Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have emerged as one of the most promising first-line therapeutics in the management of non-small cell lung cancer (NSCLC). However, only a subset of these patients responds to ICIs, highlighting the clinical need to develop better predictive and prognostic biomarkers. This study will leverage pre-treatment imaging profiles to develop survival risk models for NSCLC patients treated with first-line immunotherapy. METHODS: Advanced NSCLC patients (n = 149) were retrospectively identified from two institutions who were treated with first-line ICIs. Radiomics features extracted from pretreatment imaging scans were used to build the predictive models for progression-free survival (PFS) and overall survival (OS). A compendium of five feature selection methods and seven machine learning approaches were utilized to build the survival risk models. The concordance index (C-index) was used to evaluate model performance. RESULTS: From our results, we found several combinations of machine learning algorithms and feature selection methods to achieve similar performance. K-nearest neighbourhood (KNN) with ReliefF (RL) feature selection was the best-performing model to predict PFS (C-index = 0.61 and 0.604 in discovery and validation cohorts), while XGBoost with Mutual Information (MI) feature selection was the best-performing model for OS (C-index = 0.7 and 0.655 in discovery and validation cohorts). CONCLUSION: The results of this study highlight the importance of implementing an appropriate feature selection method coupled with a machine learning strategy to develop robust survival models. With further validation of these models on external cohorts when available, this can have the potential to improve clinical decisions by systematically analyzing routine medical images.

Aberrant PRDM9 expression impacts the pan-cancer genomic landscape.

The binding of PRDM9 to chromatin is a key step in the induction of DNA double-strand breaks associated with meiotic recombination hotspots; it is normally expressed solely in germ cells. We interrogated 1879 cancer samples in 39 different cancer types and found that PRDM9 is unexpectedly expressed in 20% of these tumors even after stringent gene homology correction. The expression levels of PRDM9 in tumors are significantly higher than those found in healthy neighboring tissues and in healthy nongerm tissue databases. Recurrently mutated regions located within 5 Mb of the PRDM9 loci, as well as differentially expressed genes in meiotic pathways, correlate with PRDM9 expression. In samples with aberrant PRDM9 expression, structural variant breakpoints frequently neighbor the DNA motif recognized by PRDM9, and there is an enrichment of structural variants at sites of known meiotic PRDM9 activity. This study is the first to provide evidence of an association between aberrant expression of the meiosis-specific gene PRDM9 with genomic instability in cancer.

Standing chromosomal variation in Lake Whitefish species pairs: the role of historical contingency and relevance for speciation.

The role of chromosome changes in speciation remains a debated topic, although demographic conditions associated with divergence should promote their appearance. We tested a potential relationship between chromosome changes and speciation by studying two Lake Whitefish (Coregonus clupeaformis) lineages that recently colonized postglacial lakes following allopatry. A dwarf limnetic species evolved repeatedly from the normal benthic species, becoming reproductively isolated. Lake Whitefish hybrids experience mitotic and meiotic instability, which may result from structurally divergent chromosomes. Motivated by this observation, we test the hypothesis that chromosome organization differs between Lake Whitefish species pairs using cytogenetics. While chromosome and fundamental numbers are conserved between the species (2n = 80, NF = 98), we observe extensive polymorphism of subtle karyotype traits. We describe intrachromosomal differences associated with heterochromatin and repetitive DNA, and test for parallelism among three sympatric species pairs. Multivariate analyses support the hypothesis that differentiation at the level of subchromosomal markers mostly appeared during allopatry. Yet we find no evidence for parallelism between species pairs among lakes, consistent with colonization effect or postcolonization differentiation. The reported intrachromosomal polymorphisms do not appear to play a central role in driving adaptive divergence between normal and dwarf Lake Whitefish. We discuss how chromosomal differentiation in the Lake Whitefish system may contribute to the destabilization of mitotic and meiotic chromosome segregation in hybrids, as documented previously. The chromosome structures detected here are still difficult to sequence and assemble, demonstrating the value of cytogenetics as a complementary approach to understand the genomic bases of speciation.

metagene Profiles Analyses Reveal Regulatory Element's Factor-Specific Recruitment Patterns.

ChIP-Sequencing (ChIP-Seq) provides a vast amount of information regarding the localization of proteins across the genome. The aggregation of ChIP-Seq enrichment signal in a metagene plot is an approach commonly used to summarize data complexity and to obtain a high level visual representation of the general occupancy pattern of a protein. Here we present the R package metagene, the graphical interface Imetagene and the companion package similaRpeak. Together, they provide a framework to integrate, summarize and compare the ChIP-Seq enrichment signal from complex experimental designs. Those packages identify and quantify similarities or dissimilarities in patterns between large numbers of ChIP-Seq profiles. We used metagene to investigate the differential occupancy of regulatory factors at noncoding regulatory regions (promoters and enhancers) in relation to transcriptional activity in GM12878 B-lymphocytes. The relationships between occupancy patterns and transcriptional activity suggest two different mechanisms of action for transcriptional control: i) a "gradient effect" where the regulatory factor occupancy levels follow transcription and ii) a "threshold effect" where the regulatory factor occupancy levels max out prior to reaching maximal transcription. metagene, Imetagene and similaRpeak are implemented in R under the Artistic license 2.0 and are available on Bioconductor.

Neutral and selective processes shape MHC gene diversity and expression in stocked brook charr populations (Salvelinus fontinalis).

The capacity of an individual to battle infection is an important fitness determinant in wild vertebrate populations. The major histocompatibility complex (MHC) genes are crucial for a host's adaptive immune system to detect pathogens. However, anthropogenic activities may disrupt natural cycles of co-evolution between hosts and pathogens. In this study, we investigated the dynamic sequence and expression variation of host parasite interactions in brook charr (Salvelinus fontinalis) in a context of past human disturbance via population supplementation from domestic individuals. To do so, we developed a new method to examine selection shaping MHC diversity within and between populations and found a complex interplay between neutral and selective processes that varied between lakes that were investigated. We provided evidence for a lower introgression rate of domestic alleles and found that parasite infection increased with domestic genomic background of individuals. We also documented an association between individual MHC alleles and parasite taxa. Finally, longer cis-regulatory minisatellites were positively correlated with MHC II down-regulation and domestic admixture, suggesting that inadvertent selection during domestication resulted in a lower immune response capacity, through a trade-off between growth and immunity, which explained the negative selection of domestic alleles at least under certain circumstances.

The impact of radiomics libraries and gray level discretization on the discovery of immunotherapy biomarkers in NSCLC patients.

OBJECTIVE: The influence of radiomics pipeline and the grey-level discretization on the discovery of immunotherapy biomarkers is still a poorly understood topic. This study is aimed at identifying robust features by comparing two radiomics libraries and their association with clinical outcomes in non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs). METHODS: A retrospective cohort of 164 NSCLC patients administered with ICIs was used in this study. Radiomic features were extracted from the pre-treatment CT scans. Univariate models were used to assess the association of radiomics features with progression free survival (PFS), PD-L1 and CD8 cell counts. We also examined the impact of gray-level discretization on feature robustness by evaluating the association of features with clinical endpoints. RESULTS: We extracted 1224, 441 radiomic features using Pyradiomics and RaCat, respectively, out of which 75 were common between them. We showed that the directionality of association between features and clinical endpoints is specific to the radiomic library used. Overall, more Pyradiomics and RaCat features were statistically associated with PFS, and PD-L1, respectively. We found intensity-based features to be more agnostic to the gray-level discretization parameters. Among features that showed significant correlation with PFS with varying gray-level discretization parameters, 45% were intensity-based, compared to PD-L1, and CD8. CONCLUSIONS: This study highlights the heterogeneity of radiomics libraries and the gray level discretization parameters that will impact the feature selection and predictive model development. Importantly, our work highlights the significance of selecting features that are agnostic to radiomics libraries for clinical translation. ADVANCES IN KNOWLEDGE: Our study emphasizes the need to select stable CT-derived handcrafted features to build immunotherapy biomarkers, which is a necessary precursor for multi-institutional validation of imaging biomarkers.

CXCL13 Positive Cells Localization Predict Response to Anti-PD-1/PD-L1 in Pulmonary Non-Small Cell Carcinoma.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized non-small cell lung cancers (NSCLCs) treatment, but only 20-30% of patients benefit from these treatments. Currently, PD-L1 expression in tumor cells is the only clinically approved predictor of ICI response in lung cancer, but concerns arise due to its low negative and positive predictive value. Recent studies suggest that CXCL13+ T cells in the tumor microenvironment (TME) may be a good predictor of response. We aimed to assess if CXCL13+ cell localization within the TME can predict ICI response in advanced NSCLC patients. Methods: This retrospective study included 65 advanced NSCLC patients treated with Nivolumab/Pembrolizumab at IUCPQ or CHUM and for whom a pretreatment surgical specimen was available. Good responders were defined as having a complete radiologic response at 1 year, and bad responders were defined as showing cancer progression at 1 year. IHC staining for CXCL13 was carried out on a representative slide from a resection specimen, and CXCL13+ cell density was evaluated in tumor (T), invasive margin (IM), non-tumor (NT), and tertiary lymphoid structure (TLS) compartments. Cox models were used to analyze progression-free survival (PFS) and overall survival (OS) probability, while the Mann-Whitney test was used to compare CXCL13+ cell density between responders and non-responders. Results: We showed that CXCL13+ cell density localization within the TME is associated with ICI efficacy. An increased density of CXCL13+ cells across all compartments was associated with a poorer prognostic (OS; HR = 1.22; 95%CI = 1.04-1.42; p = 0.01, PFS; HR = 1.16; p = 0.02), or a better prognostic when colocalized within TLSs (PFS; HR = 0.84, p = 0.03). Conclusion: Our results support the role of CXCL13+ cells in advanced NSCLC patients, with favorable prognosis when localized within TLSs and unfavorable prognosis when present elsewhere. The concomitant proximity of CXCL13+ and CD20+ cells within TLSs may favor antigen presentation to T cells, thus enhancing the effect of PD-1/PD-L1 axis inhibition. Further validation is warranted to confirm the potential relevance of this biomarker in a clinical setting.

Metabolomic Profiling of Pulmonary Neuroendocrine Neoplasms.

BACKGROUND/OBJECTIVES: Pulmonary neuroendocrine neoplasms (NENs) account for 20% of malignant lung tumors. Their management is challenging due to their diverse clinical features and aggressive nature. Currently, metabolomics offers a range of potential cancer biomarkers for diagnosis, monitoring tumor progression, and assessing therapeutic response. However, a specific metabolomic profile for early diagnosis of lung NENs has yet to be identified. This study aims to identify specific metabolomic profiles that can serve as biomarkers for early diagnosis of lung NENs. METHODS: We measured 153 metabolites using liquid chromatography combined with mass spectrometry (LC-MS) in the plasma of 120 NEN patients and compared them with those of 71 healthy individuals. Additionally, we compared these profiles with those of 466 patients with non-small-cell lung cancers (NSCLCs) to ensure clinical relevance. RESULTS: We identified 21 metabolites with consistently altered plasma concentrations in NENs. Compared to healthy controls, 18 metabolites were specific to carcinoid tumors, 5 to small-cell lung carcinomas (SCLCs), and 10 to large-cell neuroendocrine carcinomas (LCNECs). These findings revealed alterations in various metabolic pathways, such as fatty acid biosynthesis and beta-oxidation, the Warburg effect, and the citric acid cycle. CONCLUSIONS: Our study identified biomarker metabolites in the plasma of patients with each subtype of lung NENs and demonstrated significant alterations in several metabolic pathways. These metabolomic profiles could potentially serve as biomarkers for early diagnosis and better management of lung NENs.

The Cross-Scale Association between Pathomics and Radiomics Features in Immunotherapy-Treated NSCLC Patients: A Preliminary Study.

BACKGROUND: Recent advances in cancer biomarker development have led to a surge of distinct data modalities, such as medical imaging and histopathology. To develop predictive immunotherapy biomarkers, these modalities are leveraged independently, despite their orthogonality. This study aims to explore the cross-scale association between radiological scans and digitalized pathology images for immunotherapy-treated non-small cell lung cancer (NSCLC) patients. METHODS: This study involves 36 NSCLC patients who were treated with immunotherapy and for whom both radiology and pathology images were available. A total of 851 and 260 features were extracted from CT scans and cell density maps of histology images at different resolutions. We investigated the radiopathomics relationship and their association with clinical and biological endpoints. We used the Kolmogorov-Smirnov (KS) method to test the differences between the distributions of correlation coefficients with the two imaging modality features. Unsupervised clustering was done to identify which imaging modality captures poor and good survival patients. RESULTS: Our results demonstrated a significant correlation between cell density pathomics and radiomics features. Furthermore, we also found a varying distribution of correlation values between imaging-derived features and clinical endpoints. The KS test revealed that the two imaging feature distributions were different for PFS and CD8 counts, while similar for OS. In addition, clustering analysis resulted in significant differences in the two clusters generated from the radiomics and pathomics features with respect to patient survival and CD8 counts. CONCLUSION: The results of this study suggest a cross-scale association between CT scans and pathology H&E slides among ICI-treated patients. These relationships can be further explored to develop multimodal immunotherapy biomarkers to advance personalized lung cancer care.

Prognostic implication of methylation-based circulating tumor DNA detection prior to surgery in stage I non-small cell lung cancer.

BACKGROUND: Circulating tumor DNA (ctDNA) positivity at diagnosis, which is associated with worse outcomes in multiple solid tumors including stage I-III non-small cell lung cancer (NSCLC), may have utility to guide (neo)adjuvant therapy. METHODS: In this retrospective study, 260 patients with clinical stage I NSCLC (180 adenocarcinoma, 80 squamous cell carcinoma) were allocated (2:1) to high- and low-risk groups based on relapse versus disease-free status ≤5 years post-surgery. We evaluated the association of preoperative ctDNA detection by a plasma-only targeted methylation-based multi-cancer early detection (MCED) test with NSCLC relapse ≤5 years post-surgery in the overall population, followed by histology-specific subgroup analyses. RESULTS: Across clinical stage I patients, preoperative ctDNA detection did not associate with relapse within 5 years post-surgery. Sub-analyses confined to lung adenocarcinoma suggested a histology-specific association between ctDNA detection and outcome. In this group, ctDNA positivity tended to associate with relapse within 2 years, suggesting prognostic implications of MCED test positivity may be histology- and time-dependent in stage I NSCLC. Preoperative ctDNA detection was associated with upstaging of clinical stage I to pathological stage II-III NSCLC. CONCLUSIONS: Our findings suggest preoperative ctDNA detection in patients with resectable clinical stage I NSCLC using MCED, a pan-cancer screening test developed for use in an asymptomatic population, has no detectable prognostic value for relapse ≤5 years post-surgery. MCED detection may be associated with early adenocarcinoma relapse and increased pathological upstaging rates in stage I NSCLC. However, given the exploratory nature of these findings, independent validation is required.

Nonparallelism in MHCIIß diversity accompanies nonparallelism in pathogen infection of lake whitefish (Coregonus clupeaformis) species pairs as revealed by next-generation sequencing.

Major histocompatibility (MHC) immune system genes may evolve in response to pathogens in the environment. Because they also may affect mate choice, they are candidates for having great importance in ecological speciation. Here, we use next-generation sequencing to test the general hypothesis of parallelism in patterns of MHCIIß diversity and bacterial infections among five dwarf and normal whitefish sympatric pairs. A second objective was to assess the functional relationships between specific MHCIIß alleles and pathogens in natural conditions. Each individual had between one and four alleles, indicating two paralogous loci. In Cliff Lake, the dwarf ecotype was monomorphic for the most common allele. In Webster Lake, the skew in the allelic distribution was towards the same allele but in the normal ecotype, underscoring the nonparallel divergence among lakes. Our signal of balancing selection matched putative peptide binding region residues in some cases, but not in others, supporting other recent findings of substantial functional differences in fish MHCIIß compared with mammals. Individuals with fewer alleles were less likely to be infected; thus, we found no evidence for the heterozygote advantage hypothesis. MHCIIß alleles and pathogenic bacteria formed distinct clusters in multivariate analyses, and clusters of certain alleles were associated with clusters of pathogens, or sometimes the absence of pathogens, indicating functional relationships at the individual level. Given that patterns of MHCIIß and bacteria were nonparallel among dwarf and normal whitefish pairs, we conclude that pathogens driving MHCIIß evolution did not play a direct role in their parallel phenotypic evolution.

Radiomics approaches to predict PD-L1 and PFS in advanced non-small cell lung patients treated with immunotherapy: a multi-institutional study.

With the increasing use of immune checkpoint inhibitors (ICIs), there is an urgent need to identify biomarkers to stratify responders and non-responders using programmed death-ligand (PD-L1) expression, and to predict patient-specific outcomes such as progression free survival (PFS). The current study is aimed to determine the feasibility of building imaging-based predictive biomarkers for PD-L1 and PFS through systematically evaluating a combination of several machine learning algorithms with different feature selection methods. A retrospective, multicenter study of 385 advanced NSCLC patients amenable to ICIs was undertaken in two academic centers. Radiomic features extracted from pretreatment CT scans were used to build predictive models for PD-L1 and PFS (short-term vs. long-term survivors). We first employed the LASSO methodology followed by five feature selection methods and seven machine learning approaches to build the predictors. From our analyses, we found several combinations of feature selection methods and machine learning algorithms to achieve a similar performance. Logistic regression with ReliefF feature selection (AUC = 0.64, 0.59 in discovery and validation cohorts) and SVM with Anova F-test feature selection (AUC = 0.64, 0.63 in discovery and validation datasets) were the best-performing models to predict PD-L1 and PFS. This study elucidates the application of suitable feature selection approaches and machine learning algorithms to predict clinical endpoints using radiomics features. Through this study, we identified a subset of algorithms that should be considered in future investigations for building robust and clinically relevant predictive models.

Imaging-Based Biomarkers Predict Programmed Death-Ligand 1 and Survival Outcomes in Advanced NSCLC Treated With Nivolumab and Pembrolizumab: A Multi-Institutional Study.

Background: Although the immune checkpoint inhibitors, nivolumab and pembrolizumab, were found to be promising in patients with advanced NSCLC, some of them either do not respond or have recurrence after an initial response. It is still unclear who will benefit from these therapies, and, hence, there is an unmet clinical need to build robust biomarkers. Methods: Patients with advanced NSCLC (N = 323) who were treated with pembrolizumab or nivolumab were retrospectively identified from two institutions. Radiomics features extracted from baseline pretreatment computed tomography scans along with the clinical variables were used to build the predictive models for overall survival (OS), progression-free survival (PFS), and programmed death-ligand 1 (PD-L1). To develop the imaging and integrative clinical-imaging predictive models, we used the XGBoost learning algorithm with ReliefF feature selection method and validated them in an independent cohort. The concordance index for OS, PFS, and area under the curve for PD-L1 was used to evaluate model performance. Results: We developed radiomics and the ensemble radiomics-clinical predictive models for OS, PFS, and PD-L1 expression. The concordance indices of the radiomics model were 0.60 and 0.61 for predicting OS and PFS and area under the curve was 0.61 for predicting PD-L1 in the validation cohort, respectively. The combined radiomics-clinical model resulted in higher performance with 0.65, 0.63, and 0.68 to predict OS, PFS, and PD-L1 in the validation cohort, respectively. Conclusions: We found that pretreatment computed tomography imaging along with clinical data can aid as predictive biomarkers for PD-L1 and survival end points. These imaging-driven approaches may prove useful to expand the therapeutic options for nonresponders and improve the selection of patients who would benefit from immune checkpoint inhibitors.

A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy. METHODS: This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort. RESULTS: We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively. CONCLUSIONS: We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted.

Dynamics of introgressive hybridization assessed by SNP population genomics of coding genes in stocked brook charr (Salvelinus fontinalis).

Salmonid fishes rank among species being most severely affected by introgressive hybridization as a result of a long tradition of stocking with hatchery-reared conspecifics. The objectives of this study were (i) to evaluate the genetic consequences of stocking and resulting introgression rates on the genetic integrity of natural populations of brook charr, (ii) to identify genomic regions potentially associated with adaptation to natural and artificial rearing environments, and (iii) to test the null hypothesis that introgression from domesticated brook charr into wild populations is homogeneous among loci. A total of 336 individuals were sampled from nine lakes, which were stocked at different intensities with domestic fish. Individuals were genotyped at 280 SNPs located in transcribed regions and developed by means of next-generation sequencing. As previously reported with microsatellites, we observed a positive relationship between stocking intensity and genetic diversity among stocking groups, and a decrease in population differentiation. Individual admixture proportions also increased with stocking intensity. Moreover, genomic cline analysis revealed 27 SNPs, seven of which were also identified as outliers in a genome scan, which showed an introgression rate either more restricted or enhanced relative to neutral expectations. This indicated that selection, mainly for growth-related biological processes, has favored or hampered the introgression of genomic blocks into the introgressed wild populations. Overall, this study highlights the usefulness of investigating the impact of stocking on the dynamics of introgression of potentially adaptive genetic variation to better understand the consequences of such practice on the genomic integrity of wild populations.

Recombination affects allele-specific expression of deleterious variants in human populations.

How the genetic composition of a population changes through stochastic processes, such as genetic drift, in combination with deterministic processes, such as selection, is critical to understanding how phenotypes vary in space and time. Here, we show how evolutionary forces affecting selection, including recombination and effective population size, drive genomic patterns of allele-specific expression (ASE). Integrating tissue-specific genotypic and transcriptomic data from 1500 individuals from two different cohorts, we demonstrate that ASE is less often observed in regions of low recombination, and loci in high or normal recombination regions are more efficient at using ASE to underexpress harmful mutations. By tracking genetic ancestry, we discriminate between ASE variability due to past demographic effects, including subsequent bottlenecks, versus local environment. We observe that ASE is not randomly distributed along the genome and that population parameters influencing the efficacy of natural selection alter ASE levels genome wide.

Gene-by-environment interactions in urban populations modulate risk phenotypes.

Uncovering the interaction between genomes and the environment is a principal challenge of modern genomics and preventive medicine. While theoretical models are well defined, little is known of the G × E interactions in humans. We used an integrative approach to comprehensively assess the interactions between 1.6 million data points, encompassing a range of environmental exposures, health, and gene expression levels, coupled with whole-genome genetic variation. From ∼1000 individuals of a founder population in Quebec, we reveal a substantial impact of the environment on the transcriptome and clinical endophenotypes, overpowering that of genetic ancestry. Air pollution impacts gene expression and pathways affecting cardio-metabolic and respiratory traits, when controlling for genetic ancestry. Finally, we capture four expression quantitative trait loci that interact with the environment (air pollution). Our findings demonstrate how the local environment directly affects disease risk phenotypes and that genetic variation, including less common variants, can modulate individual's response to environmental challenges.

Connected Gene Communities Underlie Transcriptional Changes in Cornelia de Lange Syndrome.

Cornelia de Lange syndrome (CdLS) is a complex multisystem developmental disorder caused by mutations in cohesin subunits and regulators. While its precise molecular mechanisms are not well defined, they point toward a global deregulation of the transcriptional gene expression program. Cohesin is associated with the boundaries of chromosome domains and with enhancer and promoter regions connecting the three-dimensional genome organization with transcriptional regulation. Here, we show that connected gene communities, structures emerging from the interactions of noncoding regulatory elements and genes in the three-dimensional chromosomal space, provide a molecular explanation for the pathoetiology of CdLS associated with mutations in the cohesin-loading factor NIPBL and the cohesin subunit SMC1A NIPBL and cohesin are important constituents of connected gene communities that are centrally positioned at noncoding regulatory elements. Accordingly, genes deregulated in CdLS are positioned within reach of NIPBL- and cohesin-occupied regions through promoter-promoter interactions. Our findings suggest a dynamic model where NIPBL loads cohesin to connect genes in communities, offering an explanation for the gene expression deregulation in the CdLS.

FOXA and master transcription factors recruit Mediator and Cohesin to the core transcriptional regulatory circuitry of cancer cells.

Controlling the transcriptional program is essential to maintain the identity and the biological functions of a cell. The Mediator and Cohesin complexes have been established as central cofactors controlling the transcriptional program in normal cells. However, the distribution, recruitment and importance of these complexes in cancer cells have not been fully investigated. Here we show that FOXA and master transcription factors are part of the core transcriptional regulatory circuitry of cancer cells and are essential to recruit M ediator and Cohesin. Indeed, Mediator and Cohesin occupied the enhancer and promoter regions of actively transcribed genes and maintained the proliferation and colony forming potential. Through integration of publically available ChIP-Seq datasets, we predicted the core transcriptional regulatory circuitry of each cancer cell. Unexpectedly, for all cells investigated, the pioneer transcription factors FOXA1 and/or FOXA2 were identified in addition to cell-specific master transcription factors. Loss of both types of transcription factors phenocopied the loss of Mediator and Cohesin. Lastly, the master and pioneer transcription factors were essential to recruit Mediator and Cohesin to regulatory regions of actively transcribed genes. Our study proposes that maintenance of the cancer cell state is dependent on recruitment of Mediator and Cohesin through FOXA and master transcription factors.

Mutant cohesin affects RNA polymerase II regulation in Cornelia de Lange syndrome.

In addition to its role in sister chromatid cohesion, genome stability and integrity, the cohesin complex is involved in gene transcription. Mutations in core cohesin subunits SMC1A, SMC3 and RAD21, or their regulators NIPBL and HDAC8, cause Cornelia de Lange syndrome (CdLS). Recent evidence reveals that gene expression dysregulation could be the underlying mechanism for CdLS. These findings raise intriguing questions regarding the potential role of cohesin-mediated transcriptional control and pathogenesis. Here, we identified numerous dysregulated genes occupied by cohesin by combining the transcriptome of CdLS cell lines carrying mutations in SMC1A gene and ChIP-Seq data. Genome-wide analyses show that genes changing in expression are enriched for cohesin-binding. In addition, our results indicate that mutant cohesin impairs both RNA polymerase II (Pol II) transcription initiation at promoters and elongation in the gene body. These findings highlight the pivotal role of cohesin in transcriptional regulation and provide an explanation for the typical gene dysregulation observed in CdLS patients.