Quantification of HER2 and estrogen receptor heterogeneity in breast cancer by single-molecule RNA fluorescence in situ hybridization

Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images

BACKGROUND AND OBJECTIVE

Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinical observations. Unfortunately, most segmentation methods known today are limited to nuclei and cannot segment the cytoplasm.

METHODS

We present a new network architecture Cyto R-CNN that is able to accurately segment whole cells (with both the nucleus and the cytoplasm) in bright-field images. We also present a new dataset CytoNuke, consisting of multiple thousand manual annotations of head and neck squamous cell carcinoma cells. Utilizing this dataset, we compared the performance of Cyto R-CNN to other popular cell segmentation algorithms, including QuPath's built-in algorithm, StarDist, Cellpose and a multi-scale Attention Deeplabv3+. To evaluate segmentation performance, we calculated AP50, AP75 and measured 17 morphological and staining-related features for all detected cells. We compared these measurements to the gold standard of manual segmentation using the Kolmogorov-Smirnov test.

RESULTS

Cyto R-CNN achieved an AP50 of 58.65% and an AP75 of 11.56% in whole-cell segmentation, outperforming all other methods (QuPath 19.46/0.91%; StarDist 45.33/2.32%; Cellpose 31.85/5.61%, Deeplabv3+ 3.97/1.01%). Cell features derived from Cyto R-CNN showed the best agreement to the gold standard (D¯=0.15) outperforming QuPath (D¯=0.22), StarDist (D¯=0.25), Cellpose (D¯=0.23) and Deeplabv3+ (D¯=0.33).

CONCLUSION

Our newly proposed Cyto R-CNN architecture outperforms current algorithms in whole-cell segmentation while providing more reliable cell measurements than any other model. This could improve digital pathology workflows, potentially leading to improved diagnosis. Moreover, our published dataset can be used to develop further models in the future.

Mapping cancer biology in space: applications and perspectives on spatial omics for oncology

Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.

Gene transcription regulation by ER at the single cell and allele level

In this short review we discuss the current view of how the estrogen receptor (ER), a pivotal member of the nuclear receptor superfamily of transcription factors, regulates gene transcription at the single cell and allele level, focusing on in vitro cell line models. We discuss central topics and new trends in molecular biology including phenotypic heterogeneity, single cell sequencing, nuclear phase separated condensates, single cell imaging, and image analysis methods, with particular focus on the methodologies and results that have been reported in the last few years using microscopy-based techniques. These observations augment the results from biochemical assays that lead to a much more complex and dynamic view of how ER, and arguably most transcription factors, act to regulate gene transcription.

Dual spatially resolved transcriptomics for human host-pathogen colocalization studies in FFPE tissue sections

Technologies to study localized host-pathogen interactions are urgently needed. Here, we present a spatial transcriptomics approach to simultaneously capture host and pathogen transcriptome-wide spatial gene expression information from human formalin-fixed paraffin-embedded (FFPE) tissue sections at a near single-cell resolution. We demonstrate this methodology in lung samples from COVID-19 patients and validate our spatial detection of SARS-CoV-2 against RNAScope and in situ sequencing. Host-pathogen colocalization analysis identified putative modulators of SARS-CoV-2 infection in human lung cells. Our approach provides new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes in FFPE samples.

Quantification of spatial pharmacogene expression heterogeneity in breast tumors

BACKGROUND

Chemotherapeutic drug concentrations vary across different regions of tumors and this is thought to be involved in development of chemotherapy resistance. Insufficient drug delivery to some regions of the tumor may be due to spatial differences in expression of genes involved in the disposition, transport, and detoxification of drugs (pharmacogenes). Therefore, in this study, we analyzed the spatial expression of 286 pharmacogenes in six breast cancer tissues using the recently developed Visium spatial transcriptomics platform to (1) determine if these pharmacogenes are expressed heterogeneously across tumor tissue and (2) to determine which pharmacogenes have the most spatial expression heterogeneity.

METHODS AND RESULTS

The spatial transcriptomics technology sequences the transcriptome of 55 um diameter barcoded sections (spots) across a tissue sample. We analyzed spatial gene expression profiles of four biobank-sourced breast tumor samples in addition to two breast tumor sample datasets from 10× Genomics. We define heterogeneity as the interquartile range of read counts. Collectively, we identified 8887 spots in tumor regions, 3814 in stroma, 44 in lymphocytes, and 116 in normal regions based on pathologist annotation of the tissues. We showed statistically significant differences in expression of pharmacogenes in tumor regions compared to surrounding non-tumor regions. We also observed that the most heterogeneously expressed genes within tumor regions were involved in reactive oxygen species (ROS) handling and detoxification mechanisms. GPX4, GSTP1, MGST3, SOD1, CYP4Z1, CYB5R3, GSTK1, and NAT1 showed the most heterogeneous expression within tumor regions.

CONCLUSIONS

The heterogeneous expression of these pharmacogenes may have important implications for cancer therapy due to their ability to impact drug distribution and efficacy throughout the tumor. Our results suggest that chemoresistance caused by expression of GPX4, GSTP1, MGST3, and SOD1 may be intrinsic, not acquired, since the heterogeneity is not specific to chemotherapy-treated samples or cell type. Additionally, we identified candidate chemoresistance pharmacogenes that can be further tested through focused follow-up studies.

Loss of HER2 in breast cancer: biological mechanisms and technical pitfalls

Loss of HER2 in previously HER2-positive breast tumors is not rare, occurring in up to 50% of breast cancers; however, clinical research and practice underestimate this issue. Many studies have reported the loss of HER2 after neoadjuvant therapy and at metastatic relapse and identified clinicopathological variables more frequently associated with this event. Nevertheless, the biological mechanisms underlying HER2 loss are still poorly understood. HER2 downregulation, intratumoral heterogeneity, clonal selection, and true subtype switch have been suggested as potential causes of HER2 loss, but translational studies specifically investigating the biology behind HER2 loss are virtually absent. On the other side, technical pitfalls may justify HER2 loss in some of these samples. The best treatment strategy for patients with HER2 loss is currently unknown. Considering the prevalence of this phenomenon and its apparent correlation with worse outcomes, we believe that correlative studies specifically addressing HER2 loss are warranted.

Analysis of the Expression and Subcellular Distribution of eEF1A1 and eEF1A2 mRNAs during Neurodevelopment

Neurodevelopment is accompanied by a precise change in the expression of the translation elongation factor 1A variants from eEF1A1 to eEF1A2. These are paralogue genes that encode 92% identical proteins in mammals. The switch in the expression of eEF1A variants has been well studied in mouse motor neurons, which solely express eEF1A2 by four weeks of postnatal development. However, changes in the subcellular localization of eEF1A variants during neurodevelopment have not been studied in detail in other neuronal types because antibodies lack perfect specificity, and immunofluorescence has a low sensitivity. In hippocampal neurons, eEF1A is related to synaptic plasticity and memory consolidation, and decreased eEF1A expression is observed in the hippocampus of Alzheimer's patients. However, the specific variant involved in these functions is unknown. To distinguish eEF1A1 from eEF1A2 expression, we have designed single-molecule fluorescence in-situ hybridization probes to detect either eEF1A1 or eEF1A2 mRNAs in cultured primary hippocampal neurons and brain tissues. We have developed a computational framework, ARLIN (analysis of RNA localization in neurons), to analyze and compare the subcellular distribution of eEF1A1 and eEF1A2 mRNAs at specific developmental stages and in mature neurons. We found that eEF1A1 and eEF1A2 mRNAs differ in expression and subcellular localization over neurodevelopment, and eEF1A1 mRNAs localize in dendrites and synapses during dendritogenesis and synaptogenesis. Interestingly, mature hippocampal neurons coexpress both variant mRNAs, and eEF1A1 remains the predominant variant in dendrites.

Absolute quantitation of individual SARS-CoV-2 RNA molecules provides a new paradigm for infection dynamics and variant differences

Despite an unprecedented global research effort on SARS-CoV-2, early replication events remain poorly understood. Given the clinical importance of emergent viral variants with increased transmission, there is an urgent need to understand the early stages of viral replication and transcription. We used single-molecule fluorescence in situ hybridisation (smFISH) to quantify positive sense RNA genomes with 95% detection efficiency, while simultaneously visualising negative sense genomes, subgenomic RNAs, and viral proteins. Our absolute quantification of viral RNAs and replication factories revealed that SARS-CoV-2 genomic RNA is long-lived after entry, suggesting that it avoids degradation by cellular nucleases. Moreover, we observed that SARS-CoV-2 replication is highly variable between cells, with only a small cell population displaying high burden of viral RNA. Unexpectedly, the B.1.1.7 variant, first identified in the UK, exhibits significantly slower replication kinetics than the Victoria strain, suggesting a novel mechanism contributing to its higher transmissibility with important clinical implications.

Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions

In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.

The Known Unknowns of the Immune Response to Coccidioides

Coccidioidomycosis, otherwise known as Valley Fever, is caused by the dimorphic fungi Coccidioides immitis and C. posadasii. While most clinical cases present with self-limiting pulmonary infection, dissemination of Coccidioides spp. results in prolonged treatment and portends higher mortality rates. While the structure, genome, and niches for Coccidioides have provided some insight into the pathogenesis of disease, the underlying immunological mechanisms of clearance or inability to contain the infection in the lung are poorly understood. This review focuses on the known innate and adaptive immune responses to Coccidioides and highlights three important areas of uncertainty and potential approaches to address them. Closing these gaps in knowledge may enable new preventative and therapeutic strategies to be pursued.

Clinical and research applications of multiplexed immunohistochemistry and in situ hybridization

Over the past decade, invention and adoption of novel multiplexing technologies for tissues have made increasing impacts in basic and translational research and, to a lesser degree, clinical medicine. Platforms capable of highly multiplexed immunohistochemistry or in situ RNA measurements promise evaluation of protein or RNA targets at levels of plex and sensitivity logs above traditional methods - all with preservation of spatial context. These methods promise objective biomarker quantification, markedly increased sensitivity, and single-cell resolution. Increasingly, development of novel technologies is enabling multi-omic interrogations with spatial correlation of RNA and protein expression profiles in the same sample. Such sophisticated methods will provide unprecedented insights into tissue biology, biomarker science, and, ultimately, patient health. However, this sophistication comes at significant cost, requiring extensive time, practical knowledge, and resources to implement. This review will describe the technical features, advantages, and limitations of currently available multiplexed immunohistochemistry and spatial transcriptomic platforms. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Heterogeneity in Signaling Pathway Activity within Primary and between Primary and Metastatic Breast Cancer

Targeted therapy aims to block tumor-driving signaling pathways and is generally based on analysis of one primary tumor (PT) biopsy. Tumor heterogeneity within PT and between PT and metastatic breast lesions may, however, impact the effect of a chosen therapy. Whereas studies are available that investigate genetic heterogeneity, we present results on phenotypic heterogeneity by analyzing the variation in the functional activity of signal transduction pathways, using an earlier developed platform to measure such activity from mRNA measurements of pathways' direct target genes. Statistical analysis comparing macro-scale variation in pathway activity on up to five spatially distributed PT tissue blocks (n = 35), to micro-scale variation in activity on four adjacent samples of a single PT tissue block (n = 17), showed that macro-scale variation was not larger than micro-scale variation, except possibly for the PI3K pathway. Simulations using a "checkerboard clone-size" model showed that multiple small clones could explain the higher micro-scale variation in activity found for the TGFβ and Hedgehog pathways, and that intermediate/large clones could explain the possibly higher macro-scale variation of the PI3K pathway. While within PT, pathway activities presented a highly positive correlation, correlations weakened between PT and lymph node metastases (n = 9), becoming even worse for PT and distant metastases (n = 9), including a negative correlation for the ER pathway. While analysis of multiple sub-samples of a single biopsy may be sufficient to predict PT response to targeted therapies, metastatic breast cancer treatment prediction requires analysis of metastatic biopsies. Our findings on phenotypic intra-tumor heterogeneity are compatible with emerging ideas on a Big Bang type of cancer evolution in which macro-scale heterogeneity appears not dominant.

Hormone Receptor Loss in Breast Cancer: Molecular Mechanisms, Clinical Settings, and Therapeutic Implications

Hormone receptor-positive breast cancer (HR+ BC) accounts for approximately 75% of new BC diagnoses. Despite the undisputable progresses obtained in the treatment of HR+ BC in recent years, primary or acquired resistance to endocrine therapies still represents a clinically relevant issue, and is largely responsible for disease recurrence after curative surgery, as well as for disease progression in the metastatic setting. Among the mechanisms causing primary or acquired resistance to endocrine therapies is the loss of estrogen/progesterone receptor expression, which could make BC cells independent of estrogen stimulation and, consequently, resistant to estrogen deprivation or the pharmacological inhibition of estrogen receptors. This review aims at discussing the molecular mechanisms and the clinical implications of HR loss as a result of the therapies used in the neoadjuvant setting or for the treatment of advanced disease in HR+ BC patients.

Transcriptional Spatial Profiling of Cancer Tissues in the Era of Immunotherapy: The Potential and Promise

Intratumoral heterogeneity poses a major challenge to making an accurate diagnosis and establishing personalized treatment strategies for cancer patients. Moreover, this heterogeneity might underlie treatment resistance, disease progression, and cancer relapse. For example, while immunotherapies can confer a high success rate, selective pressures coupled with dynamic evolution within a tumour can drive the emergence of drug-resistant clones that allow tumours to persist in certain patients. To improve immunotherapy efficacy, researchers have used transcriptional spatial profiling techniques to identify and subsequently block the source of tumour heterogeneity. In this review, we describe and assess the different technologies available for such profiling within a cancer tissue. We first outline two well-known approaches, in situ hybridization and digital spatial profiling. Then, we highlight the features of an emerging technology known as Visium Spatial Gene Expression Solution. Visium generates quantitative gene expression data and maps them to the tissue architecture. By retaining spatial information, we are well positioned to identify novel biomarkers and perform computational analyses that might inform on novel combinatorial immunotherapies.

Spatially Resolved Transcriptomes-Next Generation Tools for Tissue Exploration

Recent advances in spatially resolved transcriptomics have greatly expanded the knowledge of complex multicellular biological systems. The field has quickly expanded in recent years, and several new technologies have been developed that all aim to combine gene expression data with spatial information. The vast array of methodologies displays fundamental differences in their approach to obtain this information, and thus, demonstrate method-specific advantages and shortcomings. While the field is moving forward at a rapid pace, there are still multiple challenges presented to be addressed, including sensitivity, labor extensiveness, tissue-type dependence, and limited capacity to obtain detailed single-cell information. No single method can currently address all these key parameters. In this review, available spatial transcriptomics methods are described and their applications as well as their strengths and weaknesses are discussed. Future developments are explored and where the field is heading to is deliberated upon.

Evolving concepts in HER2 evaluation in breast cancer: Heterogeneity, HER2-low carcinomas and beyond

The human epidermal growth factor receptor 2 (HER2) is a well-known negative prognostic factor in breast cancer and a target of the monoclonal antibody trastuzumab as well as of other anti-HER2 compounds. Pioneering works on HER2-positive breast cancer in the 90s' launched a new era in clinical research and oncology practice that has reshaped the natural history of this disease. In diagnostic pathology the HER2 status is routinely assessed by using a combination of immunohistochemistry (IHC, to evaluate HER2 protein expression levels) and in situ hybridization (ISH, to assess HER2 gene status). For this purpose, international recommendations have been developed by a consensus of experts in the field, which have changed over the years according to new experimental and clinical data. In this review article we will document the changes that have contributed to a better evaluation of the HER2 status in clinical practice, furthermore we will discuss HER2 heterogeneity defined by IHC and ISH as well as by transcriptomic analysis and we will critically describe the complexity of HER2 equivocal results. Finally, we will introduce the clinical impact of HER2 mutations and we will define the upcoming category of HER2-low breast cancer with respect to emerging clinical data on the efficacy of specific anti-HER2 agents in subgroups of breast carcinomas lacking the classical oncogene addition dictated by HER2 amplification.

Spatially multiplexed RNA in situ hybridization to reveal tumor heterogeneity

Multiplexed RNA in situ hybridization for the analysis of gene expression patterns plays an important role in investigating development and disease. Here, we present a method for multiplexed RNA-ISH to detect spatial tumor heterogeneity in tissue sections. We made use of a microfluidic chip to deliver ISH-probes locally to regions of a few hundred micrometers over time periods of tens of minutes. This spatial multiplexing method can be combined with ISH-approaches based on signal amplification, with bright field detection and with the commonly used format of formalin-fixed paraffin-embedded tissue sections. By using this method, we analyzed the expression of HER2 with internal positive and negative controls (ActB, dapB) as well as predictive biomarker panels (ER, PgR, HER2) in a spatially multiplexed manner on single mammary carcinoma sections. We further demonstrated the applicability of the technique for subtype differentiation in breast cancer. Local analysis of HER2 revealed medium to high spatial heterogeneity of gene expression (Cohen effect size r = 0.4) in equivocally tested tumor tissues. Thereby, we exemplify the importance of using such a complementary approach for the analysis of spatial heterogeneity, in particular for equivocally tested tumor samples. As the method is compatible with a range of ISH approaches and tissue samples, it has the potential to find broad applicability in the context of molecular analysis of human diseases.

RollFISH achieves robust quantification of single-molecule RNA biomarkers in paraffin-embedded tumor tissue samples

Single-molecule RNA fluorescence in situ hybridization (smFISH) represents a promising approach to quantify the expression of clinically useful biomarkers in tumor samples. However, routine application of smFISH to formalin-fixed, paraffin-embedded (FFPE) samples is challenging due to the low signal intensity and high background noise. Here we present RollFISH, a method combining the specificity of smFISH with the signal boosting of rolling circle amplification. We apply RollFISH to quantify widely used breast cancer biomarkers in cell lines and FFPE samples. Thanks to the high signal-to-noise ratio, we can visualize selected biomarkers at low magnification (20 × ) across entire tissue sections, and thus assess their spatial heterogeneity. Lastly, we apply RollFISH to quantify HER2 mRNA in 150 samples on a single tissue microarray, achieving a sensitivity and specificity of detection of HER2-positive samples of ~90%. RollFISH is a robust method for quantifying the expression and intratumor heterogeneity of biomarkers in FFPE tissues.

Wu et al. introduce RollFISH, a method that enables quantification of single-molecule RNA with high specificity and sensitivity by combining smFISH with rolling circle amplification. RollFISH facilitated studying heterogeneity of biomarkers in formalin-fixed and paraffin-embedded breast cancer tissue, demonstrating its clinical application.

Cholesterol signaling in single cells: lessons from STAR and sm-FISH

Cholesterol is an important regulator of cell signaling, both through direct impacts on cell membranes and through oxy-metabolites that activate specific receptors (steroids, hydroxy-cholesterols, bile acids). Cholesterol moves slowly through and between cell membranes with the assistance of specific binding proteins and transfer processes. The prototype cholesterol regulator is the Steroidogenesis Acute Regulatory (STAR), which moves cholesterol into mitochondria, where steroid synthesis is initiated by cytochrome P450 11A1 in multiple endocrine cell types. CYP27A1 generates hydroxyl cholesterol metabolites that activate LXR nuclear receptors to control cholesterol homeostatic and transport mechanisms. LXR regulation of cholesterol transport and storage as cholesterol ester droplets is shared by both steroid-producing cells and macrophage. This cholesterol signaling is crucial to brain neuron regulation by astrocytes and microglial macrophage, mediated by ApoE and sensitive to disruption by β-amyloid plaques. sm-FISH delivers appreciable insights into signaling in single cells, by resolving single RNA molecules as mRNA and by quantifying pre-mRNA at gene loci. sm-FISH has been applied to problems in physiology, embryo development and cancer biology, where single cell features have critical impacts. sm-FISH identifies novel features of STAR transcription in adrenal and testis cells, including asymmetric expression at individual gene loci, delayed splicing and 1:1 association of mRNA with mitochondria. This may represent a functional unit for the translation-dependent cholesterol transfer directed by STAR, which integrates into mitochondrial fusion dynamics. Similar cholesterol dynamics repeat with different players in the cycling of cholesterol between astrocytes and neurons in the brain, which may be abnormal in neurodegenerative diseases.

Synthetic lethality between androgen receptor signalling and the PARP pathway in prostate cancer

Emerging data demonstrate homologous recombination (HR) defects in castration-resistant prostate cancers, rendering these tumours sensitive to PARP inhibition. Here we demonstrate a direct requirement for the androgen receptor (AR) to maintain HR gene expression and HR activity in prostate cancer. We show that PARP-mediated repair pathways are upregulated in prostate cancer following androgen-deprivation therapy (ADT). Furthermore, upregulation of PARP activity is essential for the survival of prostate cancer cells and we demonstrate a synthetic lethality between ADT and PARP inhibition in vivo. Our data suggest that ADT can functionally impair HR prior to the development of castration resistance and that, this potentially could be exploited therapeutically using PARP inhibitors in combination with androgen-deprivation therapy upfront in advanced or high-risk prostate cancer.Tumours with homologous recombination (HR) defects become sensitive to PARPi. Here, the authors show that androgen receptor (AR) regulates HR and AR inhibition activates the PARP pathway in vivo, thus inhibition of both AR and PARP is required for effective treatment of high risk prostate cancer.

New Fluorescent Nanoparticles for Ultrasensitive Detection of Nucleic Acids by Optical Methods

For decades the detection of nucleic acids and their interactions at low abundances has been a challenging task that has thus far been solved by enzymatic target amplification. In this work we aimed at developing efficient tools for amplification-free nucleic acid detection, which resulted in the synthesis of new fluorescent nanoparticles. Here, the fluorescent nanoparticles were made by simple and inexpensive radical emulsion polymerization of butyl acrylate in the presence of fluorescent dyes and additional functionalization reagents. This provided ultra-bright macrofluorophores of 9-84 nm mean diameter, modified with additional alkyne and amino groups for bioconjugation. By using click and NHS chemistries, the new nanoparticles were attached to target-specific DNA probes that were used in fluorimetry and fluorescence microscopy. Overall, these fluorescent nanoparticles and their oligonucleotide derivatives have higher photostability, brighter fluorescence and hence dramatically lower limits of target detection than the individual organic dyes. These properties make them useful in approaches directed towards ultrasensitive detection of nucleic acids, in particular for imaging and in vitro diagnostics of DNA.