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Transgender and gender diverse (TGD) people in the United States face high rates of minority stressors, such as social rejection, homelessness, discrimination, and identity-based violence (James et al., 2016). Transgender and gender diverse service members are also exposed to unique military-specific stressors such as discriminatory military policies, combat stress, and military sexual trauma (Tucker et al., 2019). However, little is known about TGD troops' experiences of resilience when navigating stress and trauma exposure during their military service. A transaffirmative participatory research design and interpretative phenomenological analysis (IPA) analytic method were used to explore how troops made sense of their experiences of oppression and resilience. Researchers interviewed TGD service members (

Despite advances in precision medicine, the clinical prospects for patients with ovarian and uterine cancers have not substantially improved. Here, we analyzed genome-scale CRISPR-Cas9 loss-of-function screens across 851 human cancer cell lines and found that frequent overexpression of SLC34A2-encoding a phosphate importer-is correlated with sensitivity to loss of the phosphate exporter XPR1, both in vitro and in vivo. In patient-derived tumor samples, we observed frequent PAX8-dependent overexpression of SLC34A2, XPR1 copy number amplifications and XPR1 messenger RNA overexpression. Mechanistically, in SLC34A2-high cancer cell lines, genetic or pharmacologic inhibition of XPR1-dependent phosphate efflux leads to the toxic accumulation of intracellular phosphate. Finally, we show that XPR1 requires the novel partner protein KIDINS220 for proper cellular localization and activity, and that disruption of this protein complex results in acidic "vacuolar" structures preceding cell death. These data point to the XPR1-KIDINS220 complex and phosphate dysregulation as a therapeutic vulnerability in ovarian cancer.

Exciting therapeutic targets are emerging from CRISPR-based screens of high mutational-burden adult cancers. A key question, however, is whether functional genomic approaches will yield new targets in pediatric cancers, known for remarkably few mutations, which often encode proteins considered challenging drug targets. To address this, we created a first-generation pediatric cancer dependency map representing 13 pediatric solid and brain tumor types. Eighty-two pediatric cancer cell lines were subjected to genome-scale CRISPR–Cas9 loss-of-function screening to identify genes required for cell survival. In contrast to the finding that pediatric cancers harbor fewer somatic mutations, we found a similar complexity of genetic dependencies in pediatric cancer cell lines compared to that in adult models. Findings from the pediatric cancer dependency map provide preclinical support for ongoing precision medicine clinical trials. The vulnerabilities observed in pediatric cancers were often distinct from those in adult cancer, indicating that repurposing adult oncology drugs will be insufficient to address childhood cancers. A pediatric cancer dependency map generated with genome-scale CRISPR–Cas9 loss-of-function screens in 82 pediatric cancer cell lines highlights genetic dependencies across a range of tumor types.

Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Direct comparisons of tumor and cell line transcriptional profiles are complicated by several factors, including the variable presence of normal cells in tumor samples. We thus develop an unsupervised alignment method (Celligner) and apply it to integrate several large-scale cell line and tumor RNA-Seq datasets. Although our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor similarity across cell lines. Using this approach, we identify several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and that exhibit distinct chemical and genetic dependencies. Celligner could be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell lines., The determination of whether cancer cell lines recapitulate the molecular features of corresponding patient tumours remains essential for the selection of appropriate cell line models for preclinical studies. The method developed here, Celligner, integrates cancer cell line and tumour RNA-seq datasets and reveals large differences in their concordance across cell lines and cancer types.

Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate. Information-rich assays, such as gene-expression profiling, have generally not permitted efficient profiling of a given perturbation across multiple cellular contexts. Here, we develop MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines. We combine it with Cell Hashing to further multiplex additional experimental conditions, such as post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and enable prediction of long-term cell viability from short-term transcriptional responses to treatment., Large-scale screens of chemical and genetic vulnerabilities in cancer are typically limited to simple readouts of cell viability. Here, the authors develop a method for profiling post-perturbation transcriptional responses across large pools of cancer cell lines, enabling deep characterization of shared and context-specific responses.

Achieving precision oncology requires accurate identification of targetable cancer vulnerabilities in patients. Generally, genomic features are regarded as the state-of-the-art method for stratifying patients for targeted therapies. In this work, we conduct the first rigorous comparison of DNA- and expression-based predictive models for viability across five datasets encompassing chemical and genetic perturbations. We find that expression consistently outperforms DNA for predicting vulnerabilities, including many currently stratified by canonical DNA markers. Contrary to their perception in the literature, the most accurate expression-based models depend on few features and are amenable to biological interpretation. This work points to the importance of exploring more comprehensive expression profiling in clinical settings.

Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate or the expression of a marker gene. Information-rich assays, such as gene-expression profiling, are generally not amenable to efficient profiling of a given perturbation across multiple cellular contexts. Here, we developed MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines, and combine it with Cell Hashing to further multiplex additional experimental conditions, such as multiple post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and can be used to predict long-term cell viability from short-term transcriptional responses to treatment.

Cultured cell lines are the workhorse of cancer research, but it is unclear to what extent they recapitulate the cellular heterogeneity observed among malignant cells in tumors, given the absence of a native tumor microenvironment. Here, we used multiplexed single cell RNA-seq to profile ~200 cancer cell lines. We uncovered expression programs that are recurrently heterogeneous within many cancer cell lines and are largely independent of observed genetic diversity. These programs of heterogeneity are associated with diverse biological processes, including cell cycle, senescence, stress and interferon responses, epithelial-to-mesenchymal transition, and protein maturation and degradation. Notably, some of these recurrent programs recapitulate those seen in human tumors, suggesting a prominent role of intrinsic plasticity in generating intra-tumoral heterogeneity. Moreover, the data allowed us to prioritize specific cell lines as model systems of cellular plasticity. We used two such models to demonstrate the dynamics, regulation and drug sensitivities associated with a cancer senescence program also observed in human tumors. Our work describes the landscape of cellular heterogeneity in diverse cancer cell lines, and identifies recurrent patterns of expression heterogeneity that are shared between tumors and specific cell lines and can thus be further explored in follow up studies.

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

Transactions of the Cumberland & Westmorland Antiquarian & Archaeological Society, 10, 35-54, Presents a summary of the history of mining in the Caldbeck fells, and describes the results of recent research carried out by local enthusiasts. Their findings have not only been pivotal in extending the known date for mining and smelting to before AD 1200, but have also provided the earliest known evidence for the use of tracked transport in the UK and possibly elsewhere. A major conclusion is that the historical research and practical work, including the discovery of a hitherto unknown level, has determined that the principal seat of Elizabethan mining at Caldbeck was the Silver Gill workings. Au/LD

Transactions of the Cumberland & Westmorland Antiquarian & Archaeological Society, 1, 89-104, Investigates the first well-documented large-scale copper mining and smelting operation in the UK -- active between 1566/68 and 1695. Its location had been forgotten but historical survey plus fieldwork including that by the Mines of Lakeland Exploration Society (MOLES) reveals Silver Gill as the likely locale.