A recurrent chromosomal inversion suffices for driving escape from oncogene-induced senescence via subTAD reorganization.

Oncogene-induced senescence (OIS) is an inherent and important tumor suppressor mechanism. However, if not removed timely via immune surveillance, senescent cells also have detrimental effects. Although this has mostly been attributed to the senescence-associated secretory phenotype (SASP) of these cells, we recently proposed that "escape" from the senescent state is another unfavorable outcome. The mechanism underlying this phenomenon remains elusive. Here, we exploit genomic and functional data from a prototypical human epithelial cell model carrying an inducible CDC6 oncogene to identify an early-acquired recurrent chromosomal inversion that harbors a locus encoding the circadian transcription factor BHLHE40. This inversion alone suffices for BHLHE40 activation upon CDC6 induction and driving cell cycle re-entry of senescent cells, and malignant transformation. Ectopic overexpression of BHLHE40 prevented induction of CDC6-triggered senescence. We provide strong evidence in support of replication stress-induced genomic instability being a causative factor underlying "escape" from oncogene-induced senescence.

Adiabatic quantum algorithm for search engine ranking.

We propose an adiabatic quantum algorithm for generating a quantum pure state encoding of the PageRank vector, the most widely used tool in ranking the relative importance of internet pages. We present extensive numerical simulations which provide evidence that this algorithm can prepare the quantum PageRank state in a time which, on average, scales polylogarithmically in the number of web pages. We argue that the main topological feature of the underlying web graph allowing for such a scaling is the out-degree distribution. The top-ranked log(n) entries of the quantum PageRank state can then be estimated with a polynomial quantum speed-up. Moreover, the quantum PageRank state can be used in "q-sampling" protocols for testing properties of distributions, which require exponentially fewer measurements than all classical schemes designed for the same task. This can be used to decide whether to run a classical update of the PageRank.

Genome-wide detection of DNA double-strand breaks by in-suspension BLISS.

sBLISS (in-suspension breaks labeling in situ and sequencing) is a versatile and widely applicable method for identification of endogenous and induced DNA double-strand breaks (DSBs) in any cell type that can be brought into suspension. sBLISS provides genome-wide profiles of the most consequential DNA lesion implicated in a variety of pathological, but also physiological, processes. In sBLISS, after in situ labeling, DSB ends are linearly amplified, followed by next-generation sequencing and DSB landscape analysis. Here, we present a step-by-step experimental protocol for sBLISS, as well as a basic computational analysis. The main advantages of sBLISS are (i) the suspension setup, which renders the protocol user-friendly and easily scalable; (ii) the possibility of adapting it to a high-throughput or single-cell workflow; and (iii) its flexibility and its applicability to virtually every cell type, including patient-derived cells, organoids, and isolated nuclei. The wet-lab protocol can be completed in 1.5 weeks and is suitable for researchers with intermediate expertise in molecular biology and genomics. For the computational analyses, basic-to-intermediate bioinformatics expertise is required.

Modeling double strand break susceptibility to interrogate structural variation in cancer.

BACKGROUND: Structural variants (SVs) are known to play important roles in a variety of cancers, but their origins and functional consequences are still poorly understood. Many SVs are thought to emerge from errors in the repair processes following DNA double strand breaks (DSBs). RESULTS: We used experimentally quantified DSB frequencies in cell lines with matched chromatin and sequence features to derive the first quantitative genome-wide models of DSB susceptibility. These models are accurate and provide novel insights into the mutational mechanisms generating DSBs. Models trained in one cell type can be successfully applied to others, but a substantial proportion of DSBs appear to reflect cell type-specific processes. Using model predictions as a proxy for susceptibility to DSBs in tumors, many SV-enriched regions appear to be poorly explained by selectively neutral mutational bias alone. A substantial number of these regions show unexpectedly high SV breakpoint frequencies given their predicted susceptibility to mutation and are therefore credible targets of positive selection in tumors. These putatively positively selected SV hotspots are enriched for genes previously shown to be oncogenic. In contrast, several hundred regions across the genome show unexpectedly low levels of SVs, given their relatively high susceptibility to mutation. These novel coldspot regions appear to be subject to purifying selection in tumors and are enriched for active promoters and enhancers. CONCLUSIONS: We conclude that models of DSB susceptibility offer a rigorous approach to the inference of SVs putatively subject to selection in tumors.

CUTseq is a versatile method for preparing multiplexed DNA sequencing libraries from low-input samples.

Current multiplexing strategies for massively parallel sequencing of genomic DNA mainly rely on library indexing in the final steps of library preparation. This procedure is costly and time-consuming, because a library must be generated separately for each sample. Furthermore, library preparation is challenging in the case of fixed samples, such as DNA extracted from formalin-fixed paraffin-embedded (FFPE) tissues. Here we describe CUTseq, a method that uses restriction enzymes and in vitro transcription to barcode and amplify genomic DNA prior to library construction. We thoroughly assess the sensitivity and reproducibility of CUTseq in both cell lines and FFPE samples, and demonstrate an application of CUTseq for multi-region DNA copy number profiling within single FFPE tumor sections, to assess intratumor genetic heterogeneity at high spatial resolution. In conclusion, CUTseq is a versatile and cost-effective method for library preparation for reduced representation genome sequencing, which can find numerous applications in research and diagnostics.

Quantification of HER2 and estrogen receptor heterogeneity in breast cancer by single-molecule RNA fluorescence in situ hybridization.

Intra-tumor heterogeneity is a pervasive property of human cancers that poses a major clinical challenge. Here, we describe the characterization, at the transcriptional level, of the intra-tumor topography of two prominent breast cancer biomarkers and drug targets, epidermal growth factor receptor 2 (HER2) and estrogen receptor 1 (ER) in 49 archival breast cancer samples. We developed a protocol for single-molecule RNA FISH in formalin-fixed, paraffin-embedded tissue sections (FFPE-smFISH), which enabled us to simultaneously detect and perform absolute quantification of HER2 and ER mature transcripts in single cells and multiple tumor regions. We benchmarked our method with standard diagnostic techniques, demonstrating that FFPE-smFISH is able to correctly classify breast cancers into well-established molecular subgroups. By counting transcripts in thousands of single cells, we identified different expression modes and levels of inter-cellular variability. In samples expressing both HER2 and ER, many cells co-expressed both genes, although expression levels were typically uncorrelated. Finally, we applied diversity metrics from the field of ecology to assess the intra-tumor topography of HER2 and ER gene expression, revealing that the spatial distribution of these key biomarkers can vary substantially even among breast cancers of the same subtype. Our results demonstrate that FFPE-smFISH is a reliable diagnostic assay and a powerful method for quantification of intra-tumor transcriptional heterogeneity of selected biomarkers in clinical samples.

BLISS is a versatile and quantitative method for genome-wide profiling of DNA double-strand breaks.

Precisely measuring the location and frequency of DNA double-strand breaks (DSBs) along the genome is instrumental to understanding genomic fragility, but current methods are limited in versatility, sensitivity or practicality. Here we present Breaks Labeling In Situ and Sequencing (BLISS), featuring the following: (1) direct labelling of DSBs in fixed cells or tissue sections on a solid surface; (2) low-input requirement by linear amplification of tagged DSBs by in vitro transcription; (3) quantification of DSBs through unique molecular identifiers; and (4) easy scalability and multiplexing. We apply BLISS to profile endogenous and exogenous DSBs in low-input samples of cancer cells, embryonic stem cells and liver tissue. We demonstrate the sensitivity of BLISS by assessing the genome-wide off-target activity of two CRISPR-associated RNA-guided endonucleases, Cas9 and Cpf1, observing that Cpf1 has higher specificity than Cas9. Our results establish BLISS as a versatile, sensitive and efficient method for genome-wide DSB mapping in many applications.

Quantum algorithms for topological and geometric analysis of data.

Extracting useful information from large data sets can be a daunting task. Topological methods for analysing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying topological features and for determining how such features persist as the data is viewed at different scales. Here we present quantum machine learning algorithms for calculating Betti numbers--the numbers of connected components, holes and voids--in persistent homology, and for finding eigenvectors and eigenvalues of the combinatorial Laplacian. The algorithms provide an exponential speed-up over the best currently known classical algorithms for topological data analysis.

Phase transition of light on complex quantum networks.

Recent advances in quantum optics and atomic physics allow for an unprecedented level of control over light-matter interactions, which can be exploited to investigate new physical phenomena. In this work we are interested in the role played by the topology of quantum networks describing coupled optical cavities and local atomic degrees of freedom. In particular, using a mean-field approximation, we study the phase diagram of the Jaynes-Cummings-Hubbard model on complex networks topologies, and we characterize the transition between a Mott-like phase of localized polaritons and a superfluid phase. We found that, for complex topologies, the phase diagram is nontrivial and well defined in the thermodynamic limit only if the hopping coefficient scales like the inverse of the maximal eigenvalue of the adjacency matrix of the network. Furthermore we provide numerical evidences that, for some complex network topologies, this scaling implies an asymptotically vanishing hopping coefficient in the limit of large network sizes. The latter result suggests the interesting possibility of observing quantum phase transitions of light on complex quantum networks even with very small couplings between the optical cavities.

Fidelity approach to the disordered quantum XY model.

We study the random XY spin chain in a transverse field by analyzing the susceptibility of the ground state fidelity, numerically evaluated through a standard mapping of the model onto quasifree fermions. It is found that the fidelity susceptibility and its scaling properties provide useful information about the phase diagram. In particular it is possible to determine the Ising critical line and the Griffiths phase regions, in agreement with previous analytical and numerical results.