CREAMMIST: an integrative probabilistic database for cancer drug response prediction.

Extensive in vitro cancer drug screening datasets have enabled scientists to identify biomarkers and develop machine learning models for predicting drug sensitivity. While most advancements have focused on omics profiles, cancer drug sensitivity scores precalculated by the original sources are often used as-is, without consideration for variabilities between studies. It is well-known that significant inconsistencies exist between the drug sensitivity scores across datasets due to differences in experimental setups and preprocessing methods used to obtain the sensitivity scores. As a result, many studies opt to focus only on a single dataset, leading to underutilization of available data and a limited interpretation of cancer pharmacogenomics analysis. To overcome these caveats, we have developed CREAMMIST (https://creammist.mtms.dev), an integrative database that enables users to obtain an integrative dose-response curve, to capture uncertainty (or high certainty when multiple datasets well align) across five widely used cancer cell-line drug-response datasets. We utilized the Bayesian framework to systematically integrate all available dose-response values across datasets (>14 millions dose-response data points). CREAMMIST provides easy-to-use statistics derived from the integrative dose-response curves for various downstream analyses such as identifying biomarkers, selecting drug concentrations for experiments, and training robust machine learning models.

Emergence of SARS-CoV-2 Spike Mutations during Prolonged Infection in Immunocompromised Hosts.

Immunocompromised hosts with prolonged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have been implicated in the emergence of highly mutated SARS-CoV-2 variants. Spike mutations are of particular concern because the spike protein is a key target for vaccines and therapeutics for SARS-CoV-2. Here, we report the emergence of spike mutations in two immunocompromised patients with persistent SARS-CoV-2 reverse transcription (RT)-PCR positivity (>90 days). Whole-genome sequence analysis of samples obtained before and after coronavirus disease 2019 (COVID-19) treatment demonstrated the development of partial therapeutic escape mutations and increased intrahost SARS-CoV-2 genome diversity over time. This case series thus adds to the accumulating evidence that immunocompromised hosts with persistent infections are important sources of SARS-CoV-2 genome diversity and, in particular, clinically important spike protein diversity. IMPORTANCE The emergence of clinically important mutations described in this report highlights the need for sustained vigilance and containment measures when managing immunocompromised patients with persistent COVID-19. Even as jurisdictions across the globe start lifting pandemic control measures, immunocompromised patients with persistent COVID-19 constitute a unique group that requires close genomic monitoring and enhanced infection control measures, to ensure early detection and containment of mutations and variants of therapeutic and public health importance.

Clonal serotype 1c multidrug-resistant Shigella flexneri detected in multiple institutions by sentinel-site sequencing.

Shigella flexneri is a major diarrhoeal pathogen, and the emergence of multidrug-resistant S. flexneri is of public health concern. We report the detection of a clonal cluster of multidrug-resistant serotype 1c (7a) S. flexneri in Singapore in April 2022. Long-read whole-genome sequence analysis found five S. flexneri isolates to be clonal and harboring the extended-spectrum ß-lactamases bla CTX-M-15 and bla TEM-1. The isolates were phenotypically resistant to ceftriaxone and had intermediate susceptibility to ciprofloxacin. The S. flexneri clonal cluster was first detected in a tertiary hospital diagnostic laboratory (sentinel-site), to which the S. flexneri isolates were sent from other hospitals for routine serogrouping. Long-read whole-genome sequence analysis was performed in the sentinel-site near real-time in view of the unusually high number of S. flexneri isolates received within a short time frame. This study demonstrates that near real-time sentinel-site sequence-based surveillance of convenience samples can detect possible clonal outbreak clusters and may provide alerts useful for public health mitigations at the earliest possible opportunity.

CalmBelt: Rapid SARS-CoV-2 Genome Characterization for Outbreak Tracking.

Background: The ongoing COVID-19 pandemic is a global health crisis caused by the spread of SARS-CoV-2. Establishing links between known cases is crucial for the containment of COVID-19. In the healthcare setting, the ability to rapidly identify potential healthcare-associated COVID-19 clusters is critical for healthcare worker and patient safety. Increasing sequencing technology accessibility has allowed routine clinical diagnostic laboratories to sequence SARS-CoV-2 in clinical samples. However, these laboratories often lack specialized informatics skills required for sequence analysis. Therefore, an on-site, intuitive sequence analysis tool that enables clinical laboratory users to analyze multiple genomes and derive clinically relevant information within an actionable timeframe is needed. Results: We propose CalmBelt, an integrated framework for on-site whole genome characterization and outbreak tracking. Nanopore sequencing technology enables on-site sequencing and construction of draft genomes for multiple SARS-CoV-2 samples within 12 h. CalmBelt's interactive interface allows users to analyse multiple SARS-CoV-2 genomes by utilizing whole genome information, collection date, and additional information such as predefined potential clusters from epidemiological investigations. CalmBelt also integrates established SARS-CoV-2 nomenclature assignments, GISAID clades and PANGO lineages, allowing users to visualize relatedness between samples together with the nomenclatures. We demonstrated multiple use cases including investigation of potential hospital transmission, mining transmission patterns in a large outbreak, and monitoring possible diagnostic-escape. Conclusions: This paper presents an on-site rapid framework for SARS-CoV-2 whole genome characterization. CalmBelt interactive web application allows non-technical users, such as routine clinical laboratory users in hospitals to determine SARS-CoV-2 variants of concern, as well as investigate the presence of potential transmission clusters. The framework is designed to be compatible with routine usage in clinical laboratories as it only requires readily available sample data, and generates information that impacts immediate infection control mitigations.