Multicenter integrated analysis of noncoding CRISPRi screens.

The ENCODE Consortium's efforts to annotate noncoding cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes. Pooled, noncoding CRISPR screens offer a systematic approach to investigate cis-regulatory mechanisms. The ENCODE4 Functional Characterization Centers conducted 108 screens in human cell lines, comprising >540,000 perturbations across 24.85 megabases of the genome. Using 332 functionally confirmed CRE-gene links in K562 cells, we established guidelines for screening endogenous noncoding elements with CRISPR interference (CRISPRi), including accurate detection of CREs that exhibit variable, often low, transcriptional effects. Benchmarking five screen analysis tools, we find that CASA produces the most conservative CRE calls and is robust to artifacts of low-specificity single guide RNAs. We uncover a subtle DNA strand bias for CRISPRi in transcribed regions with implications for screen design and analysis. Together, we provide an accessible data resource, predesigned single guide RNAs for targeting 3,275,697 ENCODE SCREEN candidate CREs with CRISPRi and screening guidelines to accelerate functional characterization of the noncoding genome.

A zero-agnostic model for copy number evolution in cancer.

MOTIVATION: New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. Copy number aberrations alter multiple adjacent genomic loci, violating the standard phylogenetic assumption that loci evolve independently. Thus, specialized models to infer copy number phylogenies have been introduced. A widely used model is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. RESULTS: We introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data.

GLIDER: function prediction from GLIDE-based neighborhoods.

MOTIVATION: Protein function prediction, based on the patterns of connection in a protein-protein interaction (or association) network, is perhaps the most studied of the classical, fundamental inference problems for biological networks. A highly successful set of recent approaches use random walk-based low-dimensional embeddings that tend to place functionally similar proteins into coherent spatial regions. However, these approaches lose valuable local graph structure from the network when considering only the embedding. We introduce GLIDER, a method that replaces a protein-protein interaction or association network with a new graph-based similarity network. GLIDER is based on a variant of our previous GLIDE method, which was designed to predict missing links in protein-protein association networks, capturing implicit local and global (i.e. embedding-based) graph properties. RESULTS: GLIDER outperforms competing methods on the task of predicting GO functional labels in cross-validation on a heterogeneous collection of four human protein-protein association networks derived from the 2016 DREAM Disease Module Identification Challenge, and also on three different protein-protein association networks built from the STRING database. We show that this is due to the strong functional enrichment that is present in the local GLIDER neighborhood in multiple different types of protein-protein association networks. Furthermore, we introduce the GLIDER graph neighborhood as a way for biologists to visualize the local neighborhood of a disease gene. As an application, we look at the local GLIDER neighborhoods of a set of known Parkinson's Disease GWAS genes, rediscover many genes which have known involvement in Parkinson's disease pathways, plus suggest some new genes to study. AVAILABILITY AND IMPLEMENTATION: All code is publicly available and can be accessed here: https://github.com/kap-devkota/GLIDER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A zero-agnostic model for copy number evolution in cancer.

Motivation: New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. A widely used model to infer such copy number phylogenies is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. Results: We introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data.

Startle: A star homoplasy approach for CRISPR-Cas9 lineage tracing.

CRISPR-Cas9-based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. Although standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9-induced mutations. Here, we introduce the "star homoplasy" evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing the "non-modifiability" property of CRISPR-Cas9 mutations. We derive a combinatorial characterization of star homoplasy phylogenies and use this characterization to develop an algorithm, "Startle", that computes a maximum parsimony star homoplasy phylogeny. We demonstrate that Startle infers more accurate phylogenies on simulated lineage tracing data compared with existing methods and finds parsimonious phylogenies with fewer metastatic migrations on lineage tracing data from mouse metastatic lung adenocarcinoma.

Base editing sensor libraries for high-throughput engineering and functional analysis of cancer-associated single nucleotide variants.

Base editing can be applied to characterize single nucleotide variants of unknown function, yet defining effective combinations of single guide RNAs (sgRNAs) and base editors remains challenging. Here, we describe modular base-editing-activity 'sensors' that link sgRNAs and cognate target sites in cis and use them to systematically measure the editing efficiency and precision of thousands of sgRNAs paired with functionally distinct base editors. By quantifying sensor editing across >200,000 editor-sgRNA combinations, we provide a comprehensive resource of sgRNAs for introducing and interrogating cancer-associated single nucleotide variants in multiple model systems. We demonstrate that sensor-validated tools streamline production of in vivo cancer models and that integrating sensor modules in pooled sgRNA libraries can aid interpretation of high-throughput base editing screens. Using this approach, we identify several previously uncharacterized mutant TP53 alleles as drivers of cancer cell proliferation and in vivo tumor development. We anticipate that the framework described here will facilitate the functional interrogation of cancer variants in cell and animal models.

A Pilot Study to Non-Invasively Track PIK3CA Mutation in Head and Neck Cancer.

BACKGROUND: PIK3CA pathways are the most frequently mutated oncogenic pathway in head and neck squamous cell carcinoma (HNSCC), including virally driven HNCs. PIK3CA is involved in the PI3K-PTEN-mTOR signalling pathway. PIK3CA has been implicated in HNSCC progression and PIK3CA mutations may serve as predictive biomarkers for therapy selection. Circulating tumour DNA (ctDNA) derived from necrotic and apoptotic tumour cells are thought to harbour tumour-specific genetic alterations. As such, the detection of PIK3CA alterations detected by ctDNA holds promise as a potential biomarker in HNSCC. METHODS: Blood samples from treatment naïve HNSCC patients (n = 29) were interrogated for a commonly mutated PIK3CA hotspot mutation using low cost allele-specific Plex-PCRTM technology. RESULTS: In this pilot, cross sectional study, PIK3CA E545K mutation was detected in the plasma samples of 9/29 HNSCC patients using the Plex-PCRTM technology. CONCLUSION: The results of this pilot study support the notion of using allele-specific technologies for cost-effective testing of ctDNA, and further assert the potential utility of ctDNA in HNSCC.

A Collective Route to Head and Neck Cancer Metastasis.

Distant metastasis (DM) from head and neck cancers (HNC) portends a poor patient prognosis. Despite its important biological role, little is known about the cells which seed these DM. Circulating tumour cells (CTCs) represent a transient cancer cell population, which circulate in HNC patients' peripheral blood and seed at distant sites. Capture and analysis of CTCs offers insights into tumour metastasis and can facilitate treatment strategies. Whilst the data on singular CTCs have shown clinical significance, the role of CTC clusters in metastasis remains limited. In this pilot study, we assessed 60 treatment naïve HNC patients for CTCs with disease ranging from early to advanced stages, for CTC clusters utilizing spiral CTC enrichment technology. Single CTCs were isolated in 18/60-30% (Ranging from Stage I-IV), CTC clusters in 15/60-25% (exclusively Stage IV) with 3/15-20% of CTC clusters also containing leukocytes. The presence of CTC clusters associated with the development of distant metastatic disease(P = 0.0313). This study demonstrates that CTC clusters are found in locally advanced patients, and this may be an important prognostic marker. In vivo and in vitro studies are warranted to determine the role of these CTC clusters, in particular, whether leukocyte involvement in CTC clusters has clinical relevance.

The development of a liquid biopsy for head and neck cancers.

Developing non-invasive diagnostic tools in the field of head and neck oncology has been a challenge. Analysis of circulating tumour derivatives in a patient's blood has been explored in other solid cancers. This includes analysis of circulating tumour DNA, intact circulating tumour cells (CTCs) and exosomes. These circulating tumour derivatives provide avenues of investigation which can be representative of a patient's primary tumour signature and can be assessed from a patient's blood sample. In advanced stage cancer patients, these tumour derivatives are found in higher amounts, attributed to higher cellular turnover (apoptosis, autophagy), lysed CTCs and sloughing from necrotic tumours. Head and neck squamous cell carcinoma (HNSCC) patients often present with advanced disease associated with a poor 5-year survival of <50%. Outside of sophisticated imaging and clinical examination, there is a lack of available biomarkers to measure disease burden, and/or response to therapy. Implementation of a liquid biopsy in HNSCC through serial blood samples has the potential to detect metastatic events earlier, thereby allowing better selection of appropriate treatment choices, predict prognosis in patients with potentially curable disease, monitor systemic therapies and residual disease post-treatment.

A liquid biopsy for head and neck cancers.

Head and neck cancer patients often present with advanced metastatic disease resulting in a poor 5-year survival. Therefore, there is a need for non-invasive diagnostic tools that could complement conventional imaging to inform clinicians of patient outcomes and treatment responses. A liquid biopsy addresses this unmet clinical need; a simple peripheral blood draw could provide information about the disseminated disease in terms of circulating tumor cells and circulating tumor DNA. Moreover, detectable tumor DNA in the saliva of head and neck cancer patients could signify the early signs of the disease and present an opportunity for clinical intervention. This review provides an overview of the current literature with regard to the feasibility of such a test in the head and neck cancer field and highlights the need for such a test.