Development and Validation of a HER2-Low Focused Immunohistochemical Scoring System With High-Interobserver Concordance: The Australian HER2-Low Breast Cancer Concordance Study.

The DESTINY Breast-04 trial revealed survival advantages of trastuzumab deruxtecan for women with metastatic HER2-low breast cancer (1+ or 2+ immunohistochemistry [IHC], without amplification). Although this trial applied the 2018 Americal Society of Clinial Oncology (ASCO)/College of American Pathologists (CAP) HER2 IHC scoring criteria, the subjectivity and imprecision in IHC scoring have raised concerns that patients' treatment may be misaligned. Our group of 9 experienced breast pathologists collated a deidentified set of 60 breast cancer core biopsies from 3 laboratories, evaluated with the Ventana 4B5 HER2 assay and mostly scored locally as HER2 0 or 1+. Based on ASCO/CAP 2018 criteria and our extensive experience of reporting HER2 IHC, we specified scoring conventions for cancers with low levels of HER2 protein expression, articulating specific scoring pitfalls. Each pathologist then reviewed digitized whole slide images of the IHC slides and scored the HER2 expression for each case. At a subsequent consensus workshop, we reviewed the cases jointly to establish consensus scores for each case and determine the percentage of HER2 expressing tumor cells. Consensus was reached on all cases, with 40 classified as 1+ and 3 as 2+ (not amplified), totaling 43 (71.7%) HER2-low cancers. The remaining cases were HER2 0. In 93.3% of cases (56/60), the consensus score matched with the majority opinion of pathologists' independent scores. Seven (41.2%) of the 17 cases reported locally as HER2 0 were classified as HER2 low. Conversely, among 32 cases with local scores of 1+, 7 (21.8%) were reclassified as ultralow or null. Individual pathologists' accuracy in matching the consensus scores ranged from 73.3% to 91.67% (mean, 80.74%). Among HER2-low cancers those in which <20% of the tumor cells expressed HER2 had the lowest concordance levels. Observers Cohen's κ coefficients for concordance were excellent for 4, good in 1, and moderate in the 4 observers. This reference set of cases with expert consensus HER2 scores will be invaluable for peer training and development of our national external quality assurance program for HER2-low cancers. For assessing breast cancers at the low end of HER2 protein expression, our targeted scoring criteria and explicit instruction on pitfalls improved pathologists' accuracy and concordance.

Dataset for pathology reporting of ductal carcinoma in situ, variants of lobular carcinoma in situ and low-grade lesions: recommendations from the International Collaboration on Cancer Reporting (ICCR).

AIMS: To describe a new international dataset for pathology reporting of ductal carcinoma in situ (DCIS), variants of lobular carcinoma in situ (LCIS) and low-grade lesions (encapsulated papillary carcinoma, solid papillary carcinoma in situ, Paget's disease) produced by the International Collaboration on Cancer Reporting (ICCR). METHODS AND RESULTS: The ICCR, a global alliance of pathology bodies, uses a rigorous and efficient process for the development of evidence-based, structured datasets for pathology reporting of common cancers. Their aim is to support quality pathology reporting and engender understanding between the breast surgeon, pathologist, and oncologist for optimal and uniform patient management globally. Here we describe the dataset for DCIS, some variants of LCIS (namely the pleomorphic and the florid variants), and low-grade lesions by a multidisciplinary panel of internationally recognized experts. The agreed dataset comprises 12 core (required) and five noncore (recommended) elements suitable for both developed and low-income jurisdictions, derived from a review of current evidence. Areas of contention were addressed using a pragmatic approach in the absence of evidence. Use of all core elements is the minimum reporting standard for any individual case. Commentary is provided, explaining each element's clinical relevance, definitions to be applied where appropriate for the agreed list of value options and the rationale for considering the element as core or noncore. CONCLUSION: This first internationally agreed dataset for DCIS, variants of LCIS, and low-grade lesions reporting will enable their standardization of pathology reporting and enhance clinicopathological communication leading to improved patient outcomes. Widespread adoption will also facilitate international comparisons, multinational clinical trials, and help to improve the management of breast disease globally.

Elevated levels of tumour apolipoprotein D independently predict poor outcome in breast cancer patients.

AIMS: Apolipoprotein D (ApoD) is a protein that is regulated by androgen and oestrogen, and is a major constituent of breast cysts. Although ApoD has been reported to be a marker of breast cancer, its prognostic importance in invasive breast cancer is unclear. The aim of this study was to investigate the relationship between ApoD protein expression, oestrogen receptor-α (ERα) expression and androgen receptor (AR) expression in predicting breast cancer outcome. METHODS AND RESULTS: ApoD levels were measured by the use of immunohistochemistry and video image analysis on tissue sections from a breast cancer cohort (n = 214). We assessed the associations of ApoD expression with disease-free survival (DFS), metastasis-free survival (MFS), and overall survival (OS). We also assessed the relationship between ApoD expression, AR expression and ERα expression in predicting OS. ApoD expression (>1% ApoD positivity) was found in 72% (154/214) of tissues. High ApoD positivity (≥20.7%, fourth quartile) was an independent predictor of MFS and OS, and conferred a 2.2-fold increased risk of developing metastatic disease and a 2.1-fold increased risk of breast cancer-related death. ApoD positivity was not associated with AR or ERα nuclear positivity. However, patients with (≥1%) ERα-positive cancers with low (<20.7%) ApoD positivity, or those showing high (≥78%) AR positivity and low (<20.7%) ApoD positivity had better OS than other patient groups. CONCLUSIONS: ApoD expression could be used to predict breast cancer prognosis independently of ERα and AR expression.

ELF5 Drives Lung Metastasis in Luminal Breast Cancer through Recruitment of Gr1+ CD11b+ Myeloid-Derived Suppressor Cells.

During pregnancy, the ETS transcription factor ELF5 establishes the milk-secreting alveolar cell lineage by driving a cell fate decision of the mammary luminal progenitor cell. In breast cancer, ELF5 is a key transcriptional determinant of tumor subtype and has been implicated in the development of insensitivity to anti-estrogen therapy. In the mouse mammary tumor virus-Polyoma Middle T (MMTV-PyMT) model of luminal breast cancer, induction of ELF5 levels increased leukocyte infiltration, angiogenesis, and blood vessel permeability in primary tumors and greatly increased the size and number of lung metastasis. Myeloid-derived suppressor cells, a group of immature neutrophils recently identified as mediators of vasculogenesis and metastasis, were recruited to the tumor in response to ELF5. Depletion of these cells using specific Ly6G antibodies prevented ELF5 from driving vasculogenesis and metastasis. Expression signatures in luminal A breast cancers indicated that increased myeloid cell invasion and inflammation were correlated with ELF5 expression, and increased ELF5 immunohistochemical staining predicted much shorter metastasis-free and overall survival of luminal A patients, defining a group who experienced unexpectedly early disease progression. Thus, in the MMTV-PyMT mouse mammary model, increased ELF5 levels drive metastasis by co-opting the innate immune system. As ELF5 has been previously implicated in the development of antiestrogen resistance, this finding implicates ELF5 as a defining factor in the acquisition of the key aspects of the lethal phenotype in luminal A breast cancer.

Breast ductal carcinoma in situ carry mutational driver events representative of invasive breast cancer.

The spectrum of genomic alterations in ductal carcinoma in situ (DCIS) is relatively unexplored, but is likely to provide useful insights into its biology, its progression to invasive carcinoma and the risk of recurrence. DCIS (n=20) with a range of phenotypes was assessed by massively parallel sequencing for mutations and copy number alterations and variants validated by Sanger sequencing. PIK3CA mutations were identified in 11/20 (55%), TP53 mutations in 6/20 (30%), and GATA3 mutations in 9/20 (45%). Screening an additional 91 cases for GATA3 mutations identified a final frequency of 27% (30/111), with a high proportion of missense variants (8/30). TP53 mutations were exclusive to high grade DCIS and more frequent in PR-negative tumors compared with PR-positive tumors (P=0.037). TP53 mutant tumors also had a significantly higher fraction of the genome altered by copy number than wild-type tumors (P=0.005), including a significant positive association with amplification or gain of ERBB2 (P<0.05). The association between TP53 mutation and ERBB2 amplification was confirmed in a wider DCIS cohort using p53 immunohistochemistry as a surrogate marker for TP53 mutations (P=0.03). RUNX1 mutations and MAP2K4 copy number loss were novel findings in DCIS. Frequent copy number alterations included gains on 1q, 8q, 17q, and 20q and losses on 8p, 11q, 16q, and 17p. Patterns of genomic alterations observed in DCIS were similar to those previously reported for invasive breast cancers, with all DCIS having at least one bona fide breast cancer driver event. However, an increase in GATA3 mutations and fewer copy number changes were noted in DCIS compared with invasive carcinomas. The role of such alterations as prognostic and predictive biomarkers in DCIS is an avenue for further investigation.

MCL-1 inhibition provides a new way to suppress breast cancer metastasis and increase sensitivity to dasatinib.

BACKGROUND: Metastatic disease is largely resistant to therapy and accounts for almost all cancer deaths. Myeloid cell leukemia-1 (MCL-1) is an important regulator of cell survival and chemo-resistance in a wide range of malignancies, and thus its inhibition may prove to be therapeutically useful. METHODS: To examine whether targeting MCL-1 may provide an effective treatment for breast cancer, we constructed inducible models of BIMs2A expression (a specific MCL-1 inhibitor) in MDA-MB-468 (MDA-MB-468-2A) and MDA-MB-231 (MDA-MB-231-2A) cells. RESULTS: MCL-1 inhibition caused apoptosis of basal-like MDA-MB-468-2A cells grown as monolayers, and sensitized them to the BCL-2/BCL-XL inhibitor ABT-263, demonstrating that MCL-1 regulated cell survival. In MDA-MB-231-2A cells, grown in an organotypic model, induction of BIMs2A produced an almost complete suppression of invasion. Apoptosis was induced in such a small proportion of these cells that it could not account for the large decrease in invasion, suggesting that MCL-1 was operating via a previously undetected mechanism. MCL-1 antagonism also suppressed local invasion and distant metastasis to the lung in mouse mammary intraductal xenografts. Kinomic profiling revealed that MCL-1 antagonism modulated Src family kinases and their targets, which suggested that MCL-1 might act as an upstream modulator of invasion via this pathway. Inhibition of MCL-1 in combination with dasatinib suppressed invasion in 3D models of invasion and inhibited the establishment of tumors in vivo. CONCLUSION: These data provide the first evidence that MCL-1 drives breast cancer cell invasion and suggests that MCL-1 antagonists could be used alone or in combination with drugs targeting Src kinases such as dasatinib to suppress metastasis.

Assessment of DNA methylation profiling and copy number variation as indications of clonal relationship in ipsilateral and contralateral breast cancers to distinguish recurrent breast cancer from a second primary tumour.

BACKGROUND: Patients with breast cancer have an increased risk of developing subsequent breast cancers. It is important to distinguish whether these tumours are de novo or recurrences of the primary tumour in order to guide the appropriate therapy. Our aim was to investigate the use of DNA methylation profiling and array comparative genomic hybridization (aCGH) to determine whether the second tumour is clonally related to the first tumour. METHODS: Methylation-sensitive high-resolution melting was used to screen promoter methylation in a panel of 13 genes reported as methylated in breast cancer (RASSF1A, TWIST1, APC, WIF1, MGMT, MAL, CDH13, RARß, BRCA1, CDH1, CDKN2A, TP73, and GSTP1) in 29 tumour pairs (16 ipsilateral and 13 contralateral). Using the methylation profile of these genes, we employed a Bayesian and an empirical statistical approach to estimate clonal relationship. Copy number alterations were analysed using aCGH on the same set of tumour pairs. RESULTS: There is a higher probability of the second tumour being recurrent in ipsilateral tumours compared with contralateral tumours (38 % versus 8 %; p <0.05) based on the methylation profile. Using previously reported recurrence rates as Bayesian prior probabilities, we classified 69 % of ipsilateral and 15 % of contralateral tumours as recurrent. The inferred clonal relationship results of the tumour pairs were generally concordant between methylation profiling and aCGH. CONCLUSION: Our results show that DNA methylation profiling as well as aCGH have potential as diagnostic tools in improving the clinical decisions to differentiate recurrences from a second de novo tumour.

Methylation profiling of ductal carcinoma in situ and its relationship to histopathological features.

INTRODUCTION: DNA methylation is a well-studied biomarker in invasive breast cancer, but its role in ductal carcinoma in situ (DCIS) is less well characterized. The aims of this study are to assess the methylation profile in DCIS for a panel of well-characterized genes that are frequently methylated in breast cancer, to investigate the relationship of methylation with pathological features, and to perform a proof-of-principle study to evaluate the practicality of methylation as a biomarker in diagnostic DCIS material. METHODS: Promoter CpG island methylation for a panel of 11 breast cancer-related genes was performed by methylation-sensitive high resolution melting (MS-HRM). Formalin-fixed, paraffin-embedded (FFPE) biopsies from 72 samples of pure DCIS (DCIS occurring in the absence of synchronous invasive carcinoma), 10 samples of mixed DCIS (DCIS adjacent to invasive carcinoma), and 18 samples of normal breast epithelium adjacent to a DCIS lesion were micro-dissected prior to DNA extraction. RESULTS: Methylation was seen for all the tested genes except BRCA1. RASSF1A was the most frequently methylated gene (90% of DCIS samples) and its methylation was associated with comedo necrosis (p = 0.018). Cluster analysis based on the methylation profile revealed four groups, the highly methylated cluster being significantly associated with high nuclear grade, HER2 amplification, negative estrogen receptor (ER) α status, and negative progesterone receptor (PgR) status, (p = 0.038, p = 0.018, p <0.001, p = 0.001, respectively). Methylation of APC (p = 0.017), CDH13 (p = 0.017), and RARß (p <0.001) was associated with negative ERα status. Methylation of CDH13 (p <0.001), and RARß (p = 0.001) was associated with negative PgR status. Methylation of APC (p = 0.013) and CDH13 (p = 0.026) was associated with high nuclear grade. Methylation of CDH13 (p = 0.009), and RARß (p = 0.042) was associated with HER2-amplification. CONCLUSIONS: DNA methylation can be assessed in FFPE-derived samples using suitable methodologies. Methylation of a panel of genes that are known to be methylated in invasive breast cancer was able to classify DCIS into distinct groups and was differentially associated with phenotypic features in DCIS.

BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion.

Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment decisions and improving patient outcomes. Here we present BioFusionNet, a deep learning framework that fuses image-derived features with genetic and clinical data to obtain a holistic profile and achieve survival risk stratification of ER+ breast cancer patients. We employ multiple self-supervised feature extractors (DINO and MoCoV3) pretrained on histopathological patches to capture detailed image features. These features are then fused by a variational autoencoder and fed to a self-attention network generating patient-level features. A co-dual-cross-attention mechanism combines the histopathological features with genetic data, enabling the model to capture the interplay between them. Additionally, clinical data is incorporated using a feed-forward network, further enhancing predictive performance and achieving comprehensive multimodal feature integration. Furthermore, we introduce a weighted Cox loss function, specifically designed to handle imbalanced survival data, which is a common challenge. Our model achieves a mean concordance index of 0.77 and a time-dependent area under the curve of 0.84, outperforming state-of-the-art methods. It predicts risk (high versus low) with prognostic significance for overall survival in univariate analysis (HR=2.99, 95% CI: 1.88-4.78, p 0.005), and maintains independent significance in multivariate analysis incorporating standard clinicopathological variables (HR=2.91, 95% CI: 1.80-4.68, p 0.005).

Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases.

The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists' performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% ( P <0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% ( P ≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.

A foundation model for clinical-grade computational pathology and rare cancers detection.

The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends on the ability to model the diverse patterns observed in pathology images. To this end, we present Virchow, the largest foundation model for computational pathology to date. In addition to the evaluation of biomarker prediction and cell identification, we demonstrate that a large foundation model enables pan-cancer detection, achieving 0.95 specimen-level area under the (receiver operating characteristic) curve across nine common and seven rare cancers. Furthermore, we show that with less training data, the pan-cancer detector built on Virchow can achieve similar performance to tissue-specific clinical-grade models in production and outperform them on some rare variants of cancer. Virchow's performance gains highlight the value of a foundation model and open possibilities for many high-impact applications with limited amounts of labeled training data.

The impact of breast cosmetic and functional outcomes on quality of life: long-term results from the St. George and Wollongong randomized breast boost trial.

The aims of this study were to evaluate the impact of cosmetic and functional outcomes after breast-conserving surgery (BCS) and radiation on quality of life (QOL). In this exploratory analysis; baseline, 5 and 10 years data of patient's assessment of breast cosmesis, arm swelling/pain, limitation of movement, loss of feeling in fingers and breast sensitivity/tenderness were dichotomized and their impact on QOL (QLQ-C30) were assessed. Multivariable modelling was also performed to assess associations with QOL. The St. George and Wollongong randomized trial randomized 688 patients into the boost and no boost arms. 609, 580, and 428 patients had baseline, 5 and 10 years cosmetic data available, respectively. Similar numbers had the various functional assessments in the corresponding period. By univariate analysis, cosmesis and a number of functional outcomes were highly associated with QOL. Adjusted multivariate modelling showed that cosmesis remained associated with QOL at 5 and 10 years. Breast sensitivity, arm pain, breast separation, age and any distant cancer event were also associated with QOL on multivariate modelling at 10 years. This study highlights the importance of maintaining favorable cosmetic and functional outcomes following BCS. In addition, the clinically and statistically significant relationship between functional outcomes and QOL shows the importance for clinicians and allied health professionals in identifying, discussing, managing, and limiting these effects in women with breast cancer in order to maintain QOL.

Inositol polyphosphate 4-phosphatase II regulates PI3K/Akt signaling and is lost in human basal-like breast cancers.

Inositol polyphosphate 4-phosphatase-II (INPP4B) is a regulator of the phosphoinositide 3-kinase (PI3K) signaling pathway and is implicated as a tumor suppressor in epithelial carcinomas. INPP4B loss of heterozygosity (LOH) is detected in some human breast cancers; however, the expression of INPP4B protein in breast cancer subtypes and the normal breast is unknown. We report here that INPP4B is expressed in nonproliferative estrogen receptor (ER)-positive cells in the normal breast, and in ER-positive, but not negative, breast cancer cell lines. INPP4B knockdown in ER-positive breast cancer cells increased Akt activation, cell proliferation, and xenograft tumor growth. Conversely, reconstitution of INPP4B expression in ER-negative, INPP4B-null human breast cancer cells reduced Akt activation and anchorage-independent growth. INPP4B protein expression was frequently lost in primary human breast carcinomas, associated with high clinical grade and tumor size and loss of hormone receptors and was lost most commonly in aggressive basal-like breast carcinomas. INPP4B protein loss was also frequently observed in phosphatase and tensin homolog (PTEN)-null tumors. These studies provide evidence that INPP4B functions as a tumor suppressor by negatively regulating normal and malignant mammary epithelial cell proliferation through regulation of the PI3K/Akt signaling pathway, and that loss of INPP4B protein is a marker of aggressive basal-like breast carcinomas.

Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning.

Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address these challenges, as they provide a wealth of information about gene expression at the cell level, but they are expensive, hindering their use in large-scale clinical oncology studies. Predicting gene expression from hematoxylin and eosin stained histology images provides a more affordable alternative for such studies. Here we present BrST-Net, a deep learning framework for predicting gene expression from histopathology images using spatial transcriptomics data. Using this framework, we trained and evaluated four distinct state-of-the-art deep learning architectures, which include ResNet101, Inception-v3, EfficientNet (with six different variants), and vision transformer (with two different variants), all without utilizing pretrained weights for the prediction of 250 genes. To enhance the generalisation performance of the main network, we introduce an auxiliary network into the framework. Our methodology outperforms previous studies, with 237 genes identified with positive correlation, including 24 genes with a median correlation coefficient greater than 0.50. This is a notable improvement over previous studies, which could predict only 102 genes with positive correlation, with the highest correlation values ranging from 0.29 to 0.34.

hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images.

Gene expression can be used to subtype breast cancer with improved prediction of risk of recurrence and treatment responsiveness over that obtained using routine immunohistochemistry (IHC). However, in the clinic, molecular profiling is primarily used for ER+ breast cancer, which is costly, tissue destructive, requires specialised platforms, and takes several weeks to obtain a result. Deep learning algorithms can effectively extract morphological patterns in digital histopathology images to predict molecular phenotypes quickly and cost-effectively. We propose a new, computationally efficient approach called hist2RNA inspired by bulk RNA sequencing techniques to predict the expression of 138 genes (incorporated from 6 commercially available molecular profiling tests), including luminal PAM50 subtype, from hematoxylin and eosin (H&E)-stained whole slide images (WSIs). The training phase involves the aggregation of extracted features for each patient from a pretrained model to predict gene expression at the patient level using annotated H&E images from The Cancer Genome Atlas (TCGA, n = 335). We demonstrate successful gene prediction on a held-out test set (n = 160, corr = 0.82 across patients, corr = 0.29 across genes) and perform exploratory analysis on an external tissue microarray (TMA) dataset (n = 498) with known IHC and survival information. Our model is able to predict gene expression and luminal PAM50 subtype (Luminal A versus Luminal B) on the TMA dataset with prognostic significance for overall survival in univariate analysis (c-index = 0.56, hazard ratio = 2.16 (95% CI 1.12-3.06), p < 5 × 10-3), and independent significance in multivariate analysis incorporating standard clinicopathological variables (c-index = 0.65, hazard ratio = 1.87 (95% CI 1.30-2.68), p < 5 × 10-3). The proposed strategy achieves superior performance while requiring less training time, resulting in less energy consumption and computational cost compared to patch-based models. Additionally, hist2RNA predicts gene expression that has potential to determine luminal molecular subtypes which correlates with overall survival, without the need for expensive molecular testing.

MCP-1 expression in breast cancer and its association with distant relapse.

BACKGROUND: Distant relapse of breast cancer complicates management of the disease and accounts for 90% of breast cancer-related deaths. Monocyte chemoattractant protein-1 (MCP-1) has critical roles in breast cancer progression and is widely accepted as a pro-metastatic chemokine. METHODS: This study explored MCP-1 expression in the primary tumour of 251 breast cancer patients. A simplified 'histoscore' was used to determine if each tumour had high or low expression of MCP-1. Patient breast cancers were retrospectively staged based on available patient data. p < 0.05 was used to determine significance and changes in hazard ratios between models were considered. RESULTS: Low MCP-1 expression in the primary tumour was associated with breast cancer-related death with distant relapse in ER- breast cancers (p < 0.01); however, this was likely a result of most low MCP-1-expressing ER- breast cancers being Stage III or Stage IV, with high MCP-1 expression in the primary tumour significantly correlated with Stage I breast cancers (p < 0.05). Expression of MCP-1 in the primary ER- tumours varied across Stage I, II, III and IV and we highlighted a switch in MCP-1 expression from high in Stage I ER- cancers to low in Stage IV ER- cancers. CONCLUSION: This study has emphasised a critical need for further investigation into MCP-1's role in breast cancer progression and improved characterisation of MCP-1 in breast cancers, particularly in light of the development of anti-MCP-1, anti-metastatic therapies.

Prognostic interaction between expression of p53 and estrogen receptor in patients with node-negative breast cancer: results from IBCSG Trials VIII and IX.

INTRODUCTION: The prognostic significance of p53 protein expression in early breast cancer remains uncertain, with some but not all studies finding an association with poorer outcomes. Estrogen receptor (ER) expression is both a positive prognostic marker and predictive of response to endocrine therapies. The relationship between these biomarkers is unknown. METHODS: We constructed tissue microarrays (TMAs) from available pathological material from 1113 patients participating in two randomized clinical trials comparing endocrine therapy alone versus chemo-endocrine therapy in node-negative breast cancer. Expression of p53 defined as >10% positive nuclei was analyzed together with prior immunohistochemical assays of ER performed at central pathological review of whole tumor sections. RESULTS: ER was present (i.e. >1% positive tumor cell nuclei) in 80.1% (880/1092). p53 expression was significantly more frequent when ER was absent, 125/212 (59%) than when ER was present, 171/880 (19%), p <0.0001. A significant qualitative interaction was observed such that p53 expression was associated with better disease-free survival (DFS) and overall survival (OS) among patients whose tumors did not express ER, but worse DFS and OS among patients whose tumors expressed ER. The interaction remained significant after allowance for pathologic variables, and treatment. Similar effects were seen when luminal and non-luminal intrinsic subtypes were compared. CONCLUSIONS: Interpretation of the prognostic significance of p53 expression requires knowledge of concurrent expression of ER. The reason for the interaction between p53 and ER is unknown but may reflect qualitatively different p53 mutations underlying the p53 expression in tumors with or without ER expression. TRIAL REGISTRATION: Current Controlled Trials ACTRN12607000037404 (Trial VIII) and ACTRN12607000029493 (Trial IX).

Fecal DNA Virome Is Associated with the Development of Colorectal Neoplasia in a Murine Model of Colorectal Cancer.

Alteration of the gut virome has been associated with colorectal cancer (CRC); however, when and how the alteration takes place has not been studied. Here, we employ a longitudinal study in mice to characterize the gut virome alteration in azoxymethane (AOM)-induced colorectal neoplasia and identify important viruses associated with tumor growth. The number and size of the tumors increased as the mice aged in the AOM treated group, as compared to the control group. Tumors were first observed in the AOM group at week 12. We observed a significantly lower alpha diversity and shift in viral profile when tumors first appeared. In addition, we identified novel viruses from the genera Brunovirus, Hpunavirus that are positively associated with tumor growth and enriched at a late time point in AOM group, whereas members from Lubbockvirus show a negative correlation with tumor growth. Moreover, network analysis revealed two clusters of viruses in the AOM virome, a group that is positively correlated with tumor growth and another that is negatively correlated with tumor growth, all of which are bacteriophages. Our findings suggest that the gut virome changes along with tumor formation and provides strong evidence of a potential role for bacteriophage in the development of colorectal neoplasia.

Survival prediction in triple negative breast cancer using multiple instance learning of histopathological images.

Computational pathology is a rapidly expanding area for research due to the current global transformation of histopathology through the adoption of digital workflows. Survival prediction of breast cancer patients is an important task that currently depends on histopathology assessment of cancer morphological features, immunohistochemical biomarker expression and patient clinical findings. To facilitate the manual process of survival risk prediction, we developed a computational pathology framework for survival prediction using digitally scanned haematoxylin and eosin-stained tissue microarray images of clinically aggressive triple negative breast cancer. Our results show that the model can produce an average concordance index of 0.616. Our model predictions are analysed for independent prognostic significance in univariate analysis (hazard ratio = 3.12, 95% confidence interval [1.69,5.75], p < 0.005) and multivariate analysis using clinicopathological data (hazard ratio = 2.68, 95% confidence interval [1.44,4.99], p < 0.005). Through qualitative analysis of heatmaps generated from our model, an expert pathologist is able to associate tissue features highlighted in the attention heatmaps of high-risk predictions with morphological features associated with more aggressive behaviour such as low levels of tumour infiltrating lymphocytes, stroma rich tissues and high-grade invasive carcinoma, providing explainability of our method for triple negative breast cancer.

ALTEN: A High-Fidelity Primary Tissue-Engineering Platform to Assess Cellular Responses Ex Vivo.

To fully investigate cellular responses to stimuli and perturbations within tissues, it is essential to replicate the complex molecular interactions within the local microenvironment of cellular niches. Here, the authors introduce Alginate-based tissue engineering (ALTEN), a biomimetic tissue platform that allows ex vivo analysis of explanted tissue biopsies. This method preserves the original characteristics of the source tissue's cellular milieu, allowing multiple and diverse cell types to be maintained over an extended period of time. As a result, ALTEN enables rapid and faithful characterization of perturbations across specific cell types within a tissue. Importantly, using single-cell genomics, this approach provides integrated cellular responses at the resolution of individual cells. ALTEN is a powerful tool for the analysis of cellular responses upon exposure to cytotoxic agents and immunomodulators. Additionally, ALTEN's scalability using automated microfluidic devices for tissue encapsulation and subsequent transport, to enable centralized high-throughput analysis of samples gathered by large-scale multicenter studies, is shown.