Relevance of Spatial Heterogeneity of Immune Infiltration for Predicting Risk of Recurrence After Endocrine Therapy of ER+ Breast Cancer

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer

INTRODUCTION

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes.

AREAS COVERED

In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings.

EXPERT OPINION

The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

Spatial Distribution and Densities of CD103+ and FoxP3+ Tumor Infiltrating Lymphocytes by Digital Analysis for Outcome Prediction in Breast Cancer

BACKGROUND

The evaluation of tumor-infiltrating lymphocytes (TILs) for breast cancer prognosis is now established. However, the clinical value for their spatial distributions of specific immune subsets, namely CD103+ tissue-resident memory T cells FoxP3+ regulatory T ells, have not been thoroughly examined.

METHOD

Representative whole sections of breast cancers were subjected to CD103 and FoxP3 double staining. Their density, ratio, and spatial features were analyzed in tumor area and tumor-stromal interface. Their associations with clinicopathological parameters and patient's prognosis were analyzed.

RESULTS

CD103 TILs were closer to tumor nests than FoxP3 TILs in the tumor-stromal interface. Their densities were associated with high-grade disease, TNBC, and stromal TILs. High stromal FoxP3 (sFoxP3) TILs and close proximity of sCD103 TILs to tumor were independently associated with better survival at multivariate analysis. Subgroup analysis showed the high FoxP3 TILs density associated better survival was seen in HER2-OE and TNBC subtypes while the proximity of CD103 TILs to tumor nests associated better survival was seen in luminal cancers.

CONCLUSION

The prognostic impact of CD103 and FoxP3 TILs in breast cancer depends on their spatial localization. High sFoxP3 TIL density and the lower distance of CD103 TILs from the tumor nests had independent favorable prognostic values.

A modular microfluidic platform to study how fluid shear stress alters estrogen receptor phenotype in ER+ breast cancer cells

Metastatic breast cancer leads to poor prognoses and worse outcomes in patients due to its invasive behavior and poor response to therapy. It is still unclear what biophysical and biochemical factors drive this more aggressive phenotype in metastatic cancer; however recent studies have suggested that exposure to fluid shear stress in the vasculature could cause this. In this study a modular microfluidic platform capable of mimicking the magnitude of fluid shear stress (FSS) found in human vasculature was designed and fabricated. This device provides a platform to evaluate the effects of FSS on MCF-7 cell line, an estrogen receptor positive (ER+) breast cancer cell line, during circulation in the vessels. Elucidation of the effects of FSS on MCF-7 cells was carried out utilizing two approaches: single cell analysis and bulk analysis. For single cell analysis, cells were trapped in a microarray after exiting the serpentine channel and followed by immunostaining on the device (on-chip). Bulk analysis was performed after cells were collected in a microtube at the outlet of the microfluidic serpentine channel for western blotting (off-chip). It was found that cells exposed to an FSS magnitude of 10 dyn/cm2 with a residence time of 60 s enhanced expression of the proliferation marker Ki67 in the MCF-7 cell line at a single cell level. To understand possible mechanisms for enhanced Ki67 expression, on-chip and off-chip analyses were performed for pro-growth and survival pathways ERK, AKT, and JAK/STAT. Results demonstrated that after shearing the cells phosphorylation of p-AKT, p-mTOR, and p-STAT3 were observed. However, there was no change in p-ERK1/2. AKT is a mediator of ER rapid signaling, analysis of phosphorylated ERα was carried out and no significant differences between sheared and non-sheared populations were observed. Taken together these results demonstrate that FSS can increase phosphorylation of proteins associated with a more aggressive phenotype in circulating cancer cells. These findings provide additional information that may help inform why cancer cells located at metastatic sites are usually more aggressive than primary breast cancer cells.

Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphologic, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissect the diverse cellular communities and mosaic tissue habitats, we developed a superpixel-inspired deep learning method (MoSaicNet) that adapts to complex tissue architectures and a cell imbalance aware deep learning pipeline (AwareNet) to enable accurate detection and classification of rare cell types in multiplex immunohistochemistry images. MoSaicNet and AwareNet achieved an AUC of >0.98 for tissue and cellular classification on separate test datasets. Application of MoSaicNet and AwareNet enabled investigation of bone heterogeneity and thickness as well as spatial histology analysis of bone marrow trephine samples from monoclonal gammopathies of undetermined significance (MGUS) and from paired newly diagnosed and posttreatment multiple myeloma. The most significant difference between MGUS and newly diagnosed multiple myeloma (NDMM) samples was not related to cell density but to spatial heterogeneity, with reduced spatial proximity of BLIMP1+ tumor cells to CD8+ cells in MGUS compared with NDMM samples. Following treatment of patients with multiple myeloma, there was a reduction in the density of BLIMP1+ tumor cells, effector CD8+ T cells, and regulatory T cells, indicative of an altered immune microenvironment. Finally, bone heterogeneity decreased following treatment of patients with multiple myeloma. In summary, deep learning-based spatial mapping of bone marrow trephine biopsies can provide insights into the cellular topography of the myeloma marrow microenvironment and complement aspirate-based techniques.

SIGNIFICANCE

Spatial analysis of bone marrow trephine biopsies using histology, deep learning, and tailored algorithms reveals the bone marrow architectural heterogeneity and evolution during myeloma progression and treatment.

A Weakly Supervised Segmentation Network Embedding Cross-Scale Attention Guidance and Noise-Sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors

The presence of tertiary lymphoid structures (TLSs) on pancreatic pathological images is an important prognostic indicator of pancreatic tumors. Therefore, TLSs detection on pancreatic pathological images plays a crucial role in diagnosis and treatment for patients with pancreatic tumors. However, fully supervised detection algorithms based on deep learning usually require a large number of manual annotations, which is time-consuming and labor-intensive. In this paper, we aim to detect the TLSs in a manner of few-shot learning by proposing a weakly supervised segmentation network. We firstly obtain the lymphocyte density maps by combining a pretrained model for nuclei segmentation and a domain adversarial network for lymphocyte nuclei recognition. Then, we establish a cross-scale attention guidance mechanism by jointly learning the coarse-scale features from the original histopathology images and fine-scale features from our designed lymphocyte density attention. A noise-sensitive constraint is introduced by an embedding signed distance function loss in the training procedure to reduce tiny prediction errors. Experimental results on two collected datasets demonstrate that our proposed method significantly outperforms the state-of-the-art segmentation-based algorithms in terms of TLSs detection accuracy. Additionally, we apply our method to study the congruent relationship between the density of TLSs and peripancreatic vascular invasion and obtain some clinically statistical results.

Spatial Distribution of Immune Cells Drives Resistance to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

Neoadjuvant chemotherapy (NAC) alone or combined with target therapies represents the standard of care for localized triple-negative breast cancer (TNBC). However, only a fraction of patients have a response, necessitating better understanding of the complex elements in the TNBC ecosystem that establish continuous and multidimensional interactions. Resolving such complexity requires new spatially-defined approaches. Here, we used spatial transcriptomics to investigate the multidimensional organization of TNBC at diagnosis and explore the contribution of each cell component to response to NAC. Starting from a consecutive retrospective series of TNBC cases, we designed a case-control study including 24 patients with TNBC of which 12 experienced a pathologic complete response (pCR) and 12 no-response or progression (pNR) after NAC. Over 200 regions of interest (ROI) were profiled. Our computational approaches described a model that recapitulates clinical response to therapy. The data were validated in an independent cohort of patients. Differences in the transcriptional program were detected in the tumor, stroma, and immune infiltrate comparing patients with a pCR with those with pNR. In pCR, spatial contamination between the tumor mass and the infiltrating lymphocytes was observed, sustained by a massive activation of IFN-signaling. Conversely, pNR lesions displayed increased pro-angiogenetic signaling and oxygen-based metabolism. Only modest differences were observed in the stroma, revealing a topology-based functional heterogeneity of the immune infiltrate. Thus, spatial transcriptomics provides fundamental information on the multidimensionality of TNBC and allows an effective prediction of tumor behavior. These results open new perspectives for the improvement and personalization of therapeutic approaches to TNBCs.

Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images

Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of methods for evaluating individual spatial interactions. In this study, we introduce Ceograph, a cell spatial organization-based graph convolutional network designed to analyze cell spatial organization (for example,. the cell spatial distribution, morphology, proximity, and interactions) derived from pathology images. Ceograph identifies key cell spatial organization features by accurately predicting their influence on patient clinical outcomes. In patients with oral potentially malignant disorders, our model highlights reduced structural concordance and increased closeness in epithelial substrata as driving features for an elevated risk of malignant transformation. In lung cancer patients, Ceograph detects elongated tumor nuclei and diminished stroma-stroma closeness as biomarkers for insensitivity to EGFR tyrosine kinase inhibitors. With its potential to predict various clinical outcomes, Ceograph offers a deeper understanding of biological processes and supports the development of personalized therapeutic strategies.

Future Practices of Breast Pathology Using Digital and Computational Pathology

Pathology clinical practice has evolved by adopting technological advancements initially regarded as potentially disruptive, such as electron microscopy, immunohistochemistry, and genomic sequencing. Breast pathology has a critical role as a medical domain, where the patient's pathology diagnosis has significant implications for prognostication and treatment of diseases. The advent of digital and computational pathology has brought about significant advancements in the field, offering new possibilities for enhancing diagnostic accuracy and improving patient care. Digital slide scanning enables to conversion of glass slides into high-fidelity digital images, supporting the review of cases in a digital workflow. Digitization offers the capability to render specimen diagnoses, digital archival of patient specimens, collaboration, and telepathology. Integration of image analysis and machine learning-based systems layered atop the high-resolution digital images offers novel workflows to assist breast pathologists in their clinical, educational, and research endeavors. Decision support tools may improve the detection and classification of breast lesions and the quantification of immunohistochemical studies. Computational biomarkers may help to contribute to patient management or outcomes. Furthermore, using digital and computational pathology may increase standardization and quality assurance, especially in areas with high interobserver variability. This review explores the current landscape and possible future applications of digital and computational techniques in the field of breast pathology.

Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence

BACKGROUND

Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this study, we used artificial intelligence (AI) to assess the prognostic significance of TILs in a large well-characterised cohort of luminal BC.

METHODS

Supervised deep learning model analysis of Haematoxylin and Eosin (H&E)-stained whole slide images (WSI) was applied to a cohort of 2231 luminal early-stage BC patients with long-term follow-up. Stromal TILs (sTILs) and intratumoural TILs (tTILs) were quantified and their spatial distribution within tumour tissue, as well as the proportion of stroma involved by sTILs were assessed. The association of TILs with clinicopathological parameters and patient outcome was determined.

RESULTS

A strong positive linear correlation was observed between sTILs and tTILs. High sTILs and tTILs counts, as well as their proximity to stromal and tumour cells (co-occurrence) were associated with poor clinical outcomes and unfavourable clinicopathological parameters including high tumour grade, lymph node metastasis, large tumour size, and young age. AI-based assessment of the proportion of stroma composed of sTILs (as assessed visually in routine practice) was not predictive of patient outcome. tTILs was an independent predictor of worse patient outcome in multivariate Cox Regression analysis.

CONCLUSION

AI-based detection of TILs counts, and their spatial distribution provides prognostic value in luminal early-stage BC patients. The utilisation of AI algorithms could provide a comprehensive assessment of TILs as a morphological variable in WSIs beyond eyeballing assessment.

Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer

Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level.

A modular microfluidic platform to study how fluid shear stress alters estrogen receptor phenotype in ER+ breast cancer cells

Metastatic breast cancer leads to poor prognoses and worse outcomes in patients due to its invasive behavior and poor response to therapy. It is still unclear what biophysical and biochemical factors drive this more aggressive phenotype in metastatic cancer; however recent studies have suggested that exposure to fluid shear stress in the vasculature could cause this. In this study a modular microfluidic platform capable of mimicking the magnitude of fluid shear stress (FSS) found in human vasculature was designed and fabricated. This device provides a platform to evaluate the effects of FSS on MCF-7 cell line, a receptor positive (ER+) breast cancer cell line, during circulation in the vessels. Elucidation of the effects of FSS on MCF-7 cells was carried out utilizing two approaches: single cell analysis and bulk analysis. For single cell analysis, cells were trapped in a microarray after exiting the serpentine channel and followed by immunostaining on the device (on-chip). Bulk analysis was performed after cells were collected in a microtube at the outlet of the microfluidic serpentine channel for western blotting (off-chip). It was found that cells exposed to an FSS magnitude of 10 dyn/cm2 with a residence time of 60 seconds enhanced expression of the proliferation marker Ki67 in the MCF-7 cell line at a single cell level. To understand possible mechanisms for enhanced Ki67 expression, on-chip and off-chip analyses were performed for pro-growth and survival pathways ERK, AKT, and JAK/STAT. Results demonstrated that after shearing the cells phosphorylation of p-AKT, p-mTOR, and p-STAT3 were observed. However, there was no change in p-ERK1/2. AKT is a mediator of ER rapid signaling, analysis of phosphorylated ERα was carried out and no significant differences between sheared and non-sheared populations were observed. Taken together these results demonstrate that FSS can increase phosphorylation of proteins associated with a more aggressive phenotype in circulating cancer cells. These findings provide additional information that may help inform why cancer cells located at metastatic sites are usually more aggressive than primary breast cancer cells.

Applications of Artificial Intelligence in Breast Pathology

CONTEXT.—

Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology.

OBJECTIVE.—

To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes.

DATA SOURCES.—

We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience.

CONCLUSIONS.—

With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response.

KIF20A is a Prognostic Marker for Female Patients with Estrogen Receptor-Positive Breast Cancer and Receiving Tamoxifen as Adjuvant Endocrine Therapy

Purpose

Our aim was to verify whether KIF20A has the potential to serve as a prognostic marker for female patients with estrogen receptor (ER)-positive breast cancer (BC) and treated with tamoxifen (TAM).

Patients and Methods

Online tools were used to investigate the potential correlation between KIF20A gene expression and survival of patients with ER-positive BC and TAM treatment. Furthermore, immunohistochemistry (IHC) was conducted to assess the expression levels of KIF20A in patients included from our center. The prognostic value of KIF20A for disease-free survival (DFS) and overall survival (OS) was further evaluated using Cox regression analysis.

Results

According to the results obtained from online tools, it was found that patients with low KIF20A expression exhibited significantly better survival outcomes in terms of relapse-free survival (RFS), distant metastasis-free survival (DMFS), and OS compared to those with high KIF20A expression (P < 0.001, P < 0.001, and P = 0.008, respectively). Additionally, significantly lower gene expression of KIF20A was found in patients who responded to TAM than in those who did not respond to TAM (P < 0.001). We further included 203 patients with adjuvant TAM therapy, and IHC for KIF20A was performed on sections from paraffin-embedded blocks. Patients with low KIF20A expression had significantly better DFS and OS (P = 0.001 and 0.002, respectively, log rank test), and the expression of KIF20A was identified as an independent factor for predicting both DFS and OS (P = 0.001 and 0.008, respectively).

Conclusion

KIF20A expression is an independent prognostic factor for survival in patients with ER-positive BC who received adjuvant TAM therapy. In clinical practice, IHC evaluation of KIF20A expression in surgical samples before administering tamoxifen may assist in predicting the treatment outcomes of these patients.

Breast cancer patient-derived microtumors resemble tumor heterogeneity and enable protein-based stratification and functional validation of individualized drug treatment

Despite tremendous progress in deciphering breast cancer at the genomic level, the pronounced intra- and intertumoral heterogeneity remains a major obstacle to the advancement of novel and more effective treatment approaches. Frequent treatment failure and the development of treatment resistance highlight the need for patient-derived tumor models that reflect the individual tumors of breast cancer patients and allow a comprehensive analyses and parallel functional validation of individualized and therapeutically targetable vulnerabilities in protein signal transduction pathways. Here, we introduce the generation and application of breast cancer patient-derived 3D microtumors (BC-PDMs). Residual fresh tumor tissue specimens were collected from n = 102 patients diagnosed with breast cancer and subjected to BC-PDM isolation. BC-PDMs retained histopathological characteristics, and extracellular matrix (ECM) components together with key protein signaling pathway signatures of the corresponding primary tumor tissue. Accordingly, BC-PDMs reflect the inter- and intratumoral heterogeneity of breast cancer and its key signal transduction properties. DigiWest®-based protein expression profiling of identified treatment responder and non-responder BC-PDMs enabled the identification of potential resistance and sensitivity markers of individual drug treatments, including markers previously associated with treatment response and yet undescribed proteins. The combination of individualized drug testing with comprehensive protein profiling analyses of BC-PDMs may provide a valuable complement for personalized treatment stratification and response prediction for breast cancer.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

Virtual alignment of pathology image series for multi-gigapixel whole slide images

Interest in spatial omics is on the rise, but generation of highly multiplexed images remains challenging, due to cost, expertise, methodical constraints, and access to technology. An alternative approach is to register collections of whole slide images (WSI), generating spatially aligned datasets. WSI registration is a two-part problem, the first being the alignment itself and the second the application of transformations to huge multi-gigapixel images. To address both challenges, we developed Virtual Alignment of pathoLogy Image Series (VALIS), software which enables generation of highly multiplexed images by aligning any number of brightfield and/or immunofluorescent WSI, the results of which can be saved in the ome.tiff format. Benchmarking using publicly available datasets indicates VALIS provides state-of-the-art accuracy in WSI registration and 3D reconstruction. Leveraging existing open-source software tools, VALIS is written in Python, providing a free, fast, scalable, robust, and easy-to-use pipeline for registering multi-gigapixel WSI, facilitating downstream spatial analyses.

Spatial Positioning of Immune Hotspots Reflects the Interplay between B and T Cells in Lung Squamous Cell Carcinoma

Beyond tertiary lymphoid structures, a significant number of immune-rich areas without germinal center-like structures are observed in non-small cell lung cancer. Here, we integrated transcriptomic data and digital pathology images to study the prognostic implications, spatial locations, and constitution of immune rich areas (immune hotspots) in a cohort of 935 patients with lung cancer from The Cancer Genome Atlas. A high intratumoral immune hotspot score, which measures the proportion of immune hotspots interfacing with tumor islands, was correlated with poor overall survival in lung squamous cell carcinoma but not in lung adenocarcinoma. Lung squamous cell carcinomas with high intratumoral immune hotspot scores were characterized by consistent upregulation of B-cell signatures. Spatial statistical analyses conducted on serial multiplex IHC slides further revealed that only 4.87% of peritumoral immune hotspots and 0.26% of intratumoral immune hotspots were tertiary lymphoid structures. Significantly lower densities of CD20+CXCR5+ and CD79b+ B cells and less diverse immune cell interactions were found in intratumoral immune hotspots compared with peritumoral immune hotspots. Furthermore, there was a negative correlation between the percentages of CD8+ T cells and T regulatory cells in intratumoral but not in peritumoral immune hotspots, with tertiary lymphoid structures excluded. These findings suggest that the intratumoral immune hotspots reflect an immunosuppressive niche compared with peritumoral immune hotspots, independent of the distribution of tertiary lymphoid structures. A balance toward increased intratumoral immune hotspots is indicative of a compromised antitumor immune response and poor outcome in lung squamous cell carcinoma.

SIGNIFICANCE

Intratumoral immune hotspots beyond tertiary lymphoid structures reflect an immunosuppressive microenvironment, different from peritumoral immune hotspots, warranting further study in the context of immunotherapies.

Bridging clinic and wildlife care with AI-powered pan-species computational pathology

Cancers occur across species. Understanding what is consistent and varies across species can provide new insights into cancer initiation and evolution, with significant implications for animal welfare and wildlife conservation. We build a pan-species cancer digital pathology atlas (panspecies.ai) and conduct a pan-species study of computational comparative pathology using a supervised convolutional neural network algorithm trained on human samples. The artificial intelligence algorithm achieves high accuracy in measuring immune response through single-cell classification for two transmissible cancers (canine transmissible venereal tumour, 0.94; Tasmanian devil facial tumour disease, 0.88). In 18 other vertebrate species (mammalia = 11, reptilia = 4, aves = 2, and amphibia = 1), accuracy (range 0.57-0.94) is influenced by cell morphological similarity preserved across different taxonomic groups, tumour sites, and variations in the immune compartment. Furthermore, a spatial immune score based on artificial intelligence and spatial statistics is associated with prognosis in canine melanoma and prostate tumours. A metric, named morphospace overlap, is developed to guide veterinary pathologists towards rational deployment of this technology on new samples. This study provides the foundation and guidelines for transferring artificial intelligence technologies to veterinary pathology based on understanding of morphological conservation, which could vastly accelerate developments in veterinary medicine and comparative oncology.

Leukocytic Infiltration of Intraductal Carcinoma of the Prostate: An Exploratory Study

Intraductal carcinoma of the prostate (IDC-P) is an aggressive histological subtype of prostate cancer (PCa) detected in approximately 20% of radical prostatectomy (RP) specimens. As IDC-P has been associated with PCa-related death and poor responses to standard treatment, the purpose of this study was to explore the immune infiltrate of IDC-P. Hematoxylin- and eosin-stained slides from 96 patients with locally advanced PCa who underwent RP were reviewed to identify IDC-P. Immunohistochemical staining of CD3, CD8, CD45RO, FoxP3, CD68, CD163, CD209 and CD83 was performed. For each slide, the number of positive cells per mm² in the benign tissues, tumor margins, cancer and IDC-P was calculated. Consequently, IDC-P was found in a total of 33 patients (34%). Overall, the immune infiltrate was similar in the IDC-P-positive and the IDC-P-negative patients. However, FoxP3⁺ regulatory T cells (p < 0.001), CD68⁺ and CD163⁺ macrophages (p < 0.001 for both) and CD209⁺ and CD83⁺ dendritic cells (p = 0.002 and p = 0.013, respectively) were less abundant in the IDC-P tissues compared to the adjacent PCa. Moreover, the patients were classified as having immunologically "cold" or "hot" IDC-P, according to the immune-cell densities averaged in the total IDC-P or in the immune hotspots. The CD68/CD163/CD209-immune hotspots predicted metastatic dissemination (p = 0.014) and PCa-related death (p = 0.009) in a Kaplan-Meier survival analysis. Further studies on larger cohorts are necessary to evaluate the clinical utility of assessing the immune infiltrate of IDC-P with regards to patient prognosis and the use of immunotherapy for lethal PCa.

Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology

Breast cancer remains a highly prevalent disease with considerable inter- and intra-tumoral heterogeneity complicating prognostication and treatment decisions. The utilization and depth of genomic, transcriptomic and proteomic data for cancer has exploded over recent times and the addition of spatial context to this information, by understanding the correlating morphologic and spatial patterns of cells in tissue samples, has created an exciting frontier of research, histo-genomics. At the same time, deep learning (DL), a class of machine learning algorithms employing artificial neural networks, has rapidly progressed in the last decade with a confluence of technical developments - including the advent of modern graphic processing units (GPU), allowing efficient implementation of increasingly complex architectures at scale; advances in the theoretical and practical design of network architectures; and access to larger datasets for training - all leading to sweeping advances in image classification and object detection. In this review, we examine recent developments in the application of DL in breast cancer histology with particular emphasis of those producing biologic insights or novel biomarkers, spanning the extraction of genomic information to the use of stroma to predict cancer recurrence, with the aim of suggesting avenues for further advancing this exciting field.

Tumour-infiltrating lymphocytes: from prognosis to treatment selection

Tumour-infiltrating lymphocytes (TILs) are considered crucial in anti-tumour immunity. Accordingly, the presence of TILs contains prognostic and predictive value. In 2011, we performed a systematic review and meta-analysis on the prognostic value of TILs across cancer types. Since then, the advent of immune checkpoint blockade (ICB) has renewed interest in the analysis of TILs. In this review, we first describe how our understanding of the prognostic value of TIL has changed over the last decade. New insights on novel TIL subsets are discussed and give a broader view on the prognostic effect of TILs in cancer. Apart from prognostic value, evidence on the predictive significance of TILs in the immune therapy era are discussed, as well as new techniques, such as machine learning that strive to incorporate these predictive capacities within clinical trials.

Tumor Infiltrating Lymphocytes across Breast Cancer Subtypes: Current Issues for Biomarker Assessment

Tumor-infiltrating lymphocytes (TILs) represent a surrogate biomarker of anti-tumor, lymphocyte-mediated immunity. In early, triple-negative breast cancer, TILs have level 1B of evidence to predict clinical outcomes. TILs represent a promising biomarker to select patients who can experience a better prognosis with de-intensified cancer treatments and derive larger benefits from immune checkpoint inhibitors. However, the assessment and the validation of TILs as a biomarker require a prospective and rigorous demonstration of its clinical validity and utility, provided reproducible analytical performance. With pending data about the prospective validation of TILs' clinical validity to modulate treatments in early breast cancer, this review summarizes the most important current issues and future challenges related to the implementation of TILs assessments across all breast cancer subtypes and their potential integration into clinical practice.

Identification and validation of endocrine resistance-related and immune-related long non-coding RNA (lncRNA) signatures for predicting endocrinotherapy response and prognosis in breast cancer

Background

Endocrine resistance remains a major challenge in breast cancer (BRCA). Increasing evidence has revealed that long non-coding RNA (lncRNA) are closely implicated in tumorigenesis, drug resistance, and the immune-related pathways of cancer. However, the immune-related lncRNA remains to be thoroughly investigated in predicting the endocrine therapeutic response and prognosis of BRCA.

Methods

Based on the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, and calculating the correlation of lncRNAs with immune-related genes obtained from ImmPort and InnateDB databases, we finally obtained endocrine resistance-related and immune-related long non-coding RNAs (ERIR-lncRNAs). Univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox regression were performed to screen prognosis-associated ERIR-lncRNAs and establish signatures, using 2 separate datasets from GEO for external validation. Principal component analysis (PCA), Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and multivariate Cox regression were performed to demonstrate the robustness and predictability of the signature. We investigated tumor immune infiltration and tumor mutation burden (TMB) between high- and low-risk groups, and the role of key lncRNAs in endocrine resistant breast cancer was confirmed by quantitative real-time polymerase chain reaction (qRT-PCR), Cell Counting Kit 8 (CCK 8) and transwell assays.

Results

A total of 781 endocrine resistance related lncRNAs were identified, of which 12 lncRNAs were associated with immunity. Then, three ERIR-lncRNAs with prognostic relevance were screened to successfully construct the risk signature. Compared to sensitive patients, the endocrine resistant patients had higher risk scores in both the training and validation sets (P<0.05). The high-risk group had significantly shorter survival times (P<0.001) with area under the curve (AUC) values of 0.710, 0.649, and 0.672 at 1, 3, and 5 years. Univariate and multivariate Cox regression indicated that our signature was an independent prognostic factor (P<0.001). Through immune infiltration analysis, it was revealed that the high-risk scores were associated with T follicular helper (Tfh) differentiation and exhibited a pro-tumor phenomenon with the Th1/Th2 balance shifting toward Th2. The key lncRNAs promote cell proliferation and migration as confirmed by qRT-PCR, CCK-8 and transwell assays.

Conclusions

The ERIR-lncRNA signature is valuable in predicting endocrine therapeutic response and prognosis of BRCA, revealing a potential relationship between endocrine resistance and TME.

Comparative Evaluation of Tumor-Infiltrating Lymphocytes in Companion Animals: Immuno-Oncology as a Relevant Translational Model for Cancer Therapy

Simple Summary

Laboratory experiments studying solid tumors are limited by the inability to adequately model the tumor microenvironment and important immune interactions. Immune cells that infiltrate the tumor bed or periphery have been documented as reliable biomarkers in human studies. Veterinary oncology provides a naturally occurring cancer model that could complement biomarker discovery, clinical trials, and drug development.

Abstract

Despite the important role of preclinical experiments to characterize tumor biology and molecular pathways, there are ongoing challenges to model the tumor microenvironment, specifically the dynamic interactions between tumor cells and immune infiltrates. Comprehensive models of host-tumor immune interactions will enhance the development of emerging treatment strategies, such as immunotherapies. Although in vitro and murine models are important for the early modelling of cancer and treatment-response mechanisms, comparative research studies involving veterinary oncology may bridge the translational pathway to human studies. The natural progression of several malignancies in animals exhibits similar pathogenesis to human cancers, and previous studies have shown a relevant and evaluable immune system. Veterinary oncologists working alongside oncologists and cancer researchers have the potential to advance discovery. Understanding the host-tumor-immune interactions can accelerate drug and biomarker discovery in a clinically relevant setting. This review presents discoveries in comparative immuno-oncology and implications to cancer therapy.

High inter-follicular spatial co-localization of CD8+FOXP3+ with CD4+CD8+ cells predicts favorable outcome in follicular lymphoma

The spatial architecture of the lymphoid tissue in follicular lymphoma (FL) presents unique challenges to studying its immune microenvironment. We investigated the spatial interplay of T cells, macrophages, myeloid cells and natural killer T cells using multispectral immunofluorescence images of diagnostic biopsies of 32 patients. A deep learning-based image analysis pipeline was tailored to the needs of follicular lymphoma spatial histology research, enabling the identification of different immune cells within and outside neoplastic follicles. We analyzed the density and spatial co-localization of immune cells in the inter-follicular and intra-follicular regions of follicular lymphoma. Low inter-follicular density of CD8+FOXP3+ cells and co-localization of CD8+FOXP3+ with CD4+CD8+ cells were significantly associated with relapse (p = 0.0057 and p = 0.0019, respectively) and shorter time to progression after first-line treatment (Logrank p = 0.0097 and log-rank p = 0.0093, respectively). A low inter-follicular density of CD8+FOXP3+ cells is associated with increased risk of relapse independent of follicular lymphoma international prognostic index (FLIPI) (p = 0.038, Hazard ratio (HR) = 0.42 [0.19, 0.95], but not independent of co-localization of CD8+FOXP3+ with CD4+CD8+ cells (p = 0.43). Co-localization of CD8+FOXP3+ with CD4+CD8+ cells is predictors of time to relapse independent of the FLIPI score and density of CD8+FOXP3+ cells (p = 0.027, HR = 0.0019 [7.19 × 10-6 , 0.49], This suggests a potential role of inter-follicular CD8+FOXP3+ and CD4+CD8+ cells in the disease progression of FL, warranting further validation on larger patient cohorts.

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer

BACKGROUND

Despite the fact that tumor microenvironment (TME) and gene mutations are the main determinants of progression of the deadliest cancer in the world - lung cancer, their interrelations are not well understood. Digital pathology data provides a unique insight into the spatial composition of the TME. Various spatial metrics and machine learning approaches were proposed for prediction of either patient survival or gene mutations from this data. Still, these approaches are limited in the scope of analyzed features and in their explainability, and as such fail to transfer to clinical practice.

METHODS

Here, we generated 23,199 image patches from 26 hematoxylin-and-eosin (H&E)-stained lung cancer tissue sections and annotated them into 9 different tissue classes. Using this dataset, we trained a deep neural network ARA-CNN. Next, we applied the trained network to segment 467 lung cancer H&E images from The Cancer Genome Atlas (TCGA) database. We used the segmented images to compute human-interpretable features reflecting the heterogeneous composition of the TME, and successfully utilized them to predict patient survival and cancer gene mutations.

RESULTS

We achieved per-class AUC ranging from 0.72 to 0.99 for classifying tissue types in lung cancer with ARA-CNN. Machine learning models trained on the proposed human-interpretable features achieved a c-index of 0.723 in the task of survival prediction and AUC up to 73.5% for PDGFRB in the task of mutation classification.

CONCLUSIONS

We presented a framework that accurately predicted survival and gene mutations in lung adenocarcinoma patients based on human-interpretable features extracted from H&E slides. Our approach can provide important insights for designing novel cancer treatments, by linking the spatial structure of the TME in lung adenocarcinoma to gene mutations and patient survival. It can also expand our understanding of the effects that the TME has on tumor evolutionary processes. Our approach can be generalized to different cancer types to inform precision medicine strategies.

Multi-modality artificial intelligence in digital pathology

In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin-eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors' work and discusses the opportunities and challenges of AI.

Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy

Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.

Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) show prominent clinical activity across multiple advanced tumors. However, less than half of patients respond even after molecule-based selection. Thus, improved biomarkers are required. In this study, we use an image analysis to capture morphologic attributes relating to the spatial interaction and architecture of tumor cells and tumor-infiltrating lymphocytes (TILs) from digitized H&E images. We evaluate the association of image features with progression-free (PFS) and overall survival in non-small cell lung cancer (NSCLC) (N = 187) and gynecological cancer (N = 39) patients treated with ICIs. We demonstrated that the classifier trained with NSCLC alone was associated with PFS in independent NSCLC cohorts and also in gynecological cancer. The classifier was also associated with clinical outcome independent of clinical factors. Moreover, the classifier was associated with PFS even with low PD-L1 expression. These findings suggest that image analysis can be used to predict clinical end points in patients receiving ICI.

Clinical Significance of a CD3/CD8-Based Immunoscore in Neuroblastoma Patients Using Digital Pathology

Background

Infiltrating immune cells have been reported as prognostic markers in many cancer types. We aimed to evaluate the prognostic role of tumor-infiltrating lymphocytes, namely CD3+ T cells, CD8+ cytotoxic T cells and memory T cells (CD45RO+), in neuroblastoma.

Patients and Methods

Immunohistochemistry was used to determine the expression of CD3, CD8 and CD45RO in the tumor samples of 244 neuroblastoma patients. We then used digital pathology to calculate the densities of these markers and derived an immunoscore based on such densities.

Results

Densities of CD3+ and CD8+ T cells in tumor were positively associated with the overall survival (OS) and event-free survival (EFS), whereas density of CD45RO+ T cells in tumor was negatively associated with OS but not EFS. An immunoscore with low density of CD3 and CD8 (CD3-CD8-) was indictive of a greater risk of death (hazard ratio 6.39, 95% confidence interval 3.09-13.20) and any event (i.e., relapse at any site, progressive disease, second malignancy, or death) (hazard ratio 4.65, 95% confidence interval 2.73-7.93). Multivariable analysis revealed that the CD3-CD8- immunoscore was an independent prognostic indicator for OS, even after adjusting for other known prognostic indicators.

Conclusions

The new immunoscore based on digital pathology evaluated densities of tumor-infiltrating CD3+ and CD8+ T cells contributes to the prediction of prognosis in neuroblastoma patients.

Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer

Background

High tumor mutation burden (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patient's own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response.

Methods

Here we first developed computational approaches using whole slide images (WSIs) to predict bladder cancer patients' TMB status and TILs across tumor regions, and then investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, as well as their prognostic utility. Results: In experiments using WSIs from The Cancer Genome Atlas (TCGA) bladder cancer (BLCA), our findings show that computational pathology can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival.

Conclusions

Computational approaches using WSIs have the potential to provide rapid and cost-effective TMB testing and TILs detection. Survival analysis illuminates potential clinical utility of spatial heterogeneity and co-organization of TMB and TILs as a prognostic biomarker in BLCA which warrants further validation in future studies.

SPCS: a spatial and pattern combined smoothing method for spatial transcriptomic expression

High-dimensional, localized ribonucleic acid (RNA) sequencing is now possible owing to recent developments in spatial transcriptomics (ST). ST is based on highly multiplexed sequence analysis and uses barcodes to match the sequenced reads to their respective tissue locations. ST expression data suffer from high noise and dropout events; however, smoothing techniques have the promise to improve the data interpretability prior to performing downstream analyses. Single-cell RNA sequencing (scRNA-seq) data similarly suffer from these limitations, and smoothing methods developed for scRNA-seq can only utilize associations in transcriptome space (also known as one-factor smoothing methods). Since they do not account for spatial relationships, these one-factor smoothing methods cannot take full advantage of ST data. In this study, we present a novel two-factor smoothing technique, spatial and pattern combined smoothing (SPCS), that employs the k-nearest neighbor (kNN) technique to utilize information from transcriptome and spatial relationships. By performing SPCS on multiple ST slides from pancreatic ductal adenocarcinoma (PDAC), dorsolateral prefrontal cortex (DLPFC) and simulated high-grade serous ovarian cancer (HGSOC) datasets, smoothed ST slides have better separability, partition accuracy and biological interpretability than the ones smoothed by preexisting one-factor methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS).

Spatial interplay of lymphocytes and fibroblasts in estrogen receptor-positive HER2-negative breast cancer

In estrogen-receptor-positive, HER2-negative (ER⁺HER2^-) breast cancer, higher levels of tumor infiltrating lymphocytes (TILs) are often associated with a poor prognosis and this phenomenon is still poorly understood. Fibroblasts represent one of the most frequent cells in breast cancer and harbor immunomodulatory capabilities. Here, we evaluate the molecular and clinical impact of the spatial patterns of TILs and fibroblast in ER⁺HER2^- breast cancer. We used a deep neural network to locate and identify tumor, TILs, and fibroblasts on hematoxylin and eosin-stained slides from 179 ER⁺HER2^- breast tumors (ICGC cohort) together with a new density estimation analysis to measure the spatial patterns. We clustered tumors based on their spatial patterns and gene set enrichment analysis was performed to study their molecular characteristics. We independently assessed the spatial patterns in a second cohort of ER⁺HER2^- breast cancer (N = 630, METABRIC) and studied their prognostic value. The spatial integration of fibroblasts, TILs, and tumor cells leads to a new reproducible spatial classification of ER⁺HER2^- breast cancer and is linked to inflammation, fibroblast meddling, or immunosuppression. ER⁺HER2^- patients with high TIL did not have a significant improved overall survival (HR = 0.76, P = 0.212), except when they had received chemotherapy (HR = 0.447). A poorer survival was observed for patients with high fibroblasts that did not show a high level of TILs (HR = 1.661, P = 0.0303). Especially spatial mixing of fibroblasts and TILs was associated with a good prognosis (HR = 0.464, P = 0.013). Our findings demonstrate a reproducible pipeline for the spatial profiling of TILs and fibroblasts in ER⁺HER2^- breast cancer and suggest that this spatial interplay holds a decisive role in their cancer-immune interactions.

ESR1 mutant breast cancers show elevated basal cytokeratins and immune activation

Estrogen receptor alpha (ER/ESR1) is frequently mutated in endocrine resistant ER-positive (ER+) breast cancer and linked to ligand-independent growth and metastasis. Despite the distinct clinical features of ESR1 mutations, their role in intrinsic subtype switching remains largely unknown. Here we find that ESR1 mutant cells and clinical samples show a significant enrichment of basal subtype markers, and six basal cytokeratins (BCKs) are the most enriched genes. Induction of BCKs is independent of ER binding and instead associated with chromatin reprogramming centered around a progesterone receptor-orchestrated insulated neighborhood. BCK-high ER+ primary breast tumors exhibit a number of enriched immune pathways, shared with ESR1 mutant tumors. S100A8 and S100A9 are among the most induced immune mediators and involve in tumor-stroma paracrine crosstalk inferred by single-cell RNA-seq from metastatic tumors. Collectively, these observations demonstrate that ESR1 mutant tumors gain basal features associated with increased immune activation, encouraging additional studies of immune therapeutic vulnerabilities.

Incorporation of TILs in daily breast cancer care: how much evidence can we bear?

One of the most important developments in the breast cancer field has been an improved understanding of prognostic and predictive biomarkers, of which TILs are increasingly gaining importance. The evaluation of TILs by light microscopy on a H&E-stained section is workable in a daily practice setting. Reproducibility of reporting TILs is good, but heterogeneity is a cause of variation. TILs provide clinicians with important prognostic information for patients with TNBC, as early-stage TNBC with high TILs have > 98% 5-year survival and TILs predict benefit to immunotherapy. Importantly, while TILs do not have level of evidence IA, TILs should be used as a prognostic factor with caution and with other accepted prognostic variables, such as tumour size and lymph node status, to inform clinicians and patients on their treatment options. A framework on how to use the TILs in daily practice is proposed, including a co-assessment with PD-L1 for its predictive role to immunotherapy.

Interferon Signaling in Estrogen Receptor-positive Breast Cancer: A Revitalized Topic

Cancer immunology is the most rapidly expanding field in cancer research, with the importance of immunity in cancer pathogenesis now well accepted including in the endocrine-related cancers. The immune system plays an essential role in the development of ductal and luminal epithelial differentiation in the mammary gland. Originally identified as evolutionarily conserved antipathogen cytokines, interferons (IFNs) have shown important immune-modulatory and antineoplastic properties when administered to patients with various types of cancer, including breast cancer. Recent studies have drawn attention to the role of tumor- and stromal-infiltrating lymphocytes in dictating therapy response and outcome of breast cancer patients, which, however, is highly dependent on the breast cancer subtype. The emerging role of tumor cell-inherent IFN signaling in the subtype-defined tumor microenvironment could influence therapy response with protumor activities in breast cancer. Here we review evidence with new insights into tumor cell-intrinsic and tumor microenvironment-derived IFN signaling, and the crosstalk of IFN signaling with key signaling pathways in estrogen receptor-positive (ER+) breast cancer. We also discuss clinical implications and opportunities exploiting IFN signaling to treat advanced ER+ breast cancer.

Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models

RATIONALE AND OBJECTIVES

We aimed to investigate the value of magnetic resonance image (MRI)-based radiomics in predicting Ki-67 expression of breast cancer.

METHODS

In this retrospective study, 159 lesions from 154 patients were included. Radiomic features were extracted from contrast-enhanced T1-weighted MRI (C+MRI) and apparent diffusion coefficient (ADC) maps, with open-source software. Dimension reduction was done with reliability analysis, collinearity analysis, and feature selection. Two different Ki-67 expression cut-off values (14% vs 20%) were studied as reference standard for the classifications. Input for the models were radiomic features from individual MRI sequences or their combination. Classifications were performed using a generalized linear model.

RESULTS

Considering Ki-67 cut-off value of 14%, training and testing AUC values were 0.785 (standard deviation [SD], 0.193) and 0.849 for ADC; 0.696 (SD, 0.150) and 0.695 for C+MRI; 0.755 (SD, 0.171) and 0.635 for the combination of both sequences, respectively. Regarding Ki-67 cut-off value of 20%, training and testing AUC values were 0.744 (SD, 0.197) and 0.617 for ADC; 0.629 (SD, 0.251) and 0.741 for C+MRI; 0.761 (SD, 0.207) and 0.618 for the combination of both sequences, respectively.

CONCLUSION

ADC map-based selected radiomic features coupled with generalized linear modeling might be a promising non-invasive method to determine the Ki-67 expression level of breast cancer.

Subclonal heterogeneity and evolution in breast cancer

Subclonal heterogeneity and evolution are characteristics of breast cancer that play a fundamental role in tumour development, progression and resistance to current therapies. In this review, we focus on the recent advances in understanding the epigenetic and transcriptomic changes that occur within breast cancer and their importance in terms of cancer development, progression and therapy resistance with a particular focus on alterations at the single-cell level. Furthermore, we highlight the utility of using single-cell tracing and molecular barcoding methodologies in preclinical models to assess disease evolution and response to therapy. We discuss how the integration of single-cell profiling from patient samples can be used in conjunction with results from preclinical models to untangle the complexities of this disease and identify biomarkers of disease progression, including measures of intra-tumour heterogeneity themselves, and how enhancing this understanding has the potential to uncover new targetable vulnerabilities in breast cancer.

Intratumoral Heterogeneity and Immune Response Indicators to Predict Overall Survival in a Retrospective Study of HER2-Borderline (IHC 2+) Breast Cancer Patients

Breast cancer (BC) categorized as human epidermal growth factor receptor 2 (HER2) borderline [2+ by immunohistochemistry (IHC 2+)] presents challenges for the testing, frequently obscured by intratumoral heterogeneity (ITH). This leads to difficulties in therapy decisions. We aimed to establish prognostic models of overall survival (OS) of these patients, which take into account spatial aspects of ITH and tumor microenvironment by using hexagonal tiling analytics of digital image analysis (DIA). In particular, we assessed the prognostic value of Immunogradient indicators at the tumor–stroma interface zone (IZ) as a feature of antitumor immune response. Surgical excision samples stained for estrogen receptor (ER), progesterone receptor (PR), Ki67, HER2, and CD8 from 275 patients with HER2 IHC 2+ invasive ductal BC were used in the study. DIA outputs were subsampled by HexT for ITH quantification and tumor microenvironment extraction for Immunogradient indicators. Multiple Cox regression revealed HER2 membrane completeness (HER2 MC) (HR: 0.18, p = 0.0007), its spatial entropy (HR: 0.37, p = 0.0341), and ER contrast (HR: 0.21, p = 0.0449) as independent predictors of better OS, with worse OS predicted by pT status (HR: 6.04, p = 0.0014) in the HER2 non-amplified patients. In the HER2-amplified patients, HER2 MC contrast (HR: 0.35, p = 0.0367) and CEP17 copy number (HR: 0.19, p = 0.0035) were independent predictors of better OS along with worse OS predicted by pN status (HR: 4.75, p = 0.0018). In the non-amplified tumors, three Immunogradient indicators provided the independent prognostic value: CD8 density in the tumor aspect of the IZ and CD8 center of mass were associated with better OS (HR: 0.23, p = 0.0079 and 0.14, p = 0.0014, respectively), and CD8 density variance along the tumor edge predicted worse OS (HR: 9.45, p = 0.0002). Combining these three computational indicators of the CD8 cell spatial distribution within the tumor microenvironment augmented prognostic stratification of the patients. In the HER2-amplified group, CD8 cell density in the tumor aspect of the IZ was the only independent immune response feature to predict better OS (HR: 0.22, p = 0.0047). In conclusion, we present novel prognostic models, based on computational ITH and Immunogradient indicators of the IHC biomarkers, in HER2 IHC 2+ BC patients.

The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group

The advent of immune-checkpoint inhibitors (ICI) in modern oncology has significantly improved survival in several cancer settings. A subgroup of women with breast cancer (BC) has immunogenic infiltration of lymphocytes with expression of programmed death-ligand 1 (PD-L1). These patients may potentially benefit from ICI targeting the programmed death 1 (PD-1)/PD-L1 signaling axis. The use of tumor-infiltrating lymphocytes (TILs) as predictive and prognostic biomarkers has been under intense examination. Emerging data suggest that TILs are associated with response to both cytotoxic treatments and immunotherapy, particularly for patients with triple-negative BC. In this review from The International Immuno-Oncology Biomarker Working Group, we discuss (a) the biological understanding of TILs, (b) their analytical and clinical validity and efforts toward the clinical utility in BC, and (c) the current status of PD-L1 and TIL testing across different continents, including experiences from low-to-middle-income countries, incorporating also the view of a patient advocate. This information will help set the stage for future approaches to optimize the understanding and clinical utilization of TIL analysis in patients with BC.

The Role of Machine Learning in Cardiovascular Pathology

Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to assess digital whole-slide images. Machine learning tools have incredible potential to standardise, and likely even improve, histopathologic diagnoses, but they are not yet widely used in clinical practice. We describe the principles of these tools and technologies and some successful preclinical and pretranslational efforts in cardiovascular pathology, as well as a roadmap for moving forward. In nonhuman animal models, one proof-of-principle application is in rodent progressive cardiomyopathy, which is of particular significance to drug toxicity studies. Basic science successes include screening the quality of differentiated stem cells and characterising cardiomyocyte developmental stages, with potential applications for research and toxicology/drug safety screening using derived or native human pluripotent stem cells differentiated into cardiomyocytes. Translational studies of particular note include those with success in diagnosing the various forms of heart allograft rejection. For fully realising the value of these tools in clinical cardiovascular pathology, we identify 3 essential challenges. First is image quality standardisation to ensure that algorithms can be developed and implemented on robust, consistent data. The second is consensus diagnosis; experts don't always agree, and thus "truth" may be difficult to establish, but the algorithms themselves may provide a solution. The third is the need for large-enough data sets to facilitate robust algorithm development, necessitating large cross-institutional shared image databases. The power of histopathology-based machine learning technologies is tremendous, and we outline the next steps needed to capitalise on this power.

Artificial intelligence applied to breast pathology

The convergence of digital pathology and computer vision is increasingly enabling computers to perform tasks performed by humans. As a result, artificial intelligence (AI) is having an astoundingly positive effect on the field of pathology, including breast pathology. Research using machine learning and the development of algorithms that learn patterns from labeled digital data based on "deep learning" neural networks and feature-engineered approaches to analyze histology images have recently provided promising results. Thus far, image analysis and more complex AI-based tools have demonstrated excellent success performing tasks such as the quantification of breast biomarkers and Ki67, mitosis detection, lymph node metastasis recognition, tissue segmentation for diagnosing breast carcinoma, prognostication, computational assessment of tumor-infiltrating lymphocytes, and prediction of molecular expression as well as treatment response and benefit of therapy from routine H&E images. This review critically examines the literature regarding these applications of AI in the area of breast pathology.

Spatial heterogeneity of immune infiltration predicts the prognosis of nasopharyngeal carcinoma patients

Spatial information on the tumor immune microenvironment is of clinical relevance. Here, we aimed to quantify the spatial heterogeneity of lymphocytes and cancer cells and evaluated its prognostic value in patients with nasopharyngeal carcinoma (NPC). The scanned immunohistochemistry images of 336 NPC patients from two different hospitals were used to generate cell density maps for tumor and immune cells. Then, Getis-Ord hotspot analysis, a spatial statistic method used to describe species biodiversity in ecological habitats, was applied to identify cancer, immune, and immune-cancer hotspots. The results showed that cancer hotspots were not associated with any of the studied clinical outcomes, while immune-cancer hotspots predicted worse overall survival (OS) in the training cohort. In contrast, a high immune hotspot score was significantly associated with better OS (HR 0.41, 95% CI 0.22–0.77, P = .006), disease-free survival (DFS) (HR 0.43, 95% CI 0.24–0.75, P = .003) and distant metastasis-free survival (DMFS) (HR 0.40, 95% CI 0.20–0.81, P = .011) in NPC patients in the training cohort, and similar associations were also evident in the validation cohort. Importantly, multivariate analysis revealed that the immune hotspot score remained an independent prognostic indicator for OS, DFS, and DMFS in both cohorts. We explored the spatial heterogeneity of cancer cells and lymphocytes in the tumor microenvironment of NPC patients using digital pathology and ecological analysis methods and further constructed three spatial scores. Our study demonstrates that spatial variation may aid in the identification of the clinical prognosis of NPC patients, but further investigation is needed.

Prognostic value of tumor-infiltrating lymphocytes in estrogen receptor-positive and human epidermal growth factor receptor 2-negative breast cancer

Tumor-infiltrating lymphocytes (TILs) are a significant prognostic factor in triple-negative breast cancer. However, the clinicopathological significance of TILs in estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer remains unclear. The purpose of the present study was to evaluate the role of TILs in the prognosis of ER-positive and HER2-negative breast cancer. A total of 65 consecutive patients with ER-positive and HER2-negative breast cancer were examined. TILs in stromal tissue (str-TILs) were graded using the International TILs Working Group criteria. The association between several clinicopathological factors and TIL grade were investigated, and the prognostic impact of TILs was compared between luminal A-like and luminal B-like breast cancer. A total of 51 patients (78.5%) had low-grade (0-10%), 11 (16.9%) had intermediate (10-40%) and 3 (4.6%) had high-grade (40-90%) str-TIL levels. There was a significant association between high levels of Ki67 expression and a high str-TIL count. Relapse-free survival was significantly worse in patients with luminal B-like cancer compared with that in patients with luminal A-like cancer. Patients with an intermediate or high str-TIL count had a better prognosis compared with those with a low str-TIL count. All patients with luminal B-like cancer and intermediate or high str-TIL levels developed no recurrence during follow-up. In conclusion, there was a significant correlation between high-grade str-TIL levels and high tumor cell proliferation rate, as well as high levels of Ki67 expression.

Magnetic resonance imaging radiomics signatures for predicting endocrine resistance in hormone receptor-positive non-metastatic breast cancer

Background

One-third of patients with hormone receptor (HR)-positive breast cancers fail to respond to hormone therapy, and some patients even progress within two years of adjuvant endocrine therapy (ET) toward primary endocrine resistance. However, there is no effective way to predict endocrine resistance.

Objective

To build a model that incorporates the radiomic signature of pretreatment magnetic resonance imaging (MRI) with clinical information to predict endocrine resistance.

Methods

Clinical data of non-metastatic breast cancer patients diagnosed between May 1, 2015 and December 31, 2018 and preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were retrospectively collected from three hospitals in China. The significant clinicopathological characteristics and radiomic signatures were included in multivariable logistic regression to establish a combined model to predict endocrine resistance in the training set, and validate the internal and external validation set.

Results

A total of 744 female non-metastatic breast cancer patients from three hospitals in China were included. In the training cohort, the AUC of the Radiomic-Clinical combined model to predict endocrine resistance was 0.975, which was higher than clinical model (0.849), IHC4 model (0.682) and similar as radiomic model (0.941). Also, the AUC of the combined model in the internal (0.921) and external validation cohort (0.955) were higher than clinical model and IHC4 model. The sensitivity of combined model was higher than radiomic alone, and got the best thresholding of the AUC.

Conclusion

This study developed and validated a pretreatment multiparametric MRI-based radiomic-clinical combined model and showed good performance in predicting endocrine resistance.

•

This study first established a model to predict endocrine resistance in non-metastatic breast cancer based on radiomic.

•

This model was a combined model that contain multiparametric MRI radiomics features and clinical features.

•

The AUC of the combined model to predict endocrine resistance was 0.975 , with great potential in clinical applications.

Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler

Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of heterogeneity in the breast TME, which can holistically illuminate the biology of tumor growth, dissemination and, ultimately, response to therapy. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially resolve and quantify proteins and RNA transcripts from tissue sections. The platform is compatible with both formalin-fixed paraffin-embedded and frozen tissues. RNA profiling was developed at the whole transcriptome level for human and mouse samples and protein profiling of 100-plex for human samples. Tissue can be optically segmented for analysis of regions of interest or cell populations to study biology-directed tissue characterization. The GeoMx Breast Cancer Consortium (GBCC) is composed of breast cancer researchers who are developing innovative approaches for spatial profiling to accelerate biomarker discovery. Here, the GBCC presents best practices for GeoMx profiling to promote the collection of high-quality data, optimization of data analysis and integration of datasets to advance collaboration and meta-analyses. Although the capabilities of the platform are presented in the context of breast cancer research, they can be generalized to a variety of other tumor types that are characterized by high heterogeneity.

Intra-Tumour Heterogeneity Is One of the Main Sources of Inter-Observer Variation in Scoring Stromal Tumour Infiltrating Lymphocytes in Triple Negative Breast Cancer

Stromal tumour infiltrating lymphocytes (sTILs) are a strong prognostic marker in triple negative breast cancer (TNBC). Consistency scoring sTILs is good and was excellent when an internet-based scoring aid developed by the TIL-WG was used to score cases in a reproducibility study. This study aimed to evaluate the reproducibility of sTILs assessment using this scoring aid in cases from routine practice and to explore the potential of the tool to overcome variability in scoring. Twenty-three breast pathologists scored sTILs in digitized slides of 49 TNBC biopsies using the scoring aid. Subsequently, fields of view (FOV) from each case were selected by one pathologist and scored by the group using the tool. Inter-observer agreement was good for absolute sTILs (ICC 0.634, 95% CI 0.539-0.735, p < 0.001) but was poor to fair using binary cutpoints. sTILs heterogeneity was the main contributor to disagreement. When pathologists scored the same FOV from each case, inter-observer agreement was excellent for absolute sTILs (ICC 0.798, 95% CI 0.727-0.864, p < 0.001) and good for the 20% (ICC 0.657, 95% CI 0.561-0.756, p < 0.001) and 40% (ICC 0.644, 95% CI 0.546-0.745, p < 0.001) cutpoints. However, there was a wide range of scores for many cases. Reproducibility scoring sTILs is good when the scoring aid is used. Heterogeneity is the main contributor to variance and will need to be overcome for analytic validity to be achieved.

The Multiple Dimensions of Networks in Cancer: A Perspective

This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. It instigates the community to explore new paths and synergies under the umbrella of the Special Issue “Networks in Cancer: From Symmetry Breaking to Targeted Therapy”. The focus of the perspective is to demonstrate how networks can model the physics, analyse the interactions, and predict the evolution of the multiple processes behind tumour-host encounters across multiple scales. From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests approaches that mark a viable path towards adoption in the clinic.

Tumor Microenvironment in Breast Cancer-Updates on Therapeutic Implications and Pathologic Assessment

The tumor microenvironment (TME) in breast cancer comprises local factors, cancer cells, immune cells and stromal cells of the local and distant tissues. The interaction between cancer cells and their microenvironment plays important roles in tumor proliferation, propagation and response to therapies. There is increasing research in exploring and manipulating the non-cancerous components of the TME for breast cancer treatment. As the TME is now increasingly recognized as a treatment target, its pathologic assessment has become a critical component of breast cancer management. The latest WHO classification of tumors of the breast listed stromal response pattern/fibrotic focus as a prognostic factor and includes recommendations on the assessment of tumor infiltrating lymphocytes and PD-1/PD-L1 expression, with therapeutic implications. This review dissects the TME of breast cancer, describes pathologic assessment relevant for prognostication and treatment decision, and details therapeutic options that interacts with and/or exploits the TME in breast cancer.