Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes

Learning Phenotype Associated Signature in Spatial Transcriptomics with PASSAGE

Spatially resolved transcriptomics (SRT) is poised to advance the understanding of cellular organization within complex tissues under various physiological and pathological conditions at unprecedented resolution. Despite the development of numerous computational tools that facilitate the automatic identification of statistically significant intra-/inter-slice patterns (like spatial domains), these methods typically operate in an unsupervised manner, without leveraging sample characteristics like physiological/pathological states. Here PASSAGE (Phenotype Associated Spatial Signature Analysis with Graph-based Embedding), a rationally-designed deep learning framework is presented for characterizing phenotype-associated signatures across multiple heterogeneous spatial slices effectively. In addition to its outstanding performance in systematic benchmarks, PASSAGE's unique capability in calling sophisticated signatures has been demonstrated in multiple real-world cases. The full package of PASSAGE is available at https://github.com/gao-lab/PASSAGE.

Spatially informed graph transformers for spatially resolved transcriptomics

Spatially resolved transcriptomics (SRT) has emerged as a powerful technique for mapping gene expression landscapes within spatial contexts. However, significant challenges persist in effectively integrating gene expression with spatial information to elucidate the heterogeneity of biological tissues. Here, we present a Spatially informed Graph Transformers framework, SpaGT, which leverages both node and edge channels to model spatially aware graph representation for denoising gene expression and identifying spatial domains. Unlike conventional graph neural networks, which rely on static, localized convolutional aggregation, SpaGT employs a structure-reinforced self-attention mechanism that iteratively evolves topological structural information and transcriptional signal representation. By replacing graph convolution with global self-attention, SpaGT enables the integration of both global and spatially localized information, thereby improving the detection of fine-grained spatial domains. We demonstrate that SpaGT achieves superior performance in identifying spatial domains and denoising gene expression data across diverse platforms and species. Furthermore, SpaGT facilitates the discovery of spatially variable genes with significant prognostic potential in cancer tissues. These findings establish SpaGT as a powerful tool for unraveling the complexities of biological tissues.

Biobanking and gynecologic oncology - Special considerations, challenges and opportunities

Biobanks of gynecological tissues occupy a critical niche in oncologic research. They are essential components of contemporary research strategies on gynecologic malignancy by integrating clinical, molecular and longitudinal biospecimen data. They also implement protocols for quality control, regulate sample and data sharing, provide ethical and regulatory oversight, and establish governance mechanisms to regulate their function. Gynecologic tissue biobanks also face some unique challenges. The broad heterogeneity of disease entities encompassed under this domain include common and rare malignancies, each with unique molecular subtypes that must be integrated into biobank information systems. Specimen acquisition extends beyond conventional tissues to include cervicovaginal microbiomes and ascitic fluid. Thus, gynecologic tissue biobanks should develop tailored collection strategies and the establishment of dedicated gynecologic tissue repositories that enable the aggregation of rare specimens through collaborative networks. This article emphasizes the need for high-quality annotation of biospecimens, the incorporation of multi-omics approaches to enhance the translational approaches, challenges associated with integration of high dimensional datasets, the role of biobank networks, and various ethical and cultural considerations concerning gynecologic biobanks. Emerging technologies that integrate multi omics, spatial biology and liquid biopsies now offer enhanced opportunities that augment classical specimen collection and should be integrated into standardized protocols.

Spatial transcriptomics in breast cancer: providing insight into tumor heterogeneity and promoting individualized therapy

A comprehensive understanding of tumor heterogeneity, tumor microenvironment and the mechanisms of drug resistance is fundamental to advancing breast cancer research. While single-cell RNA sequencing has resolved the issue of "temporal dynamic expression" of genes at the single-cell level, the lack of spatial information still prevents us from gaining a comprehensive understanding of breast cancer. The introduction and application of spatial transcriptomics addresses this limitation. As the annual technical method of 2020, spatial transcriptomics preserves the spatial location of tissues and resolves RNA-seq data to help localize and differentiate the active expression of functional genes within a specific tissue region, enabling the study of spatial location attributes of gene locations and cellular tissue environments. In the context of breast cancer, spatial transcriptomics can assist in the identification of novel breast cancer subtypes and spatially discriminative features that show promise for individualized precise treatment. This article summarized the key technical approaches, recent advances in spatial transcriptomics and its applications in breast cancer, and discusses the limitations of current spatial transcriptomics methods and the prospects for future development, with a view to advancing the application of this technology in clinical practice.

Deciphering the spatial landscape and plasticity of immunosuppressive fibroblasts in breast cancer

Although heterogeneity of FAP+ Cancer-Associated Fibroblasts (CAF) has been described in breast cancer, their plasticity and spatial distribution remain poorly understood. Here, we analyze trajectory inference, deconvolute spatial transcriptomics at single-cell level and perform functional assays to generate a high-resolution integrated map of breast cancer (BC), with a focus on inflammatory and myofibroblastic (iCAF/myCAF) FAP+ CAF clusters. We identify 10 spatially-organized FAP+ CAF-related cellular niches, called EcoCellTypes, which are differentially localized within tumors. Consistent with their spatial organization, cancer cells drive the transition of detoxification-associated iCAF (Detox-iCAF) towards immunosuppressive extracellular matrix (ECM)-producing myCAF (ECM-myCAF) via a DPP4- and YAP-dependent mechanism. In turn, ECM-myCAF polarize TREM2+ macrophages, regulatory NK and T cells to induce immunosuppressive EcoCellTypes, while Detox-iCAF are associated with FOLR2+ macrophages in an immuno-protective EcoCellType. FAP+ CAF subpopulations accumulate differently according to the invasive BC status and predict invasive recurrence of ductal carcinoma in situ (DCIS), which could help in identifying low-risk DCIS patients eligible for therapeutic de-escalation.

Case report: Successful treatment of a rare HER2-positive advanced breast squamous cell carcinoma

Background: Breast squamous cell carcinoma (SCC) is an uncommon and highly aggressive variant of metaplastic breast cancer. Despite its rarity, there is currently no consensus on treatment guidelines for this specific subtype. Previous studies have demonstrated that chemotherapy alone has limited efficacy in treating breast SCC. However, the potential for targeted therapy in combination with chemotherapy holds promise for future treatment options. Case presentation: In this case report, we present a patient with advanced HER2-positive breast SCC, exhibiting a prominent breast mass, localized ulcers, and metastases in the lungs and brain. Our treatment approach involved the administration of HER2-targeted drugs in conjunction with paclitaxel, resulting in a sustained control of tumor growth. Conclusion: This case represents a rare occurrence of HER2-positive breast SCC, with limited available data on the efficacy of previous HER2-targeted drugs in treating such patients. Our study presents the first application of HER2-targeted drugs in this particular case, offering novel therapeutic insights for future considerations. Additionally, it is imperative to conduct further investigations to assess the feasibility of treatment options in a larger cohort of patients.