CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system.

MOTIVATION: The transition from evaluating a single time point to examining the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and clustering challenging. RESULTS: We developed CONNECTOR, a data-driven framework able to analyze and inspect longitudinal data in a straightforward and revealing way. When used to analyze tumor growth kinetics over time in 1599 patient-derived xenograft growth curves from ovarian and colorectal cancers, CONNECTOR allowed the aggregation of time-series data through an unsupervised approach in informative clusters. We give a new perspective of mechanism interpretation, specifically, we define novel model aggregations and we identify unanticipated molecular associations with response to clinically approved therapies. AVAILABILITY AND IMPLEMENTATION: CONNECTOR is freely available under GNU GPL license at https://qbioturin.github.io/connector and https://doi.org/10.17504/protocols.io.8epv56e74g1b/v1.

Measurement invariance of the Liebowitz Social Anxiety Scale-Self-Report.

OBJECTIVE: The Liebowitz Social Anxiety Scale-Self Report (LSAS-SR) is a self-report measure of social anxiety (SA), which has shown adequate psychometric properties across cultures. However, no study has systematically evaluated its measurement invariance (MI) between (a) individuals with and without a diagnosis of social anxiety disorder (SAD) and (b) males and females. The current study addresses this issue. METHODS: We collected data on 257 (158 females) Italian individuals diagnosed with SAD and 356 (232 females) community-dwelling adults. RESULTS: We initially found support for the unidimensionality of the Italian LSAS-SR measurement model in all samples. Using the Graded Response Model, we obtained evidence of partial MI and differential item functioning between community-dwelling and SAD-diagnosed individuals and evidence of strong MI between male and female participants. CONCLUSIONS: The results of this study suggest that the Italian LSAS-SR measures the same trait in the same way across the symptom continuum and sexes, making it a psychometrically sound tool for assessment, screening, and research purposes.

Becoming a mother during the COVID-19 pandemic: The lived experience as told by birthing mothers: A qualitative study.

AIMS AND OBJECTIVES: The aim of this study is to explore the lived experience of women who gave birth during the COVID-19 pandemic. BACKGROUND: Experiencing pregnancy during the Covid-19 pandemic exacerbates the risk of the onset of psychological problems. DESIGN: This is a descriptive, single-centre, qualitative study. METHODS: The enrolment for data collection included childbearing mothers aged 18 years and over between November 2021 and April 2022. The researchers invited them to write about their personal experiences during the isolation period of the first pandemic wave. The descriptive phenomenological analysis of the data was carried out using the method described by Mortari. RESULTS: A total of 50 mothers were recruited, of whom 28 were primiparous (56.0%) and 22 multiparous (44.0%). From the analysis of the interviews, five main themes emerged that enclose the experience of both primiparous and multiparous mothers: 'The negative feeling: between loneliness, sadness and fear'; 'The comfort of being cared for: between humanity and competence'; 'Family proximity: between comfort and stress'; 'Symbiotic intimacy: bonding; Managing physical pain and consciousness of being resilient women'. CONCLUSIONS: The study showed that the discomfort associated with the absence of family support in the phase of labour and childbirth was compensated by the professionalism of the health staff and allowed new mothers to experience moments of great intimacy with the child. IMPLICATIONS FOR NURSING MANAGEMENT: Such data could help create recommendations based on the assisted person's experiences to ensure that care is increasingly attentive and tailored to the needs of mothers and, thus, of children.

PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity.

BACKGROUND: Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking. RESULTS: We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module. CONCLUSIONS: PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data.

Evolutionary Rewiring of Human Regulatory Networks by Waves of Genome Expansion.

Genome expansion is believed to be an important driver of the evolution of gene regulation. To investigate the role of a newly arising sequence in rewiring regulatory networks, we estimated the age of each region of the human genome by applying maximum parsimony to genome-wide alignments with 100 vertebrates. We then studied the age distribution of several types of functional regions, with a focus on regulatory elements. The age distribution of regulatory elements reveals the extensive use of newly formed genomic sequence in the evolution of regulatory interactions. Many transcription factors have expanded their repertoire of targets through waves of genomic expansions that can be traced to specific evolutionary times. Repeated elements contributed a major part of such expansion: many classes of such elements are enriched in binding sites of one or a few specific transcription factors, whose binding sites are localized in specific portions of the element and characterized by distinctive motif words. These features suggest that the binding sites were available as soon as the new sequence entered the genome, rather than being created later by accumulation of point mutations. By comparing the age of regulatory regions to the evolutionary shift in expression of nearby genes, we show that rewiring through genome expansion played an important role in shaping human regulatory networks.

A functional strategy to characterize expression Quantitative Trait Loci.

The study of genetic variation has been revolutionized by the advent of high-throughput technologies able to determine the complete genomic sequence of thousands of individuals. Understanding the functional relevance of variants is, however, still a difficult task, especially when focusing on non-coding variants. Most of the variants associated with disease by Genome-Wide Association Studies (GWAS) are indeed non-coding, and presumably exert their effects by altering gene regulation. Expression Quantitative Trait Loci (eQTL) studies represent an important step in understanding the functional relevance of regulatory variants. We propose a new strategy to detect and characterize eQTLs, based on the effect of variants on the Total Binding Affinity (TBA) profiles of regulatory regions. Using a large dataset of coupled genome and expression data, we show that TBA-based inference allows the identification of eQTLs not revealed by traditional methods and helps in their interpretation in terms of altered transcription factor binding.

Genome-wide identification and characterization of fixed human-specific regulatory regions.

Changes in gene regulatory networks are believed to have played an important role in the development of human-specific anatomy and behavior. We identified the human genome regions that show the typical chromatin marks of regulatory regions but cannot be aligned to other mammalian genomes. Most of these regions have become fixed in the human genome. Their regulatory targets are enriched in genes involved in neural processes, CNS development, and diseases such as autism, depression, and schizophrenia. Specific transposable elements contributing to the rewiring of the human regulatory network can be identified by the creation of human-specific regulatory regions. Our results confirm the relevance of regulatory evolution in the emergence of human traits and cognitive abilities and the importance of newly acquired genomic elements for such evolution.

Guidance for faecal occult blood testing: quantitative immunochemical method (FIT-HB) in colorectal cancer screening programmes. / Guida per la determinazione del sangue occulto fecale: metodo immunochimico quantitativo (FIT-HB) nei programmi di screening per il carcinoma colorettale

BACKGROUND: in Italy, colorectal cancer screening is included as part of the Italian National Health Service - SSN (Servizio Sanitario Nazionale) Essential Levels of Care - LEA (Livelli Essenziali Assistenziali) and the European Guidelines, which specify quantitative FIT-Hb testing as the best strategy for organised screening programmes. To ensure consistent operating standards in Member States, European regulations require the implementation of certification and accreditation requirements for diagnostic and care-related processes. The requirement, based on ISO 17021 accreditation standards, includes ISO 9001 certification for systems and ISO 15189:2012 accreditation for laboratories. METHODOLOGY: various phases of the analytical process (pre-test, test, post-test) were evaluated in detail and provided operational guidelines for adjusting analytical and managerial procedures using: (a) feedback from members of GISCoR screening labs; (b) performance data obtained via a systematic review of the literature and the Osservatorio Nazionale Screening (ONS) Survey; (c) recommendations for laboratory practice issued by the World Endoscopy Organization "FIT for Screening" Working Group; (d) selected guidelines from the National Guidelines Clearinghouse database; and (e) Canadian, Australian and European screening programme websites. With respect to ISO 15189:2012 standards for accreditation of medical laboratories, GISCoR's guidance has been re-evaluated and revised by auditors from the Italian certification body (ACCREDIA) to assess its compliance and completeness with the aim of finalising operating procedures. CONCLUSIONS: the implementation and maintenance of operational standards required by complex systems (e.g. screening programmes) involving constant interaction between facilities and the supporting organisational structure are not easy to achieve. The guide aims to provide laboratories with the necessary guidance for proper process management.

Roar: detecting alternative polyadenylation with standard mRNA sequencing libraries.

BACKGROUND: Post-transcriptional regulation is a complex mechanism that plays a central role in defining multiple cellular identities starting from a common genome. Modifications in the length of 3'UTRs have been found to play an important role in this context, since alternative 3' UTRs could lead to differences for example in regulation by microRNAs and cellular localization of the transcripts thus altering their fate. RESULTS: We propose a strategy to identify the genes undergoing regulation of 3' UTR length using RNA sequencing data obtained from standard libraries, thus widely applicable to data originally obtained to perform classical differential expression analyses. We decided to exploit previously annotated APA sites from public databases, in contrast with other approaches recently proposed in which the location of the APA site is inferred from the data together with the relative abundance of the isoforms. We demonstrate the reliability of our method by comparing it to the results of other microarray based or specific RNA-seq libraries methods and show that using APA sites databases results in higher sensitivity compared to de novo site prediction approach. CONCLUSIONS: We implemented the algorithm in a Bioconductor package to facilitate its broad usage in the scientific community. The ability of this approach to detect shortening from libraries with a number of reads comparable to that needed for differential expression analyses makes it useful for investigating if alternative polyadenylation is relevant in a certain biological process without requiring specific experimental assays.

Using a mixed-methods approach to measure impact of a school-based nutrition and media education intervention study on fruit and vegetable intake of Italian children.

OBJECTIVE: The purpose of the present study was to investigate the impact of a school-based nutrition and media education intervention on the promotion of fruit and vegetable consumption to help prevent childhood obesity. DESIGN: The 10-week-long intervention included sessions on nutrition education and media literacy. It also included a health communication media-based campaign workshop during which the children created posters, newsletters and video commercials related to fruits and vegetables targeted to their parents. For evaluation purposes, the study utilized a mixed-methods approach, including a quasi-experimental study (with one intervention group and one control group) and a focus group study. SETTING: Four different elementary schools in Treviso (Veneto Region of Italy) agreed to participate in the research. SUBJECTS: The target population for the study included 10-year-old Italian children and their parents. RESULTS: Data indicate that this intervention was effective for children but not for parents. Evaluation results show that the intervention was effective in significantly increasing children's fruit and vegetable intake (P<0·05) and all psychosocial determinants (P<0·05). CONCLUSIONS: The study results confirm the efficacy of a school-based health and media education intervention to address the children's obesity issue and, in particular, to increase children's fruit and vegetable intake. The study also opens a new perspective on the theoretical constructs investigated, because the development of 'ability of expression' could be considered one of the most important factors to determine the efficacy of the intervention.

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer.

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers.

Auditory models of suprathreshold distortion and speech intelligibility in persons with impaired hearing.

BACKGROUND: Hearing-impaired (HI) individuals with similar ages and audiograms often demonstrate substantial differences in speech-reception performance in noise. Traditional models of speech intelligibility focus primarily on average performance for a given audiogram, failing to account for differences between listeners with similar audiograms. Improved prediction accuracy might be achieved by simulating differences in the distortion that speech may undergo when processed through an impaired ear. Although some attempts to model particular suprathreshold distortions can explain general speech-reception deficits not accounted for by audibility limitations, little has been done to model suprathreshold distortion and predict speech-reception performance for individual HI listeners. Auditory-processing models incorporating individualized measures of auditory distortion, along with audiometric thresholds, could provide a more complete understanding of speech-reception deficits by HI individuals. A computational model capable of predicting individual differences in speech-recognition performance would be a valuable tool in the development and evaluation of hearing-aid signal-processing algorithms for enhancing speech intelligibility. PURPOSE: This study investigated whether biologically inspired models simulating peripheral auditory processing for individual HI listeners produce more accurate predictions of speech-recognition performance than audiogram-based models. RESEARCH DESIGN: Psychophysical data on spectral and temporal acuity were incorporated into individualized auditory-processing models consisting of three stages: a peripheral stage, customized to reflect individual audiograms and spectral and temporal acuity; a cortical stage, which extracts spectral and temporal modulations relevant to speech; and an evaluation stage, which predicts speech-recognition performance by comparing the modulation content of clean and noisy speech. To investigate the impact of different aspects of peripheral processing on speech predictions, individualized details (absolute thresholds, frequency selectivity, spectrotemporal modulation [STM] sensitivity, compression) were incorporated progressively, culminating in a model simulating level-dependent spectral resolution and dynamic-range compression. STUDY SAMPLE: Psychophysical and speech-reception data from 11 HI and six normal-hearing listeners were used to develop the models. DATA COLLECTION AND ANALYSIS: Eleven individualized HI models were constructed and validated against psychophysical measures of threshold, frequency resolution, compression, and STM sensitivity. Speech-intelligibility predictions were compared with measured performance in stationary speech-shaped noise at signal-to-noise ratios (SNRs) of -6, -3, 0, and 3 dB. Prediction accuracy for the individualized HI models was compared to the traditional audibility-based Speech Intelligibility Index (SII). RESULTS: Models incorporating individualized measures of STM sensitivity yielded significantly more accurate within-SNR predictions than the SII. Additional individualized characteristics (frequency selectivity, compression) improved the predictions only marginally. A nonlinear model including individualized level-dependent cochlear-filter bandwidths, dynamic-range compression, and STM sensitivity predicted performance more accurately than the SII but was no more accurate than a simpler linear model. Predictions of speech-recognition performance simultaneously across SNRs and individuals were also significantly better for some of the auditory-processing models than for the SII. CONCLUSIONS: A computational model simulating individualized suprathreshold auditory-processing abilities produced more accurate speech-intelligibility predictions than the audibility-based SII. Most of this advantage was realized by a linear model incorporating audiometric and STM-sensitivity information. Although more consistent with known physiological aspects of auditory processing, modeling level-dependent changes in frequency selectivity and gain did not result in more accurate predictions of speech-reception performance.

EGAsubmitter: A software to automate submission of nucleic acid sequencing data to the European Genome-phenome Archive.

Making raw data available to the research community is one of the pillars of Findability, Accessibility, Interoperability, and Reuse (FAIR) research. However, the submission of raw data to public databases still involves many manually operated procedures that are intrinsically time-consuming and error-prone, which raises potential reliability issues for both the data themselves and the ensuing metadata. For example, submitting sequencing data to the European Genome-phenome Archive (EGA) is estimated to take 1 month overall, and mainly relies on a web interface for metadata management that requires manual completion of forms and the upload of several comma separated values (CSV) files, which are not structured from a formal point of view. To tackle these limitations, here we present EGAsubmitter, a Snakemake-based pipeline that guides the user across all the submission steps, ranging from files encryption and upload, to metadata submission. EGASubmitter is expected to streamline the automated submission of sequencing data to EGA, minimizing user errors and ensuring higher end product fidelity.

Multi-label transcriptional classification of colorectal cancer reflects tumor cell population heterogeneity.

BACKGROUND: Transcriptional classification has been used to stratify colorectal cancer (CRC) into molecular subtypes with distinct biological and clinical features. However, it is not clear whether such subtypes represent discrete, mutually exclusive entities or molecular/phenotypic states with potential overlap. Therefore, we focused on the CRC Intrinsic Subtype (CRIS) classifier and evaluated whether assigning multiple CRIS subtypes to the same sample provides additional clinically and biologically relevant information. METHODS: A multi-label version of the CRIS classifier (multiCRIS) was applied to newly generated RNA-seq profiles from 606 CRC patient-derived xenografts (PDXs), together with human CRC bulk and single-cell RNA-seq datasets. Biological and clinical associations of single- and multi-label CRIS were compared. Finally, a machine learning-based multi-label CRIS predictor (ML2CRIS) was developed for single-sample classification. RESULTS: Surprisingly, about half of the CRC cases could be significantly assigned to more than one CRIS subtype. Single-cell RNA-seq analysis revealed that multiple CRIS membership can be a consequence of the concomitant presence of cells of different CRIS class or, less frequently, of cells with hybrid phenotype. Multi-label assignments were found to improve prediction of CRC prognosis and response to treatment. Finally, the ML2CRIS classifier was validated for retaining the same biological and clinical associations also in the context of single-sample classification. CONCLUSIONS: These results show that CRIS subtypes retain their biological and clinical features even when concomitantly assigned to the same CRC sample. This approach could be potentially extended to other cancer types and classification systems.

Colorectal cancer patient-derived organoids and cell lines harboring ATRX and/or DAXX mutations lack Alternative Lengthening of Telomeres (ALT).

Telomere maintenance is necessary to maintain cancer cell unlimited viability. However, the mechanisms maintaining telomere length in colorectal cancer (CRC) have not been extensively investigated. Telomere maintenance mechanisms (TMM) include the re-expression of telomerase or alternative lengthening of telomeres (ALT). ALT is genetically associated with somatic alterations in alpha-thalassemia/mental retardation X-linked (ATRX) and death domain-associated protein (DAXX) genes. Cells displaying ALT present distinctive features including C-circles made of telomeric DNA, long and heterogenous telomeric tracts, and telomeric DNA co-localized with promyelocytic leukemia (PML) bodies forming so-called ALT-associated PML bodies (APBs). Here, we identified mutations in ATRX and/or DAXX genes in an extensive collection of CRC samples including 119 patient-derived organoids (PDOs) and 232 established CRC cell lines. C-circles measured in CRC PDOs and cell lines showed low levels overall. We also observed that CRC PDOs and cell lines did not display a significant accumulation of APBs or long telomeres with no appreciable differences between wild-type and mutated ATRX/DAXX samples. Overall, our extensive analyses indicate that CRC is not prone to engage ALT, even when carrying genetic lesions in ATRX and/or DAXX, and support the notion that ATRX/DAXX genomic footprints are not reliable predictors of ALT.

Synthetic Lethal Interaction with BCL-XL Blockade Deepens Response to Cetuximab in Patient-Derived Models of Metastatic Colorectal Cancer.

PURPOSE: Approximately 20% of patients with RAS wild-type metastatic colorectal cancer (mCRC) experience objective responses to the anti-EGFR antibody cetuximab, but disease eradication is seldom achieved. The extent of tumor shrinkage correlates with long-term outcome. We aimed to find rational combinations that potentiate cetuximab efficacy by disrupting adaptive dependencies on antiapoptotic molecules (BCL2, BCL-XL, MCL1). EXPERIMENTAL DESIGN: Experiments were conducted in patient-derived xenografts (PDX) and organoids (PDXO). Apoptotic priming was analyzed by BH3 profiling. Proapoptotic and antiapoptotic protein complexes were evaluated by co-immunoprecipitation and electroluminescence sandwich assays. The effect of combination therapies was assessed by caspase activation in PDXOs and by monitoring PDX growth. RESULTS: A population trial in 314 PDX cohorts, established from as many patients, identified 46 models (14.6%) with appreciable (>50% tumor shrinkage) but incomplete response to cetuximab. From these models, 14 PDXOs were derived. Cetuximab primed cells for apoptosis, but only concomitant blockade of BCL-XL precipitated cell death. Mechanistically, exposure to cetuximab induced upregulation of the proapoptotic protein BIM and its sequestration by BCL-XL. Inhibition of BCL-XL resulted in displacement of BIM, which was not buffered by MCL1 and thereby became competent to induce apoptosis. In five PDX models, combination of cetuximab and a selective BCL-XL inhibitor triggered apoptosis and led to more pronounced tumor regressions and longer time to relapse after treatment discontinuation than cetuximab alone. CONCLUSIONS: In mCRC tumors that respond to cetuximab, antibody treatment confers a synthetic-lethal dependency on BCL-XL. Targeting this dependency unleashes apoptosis and increases the depth of response to cetuximab.

Phosphoproteomics of patient-derived xenografts identifies targets and markers associated with sensitivity and resistance to EGFR blockade in colorectal cancer.

Epidermal growth factor receptor (EGFR) is a well-exploited therapeutic target in metastatic colorectal cancer (mCRC). Unfortunately, not all patients benefit from current EGFR inhibitors. Mass spectrometry-based proteomics and phosphoproteomics were performed on 30 genomically and pharmacologically characterized mCRC patient-derived xenografts (PDXs) to investigate the molecular basis of response to EGFR blockade and identify alternative drug targets to overcome resistance. Both the tyrosine and global phosphoproteome as well as the proteome harbored distinctive response signatures. We found that increased pathway activity related to mitogen-activated protein kinase (MAPK) inhibition and abundant tyrosine phosphorylation of cell junction proteins, such as CXADR and CLDN1/3, in sensitive tumors, whereas epithelial-mesenchymal transition and increased MAPK and AKT signaling were more prevalent in resistant tumors. Furthermore, the ranking of kinase activities in single samples confirmed the driver activity of ERBB2, EGFR, and MET in cetuximab-resistant tumors. This analysis also revealed high kinase activity of several members of the Src and ephrin kinase family in 2 CRC PDX models with genomically unexplained resistance. Inhibition of these hyperactive kinases, alone or in combination with cetuximab, resulted in growth inhibition of ex vivo PDX-derived organoids and in vivo PDXs. Together, these findings highlight the potential value of phosphoproteomics to improve our understanding of anti-EGFR treatment and response prediction in mCRC and bring to the forefront alternative drug targets in cetuximab-resistant tumors.

Evolution of promoter affinity for transcription factors in the human lineage.

Changes in gene regulation are believed to play an important role in the evolution of animals. It has been suggested that changes in cis-regulatory regions are responsible for many or most of the anatomical and behavioral differences between humans and apes. However, the study of the evolution of cis-regulatory regions is made problematic by the degeneracy of transcription factor (TF) binding sites and the shuffling of their positions. In this work, we use the predicted total affinity of a promoter for a large collection of TFs as the basis for studying the evolution of cis-regulatory regions in mammals. We introduce the human specificity of a promoter, measuring the divergence between the affinity profile of a human promoter and its orthologous promoters in other mammals. The promoters of genes involved in functional categories such as neural processes and signal transduction, among others, have higher human specificity compared with the rest of the genome. Clustering of the human-specific affinities (HSAs) of neural genes reveals patterns of promoter evolution associated with functional categories such as synaptic transmission and brain development and to diseases such as bipolar disorder and autism.

Towards precision oncology with patient-derived xenografts.

Under the selective pressure of therapy, tumours dynamically evolve multiple adaptive mechanisms that make static interrogation of genomic alterations insufficient to guide treatment decisions. Clinical research does not enable the assessment of how various regulatory circuits in tumours are affected by therapeutic insults over time and space. Likewise, testing different precision oncology approaches informed by composite and ever-changing molecular information is hard to achieve in patients. Therefore, preclinical models that incorporate the biology and genetics of human cancers, facilitate analyses of complex variables and enable adequate population throughput are needed to pinpoint randomly distributed response predictors. Patient-derived xenograft (PDX) models are dynamic entities in which cancer evolution can be monitored through serial propagation in mice. PDX models can also recapitulate interpatient diversity, thus enabling the identification of response biomarkers and therapeutic targets for molecularly defined tumour subgroups. In this Review, we discuss examples from the past decade of the use of PDX models for precision oncology, from translational research to drug discovery. We elaborate on how and to what extent preclinical observations in PDX models have confirmed and/or anticipated findings in patients. Finally, we illustrate emerging methodological efforts that could broaden the application of PDX models by honing their predictive accuracy or improving their versatility.

Chromatin Velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin.

Recent efforts have succeeded in surveying open chromatin at the single-cell level, but high-throughput, single-cell assessment of heterochromatin and its underlying genomic determinants remains challenging. We engineered a hybrid transposase including the chromodomain (CD) of the heterochromatin protein-1α (HP-1α), which is involved in heterochromatin assembly and maintenance through its binding to trimethylation of the lysine 9 on histone 3 (H3K9me3), and developed a single-cell method, single-cell genome and epigenome by transposases sequencing (scGET-seq), that, unlike single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq), comprehensively probes both open and closed chromatin and concomitantly records the underlying genomic sequences. We tested scGET-seq in cancer-derived organoids and human-derived xenograft (PDX) models and identified genetic events and plasticity-driven mechanisms contributing to cancer drug resistance. Next, building upon the differential enrichment of closed and open chromatin, we devised a method, Chromatin Velocity, that identifies the trajectories of epigenetic modifications at the single-cell level. Chromatin Velocity uncovered paths of epigenetic reorganization during stem cell reprogramming and identified key transcription factors driving these developmental processes. scGET-seq reveals the dynamics of genomic and epigenetic landscapes underlying any cellular processes.