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Breast cancer is a leading cause of female mortality and despite advancements in personalized therapeutics, metastatic disease largely remains incurable due to drug resistance. The estrogen receptor (ER, ESR1) is expressed in two-thirds of all breast cancer, and under endocrine stress, somatic ESR1 mutations arise in approximately 30% of cases that result in endocrine resistance. We and others reported ESR1 fusions as a mechanism of ER-mediated endocrine resistance. ER fusions, which retain the activation function 1- and DNA-binding domains, harbor ESR1 exons 1 to 6 fused to an in-frame gene partner resulting in loss of the ER ligand-binding domain (LBD). We demonstrate that in a no-special type (invasive ductal carcinoma [IDC]-NST) and an invasive lobular carcinoma (ILC) cell line, ER fusions exhibit robust hyperactivation of canonical ER signaling pathways independent of estradiol or antiendocrine therapies. We employ cell line models stably overexpressing ER fusions with concurrent endogenous ER knockdown to minimize endogenous ER influence. Cell lines exhibited shared transcriptomic enrichment in pathways known to be drivers of metastatic disease, notably MYC signaling. Cells expressing the 3′ fusion partners SOX9 and YAP1 consistently demonstrated enhanced growth and cell survival. ILC cells expressing the DAB2 fusion led to enhanced growth, survival, and migration, phenotypes not appreciated in the IDC-NST DAB2 model. Herein, we report that cell line activity is subtype-, fusion-, and assay-specific, suggesting that LBD loss, the fusion partner, and the cellular landscape all influence fusion activities. Therefore, it will be critical to assess fusion frequency in the context of the clinicopathology. [ABSTRACT FROM AUTHOR]

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity - e.g., MDLC with triple-negative breast cancer (TNBC) or basal ductal and estrogen receptor positive (ER+) luminal lobular regions, distinct enrichment of cell cycle arrest/senescence and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular but not ductal regions, and single-cell ductal and lobular subpopulations with unique oncogenic signatures further highlighting intraregional heterogeneity. Altogether, we demonstrated that the intratumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma. [ABSTRACT FROM AUTHOR]

While there is a great clinical need to understand the biology of metastatic cancer in order to treat it more effectively, research is hampered by limited sample availability. Research autopsy programmes can crucially advance the field through synchronous, extensive, and high‐volume sample collection. However, it remains an underused strategy in translational research. Via an extensive questionnaire, we collected information on the study design, enrolment strategy, study conduct, sample and data management, and challenges and opportunities of research autopsy programmes in oncology worldwide. Fourteen programmes participated in this study. Eight programmes operated 24 h/7 days, resulting in a lower median postmortem interval (time between death and start of the autopsy, 4 h) compared with those operating during working hours (9 h). Most programmes (n = 10) succeeded in collecting all samples within a median of 12 h after death. A large number of tumour sites were sampled during each autopsy (median 15.5 per patient). The median number of samples collected per patient was 58, including different processing methods for tumour samples but also non‐tumour tissues and liquid biopsies. Unique biological insights derived from these samples included metastatic progression, treatment resistance, disease heterogeneity, tumour dormancy, interactions with the tumour micro‐environment, and tumour representation in liquid biopsies. Tumour patient‐derived xenograft (PDX) or organoid (PDO) models were additionally established, allowing for drug discovery and treatment sensitivity assays. Apart from the opportunities and achievements, we also present the challenges related with postmortem sample collections and strategies to overcome them, based on the shared experience of these 14 programmes. Through this work, we hope to increase the transparency of postmortem tissue donation, to encourage and aid the creation of new programmes, and to foster collaborations on these unique sample collections. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

Abstract Introduction Accumulating evidence suggests that both levels and activity of the estrogen receptor (ER) and the progesterone receptor (PR) are dramatically influenced by growth-factor receptor (GFR) signaling pathways, and that this crosstalk is a major determinant of both breast cancer progression and response to therapy. The phosphatidylinositol 3-kinase (PI3K) pathway, a key mediator of GFR signaling, is one of the most altered pathways in breast cancer. We thus examined whether deregulated PI3K signaling in luminal ER+ breast tumors is associated with ER level and activity and intrinsic molecular subtype. Methods We defined two independent molecular signatures of the PI3K pathway: a proteomic (reverse-phase proteomic array) PI3K signature, based on protein measurement for PI3K signaling intermediates, and a PI3K transcriptional (mRNA) signature based on the set of genes either induced or repressed by PI3K inhibitors. By using these signatures, we scored each ER+ breast tumor represented in multiple independent expression-profiling datasets (four mRNA, n = 915; one protein, n = 429) for activation of the PI3K pathway. Effects of PI3K inhibitor BEZ-235 on ER expression and activity levels and cell growth were tested by quantitative real-time PCR and cell proliferation assays. Results Within ER+ tumors, ER levels were negatively correlated with the PI3K activation scores, both at the proteomic and transcriptional levels, in all datasets examined. PI3K signature scores were also higher in ER+ tumors and cell lines of the more aggressive luminal B molecular subtype versus those of the less aggressive luminal A subtype. Notably, BEZ-235 treatment in four different ER+ cell lines increased expression of ER and ER target genes including PR, and treatment with IGF-I (which signals via PI3K) decreased expression of ER and target genes, thus further establishing an inverse functional relation between ER and PI3K. BEZ-235 had an additional effect on tamoxifen in inhibiting the growth of a number of ER+ cell lines. Conclusions Our data suggest that luminal B tumors have hyperactive GFR/PI3K signaling associated with lower ER levels, which has been correlated with resistance to endocrine therapy. Targeting PI3K in these tumors might reverse loss of ER expression and signaling and restore hormonal sensitivity.

Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine. Author summary: Cancer research relies on models, such as cell lines, patient-derived xenografts (PDX), and patient-derived organoids (PDO), as essential alternatives to human studies. However, it is crucial to determine how well these models mimic human patients and to quantify their congruence in disease-relevant genes and regulatory pathways. As the number of cancer models grows, researchers face the challenge of selecting the most representative model based on molecular profiles. Existing methods are machine learning based and are limited to prediction using genome-wide information without mechanistic insights. To address this, we developed a comprehensive suite of bioinformatics tools, namely Congruence Analysis and Selection of CAncer Models (CASCAM). The framework develops a multi-stage systems approach to quantify pathway and gene specific congruence and allow prioritization and selection of the most congruent cancer model(s), which provides a paradigm shift towards gene regulatory and systems investigations. [ABSTRACT FROM AUTHOR]

Model organisms are instrumental substitutes for human studies to expedite basic, translational, and clinical research. Despite their indispensable role in mechanistic investigation and drug development, molecular congruence of animal models to humans has long been questioned and debated. Little effort has been made for an objective quantification and mechanistic exploration of a model organism's resemblance to humans in terms of molecular response under disease or drug treatment. We hereby propose a framework, namely Congruence Analysis for Model Organisms (CAMO), for transcriptomic response analysis by developing threshold-free differential expression analysis, quantitative concordance/discordance scores incorporating data variabilities, pathway-centric downstream investigation, knowledge retrieval by text mining, and topological gene module detection for hypothesis generation. Instead of a genome-wide vague and dichotomous answer of "poorly" or "greatly" mimicking humans, CAMO assists researchers to numerically quantify congruence, to dissect true cross-species differences from unwanted biological or cohort variabilities, and to visually identify molecular mechanisms and pathway subnetworks that are best or least mimicked by model organisms, which altogether provides foundations for hypothesis generation and subsequent translational decisions. [ABSTRACT FROM AUTHOR]

Background: There is increasing interest in better understanding the biology and clinical presentation of invasive lobular cancer (ILC), which is the most common special histological subtype of breast cancer. Limited large contemporary data sets are available allowing comparison of clinicopathologic features between ILC and invasive ductal cancer (IDC).Methods: The Great Lakes Breast Cancer Consortium was formed to compare clinical behavior of ILC (n = 3617) and IDC (n = 30 045) from 33 662 patients treated between 1990 and 2017 at 3 large clinical centers. We used Kaplan-Meier analysis, Cox proportional hazards modeling, and propensity score matching to evaluate treatment differences and outcomes. All statistical testing used 2-sided P values.Results: Compared with IDC, patients with ILC were more frequently diagnosed at later stages and with more lymph node involvement (corrected P < .001). Estrogen receptor-positive ILCs were of lower grade (grade 1 and 2: 90% in ILC vs 72% in IDC) but larger in size (T3 and 4: 14.3% in ILC vs 3.4% in IDC) (corrected P < .001), and since 1990, the mean ILC size detected at diagnosis increased yearly. Patients with estrogen receptor (ER)-positive ILC underwent statistically significantly more mastectomies compared with ER-positive IDC (57% vs 46%). Using Kaplan-Meier analysis, patients with ER-positive ILC had statistically significantly worse disease-free survival and overall survival than ER-positive IDC although 6 times more IDCs were classified as high risk by OncotypeDx Breast Recurrence Score assay.Conclusions: This large, retrospective, collaborative analysis with 3 clinical centers identified meaningful differences in clinicopathological features between ILC and IDC, providing further evidence that these are 2 different entities requiring different clinical management. [ABSTRACT FROM AUTHOR]

Cancer cells have an efficient antioxidant system to counteract their increased generation of ROS. However, whether this ability to survive high levels of ROS has an important role in the growth and metastasis of tumors is not well understood. Here, we demonstrate that the redox protein thioredoxin-like 2 (TXNL2) regulates the growth and metastasis of human breast cancer cells through a redox signaling mechanism. TXNL2 was found to be overexpressed in human cancers, including breast cancers. Knockdown of TXNL2 in human breast cancer cell lines increased ROS levels and reduced NF-ΚB activity, resulting in inhibition of in vitro proliferation, survival, and invasion. In addition, TXNL2 knockdown inhibited tumorigenesis and metastasis of these cells upon transplantation into immunodeficient mice. Furthermore, analysis of primary breast cancer samples demonstrated that enhanced TXNL2 expression correlated with metastasis to the lung and brain and with decreased overall patient survival. Our studies provided insight into redox-based mechanisms underlying tumor growth and metastasis and suggest that TXNL2 could be a target for treatment of breast cancer., Introduction ROS, including superoxide [O.sup.-2], hydroxyl radical OH, and [H.sub.2][O.sub.2], are constantly generated during intracellular metabolism and in response to environmental stimuli (1). Generally, ROS are regarded as host-defending molecules [...]