Genetic intratumour heterogeneity and clonal evolution in extramammary Paget's disease.

All tumour cells in a patient have shared and non-shared genetic alterations, and the diversity of mutations is described as intratumoural heterogeneity (ITH). Multiregion sequencing is a genome sequencing analytical technique used for multiple, spatially-separated biopsy tissues that may further our understanding of ITH and tumour evolution. Although genetic mutations in extramammary Paget's disease (EMPD) have recently been detected by next-generation sequencing analysis, there have been no reports of ITH based on multiregion sequencing in EMPD. Thus, we clarified the landscape of ITH and tumour evolution in EMPD. We performed whole-exome sequencing on 35 tissues (30 tumour tissues and five normal skin samples as a paired control), collected from five patients with EMPD. The rate of private mutations was significantly higher than that of ubiquitous and shared mutations. Ubiquitous mutations were not present in driver genes, and most driver genes exhibited private and shared mutations. The most frequent base substitution was C>T in almost all lesions, and most mutational signatures corresponded to signature 1, 2, 3, and 8. The types of proposed aetiology in most lesions were based on age and AID/APOBEC family and BRCA1/BRCA2 mutations. Evolutionary trees were characterized by short trunks and long branches due to the extremely high ratio of private mutations. In contrast, pathogenic factors, such as base substitutions, mutational signatures, and proposed aetiology, were shared. Tumour evolution in EMPD appears to be characterized by a high level of genetic ITH with shared background factors.

Genetic and phenotypic heterogeneity in early neurodevelopmental traits in the Norwegian Mother, Father and Child Cohort Study.

BACKGROUND: Autism and different neurodevelopmental conditions frequently co-occur, as do their symptoms at sub-diagnostic threshold levels. Overlapping traits and shared genetic liability are potential explanations. METHODS: In the population-based Norwegian Mother, Father, and Child Cohort study (MoBa), we leverage item-level data to explore the phenotypic factor structure and genetic architecture underlying neurodevelopmental traits at age 3 years (N = 41,708-58,630) using maternal reports on 76 items assessing children's motor and language development, social functioning, communication, attention, activity regulation, and flexibility of behaviors and interests. RESULTS: We identified 11 latent factors at the phenotypic level. These factors showed associations with diagnoses of autism and other neurodevelopmental conditions. Most shared genetic liabilities with autism, ADHD, and/or schizophrenia. Item-level GWAS revealed trait-specific genetic correlations with autism (items rg range = - 0.27-0.78), ADHD (items rg range = - 0.40-1), and schizophrenia (items rg range = - 0.24-0.34). We find little evidence of common genetic liability across all neurodevelopmental traits but more so for several genetic factors across more specific areas of neurodevelopment, particularly social and communication traits. Some of these factors, such as one capturing prosocial behavior, overlap with factors found in the phenotypic analyses. Other areas, such as motor development, seemed to have more heterogenous etiology, with specific traits showing a less consistent pattern of genetic correlations with each other. CONCLUSIONS: These exploratory findings emphasize the etiological complexity of neurodevelopmental traits at this early age. In particular, diverse associations with neurodevelopmental conditions and genetic heterogeneity could inform follow-up work to identify shared and differentiating factors in the early manifestations of neurodevelopmental traits and their relation to autism and other neurodevelopmental conditions. This in turn could have implications for clinical screening tools and programs.

Polygenic Risk Score Reveals Genetic Heterogeneity of Alzheimer's Disease between the Chinese and European Populations.

BACKGROUND: The polygenic risk score (PRS) aggregates the effects of numerous genetic variants associated with a condition across the human genome and may help to predict late-onset Alzheimer's disease (LOAD). Most of the current PRS studies on Alzheimer's disease (AD) have been conducted in Caucasian ancestry populations, while it is less studied in Chinese. OBJECTIVE: To establish and examine the validity of Chinese PRS, and explore its racial heterogeneity. DESIGN: We constructed a PRS using both discovery (N = 2012) and independent validation samples (N = 1008) from Chinese population. The associations between PRS and age at onset of LOAD or cerebrospinal fluid (CSF) biomarkers were assessed. We also replicated the PRS in an independent replication cohort with CSF data and constructed an alternative PRS using European weights. SETTING: Multi-center genetics study. PARTICIPANTS: A total of 3020 subjects were included in the study. MEASUREMENTS: PRS was calculated using genome-wide association studies data and evaluated the performance alone (PRSnoAPOE) and with other predictors (full model: LOAD ~ PRSnoAPOE + APOE+ sex + age) by measuring the area under the receiver operating curve (AUC). RESULTS: PRS of the full model achieved the highest AUC of 84.0% (95% CI = 81.4-86.5) with pT< 0.5, compared with the model containing APOE alone (61.0%). The AUC of PRS with pT<5e-8 was 77.8% in the PRSnoAPOE model, 81.5% in the full model, and only ranged from 67.5% to 75.1% in the PRS with the European weights model. A higher PRS was significantly associated with an earlier age at onset (P <0.001). The PRS also performed well in the replication cohort of the full model (AUC=83.1%, 95% CI = 74.3-92.0). The CSF biomarkers of Aß42 and the ratio of Aß42/Aß40 were significantly inversely associated with the PRS, while p-Tau181 showed a positive association. CONCLUSIONS: This finding suggests that PRS reveal genetic heterogeneity and higher prediction accuracy of the PRS for AD can be achieved using a base dataset and validation within the same ethnicity. The effective PRS model has the clinical potential to predict individuals at risk of developing LOAD at a given age and with abnormal levels of CSF biomarkers in the Chinese population.

Congenital Hyperinsulinism Caused by Mutations in ABCC8 Gene Associated with Early-Onset Neonatal Hypoglycemia: Genetic Heterogeneity Correlated with Phenotypic Variability.

Congenital hyperinsulinism (CHI) is a rare disorder of glucose metabolism and is the most common cause of severe and persistent hypoglycemia (hyperinsulinemic hypoglycemia, HH) in the neonatal period and childhood. Most cases are caused by mutations in the ABCC8 and KCNJ11 genes that encode the ATP-sensitive potassium channel (KATP). We present the correlation between genetic heterogeneity and the variable phenotype in patients with early-onset HH caused by ABCC8 gene mutations. In the first patient, who presented persistent severe hypoglycemia since the first day of life, molecular genetic testing revealed the presence of a homozygous mutation in the ABCC8 gene [deletion in the ABCC8 gene c.(2390+1_2391-1)_(3329+1_3330-1)del] that correlated with a diffuse form of hyperinsulinism (the parents being healthy heterozygous carriers). In the second patient, the onset was on the third day of life with severe hypoglycemia, and genetic testing identified a heterozygous mutation in the ABCC8 gene c.1792C>T (p.Arg598*) inherited on the paternal line, which led to the diagnosis of the focal form of hyperinsulinism. To locate the focal lesions, (18)F-DOPA (3,4-dihydroxy-6-[18F]fluoro-L-phenylalanine) positron emission tomography/computed tomography (PET/CT) was recommended (an investigation that cannot be carried out in the country), but the parents refused to carry out the investigation abroad. In this case, early surgical treatment could have been curative. In addition, the second child also presented secondary adrenal insufficiency requiring replacement therapy. At the same time, she developed early recurrent seizures that required antiepileptic treatment. We emphasize the importance of molecular genetic testing for diagnosis, management and genetic counseling in patients with HH.

Exome sequencing reveals genetic heterogeneity and clinically actionable findings in children with cerebral palsy.

Cerebral palsy (CP) is the most common motor disability in children. To ascertain the role of major genetic variants in the etiology of CP, we conducted exome sequencing on a large-scale cohort with clinical manifestations of CP. The study cohort comprised 505 girls and 1,073 boys. Utilizing the current gold standard in genetic diagnostics, 387 of these 1,578 children (24.5%) received genetic diagnoses. We identified 412 pathogenic and likely pathogenic (P/LP) variants across 219 genes associated with neurodevelopmental disorders, and 59 P/LP copy number variants. The genetic diagnostic rate of children with CP labeled at birth with perinatal asphyxia was higher than the rate in children without asphyxia (P = 0.0033). Also, 33 children with CP manifestations (8.5%, 33 of 387) had findings that were clinically actionable. These results highlight the need for early genetic testing in children with CP, especially those with risk factors like perinatal asphyxia, to enable evidence-based medical decision-making.

Characterizing carbapenemase-producing Escherichia coli isolates from Spain: high genetic heterogeneity and wide geographical spread.

Introduction: Carbapenemase-Producing Escherichia coli (CP-Eco) isolates, though less prevalent than other CP-Enterobacterales, have the capacity to rapidly disseminate antibiotic resistance genes (ARGs) and cause serious difficult-to-treat infections. The aim of this study is phenotypically and genotypically characterizing CP-Eco isolates collected from Spain to better understand their resistance mechanisms and population structure. Methods: Ninety representative isolates received from 2015 to 2020 from 25 provinces and 59 hospitals Spanish hospitals were included. Antibiotic susceptibility was determined according to EUCAST guidelines and whole-genome sequencing was performed. Antibiotic resistance and virulence-associated genes, phylogeny and population structure, and carbapenemase genes-carrying plasmids were analyzed. Results and discussion: The 90 CP-Eco isolates were highly polyclonal, where the most prevalent was ST131, detected in 14 (15.6%) of the isolates. The carbapenemase genes detected were bla OXA-48 (45.6%), bla VIM-1 (23.3%), bla NDM-1 (7.8%), bla KPC-3 (6.7%), and bla NDM-5 (6.7%). Forty (44.4%) were resistant to 6 or more antibiotic groups and the most active antibiotics were colistin (98.9%), plazomicin (92.2%) and cefiderocol (92.2%). Four of the seven cefiderocol-resistant isolates belonged to ST167 and six harbored bla NDM. Five of the plazomicin-resistant isolates harbored rmt. IncL plasmids were the most frequent (45.7%) and eight of these harbored bla VIM-1. bla OXA-48 was found in IncF plasmids in eight isolates. Metallo-ß-lactamases were more frequent in isolates with resistance to six or more antibiotic groups, with their genes often present on the same plasmid/integron. ST131 isolates were associated with sat and pap virulence genes. This study highlights the genetic versatility of CP-Eco and its potential to disseminate ARGs and cause community and nosocomial infections.

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity.

Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models-especially variational autoencoders-have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE's capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively.

Multiregional transcriptomics identifies congruent consensus subtypes with prognostic value beyond tumor heterogeneity of colorectal cancer.

Intra-tumor heterogeneity compromises the clinical value of transcriptomic classifications of colorectal cancer. We investigated the prognostic effect of transcriptomic heterogeneity and the potential for classifications less vulnerable to heterogeneity in a single-hospital series of 1093 tumor samples from 692 patients, including multiregional samples from 98 primary tumors and 35 primary-metastasis sets. We show that intra-tumor heterogeneity of the consensus molecular subtypes (CMS) is frequent and has poor-prognostic associations independently of tumor microenvironment markers. Multiregional transcriptomics uncover cancer cell-intrinsic and low-heterogeneity signals that recapitulate the intrinsic CMSs proposed by single-cell sequencing. Further subclassification identifies congruent CMSs that explain a larger proportion of variation in patient survival than intra-tumor heterogeneity. Plasticity is indicated by discordant intrinsic phenotypes of matched primary and metastatic tumors. We conclude that multiregional sampling reconciles the prognostic power of tumor classifications from single-cell and bulk transcriptomics in the context of intra-tumor heterogeneity, and phenotypic plasticity challenges the reconciliation of primary and metastatic subtypes.

Genetic heterogeneity in epilepsy and comorbidities: insights from Pakistani families.

BACKGROUND: Epilepsy, a challenging neurological condition, is often present with comorbidities that significantly impact diagnosis and management. In the Pakistani population, where financial limitations and geographical challenges hinder access to advanced diagnostic methods, understanding the genetic underpinnings of epilepsy and its associated conditions becomes crucial. METHODS: This study investigated four distinct Pakistani families, each presenting with epilepsy and a spectrum of comorbidities, using a combination of whole exome sequencing (WES) and Sanger sequencing. The epileptic patients were prescribed multiple antiseizure medications (ASMs), yet their seizures persist, indicating the challenging nature of ASM-resistant epilepsy. RESULTS: Identified genetic variants contributed to a diverse range of clinical phenotypes. In the family 1, which presented with epilepsy, developmental delay (DD), sleep disturbance, and aggressive behavior, a homozygous splice site variant, c.1339-6 C > T, in the COL18A1 gene was detected. The family 2 exhibited epilepsy, intellectual disability (ID), DD, and anxiety phenotypes, a homozygous missense variant, c.344T > A (p. Val115Glu), in the UFSP2 gene was identified. In family 3, which displayed epilepsy, ataxia, ID, DD, and speech impediment, a novel homozygous frameshift variant, c.1926_1941del (p. Tyr643MetfsX2), in the ZFYVE26 gene was found. Lastly, family 4 was presented with epilepsy, ID, DD, deafness, drooling, speech impediment, hypotonia, and a weak cry. A homozygous missense variant, c.1208 C > A (p. Ala403Glu), in the ATP13A2 gene was identified. CONCLUSION: This study highlights the genetic heterogeneity in ASM-resistant epilepsy and comorbidities among Pakistani families, emphasizing the importance of genotype-phenotype correlation and the necessity for expanded genetic testing in complex clinical cases.

Heterogeneity generating capacity in tumorigenesis and cancer therapeutics.

Cells of multicellular organisms generate heterogeneity in a controlled and transient fashion during embryogenesis, which can be reactivated in pathologies such as cancer. Although genomic heterogeneity is an important part of tumorigenesis, continuous generation of phenotypic heterogeneity is central for the adaptation of cancer cells to the challenges of tumorigenesis and response to therapy. Here I discuss the capacity of generating heterogeneity, hereafter called cell hetness, in cancer cells both as the activation of hetness oncogenes and inactivation of hetness tumor suppressor genes, which increase the generation of heterogeneity, ultimately producing an increase in adaptability and cell fitness. Transcriptomic high hetness states in therapy-tolerant cell states denote its importance in cancer resistance to therapy. The definition of the concept of hetness will allow the understanding of its origins, its control during embryogenesis, its loss of control in tumorigenesis and cancer therapeutics and its active targeting.

Pervasive structural heterogeneity rewires glioblastoma chromosomes to sustain patient-specific transcriptional programs.

Glioblastoma multiforme (GBM) encompasses brain malignancies marked by phenotypic and transcriptional heterogeneity thought to render these tumors aggressive, resistant to therapy, and inevitably recurrent. However, little is known about how the spatial organization of GBM genomes underlies this heterogeneity and its effects. Here, we compile a cohort of 28 patient-derived glioblastoma stem cell-like lines (GSCs) known to reflect the properties of their tumor-of-origin; six of these were primary-relapse tumor pairs from the same patient. We generate and analyze 5 kbp-resolution chromosome conformation capture (Hi-C) data from all GSCs to systematically map thousands of standalone and complex structural variants (SVs) and the multitude of neoloops arising as a result. By combining Hi-C, histone modification, and gene expression data with chromatin folding simulations, we explain how the pervasive, uneven, and idiosyncratic occurrence of neoloops sustains tumor-specific transcriptional programs via the formation of new enhancer-promoter contacts. We also show how even moderately recurrent neoloops can relate to patient-specific vulnerabilities. Together, our data provide a resource for dissecting GBM biology and heterogeneity, as well as for informing therapeutic approaches.

Heterogeneity and molecular landscape of melanoma: implications for targeted therapy.

Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays a critical position in shaping the behavior and response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful in overcoming radiation resistance, however, the diverse molecular makeups of UM call for a patient-specific approach in therapy procedures. We need to understand the intricate molecular landscape of UM to develop targeted treatments customized to each patient's specific genetic mutations. One of the promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), for detecting and monitoring the disease at the early stages. These non-invasive methods can help us identify the most effective treatment strategies for each patient. Single-cellular is a brand-new analysis platform that gives treasured insights into diagnosis, prognosis, and remedy. The incorporation of this data with known clinical and genomics information will give a better understanding of the complicated molecular mechanisms that UM diseases exploit. In this review, we focused on the heterogeneity and molecular panorama of UM, and to achieve this goal, the authors conducted an exhaustive literature evaluation spanning 1998 to 2023, using keywords like "uveal melanoma, "heterogeneity". "Targeted therapies"," "CTCs," and "single-cellular analysis".

Interpretation of 10 years of Alzheimer's disease genetic findings in the perspective of statistical heterogeneity.

Common genetic variants and susceptibility loci associated with Alzheimer's disease (AD) have been discovered through large-scale genome-wide association studies (GWAS), GWAS by proxy (GWAX) and meta-analysis of GWAS and GWAX (GWAS+GWAX). However, due to the very low repeatability of AD susceptibility loci and the low heritability of AD, these AD genetic findings have been questioned. We summarize AD genetic findings from the past 10 years and provide a new interpretation of these findings in the context of statistical heterogeneity. We discovered that only 17% of AD risk loci demonstrated reproducibility with a genome-wide significance of P < 5.00E-08 across all AD GWAS and GWAS+GWAX datasets. We highlighted that the AD GWAS+GWAX with the largest sample size failed to identify the most significant signals, the maximum number of genome-wide significant genetic variants or maximum heritability. Additionally, we identified widespread statistical heterogeneity in AD GWAS+GWAX datasets, but not in AD GWAS datasets. We consider that statistical heterogeneity may have attenuated the statistical power in AD GWAS+GWAX and may contribute to explaining the low repeatability (17%) of genome-wide significant AD susceptibility loci and the decreased AD heritability (40-2%) as the sample size increased. Importantly, evidence supports the idea that a decrease in statistical heterogeneity facilitates the identification of genome-wide significant genetic loci and contributes to an increase in AD heritability. Collectively, current AD GWAX and GWAS+GWAX findings should be meticulously assessed and warrant additional investigation, and AD GWAS+GWAX should employ multiple meta-analysis methods, such as random-effects inverse variance-weighted meta-analysis, which is designed specifically for statistical heterogeneity.

Integrating spatial and single-cell transcriptomics reveals tumor heterogeneity and intercellular networks in colorectal cancer.

Single cell RNA sequencing (scRNA-seq), a powerful tool for studying the tumor microenvironment (TME), does not preserve/provide spatial information on tissue morphology and cellular interactions. To understand the crosstalk between diverse cellular components in proximity in the TME, we performed scRNA-seq coupled with spatial transcriptomic (ST) assay to profile 41,700 cells from three colorectal cancer (CRC) tumor-normal-blood pairs. Standalone scRNA-seq analyses revealed eight major cell populations, including B cells, T cells, Monocytes, NK cells, Epithelial cells, Fibroblasts, Mast cells, Endothelial cells. After the identification of malignant cells from epithelial cells, we observed seven subtypes of malignant cells that reflect heterogeneous status in tumor, including tumor_CAV1, tumor_ATF3_JUN | FOS, tumor_ZEB2, tumor_VIM, tumor_WSB1, tumor_LXN, and tumor_PGM1. By transferring the cellular annotations obtained by scRNA-seq to ST spots, we annotated four regions in a cryosection from CRC patients, including tumor, stroma, immune infiltration, and colon epithelium regions. Furthermore, we observed intensive intercellular interactions between stroma and tumor regions which were extremely proximal in the cryosection. In particular, one pair of ligands and receptors (C5AR1 and RPS19) was inferred to play key roles in the crosstalk of stroma and tumor regions. For the tumor region, a typical feature of TMSB4X-high expression was identified, which could be a potential marker of CRC. The stroma region was found to be characterized by VIM-high expression, suggesting it fostered a stromal niche in the TME. Collectively, single cell and spatial analysis in our study reveal the tumor heterogeneity and molecular interactions in CRC TME, which provides insights into the mechanisms underlying CRC progression and may contribute to the development of anticancer therapies targeting on non-tumor components, such as the extracellular matrix (ECM) in CRC. The typical genes we identified may facilitate to new molecular subtypes of CRC.

Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity.

Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain. We considered factors such as genomic instability to characterize the cancer dynamics through stochastic fixed points. The fixed points were derived from centroids obtained through various clustering methods to verify our method sensitivity. This methodological foundation is based upon sample and time average equivalence, assigning an interpretative value to the data cluster centroids and supporting parameters estimation. We used stochastic simulations to reproduce the dynamics, and our results showed an alignment between experimental and simulated dataset centroids. We also computed the Waddington landscape, which provided a visual framework for validating the centroids and standard deviations as characterizations of cancer attractors. Additionally, we examined the stability and transitions between attractors and revealed a potential interplay between subtypes. These transitions might be related to cancer recurrence and progression, connecting the molecular mechanisms of cancer heterogeneity with statistical properties of gene expression dynamics. Our work advances the modeling of gene expression dynamics and paves the way for personalized therapeutic interventions.

Understanding the genomic heterogeneity of North African Imazighen: from broad to microgeographical perspectives.

The strategic location of North Africa has led to cultural and demographic shifts, shaping its genetic structure. Historical migrations brought different genetic components that are evident in present-day North African genomes, along with autochthonous components. The Imazighen (plural of Amazigh) are believed to be the descendants of autochthonous North Africans and speak various Amazigh languages, which belong to the Afro-Asiatic language family. However, the arrival of different human groups, especially during the Arab conquest, caused cultural and linguistic changes in local populations, increasing their heterogeneity. We aim to characterize the genetic structure of the region, using the largest Amazigh dataset to date and other reference samples. Our findings indicate microgeographical genetic heterogeneity among Amazigh populations, modeled by various admixture waves and different effective population sizes. A first admixture wave is detected group-wide around the twelfth century, whereas a second wave appears in some Amazigh groups around the nineteenth century. These events involved populations with higher genetic ancestry from south of the Sahara compared to the current North Africans. A plausible explanation would be the historical trans-Saharan slave trade, which lasted from the Roman times to the nineteenth century. Furthermore, our investigation shows that assortative mating in North Africa has been rare.

Spatial genomic, biochemical and cellular mechanisms underlying meningioma heterogeneity and evolution.

Intratumor heterogeneity underlies cancer evolution and treatment resistance, but targetable mechanisms driving intratumor heterogeneity are poorly understood. Meningiomas are the most common primary intracranial tumors and are resistant to all medical therapies, and high-grade meningiomas have significant intratumor heterogeneity. Here we use spatial approaches to identify genomic, biochemical and cellular mechanisms linking intratumor heterogeneity to the molecular, temporal and spatial evolution of high-grade meningiomas. We show that divergent intratumor gene and protein expression programs distinguish high-grade meningiomas that are otherwise grouped together by current classification systems. Analyses of matched pairs of primary and recurrent meningiomas reveal spatial expansion of subclonal copy number variants associated with treatment resistance. Multiplexed sequential immunofluorescence and deconvolution of meningioma spatial transcriptomes using cell types from single-cell RNA sequencing show decreased immune infiltration, decreased MAPK signaling, increased PI3K-AKT signaling and increased cell proliferation, which are associated with meningioma recurrence. To translate these findings to preclinical models, we use CRISPR interference and lineage tracing approaches to identify combination therapies that target intratumor heterogeneity in meningioma cell co-cultures.

Oral and non-oral lichen planus show genetic heterogeneity and differential risk for autoimmune disease and oral cancer.

Lichen planus (LP) is a T-cell-mediated inflammatory disease affecting squamous epithelia in many parts of the body, most often the skin and oral mucosa. Cutaneous LP is usually transient and oral LP (OLP) is most often chronic, so we performed a large-scale genetic and epidemiological study of LP to address whether the oral and non-oral subgroups have shared or distinct underlying pathologies and their overlap with autoimmune disease. Using lifelong records covering diagnoses, procedures, and clinic identity from 473,580 individuals in the FinnGen study, genome-wide association analyses were conducted on carefully constructed subcategories of OLP (n = 3,323) and non-oral LP (n = 4,356) and on the combined group. We identified 15 genome-wide significant associations in FinnGen and an additional 12 when meta-analyzed with UKBB (27 independent associations at 25 distinct genomic locations), most of which are shared between oral and non-oral LP. Many associations coincide with known autoimmune disease loci, consistent with the epidemiologic enrichment of LP with hypothyroidism and other autoimmune diseases. Notably, a third of the FinnGen associations demonstrate significant differences between OLP and non-OLP. We also observed a 13.6-fold risk for tongue cancer and an elevated risk for other oral cancers in OLP, in agreement with earlier reports that connect LP with higher cancer incidence. In addition to a large-scale dissection of LP genetics and comorbidities, our study demonstrates the use of comprehensive, multidimensional health registry data to address outstanding clinical questions and reveal underlying biological mechanisms in common but understudied diseases.

Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models.

Patient-derived xenograft (PDX) models are widely used in cancer research. To investigate the genomic fidelity of non-small cell lung cancer PDX models, we established 48 PDX models from 22 patients enrolled in the TRACERx study. Multi-region tumor sampling increased successful PDX engraftment and most models were histologically similar to their parent tumor. Whole-exome sequencing enabled comparison of tumors and PDX models and we provide an adapted mouse reference genome for improved removal of NOD scid gamma (NSG) mouse-derived reads from sequencing data. PDX model establishment caused a genomic bottleneck, with models often representing a single tumor subclone. While distinct tumor subclones were represented in independent models from the same tumor, individual PDX models did not fully recapitulate intratumor heterogeneity. On-going genomic evolution in mice contributed modestly to the genomic distance between tumors and PDX models. Our study highlights the importance of considering primary tumor heterogeneity when using PDX models and emphasizes the benefit of comprehensive tumor sampling.