Cancer therapy has traditionally focused on eliminating fast-growing populations of cells. Yet, an increasing body of evidence suggests that small subpopulations of cancer cells can evade strong selective drug pressure by entering a ‘persister' state of negligible growth. This drug-tolerant state has been hypothesized to be part of an initial strategy towards eventual acquisition of bona fide drug-resistance mechanisms. However, the diversity of drug-resistance mechanisms that can expand from a persister bottleneck is unknown. Here we compare persister-derived, erlotinib-resistant colonies that arose from a single, EGFR-addicted lung cancer cell. We find, using a combination of large-scale drug screening and whole-exome sequencing, that our erlotinib-resistant colonies acquired diverse resistance mechanisms, including the most commonly observed clinical resistance mechanisms. Thus, the drug-tolerant persister state does not limit—and may even provide a latent reservoir of cells for—the emergence of heterogeneous drug-resistance mechanisms.

BackgroundThe ability to deliver a gene of interest into a specific cell type is an essential aspect of biomedical research. Viruses can be a useful tool for this delivery, particularly in difficult to transfect cell types. Adeno-associated virus (AAV) is a useful gene transfer vector because of its ability to mediate efficient gene transduction in numerous dividing and quiescent cell types, without inducing any known pathogenicity. There are now a number of natural for that designed AAV serotypes that each has a differential ability to infect a variety of cell types. Although transduction studies have been completed, the bulk of the studies have been done in vivo, and there has never been a comprehensive study of transduction ex vivo/in vitro.MethodsEach cell type was infected with each serotype at a multiplicity of infection of 100,000 viral genomes/cell and transduction was analyzed by flow cytometry + .ResultsWe found that AAV1 and AAV6 have the greatest ability to transduce a wide range of cell types, however, for particular cell types, there are specific serotypes that provide optimal transduction.ConclusionsIn this work, we describe the transduction efficiency of ten different AAV serotypes in thirty-four different mammalian cell lines and primary cell types. Although these results may not be universal due to numerous factors such as, culture conditions and/ or cell growth rates and cell heterogeneity, these results provide an important and unique resource for investigators who use AAV as an ex vivo gene delivery vector or who work with cells that are difficult to transfect.

Non small cell lung cancer H460 clones exhibit a high degree of heterogeneity in signaling states.Clones with similar patterns of basal signaling heterogeneity have similar paclitaxel sensitivities.Models of signaling heterogeneity among the clones can be used to classify sensitivity to paclitaxel for other cancer populations.

Microscopy often reveals the existence of phenotypically distinct cellular subpopulations. However, further characterization of observed subpopulations can be limited by the number of biomolecular markers that can be simultaneously monitored. Here, we present a computational approach for extensibly profiling cellular subpopulations by freeing up one or more imaging channels to monitor additional probes. In our approach, we train classifiers to re-identify subpopulations accurately based on an enhanced collection of phenotypic features extracted from only a subset of the original markers. Subpopulation profiles were then constructed step-wise from replicate experiments, in which cells were labeled with different but overlapping marker sets. We applied our approach to characterize molecular differences among subpopulations, and functional groupings of markers within populations of differentiating mouse preadipocytes, polarizing human neutrophil-like cells, and dividing human cancer cells.

Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell’s phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring co-staining biomarkers on the same cells; instead we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the “gold standard” of co-staining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems.

We evaluated a 66-year-old woman who presented with a new inferior left breast mass. She had a history of a stage IIIC left breast cancer 3 years prior. Treatment for that cancer included neo-adjuvant chemotherapy, partial mastectomy, level I/II axillary lymph node dissection, radiation therapy, and anastrozole. She had no history of recent breast trauma to account for the new mass.The visible and palpable solid mass located in the lower inner left breast was at a location different from the previous lumpectomy scar.The mass extended to the skin. A mammogram and ultrasound revealed a 14 × 14 × 9 mm mass (Figures 1, 2). A core biopsy revealed "fibrovascular tissue with associated hemorrhage and focal endothelial hyperplasia and vascular proliferation with mild chronic inflammation including abundant histiocytes." Complete excision was recommended. The mass was completely excised. Pathology was consistent with an Masson's tumor, noting intravascular papillary endothelial hyperplasia. The lesion originated from the dermis and had Abstract A 66-year-old woman had a new left breast mass with a prior history of a stage IIIC left breast cancer. She had excision of the mass. The pathology noted intravascular papillary endothelial hyperplasia (IPEH) also known as Masson's tumor. Although a benign lesion, this remains in the differential of breast lesions with vascular morphology. IPEH has been described at multiple sites throughout the body. It must be distinguished from angiosarcoma. Although Masson's tumor has been previously documented in the breast, prior treatment for breast cancer presents a diagnostic dilemma. Treatment for the breast cancer may be a contributing event for Masson's tumor. K E Y W O R D S angiosarcoma, breast cancer, dermal lesion, endothelial hyperplasia, fibrovascular tissue, intravascular papillary endothelial hyperplasia, IPEH, Masson's pseudoangiosarcoma, Masson's tumor, papillary endothelial hyperplasia, vascular proliferation F I G U R E 1 Left breast ultrasound of the superficial mass with skin involvement How to cite this article: Steininger R, Bouton M, Marsh J. Masson's tumor of the breast: Rare differential for new or recurrent breast cancer-Case report, pathology, and review of the literature. Breast J. 2020;26:752-754. https ://doi.

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard. Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard.Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.