis. Immune-related gene sets were retrieved from the literature. Venn analysis was performed to identify the shared DEGs between immunity and cancer stemness in TNBC. Cluster analysis and survival analysis based on the expression of these genes were conducted to identify TNBC subtypes with significant survival differences. A total of 5259 TNBC-associated DEGs, 2214 CSC-related genes, 1793 immune-related genes, and 44 shared DEGs between immunity and cancer stemness were obtained. Among them, 3 shared DEGs were closely associated with TNBC survival rates ( P <0.05). Cluster and survival analyses revealed that among 3 subtypes, cluster2 exhibited the best survival rate, and cluster3 showed the worst survival rate ( P <0.05). Dendritic cells were highly infiltrated in cluster2, while plasma cells and resting mast cells were highly infiltrated in cluster3 ( P <0.05). Genes shared by immunity and cancer stemness were capable of classifying TNBC samples. TNBC patients of different subtypes exhibited significant differences in immune profiles, genetic mutations, and drug sensitivity. These findings could provide new insights into the pathogenesis of TNBC, the immune microenvironment, and the selection of therapeutic targets for drug treatment.

ABCD1 as a Novel Diagnostic Marker for Solid Pseudopapillary Neoplasm of the Pancreas

The diagnosis of solid pseudopapillary neoplasm of the pancreas (SPN) can be challenging due to potential confusion with other pancreatic neoplasms, particularly pancreatic neuroendocrine tumors (NETs), using current pathological diagnostic markers. We conducted a comprehensive analysis of bulk RNA sequencing data from SPNs, NETs, and normal pancreas, followed by experimental validation. This analysis revealed an increased accumulation of peroxisomes in SPNs. Moreover, we observed significant upregulation of the peroxisome marker ABCD1 in both primary and metastatic SPN samples compared with normal pancreas and NETs. To further investigate the potential utility of ABCD1 as a diagnostic marker for SPN via immunohistochemistry staining, we conducted verification in a large-scale patient cohort with pancreatic tumors, including 127 SPN (111 primary, 16 metastatic samples), 108 NET (98 nonfunctional pancreatic neuroendocrine tumor, NF-NET, and 10 functional pancreatic neuroendocrine tumor, F-NET), 9 acinar cell carcinoma (ACC), 3 pancreatoblastoma (PB), 54 pancreatic ductal adenocarcinoma (PDAC), 20 pancreatic serous cystadenoma (SCA), 19 pancreatic mucinous cystadenoma (MCA), 12 pancreatic ductal intraepithelial neoplasia (PanIN) and 5 intraductal papillary mucinous neoplasm (IPMN) samples. Our results indicate that ABCD1 holds promise as an easily applicable diagnostic marker with exceptional efficacy (AUC=0.999, sensitivity=99.10%, specificity=100%) for differentiating SPN from NET and other pancreatic neoplasms through immunohistochemical staining.