Single-cell heterogeneity in ductal carcinoma in situ of breast

Comparison of molecular subtype composition between independent sets of primary and brain metastatic small cell lung carcinoma and matched samples

INTRODUCTION

Recent advances in the subclassification of small cell lung carcinomas (SCLCs) may help to overcome the unmet need for targeted therapies and improve survival. However, limited information is available on how the expression of the subtype markers changes during tumour progression. Our study aimed to compare the expression of these markers in primary and brain metastatic SCLCs.

MATERIALS AND METHODS

Immunohistochemical analysis of the subtype markers was performed on 120 SCLCs (including 10 matched samples) and SCLC xenografts.

RESULTS

Compared to primary SCLCs, there was a significant increase in the proportion of mixed subtypes in brain metastases, with a rate of ASCL1high/NeuroD1high and ASCL1high/NeuroD1high/YAP1high subtypes increasing to 48 % and 18 %, respectively. The subtype of the paired samples matched in only one-third of the cases. Although we did not observe a significant change after chemotherapy, a continuous decrease in ASCL1 expression coupled with an increase in the NeuroD1 expression was detected in the xenografts in a long-term experiment.

DISCUSSION

Our results indicate that the expression of subtype markers frequently changes during disease progression, and subtype analysis of the primary SCLC may not provide accurate information about the characteristics of the recurrent or metastatic tumour. Therefore, repeated sampling and subtyping may be necessary for subtype-specific targeted therapy.

Clinical Proteomics Reveals Vulnerabilities in Noninvasive Breast Ductal Carcinoma and Drives Personalized Treatment Strategies

SIGNIFICANCE

This study provides real-world evidence for DCIS, a disease for which currently no molecular tools or biomarkers exist, and gives an unbiased, comprehensive, and deep proteomic profile, identifying >380 actionable targets.

Integrated multiplex analysis of cell death regulators in stage II colorectal cancer suggests patients with 'persister' cell profiles fail to benefit from adjuvant chemotherapy

Objective

Inducing tumour cell apoptosis is a primary objective of chemotherapy but, to date, there are no validated biomarkers of apoptosis sensitivity or resistance. Our objective was to image multiple apoptosis pathway proteins at single cell level and determine multi-protein associations with recurrence risk and chemotherapy response in patients with stage II colorectal cancer (CRC).

Methods and analysis

Multiplexed imaging of 16 proteins in the intrinsic and extrinsic apoptosis pathways at single cell resolution on resected tissue from 194 patients with stage II CRC who either received adjuvant chemotherapy (n=108) or were treated with surgery only (n=86). K-means clustering of >600 000 cancer cells and cell level intensities of APAF1, procaspase-9, procaspase-3, XIAP, SMAC, BAX, BAK, BCL2, BCL-XL, MCL-1, procaspase-8, BID, FADD, FLIP, RIP3 and CIAP1 identified distinct cell cluster profiles.

Results

Chemotherapy-treated patients with a higher percentage of cell clusters with low procaspase-3 and high XIAP had a higher risk of recurrence. This was validated in an independent cohort of adjuvant chemotherapy-treated high-risk patients with stage II CRC. We also applied two established system models of apoptosis initiation and execution to estimate cellular apoptosis sensitivity and show that these cell clusters do not appear to have impaired mitochondrial outer membrane permeabilisation sensitivity, but downstream procaspase-3 cleavage is compromised. This represents a key characteristic of drug-tolerant 'persister' cells.

Conclusion

This study represents the most comprehensive analysis to date of apoptosis protein distribution at single cell level in CRC tumours. Our study identifies a subgroup of patients with stage II CRC with an apoptosis-resistant 'persister' cell profile who do not benefit from adjuvant chemotherapy.

Identification of unique rectal cancer-specific subtypes

BACKGROUND

Existing colorectal cancer subtyping methods were generated without much consideration of potential differences in expression profiles between colon and rectal tissues. Moreover, locally advanced rectal cancers at resection often have received neoadjuvant chemoradiotherapy which likely has a significant impact on gene expression.

METHODS

We collected mRNA expression profiles for rectal and colon cancer samples (n = 2121). We observed that (i) Consensus Molecular Subtyping (CMS) had a different prognosis in treatment-naïve rectal vs. colon cancers, and (ii) that neoadjuvant chemoradiotherapy exposure produced a strong shift in CMS subtypes in rectal cancers. We therefore clustered 182 untreated rectal cancers to find rectal cancer-specific subtypes (RSSs).

RESULTS

We identified three robust subtypes. We observed that RSS1 had better, and RSS2 had worse disease-free survival. RSS1 showed high expression of MYC target genes and low activity of angiogenesis genes. RSS2 exhibited low regulatory T cell abundance, strong EMT and angiogenesis signalling, and high activation of TGF-β, NF-κB, and TNF-α signalling. RSS3 was characterised by the deactivation of EGFR, MAPK and WNT pathways.

CONCLUSIONS

We conclude that RSS subtyping allows for more accurate prognosis predictions in rectal cancers than CMS subtyping and provides new insight into targetable disease pathways within these subtypes.

AZGP1 deficiency promotes angiogenesis in prostate cancer

BACKGROUND

Loss of AZGP1 expression is a biomarker associated with progression to castration resistance, development of metastasis, and poor disease-specific survival in prostate cancer. However, high expression of AZGP1 cells in prostate cancer has been reported to increase proliferation and invasion. The exact role of AZGP1 in prostate cancer progression remains elusive.

METHOD

AZGP1 knockout and overexpressing prostate cancer cells were generated using a lentiviral system. The effects of AZGP1 under- or over-expression in prostate cancer cells were evaluated by in vitro cell proliferation, migration, and invasion assays. Heterozygous AZGP1± mice were obtained from European Mouse Mutant Archive (EMMA), and prostate tissues from homozygous knockout male mice were collected at 2, 6 and 10 months for histological analysis. In vivo xenografts generated from AZGP1 under- or over-expressing prostate cancer cells were used to determine the role of AZGP1 in prostate cancer tumor growth, and subsequent proteomics analysis was conducted to elucidate the mechanisms of AZGP1 action in prostate cancer progression. AZGP1 expression and microvessel density were measured in human prostate cancer samples on a tissue microarray of 215 independent patient samples.

RESULT

Neither the knockout nor overexpression of AZGP1 exhibited significant effects on prostate cancer cell proliferation, clonal growth, migration, or invasion in vitro. The prostates of AZGP1-/- mice initially appeared to have grossly normal morphology; however, we observed fibrosis in the periglandular stroma and higher blood vessel density in the mouse prostate by 6 months. In PC3 and DU145 mouse xenografts, over-expression of AZGP1 did not affect tumor growth. Instead, these tumors displayed decreased microvessel density compared to xenografts derived from PC3 and DU145 control cells, suggesting that AZGP1 functions to inhibit angiogenesis in prostate cancer. Proteomics profiling further indicated that, compared to control xenografts, AZGP1 overexpressing PC3 xenografts are enriched with angiogenesis pathway proteins, including YWHAZ, EPHA2, SERPINE1, and PDCD6, MMP9, GPX1, HSPB1, COL18A1, RNH1, and ANXA1. In vitro functional studies show that AZGP1 inhibits human umbilical vein endothelial cell proliferation, migration, tubular formation and branching. Additionally, tumor microarray analysis shows that AZGP1 expression is negatively correlated with blood vessel density in human prostate cancer tissues.

CONCLUSION

AZGP1 is a negative regulator of angiogenesis, such that loss of AZGP1 promotes angiogenesis in prostate cancer. AZGP1 likely exerts heterotypical effects on cells in the tumor microenvironment, such as stromal and endothelial cells. This study sheds light on the anti-angiogenic characteristics of AZGP1 in the prostate and provides a rationale to target AZGP1 to inhibit prostate cancer progression.

Applicability of Spatial Technology in Cancer Research

This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.

Pathobiology of Candida auris infection analyzed by multiplexed imaging and single cell analysis

Fungal organisms contribute to significant human morbidity and mortality and Candida auris (C. auris) infections are of utmost concern due to multi-drug resistant strains and persistence in critical care and hospital settings. Pathogenesis and pathology of C. auris is still poorly understood and in this study, we demonstrate how the use of multiplex immunofluorescent imaging (MxIF) and single-cell analysis can contribute to a deeper understanding of fungal infections within organs. We used two different neutrophil depletion murine models (treated with either 1A8-an anti-Ly6G antibody, or RB6-8C5-an anti-Ly6G/Ly6C antibody; both 1A8 and RB6-8C5 antibodies have been shown to deplete neutrophils) and compared to wildtype, non-neutropenic mice. Following pathologist assessment, fixed samples underwent MxIF imaging using a C. albicans antibody (shown to be cross-reactive to C. auris) and immune cell biomarkers-CD3 (T cells), CD68 (macrophages), B220 (B cells), CD45 (monocytes), and Ly6G (neutrophils) to quantify organ specific immune niches. MxIF analysis highlighted the heterogenous distribution of C. auris infection within heart, kidney, and brain 7 days post-infection. Size and number of fungal abscesses was greatest in the heart and lowest in brain. Infected mice had an increased count of CD3+, CD68+, B220+, and CD45+ immune cells, concentrated around C. auris abscesses. CD68+ cells were predominant in wildtype (non-neutropenic mice) and CD3+/CD45+ cells were predominant in neutropenic mice, with B cells being the least abundant. These findings suggest a Th2 driven immune response in neutropenic C. auris infection mice models. This study demonstrates the value of MxIF to broaden understanding of C. auris pathobiology, and mechanistic understanding of fungal infections.

Single-cell transcriptome analysis indicates fatty acid metabolism-mediated metastasis and immunosuppression in male breast cancer

Male breast cancer (MBC) is a rare but aggressive malignancy with cellular and immunological characteristics that remain unclear. Here, we perform transcriptomic analysis for 111,038 single cells from tumor tissues of six MBC and thirteen female breast cancer (FBC) patients. We find that that MBC has significantly lower infiltration of T cells relative to FBC. Metastasis-related programs are more active in cancer cells from MBC. The activated fatty acid metabolism involved with FASN is related to cancer cell metastasis and low immune infiltration of MBC. T cells in MBC show activation of p38 MAPK and lipid oxidation pathways, indicating a dysfunctional state. In contrast, T cells in FBC exhibit higher expression of cytotoxic markers and immune activation pathways mediated by immune-modulatory cytokines. Moreover, we identify the inhibitory interactions between cancer cells and T cells in MBC. Our study provides important information for understanding the tumor immunology and metabolism of MBC.

Spatial Proteomics for the Molecular Characterization of Breast Cancer

Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease's heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to identify early diagnostic biomarkers and therapeutic targets, and to understand protein expression levels and modifications. The subcellular localization of proteins is a key factor in their physiological function, making the study of subcellular localization a major challenge in cell biology. Achieving high resolution at the cellular and subcellular level is essential for obtaining an accurate spatial distribution of proteins, which in turn can enable the application of proteomics in clinical research. In this review, we present a comparison of current methods of spatial proteomics in BC, including untargeted and targeted strategies. Untargeted strategies enable the detection and analysis of proteins and peptides without a predetermined molecular focus, whereas targeted strategies allow the investigation of a predefined set of proteins or peptides of interest, overcoming the limitations associated with the stochastic nature of untargeted proteomics. By directly comparing these methods, we aim to provide insights into their strengths and limitations and their potential applications in BC research.

Characterisation of 3D Bioprinted Human Breast Cancer Model for In Vitro Drug and Metabolic Targeting

Monolayer cultures, the less standard three-dimensional (3D) culturing systems, and xenografts are the main tools used in current basic and drug development studies of cancer research. The aim of biofabrication is to design and construct a more representative in vivo 3D environment, replacing two-dimensional (2D) cell cultures. Here, we aim to provide a complex comparative analysis of 2D and 3D spheroid culturing, and 3D bioprinted and xenografted breast cancer models. We established a protocol to produce alginate-based hydrogel bioink for 3D bioprinting and the long-term culturing of tumour cells in vitro. Cell proliferation and tumourigenicity were assessed with various tests. Additionally, the results of rapamycin, doxycycline and doxorubicin monotreatments and combinations were also compared. The sensitivity and protein expression profile of 3D bioprinted tissue-mimetic scaffolds showed the highest similarity to the less drug-sensitive xenograft models. Several metabolic protein expressions were examined, and the in situ tissue heterogeneity representing the characteristics of human breast cancers was also verified in 3D bioprinted and cultured tissue-mimetic structures. Our results provide additional steps in the direction of representing in vivo 3D situations in in vitro studies. Future use of these models could help to reduce the number of animal experiments and increase the success rate of clinical phase trials.

Protein Profiling of Breast Cancer for Treatment Decision-Making

The increasing use of neoadjuvant therapy has resulted in therapeutic decisions being made on the basis of diagnostic needle core biopsy. For many patients, this method might yield the only fragment of tumor available for biomarker analysis, necessitating judicious use. Many multiplex protein analytic methods have been developed that employ fluorescence or other tags to overcome the limitations of immunohistochemistry while still retaining the spatial annotation. Interpretation of the data can be difficult because of the limitations of the human eye. Computational deconvolution of the signals may be necessary for some of these methods to enable identification of cell-specific localization and coexpression of biomarkers. Herein, we present the different methods that are coming of age and their application in cancer research, with a focus on breast cancer. We also discuss the limitations, which include high costs and long turnaround times. The methods are also based on the premise that preanalytical factors will have identical impact on all proteins analyzed. There is a need to establish standards to normalize the data and enable cross-sample comparisons. In spite of these limitations, the multiplex technologies are extremely valuable discovery tools and can provide novel insights into the biology of cancer and mechanisms of drug resistance.

Single-Cell Sequencing and Its Applications in Liver Cancer

As one of the most lethal cancers, primary liver cancer (PLC) has high tumor heterogeneity, including the heterogeneity between cancer cells. Traditional methods which have been used to identify tumor heterogeneity for a long time are based on large mixed cell samples, and the research results usually show average level of the cell population, ignoring the heterogeneity between cancer cells. In recent years, single-cell sequencing has been increasingly applied to the studies of PLCs. It can detect the heterogeneity between cancer cells, distinguish each cell subgroup in the tumor microenvironment (TME), and also reveal the clonal characteristics of cancer cells, contributing to understand the evolution of tumor. Here, we introduce the process of single-cell sequencing, review the applications of single-cell sequencing in the heterogeneity of cancer cells, TMEs, oncogenesis, and metastatic mechanisms of liver cancer, and discuss some of the current challenges in the field.

Stratification of chemotherapy-treated stage III colorectal cancer patients using multiplexed imaging and single-cell analysis of T-cell populations

Colorectal cancer (CRC) has one of the highest cancer incidences and mortality rates. In stage III, postoperative chemotherapy benefits <20% of patients, while more than 50% will develop distant metastases. Biomarkers for identification of patients at increased risk of disease recurrence following adjuvant chemotherapy are currently lacking. In this study, we assessed immune signatures in the tumor and tumor microenvironment (TME) using an in situ multiplexed immunofluorescence imaging and single-cell analysis technology (Cell DIVETM) and evaluated their correlations with patient outcomes. Tissue microarrays (TMAs) with up to three 1 mm diameter cores per patient were prepared from 117 stage III CRC patients treated with adjuvant fluoropyrimidine/oxaliplatin (FOLFOX) chemotherapy. Single sections underwent multiplexed immunofluorescence staining for immune cell markers (CD45, CD3, CD4, CD8, FOXP3, PD1) and tumor/cell segmentation markers (DAPI, pan-cytokeratin, AE1, NaKATPase, and S6). We used annotations and a probabilistic classification algorithm to build statistical models of immune cell types. Images were also qualitatively assessed independently by a Pathologist as 'high', 'moderate' or 'low', for stromal and total immune cell content. Excellent agreement was found between manual assessment and total automated scores (p < 0.0001). Moreover, compared to single markers, a multi-marker classification of regulatory T cells (Tregs: CD3+/CD4+FOXP3+/PD1-) was significantly associated with disease-free survival (DFS) and overall survival (OS) (p = 0.049 and 0.032) of FOLFOX-treated patients. Our results also showed that PD1- Tregs rather than PD1+ Tregs were associated with improved survival. These findings were supported by results from an independent FOLFOX-treated cohort of 191 stage III CRC patients, where higher PD1- Tregs were associated with an increase overall survival (p = 0.015) for CD3+/CD4+/FOXP3+/PD1-. Overall, compared to single markers, multi-marker classification provided more accurate quantitation of immune cell types with stronger correlations with outcomes.

The breast pre-cancer atlas illustrates the molecular and micro-environmental diversity of ductal carcinoma in situ

Microenvironmental and molecular factors mediating the progression of Breast Ductal Carcinoma In Situ (DCIS) are not well understood, impeding the development of prevention strategies and the safe testing of treatment de-escalation. We addressed methodological barriers and characterized the mutational, transcriptional, histological, and microenvironmental landscape across 85 multiple microdissected regions from 39 cases. Most somatic alterations, including whole-genome duplications, were clonal, but genetic divergence increased with physical distance. Phenotypic and subtype heterogeneity was frequently associated with underlying genetic heterogeneity and regions with low-risk features preceded those with high-risk features according to the inferred phylogeny. B- and T-lymphocytes spatial analysis identified three immune states, including an epithelial excluded state located preferentially at DCIS regions, and characterized by histological and molecular features of immune escape, independently from molecular subtypes. Such breast pre-cancer atlas with uniquely integrated observations will help scope future expansion studies and build finer models of outcomes and progression risk.

Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment

A holistic understanding of tissue and organ structure and function requires the detection of molecular constituents in their original three-dimensional (3D) context. Imaging mass cytometry (IMC) enables simultaneous detection of up to 40 antigens and transcripts using metal-tagged antibodies but has so far been restricted to two-dimensional imaging. Here we report the development of 3D IMC for multiplexed 3D tissue analysis at single-cell resolution and demonstrate the utility of the technology by analysis of human breast cancer samples. The resulting 3D models reveal cellular and microenvironmental heterogeneity and cell-level tissue organization not detectable in two dimensions. 3D IMC will prove powerful in the study of phenomena occurring in 3D space such as tumor cell invasion and is expected to provide invaluable insights into cellular microenvironments and tissue architecture.

Quantification of tumor heterogeneity: from data acquisition to metric generation

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

Spatial protein heterogeneity analysis in frozen tissues to evaluate tumor heterogeneity

A new workflow for protein-based tumor heterogeneity probing in tissues is here presented. Tumor heterogeneity is believed to be key for therapy failure and differences in prognosis in cancer patients. Comprehending tumor heterogeneity, especially at the protein level, is critical for tracking tumor evolution, and showing the presence of different phenotypical variants and their location with respect to tissue architecture. Although a variety of techniques is available for quantifying protein expression, the heterogeneity observed in the tissue is rarely addressed. The proposed method is validated in breast cancer fresh-frozen tissues derived from five patients. Protein expression is quantified on the tissue regions of interest (ROI) with a resolution of up to 100 μm in diameter. High heterogeneity values across the analyzed patients in proteins such as cytokeratin 7, β-actin and epidermal growth factor receptor (EGFR) using a Shannon entropy analysis are observed. Additionally, ROIs are clustered according to their expression levels, showing their location in the tissue section, and highlighting that similar phenotypical variants are not always located in neighboring regions. Interestingly, a patient with a phenotype related to increased aggressiveness of the tumor presents a unique protein expression pattern. In summary, a workflow for the localized extraction and protein analysis of regions of interest from frozen tissues, enabling the evaluation of tumor heterogeneity at the protein level is presented.

Single-cell evaluation reveals shifts in the tumor-immune niches that shape and maintain aggressive lesions in the breast

There is an unmet clinical need for stratification of breast lesions as indolent or aggressive to tailor treatment. Here, single-cell transcriptomics and multiparametric imaging applied to a mouse model of breast cancer reveals that the aggressive tumor niche is characterized by an expanded basal-like population, specialization of tumor subpopulations, and mixed-lineage tumor cells potentially serving as a transition state between luminal and basal phenotypes. Despite vast tumor cell-intrinsic differences, aggressive and indolent tumor cells are functionally indistinguishable once isolated from their local niche, suggesting a role for non-tumor collaborators in determining aggressiveness. Aggressive lesions harbor fewer total but more suppressed-like T cells, and elevated tumor-promoting neutrophils and IL-17 signaling, disruption of which increase tumor latency and reduce the number of aggressive lesions. Our study provides insight into tumor-immune features distinguishing indolent from aggressive lesions, identifies heterogeneous populations comprising these lesions, and supports a role for IL-17 signaling in aggressive progression.

Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response

Tumors are not only aggregates of malignant cells but also well-organized complex ecosystems. The immunological components within tumors, termed the tumor immune microenvironment (TIME), have long been shown to be strongly related to tumor development, recurrence and metastasis. However, conventional studies that underestimate the potential value of the spatial architecture of the TIME are unable to completely elucidate its complexity. As innovative high-flux and high-dimensional technologies emerge, researchers can more feasibly and accurately detect and depict the spatial architecture of the TIME. These findings have improved our understanding of the complexity and role of the TIME in tumor biology. In this review, we first epitomized some representative emerging technologies in the study of the spatial architecture of the TIME and categorized the description methods used to characterize these structures. Then, we determined the functions of the spatial architecture of the TIME in tumor biology and the effects of the gradient of extracellular nonspecific chemicals (ENSCs) on the TIME. We also discussed the potential clinical value of our understanding of the spatial architectures of the TIME, as well as current limitations and future prospects in this novel field. This review will bring spatial architectures of the TIME, an emerging dimension of tumor ecosystem research, to the attention of more researchers and promote its application in tumor research and clinical practice.

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.

Improving DCIS diagnosis and predictive outcome by applying artificial intelligence

Breast ductal carcinoma in situ (DCIS) is a preinvasive lesion that is considered to be a precursor to invasive breast cancer. Nevertheless, not all DCIS will progress to invasion. Current histopathological classification systems are unable to predict which cases will or will not progress, and therefore many women with DCIS may be overtreated. Artificial intelligence (AI) image-based analysis methods have potential to identify and analyze novel features that may facilitate tumor identification, prediction of disease outcome and response to treatment. Indeed, these methods prove promising for accurately identifying DCIS lesions, and show potential clinical utility in the therapeutic stratification of DCIS patients. Here, we review how AI techniques in histopathology may aid diagnosis and clinical decisions in regards to DCIS, and how such techniques could be incorporated into clinical practice.

Multi-protein spatial signatures in ductal carcinoma in situ (DCIS) of breast

BACKGROUND

There is limited knowledge about DCIS cellular composition and relationship with breast cancer events (BCE).

METHODS

Immunofluorescence multiplexing (MxIF) was used to image and quantify 32 cellular biomarkers in FFPE DCIS tissue microarrays. Over 75,000 DCIS cells from 51 patients (median 9 years follow-up for non-BCE cases) were analysed for profiles predictive of BCE. K-means clustering was used to evaluate cellular co-expression of epithelial markers with ER and HER2.

RESULTS

Only ER, PR and HER2 significantly correlated with BCE. Cluster analysis identified 6 distinct cell groups with different levels of ER, Her2, cMET and SLC7A5. Clusters 1 and 3 were not significant. Clusters 2 and 4 (high ER/low HER2 and SLC7A5/mixed cMET) significantly correlated with low BCE risk (P = 0.001 and P = 0.034), while cluster 6 (high HER2/low ER, cMET and SLC7A5) correlated with increased risk (P = 0.018). Cluster 5 (similar to cluster 6, except high SLC7A5) trended towards significance (P = 0.072). A continuous expression score (Escore) based on these 4 clusters predicted likelihood of BCE (AUC = 0.79, log-rank test P = 5E-05; LOOCV AUC = 0.74, log-rank test P = 0.006).

CONCLUSION

Multiplexed spatial analysis of limited tissue is a novel method for biomarker analysis and predicting BCEs. Further validation of Escore is needed in a larger cohort.

Morphological intratumor heterogeneity in ductal carcinoma in situ of the breast

Ductal carcinoma in situ (DCIS) of the breast is a heterogeneous disease in terms of morphological characteristics, protein expression profiles, genetic abnormalities, and potential for progression. Molecular heterogeneity has been extensively studied in DCIS. Yet morphological heterogeneity remains relatively undefined. This study investigated morphological intratumor heterogeneity in a series of 51 large DCIS. Nuclear atypia, DCIS architecture, necrosis, calcifications, stromal architecture, and stromal inflammation were assessed in one biopsy slide and three representative slides from each corresponding resection. For each histopathological feature, a histo-score was determined per slide and compared between the biopsy and the resection, as well as within a single resection. Statistical analysis comprised of Friedman tests, post hoc Wilcoxon tests with Bonferroni corrections, Mann-Whitney U tests, and chi-square tests. Despite substantial morphological heterogeneity in around 50% of DCIS, the histopathological assessment of the biopsy did not statistically significantly differ from the resection. Morphological heterogeneity was not significantly associated with patient age, DCIS size, or type of surgery, except for a weak association between heterogeneous stromal inflammation and smaller DCIS size. At the group level, the degree of heterogeneity did not significantly affect the representativity of a biopsy. At the individual patient level, however, the presence of necrosis, intraductal calcifications, myxoid stromal changes, and high-grade nuclear atypia was underestimated in a minority of DCIS patients. This study confirms the presence of morphological heterogeneity in DCIS for all six evaluated histopathological features. This should be kept in mind when taking biopsy-based treatment decisions for DCIS patients.

Genomic Alterations during the In Situ to Invasive Ductal Breast Carcinoma Transition Shaped by the Immune System

The drivers of ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) transition are poorly understood. Here, we conducted an integrated genomic, transcriptomic, and whole-slide image analysis to evaluate changes in copy-number profiles, mutational profiles, expression, neoantigen load, and topology in 6 cases of matched pure DCIS and recurrent IDC. We demonstrate through combined copy-number and mutational analysis that recurrent IDC can be genetically related to its pure DCIS despite long latency periods and therapeutic interventions. Immune "hot" and "cold" tumors can arise as early as DCIS and are subtype-specific. Topologic analysis showed a similar degree of pan-leukocyte-tumor mixing in both DCIS and IDC but differ when assessing specific immune subpopulations such as CD4 T cells and CD68 macrophages. Tumor-specific copy-number aberrations in MHC-I presentation machinery and losses in 3p, 4q, and 5p are associated with differences in immune signaling in estrogen receptor (ER)-negative IDC. Common oncogenic hotspot mutations in genes including TP53 and PIK3CA are predicted to be neoantigens yet are paradoxically conserved during the DCIS-to-IDC transition, and are associated with differences in immune signaling. We highlight both tumor and immune-specific changes in the transition of pure DCIS to IDC, including genetic changes in tumor cells that may have a role in modulating immune function and assist in immune escape, driving the transition to IDC. IMPLICATIONS: We demonstrate that the in situ to IDC evolutionary bottleneck is shaped by both tumor and immune cells.

Derivation of a nuclear heterogeneity image index to grade DCIS

Abnormalities in cell nuclear morphology are a hallmark of cancer. Histological assessment of cell nuclear morphology is frequently used by pathologists to grade ductal carcinoma in situ (DCIS). Objective methods that allow standardization and reproducibility of cell nuclear morphology assessment have potential to improve the criteria needed to predict DCIS progression and recurrence. Aggressive cancers are highly heterogeneous. We asked whether cell nuclear morphology heterogeneity could be incorporated into a metric to classify DCIS. We developed a nuclear heterogeneity image index to objectively, and quantitatively grade DCIS. A whole-tissue cell nuclear morphological analysis, that classified tumors by the worst ten percent in a duct-by-duct manner, identified nuclear size ranges associated with each DCIS grade. Digital image analysis further revealed increasing heterogeneity within ducts or between ducts in tissues of worsening DCIS grade. The findings illustrate how digital image analysis comprises a supplemental tool for pathologists to objectively classify DCIS and in the future, may provide a method to predict patient outcome through analysis of nuclear heterogeneity.

Mutational profiling of micro-dissected pre-malignant lesions from archived specimens

BACKGROUND

Systematic cancer screening has led to the increased detection of pre-malignant lesions (PMLs). The absence of reliable prognostic markers has led mostly to over treatment resulting in potentially unnecessary stress, or insufficient treatment and avoidable progression. Importantly, most mutational profiling studies have relied on PML synchronous to invasive cancer, or performed in patients without outcome information, hence limiting their utility for biomarker discovery. The limitations in comprehensive mutational profiling of PMLs are in large part due to the significant technical and methodological challenges: most PML specimens are small, fixed in formalin and paraffin embedded (FFPE) and lack matching normal DNA.

METHODS

Using test DNA from a highly degraded FFPE specimen, multiple targeted sequencing approaches were evaluated, varying DNA input amount (3-200 ng), library preparation strategy (BE: Blunt-End, SS: Single-Strand, AT: A-Tailing) and target size (whole exome vs. cancer gene panel). Variants in high-input DNA from FFPE and mirrored frozen specimens were used for PML-specific variant calling training and testing, respectively. The resulting approach was applied to profile and compare multiple regions micro-dissected (mean area 5 mm²) from 3 breast ductal carcinoma in situ (DCIS).

RESULTS

Using low-input FFPE DNA, BE and SS libraries resulted in 4.9 and 3.7 increase over AT libraries in the fraction of whole exome covered at 20x (BE:87%, SS:63%, AT:17%). Compared to high-confidence somatic mutations from frozen specimens, PML-specific variant filtering increased recall (BE:85%, SS:80%, AT:75%) and precision (BE:93%, SS:91%, AT:84%) to levels expected from sampling variation. Copy number alterations were consistent across all tested approaches and only impacted by the design of the capture probe-set. Applied to DNA extracted from 9 micro-dissected regions (8 PML, 1 normal epithelium), the approach achieved comparable performance, illustrated the data adequacy to identify candidate driver events (GATA3 mutations, ERBB2 or FGFR1 gains, TP53 loss) and measure intra-lesion genetic heterogeneity.

CONCLUSION

Alternate experimental and analytical strategies increased the accuracy of DNA sequencing from archived micro-dissected PML regions, supporting the deeper molecular characterization of early cancer lesions and achieving a critical milestone in the development of biology-informed prognostic markers and precision chemo-prevention strategies.

Analysis of Spatial Distribution and Prognostic Value of Different Pan Cytokeratin Immunostaining Intensities in Breast Tumor Tissue Sections

Cancer risk prognosis could improve patient survival through early personalized treatment decisions. This is the first systematic analysis of the spatial and prognostic distribution of different pan cytokeratin immunostaining intensities in breast tumors. The prognostic model included 102 breast carcinoma patients, with distant metastasis occurrence as the endpoint. We segmented the full intensity range (0–255) of pan cytokeratin digitized immunostaining into seven discrete narrow grey level ranges: 0–130, 130–160, 160–180, 180–200, 200–220, 220–240, and 240–255. These images were subsequently examined by 33 major (GLCM), fractal and first-order statistics computational analysis features. Interestingly, while moderate intensities were strongly associated with metastasis outcome, high intensities of pan cytokeratin immunostaining provided no prognostic value even after an exhaustive computational analysis. The intense pan cytokeratin immunostaining was also relatively rare, suggesting the low differentiation state of epithelial cells. The observed variability in immunostaining intensities highlighted the intratumoral heterogeneity of the malignant cells and its association with a poor disease outcome. The prognostic importance of the moderate intensity range established by complex computational morphology analyses was supported by simple measurements of its immunostaining area which was associated with favorable disease outcome. This study reveals intratumoral heterogeneity of the pan cytokeratin immunostaining together with the prognostic evaluation and spatial distribution of its discrete intensities.

T-cell-based breast cancer immunotherapy

Cancer immunotherapy has witnessed a new renaissance with the advent of immune checkpoint inhibitors, which reactivate T cells and foster endogenous anti-tumor responses. The excellent results of immunotherapy in the field of melanoma, renal cancer, lung cancer, and other cancer types that have traditionally been known to be immunogenic, rekindled the interest of the oncology community in extending the benefits to all cancers including breast cancer (BC). In this review, we highlight the current state of using T cells as both markers for clinical practice and therapeutic options for BC.

National Cancer Institute Think-Tank Meeting Report on Proteomic Cartography and Biomarkers at the Single-Cell Level: Interrogation of Premalignant Lesions

A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts' opinion on the development and application of multiomic single-cell analyses, and especially single-cell proteomics, to improve the development of a new generation of biomarkers for cancer risk, early detection, diagnosis, and prognosis as well as to discuss the discovery of new targets for prevention and therapy. It is anticipated that such markers and targets will be based on cellular, subcellular, molecular, and functional aberrations within the lesion and within individual cells. Single-cell proteomic data will be essential for the establishment of new tools with searchable and scalable features that include spatial and temporal cartographies of premalignant and malignant lesions. Challenges and potential solutions that were discussed included (i) The best way/s to analyze single-cells from fresh and preserved tissue; (ii) Detection and analysis of secreted molecules and from single cells, especially from a tissue slice; (iii) Detection of new, previously undocumented cell type/s in the premalignant and early stage cancer tissue microenvironment; (iv) Multiomic integration of data to support and inform proteomic measurements; (v) Subcellular organelles-identifying abnormal structure, function, distribution, and location within individual premalignant and malignant cells; (vi) How to improve the dynamic range of single-cell proteomic measurements for discovery of differentially expressed proteins and their post-translational modifications (PTM); (vii) The depth of coverage measured concurrently using single-cell techniques; (viii) Quantitation - absolute or semiquantitative? (ix) Single methodology or multiplexed combinations? (x) Application of analytical methods for identification of biologically significant subsets; (xi) Data visualization of N-dimensional data sets; (xii) How to construct intercellular signaling networks in individual cells within premalignant tumor microenvironments (TME); (xiii) Associations between intrinsic cellular processes and extrinsic stimuli; (xiv) How to predict cellular responses to stress-inducing stimuli; (xv) Identification of new markers for prediction of progression from precursor, benign, and localized lesions to invasive cancer, based on spatial and temporal changes within individual cells; (xvi) Identification of new targets for immunoprevention or immunotherapy-identification of neoantigens and surfactome of individual cells within a lesion.

∆Np63/p40 correlates with the location and phenotype of basal/mesenchymal cancer stem-like cells in human ER+ and HER2+ breast cancers

ΔNp63, also known as p40, regulates stemness of normal mammary gland epithelium and provides stem cell characteristics in basal and HER2‐driven murine breast cancer models. Whilst ΔNp63/p40 is a characteristic feature of normal basal cells and basal‐type triple‐negative breast cancer, some receptor‐positive breast cancers express ΔNp63/p40 and its overexpression imparts cancer stem cell‐like properties in ER⁺ cell lines. However, the incidence of ER⁺ and HER2⁺ tumours that express ΔNp63/p40 is unclear and the phenotype of ΔNp63/p40⁺ cells in these tumours remains uncertain. Using immunohistochemistry with p63 isoform‐specific antibodies, we identified a ΔNp63/p40⁺ tumour cell subpopulation in 100 of 173 (58%) non‐triple negative breast cancers and the presence of this population associated with improved survival in patients with ER⁻/HER2⁺ tumours (p = 0.006). Furthermore, 41% of ER⁺/PR⁺ and/or HER2⁺ locally metastatic breast cancers expressed ΔNp63/p40, and these cells commonly accounted for <1% of the metastatic tumour cell population that localised to the tumour/stroma interface, exhibited an undifferentiated phenotype and were CD44⁺/ALDH⁻. In vitro studies revealed that MCF7 and T47D (ER⁺) and BT‐474 (HER2⁺) breast cancer cell lines similarly contained a small subpopulation of ΔNp63/p40⁺ cells that increased in mammospheres. In vivo, MCF7 xenografts contained ΔNp63/p40⁺ cells with a similar phenotype to primary ER⁺ cancers. Consistent with tumour samples, these cells also showed a distinct location at the tumour/stroma interface, suggesting a role for paracrine factors in the induction or maintenance of ΔNp63/p40. Thus, ΔNp63/p40 is commonly present in a small population of tumour cells with a distinct phenotype and location in ER⁺ and/or HER2⁺ human breast cancers.

Single-cell sequencing and its applications in head and neck cancer

Head and neck squamous cell carcinoma (HNSCC), like many tumors, is characterized by significant intra-tumoral heterogeneity, namely transcriptional, genetic, and epigenetic differences that define distinct cellular subpopulations. While it has been established that intra-tumoral heterogeneity may have prognostic significance in HNSCC, we are only beginning to describe and define such heterogeneity at a cellular resolution. Recent advances in single-cell sequencing technologies have been critical in this regard, opening new avenues in our understanding of more nuanced tumor biology by identifying distinct cellular subpopulations, dissecting signaling within the tumor microenvironment, and characterizing cellular genomic mutations and copy number aberrations. The combined effect of these insights is likely to be robust and meaningful changes in existing diagnostic and treatment algorithms through the application of novel biomarkers as well as targeted therapeutics. Here, we review single-cell technological and computational advances at the genomic, transcriptomic, and epigenomic levels, and discuss their applications in cancer research and clinical practice, with a specific focus on HNSCC.

Ductal carcinoma in situ of breast: update 2019

Ductal carcinoma in situ is a non-invasive form of breast cancer. Its incidence is increasing due to widespread use of mammographic screening. It presents several diagnostic and management challenges in part due to its relatively indolent behaviour. Most series analysing biomarkers in these lesions are small (<100 patients) and large clinical trials have not been frequent. Herein, we review the recent progress made in understanding the biology of this entity and the tools available for prognostication.

Comparison of the genomic background of MET-altered carcinomas of the lung: biological differences and analogies

Although non-small-cell lung cancer is a leading cause of cancer-related deaths, the molecular characterization and classification of its genetic alterations has drastically changed treatment options and overall survival within the last few decades. In particular, tyrosine kinase inhibitors targeting specific molecular alterations, among other MET, have greatly improved the prognosis of non-small-cell lung cancer patients. Here, we compare the genomic background of a subset of non-small-cell lung cancer cases harboring either a MET high-level amplification (n = 24) or a MET exon 14 skipping mutation (n = 26), using next-generatison sequencing, fluorescence in situ hybridization, immunohistochemistry, and Nanostring nCounter® technology. We demonstrate that the MET-amplified cohort shows a higher genetic instability, compared with the mutant cohort (p < 0.001). Furthermore, MET mutations occur at high allele frequency and in the presence of co-occurring TP53 mutations (n = 7), as well as MDM2 (n = 7), CDK4 (n = 6), and HMGA2 (n = 5) co-amplifications. No other potential driver mutation has been detected. Conversely, in the MET-amplified group, we identify co-occurring pathogenic NRAS and KRAS mutations (n = 5) and a significantly higher number of TP53 mutations, compared with the MET-mutant cohort (p = 0.048). Of note, MET amplifications occur more frequently as subclonal events. Interestingly, despite the significantly (p = 0.00103) older age at diagnosis of stage IIIb/IV of MET-mutant patients (median 77 years), compared with MET high-level amplified patients (median 69 years), MET-mutant patients with advanced-stage tumors showed a significantly better prognosis at 12 months (p = 0.04). In conclusion, the two groups of MET genetic alterations differ, both clinically and genetically: our data strongly suggest that MET exon 14 skipping mutations represent an early driver mutation. In opposition, MET amplifications occur usually in the background of other strong genetic events and therefore MET amplifications should be interpreted in the context of each tumor's genetic background, rather than as an isolated driver event, especially when considering MET-specific treatment options.

Intratumoral Heterogeneity in Ductal Carcinoma In Situ: Chaos and Consequence

Ductal carcinoma in situ (DCIS) is a non-invasive proliferative growth in the breast that serves as a non-obligate precursor to invasive ductal carcinoma. The widespread adoption of screening mammography has led to a steep increase in the detection of DCIS, which now comprises approximately 20% of new breast cancer diagnoses in the United States. Interestingly, the intratumoral heterogeneity (ITH) that has been observed in invasive breast cancers may have been established early in tumorigenesis, given the vast and varied ITH that has been detected in DCIS. This review will discuss the intratumoral heterogeneity of DCIS, focusing on the phenotypic and genomic heterogeneity of tumor cells, as well as the compositional heterogeneity of the tumor microenvironment. In addition, we will assess the spatial heterogeneity that is now being appreciated in these lesions, and summarize new approaches to evaluate heterogeneity of tumor and stromal cells in the context of their spatial organization. Importantly, we will discuss how a growing understanding of ITH has led to a more holistic appreciation of the complex biology of DCIS, specifically its evolution and natural history. Finally, we will consider ways in which our knowledge of DCIS ITH might be translated in the future to guide clinical care for DCIS patients.

In situ characterization of stem cells-like biomarkers in meningiomas

Background

Meningioma cancer stem cells (MCSCs) contribute to tumor aggressiveness and drug resistance. Successful therapies developed for inoperable, recurrent, or metastatic tumors must target these cells and restrict their contribution to tumor progression. Unfortunately, the identity of MCSCs remains elusive, and MSCSs’ in situ spatial distribution, heterogeneity, and relationship with tumor grade, remain unclear.

Methods

Seven tumors classified as grade II or grade III, including one case of metastatic grade III, and eight grade I meningioma tumors, were analyzed for combinations of ten stem cell (SC)-related markers using immunofluorescence of consecutive sections. The correlation of expression for all markers were investigated. Three dimensional spatial distribution of markers were qualitatively analyzed using a grid, designed as a repository of information for positive staining. All statistical analyses were completed using Statistical Analysis Software Package.

Results

The patterns of expression for SC-related markers were determined in the context of two dimensional distribution and cellular features. All markers could be detected in all tumors, however, Frizzled 9 and GFAP had differential expression in grade II/III compared with grade I meningioma tissues. Correlation analysis showed significant relationships between the expression of GFAP and CD133 as well as SSEA4 and Vimentin. Data from three dimensional analysis showed a complex distribution of SC markers, with increased gene hetero-expression being associated with grade II/III tumors. Sub regions that showed multiple co-staining of markers including CD133, Frizzled 9, GFAP, Vimentin, and SSEA4, but not necessarily the proliferation marker Ki67, were highly associated with grade II/III meningiomas.

Conclusion

The distribution and level of expression of CSCs markers in meningiomas are variable and show hetero-expression patterns that have a complex spatial nature, particularly in grade II/III meningiomas. Thus, results strongly support the notion of heterogeneous populations of CSCs, even in grade I meningiomas, and call for the use of multiple markers for the accurate identification of individual CSC subgroups. Such identification will lead to practical clinical diagnostic protocols that can quantitate CSCs, predict tumor recurrence, assist in guiding treatment selection for inoperable tumors, and improve follow up of therapy.

Electronic supplementary material

The online version of this article (10.1186/s12935-018-0571-6) contains supplementary material, which is available to authorized users.