The COVID-19 pandemic has caused more than three million deaths globally. The severity of the disease is characterized, in part, by a dysregulated immune response. CD16+ monocytes are innate immune cells involved in inflammatory responses to viral infections, and tissue repair, among other functions. We characterized the transcriptional changes in CD16+ monocytes from PBMC of people with COVID-19, and from healthy individuals using publicly available single cell RNA sequencing data. CD16+ monocytes from people with COVID-19 compared to those from healthy individuals expressed transcriptional changes indicative of increased cell activation, and induction of a migratory phenotype. We also analyzed COVID-19 cases based on severity of the disease and found that mild cases were characterized by upregulation of interferon response and MHC class II related genes, whereas the severe cases had dysregulated expression of mitochondrial and antigen presentation genes, and upregulated inflammatory, cell movement, and apoptotic gene signatures. These results suggest that CD16+ monocytes in people with COVID-19 contribute to a dysregulated host response characterized by decreased antigen presentation, and an elevated inflammatory response with increased monocytic infiltration into tissues. Our results show that there are transcriptomic changes in CD16+ monocytes that may impact the functions of these cells, contributing to the pathogenesis and severity of COVID-19.

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Breast cancer subtyping is a difficult clinical and scientific challenge. The prevalent Prediction Analysis of Microarray of 50 genes (PAM50) system and its Immunohistochemistry (IHC) surrogate showed significant inconsistencies. This is because of the limited training samples, highly variable molecular features and in-efficient strategies used in these classifiers. The rapid development of early screening technologies, especially in the field of circulating tumor DNA, has also challenged the subtyping of breast cancer at the DNA level. By integrating large-scale DNA-level data and using a hierarchical structure learning algorithm, we developed Unified Genetic and Epigenetic Subtyping (UGES), a new intrinsic subtype classifier. The benchmarks showed that the use of all classes of DNA alterations worked much better than single classes, and that the multi-step hierarchical learning is crucial, which improves the overall AUC score by 0.074 compared to the one-step multi-classification method. Based on these insights, the ultimate UGES was trained as a three-step classifier on 50831 DNA features of 2065 samples, including mutations, copy number aberrations, and methylations. UGES achieved overall AUC score 0.963, and greatly improved the clinical stratification of patients, as each strata's survival difference became statistically more significant p-value=9.7e-55 (UGES) vs 2.2e-47 (PAM50). Finally, UGES identified 52 subtype-level DNA biomarkers that can be targeted in early screening technology to significantly expand the time window for precision care. The analysis code is freely available at https://github.com/labxscut/UGES.

Cancers are a mosaic of clones of varying population sizes. Any single cancer sample encodes a tumor-metagenome, since it represents the aggregate genomes of diverse clones that coexist within the sample. We quantified genomic instability as the fraction of the tumor-metagenome affected by copy number variations (CNVs) and leveraged two tumor mixture separation algorithms, EXPANDS and PyClone, to quantify genetic intra-tumor heterogeneity (ITH) from single cancer samples. We tested the potential of measures of genomic instability and ITH as prognostic biomarkers across 1,165 exome sequenced primary tumors from 12 cancer types at TCGA. Our results suggest that a tradeoff between the costs and adaptive benefits of genomic instability governs differential radiotherapy sensitivity. Between 1 and 18 clones were estimated to coexist per tumor sample at >10% cell frequency (median = 4). Clone number varied considerably within and between cancer types, with melanomas representing the most heterogeneous group. 86% all analyzed tumor samples contained at least 2 clones. Across cancer types, the presence of >2 clones was associated with worse overall survival as compared to tumors in which < = 2 clones were detected (Log-rank test: P = 8.6E-4, HR = 1.49). An exceptionally favorable outcome was observed when >75% genomic instability was shared among < = 2 clones. The highest risk was observed among individuals with an intermediate number of 4 clones - additional diversification beyond 4 clones did not impart further risk. The highest risk was also observed among individuals with 50-75% genomic instability, in both the original exome sequencing dataset and an independent SNP array dataset. Genomic instability <25% or >75% predicted reduced risk (HR = 0.15, 95% CI: 0.08-0.29). We analyzed the relation between radiotherapy intensity and overall survival among 242 individuals (21%) treated with radiotherapy and found that not all individuals did benefit equally from therapy. In order to achieve the same benefit from therapy, individuals with 25-50% genomic instability required higher therapy intensity (regression slope = 1.83; P = 0.009) than individuals with 50-75% genomic instability (slope = 2.09; P = 0.005). In contrast, individuals with <25% genomic instability did not benefit from increasing therapy intensity (slope = 0.71; P = 0.311). Radiotherapy may be particularly effective against tumors with intermediate CNV burden, by pushing them past the limit of ‘tolerable’ genomic instability. Our results from two independent pan-cancer cohorts suggest that this limit is exceeded when >75% of a tumor's metagenome is affected by CNVs. This upper limit of tolerable genomic instability may be responsible for the non-linear association we observed between genetic ITH and survival. Leveraging a clone's distance to the upper limit of tolerable genomic instability may represent a new strategy to optimize therapy intensity. Citation Format: Noemi Andor, Trevor A. Graham, Marnix Jansen, Li C. Xia, Athena Aktipis, Claudia Petritsch, Hanlee P. Ji, Carlo C. Maley. Pan-cancer analysis of clonal evolution reveals the costs and adaptive benefits of genomic instability. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2387.

High grade (grade 3) neuroendocrine neoplasms (G3 NENs) have poor survival outcomes. From a clinical standpoint, G3 NENs are usually grouped regardless of primary site and treated similarly. Little is known regarding the underlying genomics of these rare tumors, especially when compared across different primary sites. We performed whole transcriptome (n = 46), whole exome (n = 40) and gene copy number (n = 43) sequencing on G3 NEN FFPE samples from diverse organs (in total 17 were lung, 16 were gastroenteropancreatic, 13 other). G3 NENs despite arising from diverse primary sites did not have gene expression profiles that were easily segregated by organ of origin. Across all G3 NENs, TP53, APC, RB1 and CDKN2A were significantly mutated. The CDK4/6 cell cycling pathway was mutated in 95% of cases, with upregulation of oncogenes within this pathway. G3 NENs had high tumor mutation burden (mean 7.09 mutations/MB), with 20% having >10 mutations/MB. Two somatic copy number alterations were significantly associated with worse prognosis across tissue types: focal deletion 22q13.31 (HR, 7.82; p = 0.034) and arm amplification 19q (HR, 4.82; p = 0.032). This study is among the most diverse genomic study of high-grade neuroendocrine neoplasms. We uncovered genomic features previously unrecognized for this rapidly fatal and rare cancer type that could have potential prognostic and therapeutic implications.