Current breast cancer classification systems are based on molecular evaluation of tumor receptor status and do not account for distinct morphological phenotypes. In other types of cancer, taxonomy based on normal cell phenotypes has been extremely useful for diagnosis and treatment strategies. In this issue of the JCI, Santagata and colleagues developed a breast cancer classification scheme based on characterization of healthy mammary cells. Reclassification of breast cancer cells and breast cancer tissue microarrays with this system correlated with prognosis better than the standard receptor status designation. This scheme provides a major advance toward our understanding of the origin of the cells in the breast and breast cancers.

The role of immunohistochemistry (IHC) in the management of cancer has expanded to provide improved diagnostic classification, as well as guidance on disease prognosis, therapy, and relapse. These new tasks require evaluation of an increasing number of protein targets; however, conventional multiplexing, usually achieved using serial tissue sections stained for a single analyte per slide, can exhaust small biopsy specimens, complicate slide-to-slide protein expression correlation, and leave insufficient material for additional molecular assays. A new approach, mass spectrometry immunohistochemistry (MSIHC), compatible with high levels of target multiplexing and suitable for use on formalin-fixed, paraffin-embedded samples can circumvent many of these issues. The strategy employs antibodies that are labeled with elemental mass tags, such as isotopically pure lanthanides not typically found in biological specimens, rather than with typical fluorophores or chromogens. The metal-labeled antibodies are then detected in tissue using lasers or ion beams to liberate the tags for subsequent mass spectrometry detection. Within a given multiplexed IHC panel, the metal labels are selected so that their respective masses do not overlap. More than 30 antibodies have been imaged simultaneously, and up to 100 antibodies could potentially be detected at once if the full available mass spectrum is deployed. MSIHC has a number of advantages over conventional IHC techniques. Background due to autofluorescence is absent and the dynamic range is 10(5), exceeding immunofluorescence and chromogenic IHC by 100-fold and 1000-fold, respectively. Detection of labeled primary antibodies improves assay linearity over both chromogenic and fluorescent IHC. Multiplexed mass-tagged antibodies incubated simultaneously with tissue do not appear to cross-interfere, and because the mass tags do not degrade, samples are stable indefinitely. The imaging resolution of multiplexed ion-beam imaging can be better than light microscopy. With appropriate instrumentation, MSIHC has the potential to transform research and clinical pathology practice.

Metastatic neuroendocrine neoplasms to the breast are rare and histopathologic overlap with mammary carcinomas has led to misdiagnosis. We present a case of a middle-aged woman with diplopia and a right medial rectus mass. Metastatic breast cancer was initially suspected based on a history of invasive ductal carcinoma. Detailed immunohistochemistry of the orbital biopsy, gallium-68 dotatate positron emission tomography-CT, and reevaluation of her prior breast specimen, demonstrated that her initial breast carcinoma diagnosis was in error and she was ultimately diagnosed with a previously unknown gastrointestinal neuroendocrine tumour metastatic to both the orbit and breast. This case highlights the challenges of differentiating between metastatic neuroendocrine tumours and invasive mammary carcinomas with neuroendocrine differentiation both in the breast and in the orbit. It is important to recognise the overlap so that a primary neuroendocrine neoplasm is not missed, or treatment significantly delayed.

Understanding the cellular and molecular mechanisms of tumor initiation and progression for each cancer type is central to making improvements in both prevention and therapy. Identifying the cancer cells of origin and the necessary and sufficient mechanisms of transformation and progression provide opportunities for improved specific clinical interventions. In the last few decades, advanced genetic manipulation techniques have facilitated rapid progress in defining the etiologies of cancers and their cells of origin. Recent studies driven by various groups have provided experimental evidence indicating the cellular origins for each type of skin and esophageal cancer and have identified underlying mechanisms that stem/progenitor cells use to initiate tumor development. Specifically, cyclooxygenase-2 (Cox-2) is associated with tumor initiation and progression in many cancer types. Recent studies provide data demonstrating the roles of Cox-2 in skin and esophageal malignancies, especially in squamous cell carcinomas (SCCs) occurring in both sites. Here, we review experimental evidence aiming to define the origins of skin and esophageal cancers and discuss how Cox-2 contributes to tumorigenesis and differentiation.

Mouse models and genetically engineered mouse models (GEMM) are essential experimental tools for the understanding molecular mechanisms within complex biological systems. GEMM are especially useful for inferencing phenocopy information to genetic human diseases such as breast cancer. Human breast cancer modeling in mice most commonly employs mammary epithelial-specific promoters to investigate gene function(s) and, in particular, putative oncogenes. Models are specifically useful in the mammary epithelial cell in the context of the complete mammary gland environment. Gene targeted knockout mice including conditional targeting to specific mammary cells can reveal developmental defects in mammary organogenesis and demonstrate the importance of putative tumor suppressor genes. Some of these models demonstrate a non-traditional type of tumor suppression which involves interplay between the tumor susceptible cell and its host/environment. These GEMM help to reveal the processes of cancer progression beyond those intrinsic to cancer cells. Furthermore, the, analysis of mouse models requires appropriate consideration of mouse strain, background, and environmental factors. In this review, we compare aging-related factors in mouse models for breast cancer. We introduce databases of GEMM attributes and colony functional variations.

Tumor Whole Slide Images (WSI) are often heterogeneous, which hinders the discovery of biomarkers in the presence of confounding clinical factors. In this study, we present a pipeline for identifying biomarkers from the Glioblastoma Multiforme (GBM) cohort of WSIs from TCGA archive. The GBM cohort endures many technical artifacts while the discovery of GBM biomarkers is challenged because "age" is the single most confounding factor for predicting outcomes. The proposed approach relies on interpretable features (e.g., nuclear morphometric indices), effective similarity metrics for heterogeneity analysis, and robust statistics for identifying biomarkers. The pipeline first removes artifacts (e.g., pen marks) and partitions each WSI into patches for nuclear segmentation via an extended U-Net for subsequent quantitative representation. Given the variations in fixation and staining that can artificially modulate hematoxylin optical density (HOD), we extended Navab's Lab method to normalize images and reduce the impact of batch effects. The heterogeneity of each WSI is then represented either as probability density functions (PDF) per patient or as the composition of a dictionary predicted from the entire cohort of WSIs. For PDF- or dictionary-based methods, morphometric subtypes are constructed based on distances computed from optimal transport and linkage analysis or consensus clustering with Euclidean distances, respectively. For each inferred subtype, Kaplan-Meier and/or the Cox regression model are used to regress the survival time. Since age is the single most important confounder for predicting survival in GBM and there is an observed violation of the proportionality assumption in the Cox model, we use both age and age-squared coupled with the Likelihood ratio test and forest plots for evaluating competing statistics. Next, the PDF- and dictionary-based methods are combined to identify biomarkers that are predictive of survival. The combined model has the advantage of integrating global (e.g., cohort scale) and local (e.g., patient scale) attributes of morphometric heterogeneity, coupled with robust statistics, to reveal stable biomarkers. The results indicate that, after normalization of the GBM cohort, mean HOD, eccentricity, and cellularity are predictive of survival. Finally, we also stratified the GBM cohort as a function of EGFR expression and published genomic subtypes to reveal genomic-dependent morphometric biomarkers.

We previously demonstrated that the Trp53-R270H mutation can drive prostate cancer (CaP) initiation using the FVB.129S4 (Trp53^tm3Tyj/wt); FVB.129S (Nkx3-1^{tm3(cre)Mmswt}) genetically engineered mouse model (GEM). We now validate this finding in a different model (B6.129S4-Trp53^tm3.1Tyj/J mice) and use RNA-sequencing (RNA-Seq) to identify genes which may contribute to Trp53 R270H-mediated prostate carcinogenesis. Wildtype (Trp53^WT/WT), heterozygous (Trp53^R270H/WT), and homozygous mice (Trp53^R270H/R270H) were exposed to 5 Gy irradiation to activate and stabilize p53, and thereby enhance our ability to identify differences in transcriptional activity between the three groups of mice. Mouse prostates were harvested 6 h post-irradiation and processed for histological/immunohistochemistry (IHC) analysis or were snap-frozen for RNA extraction and transcriptome profiling. IHC analyses determined that presence of the Trp53-R270H mutation impacts apoptosis (lower caspase 3 activity) but not cell proliferation (Ki67). RNA-Seq analysis identified 1378 differentially expressed genes, including wildtype p53 target genes (E.g., Cdkn1a, Bax, Bcl2, Kras, Mdm2), p53 gain-of-function (GOF)-related genes (Mgmt, Id4), and CaP-related genes (Cav-1, Raf1, Kras). Further understanding the mechanisms which contribute to prostate carcinogenesis could allow for the development of improved preventive methods, diagnostics, and treatments for CaP.

Plants produce diverse specialized metabolites (SMs), but the genes responsible for their production and regulation remain largely unknown, hindering efforts to tap plant pharmacopeia. Given that genes comprising SM pathways exhibit environmentally dependent coregulation, we hypothesized that genes within a SM pathway would form tight associations (modules) with each other in coexpression networks, facilitating their identification. To evaluate this hypothesis, we used 10 global coexpression data sets, each a meta-analysis of hundreds to thousands of experiments, across eight plant species to identify hundreds of coexpressed gene modules per data set. In support of our hypothesis, 15.3 to 52.6% of modules contained two or more known SM biosynthetic genes, and module genes were enriched in SM functions. Moreover, modules recovered many experimentally validated SM pathways, including all six known to form biosynthetic gene clusters (BGCs). In contrast, bioinformatically predicted BGCs (i.e., those lacking an associated metabolite) were no more coexpressed than the null distribution for neighboring genes. These results suggest that most predicted plant BGCs are not genuine SM pathways and argue that BGCs are not a hallmark of plant specialized metabolism. We submit that global gene coexpression is a rich, largely untapped resource for discovering the genetic basis and architecture of plant natural products.

Visualizing spatial assay data in anatomical images is vital for understanding biological processes in cell, tissue, and organ organizations. Technologies requiring this functionality include traditional one-at-a-time assays, and bulk and single-cell omics experiments, including RNA-seq and proteomics. The spatialHeatmap software provides a series of powerful new methods for these needs, and allows users to work with adequately formatted anatomical images from public collections or custom images. It colors the spatial features (e.g. tissues) annotated in the images according to the measured or predicted abundance levels of biomolecules (e.g. mRNAs) using a color key. This core functionality of the package is called a spatial heatmap plot. Single-cell data can be co-visualized in composite plots that combine spatial heatmaps with embedding plots of high-dimensional data. The resulting spatial context information is essential for gaining insights into the tissue-level organization of single-cell data, or vice versa. Additional core functionalities include the automated identification of biomolecules with spatially selective abundance patterns and clusters of biomolecules sharing similar abundance profiles. To appeal to both non-expert and computational users, spatialHeatmap provides a graphical and a command-line interface, respectively. It is distributed as a free, open-source Bioconductor package (https://bioconductor.org/packages/spatialHeatmap) that users can install on personal computers, shared servers, or cloud systems.