Breast Tumor Heterogeneity Research Articles

Profiling Breast Tumor Heterogeneity and Identifying Breast Cancer Subtypes Through Tumor-Associated Immune Cell Signatures and Immuno Nano Sensors.

Breast cancer is a complex and heterogeneous disease with varying cellular, genetic, epigenetic, and molecular expressions.The detection of intratumor heterogeneity in breast cancer poses significant challenges due to its complex multifaceted characteristics, yet its identification is crucial for guiding effective treatment decisions and understanding the diseaseprogression. Currently, there exists no method capable of capturing the full extent of breast tumor heterogeneity. In this study, the aim is to identify and characterize metabolic heterogeneity in breast tumors using immune cells and an ultrafast laser-fabricated Immuno Nano Sensor.Combining spectral markers from both Natural Killer (NK)and T cells, a machine-learning approach is implemented to distinguish cancer from healthy samples, identify primary versus metastatic tumors, and determine estrogen receptor (ER)/progesterone receptor (PR) status at the single-cell level. The platform successfully distinguished heterogeneous breast cancer samples fromhealthy individuals, achieving97.8%sensitivity and92.2%specificity, andaccurately identifiedprimary tumors from metastatic tumors. Characteristic spectral signatures allow for discrimination between ER/PR-positive andnegative tumors with 97.5% sensitivity.This study demonstrates the potential of immune cell-based metabolic profiling in providing a comprehensive assessment of breast tumor heterogeneity and paving the way for minimally invasive liquid biopsy approaches in breast cancer diagnosis and management.

Breast Cancer Stem Cells and Tumor Heterogeneity: Characteristics and Therapeutic Strategies.

Breast cancer is one of the most frequently detected malignancies worldwide. It is responsible for more than 15% of all death cases caused by cancer in women. Breast cancer is a heterogeneous disease representing various histological types, molecular characteristics, and clinical profiles. However, all breast cancers are organized in a hierarchy of heterogeneous cell populations, with a small proportion of cancer stem cells (breast cancer stem cells (BCSCs)) playing a putative role in cancer progression, and they are responsible for therapeutic failure. In different molecular subtypes of breast cancer, they present different characteristics, with specific marker profiles, prognoses, and treatments. Recent efforts have focused on tackling the Wnt, Notch, Hedgehog, PI3K/Akt/mTOR, and HER2 signaling pathways. Developing diagnostics and therapeutic strategies enables more efficient elimination of the tumor mass together with the stem cell population. Thus, the knowledge about appropriate therapeutic methods targeting both "normal" breast cancer cells and breast cancer stem cell subpopulations is crucial for success in cancer elimination.

Abstract 434: Understanding metabolic heterogeneity of estrogen receptor positive metastatic breast tumors using spatial transcriptomics and geographic information systems approaches

Abstract The tumor microenvironment consists of numerous cell populations showing diverse metabolic preferences, causing metabolic heterogeneity and therapy resistance. Fulvestrant, a clinically approved selective estrogen receptor (ER) degrader, is the standard of care for ER-positive breast cancer, yet resistance has been reported among metastatic breast cancer patients. In our previous studies, we showed such resistance is attributable to metabolic heterogeneity in the tumor microenvironment. Despite metabolic heterogeneity posing a common challenge in cancer research, current methods, such as histology and Magnetic Resonance Imaging, cannot explore nutrient uptake at transcript-level resolution with spatial context, making it difficult to understand the heterogeneity that affects cellular growth and metabolism. Visium Spatial Transcriptomics, a method that shows regional gene expression, is emerging as a powerful spatial analysis tool, and we integrated this method with geographic information systems to investigate the tumor microenvironment heterogeneity and highlight the importance of cell-microenvironment interactions. We developed a computational pipeline to visualize hotspots of spatially relevant genes using breast cancer metastatic samples from xenograft models and human patients. This pipeline integrates Visium tools and geospatial methods for processing transcriptome profiling data. We analyzed the degree of local spatial association based on Local Moran’s I to estimate regions with significant co-localization of single and multiple gene expressions within a tumor. We then leveraged the co-localized areas in the process of biomarker identification and revealing biological pathways. We also used unsupervised clustering of the complete transcriptome profiles to test the resemblance between identified clusters and hotspots as well as any disruption in the spatial structure of clusters in response to therapy or metastasis. Gene set enrichment analysis of these clusters revealed several glucose or lipid metabolism pathways, and mapping of the genes corresponding to each pathway showed mutually exclusive gene expression patterns in the sample. We also observed minimal co-localization of two metabolic pathways but extensive co-localization between metabolic genes and ER target genes, suggesting distinct metabolic preferences and spatial distributions of endocrine therapy resistant cell populations. Altogether, our analysis pipeline established a computational framework for characterizing the link between tumor microenvironment, local metabolism, and changes in transcriptional response to external stimuli. Our findings support the development of strategies to target different endocrine resistant regions using a combination of ER and metabolic signaling inhibitors. Citation Format: Jin Young Yoo, Sabrina Akter, Nandana Varma, Atharva Jain, Aiman Soliman, Zeynep Madak Erdogan. Understanding metabolic heterogeneity of estrogen receptor positive metastatic breast tumors using spatial transcriptomics and geographic information systems approaches [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 434.

Ferroptosis: the emerging player in remodeling triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a highly heterogeneous breast tumor type that is highly malignant, invasive, and highly recurrent. Ferroptosis is a unique mode of programmed cell death (PCD) at the morphological, physiological, and molecular levels, mainly characterized by cell death induced by iron-dependent accumulation of lipid peroxides, which plays a substantial role in a variety of diseases, including tumors and inflammatory diseases. TNBC cells have been reported to display a peculiar equilibrium metabolic profile of iron and glutathione, which may increase the sensitivity of TNBC to ferroptosis. TNBC possesses a higher sensitivity to ferroptosis than other breast cancer types. Ferroptosis also occurred between immune cells and tumor cells, suggesting that regulating ferroptosis may remodel TNBC by modulating the immune response. Many ferroptosis-related genes or molecules have characteristic expression patterns and are expected to be diagnostic targets for TNBC. Besides, therapeutic strategies based on ferroptosis, including the isolation and extraction of natural drugs and the use of ferroptosis inducers, are urgent for TNBC personalized treatment. Thus, this review will explore the contribution of ferroptosis in TNBC progression, diagnosis, and treatment, to provide novel perspectives and therapeutic strategies for TNBC management.

Spectrum of Mucin-containing Lesions of the Breast: Multimodality Imaging Review with Pathologic Correlation.

Mucin-containing lesions of the breast encompass a wide range of benign and malignant processes. The spectrum of histologic findings includes incidental mucus-filled benign cysts or ducts, mucocele-like lesions (MLLs), mucin-producing ductal carcinoma in situ, and invasive mucinous carcinoma. MLL is characterized by the presence of mucin-containing cysts that are typically associated with extravasated stromal mucin. MLL is often benign but can be associated with epithelial atypia or malignancy. Mucinous carcinoma represents the malignant end of the spectrum of mucinous lesions of the breast. Evidence-based literature supports a conservative approach for benign MLLs without associated atypia or malignancy, reserving excision for those lesions exhibiting such pathologic features. The most common imaging finding for MLL is microcalcifications at mammography. No specific imaging feature is predictive of malignant outcome at surgical excision. Invasive mucinous carcinoma is a heterogeneous breast tumor subtype, as defined according to the World Health Organization criteria. Mucinous carcinomas are categorized into pure (>90% mucinous component) or mixed (10%-90% mucinous component) subtypes. Pure mucinous carcinomas are generally associated with excellent prognosis and survival, with a few exceptions. Mixed mucinous carcinomas do not have the same favorable prognosis and instead behave similarly to invasive breast carcinomas of no special type. Characteristic diagnostic imaging features can be identified for mucinous carcinoma based on its mucinous and nonmucinous contents. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.

Multidimensional quantitative phenotypic and molecular analysis reveals neomorphic behaviors of p53 missense mutants

Mutations in the TP53 tumor suppressor gene occur in >80% of the triple-negative or basal-like breast cancer. To test whether neomorphic functions of specific TP53 missense mutations contribute to phenotypic heterogeneity, we characterized phenotypes of non-transformed MCF10A-derived cell lines expressing the ten most common missense mutant p53 proteins and observed a wide spectrum of phenotypic changes in cell survival, resistance to apoptosis and anoikis, cell migration, invasion and 3D mammosphere architecture. The p53 mutants R248W, R273C, R248Q, and Y220C are the most aggressive while G245S and Y234C are the least, which correlates with survival rates of basal-like breast cancer patients. Interestingly, a crucial amino acid difference at one position—R273C vs. R273H—has drastic changes on cellular phenotype. RNA-Seq and ChIP-Seq analyses show distinct DNA binding properties of different p53 mutants, yielding heterogeneous transcriptomics profiles, and MD simulation provided structural basis of differential DNA binding of different p53 mutants. Integrative statistical and machine-learning-based pathway analysis on gene expression profiles with phenotype vectors across the mutant cell lines identifies quantitative association of multiple pathways including the Hippo/YAP/TAZ pathway with phenotypic aggressiveness. Further, comparative analyses of large transcriptomics datasets on breast cancer cell lines and tumors suggest that dysregulation of the Hippo/YAP/TAZ pathway plays a key role in driving the cellular phenotypes towards basal-like in the presence of more aggressive p53 mutants. Overall, our study describes distinct gain-of-function impacts on protein functions, transcriptional profiles, and cellular behaviors of different p53 missense mutants, which contribute to clinical phenotypic heterogeneity of triple-negative breast tumors.

A sneak peek into the diagnostic conundrum of neuroendocrine neoplasms of the breast: An updated review with a brief illustrative case series

Abstract Neuroendocrine neoplasms (NENs) of the breast are uncommon tumours that have been a hitherto poorly and variably defined entity. In the recent fifth Edition World Health Organisation (WHO) classification of breast tumour, NENs are dichotomously classified into neuroendocrine tumours (NETs) and neuroendocrine carcinomas (NECs), mirroring the classification of NENs in other organ systems. Both NETs and NECs are invasive tumours in the breast. However, the clinical significance of neuroendocrine differentiation (NED) in breast cancers, especially in NETs of the breast, is not yet fully understood, and a large overlap appears to exist between breast cancers showing NED and invasive breast carcinoma of no special type (IBC-NST). Although breast NECs show distinct clinical and morphological features and show overlapping molecular features with their counterpart in other organs, especially the high-grade component. Diagnosis of NETs based on the morphological characteristics alone can be challenging; one reason is that breast NETs do not necessarily have the same morphological characteristics as those of NENs arising in other organs and are genetically heterogeneous and harbour molecular alterations that differ from IBC, NST. The keynote changes in the current WHO are the exclusion of other histological subtypes that is, solid papillary carcinoma and a hypercellular variant of mucinous carcinoma which are strong immunohistochemical mimickers of NED and the inclusion of large cell neuroendocrine carcinoma (LCNEC). Thus, the heterogeneity of breast tumours with NED and the changes in their classifications over the years have left many open issues that still need to be resolved. Data regarding the prognostic significance of NED is conflicting and histologic grade and tumour stage remain the main prognostic parameters. Current management of NENs is similar to that of IBC of NST. In this review article, we will summarise the journey of NENs of the breast with an update on the recent WHO classification and discuss their differences from NENs arising in other organs by highlighting an interesting case series depicting their clinical, radiological, histological and molecular features.

A convolutional neural network STIFMap reveals associations between stromal stiffness and EMT in breast cancer

Intratumor heterogeneity associates with poor patient outcome. Stromal stiffening also accompanies cancer. Whether cancers demonstrate stiffness heterogeneity, and if this is linked to tumor cell heterogeneity remains unclear. We developed a method to measure the stiffness heterogeneity in human breast tumors that quantifies the stromal stiffness each cell experiences and permits visual registration with biomarkers of tumor progression. We present Spatially Transformed Inferential Force Map (STIFMap) which exploits computer vision to precisely automate atomic force microscopy (AFM) indentation combined with a trained convolutional neural network to predict stromal elasticity with micron-resolution using collagen morphological features and ground truth AFM data. We registered high-elasticity regions within human breast tumors colocalizing with markers of mechanical activation and an epithelial-to-mesenchymal transition (EMT). The findings highlight the utility of STIFMap to assess mechanical heterogeneity of human tumors across length scales from single cells to whole tissues and implicates stromal stiffness in tumor cell heterogeneity.

Targeted TLR9 Agonist Elicits Effective Antitumor Immunity against Spontaneously Arising Breast Tumors.

Spontaneous tumors that arise in genetically engineered mice recapitulate the natural tumor microenvironment and tumor-immune coevolution observed in human cancers, providing a more physiologically relevant preclinical model relative to implanted tumors. Similar to many cancer patients, oncogene-driven spontaneous tumors are often resistant to immunotherapy, and thus novel agents that can effectively promote antitumor immunity against these aggressive cancers show considerable promise for clinical translation, and their mechanistic assessment can broaden our understanding of tumor immunology. In this study, we performed extensive immune profiling experiments to investigate how tumor-targeted TLR9 stimulation remodels the microenvironment of spontaneously arising tumors during an effective antitumor immune response. To model the clinical scenario of multiple tumor sites, we used MMTV-PyMT transgenic mice, which spontaneously develop heterogeneous breast tumors throughout their 10 mammary glands. We found that i.v. administration of a tumor-targeting TLR9 agonist, referred to as PIP-CpG, induced a systemic T cell-mediated immune response that not only promoted regression of existing mammary tumors, but also elicited immune memory capable of delaying growth of independent newly arising tumors. Within the tumor microenvironment, PIP-CpG therapy initiated an inflammatory cascade that dramatically amplified chemokine and cytokine production, prompted robust infiltration and expansion of innate and adaptive immune cells, and led to diverse and unexpected changes in immune phenotypes. This study demonstrates that effective systemic treatment of an autochthonous multisite tumor model can be achieved using a tumor-targeted immunostimulant and provides immunological insights that will inform future therapeutic strategies.

Precision Therapyof Recurrent Breast Cancer throughTargeting Different MalignantTumor Cells witha HER2/CD44-Targeted Hydrogel Nanobot.

Heterogeneity and drug resistance of tumor cells are the leading causes of incurability and poor survival for patients with recurrent breast cancer. In order to accurately deliver the biological anticancer drugs to different subtypes of malignant tumor cells for omnidirectional targeted treatment of recurrent breast cancer, a distinct design is demonstrated by embedding liposome-based nanocomplexes containing pro-apoptotic peptide and survivin siRNA drugs (LPR) into Herceptin/hyaluronic acid cross-linked nanohydrogels (Herceptin-HA) to fabricate a HER2/CD44-targeted hydrogel nanobot (named as ALPR). ALPR delivered cargoes to the cells overexpressing CD44 and HER2, followed by Herceptin-HA biodegradation, subsequently, the exposed lipid component containing DOPE fused with the endosomal membrane and released peptide and siRNA into the cytoplasm. These experiments indicated that ALPR can specifically deliver Herceptin, peptide, and siRNA drugs to HER2-positive SKBR-3, triple-negative MDA-MB-231, and HER2-negative drug-resistant MCF-7 human breast cancer cells. ALPR completely inhibited the growth of heterogeneous breast tumors via multichannel synergistic effects: disrupting mitochondria, downregulating the survivin gene, and blocking HER2 receptors on the surface of HER2-positive cells. The present design overcomes the chemical drug resistance and opens a feasible route for the combinative treatment of recurrent breast cancer, even other solid tumors, utilizing different kinds of biological drugs.

Mass cytometry analysis of mouse nonclassical monocyte heterogeneity in breast tumors

Abstract Monocytes have emerged as important regulators of cancer progression. Our previous studies demonstrated the importance of nonclassical Ly6C lomonocytes in regulating cancer metastasis in mouse models. Here, we aimed to study monocyte heterogeneity in two different breast cancer tumors in mice using either 4T1 or EMT6 cells. 4T1 tumors exhibit an increased ability to metastasize in the lung in a BALB/c mouse breast cancer model compared to the less aggressive EMT6 tumor model. We evaluated 35 surface markers of tumor-infiltrated monocytes and dendritic cells in tumors isolated from each model using CyTOF mass cytometry. Analysis of monocytes and dendritic cells in the tumors using the Seurat package and the FlowSom algorithm identified 12 cell subsets. Significant differences were discovered between the two tumor types in frequencies of four of 12 identified subsets. However, only one of these subsets, a nonclassical monocyte subset, was higher in the less metastatic EMT6 tumors. This subset was 5-fold higher in EMT6 tumors versus more aggressive 4T1 tumors. This Ly6C lononclassical monocyte subset showed high expression of CD169 and CX3CR1, CD86 and CD80. These differentially expressed markers suggest a functional importance in this subset in migration to the tumor and antigen presentation, both of which are anti-tumoral functions. Ongoing studies are aimed towards understanding the role of this non-classical monocyte population in regulating breast cancer metastasis. This work was supported by NIH R01 CA202987, and P01 HL136275

Estrogen receptor status heterogeneity in breast cancer tumor: role in response to endocrine treatment.

Tumor heterogeneity affects diagnosis, prognosis and response to therapy. Heterogeneity is found in both normal and neoplastic human mammary gland. Indeed, luminal ER-negative cells can give rise to various phenotypes, including ER-negative and ER-positive mammary tumors. As a result, the tumor phenotype does not necessarily reflects the cell of origin of cancer. With regard to the ER status, heterogeneity can challenge endocrine therapies, where the elimination of responsive clones could lead to reduced treatment efficacy and tumor relapse through the expansion of the resistant clones. The aim of this study was to investigate breast tumor heterogeneity and its role in endocrine resistance onset. For this purpose, we used ER+ (T47D, CAMA1) and triple-negative breast cancer cell lines (TNBC; MDA-MB-231, HCC70), co-cultures using 2D and 3D models. Our results showed that ER status is modulated when ER+ cells are cultured in the presence of TNBC cells, leading to a different response to endocrine therapy, demonstrating that the response to treatment can be affected by the influence that different breast cancer cell types exert on each other. In addition, ER+ positive cells doubling time was modified after exposure to TNBC cell co-culturing. Further experiments are required to fully elucidate the molecular mechanism of these observations.

Abstract 5791: Autophagy & PIM1 kinase inhibition in breast cancer stem cells & bulk cancer cells attenuates breast cancer & metastasis

Abstract Autophagy is a self-degradation process, it is essential for growth of heterogenous of breast cancer cells. Breast tumor heterogeneity majorly comprises bulk cancer cells & cancer stem cells (CSCs). Targeting either CSCs or cancer cells could lead to interconversion between these cells due to EMT/MET transitions. Therefore, it is important to identify a common molecular targets & its mechanism of regulation in these populations for improvement of breast cancer therapy. Here, we utilized a PCR based screen & identified NUMA1 downregulation in FIP200 KO (autophagy gene) cells of three major breast cancer subtypes, FF99 (luminal), N418 (Her2+) & BF3M (basal). Further, NUMA1 KO in these cells reduced tumor formation & CSCs indicating the role of NUMA1 on regulation of CSCs. Therefore, we sorted breast CSCs, ALDH+ and CD29hi CD61+ from WT & NUMA1 KO cells, tail-vein injected nude mice depicted reduced metastatic potential with NUMA1 KO than WT CSCs. Mechanistically, NUMA1 regulation by PIM1 kinase was observed in CSCs but not in bulk cancer cells. Cell viability assays revealed that combination treatment of autophagy & PIM1 inhibitor showed higher efficacy against cancer cells of three breast cancer subtypes than individual treatments alone. Finally, the combination treatment of autophagy inhibitor, Lys05 & PIM1 inhibitor, SMI-4a reduced tumor formation & metastasis of breast cancer. Human tissue microarray results depicted high expression of NUMA1 & PIM1 in different breast cancer subtypes than non- tumor tissues. Our study demonstrated that inhibition of autophagy & PIM1 in bulk cancer cells & CSCs showed attenuation of tumor formation & metastasis, suggesting this combination treatment serves as an important therapeutic approach for treating breast cancer. Citation Format: Kanakaraju Manupati, Mingang Hao, Michael Haas, Syn Kok Yeo, Jun-Lin Guan. Autophagy & PIM1 kinase inhibition in breast cancer stem cells & bulk cancer cells attenuates breast cancer & metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5791.

Abstract 4774: Adaptation to chronic oxidative stress promotes metabolic rewiring to maintain redox balance in HER2+ breast tumors

Abstract Breast cancer is the most commonly diagnosed malignancy in women worldwide. Breast tumors possess a high degree of intra- and inter-tumoral heterogeneity, enabling the emergence of aggressive, therapy-resistant tumors. One contributor to breast tumor heterogeneity is reactive oxygen species (ROS), which are unstable oxygen-containing molecules that modify proteins, lipids, and DNA. Our lab has developed unique models of HER2+ breast cancer that evolved in vivo under conditions of low (ROSLOW) and high (ROSHIGH) chronic oxidative stress to acquire more aggressive properties. As such, we became interested in understanding the underlying mechanisms by which breast cancer cells capitalize on sustained oxidative stress to acquire more aggressive properties. We show that, relative to their parental and ROSLOW counterparts, ROSHIGH-evolved tumors are better able to maintain redox balance by coping with ROS-induced damage in vivo and following H2O2 treatment in vitro. Moreover, ROSHIGH-evolved breast cancer cells lose their aggressive phenotype in vitro, implying a necessary microenvironmental contribution. RNAseq analysis of parental and aggressive breast cancers further identified glycolytic and hypoxic transcriptional signatures that were preferentially induced in breast cancers that adapted to chronic oxidative stress. Therefore, we hypothesized that breast cancer cells conditioned to cope with chronic oxidative stress may re-wire their glucose metabolism to support ROS buffering, which may be facilitated by a hypoxic tumor microenvironment. Notably, ROSHIGH-evolved tumors are insensitive to inhibition of glycolysis using 2-deoxyglucose (2-DG); however, we show that they are specifically sensitized to inhibition of glutathione synthesis, and this sensitivity is exacerbated when treated in combination with 2-DG. As well, we show through glucose tracing that ROSHIGH-evolved tumors exhibit increased incorporation of glucose-derived carbons into glutathione. This data confirms that breast tumor adaptation to chronic oxidative stress promotes metabolic re-wiring to support ROS scavenging. To begin interrogating the role hypoxia plays in this phenotype, we assessed cell proliferation and levels of hypoxia-responsive genes following hypoxia exposure in our breast cancer models in vitro. Relative to their parental counterparts, breast cancer cells that adapted to chronic oxidative stress exhibit enhanced proliferation in hypoxia and are better able to upregulate hypoxia-responsive genes, even in response to modest changes in oxygen levels. Overall, our study shows that tumor adaptation under chronic oxidative stress promotes metabolic rewiring which may be facilitated by a hypoxic microenvironment. Citation Format: Caitlynn Nicole Mirabelli, Rachel La Selva, Matthew Annis, Ouafa Najyb, Steven Hébert, Claudia Kleinman, Julie St-Pierre, Peter Siegel, Josie Ursini-Siegel. Adaptation to chronic oxidative stress promotes metabolic rewiring to maintain redox balance in HER2+ breast tumors. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4774.

Spatial Heterogeneity of Integrins and Their Ligands in Primary Breast Tumors.

The diversity of cell-cell interactions in different regions of a tumor reflects the functional heterogeneity of cancer, which poses challenges in early diagnosis, selection of treatment strategies, and prognosis of breast cancer. Cancer cells interact with each other to form different morphological structures in the tumor and stromal host cells via integrins. The objective of this study was to characterize the morphological and spatial heterogeneity of primary breast tumors in the context of expression profiles of integrins and their ligands. We studied spatial transcriptomics using the 10X Visium approach and the Niche Interactions and Communication Heterogeneity in Extracellular Signaling (NICHES) algorithm to map ligand-receptor signaling pathways and visualize the heterogeneity of signaling archetypes in tumor clusters. Cluster analysis of the expression profiles of tumor spots from the samples indicated pronounced inter-tumoral heterogeneity. Integrin-ligand functional clusters were associated with intratumoral heterogeneity, which was manifested by the presence of several morphological loci as observed in histological tumor samples. Inter-tumoral heterogeneity was manifested by a different number of functional clusters, ranging from 2 to 9 for each tumor sample. The main characteristic of these clusters was the significant predominance of non-complementary integrin subunits. Of the 42 functional integrin-ligand pairs in 21 clusters of five samples, 41 pairs occurred only once. The exception was the laminin subunit alpha-5 (LAMA5)-integrin beta 4 (ITGB4) pair, which was detected in two clusters of different samples. The spatial heterogeneity of integrin-ligand expression clusters in breast cancer contributes significantly to the functional heterogeneity of the tumor, which sets the stage for many scenarios of parenchymatous-stromal relationships, some of which may be effective in the emergence of metastasizing tumor seed cells. The intra- and inter-tumoral spatio-functional heterogeneity of the tumor tissue that we discovered may largely explain why it is difficult to achieve success in most patients with breast cancer using any therapeutic strategy targeting one molecule of the vast array, regardless of the importance of its pathogenetic significance.

Case series: Heterogeneity and management of large breast tumors - phyllode tumor and squamous cell carcinoma

Case series: Heterogeneity and management of large breast tumors - phyllode tumor and squamous cell carcinoma

Breast Cancer- Awareness and Early Detection

Breast cancer (BC) is a heterogeneous group of neoplasms originated via epithelial cells, which line the milk ducts. Breast tumor heterogeneity is matter of discussion and research in histology and clinical outcomes for a long span of time, and these differences pave the paths to disease classification. Out of the few tumor types which successfully use the molecular classification for the design of individualized therapies, BC is one of them. These types of tumors lead to significant improvements in disease-specific survival. The classification based on comprehensive gene expression profiling, divides the breast tumors into at least three broad subtypes: luminal, human epidermal growth factor receptor 2+, and basal like.
 In both developed and under development countries, BC is the most common type of cancer in females. To be precise, one out of ten cancers identified each year in female is BC. Moreover, it’s a worldwide major cause of deaths occurring among women due to cancer. According to the WHO's 2008 GLOBOCAN, BC is the most frequent cancer in women, affecting an estimated 1.38 million people [1]. Among the Asian population, Pakistan reports the highest incidence of BC i.e., 19.33% [2]. The established risk factors of BC are linked to oestrogens. Risks are increased by several factors including early menarche, late menopause, and obesity in postmenopausal women. Increasing concentrations of endogenous oestradiol also increased risk of BC. Childbearing somehow reduces risk, with a greater protection for early first birth and a larger number of births; illustrated as breastfeeding probably has a protective effect. Moreover, the oral contraceptives and hormonal therapy for menopause both tend to cause a little increase in breast-cancer risk, which appears to diminish once use stops. Alcohol also increases risk, whereas physical activity probably declines. Mutations in certain genes like BRCA1 and BRCA2 also massively enhance the risk to BC, but these certainly account for a minority of cases. Of all the key challenges that occur during BC research, mapping of pathways that give rise to metastasis is one of them. Recent studies have done analysis of gene expression profiles to identify markers which correlate with metastasis. Clinical and pathological risk factors, such as patient age, tumor size, and steroid receptor status, are commonly used to assess the likelihood of metastasis development.
 In Pakistan socioeconomic factors are to blame for the poor health of women especially in rural communities. One in nine Pakistani women have had BC at some point in their lives. There are no screening facilities for the diagnosis of different types of cancer, and a lot of women lose their lives to BC each year. Most Pakistani women are impoverished and unable to pay for costly screening exams. Talking about BC is considered a taboo and women don’t talk about it due to shyness. Campaigns on awareness of BC are needed country wide specially targeting rural areas. Women who are above 40 should be suggested to get mammogram time to time. Women should be advised to do Breast self-examination (BSE) once a month for detection of cancer at an early stage.

Application of deep learning to identify ductal carcinoma in situ and microinvasion of the breast using ultrasound imaging.

BackgroundThe treatment and prognosis of breast ductal carcinoma in situ (DCIS) with and without microinvasion (MIC) are different. Ultrasound imaging shows that DCIS is a heterogeneous breast tumor with diverse manifestations. DCIS means that the cancer cells are confined in the duct without penetrating the basement membrane, MIC means that the cancer cells penetrate the basement membrane and the maximum diameter of any largest invasive lesion is less than or equal to 1 mm. This study was designed to evaluate how deep learning can be used to identify DCIS with MIC on ultrasound images.MethodsThe clinical and ultrasound data of 467 consecutive inpatients diagnosed with DCIS (213 with MIC) in West China Hospital of Sichuan University were collected from January 2013 to April 2019 and randomly apportioned to training and internal validation sets. An external validation set comprised data from Sichuan Provincial People's Hospital with 101 patients (33 with MIC) collected between January 2017 and December 2019. There were 2,492 original images; 66% of these were used to establish a model, and the remaining 34% were used to evaluate the model. Three experienced breast ultrasound clinicians analyzed the ultrasound images to establish a logistic regression model. Finally, the logistic regression model and five deep learning models (ResNet-50, ResNet-101, DenseNet-161, DenseNet-169, and Inception-v3) were compared and evaluated to assess their diagnostic efficiency when identifying MIC based on ultrasound image data.ResultsThe characteristics of high nuclear grade (P<0.001), necrosis (P=0.006), estrogen receptor negative (ER−; P=0.003), progesterone receptor negative (PR−; P=0.001), human epidermal growth factor receptor 2 positive (HER2+; P=0.034), lymphatic metastasis (P=0.008), and calcification (P<0.001) all showed significant correlations with MIC. The Inception-v3 model achieved the best performance (P<0.05) in MIC identification. The area under the receiver operating curve (AUC) of the Inception-v3 model was 0.803 [95% confidence interval (CI): 0.709 to 0.878], with a classification accuracy of 0.766, a sensitivity of 0.767, and a specificity of 0.765.ConclusionsDeep learning can be used to identify MIC of breast DCIS from ultrasound images. Models based on Inception-v3 can provide automated detection of DCIS with MIC from ultrasound images.

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction.

Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

Evaluation of anticancer effects of frankincense on breast cancer stem-like cells.

Relapse and metastasis in breast cancer are linked to cancer stem cells (CSCs) resistant to anticancer therapies. The presence of cancer stem-like cells (CSLCs) and their ability to self-renew is determined by in vitro spheroid formation. Many studies have found that frankincense has anticancer impacts, although these effects on breast CSLCs have never been evaluated. A population of heterogeneous breast tumor cells was extracted from the tumor mass after generating an animal model of triple-negative breast cancer (TNBC). Spheroid formation was used as an in vitro assay to determine the existence of CSLCs in these cells. MTT assay was used to determine frankincense's cytotoxic activity. An annexin V- propidium iodide (PI) staining and scratch test were used to assess the induction of apoptosis and antimetastatic effects of frankincense. The frankincense extract has significant cytotoxic and apoptotic effects on breast CSLCs. Although, the breast CSLCs are more resistant to these impacts than other breast cancer cells. Our study is the first report that indicates that frankincense extract has anticancer properties in breast CSLCs. Compared to many anticancer chemicals, which have limited potential to battle cancer stem cells, frankincense is an appropriate option to combat breast CSCs.