CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution.

Gene fusions are found as cancer drivers in diverse adult and pediatric cancers. Accurate detection of fusion transcripts is essential in cancer clinical diagnostics, prognostics, and for guiding therapeutic development. Most currently available methods for fusion transcript detection are compatible with Illumina RNA-seq involving highly accurate short read sequences. Recent advances in long read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single cell samples. Here we developed a new computational tool CTAT-LR-fusion to detect fusion transcripts from long read RNA-seq with or without companion short reads, with applications to bulk or single cell transcriptomes. We demonstrate that CTAT-LR-fusion exceeds fusion detection accuracy of alternative methods as benchmarked with simulated and real long read RNA-seq. Using short and long read RNA-seq, we further apply CTAT-LR-fusion to bulk transcriptomes of nine tumor cell lines, and to tumor single cells derived from a melanoma sample and three metastatic high grade serous ovarian carcinoma samples. In both bulk and in single cell RNA-seq, long isoform reads yielded higher sensitivity for fusion detection than short reads with notable exceptions. By combining short and long reads in CTAT-LR-fusion, we are able to further maximize detection of fusion splicing isoforms and fusion-expressing tumor cells. CTAT-LR-fusion is available at https://github.com/TrinityCTAT/CTAT-LR-fusion/wiki.

De novo detection of somatic variants in long-read single-cell RNA sequencing data.

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, possibly leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), copy-number alterations (CNAs), and gene fusions to reconstruct the tumor clonal heterogeneity. For SNV calling, LongSom distinguishes somatic SNVs from germline polymorphisms by reannotating marker gene expression-based cell types using called variants and applying strict filters. Applying LongSom to ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against single-cell and bulk panel DNA-seq data and could not be detected with short-read (SR) scRNA-seq. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo SNVs, CNAs, and fusions detection, thus enabling the study of cancer evolution, clonal heterogeneity, and treatment resistance.

How much should we sequence? An analysis of the Swiss SARS-CoV-2 surveillance effort.

During the SARS-CoV-2 pandemic, many countries directed substantial resources toward genomic surveillance to detect and track viral variants. There is a debate over how much sequencing effort is necessary in national surveillance programs for SARS-CoV-2 and future pandemic threats. We aimed to investigate the effect of reduced sequencing on surveillance outcomes in a large genomic data set from Switzerland, comprising more than 143k sequences. We employed a uniform downsampling strategy using 100 iterations each to investigate the effects of fewer available sequences on the surveillance outcomes: (i) first detection of variants of concern (VOCs), (ii) speed of introduction of VOCs, (iii) diversity of lineages, (iv) first cluster detection of VOCs, (v) density of active clusters, and (vi) geographic spread of clusters. The impact of downsampling on VOC detection is disparate for the three VOC lineages, but many outcomes including introduction and cluster detection could be recapitulated even with only 35% of the original sequencing effort. The effect on the observed speed of introduction and first detection of clusters was more sensitive to reduced sequencing effort for some VOCs, in particular Omicron and Delta, respectively. A genomic surveillance program needs a balance between societal benefits and costs. While the overall national dynamics of the pandemic could be recapitulated by a reduced sequencing effort, the effect is strongly lineage-dependent-something that is unknown at the time of sequencing-and comes at the cost of accuracy, in particular for tracking the emergence of potential VOCs.IMPORTANCESwitzerland had one of the most comprehensive genomic surveillance systems during the COVID-19 pandemic. Such programs need to strike a balance between societal benefits and program costs. Our study aims to answer the question: How would surveillance outcomes have changed had we sequenced less? We find that some outcomes but also certain viral lineages are more affected than others by sequencing less. However, sequencing to around a third of the original effort still captured many important outcomes for the variants of concern such as their first detection but affected more strongly other measures like the detection of first transmission clusters for some lineages. Our work highlights the importance of setting predefined targets for a national genomic surveillance program based on which sequencing effort should be determined. Additionally, the use of a centralized surveillance platform facilitates aggregating data on a national level for rapid public health responses as well as post-analyses.

Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity.

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes. Mutation trees, which represent the order of select mutations, were created for 207 patients from targeted panel sequencing data using 1 639 162 cells, 823 mutations, and 275 samples. In 224 distinct orderings of mutated genes, mutations related to DNA methylation typically preceded those related to cell signaling, but signaling-first cases did occur, and had higher peripheral cell counts, increased signaling mutation homozygosity, and younger patient age. Serial sample analysis suggested that NPM1 and DNA methylation mutations provide an advantage to signaling mutations in AML. Interestingly, WT1 mutation evolution shared features with signaling mutations, such as WT1-early being proliferative and occurring in younger individuals, trends that remained in multivariable regression. Some mutation orderings had a worse prognosis, but this was mediated by unfavorable mutations, not mutation order. These findings add a dimension to the mutation landscape of AML, identifying uncommon patterns of leukemogenesis and shedding light on heterogeneous phenotypes.

Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer.

Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Here, we perform long-read single-cell RNA sequencing (scRNA-seq) on clinical samples from three ovarian cancer patients presenting with omental metastasis and increase the PacBio sequencing depth to 12,000 reads per cell. Our approach captures 152,000 isoforms, of which over 52,000 were not previously reported. Isoform-level analysis accounting for non-coding isoforms reveals 20% overestimation of protein-coding gene expression on average. We also detect cell type-specific isoform and poly-adenylation site usage in tumor and mesothelial cells, and find that mesothelial cells transition into cancer-associated fibroblasts in the metastasis, partly through the TGF-ß/miR-29/Collagen axis. Furthermore, we identify gene fusions, including an experimentally validated IGF2BP2::TESPA1 fusion, which is misclassified as high TESPA1 expression in matched short-read data, and call mutations confirmed by targeted NGS cancer gene panel results. With these findings, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.

Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity.

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes. Mutation trees, which represent the order of select mutations, were created for 207 patients from targeted panel sequencing data using 1 639 162 cells, 823 mutations, and 275 samples. In 224 distinct orderings of mutated genes, mutations related to DNA methylation typically preceded those related to cell signaling, but signaling-first cases did occur, and had higher peripheral cell counts, increased signaling mutation homozygosity, and younger patient age. Serial sample analysis suggested that NPM1 and DNA methylation mutations provide an advantage to signaling mutations in AML. Interestingly, WT1 mutation evolution shared features with signaling mutations, such as WT1-early being proliferative and occurring in younger individuals, trends that remained in multivariable regression. Some mutation orderings had a worse prognosis, but this was mediated by unfavorable mutations, not mutation order. These findings add a dimension to the mutation landscape of AML, identifying uncommon patterns of leukemogenesis and shedding light on heterogenous phenotypes.

Predicting tumour content of liquid biopsies from cell-free DNA.

BACKGROUND: Liquid biopsy is a minimally-invasive method of sampling bodily fluids, capable of revealing evidence of cancer. The distribution of cell-free DNA (cfDNA) fragment lengths has been shown to differ between healthy subjects and cancer patients, whereby the distributional shift correlates with the sample's tumour content. These fragmentomic data have not yet been utilised to directly quantify the proportion of tumour-derived cfDNA in a liquid biopsy. RESULTS: We used statistical learning to predict tumour content from Fourier and wavelet transforms of cfDNA length distributions in samples from 118 cancer patients. The model was validated on an independent dilution series of patient plasma. CONCLUSIONS: This proof of concept suggests that our fragmentomic methodology could be useful for predicting tumour content in liquid biopsies.

Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues.

Cell lineages accumulate somatic mutations during organismal development, potentially leading to pathological states. The rate of somatic evolution within a cell population can vary due to multiple factors, including selection, a change in the mutation rate, or differences in the microenvironment. Here, we developed a statistical test called the Poisson Tree (PT) test to detect varying evolutionary rates among cell lineages, leveraging the phylogenetic signal of single-cell DNA sequencing (scDNA-seq) data. We applied the PT test to 24 healthy and cancer samples, rejecting a constant evolutionary rate in 11 out of 15 cancer and five out of nine healthy scDNA-seq datasets. In six cancer datasets, we identified subclonal mutations in known driver genes that could explain the rate accelerations of particular cancer lineages. Our findings demonstrate the efficacy of scDNA-seq for studying somatic evolution and suggest that cell lineages often evolve at different rates within cancer and healthy tissues.

Because I'm happy - positive affect and its predictive value for future disease activity in patients with inflammatory bowel diseases: a retrospective cohort study.

Background: While the detrimental impact of negative emotions on the clinical course of inflammatory bowel disease (IBD) and quality of life has been extensively investigated, evidence for a potential impact of positive emotions is scarce. Objectives: We aim to analyse contributing factors of positive affect and their predictive value for disease course in IBD patients. Design: In this retrospective cohort study, epidemiological, psychosocial and IBD disease characteristics of Swiss IBD cohort study patients were analysed longitudinally. Methods: Epidemiological, psychosocial and disease characteristics were extracted from the database of the Swiss IBD cohort study. Participants' positive emotions were assessed cross-sectionally with the seven-item Marburg questionnaire (range 1-6) addressing positive affect in different aspects of daily life. Predictors of positive emotions were identified by linear regression. The quantitative longitudinal impact of positive emotions on the further disease course was analysed using a multivariable Cox proportional hazards model. Results: Among 702 IBD patients, those reporting more positive emotions were found to have significantly less intense medical treatment, less pain and fewer depressive symptoms (p < 0.05). A higher percentage of variability in positive emotions was explained by pain (36%) and depressive symptoms (13%) than by epidemiological characteristics (0.3%), or characteristics of IBD and its treatment (2.4%). Patients with higher levels of positive emotions (score > 3.5) experienced longer flare-free survival, also after adjusting for confounders (adjusted hazard ratio: 0.39, p < 0.05). Conclusions: The absence of pain and depressive symptoms were the strongest drivers for high positive affect. Higher scores of positive affect were associated with longer disease-free survival in IBD patients.

Coherent pathway enrichment estimation by modeling inter-pathway dependencies using regularized regression.

MOTIVATION: Gene set enrichment methods are a common tool to improve the interpretability of gene lists as obtained, for example, from differential gene expression analyses. They are based on computing whether dysregulated genes are located in certain biological pathways more often than expected by chance. Gene set enrichment tools rely on pre-existing pathway databases such as KEGG, Reactome, or the Gene Ontology. These databases are increasing in size and in the number of redundancies between pathways, which complicates the statistical enrichment computation. RESULTS: We address this problem and develop a novel gene set enrichment method, called pareg, which is based on a regularized generalized linear model and directly incorporates dependencies between gene sets related to certain biological functions, for example, due to shared genes, in the enrichment computation. We show that pareg is more robust to noise than competing methods. Additionally, we demonstrate the ability of our method to recover known pathways as well as to suggest novel treatment targets in an exploratory analysis using breast cancer samples from TCGA. AVAILABILITY AND IMPLEMENTATION: pareg is freely available as an R package on Bioconductor (https://bioconductor.org/packages/release/bioc/html/pareg.html) as well as on https://github.com/cbg-ethz/pareg. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here.

COMPASS: joint copy number and mutation phylogeny reconstruction from amplicon single-cell sequencing data.

Reconstructing the history of somatic DNA alterations can help understand the evolution of a tumor and predict its resistance to treatment. Single-cell DNA sequencing (scDNAseq) can be used to investigate clonal heterogeneity and to inform phylogeny reconstruction. However, most existing phylogenetic methods for scDNAseq data are designed either for single nucleotide variants (SNVs) or for large copy number alterations (CNAs), or are not applicable to targeted sequencing. Here, we develop COMPASS, a computational method for inferring the joint phylogeny of SNVs and CNAs from targeted scDNAseq data. We evaluate COMPASS on simulated data and apply it to several datasets including a cohort of 123 patients with acute myeloid leukemia. COMPASS detected clonal CNAs that could be orthogonally validated with bulk data, in addition to subclonal ones that require single-cell resolution, some of which point toward convergent evolution.

Expanding epidemic of recently acquired HCV in HIV-coinfected patients over a period of 10 years.

Background & Aims: Ongoing transmission of HCV infections is associated with risk factors such as drug injection, needlestick injuries, and men who have sex with men (MSM). Ways of transmission, the course of acute infection, changes of virologic features, and incidence over time are not well known. Methods: Over a period of 10 years, n = 161 patients with recently acquired HCV infection (RAHC) (median follow-up 6.8 years) were prospectively enrolled. NS5B sequencing was performed to re-evaluate the HCV genotype (GT) and for phylogenetic analyses. Results: Patients with RAHC were mainly male (92.5%), MSM (90.1%), and HIV-coinfected (86.3%). Transmission risk factors for MSM and non-MSM were sexual risk behaviour (100 and 6.3%, respectively), injection drug use (9.7 and 37.5%, respectively), and nasal drug use (15.2 and 0%, respectively). Spontaneous and interferon- or direct-acting antiviral-based clearance rates were 13.6, 84.3 and 93.4%, respectively. Mean RAHC declined from 19.8 in the first to 13.2 in the past five study years. Although the majority of infections was caused by HCV GT1a, the frequency of HCV GT4d and slightly HCV GT3a increased over time. No relevant clustering of HCV isolates was observed in non-MSM. However, 45% of HCV GT1a and 100% of HCV GT4d MSM cases clustered with MSM isolates from other countries. Travel-associated infections were supported by personal data in an MSM subgroup. No international clustering was detected in MSM with HCV GT1b or HCV GT3a. Conclusions: RAHCs were mainly diagnosed in HIV-coinfected MSM patients and were associated with sexual risk behaviour. Spontaneous clearance rates were low, and phylogenetic clusters were observed in the majority of patients. Impact and Implications: We evaluated the occurrence and transmission of recently acquired HCV infections (RAHCs) over a period of 10 years. Our data demonstrate that the presence of RAHC was mainly found in HIV-coinfected MSM, with internationally connected transmission networks being observed in the majority of patients. Spontaneous clearance rates were low, and reinfection rates increased mainly driven by a small subset of MSM patients with high-risk behaviour.

Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees.

Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive diversity across tumors and patients. Resolving the interactions among mutations and recovering recurrent evolutionary processes may offer greater opportunities for successful therapeutic strategies. To this end, we present a novel probabilistic framework, called TreeMHN, for the joint inference of exclusivity patterns and recurrent trajectories from a cohort of intra-tumor phylogenetic trees. Through simulations, we show that TreeMHN outperforms existing alternatives that can only focus on one aspect of the task. By analyzing datasets of blood, lung, and breast cancers, we find the most likely evolutionary trajectories and mutational patterns, consistent with and enriching our current understanding of tumorigenesis. Moreover, TreeMHN facilitates the prediction of tumor evolution and provides probabilistic measures on the next mutational events given a tumor tree, a prerequisite for evolution-guided treatment strategies.

Genome-wide DNA methylation profiling of HPV-negative leukoplakia and gingivobuccal complex cancers.

BACKGROUND: Gingivobuccal complex oral squamous cell carcinoma (GBC-OSCC) is an aggressive malignancy with high mortality often preceded by premalignant lesions, including leukoplakia. Previous studies have reported genomic drivers in OSCC, but much remains to be elucidated about DNA methylation patterns across different stages of oral carcinogenesis. RESULTS: There is a serious lack of biomarkers and clinical application of biomarkers for early detection and prognosis of gingivobuccal complex cancers. Hence, in search of novel biomarkers, we measured genome-wide DNA methylation in 22 normal oral tissues, 22 leukoplakia, and 74 GBC-OSCC tissue samples. Both leukoplakia and GBC-OSCC had distinct methylation profiles as compared to normal oral tissue samples. Aberrant DNA methylation increases during the different stages of oral carcinogenesis, from premalignant lesions to carcinoma. We identified 846 and 5111 differentially methylated promoters in leukoplakia and GBC-OSCC, respectively, with a sizable fraction shared between the two sets. Further, we identified potential biomarkers from integrative analysis in gingivobuccal complex cancers and validated them in an independent cohort. Integration of genome, epigenome, and transcriptome data revealed candidate genes with gene expression synergistically regulated by copy number and DNA methylation changes. Regularised Cox regression identified 32 genes associated with patient survival. In an independent set of samples, we validated eight genes (FAT1, GLDC, HOXB13, CST7, CYB5A, MLLT11, GHR, LY75) from the integrative analysis and 30 genes from previously published reports. Bisulfite pyrosequencing validated GLDC (P = 0.036), HOXB13 (P < 0.0001) promoter hypermethylation, and FAT1 (P < 0.0001) hypomethylation in GBC-OSCC compared to normal controls. CONCLUSIONS: Our findings identified methylation signatures associated with leukoplakia and gingivobuccal complex cancers. The integrative analysis in GBC-OSCC identified putative biomarkers that enhance existing knowledge of oral carcinogenesis and may potentially help in risk stratification and prognosis of GBC-OSCC.

Biallelic ELOC-Inactivated Renal Cell Carcinoma: Molecular Features Supporting Classification as a Distinct Entity.

Approximately 70% of clear cell renal cell carcinoma (ccRCC) is characterized by the biallelic inactivation of von Hippel-Lindau (VHL) on chromosome 3p. ELOC-mutated (Elongin C-mutated) renal cell carcinoma containing biallelic ELOC inactivations with chromosome 8q deletions is considered a novel subtype of renal cancer possessing a morphologic overlap with ccRCC, renal cell carcinoma (RCC) with fibromyomatous stroma exhibiting Tuberous Sclerosis Complex (TSC)/mammalian Target of Rapamycin (mTOR) mutations, and clear cell papillary tumor. However, the frequency and consequences of ELOC alterations in wild-type VHL and mutated VHL RCC are unclear. In this study, we characterize 123 renal tumors with clear cell morphology and known VHL mutation status to assess the morphologic and molecular consequences of ELOC inactivation. Using OncoScan and whole-exome sequencing, we identify 18 ELOC-deleted RCCs, 3 of which contain ELOC mutations resulting in the biallelic inactivation of ELOC. Biallelic ELOC and biallelic VHL aberrations were mutually exclusive; however, 2 ELOC-mutated RCCs showed monoallelic VHL alterations. Furthermore, no mutations in TSC1, TSC2, or mTOR were identified in ELOC-mutated RCC with biallelic ELOC inactivation. Using High Ambiguity Driven biomolecular DOCKing, we report a novel ELOC variant containing a duplication event disrupting ELOC-VHL interaction alongside the frequently seen Y79C alteration. Using hyper reaction monitoring mass spectrometry, we show RCCs with biallelic ELOC alterations have significantly reduced ELOC expression but similar carbonic anhydrase 9 and vascular endothelial growth factor A expression compared with classical ccRCC with biallelic VHL inactivation. The absence of biallelic VHL and TSC1, TSC2, or mTOR inactivation in RCC with biallelic ELOC inactivation (ELOC mutation in combination with ELOC deletions on chromosome 8q) supports the notion of a novel, molecularly defined tumor entity.

Detection and Molecular Characterization of the SARS-CoV-2 Delta Variant and the Specific Immune Response in Companion Animals in Switzerland.

In human beings, there are five reported variants of concern of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). However, in contrast to human beings, descriptions of infections of animals with specific variants are still rare. The aim of this study is to systematically investigate SARS-CoV-2 infections in companion animals in close contact with SARS-CoV-2-positive owners ("COVID-19 households") with a focus on the Delta variant. Samples, obtained from companion animals and their owners were analyzed using a real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) and next-generation sequencing (NGS). Animals were also tested for antibodies and neutralizing activity against SARS-CoV-2. Eleven cats and three dogs in nine COVID-19-positive households were RT-qPCR and/or serologically positive for the SARS-CoV-2 Delta variant. For seven animals, the genetic sequence could be determined. The animals were infected by one of the pangolin lineages B.1.617.2, AY.4, AY.43 and AY.129 and between zero and three single-nucleotide polymorphisms (SNPs) were detected between the viral genomes of animals and their owners, indicating within-household transmission between animal and owner and in multi-pet households also between the animals. NGS data identified SNPs that occur at a higher frequency in the viral sequences of companion animals than in viral sequences of humans, as well as SNPs, which were exclusively found in the animals investigated in the current study and not in their owners. In conclusion, our study is the first to describe the SARS-CoV-2 Delta variant transmission to animals in Switzerland and provides the first-ever description of Delta-variant pangolin lineages AY.129 and AY.4 in animals. Our results reinforce the need of a One Health approach in the monitoring of SARS-CoV-2 in animals.

Swiss public health measures associated with reduced SARS-CoV-2 transmission using genome data.

Genome sequences from evolving infectious pathogens allow quantification of case introductions and local transmission dynamics. We sequenced 11,357 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from Switzerland in 2020-the sixth largest effort globally. Using a representative subset of these data, we estimated viral introductions to Switzerland and their persistence over the course of 2020. We contrasted these estimates with simple null models representing the absence of certain public health measures. We show that Switzerland's border closures decoupled case introductions from incidence in neighboring countries. Under a simple model, we estimate an 86 to 98% reduction in introductions during Switzerland's strictest border closures. Furthermore, the Swiss 2020 partial lockdown roughly halved the time for sampled introductions to die out. Last, we quantified local transmission dynamics once introductions into Switzerland occurred using a phylodynamic model. We found that transmission slowed 35 to 63% upon outbreak detection in summer 2020 but not in fall. This finding may indicate successful contact tracing over summer before overburdening in fall. The study highlights the added value of genome sequencing data for understanding transmission dynamics.

Parallel evolution and enhanced virulence upon in vivo passage of an RNA virus in Drosophila melanogaster.

Virus evolution is strongly affected by antagonistic co-evolution of virus and host. Host immunity positively selects for viruses that evade the immune response, which in turn may drive counter-adaptations in host immune genes. We investigated how host immune pressure shapes virus populations, using the fruit fly Drosophila melanogaster and its natural pathogen Drosophila C virus (DCV), as a model. We performed an experimental evolution study in which DCV was serially passaged for ten generations in three fly genotypes differing in their antiviral RNAi response: wild-type flies and flies in which the endonuclease gene Dicer-2 was either overexpressed or inactivated. All evolved virus populations replicated more efficiently in vivo and were more virulent than the parental stock. The number of polymorphisms increased in all three host genotypes with passage number, which was most pronounced in Dicer-2 knockout flies. Mutational analysis showed strong parallel evolution, as mutations accumulated in a specific region of the VP3 capsid protein in every lineage in a host genotype-independent manner. The parental tyrosine at position ninety-five of VP3 was substituted with either one of five different amino acids in fourteen out of fifteen lineages. However, no consistent amino acid changes were observed in the viral RNAi suppressor gene 1A, nor elsewhere in the genome in any of the host backgrounds. Our study indicates that the RNAi response restricts the sequence space that can be explored by viral populations. Moreover, our study illustrates how evolution towards higher virulence can be a highly reproducible, yet unpredictable process.

Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens.

The development of cancer therapies is limited by the availability of suitable drug targets. Potential candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. Here, we present SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We apply SLIdR to Project DRIVE data and find both established and potential pan-cancer and cancer type-specific SL pairs consistent with findings from literature and drug response screening data. We experimentally validate two predicted SL interactions (ARID1A-TEAD1 and AXIN1-URI1) in hepatocellular carcinoma, thus corroborating the ability of SLIdR to identify potential drug targets.

SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data.

We present SIEVE, a statistical method for the joint inference of somatic variants and cell phylogeny under the finite-sites assumption from single-cell DNA sequencing. SIEVE leverages raw read counts for all nucleotides and corrects the acquisition bias of branch lengths. In our simulations, SIEVE outperforms other methods in phylogenetic reconstruction and variant calling accuracy, especially in the inference of homozygous variants. Applying SIEVE to three datasets, one for triple-negative breast (TNBC), and two for colorectal cancer (CRC), we find that double mutant genotypes are rare in CRC but unexpectedly frequent in the TNBC samples.