Spatial transcriptomics reveals gene expression characteristics in invasive micropapillary carcinoma of the breast

Spatial transcriptomics and the anatomical pathologist: Molecular meets morphology

In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the presence of specific molecular alterations are now essential for diagnosis. Spatial transcriptomics describes a group of technologies that provide up to transcriptome-wide expression profiling while preserving the spatial origin of the data, with many of these technologies able to provide these data using a single tissue section. Spatial transcriptomics allows expression profiling of highly specific areas within a tissue section potentially to subcellular resolution, and allows correlation of expression data with morphology, tissue type and location relative to other structures. While largely still research laboratory-based, several spatial transcriptomics methods have now achieved compatibility with formalin-fixed paraffin-embedded tissue (FFPE), allowing their use in diagnostic tissue samples, and with further development potentially leading to their incorporation in routine anatomical pathology practice. This mini review provides an overview of spatial transcriptomics methods, with an emphasis on platforms compatible with FFPE tissue, approaches to assess the data and potential applications in anatomical pathology practice.

Advances in invasive micropapillary carcinoma of the breast research: A review

Invasive micropapillary carcinoma (IMPC) of the breast represents a rare subtype of breast cancer, accounting for 1% to 2% of all breast cancers worldwide. Although clinically asymptomatic, they are usually detected during routine breast screenings. The common symptoms include breast lumps, skin or nipple changes, and nipple discharge. Histopathologically, IMPCs are characterized by tumor cells forming small papillary-like structures inside the glandular spaces, and arranged in an inverted pattern, with their apex pointing toward the center of the gland. This unique morphological feature is critical for diagnosing these cases. Another notable characteristic is its high propensity for lymph node metastasis (LNM). While the precise mechanism of metastasis is not clear, unique cellular arrangement and cellular interactions with the surrounding environment might promote tumorigenesis and higher node positivity. Hence, proper lymph node dissection and assessment are particularly crucial for this type of breast cancer. This review aims to discuss the recent progress in managing IMPC cases.

Harnessing computational spatial omics to explore the spatial biology intricacies

Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.

Spatially Resolved Transcriptomics Technology Facilitates Cancer Research

Single cell RNA sequencing (scRNA-seq) provides a great convenience for studying tumor occurrence and development for its ability to study gene expression at the individual cell level. However, patient-derived tumor tissues are composed of multiple types of cells including tumor cells and adjacent non-malignant cells such as stromal cells and immune cells. The spatial locations of various cells in situ tissues plays a pivotal role in the occurrence and development of tumors, which cannot be elucidated by scRNA-seq alone. Spatially resolved transcriptomics (SRT) technology emerges timely to explore the unrecognized relationship between the spatial background of a particular cell and its functions, and is increasingly used in cancer research. This review provides a systematic overview of the SRT technologies that are developed, in particular the more widely used cutting-edge SRT technologies based on next-generation sequencing (NGS). In addition, the main achievements by SRT technologies in precisely unveiling the underappreciated spatial locations on gene expression and cell function with unprecedented high-resolution in cancer research are emphasized, with the aim of developing more effective clinical therapeutics oriented to a deeper understanding of the interaction between tumor cells and surrounding non-malignant cells.

Novel insights from spatial transcriptome analysis in solid tumors

Since its first application in 2016, spatial transcriptomics has become a rapidly evolving technology in recent years. Spatial transcriptomics enables transcriptomic data to be acquired from intact tissue sections and provides spatial distribution information and remedies the disadvantage of single-cell RNA sequencing (scRNA-seq), whose data lack spatially resolved information. Presently, spatial transcriptomics has been widely applied to various tissue types, especially for the study of tumor heterogeneity. In this review, we provide a summary of the research progress in utilizing spatial transcriptomics to investigate tumor heterogeneity and the microenvironment with a focus on solid tumors. We summarize the research breakthroughs in various fields and perspectives due to the application of spatial transcriptomics, including cell clustering and interaction, cellular metabolism, gene expression, immune cell programs and combination with other techniques. As a combination of multiple transcriptomics, single-cell multiomics shows its superiority and validity in single-cell analysis. We also discuss the application prospect of single-cell multiomics, and we believe that with the progress of data integration from various transcriptomics, a multilayered subcellular landscape will be revealed.

Nomograms for predicting overall survival and cancer-specific survival in patients with invasive micropapillary carcinoma: Based on the SEER database

BACKGROUND

Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer that lacks a prognostic prediction model. Its treatment and prognostic factors remain controversial. Our study aimed to develop nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in IMPC patients.

METHODS

A total of 2149 patients confirmed to have IMPC between 2003 and 2018 were selected from the Surveillance, Epidemiology and End Results (SEER) database. They were divided into training and validation cohorts. Univariate and multivariate Cox regression analyses were used to identify significant independent prognostic factors. The nomograms were used to predict 3- and 5-year OS and CSS. The training and validation cohorts were used to verify the nomograms internally and externally. The predictive capability of the nomograms was evaluated by the consistency index (C-index), calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) curve.

RESULTS

In the study, 2149 IMPC patients were randomized to a training group (n = 1611) and a validation group (n = 538). Age, T stage, N stage, ER, radiotherapy, and surgery were identified as independent prognostic factors for OS and CSS. These variables were selected to construct nomograms for IMPC. The C-index (0.768 for OS and 0.811 for CSS) and the time-dependent AUC (>0.7) indicated satisfactory discriminative ability of the nomograms. Additionally, DCA showed that the nomograms had higher clinical value than traditional TNM tumor staging.

CONCLUSIONS

The models can accurately predict the prognosis of IMPC patients and can aid in providing individualized treatment for patients.

Metabolome Sequencing Reveals that Protein Arginine-N-Methyltransferase 1 Promotes the Progression of Invasive Micropapillary Carcinoma of the Breast and Predicts a Poor Prognosis

Invasive micropapillary carcinoma (IMPC) of the breast is a special histopathologic type of cancer with a high recurrence rate and the biological features of invasion and metastasis. Previous spatial transcriptome studies indicated extensive metabolic reprogramming in IMPC, which contributes to tumor cell heterogeneity. However, the impact of metabolome alterations on IMPC biological behavior is unclear. Herein, endogenous metabolite-targeted metabolomic analysis was done on frozen tumor tissue samples from 25 patients with breast IMPC and 34 patients with invasive ductal carcinoma not otherwise specified (IDC-NOS) by liquid chromatography-mass spectrometry. An IMPC-like state, which is an intermediate transitional morphologic phenotype between IMPC and IDC-NOS, was observed. The metabolic type of IMPC and IDC-NOS was related to breast cancer molecular type. Arginine methylation modification and 4-hydroxy-phenylpyruvate metabolic changes play a major role in the metabolic reprogramming of IMPC. High protein arginine-N-methyltransferase (PRMT) 1 expression was an independent factor related to the poor prognosis of patients with IMPC in terms of disease-free survival. PRMT1 promoted H4R3me2a, which induced tumor cell proliferation via cell cycle regulation and facilitated tumor cell metastasis via the tumor necrosis factor signaling pathway. This study identified the metabolic type-related features and intermediate transition morphology of IMPC. The identification of potential targets of PRMT1 has the potential to provide a basis for the precise diagnosis and treatment of breast IMPC.

Nondestructive 3D Pathology with Light-Sheet Fluorescence Microscopy for Translational Research and Clinical Assays

In recent years, there has been a revived appreciation for the importance of spatial context and morphological phenotypes for both understanding disease progression and guiding treatment decisions. Compared with conventional 2D histopathology, which is the current gold standard of medical diagnostics, nondestructive 3D pathology offers researchers and clinicians the ability to visualize orders of magnitude more tissue within their natural volumetric context. This has been enabled by rapid advances in tissue-preparation methods, high-throughput 3D microscopy instrumentation, and computational tools for processing these massive feature-rich data sets. Here, we provide a brief overview of many of these technical advances along with remaining challenges to be overcome. We also speculate on the future of 3D pathology as applied in translational investigations, preclinical drug development, and clinical decision-support assays.

Label-free detection of invasive micropapillary carcinoma of the breast using multiphoton microscopy

Invasive micropapillary carcinoma of the breast (IMPC) is a rare form of breast cancer with unique histological features, and is associated with high axillary lymph node metastasis and poor clinical prognosis. Thus, IMPC should be diagnosed in time to improve the treatment and management of patients. In this study, multiphoton microscopy (MPM) is used to label-free visualize the morphological features of IMPC. Our results demonstrate that MPM images are well in agreement with hematoxylin and eosin staining and epithelial membrane antigen staining, indicating MPM is comparable to traditional histological analysis in identifying the tissue structure and cell morphology. Statistical analysis shows significant differences in the circumference and area of the glandular lumen and cancer nest between the different IMPC cell clusters with complete glandular lumen morphology, and also shows difference in collagen length, width, and orientation, indicating the invasive ability of different morphologies of IMPC may be different.

Improvements and challenges of tissue preparation for spatial transcriptome analysis of skull base tumors

Background

Spatial transcriptome (ST) provides molecular profiles of tumor cells at the spatial level, which brings new progress to the research of tumors and the tumor microenvironment. This study summarizes the experiences and lessons learned in the spatial section preparation of two different pathological types of nose and skull base tumors at our institution, with the aim of offering guidelines to researchers to avoid wasting precious samples and provide a basis for the application of ST in clinical practice.

Methods

Frozen tissue blocks from patients with squamous cell carcinoma and adenocarcinoma of the nose and skull base diagnosed at our institution were prepared. The effects of different procedures and pathological tissue types on slide quality were explored and evaluated using RNA integrity number (RIN) and HE scores as criteria. The effects of different RIN values on ST sequencing data were explored.

Results

A total of 43 samples were obtained from 26 patients, including 22 with squamous carcinomas and 21 with adenocarcinomas. Thirteen samples with satisfactory RNA quality control and good histological morphology were sequenced for ST. Sample isolation time <15 min and abandonment of snap-frozen isopentane significantly improved RNA quality (p = 0.004, p < 0.0001) and histomorphological integrity (p = 0.02, p = 0.02). Selection of a suitable tissue RNA extraction kit was critical for RNA quality (p < 0.0001). No difference between 6 ≤ RIN <7 and RIN >7 in ST sequencing results was found, indicating that RIN ≥6 can be used as a criterion for qualified RNA quality control. Therefore, fresh tissues washed as soon as possible with cold PBS and then dried using OCT for snap freezing are currently the best method for preparing spatial sections of nose and skull base tumor tissues of different pathological types.

Conclusion

This study is the first to investigate the feasibility of applying ST to different pathological types of nose and skull base tumors and to demonstrate the widespread application of ST in tumors. Rational optimization of spatial slide preparation procedures and exploration of individualized pre-sequencing protocols are used as the first stage to ensure the quality of spatial sequencing and lay the foundation for subsequent spatial analysis.

Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment

Cancer metastasis often leads to death and therapeutic resistance. This process involves the participation of a variety of cell components, especially cellular and intercellular communications in the tumor microenvironment (TME). Using genetic sequencing technology to comprehensively characterize the tumor and TME is therefore key to understanding metastasis and therapeutic resistance. The use of spatial transcriptome sequencing enables the localization of gene expressions and cell activities in tissue sections. By examining the localization change as well as gene expression of these cells, it is possible to characterize the progress of tumor metastasis and TME formation. With improvements of this technology, spatial transcriptome sequencing technology has been extended from local regions to whole tissues, and from single sequencing technology to multimodal analysis combined with a variety of datasets. This has enabled the detection of every single cell in tissue slides, with high resolution, to provide more accurate predictive information for tumor treatments. In this review, we summarize the results of recent studies dealing with new multimodal methods and spatial transcriptome sequencing methods in tumors to illustrate recent developments in the imaging resolution of micro-tissues.

PRMT3 regulates the progression of invasive micropapillary carcinoma of the breast

Invasive micropapillary carcinoma (IMPC) is a special histopathological subtype of breast cancer. Clinically, IMPC exhibits a higher incidence of lymphovascular invasion and lymph node metastasis compared with that of invasive ductal carcinoma (IDC), the most common type. However, the metabolic characteristics and related mechanisms underlying malignant IMPC biological behaviors are unknown. We performed large-scale targeted metabolomics analysis on resected tumors obtained from chemotherapy-naïve IMPC (n = 25) and IDC (n = 26) patients to investigate metabolic alterations, and we integrated mass spectrometry analysis, RNA sequencing, and ChIP-sequencing data to elucidate the potential molecular mechanisms. The metabolomics revealed distinct metabolic profiles between IMPC and IDC. For IMPC patients, the metabolomic profile was characterized by significantly high levels of arginine methylation marks, and protein arginine methyltransferase 3 (PRMT3) was identified as a critical regulator that catalyzed the formation of these arginine methylation marks. Notably, overexpression of PRMT3 was an independent risk factor for poor IMPC prognosis. Furthermore, we demonstrated that PRMT3 was a key regulator of breast cancer cell proliferation and metastasis both in vitro and in vivo, and treatment with a preclinical PRMT3 inhibitor decreased the xenograft tumorigenic capacity. Mechanistically, PRMT3 regulated the endoplasmic reticulum (ER) stress signaling pathway by facilitating histone H4 arginine 3 asymmetric dimethylation (H4R3me2a), which may endow breast cancer cells with great proliferative and metastatic capacity. Our findings highlight PRMT3 importance in regulating the malignant biological behavior of IMPC and suggest that small-molecule inhibitors of PRMT3 activity might be promising breast cancer treatments.

SPASCER: spatial transcriptomics annotation at single-cell resolution

In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location and provides novel insights into diverse spatially related biological contexts. Even though two spatial transcriptomics databases exist, they provide limited analytical information. Information such as spatial heterogeneity of genes and cells, cell-cell communication activities in space, and the cell type compositions in the microenvironment are critical clues to unveil the mechanism of tumorigenesis and embryo differentiation. Therefore, we constructed a new spatial transcriptomics database, named SPASCER (https://ccsm.uth.edu/SPASCER), designed to help understand the heterogeneity of tissue organizations, region-specific microenvironment, and intercellular interactions across tissue architectures at multiple levels. SPASCER contains datasets from 43 studies, including 1082 sub-datasets from 16 organ types across four species. scRNA-seq was integrated to deconvolve/map spatial transcriptomics, and processed with spatial cell-cell interaction, gene pattern and pathway enrichment analysis. Cell-cell interactions and gene regulation network of scRNA-seq from matched spatial transcriptomics were performed as well. The application of SPASCER will provide new insights into tissue architecture and a solid foundation for the mechanistic understanding of many biological processes in healthy and diseased tissues.

A Primer on Preprocessing, Visualization, Clustering, and Phenotyping of Barcode-Based Spatial Transcriptomics Data

Recent developments in spatially resolved transcriptomics (ST) have resulted in a large number of studies characterizing the architecture of tissues, the spatial distribution of cell types, and their interactions. Furthermore, ST promises to enable the discovery of more accurate drug targets while also providing a better understanding of the etiology and evolution of complex diseases. The analysis of ST brings similar challenges as seen in other gene expression assays such as scRNA-seq; however, there is the additional spatial information that warrants the development of suitable algorithms for the quality control, preprocessing, visualization, and other discovery-enabling approaches (e.g., clustering, cell phenotyping). In this chapter, we review some of the existing algorithms to perform these analytical tasks and highlight some of the unmet analytical challenges in the analysis of ST data. Given the diversity of available ST technologies, we focus this chapter on the analysis of barcode-based RNA quantitation techniques.

Visualizing the Interactions Shaping the Imaging of the Microenvironment in Human Cancers

The Visium Spatial Gene Expression Solution (Visium 10×) allows for the mRNA analysis using high throughput sequencing and maps a transcriptional expression pattern in tissue sections using high-resolution microscope imaging in ex-vivo human and mice samples. The workflow surveys spatial global gene expression in tissue sections, exploiting the whole transcriptome profiling and defining the set of transcripts via targeted gene panels. An automated cell type annotation allows a comparison with control tissue samples. This technique delineates cancerous or diseased tissue boundaries and details gene expression gradients in the tissue surrounding the tumor or pathologic nests. Remarkably, the Visium 10× allows for whole transcriptome and targeted analysis without the loss of spatial information. This approach provides gene expression data within the context of tissue architecture, tissue microenvironments, and cell groups. It can be used in association with therapy, anti-angiogenic therapy, and immunotherapy to improve treatment response.

Integrated spatial analysis of gene mutation and gene expression for understanding tumor diversity in formalin-fixed paraffin-embedded lung adenocarcinoma

Introduction

A deeper understanding of intratumoral heterogeneity is essential for prognosis prediction or accurate treatment plan decisions in clinical practice. However, due to the cross-links and degradation of biomolecules within formalin-fixed paraffin-embedded (FFPE) specimens, it is challenging to analyze them. In this study, we aimed to optimize the simultaneous extraction of mRNA and DNA from microdissected FFPE tissues (φ = 100 µm) and apply the method to analyze tumor diversity in lung adenocarcinoma before and after erlotinib administration.

Method

Two magnetic beads were used for the simultaneous extraction of mRNA and DNA. The decross-linking conditions were evaluated for gene mutation and gene expression analyses of microdissected FFPE tissues. Lung lymph nodes before treatment and lung adenocarcinoma after erlotinib administration were collected from the same patient and were preserved as FFPE specimens for 4 years. Gene expression and gene mutations between histologically classified regions of lung adenocarcinoma (pre-treatment tumor in lung lymph node biopsies and post-treatment tumor, normal lung, tumor stroma, and remission stroma, in resected lung tissue) were compared in a microdissection-based approach.

Results

Using the optimized simultaneous extraction of DNA and mRNA and whole-genome amplification, we detected approximately 4,000-10,000 expressed genes and the epidermal growth factor receptor (EGFR) driver gene mutations from microdissected FFPE tissues. We found the differences in the highly expressed cancer-associated genes and the positive rate of EGFR exon 19 deletions among the tumor before and after treatment and tumor stroma, even though they were collected from tumors of the same patient or close regions of the same specimen.

Conclusion

Our integrated spatial analysis method would be applied to various FFPE pathology specimens providing area-specific gene expression and gene mutation information.

Spatial transcriptomics technology in cancer research

In recent years, spatial transcriptomics (ST) technologies have developed rapidly and have been widely used in constructing spatial tissue atlases and characterizing spatiotemporal heterogeneity of cancers. Currently, ST has been used to profile spatial heterogeneity in multiple cancer types. Besides, ST is a benefit for identifying and comprehensively understanding special spatial areas such as tumor interface and tertiary lymphoid structures (TLSs), which exhibit unique tumor microenvironments (TMEs). Therefore, ST has also shown great potential to improve pathological diagnosis and identify novel prognostic factors in cancer. This review presents recent advances and prospects of applications on cancer research based on ST technologies as well as the challenges.

Whole-Exome Sequencing Reveals the Genomic Features of the Micropapillary Component in Ground-Glass Opacities

Background: Micropapillary components are observed in a considerable proportion of ground-glass opacities (GGOs) and contribute to the poor prognosis of patients with invasive lung adenocarcinoma (LUAD). However, the underlying mutational processes related to the presence of micropapillary components remain obscure, limiting the development of clinical interventions. Methods: We collected 31 GGOs, which were separated into paired micropapillary and non-micropapillary components using microdissection. Whole-exome sequencing (WES) was performed on the GGO components, and bioinformatics analysis was conducted to reveal the genomic features of the micropapillary component in invasive LUAD. Results: The micropapillary component had more genomic variations, including tumor mutation burden, intratumoral heterogeneity, and copy number variation. We also observed the enrichment of AID/APOBEC mutation signatures and an increased activation of the RTK/Ras, Notch, and Wnt oncogenic pathways within the micropapillary component. A phylogenetic analysis further suggested that ERBB2/3/4, NCOR1/2, TP53, and ZNF469 contributed to the micropapillary component’s progression during the early invasion of LUAD, a finding that was validated in the TCGA cohort. Conclusions: Our results revealed specific mutational characteristics of the micropapillary component of invasive LUAD in an Asian population. These characteristics were associated with the formation of high-grade invasive patterns. These preliminary findings demonstrated the potential of targeting the micropapillary component in patients with early-stage LUAD.

A Very rare case report of male invasive micropapillary breast carcinoma in China and review of literature

INTRODUCTION

To report a rare case of male breast micropapillary carcinoma (MBMC) with early metastasis of axillary lymph nodes, the molecular characteristics were further studied in both primary and metastatic foci. In addition, we have reviewed similar published cases in the literature and tried to outline the molecular characteristics of this disease.

PRESENTATION OF CASE

A 63-year-old male patient presented with a painless mass on the medial side of left breast and was pathologically diagnosed with MBMC. Postoperative examination revealed 80 % invasive ductal carcinoma (IDC) and 20 % invasive micropapillary carcinoma (IMPC) in the mass, with a histological grade WHO III. There were 25 axillary lymph nodes, 11 of which were metastatic, including 5 macrometastasis and 1 micrometastasis, with a lymph node metastasis rate of 44 % (11/25). Pathological TNM stage: pT2N2M0. Immunohistochemical results in primary foci: AR (90 %, +), HER- 2 (1 +) and ER (90 %, +), PR (60 %, +), E - cadherin (+), EGFR (-), GATA - 3 (90 %, 3 +), Ki - 67 (50 %). Lymph node metastasis: AR (40 %, strong +), HER-2 (2+), ER (90 %, strong +), PR (40 %, strong +), Ki-67 (50 %). AR and Ki-67 were obviously expressed in both primary and metastatic foci. A mixture of IDC and IMPC was found in lymph node metastases, both of which expressed varying degrees of AR and Ki-67.

CLINICAL DISCUSSION

MBMC is easy to early metastasized to lymph node. In this case, there was no significant difference between primary and metastatic cancer in molecular results. It is positive for ER and PR, but negative for HER-2 in this patient. There is few data on male HER-2 expression, HER-2 expression is deficient in this case. AR is found to be positive in 50 % of MBMC cases, although their clinical relevance has not been established yet. The significance of EGFR in the prognosis of MBMC remains unclear, however, EGFR positive expression is not found in this patient.

CONCLUSIONS

MBMC is a rare disease characterized by early lymph node metastasis, high histological grade, positive ER and PR, and generally negative HER-2. The molecular biological characteristics and prognostic significance of MBMC need to be further studied in order to develop the optimal treatment strategy.

Application and prospect of single-cell sequencing in cancer metastasis

Cancer metastasis is a complicated process driven by internal genetic variations and developed through interactions with the external environment. This process usually causes therapeutic resistance and results in a low survival rate. In recent years, single-cell sequencing has become a popular method for revealing the tumor evolutionary genetic lineage, intra-tumoral heterogeneity and tumor microenvironment of the metastasis process. So as to find more therapeutic targets for clinical application, the spatial transcriptomics method has become a new rising field of cancer studies, which promotes the combination between clinical medicine and molecular biology. In future prospects, more accurate and personalized treatment models will come into reality.