Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm

BACKGROUND AND OBJECTIVE

Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome.

METHODS

We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity.

RESULTS

WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes.

CONCLUSIONS

This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.

Transcription as source of genetic heterogeneity in budding yeast

Transcription presents challenges to genome stability both directly, by altering genome topology and exposing single-stranded DNA to chemical insults and nucleases, and indirectly by introducing obstacles to the DNA replication machinery. Such obstacles include the RNA polymerase holoenzyme itself, DNA-bound regulatory factors, G-quadruplexes and RNA-DNA hybrid structures known as R-loops. Here, we review the detrimental impacts of transcription on genome stability in budding yeast, as well as the mitigating effects of transcription-coupled nucleotide excision repair and of systems that maintain DNA replication fork processivity and integrity. Interactions between DNA replication and transcription have particular potential to induce mutation and structural variation, but we conclude that such interactions must have only minor effects on DNA replication by the replisome with little if any direct mutagenic outcome. However, transcription can significantly impair the fidelity of replication fork rescue mechanisms, particularly Break Induced Replication, which is used to restart collapsed replication forks when other means fail. This leads to de novo mutations, structural variation and extrachromosomal circular DNA formation that contribute to genetic heterogeneity, but only under particular conditions and in particular genetic contexts, ensuring that the bulk of the genome remains extremely stable despite the seemingly frequent interactions between transcription and DNA replication.

Statistical methods for assessing the effects of de novo variants on birth defects

With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is still limited for such studies because of the small sample size due to the high cost of recruiting and sequencing samples and the low occurrence of DNVs. DNV analysis is further complicated by genetic heterogeneity across diseased individuals. Therefore, it is critical to jointly analyze DNVs with other types of genomic/biological information to improve statistical power to identify genes associated with birth defects. In this review, we discuss the general workflow, recent developments in statistical methods, and future directions for DNV analysis.

Treatments, prognostic factors, and genetic heterogeneity in advanced cholangiocarcinoma: A multicenter real-world study

BACKGROUND AND AIMS

Cholangiocarcinoma (CCA), a rare and aggressive hepatobiliary malignancy, presents significant clinical management challenges. Despite rising incidence and evolving treatment options, prognosis remains poor, motivating the exploration of real-world data for enhanced understanding and patient care.

METHODS

This multicenter study analyzed data from 120 metastatic CCA patients at three institutions from 2016 to 2023. Kaplan-Meier curves assessed overall survival (OS), while univariate and multivariate analyses evaluated links between clinical variables (age, gender, tumor site, metastatic burden, ECOG performance status, response to first-line chemotherapy) and OS. Genetic profiling was conducted selectively.

RESULTS

Enrolled patients had a median age of 68.5 years, with intrahepatic tumors predominant in 79 cases (65.8%). Among 85 patients treated with first-line chemotherapy, cisplatin and gemcitabine (41.1%) was the most common regimen. Notably, one-third received no systemic treatment. After a median 14-month follow-up, 81 CCA-related deaths occurred, with a median survival of 13.1 months. Two clinical variables independently predicted survival: response to first-line chemotherapy (disease control vs. no disease control; HR: 0.27; 95% CI: 0.14-0.50; p < 0.0001) and metastatic involvement (>1 site vs. 1 site; HR: 1.99; 95% CI: 1.04-3.80; p = 0.0366). The three most common genetic alterations involved the ARID1A, tp53, and CDKN2A genes.

CONCLUSIONS

Advanced CCA displays aggressive clinical behavior, emphasizing the need for treatments beyond chemotherapy. Genetic diversity supports potential personalized therapies. Collaborative research and deeper CCA biology understanding are crucial to enhance patient outcomes in this challenging malignancy.

Genetic Intratumor Heterogeneity Remodels the Immune Microenvironment and Induces Immune Evasion in Brain Metastasis of Lung Cancer

INTRODUCTION

Brain metastasis, with the highest incidence in patients with lung cancer, significantly worsens prognosis and poses challenges to clinical management. To date, how brain metastasis evades immune elimination remains unknown.

METHODS

Whole-exome sequencing and RNA sequencing were performed on 30 matched brain metastasis, primary lung adenocarcinoma, and normal tissues. Data from The Cancer Genome Atlas primary lung adenocarcinoma cohort, including multiplex immunofluorescence, were used to support the findings of bioinformatics analysis.

RESULTS

Our study highlights the key role of intratumor heterogeneity of genomic alterations in the metastasis process, mainly caused by homologous recombination deficiency or other somatic copy number alteration-associated mutation mechanisms, leading to increased genomic instability and genomic complexity. We further proposed a selection model of brain metastatic evolution in which intratumor heterogeneity drives immune remodeling, leading to immune escape through different mechanisms under local immune pressure.

CONCLUSIONS

Our findings provide novel insights into the metastatic process and immune escape mechanisms of brain metastasis and pave the way for precise immunotherapeutic strategies for patients with lung cancer with brain metastasis.

Intratumor genetic heterogeneity and head and neck cancer relapse

BACKGROUND

Head and neck squamous cell carcinomas are treated by surgery, radiotherapy (RT), chemoradiotherapy (CRT) or combinations thereof, but locoregional recurrences (LRs) occur in 30-40% of treated patients. We have previously shown that in approximately half of the LRs after CRT, cancer driver mutations are not shared with the index tumor.

AIM

To investigate two possible explanations for these genetically unrelated relapses, treatment-induced genetic changes and intratumor genetic heterogeneity.

METHODS

To investigate treatment-induced clonal DNA changes, we compared copy number alterations (CNAs) and mutations between primary and recurrent xenografted tumors after treatment with (C)RT. Intratumor genetic heterogeneity was studied by multi-region sequencing on DNA from 31 biopsies of 11 surgically treated tumors.

RESULTS

Induction of clonal DNA changes by (C)RT was not observed in the xenograft models. Multi-region sequencing demonstrated variations in CNA profiles between paired biopsies of individual tumors, with copy number heterogeneity scores varying from 0.027 to 0.333. In total, 32 cancer driver mutations could be identified and were shared in all biopsies of each tumor. Remarkably, multi-clonal mutations in these same cancer driver genes were observed in 6 of 11 tumors. Genetically distinct heterogeneous cell cultures could also be established from single tumors, with different biomarker profiles and drug sensitivities.

CONCLUSION

Intratumor genetic heterogeneity at the level of the cancer driver mutations might explain the discordant mutational profiles in LRs after CRT, while there are no indications in xenograft models that these changes are induced by CRT.

Measures of genetic diversification in somatic tissues at bulk and single-cell resolution

Intra-tissue genetic heterogeneity is universal to both healthy and cancerous tissues. It emerges from the stochastic accumulation of somatic mutations throughout development and homeostasis. By combining population genetics theory and genomic information, genetic heterogeneity can be exploited to infer tissue organization and dynamics in vivo. However, many basic quantities, for example the dynamics of tissue-specific stem cells remain difficult to quantify precisely. Here, we show that single-cell and bulk sequencing data inform on different aspects of the underlying stochastic processes. Bulk-derived variant allele frequency spectra (VAF) show transitions from growing to constant stem cell populations with age in samples of healthy esophagus epithelium. Single-cell mutational burden distributions allow a sample size independent measure of mutation and proliferation rates. Mutation rates in adult hematopietic stem cells are higher compared to inferences during development, suggesting additional proliferation-independent effects. Furthermore, single-cell derived VAF spectra contain information on the number of tissue-specific stem cells. In hematopiesis, we find approximately 2 × 105 HSCs, if all stem cells divide symmetrically. However, the single-cell mutational burden distribution is over-dispersed compared to a model of Poisson distributed random mutations. A time-associated model of mutation accumulation with a constant rate alone cannot generate such a pattern. At least one additional source of stochasticity would be needed. Possible candidates for these processes may be occasional bursts of stem cell divisions, potentially in response to injury, or non-constant mutation rates either through environmental exposures or cell-intrinsic variation.

Glioblastoma evolution and heterogeneity from a 3D whole-tumor perspective

Treatment failure for the lethal brain tumor glioblastoma (GBM) is attributed to intratumoral heterogeneity and tumor evolution. We utilized 3D neuronavigation during surgical resection to acquire samples representing the whole tumor mapped by 3D spatial coordinates. Integrative tissue and single-cell analysis revealed sources of genomic, epigenomic, and microenvironmental intratumoral heterogeneity and their spatial patterning. By distinguishing tumor-wide molecular features from those with regional specificity, we inferred GBM evolutionary trajectories from neurodevelopmental lineage origins and initiating events such as chromothripsis to emergence of genetic subclones and spatially restricted activation of differential tumor and microenvironmental programs in the core, periphery, and contrast-enhancing regions. Our work depicts GBM evolution and heterogeneity from a 3D whole-tumor perspective, highlights potential therapeutic targets that might circumvent heterogeneity-related failures, and establishes an interactive platform enabling 360° visualization and analysis of 3D spatial patterns for user-selected genes, programs, and other features across whole GBM tumors.

Understanding genetic heterogeneity in gene-edited hematopoietic stem cell products

CRISPR/Cas gene editing has transformed genetic research and is poised to drive the next generation of gene therapies targeting hematopoietic stem cells (HSCs). However, the installation of the "desired" edit is most often only achieved in a minor subset of alleles. The array of cellular pathways triggered by gene editing tools produces a broad spectrum of "undesired" editing outcomes, including short insertions and deletions (indels) and chromosome rearrangements, leading to considerable genetic heterogeneity in gene-edited HSC populations. This heterogeneity may undermine the effect of the genetic intervention since only a subset of cells will carry the intended modification. Also, undesired mutations represent a potential safety concern as gene editing advances toward broader clinical use. Here, we will review the different sources of "undesired" edits and will discuss strategies for their mitigation and control.

Genetic heterogeneity in p53-null leukemia increases transiently with spindle assembly checkpoint inhibition and is not rescued by p53

Chromosome gains or losses often lead to copy number variations (CNV) and loss of heterozygosity (LOH). Both quantities are low in hematologic "liquid" cancers versus solid tumors in data of The Cancer Genome Atlas (TCGA) that also shows the fraction of a genome affected by LOH is ~ one-half of that with CNV. Suspension cultures of p53-null THP-1 leukemia-derived cells conform to these trends, despite novel evidence here of genetic heterogeneity and transiently elevated CNV after perturbation. Single-cell DNAseq indeed reveals at least 8 distinct THP-1 aneuploid clones with further intra-clonal variation, suggesting ongoing genetic evolution. Importantly, acute inhibition of the mitotic spindle assembly checkpoint (SAC) produces CNV levels that are typical of high-CNV solid tumors, with subsequent cell death and down-selection to novel CNV. Pan-cancer analyses show p53 inactivation associates with aneuploidy, but leukemias exhibit a weaker trend even though p53 inactivation correlates with poor survival. Overexpression of p53 in THP-1 does not rescue established aneuploidy or LOH but slightly increases cell death under oxidative or confinement stress, and triggers p21, a key p53 target, but without affecting net growth. Our results suggest that factors other than p53 exert stronger pressures against aneuploidy in liquid cancers, and identifying such CNV suppressors could be useful across liquid and solid tumor types.

Genetic Heterogeneity Underlying Phenotypes with Early-Onset Cerebellar Atrophy

Cerebellar atrophy (CA) is a frequent neuroimaging finding in paediatric neurology, usually associated with cerebellar ataxia. The list of genes involved in hereditary forms of CA is continuously growing and reveals its genetic complexity. We investigated ten cases with early-onset cerebellar involvement with and without ataxia by exome sequencing or by a targeted panel with 363 genes involved in ataxia or spastic paraplegia. Novel variants were investigated by in silico or experimental approaches. Seven probands carry causative variants in well-known genes associated with CA or cerebellar hypoplasia: SETX, CACNA1G, CACNA1A, CLN6, CPLANE1, and TBCD. The remaining three cases deserve special attention; they harbour variants in MAST1, PI4KA and CLK2 genes. MAST1 is responsible for an ultrarare condition characterised by global developmental delay and cognitive decline; our index case added ataxia to the list of concomitant associated symptoms. PIK4A is mainly related to hypomyelinating leukodystrophy; our proband presented with pure spastic paraplegia and normal intellectual capacity. Finally, in a patient who suffers from mild ataxia with oculomotor apraxia, the de novo novel CLK2 c.1120T>C variant was found. The protein expression of the mutated protein was reduced, which may indicate instability that would affect its kinase activity.

Genetic heterogeneity of cardiomyopathy and its correlation with patient care

BACKGROUND

Cardiomyopathy, which is a genetically and phenotypically heterogeneous pathological condition, is associated with increased morbidity and mortality. Genetic diagnosis of cardiomyopathy enables accurate phenotypic classification and optimum patient management and counseling. This study investigated the genetic spectrum of cardiomyopathy and its correlation with the clinical course of the disease.

METHODS

The samples of 72 Korean patients with cardiomyopathy (43 males and 29 females) were subjected to whole-exome sequencing (WES). The familial information and clinical characteristics of the patients were reviewed and analyzed according to their genotypes.

RESULTS

Dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), left ventricular non-compaction cardiomyopathy, and restrictive cardiomyopathy was detected in 41 (56.9%), 25 (34.7%), 4 (5.6%), and 2 (2.8%) patients, respectively. WES analysis revealed positive results in 37 (51.4%) patients. Subsequent familial testing identified ten additional familial cases. Among DCM cases, 19 (46.3%) patients exhibited positive results, with TTN variants being the most common alteration, followed by LMNA and MYH7 variants. Meanwhile, among HCM cases, 15 (60%) patients exhibited positive results with MYH7 variants being the most common alteration. In six patients with positive results, extracardiac surveillance was warranted based on disease information. The incidence of worse outcomes, such as mortality and life-threatening arrhythmic events, in patients with DCM harboring LMNA variants, was higher than that in patients with DCM harboring TTN or MYH7 variants.

CONCLUSIONS

Diverse genotypes were identified in a substantial proportion of patients with cardiomyopathy. Genetic diagnosis enables personalized disease surveillance and management.

Development and validation of an individualized gene expression-based signature to predict overall survival of patients with high-grade serous ovarian carcinoma

BACKGROUND

High-grade serious ovarian carcinoma (HGSOC) is a subtype of ovarian cancer with a different prognosis attributable to genetic heterogeneity. The prognosis of patients with advanced HGSOC requires prediction by genetic markers. This study systematically analyzed gene expression profile data to establish a genetic marker for predicting HGSOC prognosis.

METHODS

The RNA-seq data set and information on clinical follow-up of HGSOC were retrieved from Gene Expression Omnibus (GEO) database, and the data were standardized by DESeq2 as a training set. On the other hand, HGSOC RNA sequence data and information on clinical follow-up were retrieved from The Cancer Genome Atlas (TCGA) as a test set. Additionally, ovarian cancer microarray data set was obtained from GEO as the external validation set. Prognostic genes were screened from the training set, and characteristic selection was performed using the least absolute shrinkage and selection operator (LASSO) with 80% re-sampling for 5000 times. Genes with a frequency of more than 2000 were selected as robust biomarkers. Finally, a gene-related prognostic model was validated in both the test and GEO validation sets.

RESULTS

A total of 148 genes were found to be significantly correlated with HGSOC prognosis. The expression profile of these genes could stratify HGSOC prognosis and they were enriched to multiple tumor-related regulatory pathways such as tyrosine metabolism and AMPK signaling pathway. AKR1B10 and ANGPT4 were obtained after 5000-time re-sampling by LASSO regression. AKR1B10 was associated with the metastasis and progression of several tumors. In this study, Cox regression analysis was performed to create a 2-gene signature as an independent prognostic factor for HGSOC, which has the ability to stratify risk samples in all three data sets (p < 0.05). The Gene Set Enrichment Analysis (GSEA) discovered abnormally active REGULATION_OF_AUTOPHAGY and OLFACTORY_TRANSDUCTION pathways in the high-risk group samples.

CONCLUSION

This study resulted in the creation of a 2-gene molecular prognostic classifier that distinguished clinical features and was a promising novel prognostic tool for assessing the prognosis of HGSOC. RiskScore was a novel prognostic model which might be effective in guiding accurate prognosis of HGSOC.

High genetic heterogeneity of leukodystrophies in Iranian children: the first report of Iranian Leukodystrophy Registry

Leukodystrophies (LDs) are a heterogeneous group of progressive neurological disorders and characterized by primary involvement of white matter of the central nervous system (CNS). This is the first report of the Iranian LD Registry database to describe the clinical, radiological, and genomic data of Persian patients with leukodystrophies. From 2016 to 2019, patients suspicious of LDs were examined followed by a brain magnetic resonance imaging (MRI). A single gene testing or whole-exome sequencing (WES) was used depending on the neuroradiologic phenotypes. In a few cases, the diagnosis was made by metabolic studies. Based on the MRI pattern, diagnosed patients were divided into cohorts A (hypomyelinating LDs) versus cohort B (Other LDs). The most recent LD classification was utilized for classification of diagnosed patients. For novel variants, in silico analyses were performed to verify their pathogenicity. Out of 680 registered patients, 342 completed the diagnostic evaluations. In total, 245 patients met a diagnosis which in turn 24.5% were categorized in cohort A and the remaining in cohort B. Genetic tests revealed causal variants in 228 patients consisting of 213 variants in 110 genes with 78 novel variants. WES and single gene testing identified a causal variant in 65.5% and 34.5% cases, respectively. The total diagnostic rate of WES was 60.7%. Lysosomal disorders (27.3%; GM2-gangliosidosis-9.8%, MLD-6.1%, KD-4.5%), amino and organic acid disorders (17.15%; Canavan disease-4.5%, L-2-HGA-3.6%), mitochondrial leukodystrophies (12.6%), ion and water homeostasis disorders (7.3%; MLC-4.5%), peroxisomal disorders (6.5%; X-ALD-3.6%), and myelin protein disorders (3.6%; PMLD-3.6%) were the most commonly diagnosed disorders. Thirty-seven percent of cases had a pathogenic variant in nine genes (ARSA, HEXA, ASPA, MLC1, GALC, GJC2, ABCD1, L2HGDH, GCDH). This study highlights the most common types as well as the genetic heterogeneity of LDs in Iranian children.

Intratumor heterogeneity in colorectal cancer: Distribution of tumor suppressor gene variants with regard to patient lymph node status

Intratumor heterogeneity (ITH) results from accumulation of somatic mutations in the fractions of successive cancer cell generations. We aimed to use deep sequencing to investigate ITH in colorectal tumors with particular emphasis on variants in oncogenes (ONC) and tumor suppressor genes (TSG). Samples were collected from 16 patients with colorectal cancer and negative or positive lymph node status (n = 8 each). We deep-sequenced a panel of 56 cancer-related genes in the central and peripheral locations of T3 size primary tumors and healthy mucosa. The central region of T3 tumors has a different frequency profile and composition of genetic variants. This mutation profile is capable of independently discriminating patients with different lymph node status (p = 0.028) in the central region. We noted an increasing number of mutations outside of the central region of the tumor and a higher number of mutations in tumors from node-positive patients. Unexpectedly, in the healthy mucosa, we identified somatic mutations with variant allele frequencies, characteristic not only of heterozygotes and homozygotes but also of other discrete peaks (e.g., around 10%, 20%), suggestive of clonal expansion of certain mutant alleles. We found differences in the distribution of variant allele frequencies in TSGs when comparing node-negative and node-positive tumors (p = 0.029), as well as central and peripheral regions (p = 0.00399). TSGs may play an important role in the escape of the tumor toward metastatic colonization.

Phylogenetic inference using generative adversarial networks

MOTIVATION

The application of machine learning approaches in phylogenetics has been impeded by the vast model space associated with inference. Supervised machine learning approaches require data from across this space to train models. Because of this, previous approaches have typically been limited to inferring relationships among unrooted quartets of taxa, where there are only three possible topologies. Here, we explore the potential of generative adversarial networks (GANs) to address this limitation. GANs consist of a generator and a discriminator: at each step, the generator aims to create data that is similar to real data, while the discriminator attempts to distinguish generated and real data. By using an evolutionary model as the generator, we use GANs to make evolutionary inferences. Since a new model can be considered at each iteration, heuristic searches of complex model spaces are possible. Thus, GANs offer a potential solution to the challenges of applying machine learning in phylogenetics.

RESULTS

We developed phyloGAN, a GAN that infers phylogenetic relationships among species. phyloGAN takes as input a concatenated alignment, or a set of gene alignments, and infers a phylogenetic tree either considering or ignoring gene tree heterogeneity. We explored the performance of phyloGAN for up to 15 taxa in the concatenation case and 6 taxa when considering gene tree heterogeneity. Error rates are relatively low in these simple cases. However, run times are slow and performance metrics suggest issues during training. Future work should explore novel architectures that may result in more stable and efficient GANs for phylogenetics.

AVAILABILITY AND IMPLEMENTATION

phyloGAN is available on github: https://github.com/meganlsmith/phyloGAN/.

Genetic heterogeneity within a consanguineous family involving TTPA and SETX genes

Autosomal recessive cerebellar ataxias (ARCA) constitute a highly heterogeneous group of progressive neurodegenerative disorders that typically occur prior to adulthood. Despite some clinical resemblance between these disorders, different genes are involved. We report in this study four Tunisian patients belonging to the same large consanguineous family, sharing autosomal recessive cerebellar ataxia phenotypes but with clinical, biological, electrophysiological, and radiological differences leading to the diagnosis of two distinct ARCA caused by two distinct gene defects. Two of our patients presented ataxia with the vitamin E deficiency (AVED) phenotype, and the other two presented ataxia with oculo-motor apraxia 2 (AOA2). Genetic testing confirmed the clinical diagnosis by the detection of a frameshift c.744delA pathogenic variant in the TTPA gene, which is the most frequent in Tunisia, and a new variant c.1075dupT in the SETX gene. In Tunisia, data suggest that genetic disorders are common. The combined effects of the founder effect and inbreeding, added to genetic drift, may increase the frequency of detrimental rare disorders. The genetic heterogeneity observed in this family highlights the difficulty of genetic counseling in an inbred population. The examination and genetic testing of all affected patients, not just the index patient, is essential to not miss a treatable ataxia such as AVED, as in the case of this family.

Emergence of NDM-1 and KPC-3 carbapenemases in Kluyvera cryocrescens: Investigating genetic heterogeneity and acquisition routes of blaNDM-1 in Enterobacterales species in Portugal

OBJECTIVES

A higher diversity of species, clones and genes have been increasingly implicated in carbapenemases spread, though the mobile genetic elements responsible for their acquisition and dispersion at local and global levels are less explored, particularly in species other than Klebsiella pneumoniae or Escherichia coli. We aim to explain the emergence of NDM-1 and KPC-3 carbapenemases in a Kluyvera cryocrescens isolate, and to shed light on the heterogeneity of genetic platforms and acquisition routes of blaNDM-1 in diverse Enterobacterales species in Portugal.

METHODS

A KPC-3 and NDM-1-producing K. cryocrescens colonizing a hospitalized patient in 2019 was characterized by whole-genome sequencing and antibiotic resistance profiling following standard methods. Conjugative transfer of carbapenemases genes was assessed by filter mating. Plasmids were reconstructed with in silico and in vitro approaches. blaNDM-1 genetic context was compared with that of diverse NDM-1-producing Enterobacterales species, previously described in Portugal.

RESULTS

K. cryocrescens K629 showed a multidrug resistance profile. Resistance gene blaKPC-3 was harboured by a Tn4401d transposon within a worldwide-spread IncN-ST15 plasmid (pKLU-KPC3), whereas blaNDM-1 was located in a Tn3000 within a non-typeable mosaic plasmid (pKLU-NDM1). The heterogeneous blaNDM-1 genetic platforms and variable plasmid backbones identified in various Enterobacterales species suggested multiple introductions of blaNDM-1 in Portugal, mediated by variable insertion sequences.

CONCLUSIONS

We report the convergence of KPC-3 and NDM-1 in K. cryocrescens and the variable dissemination modes of these carbapenemases in different Enterobacterales species, underlining the need to track down genetic platforms responsible for carbapenemases diffusion.

Multi-site pre-therapeutic biopsies demonstrate genetic heterogeneity in patients with newly diagnosed diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, both regarding clinical presentation, response to treatment and outcome. Recently, subclassification of DLBCL based on mutational profile has been suggested, and next generation sequencing (NGS) analysis may be relevant as part of the diagnostic workflow. This will, however, often be based on analysis of one tumor biopsy. Here, we present a prospective study where multi-site sampling was performed prior to treatment in patients with newly diagnosed DLBCL. Two spatially different biopsies from 16 patients were analyzed using NGS with an in-house 59-gene lymphoma panel. In 8/16 (50%) patients, mutational differences were found between the two biopsy sites, including differences in TP53 mutational status. Our data indicate that a biopsy from the extra-nodal site may represent the most advanced clone, and an extra-nodal biopsy should be preferred for analysis, if safely accessible. This will help ensure a standardized stratification and treatment decision.

Metabolic protein kinase signalling in neuroblastoma

BACKGROUND

Neuroblastoma is a paediatric malignancy of incredibly complex aetiology. Oncogenic protein kinase signalling in neuroblastoma has conventionally focussed on transduction through the well-characterised PI3K/Akt and MAPK pathways, in which the latter has been implicated in treatment resistance. The discovery of the receptor tyrosine kinase ALK as a target of genetic alterations in cases of familial and sporadic neuroblastoma, was a breakthrough in the understanding of the complex genetic heterogeneity of neuroblastoma. However, despite progress in the development of small-molecule inhibitors of ALK, treatment resistance frequently arises and appears to be a feature of the disease. Moreover, since the identification of ALK, several additional protein kinases, including the PIM and Aurora kinases, have emerged not only as drivers of the disease phenotype, but also as promising druggable targets. This is particularly the case for Aurora-A, given its intimate engagement with MYCN, a driver oncogene of aggressive neuroblastoma previously considered 'undruggable.'

SCOPE OF REVIEW

Aided by significant advances in structural biology and a broader understanding of the mechanisms of protein kinase function and regulation, we comprehensively outline the role of protein kinase signalling, emphasising ALK, PIM and Aurora in neuroblastoma, their respective metabolic outputs, and broader implications for targeted therapies.

MAJOR CONCLUSIONS

Despite massively divergent regulatory mechanisms, ALK, PIM and Aurora kinases all obtain significant roles in cellular glycolytic and mitochondrial metabolism and neuroblastoma progression, and in several instances are implicated in treatment resistance. While metabolism of neuroblastoma tends to display hallmarks of the glycolytic "Warburg effect," aggressive, in particular MYCN-amplified tumours, retain functional mitochondrial metabolism, allowing for survival and proliferation under nutrient stress. Future strategies employing specific kinase inhibitors as part of the treatment regimen should consider combinatorial attempts at interfering with tumour metabolism, either through metabolic pathway inhibitors, or by dietary means, with a view to abolish metabolic flexibility that endows cancerous cells with a survival advantage.

Quantification of Genetic Heterogeneity Using Long-Read Targeted Individual DNA Molecule Sequencing

Understanding genetic heterogeneity is of paramount importance in unraveling the intricate functioning of biological systems, as it contributes to the diversity of phenotypes of gene-environment interactions. We have developed a method termed targeted Individual DNA Molecule Sequencing (IDMseq) to accurately quantify genetic heterogeneity within cell populations, even those with rare variants present at low frequencies. IDMseq ensures that each original DNA molecule is distinctively represented by one unique molecule identifier (UMI) group, preventing false UMI groups and enabling precise quantification of allele frequency within the original population. IDMseq is a versatile sequencing technique that combines error correction and long-read sequencing, enabling sensitive detection of various genetic variants, including single nucleotide variants and large structural variants in both basic and clinical research settings. This protocol provides a comprehensive, step-by-step guide to preparing samples and performing IDMseq to determine genetic variations. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: UMI labeling and amplification of DNA Support Protocol 1: AMPure XP beads cleanup Support Protocol 2: Suggested data analysis pipeline.

Analysis of genomic and immune intratumor heterogeneity in linitis plastica via multiregional exome and T-cell receptor sequencing

The molecular landscape and the intratumor heterogeneity (ITH) architecture of gastric linitis plastica (LP) are poorly understood. We performed whole-exome sequencing (WES) and T-cell receptor (TCR) sequencing on 40 tumor regions from four LP patients. The landscape and ITH at the genomic and immunological levels in LP tumors were compared with multiple cancers that have previously been reported. The lymphocyte infiltration was further assessed by immunohistochemistry (IHC) in LP tumors. In total, we identified 6339 non-silent mutations from multi-samples, with a median tumor mutation burden (TMB) of 3.30 mutations per Mb, comparable to gastric adenocarcinoma from the Cancer Genome Atlas (TCGA) cohort (P = 0.53). An extremely high level of genomic ITH was observed, with only 12.42%, 5.37%, 5.35%, and 30.67% of mutations detectable across 10 regions within the same tumors of each patient, respectively. TCR sequencing revealed that TCR clonality was substantially lower in LP than in multi-cancers. IHC using antibodies against CD4, CD8, and PD-L1 demonstrated scant T-cell infiltration in the four LP tumors. Furthermore, profound TCR ITH was observed in all LP tumors, with no T-cell clones shared across tumor regions in any of the patients, while over 94% of T-cell clones were restricted to individual tumor regions. The Morisita overlap index (MOI) ranged from 0.21 to 0.66 among multi-regions within the same tumors, significantly lower than that of lung cancer (P = 0.002). Our results show that LP harbored extremely high genomic and TCR ITH and suppressed T-cell infiltration, suggesting a potential contribution to the frequent recurrence and poor therapeutic response of this adenocarcinoma.

Characterization of crAss-like phage isolates highlights Crassvirales genetic heterogeneity and worldwide distribution

Crassvirales (crAss-like phages) are an abundant group of human gut-specific bacteriophages discovered in silico. The use of crAss-like phages as human fecal indicators is proposed but the isolation of only seven cultured strains of crAss-like phages to date has greatly hindered their study. Here, we report the isolation and genetic characterization of 25 new crAss-like phages (termed crAssBcn) infecting Bacteroides intestinalis, belonging to the order Crassvirales, genus Kehishuvirus and, based on their genomic variability, classified into six species. CrAssBcn phage genomes are similar to ΦCrAss001 but show genomic and aminoacidic differences when compared to other crAss-like phages of the same family. CrAssBcn phages are detected in fecal metagenomes around the world at a higher frequency than ΦCrAss001. This study increases the known crAss-like phage isolates and their abundance and heterogeneity open the question of what member of the Crassvirales group should be selected as human fecal marker.

Controlling genetic heterogeneity in gene-edited hematopoietic stem cells by single-cell expansion

Gene editing using engineered nucleases frequently produces unintended genetic lesions in hematopoietic stem cells (HSCs). Gene-edited HSC cultures thus contain heterogeneous populations, the majority of which either do not carry the desired edit or harbor unwanted mutations. In consequence, transplanting edited HSCs carries the risks of suboptimal efficiency and of unwanted mutations in the graft. Here, we present an approach for expanding gene-edited HSCs at clonal density, allowing for genetic profiling of individual clones before transplantation. We achieved this by developing a defined, polymer-based expansion system and identifying long-term expanding clones within the CD201+CD150+CD48-c-Kit+Sca-1+Lin- population of precultured HSCs. Using the Prkdcscid immunodeficiency model, we demonstrate that we can expand and profile edited HSC clones to check for desired and unintended modifications, including large deletions. Transplantation of Prkdc-corrected HSCs rescued the immunodeficient phenotype. Our ex vivo manipulation platform establishes a paradigm to control genetic heterogeneity in HSC gene editing and therapy.

Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours

Each tumour contains diverse cellular states that underlie intratumour heterogeneity (ITH), a central challenge of cancer therapeutics1. Dozens of recent studies have begun to describe ITH by single-cell RNA sequencing, but each study typically profiled only a small number of tumours and provided a narrow view of transcriptional ITH2. Here we curate, annotate and integrate the data from 77 different studies to reveal the patterns of transcriptional ITH across 1,163 tumour samples covering 24 tumour types. Among the malignant cells, we identify 41 consensus meta-programs, each consisting of dozens of genes that are coordinately upregulated in subpopulations of cells within many tumours. The meta-programs cover diverse cellular processes including both generic (for example, cell cycle and stress) and lineage-specific patterns that we map into 11 hallmarks of transcriptional ITH. Most meta-programs of carcinoma cells are similar to those identified in non-malignant epithelial cells, suggesting that a large fraction of malignant ITH programs are variable even before oncogenesis, reflecting the biology of their cell of origin. We further extended the meta-program analysis to six common non-malignant cell types and utilize these to map cell-cell interactions within the tumour microenvironment. In summary, we have assembled a comprehensive pan-cancer single-cell RNA-sequencing dataset, which is available through the Curated Cancer Cell Atlas website, and leveraged this dataset to carry out a systematic characterization of transcriptional ITH.

Widespread genetic heterogeneity and genotypic grouping associated with fungicide resistance among barley spot form net blotch isolates in Australia

Spot form net blotch, caused by Pyrenophora teres f. maculata, is a major foliar disease of barley worldwide. Knowledge of the pathogen's genetic diversity and population structure is critical for a better understanding of inherent evolutionary capacity and for the development of sustainable disease management strategies. Genome-wide, single nucleotide polymorphism data of 254 Australian isolates revealed genotypic diversity and an absence of population structure, either between states, or between fields and cultivars in different agro-ecological zones. This indicates there is little geographical isolation or cultivar directional selection and that the pathogen is highly mobile across the continent. However, two cryptic genotypic groups were found only in Western Australia, predominantly associated with genes involved in fungicide resistance. The findings in this study are discussed in the context of current cultivar resistance and the pathogen's adaptive potential.

DGH-GO: dissecting the genetic heterogeneity of complex diseases using gene ontology

BACKGROUND

Complex diseases such as neurodevelopmental disorders (NDDs) exhibit multiple etiologies. The multi-etiological nature of complex-diseases emerges from distinct but functionally similar group of genes. Different diseases sharing genes of such groups show related clinical outcomes that further restrict our understanding of disease mechanisms, thus, limiting the applications of personalized medicine approaches to complex genetic disorders.

RESULTS

Here, we present an interactive and user-friendly application, called DGH-GO. DGH-GO allows biologists to dissect the genetic heterogeneity of complex diseases by stratifying the putative disease-causing genes into clusters that may contribute to distinct disease outcome development. It can also be used to study the shared etiology of complex-diseases. DGH-GO creates a semantic similarity matrix for the input genes by using Gene Ontology (GO). The resultant matrix can be visualized in 2D plots using different dimension reduction methods (T-SNE, Principal component analysis, umap and Principal coordinate analysis). In the next step, clusters of functionally similar genes are identified from genes functional similarities assessed through GO. This is achieved by employing four different clustering methods (K-means, Hierarchical, Fuzzy and PAM). The user may change the clustering parameters and explore their effect on stratification immediately. DGH-GO was applied to genes disrupted by rare genetic variants in Autism Spectrum Disorder (ASD) patients. The analysis confirmed the multi-etiological nature of ASD by identifying four clusters of genes that were enriched for distinct biological mechanisms and clinical outcome. In the second case study, the analysis of genes shared by different NDDs showed that genes causing multiple disorders tend to aggregate in similar clusters, indicating a possible shared etiology.

CONCLUSION

DGH-GO is a user-friendly application that allows biologists to study the multi-etiological nature of complex diseases by dissecting their genetic heterogeneity. In summary, functional similarities, dimension reduction and clustering methods, coupled with interactive visualization and control over analysis allows biologists to explore and analyze their datasets without requiring expert knowledge on these methods. The source code of proposed application is available at https://github.com/Muh-Asif/DGH-GO.

Genetic population structure of the Xiongnu Empire at imperial and local scales

The Xiongnu established the first nomadic imperial power, controlling the Eastern Eurasian steppe from ca. 200 BCE to 100 CE. Recent archaeogenetic studies identified extreme levels of genetic diversity across the empire, corroborating historical records of the Xiongnu Empire being multiethnic. However, it has remained unknown how this diversity was structured at the local community level or by sociopolitical status. To address this, we investigated aristocratic and local elite cemeteries at the western frontier of the empire. Analyzing genome-wide data from 18 individuals, we show that genetic diversity within these communities was comparable to the empire as a whole, and that high diversity was also observed within extended families. Genetic heterogeneity was highest among the lowest-status individuals, implying diverse origins, while higher-status individuals harbored less genetic diversity, suggesting that elite status and power was concentrated within specific subsets of the broader Xiongnu population.

Genome-wide association study of obstructive sleep apnoea in the Million Veteran Program uncovers genetic heterogeneity by sex

BACKGROUND

Genome-wide association studies (GWAS) for obstructive sleep apnoea (OSA) are limited due to the underdiagnosis of OSA, leading to misclassification of OSA, which consequently reduces statistical power. We performed a GWAS of OSA in the Million Veteran Program (MVP) of the U.S. Department of Veterans Affairs (VA) healthcare system, where OSA prevalence is close to its true population prevalence.

METHODS

We performed GWAS of 568,576 MVP participants, stratified by biological sex and by harmonized race/ethnicity and genetic ancestry (HARE) groups of White, Black, Hispanic, and Asian individuals. We considered both BMI adjusted (BMI-adj) and unadjusted (BMI-unadj) models. We replicated associations in independent datasets, and analysed the heterogeneity of OSA genetic associations across HARE and sex groups. We finally performed a larger meta-analysis GWAS of MVP, FinnGen, and the MGB Biobank, totalling 916,696 individuals.

FINDINGS

MVP participants are 91% male. OSA prevalence is 21%. In MVP there were 18 and 6 genome-wide significant loci in BMI-unadj and BMI-adj analyses, respectively, corresponding to 21 association regions. Of these, 17 were not previously reported in association with OSA, and 13 replicated in FinnGen (False Discovery Rate p-value < 0.05). There were widespread significant differences in genetic effects between men and women, but less so across HARE groups. Meta-analysis of MVP, FinnGen, and MGB biobank revealed 17 additional, previously unreported, genome-wide significant regions.

INTERPRETATION

Sex differences in genetic associations with OSA are widespread, likely associated with multiple OSA risk factors. OSA shares genetic underpinnings with several sleep phenotypes, suggesting shared aetiology and causal pathways.

FUNDING

Described in acknowledgements.

An Extremes of Phenotype Approach Confirms Significant Genetic Heterogeneity in Patients with Ulcerative Colitis

BACKGROUND AND AIMS

Ulcerative colitis [UC] is a major form of inflammatory bowel disease globally. Phenotypic heterogeneity is defined by several variables including age of onset and disease extent. The genetics of disease severity remains poorly understood. To further investigate this, we performed a genome wide association [GWA] study using an extremes of phenotype strategy.

METHODS

We conducted GWA analyses in 311 patients with medically refractory UC [MRUC], 287 with non-medically refractory UC [non-MRUC] and 583 controls. Odds ratios [ORs] were calculated for known risk variants comparing MRUC and non-MRUC, and controls.

RESULTS

MRUC-control analysis had the greatest yield of genome-wide significant single nucleotide polymorphisms [SNPs] [2018], including lead SNP = rs111838972 [OR = 1.82, p = 6.28 × 10-9] near MMEL1 and a locus in the human leukocyte antigen [HLA] region [lead SNP = rs144717024, OR = 12.23, p = 1.7 × 10-19]. ORs for the lead SNPs were significantly higher in MRUC compared to non-MRUC [p < 9.0 × 10-6]. No SNPs reached significance in the non-MRUC-control analysis (top SNP, rs7680780 [OR 2.70, p = 5.56 × 10-8). We replicate findings for rs4151651 in the Complement Factor B [CFB] gene and demonstrate significant changes in CFB gene expression in active UC. Detailed HLA analyses support the strong associations with MHC II genes, particularly HLA-DQA1, HLA-DQB1 and HLA-DRB1 in MRUC.

CONCLUSIONS

Our MRUC subgroup replicates multiple known UC risk variants in contrast to non-MRUC and demonstrates significant differences in effect sizes compared to those published. Non-MRUC cases demonstrate lower ORs similar to those published. Additional risk and prognostic loci may be identified by targeted recruitment of individuals with severe disease.

Genetic heterogeneity and enrichment of variants in DNA-repair genes in ameloblastoma

OBJECTIVE

Ameloblastomas are a group of relatively common odontogenic tumors that frequently originate from the dental epithelium. These tumors are aggressive in nature and present as slow-growing painless cortical expansion of the jaw. Histologically, the follicular and plexiform subtypes constitute two-thirds of solid/multicystic ameloblastomas. The objective of this study was to understand the genetic architecture of follicular and plexiform ameloblastomas using deep whole-exome sequencing.

METHODS

Archived formalin-fixed paraffin-embedded tissue blocks of follicular (n = 4) and plexiform (n = 6) ameloblastomas were retrieved and genomic DNAs were isolated from the tumor tissue dissected from the formalin-fixed paraffin-embedded block. The exomes were enriched using the Integrated DNA Technologies Exome Research Panel (IDT, Coralville, IA) and paired-end sequencing was completed on an Illumina NovaSeq 6000 with an average output of 20 GB of data resulting in a mean coverage of 400×. Variant analysis was completed using custom-developed software: Rapid Understanding of Nucleotide variant Effect Software and variant integration and knowledge interpretation in genomes.

RESULTS

Our analyses focused on examining somatic variants (gnomAD minor allele frequency ≤1%) in genes found on an Food and Drug Administration -approved clinical cancer sequencing panel (FoundationOne®CDx). In follicular tumors, variants (>20% of the reads) were identified in BRAF, KMT2D, and ABL1 genes. In plexiform tumors, variants (>20% of the reads) were identified in ALK, BRAF, KRAS, KMT2D, SMO, KMT2A, and BRCA2 genes. Enrichment analysis showed a significant role of DNA repair genes in the development of these tumors.

CONCLUSION

The variants identified in follicular and plexiform ameloblastomas were enriched in DNA-repair genes. The observed genetic heterogeneity in these ameloblastomas may contribute to the aggressive nature and recurrence risk of these tumors.

Congenital Microcephaly: A Debate on Diagnostic Challenges and Etiological Paradigm of the Shift from Isolated/Non-Syndromic to Syndromic Microcephaly

Congenital microcephaly (CM) exhibits broad clinical and genetic heterogeneity and is thus categorized into several subtypes. However, the recent bloom of disease-gene discoveries has revealed more overlaps than differences in the underlying genetic architecture for these clinical sub-categories, complicating the differential diagnosis. Moreover, the mechanism of the paradigm shift from a brain-restricted to a multi-organ phenotype is only vaguely understood. This review article highlights the critical factors considered while defining CM subtypes. It also presents possible arguments on long-standing questions of the brain-specific nature of CM caused by a dysfunction of the ubiquitously expressed proteins. We argue that brain-specific splicing events and organ-restricted protein expression may contribute in part to disparate clinical manifestations. We also highlight the role of genetic modifiers and de novo variants in the multi-organ phenotype of CM and emphasize their consideration in molecular characterization. This review thus attempts to expand our understanding of the phenotypic and etiological variability in CM and invites the development of more comprehensive guidelines.

Intratumour heterogeneity, from hypothesis to the clinic

Recent technological advances uncovered intricate biological processes underlying intratumor heterogeneity with clinical implications. These insights led to novel biomarkers for immunotherapies, justified serial tumour biopsies for therapeutic target profiling, inspired new treatment strategies, and ultimately might yield novel therapeutics that target clonal interdependence.

Tumour Genetic Heterogeneity in Relation to Oral Squamous Cell Carcinoma and Anti-Cancer Treatment

Oral squamous cell carcinoma (SCC) represents more than 90% of all oral cancers and is the most frequent SCC of the head and neck region. It may affect any oral mucosal subsite but most frequently the tongue, followed by the floor of the mouth. The use of tobacco and betel nut, either smoked or chewed, and abuse of alcohol are the main risk factors for oral SCC. Oral SCC is characterized by considerable genetic heterogeneity and diversity, which together have a significant impact on the biological behaviour, clinical course, and response to treatment and on the generally poor prognosis of this carcinoma. Characterization of spatial and temporal tumour-specific molecular profiles and of person-specific resource availability and environmental and biological selective pressures could assist in personalizing anti-cancer treatment for individual patients, with the aim of improving treatment outcomes. In this narrative review, we discuss some of the events in cancer evolution and the functional significance of driver-mutations in carcinoma-related genes in general and elaborate on mechanisms mediating resistance to anti-cancer treatment.

Genetic Heterogeneity Shapes Brain Connectivity in Psychiatry

BACKGROUND

Polygenicity and genetic heterogeneity pose great challenges for studying psychiatric conditions. Genetically informed approaches have been implemented in neuroimaging studies to address this issue. However, the effects on functional connectivity of rare and common genetic risks for psychiatric disorders are largely unknown. Our objectives were to estimate and compare the effect sizes on brain connectivity of psychiatric genomic risk factors with various levels of complexity: oligogenic copy number variants (CNVs), multigenic CNVs, and polygenic risk scores (PRSs) as well as idiopathic psychiatric conditions and traits.

METHODS

Resting-state functional magnetic resonance imaging data were processed using the same pipeline across 9 datasets. Twenty-nine connectome-wide association studies were performed to characterize the effects of 15 CNVs (1003 carriers), 7 PRSs, 4 idiopathic psychiatric conditions (1022 individuals with autism, schizophrenia, bipolar conditions, or attention-deficit/hyperactivity disorder), and 2 traits (31,424 unaffected control subjects).

RESULTS

Effect sizes on connectivity were largest for psychiatric CNVs (estimates: 0.2-0.65 z score), followed by psychiatric conditions (0.15-0.42), neuroticism and fluid intelligence (0.02-0.03), and PRSs (0.01-0.02). Effect sizes of CNVs on connectivity were correlated to their effects on cognition and risk for disease (r = 0.9, p = 5.93 × 10-6). However, effect sizes of CNVs adjusted for the number of genes significantly decreased from small oligogenic to large multigenic CNVs (r = -0.88, p = 8.78 × 10-6). PRSs had disproportionately low effect sizes on connectivity compared with CNVs conferring similar risk for disease.

CONCLUSIONS

Heterogeneity and polygenicity affect our ability to detect brain connectivity alterations underlying psychiatric manifestations.

Genome sequence analysis of a novel rotavirus strain indicates a broad genetic diversity of rotavirus A in shrews

Rotavirus A (RVA) is an etiologic agent of diarrhea in humans and animals. It shows a high degree of genetic heterogeneity. Although distinct associations of RVA genotypes with certain host species are common, interspecies-transmission has also been described. Recently, RVA strains, which are genetically distinct and cluster basally to all other RVA strains in phylogenetic trees, have been identified in common shrews (Sorex araneus). Here, the genome sequence analysis of another RVA strain (RVA/Common Shrew-wt/GER/KS11-0893/2010/G42P[58]) from a common shrew from Germany is described. Generally, the strain shows low sequence identities to established strains, which is reflected by the assessment of the novel genotypes G42-P[58]-I32-R28-C24-M24-A39-N28-T28-E32-H28 to its genome segments. Specifically, the strain is phylogenetically distant from previously described RVA strains of common shrews, whereas it is more closely related to other avian and mammalian RVA strains including those from Asian house shrews (Suncus murinus). The results indicate that a broad variety of diverse RVA strains can be found in shrews suggesting a significant role of these animals in rotavirus evolution.

Developmental and epileptic encephalopathies: from genetic heterogeneity to phenotypic continuum

Developmental and epileptic encephalopathies (DEEs) are a heterogeneous group of disorders characterized by early-onset, often severe epileptic seizures and EEG abnormalities on a background of developmental impairment that tends to worsen as a consequence of epilepsy. DEEs may result from both nongenetic and genetic etiologies. Genetic DEEs have been associated with mutations in many genes involved in different functions including cell migration, proliferation, and organization, neuronal excitability, and synapse transmission and plasticity. Functional studies performed in different animal models and clinical trials on patients have contributed to elucidate pathophysiological mechanisms underlying many DEEs and have explored the efficacy of different treatments. Here, we provide an extensive review of the phenotypic spectrum included in the DEEs and of the genetic determinants and pathophysiological mechanisms underlying these conditions. We also provide a brief overview of the most effective treatment now available and of the emerging therapeutic approaches.

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Genetic Heterogeneity, Tumor Microenvironment and Immunotherapy in Triple-Negative Breast Cancer

Heterogeneity of triple-negative breast cancer is well known at clinical, histopathological, and molecular levels. Genomic instability and greater mutation rates, which may result in the creation of neoantigens and enhanced immunogenicity, are additional characteristics of this breast cancer type. Clinical outcome is poor due to early age of onset, high metastatic potential, and increased likelihood of distant recurrence. Consequently, efforts to elucidate molecular mechanisms of breast cancer development, progression, and metastatic spread have been initiated to improve treatment options and improve outcomes for these patients. The extremely complex and heterogeneous tumor immune microenvironment is made up of several cell types and commonly possesses disorganized gene expression. Altered signaling pathways are mainly associated with mutated genes including p53, PIK3CA, and MAPK, and which are positively correlated with genes regulating immune response. Of note, particular immunity-associated genes could be used in prognostic indexes to assess the most effective management. Recent findings highlight the fact that long non-coding RNAs also play an important role in shaping tumor microenvironment formation, and can mediate tumor immune evasion. Identification of molecular signatures, through the use of multi-omics approaches, and effector pathways that drive early stages of the carcinogenic process are important steps in developing new strategies for targeted cancer treatment and prevention. Advances in immunotherapy by remodeling the host immune system to eradicate tumor cells have great promise to lead to novel therapeutic strategies. Current research is focused on combining immune checkpoint inhibition with chemotherapy, PARP inhibitors, cancer vaccines, or natural killer cell therapy. Targeted therapies may improve therapeutic response, eliminate therapeutic resistance, and improve overall patient survival. In the future, these evolving advancements should be implemented for personalized medicine and state-of-art management of cancer patients.

Multiregional Sequencing Analysis Reveals Extensive Genetic Heterogeneity in Gastric Tumors from Latinos

Gastric cancer is a leading cause of cancer mortality and health disparities in Latinos. We evaluated gastric intratumoral heterogeneity using multiregional sequencing of >700 cancer genes in 115 tumor biopsies from 32 patients, 29 who were Latinos. Analyses focused on comparisons with The Cancer Genome Atlas (TCGA) and on mutation clonality, druggability, and signatures. We found that only approximately 30% of all mutations were clonal and that only 61% of the known TCGA gastric cancer drivers harbored clonal mutations. Multiple clonal mutations were found in new candidate gastric cancer drivers such as EYS, FAT4, PCDHA1, RAD50, EXO1, RECQL4, and FSIP2. The genomically stable (GS) molecular subtype, which has the worse prognosis, was identified in 48% of our Latino patients, a fraction that was >2.3-fold higher than in TCGA Asian and White patients. Only a third of all tumors harbored clonal pathogenic mutations in druggable genes, with most (93%) GS tumors lacking actionable clonal mutations. Mutation signature analyses revealed that, in microsatellite-stable (MSS) tumors, DNA repair mutations were common for both tumor initiation and progression, while tobacco, POLE, and inflammation signatures likely initiate carcinogenesis. MSS tumor progression was likely driven by aging- and aflatoxin-associated mutations, as these latter changes were usually nonclonal. In microsatellite-unstable tumors, nonclonal tobacco-associated mutations were common. Our study, therefore, contributed to advancing gastric cancer molecular diagnostics and suggests clonal status is important to understanding gastric tumorigenesis. Our findings of a higher frequency of a poor prognosis associated molecular subtype in Latinos and a possible new aflatoxin gastric cancer etiology also advance cancer disparities research.

SIGNIFICANCE

Our study contributes to advancing our knowledge of gastric carcinogenesis, diagnostics, and cancer health disparities.

Chromatin Variants Reveal the Genetic Determinants of Oncogenesis in Breast Cancer

Breast cancer presents as multiple distinct disease entities. Each tumor harbors diverse cell populations defining a phenotypic heterogeneity that impinges on our ability to treat patients. To date, efforts mainly focused on genetic variants to find drivers of inter- and intratumor phenotypic heterogeneity. However, these efforts have failed to fully capture the genetic basis of breast cancer. Through recent technological and analytical approaches, the genetic basis of phenotypes can now be decoded by characterizing chromatin variants. These variants correspond to polymorphisms in chromatin states at DNA sequences that serve a distinct role across cell populations. Here, we review the function and causes of chromatin variants as they relate to breast cancer inter- and intratumor heterogeneity and how they can guide the development of treatment alternatives to fulfill the goal of precision cancer medicine.

Individual Genetic Heterogeneity

Genetic variation has been widely covered in literature, however, not from the perspective of an individual in any species. Here, a synthesis of genetic concepts and variations relevant for individual genetic constitution is provided. All the different levels of genetic information and variation are covered, ranging from whether an organism is unmixed or hybrid, has variations in genome, chromosomes, and more locally in DNA regions, to epigenetic variants or alterations in selfish genetic elements. Genetic constitution and heterogeneity of microbiota are highly relevant for health and wellbeing of an individual. Mutation rates vary widely for variation types, e.g., due to the sequence context. Genetic information guides numerous aspects in organisms. Types of inheritance, whether Mendelian or non-Mendelian, zygosity, sexual reproduction, and sex determination are covered. Functions of DNA and functional effects of variations are introduced, along with mechanism that reduce and modulate functional effects, including TARAR countermeasures and intraindividual genetic conflict. TARAR countermeasures for tolerance, avoidance, repair, attenuation, and resistance are essential for life, integrity of genetic information, and gene expression. The genetic composition, effects of variations, and their expression are considered also in diseases and personalized medicine. The text synthesizes knowledge and insight on individual genetic heterogeneity and organizes and systematizes the central concepts.

Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network

Glioblastoma multiforme (GBM) is the most malignant brain cancer with great heterogeneities in many aspects, such as prognosis, clinicopathological features, immune landscapes, and immunotherapeutic responses. Considering that gene interaction network is relatively stable in a healthy state but widely perturbed in cancers, we sought to explore the multidimensional heterogeneities of GBM through evaluating the degree of network perturbations. The gene interaction network perturbations of GBM samples (TCGA cohort) and normal samples (GTEx database) were characterized by edge perturbations, which were quantized through evaluating the change in relative gene expression value. An unsupervised consensus clustering analysis was performed to identify edge perturbation-based clusters of GBM samples. Results revealed that the edge perturbation of GBM samples was stronger than that of normal samples. Four edge perturbation-based clusters of GBM samples were identified and showed prominent heterogeneities in prognosis, clinicopathological features, somatic genomic alterations, immune landscapes, and immunotherapeutic responses. In addition, a sample-specific perturbation of gene interaction score (SPGIScore) was constructed based on the differently expressed genes (DEGs) among four clusters, and exhibited a robust ability to predict prognosis. In conclusion, the bioinformatics approach based on sample-specific edge perturbation in gene interaction network provided a new perspective to understanding the multidimensional heterogeneities of GBM.

Multiregional single-cell proteogenomic analysis of ccRCC reveals cytokine drivers of intratumor spatial heterogeneity

Intratumor heterogeneity (ITH) represents a major challenge for anticancer therapies. An integrated, multidimensional, multiregional approach dissecting ITH of the clear cell renal cell carcinoma (ccRCC) tumor microenvironment (TME) is employed at the single-cell level with mass cytometry (CyTOF), multiplex immunofluorescence (MxIF), and single-nucleus RNA sequencing (snRNA-seq) and at the bulk level with whole-exome sequencing (WES), RNA-seq, and methylation profiling. Multiregional analyses reveal unexpected conservation of immune composition within each individual patient, with profound differences among patients, presenting patient-specific tumor immune microenvironment signatures despite underlying genetic heterogeneity from clonal evolution. Spatial proteogenomic TME analysis using MxIF identifies 14 distinct cellular neighborhoods and, conversely, demonstrated architectural heterogeneity among different tumor regions. Tumor-expressed cytokines are identified as key determinants of the TME and correlate with clinical outcome. Overall, this work signifies that spatial ITH occurs in ccRCC, which may drive clinical heterogeneity and warrants further interrogation to improve patient outcomes.

In Vivo Modeling of Patient Genetic Heterogeneity Identifies New Ways to Target Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is an aggressive malignancy of the bile ducts within the liver characterized by high levels of genetic heterogeneity. In the context of such genetic variability, determining which oncogenic mutations drive ICC growth has been difficult, and developing modes of patient stratification and targeted therapies remains challenging. Here we model the interactions between rare mutations with more common driver genes and combine in silico analysis of patient data with highly multiplexed in vivo CRISPR-spCas9 screens to perform a functional in vivo study into the role genetic heterogeneity plays in driving ICC. Novel tumor suppressors were uncovered, which, when lost, cooperate with the RAS oncoprotein to drive ICC growth. Focusing on a set of driver mutations that interact with KRAS to initiate aggressive, sarcomatoid-type ICC revealed that tumor growth relies on Wnt and PI3K signaling. Pharmacologic coinhibition of Wnt and PI3K in vivo impeded ICC growth regardless of mutational profile. Therefore, Wnt and PI3K activity should be considered as a signature by which patients can be stratified for treatment independent of tumor genotype, and inhibitors of these pathways should be levied to treat ICC.

SIGNIFICANCE

This work shows that, despite significant genetic heterogeneity, intrahepatic cholangiocarcinoma relies on a limited number of signaling pathways to grow, suggesting common therapeutic vulnerabilities across patients.