SCONCE2: jointly inferring single cell copy number profiles and tumor evolutionary distances.

BACKGROUND: Single cell whole genome tumor sequencing can yield novel insights into the evolutionary history of somatic copy number alterations. Existing single cell copy number calling methods do not explicitly model the shared evolutionary process of multiple cells, and generally analyze cells independently. Additionally, existing methods for estimating tumor cell phylogenies using copy number profiles are sensitive to profile estimation errors. RESULTS: We present SCONCE2, a method for jointly calling copy number alterations and estimating pairwise distances for single cell sequencing data. Using simulations, we show that SCONCE2 has higher accuracy in copy number calling and phylogeny estimation than competing methods. We apply SCONCE2 to previously published single cell sequencing data to illustrate the utility of the method. CONCLUSIONS: SCONCE2 jointly estimates copy number profiles and a distance metric for inferring tumor phylogenies in single cell whole genome tumor sequencing across multiple cells, enabling deeper understandings of tumor evolution.

SCONCE: a method for profiling copy number alterations in cancer evolution using single-cell whole genome sequencing.

MOTIVATION: Copy number alterations (CNAs) are a significant driver in cancer growth and development, but remain poorly characterized on the single cell level. Although genome evolution in cancer cells is Markovian through evolutionary time, CNAs are not Markovian along the genome. However, existing methods call copy number profiles with Hidden Markov Models or change point detection algorithms based on changes in observed read depth, corrected by genome content and do not account for the stochastic evolutionary process. RESULTS: We present a theoretical framework to use tumor evolutionary history to accurately call CNAs in a principled manner. To model the tumor evolutionary process and account for technical noise from low coverage single-cell whole genome sequencing data, we developed SCONCE, a method based on a Hidden Markov Model to analyze read depth data from tumor cells using matched normal cells as negative controls. Using a combination of public data sets and simulations, we show SCONCE accurately decodes copy number profiles, and provides a useful tool for understanding tumor evolution. AVAILABILITYAND IMPLEMENTATION: SCONCE is implemented in C++11 and is freely available from https://github.com/NielsenBerkeleyLab/sconce. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Chronic Cough With Non-Resolving Mass-like Consolidation.

CASE PRESENTATION: A 72-year-old Chinese man presented with a 5-month history of chronic dry cough, weight loss, and progressive dyspnea. There was no associated hemoptysis, hoarseness, epistaxis, or fever on systemic review. He was a nonsmoker and had no family history of malignancy. He was treated for pulmonary TB 40 years ago. A chest radiograph (Fig 1) showed mass-like consolidation in the right midzone with loss of the right hilar border, a small right pleural effusion, and bi-apical scarring. On physical examination, he was afebrile and normotensive, and he had pulse oxygen saturation of 97%. Examination of the chest was remarkable only for reduced breath sounds over the right chest. He did not have digital clubbing, distended neck veins, or cervical lymphadenopathy.

Haploinsufficiency networks identify targetable patterns of allelic deficiency in low mutation ovarian cancer.

Identification of specific oncogenic gene changes has enabled the modern generation of targeted cancer therapeutics. In high-grade serous ovarian cancer (OV), the bulk of genetic changes is not somatic point mutations, but rather somatic copy-number alterations (SCNAs). The impact of SCNAs on tumour biology remains poorly understood. Here we build haploinsufficiency network analyses to identify which SCNA patterns are most disruptive in OV. Of all KEGG pathways (N=187), autophagy is the most significantly disrupted by coincident gene deletions. Compared with 20 other cancer types, OV is most severely disrupted in autophagy and in compensatory proteostasis pathways. Network analysis prioritizes MAP1LC3B (LC3) and BECN1 as most impactful. Knockdown of LC3 and BECN1 expression confers sensitivity to cells undergoing autophagic stress independent of platinum resistance status. The results support the use of pathway network tools to evaluate how the copy-number landscape of a tumour may guide therapy.

Does this patient really have chronic obstructive pulmonary disease?

Chronic obstructive pulmonary disease (COPD) is a condition commonly encountered by primary care practitioners. The disease should be detected in its early stages to prevent disease progression and to reduce the burden of symptoms. Early treatment also results in improved mortality and reduced morbidity. COPD should be differentiated from other similar conditions such as asthma, as the basis of treatment differs in these conditions, and misdiagnosis can lead to poorer patient outcomes. Non-pharmacological treatment such as smoking cessation and vaccinations are important in the management of COPD, while pharmacotherapy such as bronchodilators and antimuscarinics are the mainstay of therapy in COPD. Referral to a specialist is recommended when there is progression of the disease or uncertainty regarding the diagnosis.