The Brahma (BRM) and Brahma-related Gene 1 (BRG1) ATPases are highly conserved homologs that catalyze the chromatin remodeling functions of the multi-subunit human SWI/SNF chromatin remodeling enzymes in a mutually exclusive manner. SWI/SNF enzyme subunits are mutated or missing in many cancer types, but are overexpressed without apparent mutation in other cancers. Here, we report that both BRG1 and BRM are overexpressed in most primary breast cancers independent of the tumor's receptor status. Knockdown of either ATPase in a triple negative breast cancer cell line reduced tumor formation in vivo and cell proliferation in vitro. Fewer cells in S phase and an extended cell cycle progression time were observed without any indication of apoptosis, senescence, or alterations in migration or attachment properties. Combined knockdown of BRM and BRG1 showed additive effects in the reduction of cell proliferation and time required for completion of cell cycle, suggesting that these enzymes promote cell cycle progression through independent mechanisms. Knockout of BRG1 or BRM using CRISPR/Cas9 technology resulted in the loss of viability, consistent with a requirement for both enzymes in triple negative breast cancer cells.

Vibrational spectroscopic imaging is now used in several fields to acquire molecular information from microscopically heterogeneous systems. Recent advances have led to promising applications in tissue analysis for cancer research, where chemical information can be used to identify cell types and disease. However, recorded spectra are affected by the morphology of the tissue sample, making identification of chemical structures difficult.

Typically, multiplexing high nanoparticle uptake, imaging, and therapy requires careful integration of three different functions of a multiscale molecular-particle assembly. Here, we present a simpler approach to multiplexing by utilizing one component of the system for multiple functions. Specifically, we successfully synthesized and characterized colloidal carotene carbon nanoparticle (C(3)-NP), in which a single functional molecule served a threefold purpose. First, the presence of carotene moieties promoted the passage of the particle through the cell membrane and into the cells. Second, the ligand acted as a potent detrimental moiety for cancer cells and, finally, the ligands produced optical contrast for robust microscopic detection in complex cellular environments. In comparative tests, C(3)-NP were found to provide effective intracellular delivery that enables both robust detection at cellular and tissue level and presents significant therapeutic potential without altering the mechanism of intracellular action of β-carotene. Surface coating of C(3) with phospholipid was used to generate C(3)-Lipocoat nanoparticles with further improved function and biocompatibility, paving the path to eventual in vivo studies.

Tall cell carcinoma with reverse polarity is a rare subtype of breast carcinoma with solid and papillary growth and nuclear features reminiscent of those of the tall cell variant of papillary thyroid carcinoma. These tumors harbor recurrent IDH2 R172 hotspot mutations or TET2 mutations, co-occurring with mutations in PI3K pathway genes. Diagnosis of tall cell carcinomas with reverse polarity is challenging in view of their rarity and the range of differential diagnosis. We sought to determine the sensitivity and specificity of IDH2 R172 immunohistochemistry for the detection of IDH2 R172 hotspot mutations in this entity. We evaluated 14 tall cell carcinomas with reverse polarity (ten excision and five core needle biopsy specimens), 13 intraductal papillomas, 16 solid papillary carcinomas, and 5 encapsulated papillary carcinomas by Sanger sequencing of the IDH2 R172 hotspot locus and of exons 9 and 20 of PIK3CA, and by immunohistochemistry using monoclonal antibodies (11C8B1) to the IDH2 R172S mutation. The 14 tall cell carcinomas with reverse polarity studied harbored IDH2 R172 hotspot mutations, which co-occurred with PIK3CA hotspot mutations in 50% of cases. None of the other papillary neoplasms analyzed displayed IDH2 R172 mutations, however PIK3CA hotspot mutations were detected in 54% of intraductal papillomas, 6% of solid papillary carcinomas, and 20% of encapsulated papillary carcinomas tested. Immunohistochemical analysis with anti-IDH2 R172S antibodies (11C8B1) detected IDH2 R172 mutated protein in 93% (14/15) of tall cell carcinomas with reverse polarity samples including excision (n = 9/10) and core needle biopsy specimens (n = 5), whereas the remaining papillary neoplasms (n = 34) were negative. Our findings demonstrate that immunohistochemical analysis of IDH2 R172 is highly sensitive and specific for the detection of IDH2 R172 hotspot mutations, and likely suitable as a diagnostic tool in the evaluation of excision and core needle biopsy material of tall cell carcinomas with reverse polarity.

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.

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.

With the rapid development and application of deep learning medical image recognition, natural language processing, and other fields, at the same time, deep learning has become the most popular research direction in the field of image processing and recognition. Through deep learning medical image recognition technology, it is of great significance to explore the research of miR-1301. The purpose of this article is to use an improved CNN neural network model algorithm combined to contrast the experimental groups and use deep learning medical imaging technology to study the mechanism by which miR-1301 inhibits the proliferation of carcinoma YD-38 cells through the PI3K/AKT pathway. This paper studies the method of image recognition of squamous cell carcinoma YD-38 cells using a convolutional neural network (CNN). First, a CNN classification model for the characteristics of YD-38 cell images is constructed. Then, pretraining and dropout technology are used to improve and optimize the proposed CNN model to improve the robustness of the model. In this paper, the miR mimic group and the miR blank group and the PI3K/AKT pathway inhibitor Wortmannin were selected to jointly treat YD-38 cells. The expression of mRNA in miR-1301 in HGF-1 was determined using RT-PCR (real and real-time fluorescence and YD-38 cells). The blank plasmids and the miR-1301 mimic (miR-1301 mimic) were transfected into YD-38 cells. The experiments were divided into two groups in the miR-1301 blank group and the miR-1301 simulation groups, respectively. The proliferation capacity of YD-38 cells was prepared in 1.5 ml sterile EP tubes and then diluted with medium for the proliferation of the cells. The scratch test and Transwell test were used to detect the effect of miR-1-3p on the migration and invasion of liver cancer cells. In this paper, CCK-8 experiment, clone formation experiment, flow cytometry, scratch experiment, and Transwell chamber experiment are used to analyze the effects of target gene CAAP1 on the proliferation, apoptosis, migration, and invasion of liver cancer cells. This paper uses CCK-8 to detect five kinds of the effect of miRNA on the proliferation ability of liver cancer cells and the effect of miR-1-3p on the proliferation ability of liver cancer cells. Experimental studies have shown that, compared with the miR blank group, the expression of PI3K and p-AKT was significantly downregulated in the miR mimic group after 24, 48, and 72 hours and the phosphorylation level of AKT was also significantly reduced (P < 0.05).

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.

Molecular analysis techniques such as gene expression analysis and proteomics have contributed greatly to our understanding of cancer heterogeneity. In prior studies, gene expression analysis was shown to stratify patient outcome on the basis of tumor-microenvironment associated genes. A specific gene expression profile, referred to as ECM3 (Extracellular Matrix Cluster 3), indicated poorer survival in patients with grade III tumors. In this work, we aimed to visualize the downstream effects of this gene expression profile onto the tissue, thus providing a spatial context to altered gene expression profiles. Using infrared spectroscopic imaging, we identified spectral patterns specific to the ECM3 gene expression profile, achieving a high spectral classification performance of 0.87 as measured by the area under the curve of the receiver operating characteristic curve. On a patient level, we correctly identified 20 out of 22 ECM3 group patients and 19 out of 20 non-ECM3 group patients by using this spectroscopic imaging-based classifier. By comparing pixels that were identified as ECM3 or non-ECM3 with H&E and IHC images, we were also able to observe an association between tissue morphology and the gene expression clusters, showing the ability of our method to capture broad outcome associated features from infrared images.

Screening agrochemicals and pharmaceuticals for potential liver toxicity is required for regulatory approval and is an expensive and time-consuming process. The identification and utilization of early exposure gene signatures and robust predictive models in regulatory toxicity testing has the potential to reduce time and costs substantially. In this study, comparative supervised machine learning approaches were applied to the rat liver TG-GATEs dataset to develop feature selection and predictive testing. We identified ten gene biomarkers using three different feature selection methods that predicted liver necrosis with high specificity and selectivity in an independent validation dataset from the Microarray Quality Control (MAQC)-II study. Nine of the ten genes that were selected with the supervised methods are involved in metabolism and detoxification (Car3, Crat, Cyp39a1, Dcd, Lbp, Scly, Slc23a1, and Tkfc) and transcriptional regulation (Ablim3). Several of these genes are also implicated in liver carcinogenesis, including Crat, Car3 and Slc23a1. Our biomarker gene signature provides high statistical accuracy and a manageable number of genes to study as indicators to potentially accelerate toxicity testing based on their ability to induce liver necrosis and, eventually, liver cancer.

The structure and organization of a tumor and its microenvironment are often associated with cancer outcomes due to spatially varying molecular composition and signaling. A persistent challenge is to use this physical and chemical spatial organization to understand cancer progression. Here, we present a high-definition infrared imaging-based organizational measurement framework (INFORM) that leverages intrinsic chemical contrast of tissue to label unique components of the tumor and its microenvironment. Using objective and automated computational methods, further, we determine organization characteristics important for prediction. We show that the tumor spatial organization assessed with this framework is predictive of overall survival in colon cancer that adds to capability from clinical variables such as stage and grade, approximately doubling the risk of death in high-risk individuals. Our results open an all-digital avenue for measuring and studying the association between tumor spatial organization and disease progression.

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.

Histopathology has remained a cornerstone for biomedical tissue assessment for over a century, with a resource-intensive workflow involving biopsy or excision, gross examination, sampling, tissue processing to snap frozen or formalin-fixed paraffin-embedded blocks, sectioning, staining, optical imaging, and microscopic assessment. Emerging chemical imaging approaches, including stimulated Raman scattering (SRS) microscopy, can directly measure inherent molecular composition in tissue (thereby dispensing with the need for tissue processing, sectioning, and using dyes) and can use artificial intelligence (AI) algorithms to provide high-quality images. Here we show the integration of SRS microscopy in a pathology workflow to rapidly record chemical information from minimally processed fresh-frozen prostate tissue. Instead of using thin sections, we record data from intact thick tissues and use optical sectioning to generate images from multiple planes. We use a deep learning–based processing pipeline to generate virtual hematoxylin and eosin images. Next, we extend the computational method to generate archival-quality images in minutes, which are equivalent to those obtained from hours/days-long formalin-fixed, paraffin-embedded processing. We assessed the quality of images from the perspective of enabling pathologists to make decisions, demonstrating that the virtual stained image quality was diagnostically useful and the interpathologist agreement on prostate cancer grade was not impacted. Finally, because this method does not wash away lipids and small molecules, we assessed the utility of lipid chemical composition in determining grade. Together, the combination of chemical imaging and AI provides novel capabilities for rapid assessments in pathology by reducing the complexity and burden of current workflows.

Breast cancer in premenopausal women (preM) is frequently associated with worse prognosis compared to that in postmenopausal women (postM), and there is evidence that preM estrogen receptor-positive (ER+) tumors may respond poorly to endocrine therapy. There is, however, a paucity of studies characterizing molecular alterations in premenopausal tumors, a potential avenue for personalizing therapy for this group of women.

In clinical diagnostics and research involving histopathology, formalin-fixed paraffin-embedded (FFPE) tissue is almost universally favored for its superb image quality. However, tissue processing time (>24 h) can slow decision-making. In contrast, fresh frozen (FF) processing (<1 h) can yield rapid information but diagnostic accuracy is suboptimal due to lack of clearing, morphologic deformation and more frequent artifacts. Here, we bridge this gap using artificial intelligence. We synthesize FFPE-like images ("virtual FFPE") from FF images using a generative adversarial network (GAN) from 98 paired kidney samples derived from 40 patients. Five board-certified pathologists evaluated the results in a blinded test. Image quality of the virtual FFPE data was assessed to be high and showed a close resemblance to real FFPE images. Clinical assessments of disease on the virtual FFPE images showed a higher inter-observer agreement compared to FF images. The nearly instantaneously generated virtual FFPE images can not only reduce time to information but can facilitate more precise diagnosis from routine FF images without extraneous costs and effort.

Chromosome changes are one of the hallmarks of human malignancies. Chromosomal rearrangement is frequent in human cancers. One of the consequences of chromosomal rearrangement is gene fusions in the cancer genome. We have previously identified a panel of fusion genes in aggressive prostate cancers. In this study, we showed that 6 of these fusion genes are present in 7 different types of human malignancies with variable frequencies. Among them, the CCNH-C5orf30 and TRMT11-GRIK2 gene fusions were found in breast cancer, colon cancer, non-small cell lung cancer, esophageal adenocarcinoma, glioblastoma multiforme, ovarian cancer and liver cancer, with frequencies ranging from 12.9% to 85%. In contrast, four other gene fusions (mTOR-TP53BP1, TMEM135-CCDC67, KDM4-AC011523.2 and LRRC59-FLJ60017) are less frequent. Both TRMT11-GRIK2 and CCNH-C5orf30 are also frequently present in lymph node metastatic cancer samples from the breast, colon and ovary. Thus, detecting these fusion transcripts may have significant biological and clinical implications in cancer patient management.

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.

Atomic force microscopy-infrared (AFM-IR) spectroscopic imaging offers non-perturbative, molecular contrast for nanoscale characterization. The need to mitigate measurement artifacts and enhance sensitivity, however, requires narrowly-defined and strict sample preparation protocols. This limits reliable and facile characterization; for example, when using common substrates such as Silicon or glass. Here, we demonstrate a closed-loop (CL) piezo controller design for responsivity-corrected AFM-IR imaging. Instead of the usual mode of recording cantilever deflection driven by sample expansion, the principle of our approach is to maintain a zero amplitude harmonic cantilever deflection by CL control of a subsample piezo. We show that the piezo voltage used to maintain a null deflection provides a reliable measure of the local IR absorption with significantly reduced noise. A complete analytical description of the CL operation and characterization of the controller for achieving robust performance are presented. Accurate measurement of IR absorption of nanothin PMMA films on glass and Silicon validates the robust capability of CL AFM-IR in routine mapping of nanoscale molecular information.

This study examined the accuracy of radioactive seed localization (RSL) of lymph nodes (LNs) following neoadjuvant chemotherapy (NAC) for invasive breast carcinoma, recorded pathologic features of LNs following NAC, evaluated concordance of response between breast and LNs, and identified clinicopathologic factors associated with higher risk of residual lymph node involvement.

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.