The molecular basis of breast cancer pathological phenotypes

Prediction of gene expression-based breast cancer proliferation scores from histopathology whole slide images using deep learning

BACKGROUND

In breast cancer, several gene expression assays have been developed to provide a more personalised treatment. This study focuses on the prediction of two molecular proliferation signatures: an 11-gene proliferation score and the MKI67 proliferation marker gene. The aim was to assess whether these could be predicted from digital whole slide images (WSIs) using deep learning models.

METHODS

WSIs and RNA-sequencing data from 819 invasive breast cancer patients were included for training, and models were evaluated on an internal test set of 172 cases as well as on 997 cases from a fully independent external test set. Two deep Convolutional Neural Network (CNN) models were optimised using WSIs and gene expression readouts from RNA-sequencing data of either the proliferation signature or the proliferation marker, and assessed using Spearman correlation (r). Prognostic performance was assessed through Cox proportional hazard modelling, estimating hazard ratios (HR).

RESULTS

Optimised CNNs successfully predicted the proliferation score and proliferation marker on the unseen internal test set (ρ = 0.691(p < 0.001) with R2 = 0.438, and ρ = 0.564 (p < 0.001) with R2 = 0.251 respectively) and on the external test set (ρ = 0.502 (p < 0.001) with R2 = 0.319, and ρ = 0.403 (p < 0.001) with R2 = 0.222 respectively). Patients with a high proliferation score or marker were significantly associated with a higher risk of recurrence or death in the external test set (HR = 1.65 (95% CI: 1.05-2.61) and HR = 1.84 (95% CI: 1.17-2.89), respectively).

CONCLUSIONS

The results from this study suggest that gene expression levels of proliferation scores can be predicted directly from breast cancer morphology in WSIs using CNNs and that the predictions provide prognostic information that could be used in research as well as in the clinical setting.

Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of molecular features. These approaches distill cancer histologic images into high-level features, which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network-HistoXGAN-capable of reconstructing representative histology using feature vectors produced by common feature extractors. We evaluate HistoXGAN across 29 cancer subtypes and demonstrate that reconstructed images retain information regarding tumor grade, histologic subtype, and gene expression patterns. We leverage HistoXGAN to illustrate the underlying histologic features for deep learning models for actionable mutations, identify model reliance on histologic batch effect in predictions, and demonstrate accurate reconstruction of tumor histology from radiographic imaging for a "virtual biopsy."

Tumor-Associated Antigen Burden Correlates with Immune Checkpoint Blockade Benefit in Tumors with Low Levels of T-cell Exhaustion

Tumor-associated antigens (TAA) are important targets for cancer vaccines. However, TAA-based vaccines have not yet achieved their full potential in clinical trials. In contrast, immune checkpoint blockade (ICB) has emerged as an effective therapy, leading to durable responses in selected patients with cancer. To date, few generalizable associations between TAAs and ICB benefit have been reported, with most studies focusing on melanoma, which has the highest mutation rate in cancer. In this study, we developed a TAA burden (TAB) algorithm based on known and putative TAAs and investigated the association of TAB with ICB benefit. Analysis of the IMvigor210 patient cohort of urothelial carcinoma treated with anti-PDL1 revealed that high tumor mutation burden weakened the association of TAB with ICB benefit. Furthermore, TAB correlated with ICB efficacy in tumors characterized by negative PDL1 staining on immune cells; however, high levels of PDL1 staining on immune cells were linked to T-cell exhaustion. Validation across independent clinical datasets-including urothelial carcinoma cohorts treated with anti-PD1/PDL1 agents and neoadjuvant anti-PD1 trials for head and neck cancers-corroborated the finding that TAB correlates with ICB benefit in tumors with low T-cell exhaustion. Pan-cancer analyses revealed that in most cancer entities, tumors with higher T-cell exhaustion exhibited lower TAB levels, implying possible immunoediting of TAAs in tumors with established antitumor immunity. Our study challenges the prevailing notion of a lack of association between TAAs and ICB response. It also underscores the need for future investigations into the immunogenicity of TAAs and TAA-based vaccine strategies in tumors with low levels of T-cell exhaustion.

Molecular Genetic Factors of Risk Stratification of Lymph Node Metastasis in Endometrial Carcinoma

BACKGROUND

According to epidemiological studies, endometrial carcinoma is one of the most frequently diagnosed malignancies of the female reproductive system, with an increasing incidence. Currently, the risk stratification of this neoplasm takes into account the stage, degree of tumor differentiation, histological type and depth of myometrial invasion. Since the publication of the last International Federation of Gynecology and Obstetrics (FIGO) staging system for endometrial cancer in 2009, numerous reports have appeared on the molecular characteristics of different types of endometrial carcinoma. Taking this into account, the FIGO Committee determined in 2023 that changes and updates to the staging system are justified to reflect new information about this tumor. Due to the high prevalence of the disease and mortality from endometrial cancer, an in-depth study of the molecular genetic characteristics of tumor cells is relevant; the results of such studies can be used to improve the efficiency of diagnosis, assess the risk of metastasis and prognosis of the disease. Lymph node assessment is crucial for the choice of treatment strategy for endometrial cancer, since metastatic lymph node involvement is one of the main factors affecting prognosis. At the same time, the criteria for the appropriateness of lymphadenectomy in low-differentiated malignant tumors are not clearly defined. Various molecular methods have been proposed to assess the status of lymph nodes; candidate genes are being studied as potential diagnostic biomarkers, as well as microRNA. The aim of the study was to analyze the literature data on numerous studies of molecular risk factors for progression in endometrioid carcinoma, as well as to preserve the most important marker changes in relation to the prognostic development of this disease.

METHODS

A literature review was conducted using data from the electronic databases PubMed, Google Scholar, and Wiley Cochrane Library for the period from 2018 to 2023 using the specific keywords.

RESULTS

The current scientific genetic studies on metastasis and prognostic factors in uterine cancer were analyzed, and a systematization of the reviewed data from the modern literature was done.

CONCLUSIONS

To select the most effective treatment - intraoperative, adjuvant or combination therapy, minimize postoperative risks of lymphadenectomy and clearly predict the results - further study of the molecular genetic features of endometrial cancer is necessary.

Potential biomarkers as a predictive factor of response to primary chemotherapy in breast cancer patients

In this study, we identified miRNAs and their potential mRNA targets that are intricately linked to primary chemotherapy response in patients with invasive ductal carcinomas. A cohort of individuals diagnosed with advanced invasive breast ductal carcinoma who underwent primary chemotherapy served as the cornerstone of our study. We conducted a comparative analysis of microRNA expression among patients who either responded or did not respond to primary systemic therapy. To analyze the correlation between the expression of the whole transcriptome and the 24 differentially expressed (DE) miRNAs, we harnessed the extensive repository of The Cancer Genome Atlas (TCGA) database. We mapped molecular mechanisms associated with these miRNAs and their targets from TCGA breast carcinomas. The resultant expression profile of the 24 DE miRNAs emerged as a potent and promising predictive model, offering insights into the intricate dynamics of chemotherapy responsiveness of advanced breast tumors. The discriminative analysis based on the principal component analysis identified the most representative miRNAs across breast cancer samples (miR-210, miR-197, miR-328, miR-519a, and miR-628). Moreover, the consensus clustering generated four possible clusters of TCGA patients. Further studies should be conducted to advance these findings.

Multistate Gene Cluster Switches Determine the Adaptive Mitochondrial and Metabolic Landscape of Breast Cancer

Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution. Metabolic adaptations represent important therapeutic targets in tumors, highlighting the need to characterize the full spectrum, characteristics, and regulation of the metabolic switches. To investigate the hypothesis that metabolic switches associated with specific metabolic states can be recognized by locating large alternating gene expression patterns, we developed a method to identify interspersed gene sets by massive correlated biclustering and to predict their metabolic wiring. Testing the method on breast cancer transcriptome datasets revealed a series of gene sets with switch-like behavior that could be used to predict mitochondrial content, metabolic activity, and central carbon flux in tumors. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labeled substrates. The metabolic switch positions also distinguished between cellular states, correlating with tumor pathology, prognosis, and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states. Significance: A method for identifying the transcriptomic signatures of metabolic switches underlying divergent routes of cellular transformation stratifies breast cancer into metabolic subtypes, predicting their biology, architecture, and clinical outcome.

The VEGF-Hypoxia Signature Is Upregulated in Basal-like Breast Tumors from Women of African Ancestry and Associated with Poor Outcomes in Breast Cancer

PURPOSE

Black women experience the highest breast cancer mortality rate compared with women of other racial/ethnic groups. To gain a deeper understanding of breast cancer heterogeneity across diverse populations, we examined a VEGF-hypoxia gene expression signature in breast tumors from women of diverse ancestry.

EXPERIMENTAL DESIGN

We developed a NanoString nCounter gene expression panel and applied it to breast tumors from Nigeria (n = 182) and the University of Chicago (Chicago, IL; n = 161). We also analyzed RNA sequencing data from Nigeria (n = 84) and The Cancer Genome Atlas (TCGA) datasets (n = 863). Patient prognosis was analyzed using multiple datasets.

RESULTS

The VEGF-hypoxia signature was highest in the basal-like subtype compared with other subtypes, with greater expression in Black women compared with White women. In TCGA dataset, necrotic breast tumors had higher scores for the VEGF-hypoxia signature compared with non-necrosis tumors (P < 0.001), with the highest proportion in the basal-like subtype. Furthermore, necrotic breast tumors have higher scores for the proliferation signature, suggesting an interaction between the VEGF-hypoxia signature, proliferation, and necrosis. T-cell gene expression signatures also correlated with the VEGF-hypoxia signature when testing all tumors in TCGA dataset. Finally, we found a significant association of the VEGF-hypoxia profile with poor outcomes when using all patients in the METABRIC (P < 0.0001) and SCAN-B datasets (P = 0.002).

CONCLUSIONS

These data provide further evidence for breast cancer heterogeneity across diverse populations and molecular subtypes. Interventions selectively targeting VEGF-hypoxia and the immune microenvironment have the potential to improve overall survival in aggressive breast cancers that disproportionately impact Black women in the African Diaspora.

Targeting systemic and gut microbial metabolism in ER+ breast cancer

Estrogen receptor-positive (ER+) breast tumors have a better overall prognosis than ER- tumors; however, there is a sustained risk of recurrence. Mounting evidence indicates that genetic and epigenetic changes associated with resistance impact critical signaling pathways governing cell metabolism. This review delves into recent literature concerning the metabolic pathways regulated in ER+ breast tumors by the availability of nutrients and endocrine therapies and summarizes research on how changes in systemic and gut microbial metabolism can affect ER activity and responsiveness to endocrine therapy. As targeting of metabolic pathways using dietary or pharmacological approaches enters the clinic, we provide an overview of the supporting literature and suggest future directions.

Treatment Response, Tumor Infiltrating Lymphocytes and Clinical Outcomes in Inflammatory Breast Cancer-Treated with Neoadjuvant Systemic Therapy

Inflammatory breast cancer (IBC) is a rare (1%-5%), aggressive form of breast cancer, accounting for approximately 10% of breast cancer mortality. In the localized setting, standard of care is neoadjuvant chemotherapy (NACT) ± anti-HER2 therapy, followed by surgery. Here we investigated associations between clinicopathologic variables, stromal tumor-infiltrating lymphocytes (sTIL), and pathologic complete response (pCR), and the prognostic value of pCR. We included 494 localized patients with IBC treated with NACT from October 1996 to October 2021 in eight European hospitals. Standard clinicopathologic variables were collected and central pathologic review was performed, including sTIL. Associations were assessed using Firth logistic regression models. Cox regressions were used to evaluate the role of pCR and residual cancer burden (RCB) on disease-free survival (DFS), distant recurrence-free survival (DRFS), and overall survival (OS). Distribution according to receptor status was as follows: 26.4% estrogen receptor negative (ER-)/HER2-; 22.0% ER-/HER2+; 37.4% ER+/HER2-, and 14.1% ER+/HER2+. Overall pCR rate was 26.3%, being highest in the HER2+ groups (45.9% for ER-/HER2+ and 42.9% for ER+/HER2+). sTILs were low (median: 5.3%), being highest in the ER-/HER2- group (median: 10%). High tumor grade, ER negativity, HER2 positivity, higher sTILs, and taxane-based NACT were significantly associated with pCR. pCR was associated with improved DFS, DRFS, and OS in multivariable analyses. RCB score in patients not achieving pCR was independently associated with survival. In conclusion, sTILs were low in IBC, but were predictive of pCR. Both pCR and RCB have an independent prognostic role in IBC treated with NACT.

SIGNIFICANCE

IBC is a rare, but very aggressive type of breast cancer. The prognostic role of pCR after systemic therapy and the predictive value of sTILs for pCR are well established in the general breast cancer population; however, only limited information is available in IBC. We assembled the largest retrospective IBC series so far and demonstrated that sTIL is predictive of pCR. We emphasize that reaching pCR remains of utmost importance in IBC.

Gene-Expression Profiling to Decipher Breast Cancer Inter- and Intratumor Heterogeneity

Breast cancer is heterogeneous and differs substantially across different tumors (intertumor heterogeneity) and even within an individual tumor (intratumor heterogeneity). Gene-expression profiling has considerably impacted our understanding of breast cancer biology. Four main "intrinsic subtypes" of breast cancer (i.e., luminal A, luminal B, HER2-enriched, and basal-like) have been consistently identified by gene expression, showing significant prognostic and predictive value in multiple clinical scenarios. Thanks to the molecular profiling of breast tumors, breast cancer is a paradigm of treatment personalization. Several standardized prognostic gene-expression assays are presently being used in the clinic to guide treatment decisions. Moreover, the development of single-cell-level resolution molecular profiling has allowed us to appreciate that breast cancer is also heterogeneous within a single tumor. There is an evident functional heterogeneity within the neoplastic and tumor microenvironment cells. Finally, emerging insights from these studies suggest a substantial cellular organization of neoplastic and tumor microenvironment cells, thus defining breast cancer ecosystems and highlighting the importance of spatial localizations.

Cross-linking breast tumor transcriptomic states and tissue histology

Identification of the gene expression state of a cancer patient from routine pathology imaging and characterization of its phenotypic effects have significant clinical and therapeutic implications. However, prediction of expression of individual genes from whole slide images (WSIs) is challenging due to co-dependent or correlated expression of multiple genes. Here, we use a purely data-driven approach to first identify groups of genes with co-dependent expression and then predict their status from WSIs using a bespoke graph neural network. These gene groups allow us to capture the gene expression state of a patient with a small number of binary variables that are biologically meaningful and carry histopathological insights for clinical and therapeutic use cases. Prediction of gene expression state based on these gene groups allows associating histological phenotypes (cellular composition, mitotic counts, grading, etc.) with underlying gene expression patterns and opens avenues for gaining biological insights from routine pathology imaging directly.

Luminal breast cancer identity is determined by loss of glucocorticoid receptor activity

Glucocorticoid receptor (GR) is a transcription factor that plays a crucial role in cancer biology. In this study, we utilized an in silico-designed GR activity signature to demonstrate that GR relates to the proliferative capacity of numerous primary cancer types. In breast cancer, the GR activity status determines luminal subtype identity and has implications for patient outcomes. We reveal that GR engages with estrogen receptor (ER), leading to redistribution of ER on the chromatin. Notably, GR activation leads to upregulation of the ZBTB16 gene, encoding for a transcriptional repressor, which controls growth in ER-positive breast cancer and associates with prognosis in luminal A patients. In relation to ZBTB16's repressive nature, GR activation leads to epigenetic remodeling and loss of histone acetylation at sites proximal to cancer-driving genes. Based on these findings, epigenetic inhibitors reduce viability of ER-positive breast cancer cells that display absence of GR activity. Our findings provide insights into how GR controls ER-positive breast cancer growth and may have implications for patients' prognostication and provide novel therapeutic candidates for breast cancer treatment.

A primary luminal/HER2 negative breast cancer patient with mismatch repair deficiency

Here, we present the case of a 47-year-old woman diagnosed with luminal B breast cancer subtype and provide an in-depth analysis of her gene mutations, chromosomal alterations, mRNA and protein expression changes. We found a point mutation in the FGFR2 gene, which is potentially hyper-activating the receptor function, along with over-expression of its ligand FGF20 due to genomic amplification. The patient also harbors somatic and germline mutations in some mismatch repair (MMR) genes, with a strong MMR mutational signature. The patient displays high microsatellite instability (MSI) and tumor mutational burden (TMB) status and increased levels of CTLA-4 and PD-1 expression. Altogether, these data strongly implicate that aberrant FGFR signaling, and defective MMR system might be involved in the development of this breast tumor. In addition, high MSI and TMB in the context of CTLA-4 and PD-L1 positivity, suggest the potential benefit of immune checkpoint inhibitors. Accurate characterization of molecular subtypes, based on gene mutational and expression profiling analyses, will be certainly helpful for individualized treatment and targeted therapy of breast cancer patients, especially for those subtypes with adverse outcome.

Recent Advances and Applications of Ambient Mass Spectrometry Imaging in Cancer Research: An Overview

Cancer metabolic variability has a significant impact on both diagnosis and treatment outcomes. The discovery of novel biological indicators and metabolic dysregulation, can significantly rely on comprehension of the modified metabolism in cancer, is a research focus. Tissue histology is a critical feature in the diagnostic testing of many ailments, such as cancer. To assess the surgical margin of the tumour on patients, frozen section histology is a tedious, laborious, and typically arbitrary method. Concurrent monitoring of ion images in tissues facilitated by the latest advancements in mass spectrometry imaging (MSI) is far more efficient than optical tissue image analysis utilized in conventional histopathology examination. This article focuses on the "desorption electrospray ionization (DESI)-MSI" technique's most recent advancements and uses in cancer research. DESI-MSI can provide wealthy information based on the variances in metabolites and lipids in normal and cancerous tissues by acquiring ion images of the lipid and metabolite variances on biopsy samples. As opposed to a systematic review, this article offers a synopsis of the most widely employed cutting-edge DESI-MSI techniques in cancer research.

Haematological and biochemical analysis of blood samples from early and late stage breast cancer patients in India

Breast cancer is the most prevalent cancer with the maximum number of cases worldwide. Early diagnosis of the cancer is necessary for an effective treatment plan. Due to a lack of awareness, diagnosis of breast cancer at an early stage is difficult. The present study aims to evaluate and compare the haematological and biochemical profiles of the early and late-stage breast cancer patient's data records. A retrospective cohort study was conducted on 56 breast cancer patients at the Institute of Medical Sciences, Banaras Hindu University India. Patient data records were obtained and haematological and biochemical parameters were arranged on an Excel sheet and analyzed. Random blood sugar (RBS), alkaline phosphates (ALP) levels, and urea levels were significantly high in patients with late-stage breast cancer (Tumor stage III and IV). At the advanced stage of breast cancer hemoglobin level falls and patients became anemic. Further large-scale studies with a greater number of patient data can help establish these parameters individually or in combination as prognostic and diagnostic markers in breast cancer staging.

Stratification of Tamoxifen Synergistic Combinations for the Treatment of ER+ Breast Cancer

Breast cancer alone accounts for the majority of cancer deaths among women, with the most commonly diagnosed subtype being estrogen receptor positive (ER+). Survival has greatly improved for patients with ER+ breast cancer, due in part to the development of antiestrogen compounds, such as tamoxifen. While treatment of the primary disease is often successful, as many as 30% of patients will experience recurrence and metastasis, mainly due to developed endocrine therapy resistance. In this study, we discovered two tamoxifen combination therapies, with simeprevir and VX-680, that reduce the tumor burden in animal models of ER+ breast cancer more than either compound or tamoxifen alone. Additionally, these tamoxifen combinations reduced the expression of HER2, a hallmark of tamoxifen treatment, which can facilitate acquisition of a treatment-resistant phenotype. These combinations could provide clinical benefit by potentiating tamoxifen treatment in ER+ breast cancer.

Familial CCM Genes Might Not Be Main Drivers for Pathogenesis of Sporadic CCMs-Genetic Similarity between Cancers and Vascular Malformations

Cerebral cavernous malformations (CCMs) are abnormally dilated intracranial capillaries that form cerebrovascular lesions with a high risk of hemorrhagic stroke. Recently, several somatic "activating" gain-of-function (GOF) point mutations in PIK3CA (phosphatidylinositol-4, 5-bisphosphate 3-kinase catalytic subunit p110α) were discovered as a dominant mutation in the lesions of sporadic forms of cerebral cavernous malformation (sCCM), raising the possibility that CCMs, like other types of vascular malformations, fall in the PIK3CA-related overgrowth spectrum (PROS). However, this possibility has been challenged with different interpretations. In this review, we will continue our efforts to expound the phenomenon of the coexistence of gain-of-function (GOF) point mutations in the PIK3CA gene and loss-of-function (LOF) mutations in CCM genes in the CCM lesions of sCCM and try to delineate the relationship between mutagenic events with CCM lesions in a temporospatial manner. Since GOF PIK3CA point mutations have been well studied in reproductive cancers, especially breast cancer as a driver oncogene, we will perform a comparative meta-analysis for GOF PIK3CA point mutations in an attempt to demonstrate the genetic similarities shared by both cancers and vascular anomalies.

Lack of Association of C677T Methylenetetrahydrofolate Reductase Polymorphism with Breast Cancer Risk in Mali

Methylenetetrahydrofolate reductase (MTHFR) plays a major role in the metabolism of folates and homocysteine, which in turn can affect gene expression and ultimately promote the development of breast cancer. Thus, mutations in the MTHFR gene could influence homocysteine, methionine, and S-adenosylmethionine levels and, indirectly, nucleotide levels. Imbalance in methionine and S-adenosylmethionine synthesis affects protein synthesis and methylation. These changes, which affect gene expression, may ultimately promote the development of breast cancer. We therefore hypothesized that such mutations could also play an important role in the occurrence and pathogenesis of breast cancer in a Malian population. In this study, we used the PCR-RFLP technique to identify the different genotypic profiles of the C677T MTHFR polymorphism in 127 breast cancer women and 160 healthy controls. The genotypic distribution of the C677T polymorphism in breast cancer cases was 88.2% for CC, 11.0% for CT, and 0.8% for TT. Healthy controls showed a similar distribution with 90.6% for CC, 8.8% for CT, and 0.6% for TT. We found no statistical association between the C677T polymorphism and breast cancer risk for the codominant models CT and TT (p > 0.05). The same trend was observed when the analysis was extended to other genetic models, including dominant (p = 0.50), recessive (p = 0.87), and additive (p = 0.50) models. The C677T polymorphism of MTHFR gene did not influence the risk of breast cancer in the Malian samples.

Tumor-Infiltrating Lymphocytes (TIL), Tertiary Lymphoid Structures (TLS), and Expression of PD-1, TIM-3, LAG-3 on TIL in Invasive and In Situ Ductal Breast Carcinomas and Their Relationship with Prognostic Factors

INTRODUCTION

Immunotherapy has been determined as an important choice in breast carcinomas, especially in tumors with markedly inflammatory response. About this promising subject, tumor-infiltrating lymphocytes (TIL) and the expression of immune control point receptors on TIL have gained importance.

MATERIALS AND METHODS

In this study, stromal TIL and tertiary lymphoid structures (TLS) were determined in tumor tissues of 312 invasive and 68 in situ breast cancer patients. Expression rates of PD-1, LAG-3, and TIM-3 on intratumoral and stromal TIL were immunohistochemically evaluated.

RESULTS

In invasive breast carcinomas, stromal TIL was found to be significantly associated with lymph node metastasis, HR and HER2 expression, and basal-like phenotype, as the presence of TLS with neoadjuvant therapy, recurrence, death, and expression of HR and HER2. PD-1, LAG-3, and TIM-3 expressions were found to be associated with HR and HER2 status, stromal TIL rates, and TLS. In multivariate analysis, high stromal TIL and PD-1 expression in intratumoral TIL were found to be independent prognostic factors in terms of overall survival and disease-free survival.

CONCLUSION

Evaluation of TIL and immune control point receptor expressions in breast cancer is particularly important in terms of planning the therapeutic approaches based on immunotherapy protocols.

Loss of PTEN Expression, PIK3CA Mutations, and Breast Cancer Survival in the Nurses' Health Studies

BACKGROUND

The relationships between PTEN loss and/or PIK3CA mutation and breast cancer prognosis remain controversial. We aim to examine the associations in large epidemiologic cohorts.

METHODS

We followed women with invasive breast cancer from the Nurses' Health Studies with available data on tumor PTEN expression (n = 4,111) and PIK3CA mutation (n = 2,930). PTEN expression was evaluated by IHC and digitally scored (0%-100%). Pyrosequencing of six hotspot mutations of PIK3CA was performed.

RESULTS

We found loss of PTEN expression (≤10%) occurred in 17% of cases, and PIK3CA mutations were detected in 11% of cases. After adjusting for clinical and lifestyle factors, PTEN loss was not associated with worse breast cancer-specific mortality among all samples [HR, 0.85; 95% confidence intervals (CI), 0.71-1.03] or among estrogen receptor (ER)-positive tumors (HR, 0.99; 95% CI, 0.79-1.24). However, among ER-negative tumors, PTEN loss was associated with lower breast cancer-specific mortality (HR, 0.68; 95% CI, 0.48-0.95). PIK3CA mutation was not strongly associated with breast cancer-specific mortality (HR, 0.89; 95% CI, 0.67-1.17). Compared with tumors without PTEN loss and without PIK3CA mutation, those with alterations (n = 540) were not at higher risk (HR, 1.07; 95% CI, 0.86-1.34). However, women with both PTEN loss and PIK3CA mutation (n = 38) were at an increased risk of breast cancer-specific mortality (HR, 1.65; 95% CI, 0.83-3.26).

CONCLUSIONS

In this large epidemiologic study, the PTEN-mortality association was more pronounced for ER-negative tumors, and the joint PTEN loss and PIK3CA mutation may be associated with worse prognosis.

IMPACT

Further studies with a larger sample of ER-negative tumors are needed to replicate our findings and elucidate underlying mechanisms.

Local data commons: the sleeping beauty in the community of data commons

Background

Public Data Commons (PDC) have been highlighted in the scientific literature for their capacity to collect and harmonize big data. On the other hand, local data commons (LDC), located within an institution or organization, have been underrepresented in the scientific literature, even though they are a critical part of research infrastructure. Being closest to the sources of data, LDCs provide the ability to collect and maintain the most up-to-date, high-quality data within an organization, closest to the sources of the data. As a data provider, LDCs have many challenges in both collecting and standardizing data, moreover, as a consumer of PDC, they face problems of data harmonization stemming from the monolithic harmonization pipeline designs commonly adapted by many PDCs. Unfortunately, existing guidelines and resources for building and maintaining data commons exclusively focus on PDC and provide very little information on LDC.

Results

This article focuses on four important observations. First, there are three different types of LDC service models that are defined based on their roles and requirements. These can be used as guidelines for building new LDC or enhancing the services of existing LDC. Second, the seven core services of LDC are discussed, including cohort identification and facilitation of genomic sequencing, the management of molecular reports and associated infrastructure, quality control, data harmonization, data integration, data sharing, and data access control. Third, instead of commonly developed monolithic systems, we propose a new data sharing method for data harmonization that combines both divide-and-conquer and bottom-up approaches. Finally, an end-to-end LDC implementation is introduced with real-world examples.

Conclusions

Although LDCs are an optimal place to identify and address data quality issues, they have traditionally been relegated to the role of passive data provider for much larger PDC. Indeed, many LDCs limit their functions to only conducting routine data storage and transmission tasks due to a lack of information on how to design, develop, and improve their services using limited resources. We hope that this work will be the first small step in raising awareness among the LDCs of their expanded utility and to publicize to a wider audience the importance of LDC.

Understanding Breast Cancers through Spatial and High-Resolution Visualization Using Imaging Technologies

Breast cancer is the most common cancer affecting women worldwide. Although many analyses and treatments have traditionally targeted the breast cancer cells themselves, recent studies have focused on investigating entire cancer tissues, including breast cancer cells. To understand the structure of breast cancer tissues, including breast cancer cells, it is necessary to investigate the three-dimensional location of the cells and/or proteins comprising the tissues and to clarify the relationship between the three-dimensional structure and malignant transformation or metastasis of breast cancers. In this review, we aim to summarize the methods for analyzing the three-dimensional structure of breast cancer tissue, paying particular attention to the recent technological advances in the combination of the tissue-clearing method and optical three-dimensional imaging. We also aimed to identify the latest methods for exploring the relationship between the three-dimensional cell arrangement in breast cancer tissues and the gene expression of each cell. Finally, we aimed to describe the three-dimensional imaging features of breast cancer tissues using noninvasive photoacoustic imaging methods.

Comparative analysis of the molecular subtype landscape in canine and human mammary gland tumors

Breast cancers in humans belong to one of several intrinsic molecular subtypes each with different tumor biology and different clinical impact. Mammary gland tumors in dogs are proposed as a relevant comparative model for human breast cancer; however, it is still unclear whether the intrinsic molecular subtypes have the same significance in dogs and humans. Using publicly available data, we analyzed gene expression and whole-exome sequencing data from 158 canine mammary gland tumors. We performed molecular subtyping using the PAM50 method followed by subtype-specific comparisons of gene expression characteristics, mutation patterns and copy number profiles between canine tumors and human breast tumors from The Cancer Genome Atlas (TCGA) breast cancer cohort (n = 1097). We found that luminal A canine tumors greatly resemble luminal A human tumors both in gene expression characteristics, mutations and copy number profiles. Also, the basal-like canine and human tumors were relatively similar, with low expression of luminal epithelial markers and high expression of genes involved in cell proliferation. There were, however, distinct differences in immune-related gene expression patterns in basal-like tumors between the two species. Characteristic HER2-enriched and luminal B subtypes were not present in the canine cohort, and we found no tumors with high-level ERBB2 amplifications. Benign and malignant canine tumors displayed similar PAM50 subtype characteristics. Our findings indicate that deeper understanding of the different molecular subtypes in canine mammary gland tumors will further improve the value of canines as comparative models for human breast cancer.

Feature Generalization for Breast Cancer Detection in Histopathological Images

Recent period has witnessed benchmarked performance of transfer learning using deep architectures in computer-aided diagnosis (CAD) of breast cancer. In this perspective, the pre-trained neural network needs to be fine-tuned with relevant data to extract useful features from the dataset. However, in addition to the computational overhead, it suffers the curse of overfitting in case of feature extraction from smaller datasets. Handcrafted feature extraction techniques as well as feature extraction using pre-trained deep networks come into rescue in aforementioned situation and have proved to be much more efficient and lightweight compared to deep architecture-based transfer learning techniques. This research has identified the competence of classifying breast cancer images using feature engineering and representation learning over the established and contemporary notion of using transfer learning techniques. Moreover, it has revealed superior feature learning capacity with feature fusion in contrast to the conventional belief of understanding unknown feature patterns better with representation learning alone. Experiments have been conducted on two different and popular breast cancer image datasets, namely, KIMIA Path960 and BreakHis datasets. A comparison of image-level accuracy is performed on these datasets using the above-mentioned feature extraction techniques. Image level accuracy of 97.81% is achieved for KIMIA Path960 dataset using individual features extracted with handcrafted (color histogram) technique. Fusion of uniform Local Binary Pattern (uLBP) and color histogram features has resulted in 99.17% of highest accuracy for the same dataset. Experimentation with BreakHis dataset has resulted in highest classification accuracy of 88.41% with color histogram features for images with 200X magnification factor. Finally, the results are contrasted to that of state-of-the-art and superior performances are observed on many occasions with the proposed fusion-based techniques. In case of BreakHis dataset, the highest accuracies 87.60% (with least standard deviation) and 85.77% are recorded for 200X and 400X magnification factors, respectively, and the results for the aforesaid magnification factors of images have exceeded the state-of-the-art.

Molecular phenotypes and clinical characterization of familial hereditary breast cancer among half and full sisters

Background

Preliminary clinical observations show that contemporaneous hereditary breast cancer (CHBC) patients suffered breast cancer at an early age, which requires further analysis.

Methods

38 familial hereditary breast cancer patients (18 CHBC patients and 20 non-CHBC patients) were screened out and 152 non-hereditary breast cancer patients were used as control subjects. Clinical pathologic subtypes, age, tumor location, histological grade, lymph node metastasis, and molecular phenotype expression (ER, PR, HER-2, Ki-67, CK5/6, E-cad, P63, and P120) were compared across all subgroups.

Results

The incidence of CHBC was 9.47% (18/190) in breast cancer patients. The average ages of onset of CHBC patients, non-CHBC patients, and non-hereditary breast cancer patients were 49.06 ± 6.42, 60.75 ± 9.95 and 61.69 ± 14.34 respectively; whereas there were no significant differences with respect to pathological type or tumor location. There were significant differences in some histological grading (grade II/III), lymph node metastasis and PR expression between hereditary and non-hereditary breast cancers (P < 0.05; P < 0.05 and P < 0.005, respectively). Significantly different HER-2 expression was observed when comparing all hereditary or CHBC patients with non-hereditary breast cancers (P < 0.05 and P < 0.005, respectively). There were significant differences in E-cad and P63 between contemporaneous hereditary and non-hereditary breast cancers (P < 0.005 and P < 0.05, respectively).

Conclusions

CHBC patients accounted for 9.47% (18/190) of breast cancer patients, had earlier disease onset, and showed differences compared to non-hereditary breast cancer patients with respect to molecular phenotype and clinical characteristics.

Genomic Correlates of Outcome in Tumor-Infiltrating Lymphocyte Therapy for Metastatic Melanoma

PURPOSE

Adoptive cell therapy (ACT) of tumor-infiltrating lymphocytes (TIL) historically yields a 40%-50% response rate in metastatic melanoma. However, the determinants of outcome are largely unknown.

EXPERIMENTAL DESIGN

We investigated tumor-based genomic correlates of overall survival (OS), progression-free survival (PFS), and response to therapy by interrogating tumor samples initially collected to generate TIL infusion products.

RESULTS

Whole-exome sequencing (WES) data from 64 samples indicated a positive correlation between neoantigen load and OS, but not PFS or response to therapy. RNA sequencing analysis of 34 samples showed that expression of PDE1C, RTKN2, and NGFR was enriched in responders who had improved PFS and OS. In contrast, the expression of ELFN1 was enriched in patients with unfavorable response, poor PFS and OS, whereas enhanced methylation of ELFN1 was observed in patients with favorable outcomes. Expression of ELFN1, NGFR, and PDE1C was mainly found in cancer-associated fibroblasts and endothelial cells in tumor tissues across different cancer types in publicly available single-cell RNA sequencing datasets, suggesting a role for elements of the tumor microenvironment in defining the outcome of TIL therapy.

CONCLUSIONS

Our findings suggest that transcriptional features of melanomas correlate with outcomes after TIL therapy and may provide candidates to guide patient selection.

Spatial Characterization of Tumor-Infiltrating Lymphocytes and Breast Cancer Progression

Tumor-infiltrating lymphocytes (TILs) have been established as a robust prognostic biomarker in breast cancer, with emerging utility in predicting treatment response in the adjuvant and neoadjuvant settings. In this study, the role of TILs in predicting overall survival and progression-free interval was evaluated in two independent cohorts of breast cancer from the Cancer Genome Atlas (TCGA BRCA) and the Carolina Breast Cancer Study (UNC CBCS). We utilized machine learning and computer vision algorithms to characterize TIL infiltrates in digital whole-slide images (WSIs) of breast cancer stained with hematoxylin and eosin (H&E). Multiple parameters were used to characterize the global abundance and spatial features of TIL infiltrates. Univariate and multivariate analyses show that large aggregates of peritumoral and intratumoral TILs (forests) were associated with longer survival, whereas the absence of intratumoral TILs (deserts) is associated with increased risk of recurrence. Patients with two or more high-risk spatial features were associated with significantly shorter progression-free interval (PFI). This study demonstrates the practical utility of Pathomics in evaluating the clinical significance of the abundance and spatial patterns of distribution of TIL infiltrates as important biomarkers in breast cancer.

Molecular analysis of TCGA breast cancer histologic types

Breast cancer is classified into multiple distinct histologic types, and many of the rarer types have limited characterization. Here, we extend The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset with additional histologic type annotations, in a total of 1063 breast cancers. We analyze this extended dataset to define transcriptomic and genomic profiles of six rare special histologic types: cribriform, micropapillary, mucinous, papillary, metaplastic, and invasive carcinoma with medullary pattern. We show the broader applicability of our constructed special histologic type gene signatures in the TCGA Pan-Cancer Atlas dataset with a predictive model that detects mucinous histologic type across cancers of other organ systems. Using a normal mammary cell differentiation score analysis, we order histologic types into a continuum from stem cell-like to luminal progenitor-like to mature luminal-like. Finally, we classify TCGA-BRCA into 12 consensus groups based on integrated genomic and histological features. We present a rich openly accessible resource of histologic and genomic characterization of TCGA-BRCA to enable studies of the range of breast cancers.

JOINT AND INDIVIDUAL ANALYSIS OF BREAST CANCER HISTOLOGIC IMAGES AND GENOMIC COVARIATES

The two main approaches in the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genomics. While both histopathology and genomics are fundamental to cancer research, the connections between these fields have been relatively superficial. We bridge this gap by investigating the Carolina Breast Cancer Study through the development of an integrative, exploratory analysis framework. Our analysis gives insights - some known, some novel - that are engaging to both pathologists and geneticists. Our analysis framework is based on Angle-based Joint and Individual Variation Explained (AJIVE) for statistical data integration and exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction. CNNs raise interpretability issues that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features.

Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression-Morphology Analysis in Breast Cancer

Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor average profiles. Morphologic patterns observable in histopathology sections from tumors are determined by the underlying molecular phenotype and therefore have the potential to be exploited for prediction of molecular phenotypes. We report here the first transcriptome-wide expression-morphology (EMO) analysis in breast cancer, where individual deep convolutional neural networks were optimized and validated for prediction of mRNA expression in 17,695 genes from hematoxylin and eosin-stained whole slide images. Predicted expressions in 9,334 (52.75%) genes were significantly associated with RNA sequencing estimates. We also demonstrated successful prediction of an mRNA-based proliferation score with established clinical value. The results were validated in independent internal and external test datasets. Predicted spatial intratumor variabilities in expression were validated through spatial transcriptomics profiling. These results suggest that EMO provides a cost-efficient and scalable approach to predict both tumor average and intratumor spatial expression from histopathology images. SIGNIFICANCE: Transcriptome-wide expression morphology deep learning analysis enables prediction of mRNA expression and proliferation markers from routine histopathology whole slide images in breast cancer.

Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology

Recent advances in computational and algorithmic power are evolving the field of medical imaging rapidly. In cancer research, many new directions are sought to characterize patients with additional imaging features derived from radiology and pathology images. The emerging field of Computational Pathology targets the high-throughput extraction and analysis of the spatial distribution of cells from digital histopathology images. The associated morphological and architectural features allow researchers to quantify and characterize new imaging biomarkers for cancer diagnosis, prognosis, and treatment decisions. However, while the image feature space grows, exploration and analysis become more difficult and ineffective. There is a need for dedicated interfaces for interactive data manipulation and visual analysis of computational pathology and clinical data. For this purpose, we present IIComPath, a visual analytics approach that enables clinical researchers to formulate hypotheses and create computational pathology pipelines involving cohort construction, spatial analysis of image-derived features, and cohort analysis. We demonstrate our approach through use cases that investigate the prognostic value of current diagnostic features and new computational pathology biomarkers.

Autocrine and paracrine purinergic signaling in the most lethal types of cancer

Cancer comprises a collection of diseases that occur in almost any tissue and it is characterized by an abnormal and uncontrolled cell growth that results in tumor formation and propagation to other tissues, causing tissue and organ malfunction and death. Despite the undeniable improvement in cancer diagnostics and therapy, there is an urgent need for new therapeutic and preventive strategies with improved efficacy and fewer side effects. In this context, purinergic signaling emerges as an interesting candidate as a cancer biomarker or therapeutic target. There is abundant evidence that tumor cells have significant changes in the expression of purinergic receptors, which comprise the G-protein coupled P2Y and AdoR families of receptors and the ligand-gated ion channel P2X receptors. Tumor cells also exhibit changes in the expression of nucleotidases and other enzymes involved in nucleotide metabolism, and the concentrations of extracellular nucleotides are significantly higher than those observed in normal cells. In this review, we will focus on the potential role of purinergic signaling in the ten most lethal cancers (lung, breast, colorectal, liver, stomach, prostate, cervical, esophagus, pancreas, and ovary), which together are responsible for more than 5 million annual deaths.

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein-protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.

Integrated multi-omics profiling of high-grade estrogen receptor-positive, HER2-negative breast cancer

Estrogen receptor‐positive and human epidermal growth factor receptor 2‐negative (ER⁺HER2⁻) breast cancer accounts for ~ 60–70% of all cases of invasive breast carcinoma. High‐grade ER⁺HER2⁻ tumors respond poorly to endocrine therapy. In this study, we systematically analyzed clinical and multi‐omics data to find potential strategies for personalized therapy of patients with high‐grade ER⁺HER2⁻ disease. Six different cohorts were analyzed, for which multi‐omics data were available. Grade III ER⁺HER2⁻ cases harbored higher proportions of large tumor size (> 5 cm), lymph node metastasis, chemotherapy use, and luminal B subtypes defined by PAM50, as compared with grade I/II tumors. DNA methylation (HM450) data and methylation‐specific PCR indicated that the cg18629132 locus in the MKI67 promoter was hypermethylated in grade I/II cases and normal tissue, but hypomethylated in grade III cases or triple‐negative breast cancer, resulting in higher expression of MKI67. Mutations in ESR1 and TP53 were detected in post‐endocrine treatment metastatic samples at a higher rate than in treatment‐naive tumors in grade III cases. We identified 42 and 20 focal copy number events in nonmetastatic and metastatic high‐grade ER⁺HER2⁻ cases, respectively, with either MYC or MDM2 amplification representing an independent prognostic event in grade III cases. Transcriptional profiling within grade III tumors highlighted ER signaling downregulation and upregulation of immune‐related pathways in non‐luminal‐like tumors defined by PAM50. Recursive partitioning analysis was employed to construct a decision tree of an endocrine‐resistant subgroup (GATA3‐negative and AGR‐negative) of two genes that was validated by immunohistochemistry in a Chinese cohort. All together, these data suggest that grade III ER⁺HER2⁻ tumors have distinct clinical and molecular characteristics compared with low‐grade tumors, particularly in cases with non‐luminal‐like biology. Due to the dismal prognosis in this group, clinical trials are warranted to test the efficacy of potential novel therapies.

Artificial intelligence grading of breast cancer: a promising method to refine prognostic classification for management precision

AIM

Artificial intelligence (AI)-based breast cancer grading may help to overcome perceived limitations of human assessment. Here, the potential value of AI grade was evaluated at the molecular level and in predicting patient outcome.

METHODS AND RESULTS

A supervised convolutional neural network (CNN) model was trained on images of 612 breast cancers from The Cancer Genome Atlas (TCGA). The test set, obtained from the Cooperative Human Tissue Network (CHTN), comprised 1058 cancers with corresponding survival data. Upon reversal, a CNN was trained from images of 1537 CHTN cancers and tested on 397 TCGA cancers. In TCGA, mRNA models were trained using AI grade and Nottingham grade (NG) as labels. Performance of mRNA models in predicting patient outcome was evaluated using data from 1807 cancers from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. In selecting images for training, nucleolar prominence determined high- versus low-grade cancer cells. In CHTN, NG corresponded to significant survival stratification in stages 1, 2 and 3 cancers, while AI grade showed significance in stages 1 and 2 and borderline in stage 3 tumours. In METABRIC, the mRNA model trained from AI grade was not significantly different to the NG-based model. The gene which best described AI grade was TRIP13, a gene involved with mitotic spindle assembly.

CONCLUSION

An AI grade trained from the morphologically distinctive feature of nucleolar prominence could transmit significant patient outcome information across three independent patient cohorts. AI grade shows promise in gene discovery and for second opinions.

So alike yet so different. Differential expression of the long non-coding RNAs NORAD and HCG11 in breast cancer subtypes

Breast cancer (BC) is a heterogeneous disease, and it is the leading cause of death among women. NORAD and HCG11 are highly similar lncRNAs that present binding sites for PUMILIO proteins. PUMILIO acts on hundreds of mRNA targets, contributing to the modulation of gene expression. We analyzed the expression levels of NORAD and HCG11 in the BC subtypes luminal A (LA) and basal-like (BL), and the regulatory networks associated with these lncRNAs. In the analysis of TCGA cohort (n=329) and Brazilian BC samples (n=44), NORAD was up-regulated in LA while HCG11 was up-regulated in BL subtype. An increased expression of NORAD is associated with reduced disease-free survival in basal-like patients (p = 0.002), which suggests that its prognostic value could be different in specific subtypes. The biological pathways observed for the HCG11 network are linked to the epithelial-to-mesenchymal transition; while NORAD associated pathways appear to be related to luminal epithelial cell transformation. NORAD and HCG11 regulons respectively present 36% and 21.5% of PUMILIO targets, which suggests that these lncRNAs act as a decoy for PUMILIO. These lncRNAs seem to work as players in the differentiation process that drives breast cells to acquire distinct phenotypes related to a specific BC subtype.

Cluster analyses of the TCGA and a TMA dataset using the coexpression of HSP27 and CRYAB improves alignment with clinical-pathological parameters of breast cancer and suggests different epichaperome influences for each sHSP

Our cluster analysis of the Cancer Genome Atlas for co-expression of HSP27 and CRYAB in breast cancer patients identified three patient groups based on their expression level combination (high HSP27 + low CRYAB; low HSP27 + high CRYAB; similar HSP27 + CRYAB). Our analyses also suggest that there is a statistically significant inverse relationship between HSP27 and CRYAB and known clinicopathological markers in breast cancer. Screening an unbiased 248 breast cancer patient tissue microarray (TMA) for the protein expression of HSP27 and phosphorylated HSP27 (HSP27-82pS) with CRYAB also identified three patient groups based on HSP27 and CRYAB expression levels. TMA24 also had recorded clinical-pathological parameters, such as ER and PR receptor status, patient survival, and TP53 mutation status. High HSP27 protein levels were significant with ER and PR expression. HSP27-82pS associated with the best patient survival (Log Rank test). High CRYAB expression in combination with wild-type TP53 was significant for patient survival, but a different patient outcome was observed when mutant TP53 was combined with high CRYAB expression. Our data suggest that HSP27 and CRYAB have different epichaperome influences in breast cancer, but more importantly evidence the value of a cluster analysis that considers their coexpression. Our approach can deliver convergence for archival datasets as well as those from recent treatment and patient cohorts and can align HSP27 and CRYAB expression to important clinical-pathological features of breast cancer.

Deep Consensus Network: Aggregating predictions to improve object detection in microscopy images

Detection of cells and particles in microscopy images is a common and challenging task. In recent years, detection approaches in computer vision achieved remarkable improvements by leveraging deep learning. Microscopy images pose challenges like small and clustered objects, low signal to noise, and complex shape and appearance, for which current approaches still struggle. We introduce Deep Consensus Network, a new deep neural network for object detection in microscopy images based on object centroids. Our network is trainable end-to-end and comprises a Feature Pyramid Network-based feature extractor, a Centroid Proposal Network, and a layer for ensembling detection hypotheses over all image scales and anchors. We suggest an anchor regularization scheme that favours prior anchors over regressed locations. We also propose a novel loss function based on Normalized Mutual Information to cope with strong class imbalance, which we derive within a Bayesian framework. In addition, we introduce an improved algorithm for Non-Maximum Suppression which significantly reduces the algorithmic complexity. Experiments on synthetic data are performed to provide insights into the properties of the proposed loss function and its robustness. We also applied our method to challenging data from the TUPAC16 mitosis detection challenge and the Particle Tracking Challenge, and achieved results competitive or better than state-of-the-art.

SNHG3 promotes migration, invasion, and epithelial-mesenchymal transition of breast cancer cells through the miR-186-5p/ZEB1 axis

Increasing evidence suggests that the long non-coding RNAs (lncRNAs) participate in the development and progression of breast cancer. The lncRNA small nucleolar RNA host gene 3 (SNHG3) reportedly acts as an oncogene in hepatocellular carcinoma and colorectal cancer; however, little is known about the biological function and oncogenic mechanisms of SNHG3 in breast cancer. We demonstrated that the expression of SNHG3 was abnormally high in breast cancer tissues and cells, and transgenic expression of SNHG3 promoted the proliferation, migration, and invasion of breast cancer cell lines (MCF-7 and MDA-MB-231). The mean volume of the xenografts from the SNHG3-knockdown MCF-7 cells was lower than that of the control tumor cells. Moreover, the expression of zinc finger E-box binding homeobox 1 (ZEB1) increased after SNHG3 overexpression and vice versa. Overexpression of ZEB1 triggered cellular migration and invasion behaviors. Analysis of the mechanism underlying these effects suggested that SNHG3 is an effective sink for miR-186-5p and modulates ZEB1 repression, conferring an additional level to its post-transcriptional regulation. In conclusion, SNHG3 promotes the migration and invasion of breast cancer cells through miR-186-5p/ZEB1 regulation and the induction of the epithelial to mesenchymal transition, indicating that SNHG3 is a potential treatment target for breast cancer.

Inherited predisposition to breast cancer in the Carolina Breast Cancer Study

The Carolina Breast Cancer Study (CBCS) phases I-II was a case-control study of biological and social risk factors for invasive breast cancer that enrolled cases and controls between 1993 and 1999. Case selection was population-based and stratified by ancestry and age at diagnosis. Controls were matched to cases by age, self-identified race, and neighborhood of residence. Sequencing genomic DNA from 1370 cases and 1635 controls yielded odds ratios (with 95% confidence limits) for breast cancer of all subtypes of 26.7 (3.59, 189.1) for BRCA1, 8.8 (3.44, 22.48) for BRCA2, and 9.0 (2.06, 39.60) for PALB2; and for triple-negative breast cancer (TNBC) of 55.0 (7.01, 431.4) for BRCA1, 12.1 (4.18, 35.12) for BRCA2, and 10.8 (1.97, 59.11) for PALB2. Overall, 5.6% of patients carried a pathogenic variant in BRCA1, BRCA2, PALB2, or TP53, the four most highly penetrant breast cancer genes. Analysis of cases by tumor subtype revealed the expected association of TNBC versus other tumor subtypes with BRCA1, and suggested a significant association between TNBC versus other tumor subtypes with BRCA2 or PALB2 among African-American (AA) patients [2.95 (1.18, 7.37)], but not among European-American (EA) patients [0.62 (0.18, 2.09)]. AA patients with pathogenic variants in BRCA2 or PALB2 were 11 times more likely to be diagnosed with TNBC versus another tumor subtype than were EA patients with pathogenic variants in either of these genes (P = 0.001). If this pattern is confirmed in other comparisons of similarly ascertained AA and EA breast cancer patients, it could in part explain the higher prevalence of TNBC among AA breast cancer patients.

Lipidomics investigations into the tissue phospholipidomic landscape of invasive ductal carcinoma of the breast

The need of identifying alternative therapeutic targets for invasive ductal carcinoma (IDC) of the breast with high specificity and sensitivity for effective therapeutic intervention is crucial for lowering the risk of fatality. Lipidomics has emerged as a key area for the discovery of potential candidates owing to its several shared pathways between cancer cell proliferation and survival. In the current study, we performed comparative phospholipidomic analysis of IDC, benign and control tissue samples of the breast to identify the significant lipid alterations associated with malignant transformation. A total of 33 each age-matched tissue samples from malignant, benign and control were analyzed to identify the altered phospholipids by using liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM/MS). A combination of univariate and multivariate statistical approaches was used to select the phospholipid species with the highest contribution in group segregation. Furthermore, these altered phospholipids were structurally confirmed by tandem mass spectrometry. A total of 244 phospholipids were detected consistently at quantifiable levels, out of which 32 were significantly altered in IDC of the breast. Moreover, in pairwise comparison of IDC against benign and control samples, 11 phospholipids were found to be significantly differentially expressed. Particularly, LPI 20:3, PE (22:1/22:2), LPE 20:0 and PC (20:4/22:4) were observed to be most significantly associated with IDC tissue samples. Apart from that, we also identified that long-chain unsaturated fatty acids were enriched in the IDC tissue samples as compared to benign and control samples, indicating its possible association with the invasive phenotype.

Cancer Cell Metabolites: Updates on Current Tracing Methods

Cancer is the second leading cause of death-1 in 6 deaths globally is due to cancer. Cancer metabolism is a complex and one of the most actively researched area in cancer biology. Metabolic reprogramming in cancer cells entails activities that involve several enzymes and metabolites to convert nutrient into building blocks that alter energy metabolism to fuel rapid cell division. Metabolic dependencies in cancer generate signature metabolites that have key regulatory roles in tumorigenesis. In this minireview, we highlight recent advances in the popular methods ingrained in biochemistry research such as stable and flux isotope analysis, as well as radioisotope labeling, which are valuable in elucidating cancer metabolites. These methods together with analytical tools such as chromatography, nuclear magnetic resonance spectroscopy and mass spectrometry have helped to bring about exploratory work in understanding the role of important as well as obscure metabolites in cancer cells. Information obtained from these analyses significantly contribute in the diagnosis and prognosis of tumors leading to potential therapeutic targets for cancer therapy.

Morphological correlation of urinary bladder cancer molecular subtypes in radical cystectomies

Several molecular subtypes of bladder cancer were identified with differing clinical behavior and responses to platinum-based chemotherapy. But so far, their urothelial histomorphologic features, besides association with some variant histologies, have remained fully undefined. We sought to characterize the histological features of genomically classified bladder cancers more extensively to tumor in radical cystectomy (RC) specimens. Forty-eight bladder cancers submitted to The Cancer Genome Atlas (TCGA) were classified using the BASE47 genomic classifier into luminal subtype (LS) (14 cases), basal subtype (BS) (18 cases), and claudin-low subtype (CLS) (16 cases), and TCGA samples and the corresponding RC specimens were histologically assessed. Marked pleomorphism was more extensive in CLS tumors (87.5% had >15% extent) than in LS tumors (21.4%) (p = 0.0006), whereas the extent in BS tumors was in between LS and CLS tumors. Pleomorphism in distant carcinoma in situ appeared to correlate with that in the main tumor. Ki-67 proliferation was higher in CLS tumors (mean = 61%) than in LS tumors (mean = 29%) or BS (mean = 30%) (p < 0.001). Squamous differentiation was more extensive in BS and CLS tumors (38.2% of BS and CLS tumors versus 7.1% of LS tumors had >30% squamous, p = 0.040). Sarcomatoid change was present in BS and CLS tumors only. The micropapillary variant was identified in LS (3/14) and BS (4/18) tumors only. Histologic features associated with aggressiveness (eg, marked pleomorphism, high proliferation, and sarcomatoid change) are enriched in CLS tumors, correlating with its known poorer outcome that may provide hints in their microscopic distinction. Features more associated with BS than with LS tumors (eg, squamous, marked pleomorphism, and sarcomatoid change) are also identified or enhanced in CLS tumors, supporting the genomic findings suggesting CLS tumor as a hyperbasal form of BS tumor.

The tumor suppressor Zinc finger protein 471 suppresses breast cancer growth and metastasis through inhibiting AKT and Wnt/β-catenin signaling

Background

Zinc-finger protein 471 (ZNF471) is a member of the Krüppel-associated box domain zinc finger protein (KRAB-ZFP) family. ZNF471 is methylated in squamous cell carcinomas of tongue, stomach and esophageal. However, its role in breast carcinogenesis remains elusive. Here, we studied its expression, functions, and molecular mechanisms in breast cancer.

Methods

We examined ZNF471 expression by RT-PCR and qPCR. Methylation-specific PCR determined its promoter methylation. Its biological functions and related molecular mechanisms were assessed by CCK-8, clonogenicity, wound healing, Transwell, nude mice tumorigenicity, flow cytometry, BrdU-ELISA, immunohistochemistry and Western blot assays.

Results

ZNF471 was significantly downregulated in breast cell lines and tissues due to its promoter CpG methylation, compared with normal mammary epithelial cells and paired surgical-margin tissues. Ectopic expression of ZNF471 substantially inhibited breast tumor cell growth in vitro and in vivo, arrested cell cycle at S phase, and promoted cell apoptosis, as well as suppressed metastasis. Further knockdown of ZNF471 verified its tumor-suppressive effects. We also found that ZNF471 exerted its tumor-suppressive functions through suppressing epithelial-mesenchymal transition, tumor cell stemness and AKT and Wnt/β-catenin signaling.

Conclusions

ZNF471 functions as a tumor suppressor that was epigenetically inactivated in breast cancer. Its inhibition of AKT and Wnt/β-catenin signaling pathways is one of the mechanisms underlying its anti-cancer effects.

Visual histological assessment of morphological features reflects the underlying molecular profile in invasive breast cancer: a morphomolecular study

AIMS

Tumour genotype and phenotype are related and can predict outcome. In this study, we hypothesised that the visual assessment of breast cancer (BC) morphological features can provide valuable insight into underlying molecular profiles.

METHODS AND RESULTS

The Cancer Genome Atlas (TCGA) BC cohort was used (n = 743) and morphological features, including Nottingham grade and its components and nucleolar prominence, were assessed utilising whole-slide images (WSIs). Two independent scores were assigned, and discordant cases were utilised to represent cases with intermediate morphological features. Differentially expressed genes (DEGs) were identified for each feature, compared among concordant/discordant cases and tested for specific pathways. Concordant grading was observed in 467 of 743 (63%) of cases. Among concordant case groups, eight common DEGs (UGT8, DDC, RGR, RLBP1, SPRR1B, CXorf49B, PSAPL1 and SPRR2G) were associated with overall tumour grade and its components. These genes are related mainly to cellular proliferation, differentiation and metabolism. The number of DEGs in cases with discordant grading was larger than those identified in concordant cases. The largest number of DEGs was observed in discordant grade 1:3 cases (n = 1185). DEGs were identified for each discordant component. Some DEGs were uniquely associated with well-defined specific morphological features, whereas expression/co-expression of other genes was identified across multiple features and underlined intermediate morphological features.

CONCLUSION

Morphological features are probably related to distinct underlying molecular profiles that drive both morphology and behaviour. This study provides further evidence to support the use of image-based analysis of WSIs, including artificial intelligence algorithms, to predict tumour molecular profiles and outcome.

Individualized multi-omic pathway deviation scores using multiple factor analysis

Malignant progression of normal tissue is typically driven by complex networks of somatic changes, including genetic mutations, copy number aberrations, epigenetic changes, and transcriptional reprogramming. To delineate aberrant multi-omic tumor features that correlate with clinical outcomes, we present a novel pathway-centric tool based on the multiple factor analysis framework called padma. Using a multi-omic consensus representation, padma quantifies and characterizes individualized pathway-specific multi-omic deviations and their underlying drivers, with respect to the sampled population. We demonstrate the utility of padma to correlate patient outcomes with complex genetic, epigenetic, and transcriptomic perturbations in clinically actionable pathways in breast and lung cancer.

Wide-ranging analysis of survival-related alternative splicing events in invasive breast carcinoma

Invasive breast carcinoma (BRCA) is a serious disease that threatens the survival time of those affected. Alternative splicing (AS) involved in BRCA pathogenesis may be a potential therapeutic target. However, to the best of our knowledge, a systematic analysis of survival-related alternative splicing events (SREs) has not yet been reported. The aim of the present study was to identify SREs and analyze their potential biological functions as BRCA prognostic biomarkers. An UpSet plot demonstrated AS global characteristics. Cox's proportional hazards regression model quantitatively demonstrated the prognostic relevance of AS events. Functional enrichment analysis investigated the potential pathways through which AS events affect BRCA progression. The receiver operating characteristic curve model determined the clinical significance of AS events represented using percent-spliced-in (PSI) values. The regulatory network of splicing factors (SFs) and AS events laid the foundation for studying the role of SFs in BRCA. The present study identified 1,215 SREs and their distribution characteristics, suggesting that AS events in exon skipping (ES) primarily exerted normal physiological functions, while AS events in alternative terminator sites had the most significant prognostic effect. The present study demonstrated that survival-associated genes are involved primarily in certain biological processes of ribosomal proteins. In the diagnostic model, the alternative acceptor site, alternative donor site, alternative promoter site and ES performed well. ELAVL4 was the key gene associated with prognosis and SREs. In conclusion, a number of AS events affect BRCA initiation, progression and prognosis. The PSI value of AS events has the potential to diagnose BRCA and predict a prognosis; however, this must be confirmed in additional studies.

Landscape analysis of adjacent gene rearrangements reveals BCL2L14-ETV6 gene fusions in more aggressive triple-negative breast cancer

Triple-negative breast cancer (TNBC) accounts for 10 to 20% of breast cancer, with chemotherapy as its mainstay of treatment due to lack of well-defined targets, and recent genomic sequencing studies have revealed a paucity of TNBC-specific mutations. Recurrent gene fusions comprise a class of viable genetic targets in solid tumors; however, their role in breast cancer remains underappreciated due to the complexity of genomic rearrangements in this cancer. Our interrogation of the whole-genome sequencing data for 215 breast tumors catalogued 99 recurrent gene fusions, 57% of which are cryptic adjacent gene rearrangements (AGRs). The most frequent AGRs, BCL2L14-ETV6, TTC6-MIPOL1, ESR1-CCDC170, and AKAP8-BRD4, were preferentially found in the more aggressive forms of breast cancers that lack well-defined genetic targets. Among these, BCL2L14-ETV6 was exclusively detected in TNBC, and interrogation of four independent patient cohorts detected BCL2L14-ETV6 in 4.4 to 12.2% of TNBC tumors. Interestingly, these fusion-positive tumors exhibit more aggressive histopathological features, such as gross necrosis and high tumor grade. Amid TNBC subtypes, BCL2L14-ETV6 is most frequently detected in the mesenchymal entity, accounting for ∼19% of these tumors. Ectopic expression of BCL2L14-ETV6 fusions induce distinct expression changes from wild-type ETV6 and enhance cell motility and invasiveness of TNBC and benign breast epithelial cells. Furthermore, BCL2L14-ETV6 fusions prime partial epithelial-mesenchymal transition and endow resistance to paclitaxel treatment. Together, these data reveal AGRs as a class of underexplored genetic aberrations that could be pathological in breast cancer, and identify BCL2L14-ETV6 as a recurrent gene fusion in more aggressive form of TNBC tumors.