Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer.

High-grade serous ovarian cancer (HGSC) exhibits extensive malignant clonal diversity with widespread but non-random patterns of disease dissemination. We investigated whether local immune microenvironment factors shape tumor progression properties at the interface of tumor-infiltrating lymphocytes (TILs) and cancer cells. Through multi-region study of 212 samples from 38 patients with whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T and B cell receptor sequencing, we identified three immunologic subtypes across samples and extensive within-patient diversity. Epithelial CD8+ TILs negatively associated with malignant diversity, reflecting immunological pruning of tumor clones inferred by neoantigen depletion, HLA I loss of heterozygosity, and spatial tracking between T cell and tumor clones. In addition, combinatorial prognostic effects of mutational processes and immune properties were observed, illuminating how specific genomic aberration types associate with immune response and impact survival. We conclude that within-patient spatial immune microenvironment variation shapes intraperitoneal malignant spread, provoking new evolutionary perspectives on HGSC clonal dispersion.

The tumour ecology of quiescence: Niches across scales of complexity.

Quiescence is a state of cell cycle arrest, allowing cancer cells to evade anti-proliferative cancer therapies. Quiescent cancer stem cells are thought to be responsible for treatment resistance in glioblastoma, an aggressive brain cancer with poor patient outcomes. However, the regulation of quiescence in glioblastoma cells involves a myriad of intrinsic and extrinsic mechanisms that are not fully understood. In this review, we synthesise the literature on quiescence regulatory mechanisms in the context of glioblastoma and propose an ecological perspective to stemness-like phenotypes anchored to the contemporary concepts of niche theory. From this perspective, the cell cycle regulation is multiscale and multidimensional, where the niche dimensions extend to extrinsic variables in the tumour microenvironment that shape cell fate. Within this conceptual framework and powered by ecological niche modelling, the discovery of microenvironmental variables related to hypoxia and mechanosignalling that modulate proliferative plasticity and intratumor immune activity may open new avenues for therapeutic targeting of emerging biological vulnerabilities in glioblastoma.

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.

Biomarkers for site-specific response to neoadjuvant chemotherapy in epithelial ovarian cancer: relating MRI changes to tumour cell load and necrosis.

BACKGROUND: Diffusion-weighted magnetic resonance imaging (DW-MRI) potentially interrogates site-specific response to neoadjuvant chemotherapy (NAC) in epithelial ovarian cancer (EOC). METHODS: Participants with newly diagnosed EOC due for platinum-based chemotherapy and interval debulking surgery were recruited prospectively in a multicentre study (n = 47 participants). Apparent diffusion coefficient (ADC) and solid tumour volume (up to 10 lesions per participant) were obtained from DW-MRI before and after NAC (including double-baseline for repeatability assessment in n = 19). Anatomically matched lesions were analysed after surgical excision (65 lesions obtained from 25 participants). A trained algorithm determined tumour cell fraction, percentage tumour and percentage necrosis on histology. Whole-lesion post-NAC ADC and pre/post-NAC ADC changes were compared with histological metrics (residual tumour/necrosis) for each tumour site (ovarian, omental, peritoneal, lymph node). RESULTS: Tumour volume reduced at all sites after NAC. ADC increased between pre- and post-NAC measurements. Post-NAC ADC correlated negatively with tumour cell fraction. Pre/post-NAC changes in ADC correlated positively with percentage necrosis. Significant correlations were driven by peritoneal lesions. CONCLUSIONS: Following NAC in EOC, the ADC (measured using DW-MRI) increases differentially at disease sites despite similar tumour shrinkage, making its utility site-specific. After NAC, ADC correlates negatively with tumour cell fraction; change in ADC correlates positively with percentage necrosis. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT01505829.

The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups.

The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ~40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA­RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the 'CNA-devoid' subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.

Systematic evaluation of quantotypic peptides for targeted analysis of the human kinome.

In targeted proteomics it is critical that peptides are not only proteotypic but also accurately represent the level of the protein (quantotypic). Numerous approaches are used to identify proteotypic peptides, but quantotypic properties are rarely assessed. We show that measuring ratios of proteotypic peptides across biological samples can be used to empirically identify peptides with good quantotypic properties. We applied this technique to identify quantotypic peptides for 21% of the human kinome.

Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology-Genomic Integration Analysis.

BACKGROUND: The intra-tumor diversity of cancer cells is under intense investigation; however, little is known about the heterogeneity of the tumor microenvironment that is key to cancer progression and evolution. We aimed to assess the degree of microenvironmental heterogeneity in breast cancer and correlate this with genomic and clinical parameters. METHODS AND FINDINGS: We developed a quantitative measure of microenvironmental heterogeneity along three spatial dimensions (3-D) in solid tumors, termed the tumor ecosystem diversity index (EDI), using fully automated histology image analysis coupled with statistical measures commonly used in ecology. This measure was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set. In high-grade (grade 3) breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types. However, this association was not observed in low-grade (grade 1 and 2) breast cancers. The prognostic value of EDI was superior to known prognostic factors and was enhanced with the addition of TP53 mutation status (multivariate analysis test set, p = 9 × 10-4, hazard ratio = 1.47, 95% CI 1.17-1.84; validation set, p = 0.0011, hazard ratio = 1.78, 95% CI 1.26-2.52). Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. Limitations of this study include the number of cell types included in the model, that EDI has prognostic value only in grade 3 tumors, and that our spatial heterogeneity measure was dependent on spatial scale and tumor size. CONCLUSIONS: To our knowledge, this is the first study to couple unbiased measures of microenvironmental heterogeneity with genomic alterations to predict breast cancer clinical outcome. We propose a clinically relevant role of microenvironmental heterogeneity for advanced breast tumors, and highlight that ecological statistics can be translated into medical advances for identifying a new type of biomarker and, furthermore, for understanding the synergistic interplay of microenvironmental heterogeneity with genomic alterations in cancer cells.

Diffusion-weighted MRI for early detection and characterization of prostate cancer in the transgenic adenocarcinoma of the mouse prostate model.

PURPOSE: To improve early diagnosis of prostate cancer to aid clinical decision-making. Diffusion-weighted magnetic resonance imaging (DW-MRI) is sensitive to water diffusion throughout tissues, which correlates with Gleason score, a histological measure of prostate cancer aggressiveness. In this study the ability of DW-MRI to detect prostate cancer onset and development was evaluated in transgenic adenocarcinoma of the mouse prostate (TRAMP) mice. MATERIALS AND METHODS: T2 -weighted and DW-MRI were acquired using a 7T MR scanner, 200 mm bore diameter; 10 TRAMP and 6 C57BL/6 control mice were scanned every 4 weeks from 8 weeks of age until sacrifice at 28-30 weeks. After sacrifice, the genitourinary tract was excised and sectioned for histological analysis. Histology slides registered with DW-MR images allowed for validation of DW-MR images and the apparent diffusion coefficient (ADC) as tools for cancer detection and disease stratification. An automated early assessment tool based on ADC threshold values was developed to aid cancer detection and progression monitoring. RESULTS: The ADC differentiated between control prostate ((1.86 ± 0.20) × 10(-3) mm(2) /s) and normal TRAMP prostate ((1.38 ± 0.10) × 10(-3) mm(2) /s) (P = 0.0001), between TRAMP prostate and well-differentiated cancer ((0.93 ± 0.18) × 10(-3) mm(2) /s) (P = 0.0006), and between well-differentiated cancer and poorly differentiated cancer ((0.63 ± 0.06) × 10(-3) mm(2) /s) (P = 0.02). CONCLUSION: DW-MRI is a tool for early detection of cancer, and discrimination between cancer stages in the TRAMP model. The incorporation of DW-MRI-based prostate cancer stratification and monitoring could increase the accuracy of preclinical trials using TRAMP mice.

Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology.

The emergent field of digital pathology employing automated image analysis techniques is to revolutionize traditional pathology at the center of clinical diagnostics. Histological images provide important tumor features unavailable in molecular profiling or omics data- the spatial context of tumor and stromal cells at single-cell resolution. Methods to map the spatial and morphological patterns of cancer and normal cells can contribute to a more comprehensive understanding of the highly heterogeneous tumor microenvironment. This review focuses on methods that help expand our knowledge of intra-tumoral spatial heterogeneity of the tumor microenvironment and their potential synergies with molecular profiling technologies.

An ecological measure of immune-cancer colocalization as a prognostic factor for breast cancer.

INTRODUCTION: Abundance of immune cells has been shown to have prognostic and predictive significance in many tumor types. Beyond abundance, the spatial organization of immune cells in relation to cancer cells may also have significant functional and clinical implications. However there is a lack of systematic methods to quantify spatial associations between immune and cancer cells. METHODS: We applied ecological measures of species interactions to digital pathology images for investigating the spatial associations of immune and cancer cells in breast cancer. We used the Morisita-Horn similarity index, an ecological measure of community structure and predator-prey interactions, to quantify the extent to which cancer cells and immune cells colocalize in whole-tumor histology sections. We related this index to disease-specific survival of 486 women with breast cancer and validated our findings in a set of 516 patients from different hospitals. RESULTS: Colocalization of immune cells with cancer cells was significantly associated with a disease-specific survival benefit for all breast cancers combined. In HER2-positive subtypes, the prognostic value of immune-cancer cell colocalization was highly significant and exceeded those of known clinical variables. Furthermore, colocalization was a significant predictive factor for long-term outcome following chemotherapy and radiotherapy in HER2 and Luminal A subtypes, independent of and stronger than all known clinical variables. CONCLUSIONS: Our study demonstrates how ecological methods applied to the tumor microenvironment using routine histology can provide reproducible, quantitative biomarkers for identifying high-risk breast cancer patients. We found that the clinical value of immune-cancer interaction patterns is highly subtype-specific but substantial and independent to known clinicopathologic variables that mostly focused on cancer itself. Our approach can be developed into computer-assisted prediction based on histology samples that are already routinely collected.

Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer.

The abundance of tumor-infiltrating lymphocytes has been associated with a favorable prognosis in estrogen receptor-negative breast cancer. However, a high degree of spatial heterogeneity in lymphocytic infiltration is often observed and its clinical implication remains unclear. Here we combine automated histological image processing with methods of spatial statistics used in ecological data analysis to quantify spatial heterogeneity in the distribution patterns of tumor-infiltrating lymphocytes. Hematoxylin and eosin-stained sections from two cohorts of estrogen receptor-negative breast cancer patients (discovery: n=120; validation: n=125) were processed with our automated cell classification algorithm to identify the location of lymphocytes and cancer cells. Subsequently, hotspot analysis (Getis-Ord Gi*) was applied to identify statistically significant hotspots of cancer and immune cells, defined as tumor regions with a significantly high number of cancer cells or immune cells, respectively. We found that the amount of co-localized cancer and immune hotspots weighted by tumor area, rather than number of cancer or immune hotspots, correlates with a better prognosis in estrogen receptor-negative breast cancer in univariate and multivariate analysis. Moreover, co-localization of cancer and immune hotspots further stratified patients with immune cell-rich tumors. Our study demonstrates the importance of quantifying not only the abundance of lymphocytes but also their spatial variation in the tumor specimen for which methods from other disciplines such as spatial statistics can be successfully applied.

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.

Longitudinal Assessment of Tumor-Infiltrating Lymphocytes in Primary Breast Cancer Following Neoadjuvant Radiation Therapy.

PURPOSE: Tumor-infiltrating lymphocytes (TILs) have prognostic significance in several cancers, including breast cancer. Despite interest in combining radiation therapy with immunotherapy, little is known about the effect of radiation therapy itself on the tumor-immune microenvironment, including TILs. Here, we interrogated longitudinal dynamics of TILs and systemic lymphocytes in patient samples taken before, during, and after neoadjuvant radiation therapy (NART) from PRADA and Neo-RT breast clinical trials. METHODS AND MATERIALS: We manually scored stromal TILs (sTILs) from longitudinal tumor samples using standardized guidelines as well as deep learning-based scores at cell-level (cTIL) and cell- and tissue-level combination analyses (SuperTIL). In parallel, we interrogated absolute lymphocyte counts from routine blood tests at corresponding time points during treatment. Exploratory analyses studied the relationship between TILs and pathologic complete response (pCR) and long-term outcomes. RESULTS: Patients receiving NART experienced a significant and uniform decrease in sTILs that did not recover at the time of surgery (P < .0001). This lymphodepletive effect was also mirrored in peripheral blood. Our SuperTIL deep learning score showed good concordance with manual sTILs and importantly performed comparably to manual scores in predicting pCR from diagnostic biopsies. The analysis suggested an association between baseline sTILs and pCR, as well as sTILs at surgery and relapse, in patients receiving NART. CONCLUSIONS: This study provides novel insights into TIL dynamics in the context of NART in breast cancer and demonstrates the potential for artificial intelligence to assist routine pathology. We have identified trends that warrant further interrogation and have a bearing on future radioimmunotherapy trials.

The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma.

The introduction of the International Association for the Study of Lung Cancer grading system has furthered interest in histopathological grading for risk stratification in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present challenges to pathologists, prompting the development of artificial intelligence (AI) methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK), encoding multiresolution inputs with an attention mechanism, to delineate growth patterns from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas across four independent cohorts, AI-based grading was prognostic of disease-free survival, and further assisted pathologists by consistently improving prognostication in stage I tumors. Tumors with discrepant patterns between AI and pathologists had notably higher intratumoral heterogeneity. Furthermore, ANORAK facilitates the morphological and spatial assessment of the acinar pattern, capturing acinus variations with pattern transition. Collectively, our AI method enabled the precision quantification and morphology investigation of growth patterns, reflecting intratumoral histological transitions in lung adenocarcinoma.

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.

Self-supervised deep learning for highly efficient spatial immunophenotyping.

BACKGROUND: Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets. METHODS: This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model. FINDINGS: With 1% annotations (18-114 cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828-11,459 annotated cells (-0.002 to -0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P = 0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression. INTERPRETATION: By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data. FUNDING: This study was funded by the Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre.

Glioblastoma cell fate is differentially regulated by the microenvironments of the tumor bulk and infiltrative margin.

Glioblastoma (GBM) recurrence originates from invasive margin cells that escape surgical debulking, but to what extent these cells resemble their bulk counterparts remains unclear. Here, we generated three immunocompetent somatic GBM mouse models, driven by subtype-associated mutations, to compare matched bulk and margin cells. We find that, regardless of mutations, tumors converge on common sets of neural-like cellular states. However, bulk and margin have distinct biology. Injury-like programs associated with immune infiltration dominate in the bulk, leading to the generation of lowly proliferative injured neural progenitor-like cells (iNPCs). iNPCs account for a significant proportion of dormant GBM cells and are induced by interferon signaling within T cell niches. In contrast, developmental-like trajectories are favored within the immune-cold margin microenvironment resulting in differentiation toward invasive astrocyte-like cells. These findings suggest that the regional tumor microenvironment dominantly controls GBM cell fate and biological vulnerabilities identified in the bulk may not extend to the margin residuum.

Artificial intelligence-based morphometric signature to identify ductal carcinoma in situ with low risk of progression to invasive breast cancer.

Ductal carcinoma in situ (DCIS) may progress to ipsilateral invasive breast cancer (iIBC), but often never will. Because DCIS is treated as early breast cancer, many women with harmless DCIS face overtreatment. To identify these women that may forego treatment, we hypothesized that DCIS morphometric features relate to the risk of subsequent iIBC. We developed an artificial intelligence-based DCIS morphometric analysis pipeline (AIDmap) to detect DCIS as a pathologist and measure morphological structures in hematoxylin-eosin-stained (H&E) tissue sections. These were from a case-control study of patients diagnosed with primary DCIS, treated by breast-conserving surgery without radiotherapy. We analyzed 689 WSIs of DCIS of which 226 were diagnosed with subsequent iIBC (cases) and 463 were not (controls). The distribution of 15 duct morphological measurements in each H&E was summarized in 55 morphometric variables. A ridge regression classifier with cross validation predicted 5-years-free of iIBC with an area-under the curve of 0.65 (95% CI 0.55-0.76). A morphometric signature based on the 30 variables most associated with outcome, identified lesions containing small-sized ducts, low number of cells and low DCIS/stroma area ratio. This signature was associated with lower iIBC risk in a multivariate regression model including grade, ER, HER2 and COX-2 expression (HR = 0.56; 95% CI 0.28-0.78). AIDmap has potential to identify harmless DCIS that may not need treatment.

Penalized regression elucidates aberration hotspots mediating subtype-specific transcriptional responses in breast cancer.

MOTIVATION: Copy number alterations (CNAs) associated with cancer are known to contribute to genomic instability and gene deregulation. Integrating CNAs with gene expression helps to elucidate the mechanisms by which CNAs act and to identify the transcriptional downstream targets of CNAs. Such analyses can help to sort functional driver events from the many accompanying passenger alterations. However, the way CNAs affect gene expression can vary in different cellular contexts, for example between different subtypes of the same cancer. Thus, it is important to develop computational approaches capable of inferring differential connectivity of regulatory networks in different cellular contexts. RESULTS: We propose a statistical deregulation model that integrates copy number and expression data of different disease subtypes to jointly model common and differential regulatory relationships. Our model not only identifies CNAs driving gene expression changes, but at the same time also predicts differences in regulation that distinguish one cancer subtype from the other. We implement our model in a penalized regression framework and demonstrate in a simulation study the feasibility and accuracy of our approach. Subsequently, we show that this model can identify both known and novel aspects of cross-talk between the ER and NOTCH pathways in ER-negative-specific deregulations, when compared with ER-positive breast cancer. This flexible model can be applied on other modalities such as methylation or microRNA and expression to disentangle cancer signaling pathways. AVAILABILITY: The Bioconductor-compliant R package DANCE is available from www.markowetzlab.org/software/ CONTACT: yinyin.yuan@cancer.org.uk; florian.markowetz@cancer.org.uk.