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The glioblastoma (GBM) tumor microenvironment is heterogeneous, complex, and being increasingly understood as a significant contributor to tumor progression. In brain tumors, the extracellular matrix contains a large concentration of hyaluronic acid (HA) that makes it important to study its role in cancer progression. In particular, abnormal accumulation of HA is observed in gliomas and is often associated with poor prognosis. In addition, HA is a polymer and its molecular weight (MW) distribution may influence tumor cell activity. Herein, the influence of the MW of HA on tumor cell metabolism is evaluated. A 2D cell culture approach is used to expose the U87‐MG (medium glucose [MG]) cell line to different HA MWs (10, 60, and 500 kDa) and glucose concentrations (0, 5.5, and 25 mm). Notably, it is found that HA influences GBM amino acid metabolism via reduction in LAT1 transporter protein expression. Also an influence on mitochondrial respiration levels and a difference in the accumulation of some key products of cell metabolic activity (lactic acid, glutamic acid, and succinic acid) are reported. Overall, in these results, it is indicated that HA MW can influence GBM metabolic state, with implications for cell invasion and tumor progression.

Abstract Medical image segmentation has made a significant contribution towards delivering affordable healthcare by facilitating the automatic identification of anatomical structures and other regions of interest. Although convolution neural networks have become prominent in the field of medical image segmentation, they suffer from certain limitations. In this study, we present a reliable framework for producing performant outcomes for the segmentation of pathological structures of 2D medical images. Our framework consists of a novel deep learning architecture, called deep multi-level attention dilated residual neural network (MADR-Net), designed to improve the performance of medical image segmentation. MADR-Net uses a U-Net encoder/decoder backbone in combination with multi-level residual blocks and atrous pyramid scene parsing pooling. To improve the segmentation results, channel-spatial attention blocks were added in the skip connection to capture both the global and local features and superseded the bottleneck layer with an ASPP block. Furthermore, we introduce a hybrid loss function that has an excellent convergence property and enhances the performance of the medical image segmentation task. We extensively validated the proposed MADR-Net on four typical yet challenging medical image segmentation tasks: (1) Left ventricle, left atrium, and myocardial wall segmentation from Echocardiogram images in the CAMUS dataset, (2) Skin cancer segmentation from dermoscopy images in ISIC 2017 dataset, (3) Electron microscopy in FIB-SEM dataset, and (4) Fluid attenuated inversion recovery abnormality from MR images in LGG segmentation dataset. The proposed algorithm yielded significant results when compared to state-of-the-art architectures such as U-Net, Residual U-Net, and Attention U-Net. The proposed MADR-Net consistently outperformed the classical U-Net by 5.43%, 3.43%, and 3.92% relative improvement in terms of dice coefficient, respectively, for electron microscopy, dermoscopy, and MRI. The experimental results demonstrate superior performance on single and multi-class datasets and that the proposed MADR-Net can be utilized as a baseline for the assessment of cross-dataset and segmentation tasks.

Abstract Calcification of aortic valve leaflets is a growing mortality threat for the 18 million human lives claimed globally each year by heart disease. Extensive research has focused on the cellular and molecular pathophysiology associated with calcification, yet the detailed composition, structure, distribution and etiological history of mineral deposition remains unknown. Here transdisciplinary geology, biology and medicine (GeoBioMed) approaches prove that leaflet calcification is driven by amorphous calcium phosphate (ACP), ACP at the threshold of transformation toward hydroxyapatite (HAP) and cholesterol biomineralization. A paragenetic sequence of events is observed that includes: (1) original formation of unaltered leaflet tissues: (2) individual and coalescing 100’s nm- to 1 μm-scale ACP spherules and cholesterol crystals biomineralizing collagen fibers and smooth muscle cell myofilaments; (3) osteopontin coatings that stabilize ACP and collagen containment of nodules preventing exposure to the solution chemistry and water content of pumping blood, which combine to slow transformation to HAP; (4) mm-scale nodule growth via ACP spherule coalescence, diagenetic incorporation of altered collagen and aggregation with other ACP nodules; and (5) leaflet diastole and systole flexure causing nodules to twist, fold their encasing collagen fibers and increase stiffness. These in vivo mechanisms combine to slow leaflet calcification and establish previously unexplored hypotheses for testing novel drug therapies and clinical interventions as viable alternatives to current reliance on surgical/percutaneous valve implants.

Understanding the molecular and physical complexity of the tissue microenvironment (TiME) in the context of its spatiotemporal organization has remained an enduring challenge. Recent advances in engineering and data science are now promising the ability to study the structure, functions, and dynamics of the TiME in unprecedented detail; however, many advances still occur in silos that rarely integrate information to study the TiME in its full detail. This review provides an integrative overview of the engineering principles underlying chemical, optical, electrical, mechanical, and computational science to probe, sense, model, and fabricate the TiME. In individual sections, we first summarize the underlying principles, capabilities, and scope of emerging technologies, the breakthrough discoveries enabled by each technology and recent, promising innovations. We provide perspectives on the potential of these advances in answering critical questions about the TiME and its role in various disease and developmental processes. Finally, we present an integrative view that appreciates the major scientific and educational aspects in the study of the TiME.

Infrared (IR) spectroscopic imaging is of potentially wide use in medical imaging applications due to its ability to capture both chemical and spatial information. This complexity of the data both necessitates using machine intelligence as well as presents an opportunity to harness a high-dimensionality data set that offers far more information than today’s manually-interpreted images. While convolutional neural networks (CNNs), including the well-known U-Net model, have demonstrated impressive performance in image segmentation, the inherent locality of convolution limits the effectiveness of these models for encoding IR data, resulting in suboptimal performance. In this work, we propose an INfrared Spectroscopic imaging-based TRAnsformers for medical image Segmentation (INSTRAS). This novel model leverages the strength of the transformer encoders to segment IR breast images effectively. Incorporating skip-connection and transformer encoders, INSTRAS overcomes the issue of pure convolution models, such as the difficulty of capturing long-range dependencies. To evaluate the performance of our model and existing convolutional models, we conducted training on various encoder–decoder models using a breast dataset of IR images. INSTRAS, utilizing 9 spectral bands for segmentation, achieved a remarkable AUC score of 0.9788, underscoring its superior capabilities compared to purely convolutional models. These experimental results attest to INSTRAS’s advanced and improved segmentation abilities for IR imaging.

Abstract Chemical imaging, especially mid-infrared spectroscopic microscopy, enables label-free biomedical analyses while achieving expansive molecular sensitivity. However, its slow speed and poor image quality impede widespread adoption. We present a microscope that provides high-throughput recording, low noise, and high spatial resolution where the bottom-up design of its optical train facilitates dual-axis galvo laser scanning of a diffraction-limited focal point over large areas using custom, compound, infinity-corrected refractive objectives. We demonstrate whole-slide, speckle-free imaging in ~3 min per discrete wavelength at 10× magnification (2 μm/pixel) and high-resolution capability with its 20× counterpart (1 μm/pixel), both offering spatial quality at theoretical limits while maintaining high signal-to-noise ratios (>100:1). The data quality enables applications of modern machine learning and capabilities not previously feasible – 3D reconstructions using serial sections, comprehensive assessments of whole model organisms, and histological assessments of disease in time comparable to clinical workflows. Distinct from conventional approaches that focus on morphological investigations or immunostaining techniques, this development makes label-free imaging of minimally processed tissue practical.

Abstract This study describes “lobular-like invasive mammary carcinomas” (LLIMCas), a group of low- to intermediate-grade invasive mammary carcinomas with discohesive, diffusely infiltrative cells showing retained circumferential membranous immunoreactivity for both E-cadherin and p120. We analyzed the clinical-pathologic features of 166 LLIMCas compared to 104 classical invasive lobular carcinomas (ILCs) and 100 grade 1 and 2 invasive ductal carcinomas (IDCs). Tumor size and pT stage of LLIMCas were intermediate between IDCs and ILCs, and yet often underestimated on imaging and showed frequent positive margins on the first resection. Despite histomorphologic similarities to classical ILC, the discohesion in LLIMCa was independent of E-cadherin/p120 immunophenotypic alteration. An exploratory, hypothesis-generating analysis of the genomic features of 14 randomly selected LLIMCas and classical ILCs (7 from each category) was performed utilizing an FDA-authorized targeted capture sequencing assay (MSK-IMPACT). None of the seven LLIMCas harbored CDH1 loss-of-function mutations, and none of the CDH1 alterations detected in two of the LLIMCas was pathogenic. In contrast, all seven ILCs harbored CDH1 loss-of-function mutations coupled with the loss of heterozygosity of the CDH1 wild-type allele. Four of the six evaluable LLIMCas were positive for CDH1 promoter methylation, which may partially explain the single-cell infiltrative morphology seen in LLIMCa. Further studies are warranted to better define the molecular basis of the discohesive cellular morphology in LLIMCa. Until more data becomes available, identifying LLIMCas and distinguishing them from typical IDCs and ILCs would be justified. In patients with LLIMCas, preoperative MRI should be entertained to guide surgical management.

Background: Differentiated vulvar intraepithelial neoplasia (dVIN) is a non-human papilloma virus (HPV)-related high-grade precursor lesion to vulvar squamous cell carcinoma (vSCCa). Although TP53 gene mutations have been identified in 80% of dVIN, its role in dVIN pathogenesis as well as malignant transformation is still being poorly understood. Poor reproducible diagnostic criteria and ambiguous p53 immunostaining patterns, along with morphologic discordance still pose a diagnostic challenge. Methods: A series of 60 cases of dVIN-related vSCCa along with adjacent dVIN were evaluated. Clinicopathological features as well as immunohistochemical results were recorded on the resection-confirmed dVIN-related vSCCa. Results: The average age of the patients was 71 years. Thirty-five cases (58.4%) of dVIN-related vSCCa were moderately differentiated, fourteen cases (23.3%) were poorly differentiated, and the remaining eleven cases (18.3%) were well-differentiated. Twenty-nine cases (48.3%) were found to have lichen sclerosus adjacent to dVIN. In terms of p53 and p16 expression in dVIN-related vSCCa and the adjacent dVIN, fifty-five (91.7%) dVIN showed mutant p53 immunostaining pattern with strong positive expression in 80% cases (basal/para-basal expression) and null pattern expression in 11.7% cases. Five (8.3%) dVIN showed p53 wild-type staining pattern. The wild-type pattern were seen in 5% of vSCCa and p53 null pattern were seen in 13.3% vSCCa. Six cases demonstrated atypical staining patterns: two cases showed p53 null expression in dVIN but p53 overexpression in invasive carcinoma; three cases exhibited p53 null expression in invasive carcinoma, with the adjacent dVIN showing basal and para-basal mutant (2 cases) and wild-type (1 case) p53 expression patterns. A single case demonstrated p53 wild-type pattern in dVIN and overexpression in invasive carcinoma. In addition, 65% dVIN were p16 negative and 31.7% dVIN had patchy p16 staining. Conclusion: Clinical and prognostic value of the ambiguous/inconsistent patterns are uncertain and molecular studies are needed for further characterization.

Abstract Triple-negative breast cancers (TNBCs) often have a high Ki-67 proliferation index and respond favorably to neoadjuvant chemotherapy (NACT) with pathologic complete response (pCR) resulting in ~40% of cases. Nevertheless, morbidity/mortality remain high, mostly due to recurrence in patients with residual disease. In contrast, the incidence and clinical features of TNBC with low proliferation (TNLP), defined as TNBC with a Ki-67 index of ≤30% remains unknown. We report 70 cases of TNLP identified at our center from 2008 to 2018, including 18 treated with NACT. TNLP tumors represent

Chromosome rearrangement is one of the hallmarks of human malignancies. Gene fusion is one of the consequences of chromosome rearrangements. In this report, we show that gene fusion between solute carrier family 45 member 2 (SLC45A2) and alpha‐methylacyl‐coenzyme A racemase (AMACR) occurs in eight different types of human malignancies, with frequencies ranging from 45% to 97%. The chimeric protein is translocated to the lysosomal membrane and activates the extracellular signal‐regulated kinase signaling cascade. The fusion protein promotes cell growth, accelerates migration, resists serum starvation‐induced cell death, and is essential for cancer growth in mouse xenograft cancer models. Introduction of SLC45A2‐AMACR into the mouse liver using a sleeping beauty transposon system and somatic knockout of phosphatase and TENsin homolog (Pten) generated spontaneous liver cancers within a short period. Conclusion: The gene fusion between SLC45A2 and AMACR may be a driving event for human liver cancer development.

Abstract Background A very pure multi-walled carbon nanotube (MWCNT) that was shown to have very low toxicity in vitro, was evaluated for lung and systemic effects and distribution following inhalation exposure. Methods B6C3F1/N mice were exposed to varying doses (0, 0.06, 0.2, and 0.6 mg/m3) of the (99.1% carbon) MWCNT by inhalation for 30 days (excluding weekends). Ten days following the last exposure, the lungs and spleen were harvested and processed for histology and immune cell population assessment. In addition, lung lavage cells and fluid were analyzed. Stimulated Raman scattering (SRS) was used to identify particles in the lungs, spleen, kidneys, liver, mediastinal and brachial lymph nodes, and olfactory bulb. Splenic tissue sections were stained with hematoxylin and eosin (H&E) for light microscopic histopathology assessment. Blood plasma was analyzed for cytokines and cathepsins. A section of the spleen was processed for RNA isolation and relative gene expression for 84 inflammation-related cytokines/chemokines. Results Following MWCNT exposure, particles were clearly evident in the lungs, spleens, lymph nodes and olfactory bulbs, (but not livers or kidneys) of exposed mice in a dose-dependent manner. Examination of the lavaged lung cells was unremarkable with no significant inflammation indicated at all particle doses. In contrast, histological examination of the spleen indicated the presence of apoptotic bodies within T cells regions of the white pulp area. Isolated splenic leukocytes had significant changes in various cells including an increased number of proinflammatory CD11b+Ly6C+ splenic cells. The gene expression studies confirmed this observation as several inflammation-related genes were upregulated particularly in the high dose exposure (0.6 mg/m3). Blood plasma evaluations showed a systemic down-regulation of inflammatory cytokines and a dose-dependent up-regulation of lysosomal cathepsins. Conclusions The findings in the lungs were consistent with our hypothesis that this MWCNT exposure would result in minimal lung inflammation and injury. However, the low toxicity of the MWCNT to lung macrophages may have contributed to enhanced migration of the MWCNT to the spleen through the lymph nodes, resulting in splenic toxicity and systemic changes in inflammatory mediators.

Atomic force microscopy-infrared (AFM-IR) spectroscopic imaging techniques offer a non-perturbative, molecular contrast for characterization of nanomaterials; however, data are often complicated by the measurement apparatus, sample preparation conditions and low signal-to-noise ratio. Here, the authors demonstrate a closed-loop controlled AFM-IR instrument design to address measurement artifacts and reduce noise up to 5x compared to previous methods.

Objective: Oncotype DX® recurrence score (RS) can be predicted from Magee EquationsTM (MS) postoperatively. The aim of this study is to investigate correlation of MS with RS from pretreatment core needle biopsy (CNB) tissues, and their clinical usefulness in prediction of response to neoadjuvant chemotherapy (NCT) in estrogen receptor-positive and human epidermal growth factor receptor 2-negative (ER+/HER2-) breast cancer (BC).Materials and Methods: Pretreatment CNB tissue samples from 60 patients with ER+/HER2- invasive BC were analyzed for MS and RS correlation. MS and RS were categorized as follows: low (

Summary: Age is one of the most important risk factors for the development of breast cancer. Nearly a third of all breast cancer cases occur in older women (aged ≥70 years), with most cases being oestrogen receptor-positive (ER+). Such tumours are often indolent and unlikely to be the ultimate cause of death for older women, particularly when considering other comorbidities. This Review focuses on unique clinical considerations for screening, detection, and treatment regimens for older women who develop ER+ breast cancers—specifically, we focus on recent trends for de-implementation of screening, staging, surgery, and adjuvant therapies along the continuum of care. Additionally, we also review emerging basic and translational research that will further uncover the unique underlying biology of these tumours, which develop in the context of systemic age-related inflammation and changing hormone profiles. With prevailing trends of clinical de-implementation, new insights into mechanistic biology might provide an opportunity for precision medicine approaches to treat patients with well tolerated, low-toxicity agents to extend patients’ lives with a higher quality of life, prevent tumour recurrences, and reduce cancer-related burdens.

Herein we describe the fabrication and characterization of Ag and Au bimetallic plasmonic crystals as a system that exhibits improved capabilities for quantitative, bulk refractive index (RI) sensing and surface-enhanced Raman spectroscopy (SERS) as compared to monometallic plasmonic crystals of similar form. The sensing optics, which are bimetallic plasmonic crystals consisting of sequential nanoscale layers of Ag coated by Au, are chemically stable and useful for quantitative, multispectral, refractive index and spectroscopic chemical sensing. Compared to previously reported homometallic devices, the results presented herein illustrate improvements in performance that stem from the distinctive plasmonic features and strong localized electric fields produced by the Ag and Au layers, which are optimized in terms of metal thickness and geometric features. Finite-difference time-domain (FDTD) simulations theoretically verify the nature of the multimode plasmonic resonances generated by the devices and allow for a better understanding of the enhancements in multispectral refractive index and SERS-based sensing. Taken together, these results demonstrate a robust and potentially useful new platform for chemical/spectroscopic sensing.

Current histopathological diagnosis involves human expert interpretation of stained images for diagnosis. This process is prone to inter-observer variability, often leading to low concordance rates amongst pathologists across many types of tissues. Further, since structural features are mostly just defined for epithelial alterations during tumor progression, the use of associated stromal changes is limited. Here we sought to examine whether digital analysis of commonly used hematoxylin and eosin-stained images could provide precise and quantitative metrics of disease from both epithelial and stromal cells. We developed a convolutional neural network approach to identify epithelial breast cells from their microenvironment. Second, we analyzed the microenvironment to further observe different constituent cells using unsupervised clustering. Finally, we categorized breast cancer by the combined effects of stromal and epithelial inertia. Together, the work provides insight and evidence of cancer association for interpretable features from deep learning methods that provide new opportunities for comprehensive analysis of standard pathology images.

Chemical hyperspectral imaging (HSI) data is naturally high dimensional and large. There are thus inherent manual trade-offs in acquisition time, and the quality of data. Minimum Noise Fraction (MNF) developed by Green et al. [1] has been extensively studied as a method for noise removal in HSI data. It too, however entails a manual speed-accuracy trade-off, namely the process of manually selecting the relevant bands in the MNF space. This process currently takes roughly around a month's time for acquiring and pre-processing an entire TMA with acceptable signal to noise ratio. We present three approaches termed 'Fast MNF', 'Approx MNF' and 'Rand MNF' where the computational time of the algorithm is reduced, as well as the entire process of band selection is fully automated. This automated approach is shown to perform at the same level of accuracy as MNF with now large speedup factors, resulting in the same task to be accomplished in hours. The different approximations produced by the three algorithms, show the reconstruction accuracy vs storage (50×) and runtime speed (60×) trade-off. We apply the approach for automating the denoising of different tissue histology samples, in which the accuracy of classification (differentiating between the different histologic and pathologic classes) strongly depends on the SNR (signal to noise ratio) of recovered data. Therefore, we also compare the effect of the proposed denoising algorithms on classification accuracy. Since denoising HSI data is done unsupervised, we also use a metric that assesses the quality of denoising in the image domain between the noisy and denoised image in the absence of ground truth.

The human genome encodes thousands of long noncoding RNA (lncRNA) genes; the function of majority of them is poorly understood. Aberrant expression of a significant number of lncRNAs is observed in various diseases, including cancer. To gain insights into the role of lncRNAs in breast cancer progression, we performed genome-wide transcriptome analyses in an isogenic, triple negative breast cancer (TNBC/basal-like) progression cell lines using a 3D cell culture model. We identified significantly altered expression of 1853 lncRNAs, including ~500 natural antisense transcript (NATs) lncRNAs. A significant number of breast cancer-deregulated NATs displayed co-regulated expression with oncogenic and tumor suppressor protein-coding genes in cis. Further studies on one such NAT, PDCD4-AS1 lncRNA reveal that it positively regulates the expression and activity of the tumor suppressor PDCD4 in mammary epithelial cells. Both PDCD4-AS1 and PDCD4 show reduced expression in TNBC cell lines and in patients, and depletion of PDCD4-AS1 compromised the cellular levels and activity of PDCD4. Further, tumorigenic properties of PDCD4-AS1-depleted TNBC cells were rescued by exogenous expression of PDCD4, implying that PDCD4-AS1 acts upstream of PDCD4. Mechanistically, PDCD4-AS1 stabilizes PDCD4 RNA by forming RNA duplex and controls the interaction between PDCD4 RNA and RNA decay promoting factors such as HuR. Our studies demonstrate crucial roles played by NAT lncRNAs in regulating post-transcriptional gene expression of key oncogenic or tumor suppressor genes, thereby contributing to TNBC progression.

Objective: The biology of high grade serous ovarian carcinoma (HGSOC) is poorly understood. Little has been reported on intratumoral homogeneity or heterogeneity of primary HGSOC tumors and their metastases. We evaluated the global protein expression profiles of paired primary and metastatic HGSOC from formalin-fixed, paraffin-embedded (FFPE) tissue samples. Methods: After IRB approval, six patients with advanced HGSOC were identified with tumor in both ovaries at initial surgery. Laser capture microdissection (LCM) was used to extract tumor for protein digestion. Peptides were extracted and analyzed by reversed-phase liquid chromatography coupled to a linear ion trap mass spectrometer. Tandem mass spectra were searched against the UniProt human protein database. Differences in protein abundance between samples were assessed and analyzed by Ingenuity Pathway Analysis software. Immunohistochemistry (IHC) for select proteins from the original and an additional validation set of five patients was performed. Results: Unsupervised clustering of the abundance profiles placed the paired specimens adjacent to each other. IHC H-score analysis of the validation set revealed a strong correlation between paired samples for all proteins. For the similarly expressed proteins, the estimated correlation coefficients in two of three experimental samples and all validation samples were statistically significant (p < 0.05). The estimated correlation coefficients in the experimental sample proteins classified as differentially expressed were not statistically significant. Conclusion: A global proteomic screen of primary HGSOC tumors and their metastatic lesions identifies tumoral homogeneity and heterogeneity and provides preliminary insight into these protein profiles and the cellular pathways they constitute.

Rejection is a common problem after cardiac transplants leading to significant number of adverse events and deaths, particularly in the first year of transplantation. The gold standard to identify rejection is endomyocardial biopsy. This technique is complex, cumbersome and requires a lot of expertise in the correct interpretation of stained biopsy sections. Traditional histopathology cannot be used actively or quickly during cardiac interventions or surgery. Our objective was to develop a stain-less approach using an emerging technology, Fourier transform infrared (FT-IR) spectroscopic imaging to identify different components of cardiac tissue by their chemical and molecular basis aided by computer recognition, rather than by visual examination using optical microscopy. We studied this technique in assessment of cardiac transplant rejection to evaluate efficacy in an example of complex cardiovascular pathology. We recorded data from human cardiac transplant patients' biopsies, used a Bayesian classification protocol and developed a visualization scheme to observe chemical differences without the need of stains or human supervision. Using receiver operating characteristic curves, we observed probabilities of detection greater than 95% for four out of five histological classes at 10% probability of false alarm at the cellular level while correctly identifying samples with the hallmarks of the immune response in all cases. The efficacy of manual examination can be significantly increased by observing the inherent biochemical changes in tissues, which enables us to achieve greater diagnostic confidence in an automated, label-free manner. We developed a computational pathology system that gives high contrast images and seems superior to traditional staining procedures. This study is a prelude to the development of real time in situ imaging systems, which can assist interventionists and surgeons actively during procedures.

Chemical imaging is a rapidly emerging field in which molecular information within samples can be used to predict biological function and recognize disease without the use of stains or manual identification. In Fourier transform infrared (FT-IR) spectroscopic imaging, molecular absorption contrast provides a large signal relative to noise. Due to the long mid-IR wavelengths and sub-optimal instrument design, however, pixel sizes have historically been much larger than cells. This limits both the accuracy of the technique in identifying small regions, as well as the ability to visualize single cells. Here we obtain data with micron-sized sampling using a tabletop FT-IR instrument, and demonstrate that the high-definition (HD) data lead to accurate identification of multiple cells in lymph nodes that was not previously possible. Highly accurate recognition of eight distinct classes - naïve and memory B cells, T cells, erythrocytes, connective tissue, fibrovascular network, smooth muscle, and light and dark zone activated B cells was achieved in healthy, reactive, and malignant lymph node biopsies using a random forest classifier. The results demonstrate that cells currently identifiable only through immunohistochemical stains and cumbersome manual recognition of optical microscopy images can now be distinguished to a similar level through a single IR spectroscopic image from a lymph node biopsy.

The tumor microenvironment is known to play a key role in altering the properties and behavior of nearby cancer cells. Its influence on resistance to endocrine therapy and cancer relapse, however, is poorly understood. Here we investigate the interaction of mammary fibroblasts and estrogen receptor-positive breast cancer cells in three-dimensional culture models in order to characterize gene expression, cellular changes, and the secreted protein factors involved in the cellular cross-talk. We show that fibroblasts, which are the predominant cell type found in the stroma adjacent to the cancer cells in a tumor, induce an epithelial-to-mesenchymal transition in the cancer cells, leading to hormone-independent growth, a more invasive phenotype, and resistance to endocrine therapy. Here, we applied a label-free chemical imaging modality, Fourier transform infrared (FT-IR) spectroscopic imaging, to identify cells that had transitioned to hormone-independent growth. Both the molecular and chemical profiles identified here were translated from cell culture to patient samples: a secreted protein signature was used to stratify patient populations based on gene expression and FT-IR was used to characterize breast tumor patient biopsies. Our findings underscore the role of mammary fibroblasts in promoting aggressiveness and endocrine therapy resistance in ER-positive breast cancers and highlight the utility of FT-IR for the further characterization of breast cancer samples.