A versatile platform for locus-scale genome rewriting and verification.

Routine rewriting of loci associated with human traits and diseases would facilitate their functional analysis. However, existing DNA integration approaches are limited in terms of scalability and portability across genomic loci and cellular contexts. We describe Big-IN, a versatile platform for targeted integration of large DNAs into mammalian cells. CRISPR/Cas9-mediated targeting of a landing pad enables subsequent recombinase-mediated delivery of variant payloads and efficient positive/negative selection for correct clones in mammalian stem cells. We demonstrate integration of constructs up to 143 kb, and an approach for one-step scarless delivery. We developed a staged pipeline combining PCR genotyping and targeted capture sequencing for economical and comprehensive verification of engineered stem cells. Our approach should enable combinatorial interrogation of genomic functional elements and systematic locus-scale analysis of genome function.

Sequencing identifies multiple early introductions of SARS-CoV-2 to the New York City region.

Effective public response to a pandemic relies upon accurate measurement of the extent and dynamics of an outbreak. Viral genome sequencing has emerged as a powerful approach to link seemingly unrelated cases, and large-scale sequencing surveillance can inform on critical epidemiological parameters. Here, we report the analysis of 864 SARS-CoV-2 sequences from cases in the New York City metropolitan area during the COVID-19 outbreak in spring 2020. The majority of cases had no recent travel history or known exposure, and genetically linked cases were spread throughout the region. Comparison to global viral sequences showed that early transmission was most linked to cases from Europe. Our data are consistent with numerous seeds from multiple sources and a prolonged period of unrecognized community spreading. This work highlights the complementary role of genomic surveillance in addition to traditional epidemiological indicators.

Tumor immunogenomic signatures improve a prognostic model of melanoma survival.

BACKGROUND: Tumor mutation burden (TMB) has been associated with melanoma immunotherapy (IT) outcomes, including survival. We explored whether combining TMB with immunogenomic signatures recently identified by The Cancer Genome Atlas (TCGA) can refine melanoma prognostic models of overall survival (OS) in patients not treated by IT. METHODS: Cox proportional-hazards (Cox PH) analysis was performed on 278 metastatic melanomas from TCGA not treated by IT. In a discovery and two validation cohorts Cox PH models assessed the interaction between TMB and 53 melanoma immunogenomic features to refine prediction of melanoma OS. RESULTS: Interferon-γ response (IFNγRes) and macrophage regulation gene signatures (MacReg) combined with TMB significantly associated with OS (p = 8.80E-14). We observed that patients with high TMB, high IFNγRes and high MacReg had significantly better OS compared to high TMB, low IFNγRes and low MacReg (HR = 2.8, p = 3.55E-08). This association was not observed in low TMB patients. CONCLUSIONS: We report a model combining TMB and tumor immune features that significantly improves prediction of melanoma OS, independent of IT. Our analysis revealed that patients with high TMB, high levels of IFNγRes and MacReg had significantly more favorable OS compared to high TMB patients with low IFNγRes and low MacReg. These findings may substantially improve current melanoma prognostic models.

Application of Machine Learning to the Prediction of Cancer-Associated Venous Thromboembolism.

Venous thromboembolism (VTE) is a common and impactful complication of cancer. Several clinical prediction rules have been devised to estimate the risk of a thrombotic event in this patient population, however they are associated with limitations. We aimed to develop a predictive model of cancer-associated VTE using machine learning as a means to better integrate all available data, improve prediction accuracy and allow applicability regardless of timing for systemic therapy administration. A retrospective cohort was used to fit and validate the models, consisting of adult patients who had next generation sequencing performed on their solid tumor for the years 2014 to 2019. A deep learning survival model limited to demographic, cancer-specific, laboratory and pharmacological predictors was selected based on results from training data for 23,800 individuals and was evaluated on an internal validation set including 5,951 individuals, yielding a time-dependent concordance index of 0.72 (95% CI = 0.70-0.74) for the first 6 months of observation. Adapted models also performed well overall compared to the Khorana Score (KS) in two external cohorts of individuals starting systemic therapy; in an external validation set of 1,250 patients, the C-index was 0.71 (95% CI = 0.65-0.77) for the deep learning model vs 0.66 (95% CI = 0.59-0.72) for the KS and in a smaller external cohort of 358 patients the C-index was 0.59 (95% CI = 0.50-0.69) for the deep learning model vs 0.56 (95% CI = 0.48-0.64) for the KS. The proportions of patients accurately reclassified by the deep learning model were 25% and 26% respectively. In this large cohort of patients with a broad range of solid malignancies and at different phases of systemic therapy, the use of deep learning resulted in improved accuracy for VTE incidence predictions. Additional studies are needed to further assess the validity of this model.

Synthetic regulatory reconstitution reveals principles of mammalian Hox cluster regulation.

Precise Hox gene expression is crucial for embryonic patterning. Intra-Hox transcription factor binding and distal enhancer elements have emerged as the major regulatory modules controlling Hox gene expression. However, quantifying their relative contributions has remained elusive. Here, we introduce "synthetic regulatory reconstitution," a conceptual framework for studying gene regulation, and apply it to the HoxA cluster. We synthesized and delivered variant rat HoxA clusters (130 to 170 kilobases) to an ectopic location in the mouse genome. We found that a minimal HoxA cluster recapitulated correct patterns of chromatin remodeling and transcription in response to patterning signals, whereas the addition of distal enhancers was needed for full transcriptional output. Synthetic regulatory reconstitution could provide a generalizable strategy for deciphering the regulatory logic of gene expression in complex genomes.

Outcomes and Molecular Features of Brain Metastasis in Gastroesophageal Adenocarcinoma.

Importance: Brain metastasis (BrM) in gastroesophageal adenocarcinoma (GEA) is a rare and poorly understood phenomenon associated with poor prognosis. Objectives: To examine the clinical and genomic features of patients with BrM from GEA and evaluate factors associated with survival. Design, Setting, and Participants: In this single-institution retrospective cohort study, 68 patients with BrM from GEA diagnosed between January 1, 2008, and December 31, 2020, were identified via review of billing codes and imaging reports from the electronic medical record with follow-up through November 3, 2021. Genomic data were derived from the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets clinical sequencing platform. Exposures: Treatment with BrM resection and/or radiotherapy. Main Outcomes and Measures: Overall survival after BrM diagnosis. Results: Sixty-eight patients (median age at diagnosis, 57.4 years [IQR, 49.8-66.4 years]; 59 [86.8%] male; 55 [85.9%] White) participated in the study. A total of 57 (83.8%) had primary tumors in the distal esophagus or gastroesophageal junction. Median time from initial diagnosis to BrM diagnosis was 16.9 months (IQR, 8.5-27.7 months). Median survival from BrM diagnosis was 8.7 months (95% CI, 5.5-11.5 months). Overall survival was 35% (95% CI, 25%-48%) at 1 year and 24% (95% CI, 16%-37%) at 2 years. In a multivariable analysis, an Eastern Cooperative Oncology Group performance status of 2 or greater (hazard ratio [HR], 4.66; 95% CI, 1.47-14.70; P = .009) and lack of surgical or radiotherapeutic intervention (HR, 7.71; 95% CI, 2.01-29.60; P = .003) were associated with increased risk of all-cause mortality, whereas 3 or more extracranial sites of disease (HR, 1.85; 95% CI, 0.64-5.29; P = .25) and 4 or more BrMs (HR, 2.15; 95% CI, 0.93-4.98; P = .07) were not statistically significant. A total of 31 patients (45.6%) had ERBB2 (formerly HER2 or HER2/neu)-positive tumors, and alterations in ERBB2 were enriched in BrM relative to primary tumors (8 [47.1%] vs 7 [20.6%], P = .05), as were alterations in PTPRT (7 [41.2%] vs 4 [11.8%], P = .03). Conclusions and Relevance: This study suggests that that a notable proportion of patients with BrM from GEA achieve survival exceeding 1 and 2 years from BrM diagnosis, a more favorable prognosis than previously reported. Good performance status and treatment with combination surgery and radiotherapy were associated with the best outcomes. ERBB2 positivity and amplification as well as PTPRT alterations were enriched in BrM tissue compared with primary tumors; therefore, further study should be pursued to identify whether these variables represent genomic risk factors for BrM development.

Sequencing identifies multiple early introductions of SARS-CoV-2 to the New York City Region.

Effective public response to a pandemic relies upon accurate measurement of the extent and dynamics of an outbreak. Viral genome sequencing has emerged as a powerful approach to link seemingly unrelated cases, and large-scale sequencing surveillance can inform on critical epidemiological parameters. Here, we report the analysis of 864 SARS-CoV-2 sequences from cases in the New York City metropolitan area during the COVID-19 outbreak in Spring 2020. The majority of cases had no recent travel history or known exposure, and genetically linked cases were spread throughout the region. Comparison to global viral sequences showed that early transmission was most linked to cases from Europe. Our data are consistent with numerous seeds from multiple sources and a prolonged period of unrecognized community spreading. This work highlights the complementary role of genomic surveillance in addition to traditional epidemiological indicators.