The Repertoire of Serous Ovarian Cancer Non-genetic Heterogeneity Revealed by Single-Cell Sequencing of Normal Fallopian Tube Epithelial Cells

The inter-differentiation between cell states promotes cancer cell survival under stress and fosters non-genetic heterogeneity (NGH). NGH is, therefore, a surrogate of tumor resilience but its quantification is confounded by genetic heterogeneity. Here we show that NGH in serous ovarian cancer (SOC) can be accurately measured when informed by the molecular signatures of the normal fallopian tube epithelium (FTE) cells, the cells of origin of SOC. Surveying the transcriptomes of ∼6,000 FTE cells, predominantly from non-ovarian cancer patients, identified 6 FTE subtypes. We used subtype signatures to deconvolute SOC expression data and found substantial intra-tumor NGH. Importantly, NGH-based stratification of ∼1,700 tumors robustly correlated with survival. Our findings lay the foundation for accurate prognostic and therapeutic stratification of SOC. Using single-cell RNA sequencing, Hu et al. identify six subtypes of fallopian tube epithelium (FTE) cells in normal human fallopian tube tissues. The FTE cellular subtypes reveal intra-tumoral heterogeneity in serous ovarian cancer (SOC) and define SOC subtypes that correlate with patient prognosis.</p

Diagnosis of mixed infection and a primary immunodeficiency disease using next-generation sequencing: a case report

Major Histocompatibility Complex Class II (MHC II) deficiency is a rare primary immunodeficiency disorder (PID) with autosomal recessive inheritance pattern. The outcome is almost fatal owing to delayed diagnosis and lacking of effective therapy. Therefore, prompt diagnosis, timely and effective treatment are critical. Here, we report a 117-day-old boy with diarrhea, cough, cyanosis and tachypnea who was failed to be cured by empiric antimicrobial therapy initially and progressed to severe pneumonia and respiratory failure. The patient was admitted to the pediatric intensive care unit (PICU) immediately and underwent a series of tests. Blood examination revealed elevated levels of inflammatory markers and cytomegalovirus DNA. Imaging findings showed signs of severe infection of lungs. Finally, the diagnosis was obtained mainly through next-generation sequencing (NGS). We found out what pathogenic microorganism he was infected via repeated conventional detection methods and metagenomic next-generation sequencing (mNGS) of sputum and bronchoalveolar lavage fluid (BALF). And his whole exome sequencing (WES) examination suggested that CIITA gene was heterozygous mutation, a kind of MHC II deficiency diseases. After aggressive respiratory support and repeated adjustment of antimicrobial regimens, the patient was weaned from ventilator on the 56th day of admission and transferred to the immunology ward on the 60th day. The patient was successful discharged after hospitalizing for 91 days, taking antimicrobials orally to prevent infections post-discharge and waiting for stem cell transplantation. This case highlights the potential importance of NGS in providing better diagnostic testing for unexplained infection and illness. Furthermore, pathogens would be identified more accurately if conventional detection techniques were combined with mNGS

Examination of Independent Prognostic Power of Gene Expressions and Histopathological Imaging Features in Cancer

Cancer prognosis is of essential interest, and extensive research has been conducted searching for biomarkers with prognostic power. Recent studies have shown that both omics profiles and histopathological imaging features have prognostic power. There are also studies exploring integrating the two types of measurements for prognosis modeling. However, there is a lack of study rigorously examining whether omics measurements have independent prognostic power conditional on histopathological imaging features, and vice versa. In this article, we adopt a rigorous statistical testing framework and test whether an individual gene expression measurement can improve prognosis modeling conditional on high-dimensional imaging features, and a parallel analysis is conducted reversing the roles of gene expressions and imaging features. In the analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma and liver hepatocellular carcinoma data, it is found that multiple individual genes, conditional on imaging features, can lead to significant improvement in prognosis modeling; however, individual imaging features, conditional on gene expressions, only offer limited prognostic power. Being among the first to examine the independent prognostic power, this study may assist better understanding the &ldquo;connectedness&rdquo; between omics profiles and histopathological imaging features and provide important insights for data integration in cancer modeling

Histopathological Imaging–Environment Interactions in Cancer Modeling

Histopathological imaging has been routinely conducted in cancer diagnosis and recently used for modeling other cancer outcomes/phenotypes such as prognosis. Clinical/environmental factors have long been extensively used in cancer modeling. However, there is still a lack of study exploring possible interactions of histopathological imaging features and clinical/environmental risk factors in cancer modeling. In this article, we explore such a possibility and conduct both marginal and joint interaction analysis. Novel statistical methods, which are “borrowed” from gene–environment interaction analysis, are employed. Analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) data is conducted. More specifically, we examine a biomarker of lung function as well as overall survival. Possible interaction effects are identified. Overall, this study can suggest an alternative way of cancer modeling that innovatively combines histopathological imaging and clinical/environmental data

DASAF: An R Package for Deep Sequencing-Based Detection of Fetal Autosomal Abnormalities from Maternal Cell-Free DNA

Background. With the development of massively parallel sequencing (MPS), noninvasive prenatal diagnosis using maternal cell-free DNA is fast becoming the preferred method of fetal chromosomal abnormality detection, due to its inherent high accuracy and low risk. Typically, MPS data is parsed to calculate a risk score, which is used to predict whether a fetal chromosome is normal or not. Although there are several highly sensitive and specific MPS data-parsing algorithms, there are currently no tools that implement these methods. Results. We developed an R package, detection of autosomal abnormalities for fetus (DASAF), that implements the three most popular trisomy detection methods—the standard Z-score (STDZ) method, the GC correction Z-score (GCCZ) method, and the internal reference Z-score (IRZ) method—together with one subchromosome abnormality identification method (SCAZ). Conclusions. With the cost of DNA sequencing declining and with advances in personalized medicine, the demand for noninvasive prenatal testing will undoubtedly increase, which will in turn trigger an increase in the tools available for subsequent analysis. DASAF is a user-friendly tool, implemented in R, that supports identification of whole-chromosome as well as subchromosome abnormalities, based on maternal cell-free DNA sequencing data after genome mapping