COVID-19 is a pandemic viral disease with catastrophic global impact. This disease is more contagious than influenza such that cluster outbreaks occur frequently. If patients with symptoms quickly underwent testing and contact tracing, these outbreaks could be contained. Unfortunately, COVID-19 patients have symptoms similar to other common illnesses. Here, we hypothesize the order of symptom occurrence could help patients and medical professionals more quickly distinguish COVID-19 from other respiratory diseases, yet such essential information is largely unavailable. To this end, we apply a Markov Process to a graded partially ordered set based on clinical observations of COVID-19 cases to ascertain the most likely order of discernible symptoms (i.e., fever, cough, nausea/vomiting, and diarrhea) in COVID-19 patients. We then compared the progression of these symptoms in COVID-19 to other respiratory diseases, such as influenza, SARS, and MERS, to observe if the diseases present differently. Our model predicts that influenza initiates with cough, whereas COVID-19 like other coronavirus-related diseases initiates with fever. However, COVID-19 differs from SARS and MERS in the order of gastrointestinal symptoms. Our results support the notion that fever should be used to screen for entry into facilities as regions begin to reopen after the outbreak of Spring 2020. Additionally, our findings suggest that good clinical practice should involve recording the order of symptom occurrence in COVID-19 and other diseases. If such a systemic clinical practice had been standard since ancient diseases, perhaps the transition from local outbreak to pandemic could have been avoided.

Hematogenous metastasis is initiated by a subset of circulating tumor cells (CTC) shed from primary or metastatic tumors into the blood circulation. Thus, CTCs provide a unique patient biopsy resource to decipher the cellular subpopulations that initiate metastasis and their molecular properties. However, one crucial question is whether CTCs derived and expanded ex vivo from patients recapitulate human metastatic disease in an animal model. Here, we show that CTC lines established from patients with breast cancer are capable of generating metastases in mice with a pattern recapitulating most major organs from corresponding patients. Genome-wide sequencing analyses of metastatic variants identified semaphorin 4D as a regulator of tumor cell transmigration through the blood–brain barrier and MYC as a crucial regulator for the adaptation of disseminated tumor cells to the activated brain microenvironment. These data provide the direct experimental evidence of the promising role of CTCs as a prognostic factor for site-specific metastasis. Significance: Interests abound in gaining new knowledge of the physiopathology of brain metastasis. In a direct metastatic tropism analysis, we demonstrated that ex vivo–cultured CTCs from 4 patients with breast cancer showed organotropism, revealing molecular features that allow a subset of CTCs to enter and grow in the brain. This article is highlighted in the In This Issue feature, p. 1

A substantial proportion of tumors consist of genotypically distinct subpopulations of cancer cells. This intratumor genetic heterogeneity poses a substantial challenge for the implementation of precision medicine. Single-cell genomics constitutes a powerful approach to resolve complex mixtures of cancer cells by tracing cell lineages and discovering cryptic genetic variations that would otherwise be obscured in tumor bulk analyses. Because of the chemical alterations that result from formalin fixation, single-cell genomic approaches have largely remained limited to fresh or rapidly frozen specimens. Here we describe the development and validation of a robust and accurate methodology to perform whole-genome copy-number profiling of single nuclei obtained from formalin-fixed paraffin-embedded clinical tumor samples. We applied the single-cell sequencing approach described here to study the progression from in situ to invasive breast cancer, which revealed that ductal carcinomas in situ show intratumor genetic heterogeneity at diagnosis and that these lesions may progress to invasive breast cancer through a variety of evolutionary processes.

Purpose: Ductal carcinoma in situ (DCIS) is a nonobligate precursor of invasive breast cancer. Here, we sought to investigate the level of intralesion genetic heterogeneity in DCIS and the patterns of clonal architecture changes in the progression from DCIS to invasive disease.Experimental Design: Synchronous DCIS (n = 27) and invasive ductal carcinomas of no special type (IDC-NSTs; n = 26) from 25 patients, and pure DCIS (n = 7) from 7 patients were microdissected separately and subjected to high-depth whole-exome (n = 56) or massively parallel sequencing targeting ≥410 key cancer-related genes (n = 4). Somatic genetic alterations, mutational signatures, clonal composition, and phylogenetic analyses were defined using validated computational methods.Results: DCIS revealed genetic alterations similar to those of synchronously diagnosed IDC-NSTs and of non-related IDC-NSTs from The Cancer Genome Atlas (TCGA), whereas pure DCIS lacked PIK3CA mutations. Clonal decomposition and phylogenetic analyses based on somatic mutations and copy number alterations revealed that the mechanisms of progression of DCIS to invasive carcinoma are diverse, and that clonal selection might have constituted the mechanism of progression from DCIS to invasive disease in 28% (7/25) of patients. DCIS displaying a pattern of clonal selection in the progression to invasive cancer harbored higher levels of intralesion genetic heterogeneity than DCIS where no clonal selection was observed.Conclusions: Intralesion genetic heterogeneity is a common feature in DCIS synchronously diagnosed with IDC-NST. DCIS is a nonobligate precursor of IDC-NST, whose mechanisms of progression to invasive breast cancer are diverse and vary from case to case.

Molecular analysis of circulating and disseminated tumor cells (CTCs/DTCs) has great potential as a means for continuous evaluation of prognosis and treatment efficacy in near-real time through minimally invasive liquid biopsies. To realize this potential, however, methods for molecular analysis of these rare cells must be developed and validated. Here, we describe the integration of imaging mass cytometry (IMC) using metal-labeled antibodies as implemented on the Fluidigm Hyperion Imaging System into the workflow of the previously established high definition single cell analysis (HD-SCA) assay for liquid biopsies, along with methods for image analysis and signal normalization. Using liquid biopsies from a metastatic prostate cancer case, we demonstrate that IMC can extend the reach of CTC characterization to include dozens of protein biomarkers, with the potential to understand a range of biological properties that could affect therapeutic response, metastasis and immune surveillance when coupled with simultaneous phenotyping of thousands of leukocytes.

Copy number alterations (CNAs) play an important role in molding the genomes of breast cancers and have been shown to be clinically useful for prognostic and therapeutic purposes. However, our knowledge of intra-tumoral genetic heterogeneity of this important class of somatic alterations is limited. Here, using single-cell sequencing, we comprehensively map out the facets of copy number alteration heterogeneity in a cohort of breast cancer tumors. Ou/var/www/html/elife/12-05-2020/backup/r analyses reveal: genetic heterogeneity of non-tumor cells (i.e. stroma) within the tumor mass; the extent to which copy number heterogeneity impacts breast cancer genomes and the importance of both the genomic location and dosage of sub-clonal events; the pervasive nature of genetic heterogeneity of chromosomal amplifications; and the association of copy number heterogeneity with clinical and biological parameters such as polyploidy and estrogen receptor negative status. Our data highlight the power of single-cell genomics in dissecting, in its many forms, intra-tumoral genetic heterogeneity of CNAs, the magnitude with which CNA heterogeneity affects the genomes of breast cancers, and the potential importance of CNA heterogeneity in phenomena such as therapeutic resistance and disease relapse.