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.

Pulmonary Function in People Living With Human Immunodeficiency Virus: A Meta-Analysis

Background: HIV can infect bronchial epithelial cells rendering individuals susceptible to lung damage. Our objective was to determine the effects of human immunodeficiency virus (HIV) infection on pulmonary function tests. Methods: We performed a meta-analysis after conducting a literature search in PubMed, Embase, Cochrane Library and Virtual Health Library databases from inception to December 31st, 2022. We employed the inverse variance method with a random effects model to calculate the effect estimate as the mean difference (MD) and 95% confidence interval (CI). We calculated the heterogeneity with the I2 statistic and performed a meta-regression analysis by age, sex, smoking, CD4 T-cells count and antiretroviral therapy. We also conducted a sensitivity analysis according to the studies’ publication date, and excluding the study with the greatest weight in the effect. The PROSPERO registry number was CRD42023401105. Results: The meta-analysis included 20 studies, with 7621 living with HIV and 7410 control participants. The pooled MD (95%CI) for the predicted percentage of FEV1, FVC and DLCO were −3.12 (−5.17, −1.06); p=0.003, −1.51 (−3.04, 0.02); p=0.05, and −5.26 (−6.64, −3.87); p<0.001, respectively. The pooled MD for FEV1/FVC was −0.01 (−0.02, −0.01); p=0.002. In all cases, there was a considerable heterogeneity. The meta-regression analysis showed that among studies heterogeneity was not explained by patient age, smoking, CD4 T-cells count or antiretroviral therapy. Conclusion: Pulmonary function tests are impaired in people living with HIV, independently of age, smoking, CD4 T-cells count, and geographical region. (AU)

Patient genetic heterogeneities acting as indicators of post-operative pain and opioid requirement in orthopedic surgery: A systematic review.

INTRODUCTION: Orthopedic surgical procedures are expected to increase annually, making it imperative to understand the correlations between patient genetic makeup and post-operative pain levels. METHODS: We performed a systematic literature review using PubMed and Cochrane databases in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 299 articles were initially selected, 20 articles remained after title and abstract review, and nine articles were selected for inclusion upon full text review. RESULTS: Genetic risk factors identified included the A allele of the 5HT2A gene single nucleotide polymorphism, the AA genotype of the ADRB2 gene, the CG genotype of the IL6 gene, the genotypes CT and TT of the NTRK1 gene, genotypes AA and GA of the OPRM gene, and the AA and GA genotypes of the COMT gene. Additional studies in the review discuss statistical significance of other variants of the COMT gene. CONCLUSION: There have been genetic association studies performed on the patient heterogeneity and its relationship on patient pain levels, but more data need to be collected to understand the clinical utility of stratifying patients based on genomic sequence.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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/.

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.