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At what point should curative cancer therapy stop and palliative care begin? What constitutes adequate palliation? Here are some up-to-date, authoritative solutions to these thorny questions. [ABSTRACT FROM AUTHOR]

Identifying cellular identities is a key use case in single-cell transcriptomics. While machine learning has been leveraged to automate cell annotation predictions for some time, there has been little progress in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues. Here, we propose scTab, an automated cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million cells). In this context, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales both in terms of training dataset size and model size. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets and demonstrate the benefits of using deep learning methods in this paradigm., (© 2024. The Author(s).)

Intravesical therapy (IVT) is the standard of care to decrease risk of recurrence and progression for high-grade nonmuscle-invasive bladder cancer. However, post-IVT recurrence remains common and the ability to risk-stratify patients before or after IVT is limited. In this prospectively designed and accrued cohort study, we examine the utility of urinary comprehensive genomic profiling (uCGP) for predicting recurrence risk following transurethral resection of bladder tumor (TURBT) and evaluating longitudinal IVT response. Urine was collected before and after IVT instillation and uCGP testing was done using the UroAmp™ platform. Baseline uCGP following TURBT identified patients with high (61%) and low (39%) recurrence risk. At 24 months, recurrence-free survival (RFS) was 100% for low-risk and 45% for high-risk patients with a hazard ratio (HR) of 9.3. Longitudinal uCGP classified patients as minimal residual disease (MRD) Negative, IVT Responder, or IVT Refractory with 24-month RFS of 100%, 50%, and 32%, respectively. Compared with MRD Negative patients, IVT Refractory patients had a HR of 10.5. Collectively, uCGP enables noninvasive risk assessment of patients following TURBT and induction IVT. uCGP could inform surveillance cystoscopy schedules and identify high-risk patients in need of additional therapy., (© 2023 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Single-cell ATAC sequencing coverage in regulatory regions is typically binarized as an indicator of open chromatin. Here we show that binarization is an unnecessary step that neither improves goodness of fit, clustering, cell type identification nor batch integration. Fragment counts, but not read counts, should instead be modeled, which preserves quantitative regulatory information. These results have immediate implications for single-cell ATAC sequencing analysis., (© 2023. The Author(s).)

Purpose: Urinary comprehensive genomic profiling (uCGP) uses next-generation sequencing to identify mutations associated with urothelial carcinoma and has the potential to improve patient outcomes by noninvasively diagnosing disease, predicting grade and stage, and estimating recurrence risk., Experimental Design: This is a multicenter case-control study using banked urine specimens collected from patients undergoing initial diagnosis/hematuria workup or urothelial carcinoma surveillance. A total of 581 samples were analyzed by uCGP: 333 for disease classification and grading algorithm development, and 248 for blinded validation. uCGP testing was done using the UroAmp platform, which identifies five classes of mutation: single-nucleotide variants, copy-number variants, small insertion-deletions, copy-neutral loss of heterozygosity, and aneuploidy. UroAmp algorithms predicting urothelial carcinoma tumor presence, grade, and recurrence risk were compared with cytology, cystoscopy, and pathology., Results: uCGP algorithms had a validation sensitivity/specificity of 95%/90% for initial cancer diagnosis in patients with hematuria and demonstrated a negative predictive value (NPV) of 99%. A positive diagnostic likelihood ratio (DLR) of 9.2 and a negative DLR of 0.05 demonstrate the ability to risk-stratify patients presenting with hematuria. In surveillance patients, binary urothelial carcinoma classification demonstrated an NPV of 91%. uCGP recurrence-risk prediction significantly prognosticated future recurrence (hazard ratio, 6.2), whereas clinical risk factors did not. uCGP demonstrated positive predictive value (PPV) comparable with cytology (45% vs. 42%) with much higher sensitivity (79% vs. 25%). Finally, molecular grade predictions had a PPV of 88% and a specificity of 95%., Conclusions: uCGP enables noninvasive, accurate urothelial carcinoma diagnosis and risk stratification in both hematuria and urothelial carcinoma surveillance patients., (©2023 The Authors; Published by the American Association for Cancer Research.)

Models of intercellular communication in tissues are based on molecular profiles of dissociated cells, are limited to receptor-ligand signaling and ignore spatial proximity in situ. We present node-centric expression modeling, a method based on graph neural networks that estimates the effects of niche composition on gene expression in an unbiased manner from spatial molecular profiling data. We recover signatures of molecular processes known to underlie cell communication., (© 2022. The Author(s).)

The clinical standard of care for urothelial carcinoma (UC) relies on invasive procedures with suboptimal performance. To enhance UC treatment, we developed a urinary comprehensive genomic profiling (uCGP) test, UroAmplitude, that measures mutations from tumor DNA present in urine. In this study, we performed a blinded, prospective validation of technical sensitivity and positive predictive value (PPV) using reference standards, and found at 1% allele frequency, mutation detection performs at 97.4% sensitivity and 80.4% PPV. We then prospectively compared the mutation profiles of urine-extracted DNA to those of matched tumor tissue to validate clinical performance. Here, we found tumor single-nucleotide variants were observed in the urine with a median concordance of 91.7% and uCGP revealed distinct patterns of genomic lesions enriched in low- and high-grade disease. Finally, we retrospectively explored longitudinal case studies to quantify residual disease following bladder-sparing treatments, and found uCGP detected residual disease in patients receiving bladder-sparing treatment and predicted recurrence and disease progression. These findings demonstrate the potential of the UroAmplitude platform to reliably identify and track mutations associated with UC at each stage of disease: diagnosis, treatment, and surveillance. Multiple case studies demonstrate utility for patient risk classification to guide both surgical and therapeutic interventions.

Despite the therapeutic promise of direct reprogramming, basic principles concerning fate erasure and the mechanisms to resolve cell identity conflicts remain unclear. To tackle these fundamental questions, we established a single-cell protocol for the simultaneous analysis of multiple cell fate conversion events based on combinatorial and traceable reprogramming factor expression: Collide-seq. Collide-seq revealed the lack of a common mechanism through which fibroblast-specific gene expression loss is initiated. Moreover, we found that the transcriptome of converting cells abruptly changes when a critical level of each reprogramming factor is attained, with higher or lower levels not contributing to major changes. By simultaneously inducing multiple competing reprogramming factors, we also found a deterministic system, in which titration of fates against each other yields dominant or colliding fates. By investigating one collision in detail, we show that reprogramming factors can disturb cell identity programs independent of their ability to bind their target genes. Taken together, Collide-seq has shed light on several fundamental principles of fate conversion that may aid in improving current reprogramming paradigms., (© 2022 The Authors. Published under the terms of the CC BY 4.0 license.)

Objective: A fine-tuned balance of glucocorticoid receptor (GR) activation is essential for organ formation, with disturbances influencing many health outcomes. In utero, glucocorticoids have been linked to brain-related negative outcomes, with unclear underlying mechanisms, especially regarding cell-type-specific effects. An in vitro model of fetal human brain development, induced human pluripotent stem cell (hiPSC)-derived cerebral organoids, was used to test whether cerebral organoids are suitable for studying the impact of prenatal glucocorticoid exposure on the developing brain., Methods: The GR was activated with the synthetic glucocorticoid dexamethasone, and the effects were mapped using single-cell transcriptomics across development., Results: The GR was expressed in all cell types, with increasing expression levels through development. Not only did its activation elicit translocation to the nucleus and the expected effects on known GR-regulated pathways, but also neurons and progenitor cells showed targeted regulation of differentiation- and maturation-related transcripts. Uniquely in neurons, differentially expressed transcripts were significantly enriched for genes associated with behavior-related phenotypes and disorders. This human neuronal glucocorticoid response profile was validated across organoids from three independent hiPSC lines reprogrammed from different source tissues from both male and female donors., Conclusions: These findings suggest that excessive glucocorticoid exposure could interfere with neuronal maturation in utero, leading to increased disease susceptibility through neurodevelopmental processes at the interface of genetic susceptibility and environmental exposure. Cerebral organoids are a valuable translational resource for exploring the effects of glucocorticoids on early human brain development.

Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing. However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow-rate chromatography, and a novel trapped ion mobility mass spectrometer, resulting in a more than 10-fold improved sensitivity. We precisely and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single-cell proteomes to transcriptome data revealed a stable-core proteome despite perturbation, while the transcriptome appears stochastic. Our technology can readily be applied to ultra-high sensitivity analyses of tissue material, posttranslational modifications, and small molecule studies from small cell counts to gain unprecedented insights into cellular heterogeneity in health and disease., (© 2022 The Authors Published under the terms of the CC BY 4.0 license.)

Background: Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge., Scope of Review: We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals., Major Conclusions: Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models., (Copyright © 2021. Published by Elsevier GmbH.)

Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics., (© 2022. Springer Nature America, Inc.)

EpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. To address the modality specific challenges from epigenomics data, epiScanpy quantifies the epigenome using multiple feature space constructions and builds a nearest neighbour graph using epigenomic distance between cells. EpiScanpy makes the many existing scRNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities, including methods for common clustering, dimension reduction, cell type identification and trajectory learning techniques, as well as an atlas integration tool for scATAC-seq datasets. The toolkit also features numerous useful downstream functions, such as differential methylation and differential openness calling, mapping epigenomic features of interest to their nearest gene, or constructing gene activity matrices using chromatin openness. We successfully benchmark epiScanpy against other scATAC-seq analysis tools and show its outperformance at discriminating cell types., (© 2021. The Author(s).)

Single-cell RNA-seq datasets are often first analyzed independently without harnessing model fits from previous studies, and are then contextualized with public data sets, requiring time-consuming data wrangling. We address these issues with sfaira, a single-cell data zoo for public data sets paired with a model zoo for executable pre-trained models. The data zoo is designed to facilitate contribution of data sets using ontologies for metadata. We propose an adaption of cross-entropy loss for cell type classification tailored to datasets annotated at different levels of coarseness. We demonstrate the utility of sfaira by training models across anatomic data partitions on 8 million cells., (© 2021. The Author(s).)

Background: Due to the ongoing COVID-19 pandemic, demand for diagnostic testing has increased drastically, resulting in shortages of necessary materials to conduct the tests and overwhelming the capacity of testing laboratories. The supply scarcity and capacity limits affect test administration: priority must be given to hospitalized patients and symptomatic individuals, which can prevent the identification of asymptomatic and presymptomatic individuals and hence effective tracking and tracing policies. We describe optimized group testing strategies applicable to SARS-CoV-2 tests in scenarios tailored to the current COVID-19 pandemic and assess significant gains compared to individual testing. Methods: We account for biochemically realistic scenarios in the context of dilution effects on SARS-CoV-2 samples and consider evidence on specificity and sensitivity of PCR-based tests for the novel coronavirus. Because of the current uncertainty and the temporal and spatial changes in the prevalence regime, we provide analysis for several realistic scenarios and propose fast and reliable strategies for massive testing procedures. Key Findings: We find significant efficiency gaps between different group testing strategies in realistic scenarios for SARS-CoV-2 testing, highlighting the need for an informed decision of the pooling protocol depending on estimated prevalence, target specificity, and high- vs. low-risk population. For example, using one of the presented methods, all 1.47 million inhabitants of Munich, Germany, could be tested using only around 141 thousand tests if the infection rate is below 0.4% is assumed. Using 1 million tests, the 6.69 million inhabitants from the city of Rio de Janeiro, Brazil, could be tested as long as the infection rate does not exceed 1%. Moreover, we provide an interactive web application, available at www.grouptexting.com, for visualizing the different strategies and designing pooling schemes according to specific prevalence scenarios and test configurations. Interpretation: Altogether, this work may help provide a basis for an efficient upscaling of current testing procedures, which takes the population heterogeneity into account and is fine-grained towards the desired study populations, e.g., mild/asymptomatic individuals vs. symptomatic ones but also mixtures thereof. Funding: German Science Foundation (DFG), German Federal Ministry of Education and Research (BMBF), Chan Zuckerberg Initiative DAF, and Austrian Science Fund (FWF)., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Verdun, Fuchs, Harar, Elbrächter, Fischer, Berner, Grohs, Theis and Krahmer.)

The in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for 'reverse phenotyping'. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states., (© 2021. The Author(s).)

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2 + TMPRSS2 + cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.

It has recently become possible to simultaneously assay T-cell specificity with respect to large sets of antigens and the T-cell receptor sequence in high-throughput single-cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T-cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single-cell immune repertoire screens can be mitigated by modeling cell-specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell-to-dextramer binding strength and receptor-to-pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single-cell RNA-seq studies on T cells without the need for MHC staining., (© 2020 The Authors. Published under the terms of the CC BY 4.0 license.)

Motivation: Dimensionality reduction is a key step in the analysis of single-cell RNA-sequencing data. It produces a low-dimensional embedding for visualization and as a calculation base for downstream analysis. Nonlinear techniques are most suitable to handle the intrinsic complexity of large, heterogeneous single-cell data. However, with no linear relation between gene and embedding coordinate, there is no way to extract the identity of genes driving any cell's position in the low-dimensional embedding, making it difficult to characterize the underlying biological processes., Results: In this article, we introduce the concepts of local and global gene relevance to compute an equivalent of principal component analysis loadings for non-linear low-dimensional embeddings. Global gene relevance identifies drivers of the overall embedding, while local gene relevance identifies those of a defined sub-region. We apply our method to single-cell RNA-seq datasets from different experimental protocols and to different low-dimensional embedding techniques. This shows our method's versatility to identify key genes for a variety of biological processes., Availability and Implementation: To ensure reproducibility and ease of use, our method is released as part of destiny 3.0, a popular R package for building diffusion maps from single-cell transcriptomic data. It is readily available through Bioconductor., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

Massively parallel reporter assays (MPRAs) can measure the regulatory function of thousands of DNA sequences in a single experiment. Despite growing popularity, MPRA studies are limited by a lack of a unified framework for analyzing the resulting data. Here we present MPRAnalyze: a statistical framework for analyzing MPRA count data. Our model leverages the unique structure of MPRA data to quantify the function of regulatory sequences, compare sequences' activity across different conditions, and provide necessary flexibility in an evolving field. We demonstrate the accuracy and applicability of MPRAnalyze on simulated and published data and compare it with existing methods.

Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. This approach can also delineate lineage hierarchies and identify molecular programmes of cell-fate acquisition and segregation. Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. However, interpretation of the resulting data is challenging and requires computational models at multiple levels of abstraction. In contrast to other applications of single cell sequencing, where clustering approaches dominate, developmental systems are generally modelled using continuous structures, trajectories and trees. These trajectory models carry the promise of elucidating mechanisms of development, disease and stimulation response at very high molecular resolution. However, their reliable analysis and biological interpretation requires an understanding of their underlying assumptions and limitations. Here, we review the basic concepts of such computational approaches and discuss the characteristics of developmental processes that can be learnt from trajectory models., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2019. Published by The Company of Biologists Ltd.)

Recent single-cell RNA-sequencing studies have suggested that cells follow continuous transcriptomic trajectories in an asynchronous fashion during development. However, observations of cell flux along trajectories are confounded with population size effects in snapshot experiments and are therefore hard to interpret. In particular, changes in proliferation and death rates can be mistaken for cell flux. Here we present pseudodynamics, a mathematical framework that reconciles population dynamics with the concepts underlying developmental trajectories inferred from time-series single-cell data. Pseudodynamics models population distribution shifts across trajectories to quantify selection pressure, population expansion, and developmental potentials. Applying this model to time-resolved single-cell RNA-sequencing of T-cell and pancreatic beta cell maturation, we characterize proliferation and apoptosis rates and identify key developmental checkpoints, data inaccessible to existing approaches.

Temporal changes to the concentration of molecular species such as mRNA, which take place in response to various environmental cues, can often be modeled as simple continuous functions such as a single pulse (impulse) model. The simplicity of such functional representations can provide an improved performance on fundamental tasks such as noise reduction, imputation and differential expression analysis. However, temporal gene expression profiles are often studied with models that treat time as a categorical variable, neglecting the dependence between time points. Here, we present ImpulseDE2, a framework for differential expression analysis that combines the power of the impulse model as a continuous representation of temporal responses along with a noise model tailored specifically to sequencing data. We compare the simple categorical models to ImpulseDE2 and to other continuous models based on natural cubic splines and demonstrate the utility of the continuous approach for studying differential expression in time course sequencing experiments. A unique feature of ImpulseDE2 is the ability to distinguish permanently from transiently up- or down-regulated genes. Using an in vitro differentiation dataset, we demonstrate that this gene classification scheme can be used to highlight distinct transcriptional programs that are associated with different phases of the differentiation process.

A policy regarding rapid response to chemotherapy overdoses was developed by the authors in an attempt to minimize morbidity and mortality. The parameters of a chemotherapy overdose were defined to promote early recognition of an overdose incident. Resources needed to guide potential therapeutic interventions and required monitoring were developed. The policy defines the immediate actions to be taken in the event of a chemotherapy overdose. The availability of a chemotherapy overdose policy provides an enhanced level of safety for patients by ensuring that appropriate treatment is initiated without delay. The development of the policy was in response to the reporting of a tragic error at another institution. Healthcare providers must recognize and address potential areas of vulnerability to maximize patient safety.

Penicillin binding proteins (PBPs) catalyze steps in the biosynthesis of bacterial cell walls and are the targets for the beta-lactam antibiotics. Non-beta-lactam based antibiotics that target PBPs are of interest because bacteria have evolved resistance to the beta-lactam antibiotics. Boronic acids have been developed as inhibitors of the mechanistically related serine beta-lactamases and serine proteases; however, they have not been explored extensively as PBP inhibitors. Here we report aromatic boronic acid inhibitors of the D,D-carboxypeptidase R39 from Actinomadura sp. strain. Analogues of an initially identified inhibitor [3-(dihydroxyboryl)benzoic acid 1, IC(50) 400 microM] were prepared via routes involving pinacol boronate esters, which were deprotected via a two-stage procedure involving intermediate trifluorborate salts that were hydrolyzed to provide the free boronic acids. 3-(Dihydroxyboryl)benzoic acid analogues containing an amide substituent in the meta, but not ortho position were up to 17-fold more potent inhibitors of the R39 PBP and displayed some activity against other PBPs. These compounds may be useful for the development of even more potent boronic acid based PBP inhibitors with a broad spectrum of antibacterial activity.

Beta-lactam antibiotics, including penicillins and cephalosporins, inhibit penicillin-binding proteins (PBPs), which are essential for bacterial cell wall biogenesis. Pathogenic bacteria have evolved efficient antibiotic resistance mechanisms that, in Gram-positive bacteria, include mutations to PBPs that enable them to avoid beta-lactam inhibition. Lactivicin (LTV; 1) contains separate cycloserine and gamma-lactone rings and is the only known natural PBP inhibitor that does not contain a beta-lactam. Here we show that LTV and a more potent analog, phenoxyacetyl-LTV (PLTV; 2), are active against clinically isolated, penicillin-resistant Streptococcus pneumoniae strains. Crystallographic analyses of S. pneumoniae PBP1b reveal that LTV and PLTV inhibition involves opening of both monocyclic cycloserine and gamma-lactone rings. In PBP1b complexes, the ring-derived atoms from LTV and PLTV show a notable structural convergence with those derived from a complexed cephalosporin (cefotaxime; 3). The structures imply that derivatives of LTV will be useful in the search for new antibiotics with activity against beta-lactam-resistant bacteria.

17Beta-hydroxysteroid dehydrogenase Type 1 (17beta-HSD1) has a pivotal role in regulating the synthesis of oestradiol (E2) within breast tumours. In whole body studies in postmenopausal women with breast cancer the conversion of oestrone (E1) to E2 (4.4+/-1.1%) was much lower than the inactivation of E2 to E1 (17.3+/-5.0%). In contrast, an examination of in vivo oestrogen metabolism within breast tumours revealed that whereas little metabolism of E2 occurred, E1 was converted to E2 to a much greater extent in malignant (48+/-14%) than in normal (19+/-6%) breast tissue. Findings from these studies originally suggested that oestrogen metabolism within breast tumours may differ from the mainly oxidative direction found in most other body tissues and that the activity of 17beta-HSD1 might be regulated by tumour-derived factors. Several growth factors (e.g. IGF-I, IGF-II) and cytokines (e.g. IL-6, TNFalpha) have now been identified which can markedly stimulate the activity of 17beta-HSD1 and such a mechanism may account for the high concentrations of E2 found in most breast tumours. Cells of the immune system, which can infiltrate breast tumours, are thought to be a major source of the growth factors and cytokines which can modulate 17beta-HSD1 activity. Given the central role that 17beta-HSD1 has in regulating breast tumour E2 concentrations the development of potent inhibitors of this enzyme has recently attracted considerable attention. Our initial studies in this area explored the use of derivatives of E1 as inhibitors, with 2-ethyl- and 2-methoxy E1 being found to inhibit 17beta-HSD1 activity in T-47D breast cancer cells by 96+/-2 and 91+/-1% respectively at 10 microM, but with a lack of specificity. Using the E1 scaffold a number of potent, selective 17beta-HSD1 inhibitors have now been identified including E1- and 2-ethyl-E1 containing a side chain with a m-pyridylmethylamidomethyl functionality extending from the 16beta position of the steroid nucleus. At 10 microM these compounds both inhibited 17beta-HSD1 activity by >90%, however some inhibition of 17beta-HSD2 activity was exhibited by the E1 derivative (25%) but not the 2-ethyl analogue. It is now apparent that 17beta-HSD1 activity contributes to the high E2 concentrations found in most breast tumours. The identification of potent, selective novel 17beta-HSD1 inhibitors will allow their efficacy to be tested in in vitro and in vivo studies.

The 17beta-hydroxysteroid dehydrogenases (17beta-HSDs) catalyze the interconversion between the oxidized and reduced forms of androgens and estrogens at the 17 position. The 17beta-HSD type 1 enzyme (17beta-HSD1) catalyzes the reduction of estrone to estradiol and is expressed in malignant breast cells. Inhibitors of this enzyme thus have potential as treatments for hormone dependent breast cancer. Here we report the syntheses and biological evaluation of novel inhibitors based on the estrone or estradiol template. These have been investigated by modification at the 6, 16 or 17 positions or combinations of these in order to gain information about structure-activity relationships by probing different areas in the enzyme active site. Activity data have been incorporated into a QSAR with predictive power, and the X-ray crystal structures of compounds 15 and 16c have been determined. Compound 15 has an IC50 of 320 nM for 17beta-HSD1 and is selective for 17beta-HSD1 over 17beta-HSD2. Three libraries of amides are also reported that led to the identification of inhibitors 19e and 20a, which have IC50 values of 510 and 380 nM respectively, and 20 h which, having an IC50 value of 37 nM, is the most potent inhibitor of 17beta-HSD1 reported to date. These amides are also selective for 17beta-HSD1 over 17beta-HSD2.

17beta-Hydroxysteroid dehydrogenases (17beta-HSDs) are an important class of steroidogenic enzymes that regulate the bioavailability of active estrogens and androgens and are as yet a relatively unexploited therapeutic target. Based on our investigations and those of others, E-ring modified steroids were identified as a useful template for the design of inhibitors of 17beta-HSD type 1, an enzyme involved in the conversion of estrone into estradiol. The synthesis and biological evaluation of a new series of N- and C-substituted 1,3,5(10)-estratrien-[17,16-c]-pyrazoles and the corresponding SAR are discussed. Among the N-alkylated analogues, the most potent inhibitor was the 1'-methoxyethyl derivative, 41, with an IC(50) of 530 nM in T47-D human breast cancer cells. The X-ray crystal structure of the 1'-isobutyl derivative, was determined. Further optimization of the template using parallel synthesis resulted in a library of C5'-linked amides from which 73 emerged. This pyridylethyl amide had an IC(50) of 300 nM and its activity, with that of 41, suggests the importance of hydrogen bond acceptor groups in the pyrazole side chain. Both 41 and 73 displayed selectivity over 17beta-HSD type 2, and preliminary investigations showed 41 to be nonestrogenic in vitro in a luciferase reporter gene assay in contrast to the parent pyrazole 25. Molecular modeling studies, which support these findings, and a QSAR, the predictive power of which was demonstrated, are also presented.

Background: The purpose of this study of sentinel lymph node biopsies (SLN) was threefold: to compare the reliability of lymphazurin blue dye to radioactive technetium 99m sulfur colloid (TC); to evaluate the reliability of frozen section examinations of sentinel lymph nodes; and to determine how much SLN dissections prolonged operative time., Study Design: We evaluated the records of 263 consecutive patients with intermediate and high-risk melanomas (1.0 mm or thicker, or Clark Level IV or greater), who were treated by a single surgeon at the Yale Melanoma Unit between October 1, 1997, and September 30, 2001, and followed for more than 18 months., Results: A total of 655 SLN were identified and removed from these 263 consecutive patients. Radioactive colloid was found to be more reliable (100%) in identifying the SLN than lymphazurin blue dye (51%) in the nodes of the patients. Twenty-eight patients (11%) had positive sentinel lymph nodes, and 2 patients (7%) had false-negative frozen sections. Three patients (11%) had false-negative frozen sections; tumor was found subsequently on permanent sections only after special immunohistochemical stains were used. The location or removal of SLN did not prolong the operative procedure unreasonably, requiring an average of 7 to 20 minutes for removal of SLN, and 33 minutes for frozen section reports, during which time the primary tumor resection and wound coverage were performed., Conclusions: SLN were found in all 263 patients. All SLN were identified reliably with radioactive colloid. Because blue dye was found in only half of the radioactive nodes, it is not appropriate to use this as the only marker for locating the SLN. This large series of patients attests to the reliability of frozen sections in identifying SLN harboring metastases, with 82% of the patients with nodal metastases identified in this fashion.

A series of novel D-ring modified derivatives of estrone was synthesized and tested as inhibitors of steroid sulfatase (STS). The steroidal D-ring was cleaved via an iodoform reaction and thermal condensation of the resulting marrianolic acid derivative gave 16,17-seco-estra-1,3,5(10)-triene-16,17-imide derivatives, where a piperidinedione moiety is in place of the D-ring. This synthetic approach was found to give a higher overall yield than the literature method of Beckmann rearrangement. A range of alkyl side chains have been introduced on the nitrogen atom of the imido-ring and the corresponding 3-O-sulfamates synthesized. The new D-ring modified estrone derivatives bearing a propyl (39) and a 1-pyridin-3-ylmethyl (46) moiety had IC(50) values of 1 nM when tested in placental microsomes for the inhibition of STS. These compounds are therefore up to 18-fold more potent than EMATE, the very first highly potent irreversible steroidal STS inhibitor.

In pursuit of novel steroid sulfatase (STS) inhibitors devoid of estrogenicity, several D-ring modified steroid derivatives were synthesised. In vitro evaluation of the compounds identified two highly potent inhibitors, 4a and 4b, which were 18 times more active than estrone-3-O-sulfamate (EMATE), both having IC(50) values of ca. 1nM. These 16,17-seco-estra-1,3,5(10)-triene-16,17-imide derivatives were synthesised from estrone, via the intermediate 1, which was easily alkylated, deprotected and sulfamoylated affording the final compounds in high yields. In order to assess their biological profile, the selected inhibitors were tested for their in vivo inhibitory potency and estrogenicity in ovariectomised rats. After an oral dose of 10mg/kg per day for 5 days, 4a and 4b were found to inhibit rat liver steroid sulfatase by 99%. They were also devoid of estrogenic activity in the uterine weight gain assay, indicating that these two leads have therapeutic potential for the treatment of hormone-dependent breast cancer.

Purpose: Reports of the tragic consequences of erroneous cancer chemotherapy overdoses at a prominent cancer center and a university hospital prompted a review of our institution's practices and those of 123 other hospitals to ascertain for each the current in-house process to prevent chemotherapy errors., Methods: A multidisciplinary committee of oncologists, nurses, and pharmacists reviewed the chemotherapy use process and identified opportunities for improvement. A 1-page facsimile survey was answered by 150 of 215 members of the American Society of Clinical Oncology (ASCO) who received it., Results: We further restricted the writing of cytotoxic chemotherapy orders to physicians who were board-certified or -eligible in hematology or medical, pediatric, and gynecologic oncology and their approved fellows. Dispensation of drugs is limited to oncology-certified pharmacists, and administration to chemotherapy-certified nurses. Standard orders are used either on special oncology forms or designated order sets in the computer. Procedures to regulate the ordering of antineoplastic drugs for nonmalignant indications by nononcology specialists are outlined. A process to prevent chemotherapy errors is in place in 95% of hospitals. Dedicated medical oncology units are ubiquitous, and most cancer centers and university hospitals have dedicated gynecologic and pediatric oncology units. Chemotherapy orders are generally written by oncology fellows and countersigned by an attending oncologist in cancer centers and university hospitals, whereas private oncology attending physicians write them in most community hospitals. Drugs are administered by oncology-certified nurses in most institutions., Conclusions: These recommendations should improve the safety and effective use of chemotherapy and reduce the error rate to as close to zero as human fallibility will allow.

Progress in medical diagnosis and therapy has raised new problems with far-reaching ethical implications. Medicine must remain a profession and not become a business. Textbooks must address ethical problems in the context of health care decisions and not restrict themselves to pathophysiology and practical therapeutics alone. The relative roles of the principles of autonomy, non-maleficence, beneficence, and justice must be balanced and appropriately applied to individual situations in biomedical ethics. When therapy becomes futile and the suffering of the patient does not justify any anticipated benefit, the patient (and/or patient surrogate) may request withholding or even withdrawing life-prolonging interventions. In the persistent vegetative state, even nutritional support by an unnatural (tube) route may ethically be denied at the patient's (or surrogate's) informed decision. New areas of ethical evaluation have been raised by the desire of some individuals to prolongation of their lives at high expense to the society such that other individuals are denied services because of limitation of available resources. There has been a long-standing conflict of interest between the acceptance by physicians and/or medical institutions of money or gifts from pharmaceutical companies whose drugs they prescribe, stock, or sell. This practice increases the cost of the drugs and is, in effect, a "sick tax," which is morally wrong.

A prospective randomized trial of low versus high doses of human leukocyte alpha-interferon (1 X 10(6) units/day for 28 days versus 10 X 10(6) units/day for 28 days) was carried out in 30 patients with metastatic renal cell carcinoma, to test the tolerance and relative antitumor effects of these interferon doses. Both doses were tolerated well, and responses to the human leukocyte alpha-interferon were observed overall in seven individuals, including complete, partial, and minimal tumor regressions. Six of the seven responses occurred in patients who received the high dosage, and three of these responses were major responses. While not statistically significant, this result suggested a dose-response relationship. One minimal response was observed in a patient treated at low dosage. Nine individuals who were stable after 1 month of therapy at low dosage were randomized to a further month of therapy at low or high dosage, during which one of four at high dosage had a partial response, and none of five at low dosage manifested response. Regression of pulmonary disease in one individual was delayed, occurring 3 months after therapy at the high dose and enduring for a period of 28 months. Major objective responses in other patients were of 4 and 15 months duration. Human leukocyte alpha-interferon is an active agent in renal cell carcinoma at the dosage of 10 million units daily. No relationship of toxicity to response was evident in this trial. Optimum dosage and duration of treatment have yet to be established.

Association of a circulating factor XI anticoagulant and disseminated intravascular coagulation (DIC) is described in a 33-year-old woman. Although the patient had rheumatoid arthritis and a bacterial infection treated with antibiotics, the anticoagulant was thought to be secondary to systemic lupus erythematosus. Curiously, the low levels of factor XI did not prevent the DIC from developing.

High-dose pulse chlorambucil was given orally at a dose of 16 mg/m2 daily for 5 consecutive days each month, as reported by Cadman et al. It was used to treat 33 patients with advanced, low-grade non-Hodgkin's lymphoma. With median follow-up of 4.2+ years, 70% of the patients achieved objective response. Eleven of 24 patients with follicular small cleaved cell lymphoma (FSCL) had pathological complete response; nine of 24 with FSCL and three of seven with small lymphocytic lymphoma had partial response. Median disease-free survival was 28 months. Actuarial survival for all patients was 60% at 5 years from initiation of therapy. Treatment toxicity was minimal. Pulse chlorambucil is an effective and minimally toxic palliative therapy for advanced FSCL.

A 60-year-old woman had a solitary mass in her left eyebrow that was first thought to be a dermoid cyst and following excision and histological examination was found to be a localized, malignant, non-Hodgkin's lymphoma of the mixed lymphocytic and histiocytic type. Subsequent lymphangiography after excisional biopsy of the left eyebrow mass demonstrated extensive para-aortic and inguinal lymph node involvement.