Gene Expression Analysis of In Vitro Cocultures to Study Interactions between Breast Epithelium and Stroma

The interactions between breast epithelium and stroma are fundamental to normal tissue homeostasis and for tumor initiation and progression. Gene expression studies of in vitro coculture models demonstrate that in vitro models have relevance for tumor progression in vivo. For example, stromal gene expression has been shown to vary in association with tumor subtype in vivo, and analogous in vitro cocultures recapitulate subtype-specific biological interactions. Cocultures can be used to study cancer cell interactions with specific stromal components (e.g., immune cells, fibroblasts, endothelium) and different representative cell lines (e.g., cancer-associated versus normal-associated fibroblasts versus established, immortalized fibroblasts) can help elucidate the role of stromal variation in tumor phenotypes. Gene expression data can also be combined with cell-based assays to identify cellular phenotypes associated with gene expression changes. Coculture systems are manipulable systems that can yield important insights about cell-cell interactions and the cellular phenotypes that occur as tumor and stroma co-evolve

Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology

Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an image-level classification by using the quantile function. The quantile function provides a more complete description of the heterogeneity within each image, improving image-level classification. We also adapt image augmentation to the MI framework by randomly selecting cropped regions on which to apply MI aggregation during each epoch of training. This provides a mechanism to study the importance of MI learning. We validate our method on five different classification tasks for breast tumor histology and provide a visualization method for interpreting local image classifications that could lead to future insights into tumor heterogeneity

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability 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. Our results provide many interpretable connections and contrasts between histopathology and genetics

Down-regulation of sfrp1 in a mammary epithelial cell line promotes the development of a cd44high/cd24low population which is invasive and resistant to anoikis

<p>Abstract</p> <p>Background</p> <p>The Wnt family of secreted proteins is implicated in the regulation of cell fate during development, as well as in cell proliferation, morphology, and migration. Aberrant activation of the Wnt/β-catenin signaling pathway leads to the development of several human cancers, including breast cancer. Secreted frizzled-related protein 1 (SFRP1) antagonizes this pathway by competing with the Frizzled receptor for Wnt ligands resulting in an attenuation of the signal transduction cascade. Loss of SFRP1 expression is observed in breast cancer, along with several other cancers, and is associated with poor patient prognosis. However, it is not clear whether the loss of SFRP1 expression predisposes the mammary gland to tumorigenesis.</p> <p>Results</p> <p>When SFRP1 is knocked down in a non-malignant immortalized mammary epithelial cell line (76 N TERT), nuclear levels of β-catenin rise and the Wnt pathway is stimulated. The SFRP1 knockdown cells exhibit increased expression of the pro-proliferative Cyclin D1 gene and increased cellular proliferation, undergo a partial epithelial-mesenchymal transition (EMT), are resistant to anchorage-independent cell death, exhibit increased migration, are significantly more invasive, and exhibit a CD24^low/CD44^highcell surface marker expression pattern.</p> <p>Conclusion</p> <p>Our study suggests that loss of SFRP1 allows non-malignant cells to acquire characteristics associated with breast cancer cells.</p

PREDICTION OF TOXICANT-SPECIFIC GENE EXPRESSION SIGNATURES FOLLOWING CHEMOTHERAPEUTIC TREATMENT OF BREAST CELL LINES

Global gene expression profiling has demonstrated that the predominant cellular response to a range of toxicants is a general stress response. This stereotyped environmental stress response commonly includes repression of protein synthesis and cell-cycle–regulated genes and induction of DNA damage and oxidative stress–responsive genes. Our laboratory recently characterized the general stress response of breast cell lines derived from basal-like and luminal epithelium after treatment with doxorubicin (DOX) or 5-fluorouracil (5FU) and showed that each cell type has a distinct response. However, we expected that some of the expression changes induced by DOX and 5FU would be unique to each compound and might reflect the underlying mechanisms of action of these agents. Therefore, we employed supervised analyses (significance analysis of microarrays) to identify genes that showed differential expression between DOX-treated and 5FU-treated cell lines. We then used cross-validation analyses and identified genes that afforded high predictive accuracy in classifying samples into the two treatment classes. To test whether these gene lists had good predictive accuracy in an independent data set, we treated our panel of cell lines with etoposide, a compound mechanistically similar to DOX. We demonstrated that using expression patterns of 100 genes we were able to obtain 100% predictive accuracy in classifying the etoposide samples as being more similar in expression to DOX-treated than to 5FU-treated samples. These analyses also showed that toxicant-specific gene expression patterns, similar to general stress responses, vary according to cell type

The MLL-Menin Interaction is a Therapeutic Vulnerability in <em>NUP98</em>-rearranged AML

\ua9 2023 Wolters Kluwer Health. All rights reserved. Chromosomal translocations involving the NUP98 locus are among the most prevalent rearrangements in pediatric acute myeloid leukemia (AML). AML with NUP98 fusions is characterized by high expression of HOXA and MEIS1 genes and is associated with poor clinical outcome. NUP98 fusion proteins are recruited to their target genes by the mixed lineage leukemia (MLL) complex, which involves a direct interaction between MLL and Menin. Here, we show that therapeutic targeting of the Menin-MLL interaction inhibits the propagation of NUP98-rearrranged AML both ex vivo and in vivo. Treatment of primary AML cells with the Menin inhibitor revumenib (SNDX-5613) impairs proliferation and clonogenicity ex vivo in long-term coculture and drives myeloid differentiation. These phenotypic effects are associated with global gene expression changes in primary AML samples that involve the downregulation of many critical NUP98 fusion protein-target genes, such as MEIS1 and CDK6. In addition, Menin inhibition reduces the expression of both wild-type FLT3 and mutated FLT3-ITD, and in combination with FLT3 inhibitor, suppresses patient-derived NUP98-r AML cells in a synergistic manner. Revumenib treatment blocks leukemic engraftment and prevents leukemia-associated death of immunodeficient mice transplanted with NUP98::NSD1 FLT3-ITD-positive patient-derived AML cells. These results demonstrate that NUP98-rearranged AMLs are highly susceptible to inhibition of the MLL-Menin interaction and suggest the inclusion of AML patients harboring NUP98 fusions into the clinical evaluation of Menin inhibitors

Gene-Level Germline Contributions to Clinical Risk of Recurrence Scores in Black and White Patients with Breast Cancer

Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReXprioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings

Nuclear Localized LSR: A Novel Regulator of Breast Cancer Behavior and Tumorigenesis

Lipolysis Stimulated Lipoprotein Receptor (LSR) has been found in the plasma membrane and is believed to function in lipoprotein endocytosis and tight junctions. Given the impact of cellular metabolism and junction signaling pathways on tumor phenotypes and patient outcome, it is important to understand how LSR cellular localization mediates its functions. We conducted localization studies, evaluated DNA binding, and examined the effects of nuclear LSR in cells, xenografts, and clinical specimens. We found LSR within the membrane, cytoplasm, and the nucleus of breast cancer cells representing multiple intrinsic subtypes. Chromatin immunoprecipitation (ChIP) showed direct binding of LSR to DNA, and sequence analysis identified putative functional motifs and post-translational modifications of the LSR protein. While neither overexpression of transcript variants, nor pharmacological manipulation of post-translational modification significantly altered localization, inhibition of nuclear export enhanced nuclear localization, suggesting a mechanism for nuclear retention. Co-immunoprecipitation and proximal ligation assays indicated LSR-pericentrin interactions, presenting potential mechanisms for nuclear-localized LSR. The clinical significance of LSR was evaluated using data from over 1,100 primary breast tumors, which showed high LSR levels in basal-like tumors and tumors from African-Americans. In tumors histosections, nuclear localization was significantly associated with poor outcomes. Finally, in vivo xenograft studies revealed that basal-like breast cancer cells that over-express LSR exhibited both membrane and nuclear localization, and developed tumors with 100% penetrance, while control cells lacking LSR developed no tumors. These results show that nuclear LSR alters gene expression and may promote aggressive cancer phenotypes

A framework for transcriptome-wide association studies in breast cancer in diverse study populations

Background: The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry- or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking. Results: We provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women near AURKA, CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS. Conclusions: We show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations