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.

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.

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.

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.

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.

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.

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.

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.

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.

ESDN inhibits melanoma progression by blocking E-selectin expression in endothelial cells via STAT3.

An interactive crosstalk between tumor and stroma cells is essential for metastatic melanoma progression. We evidenced that ESDN/DCBLD2/CLCP1 plays a crucial role in endothelial cells during the spread of melanoma. Precisely, increased extravasation and metastasis formation were revealed in ESDN-null mice injected with melanoma cells, even if the primary tumor growth, vessel permeability, and angiogenesis were not enhanced. Interestingly, improved adhesion of melanoma cells to ESDN-depleted endothelial cells was observed, due to the presence of higher levels of E-selectin transcripts/proteins in ESDN-defective cells. In accordance with these results, anticorrelation was observed between ESDN and E-selectin in human endothelial cells. Most importantly, our data revealed that cimetidine, an E-selectin inhibitor, was able to block cell adhesion, extravasation, and metastasis formation in ESDN-null mice, underlying a major role of ESDN in E-selectin transcription upregulation, which according to our data, may presumably be linked to STAT3. Based on our results, we propose a protective role for ESDN during the spread of melanoma and reveal its therapeutic potential.

Pertuzumab and trastuzumab emtansine in patients with HER2-amplified metastatic colorectal cancer: the phase II HERACLES-B trial.

BACKGROUND: HER2 is a therapeutic target for metastatic colorectal cancer (mCRC), as demonstrated in the pivotal HERACLES-A (HER2 Amplification for Colo-rectaL cancer Enhanced Stratification) trial with trastuzumab and lapatinib. The aim of HERACLES-B trial is to assess the efficacy of the combination of pertuzumab and trastuzumab-emtansine (T-DM1) in this setting. METHODS: HERACLES-B was a single-arm, phase II trial, in patients with histologically confirmed RAS/BRAF wild-type and HER2+ mCRC refractory to standard treatments. HER2 positivity was assessed by immunohistochemistry and in situ hybridisation according to HERACLES criteria. Patients were treated with pertuzumab (840 mg intravenous load followed by 420 mg intravenous every 3 weeks) and T-DM1 (3.6 mg/kg every 3 weeks) until disease progression or toxicity. Primary and secondary end points were objective response rate (ORR) and progression-free survival (PFS). With a Fleming/Hern design (H0=ORR 10%; α=0.05; power=0.85), 7/30 responses were required to demonstrate an ORR ≥30% (H1). RESULTS: Thirty-one patients, 48% with ≥4 lines of previous therapies, were treated and evaluable. ORR was 9.7% (95% CI: 0 to 28) and stable disease (SD) 67.7% (95% CI: 50 to 85). OR/SD ≥4 months was associated with higher HER2 immunohistochemistry score (3+ vs 2+) (p = 0.03). Median PFS was 4.1 months (95% CI: 3.6 to 5.9). Drug-related grade (G) 3 adverse events were observed in two patients (thrombocytopaenia); G≤2 AE in 84% of cycles (n = 296), mainly nausea and fatigue. CONCLUSIONS: HERACLES-B trial did not reach its primary end point of ORR; however, based on high disease control, PFS similar to other anti-HER2 regimens, and low toxicity, pertuzumab in combination with T-DM1 can be considered for HER2+mCRC as a potential therapeutic resource. TRIAL REGISTRATION NUMBER: 2012-002128-33 and NCT03225937.

Colorectal cancer residual disease at maximal response to EGFR blockade displays a druggable Paneth cell-like phenotype.

Blockade of epidermal growth factor receptor (EGFR) causes tumor regression in some patients with metastatic colorectal cancer (mCRC). However, residual disease reservoirs typically remain even after maximal response to therapy, leading to relapse. Using patient-derived xenografts (PDXs), we observed that mCRC cells surviving EGFR inhibition exhibited gene expression patterns similar to those of a quiescent subpopulation of normal intestinal secretory precursors with Paneth cell characteristics. Compared with untreated tumors, these pseudodifferentiated tumor remnants had reduced expression of genes encoding EGFR-activating ligands, enhanced activity of human epidermal growth factor receptor 2 (HER2) and HER3, and persistent signaling along the phosphatidylinositol 3-kinase (PI3K) pathway. Clinically, properties of residual disease cells from the PDX models were detected in lingering tumors of responsive patients and in tumors of individuals who had experienced early recurrence. Mechanistically, residual tumor reprogramming after EGFR neutralization was mediated by inactivation of Yes-associated protein (YAP), a master regulator of intestinal epithelium recovery from injury. In preclinical trials, Pan-HER antibodies minimized residual disease, blunted PI3K signaling, and induced long-term tumor control after treatment discontinuation. We found that tolerance to EGFR inhibition is characterized by inactivation of an intrinsic lineage program that drives both regenerative signaling during intestinal repair and EGFR-dependent tumorigenesis. Thus, our results shed light on CRC lineage plasticity as an adaptive escape mechanism from EGFR-targeted therapy and suggest opportunities to preemptively target residual disease.

eQTL Mapping Using Transcription Factor Affinity.

In the last decades, thousands of common genetic variants have been associated with human diseases by genome-wide association studies (GWAS). However, the functional interpretation of GWAS hits is usually nontrivial, especially because most of them lay outside the coding genome. These noncoding variants presumably exert their effect by altering gene expression levels; therefore, expression quantitative trait loci (eQTL) mapping analyses represent an important step in understanding their functional relevance and identifying the target genes. Here we describe an alternative strategy for the detection of eQTL that takes into account the combined effect of genetic variants within regulatory regions and leverages the idea that changes in gene expression often are the consequence of the alteration of transcription factor (TF) binding.

Immunogenomics of Colorectal Tumors: Facts and Hypotheses on an Evolving Saga.

Immunotherapy with immune checkpoint inhibitors is an approved treatment option for a subpopulation of patients with colorectal cancers that display microsatellite instability. However, not all individuals within this subgroup respond to immunotherapy, and molecular biomarkers for effective patient stratification are still lacking. In this opinion article, we provide an overview of the different biological parameters that contribute to rendering colorectal cancers with microsatellite instability potentially sensitive to immunotherapy. We critically discuss the reasons why such parameters have limited predictive value and the implications therein. We also consider that a more informed knowledge of response determinants in this tumor subtype could help understand the mechanisms of immunotherapy resistance in microsatellite stable tumors. We conclude that the dynamic nature of the interactions between cancer and immune cells complicates conventional biomarker development and argue that a new generation of adaptive metrics, borrowed from evolutionary genetics, may improve the effectiveness and reliability of clinical decision making.

The Length of the Expressed 3' UTR Is an Intermediate Molecular Phenotype Linking Genetic Variants to Complex Diseases.

In the last decades, genome-wide association studies (GWAS) have uncovered tens of thousands of associations between common genetic variants and complex diseases. However, these statistical associations can rarely be interpreted functionally and mechanistically. As the majority of the disease-associated variants are located far from coding sequences, even the relevant gene is often unclear. A way to gain insight into the relevant mechanisms is to study the genetic determinants of intermediate molecular phenotypes, such as gene expression and transcript structure. We propose a computational strategy to discover genetic variants affecting the relative expression of alternative 3' untranslated region (UTR) isoforms, generated through alternative polyadenylation, a widespread posttranscriptional regulatory mechanism known to have relevant functional consequences. When applied to a large dataset in which whole genome and RNA sequencing data are available for 373 European individuals, 2,530 genes with alternative polyadenylation quantitative trait loci (apaQTL) were identified. We analyze and discuss possible mechanisms of action of these variants, and we show that they are significantly enriched in GWAS hits, in particular those concerning immune-related and neurological disorders. Our results point to an important role for genetically determined alternative polyadenylation in affecting predisposition to complex diseases, and suggest new ways to extract functional information from GWAS 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.