Characterization Of Genetic Heterogeneity Research Articles

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.

Abstract 1692: Single-cell DNA sequencing from frozen endometrial tumors to address clonal evolution of somatic mutations

Abstract Background: Single-cell DNA sequencing with droplet microfluidics has enabled the characterization of genetic heterogeneity in hematologic malignancies and fresh solid tumors through the direct DNA sequencing of thousands of single cells from individual samples without the need of whole-genome amplification. The application of this technology in banked solid tumor samples has been rather limited to date, however. Here, we describe and validate a method to characterize clonal evolution of somatic mutations from frozen tumor specimens, utilizing endometrial carcinomas (ECs) as an example. Methods: Tissue cores were obtained from frozen ECs and the extracellular matrix was enzymatically digested. A subset of extracted nuclei were used for bulk DNA extraction and targeted sequencing. The remaining nuclei were encapsulated with cell lysis buffer, and the DNA was primed and barcoded with hydrogel beads containing 317 cancer gene amplicons using a Tapestri instrument (Mission Bio). Following droplet PCR, Illumina libraries were generated and sequenced on a NovaSeq6000. Sequencing reads were trimmed and mapped to the reference genome. Barcode sequences were error-corrected and cells were detected from barcodes based on number of reads assigned to each barcode and amplicon read completeness. Genotyping was performed using GATK and the allele drop-out (ADO) rate was calculated. Non-neoplastic cells were defined by the absence of somatic mutations. Allele-specific copy number (ASCN) profiles for each tumor cell were estimated by normalizing mean reads per cell per amplicon to reads of non-neoplastic cells. Results: We performed single-cell genotyping and ASCN profiling on two ECs with matched bulk sequencing data. For EC1, 5,004 tumor and 132 non-neoplastic cells were sequenced, for a 97% tumor purity. For EC2, 2,981 tumor and 165 non-neoplastic cells were obtained, for a 95% tumor purity. The average number of reads per amplicon per cell for EC1 and EC2 was 116 and 146 with an ADO rate of 6% and 8%, respectively. The data obtained from single-cell analysis were concordant with the matched bulk sequencing data in terms of clonal and subclonal mutations, and their respective cancer cell fraction. Single-cell sequencing further revealed the presence of loss of heterozygosity and emergence of minor subclones as late events in tumor evolution, which could not be inferred by bulk sequencing. In EC2, branch mutations displayed a spectrum consistent with microsatellite instability (MSI), reflecting its acquired MSI phenotype. Conclusions: Here we describe a robust method to perform single-cell targeted DNA sequencing with droplet microfluidics of individual nuclei isolated from frozen tissue samples, providing the opportunity to determine the repertoire of somatic mutations at single cell resolution and to infer cancer clonal architecture and dynamics utilizing banked solid cancer tissue samples. Citation Format: Arnaud Da Cruz Paula, Yingjie Zhu, Shirin Issa Bhaloo, Fresia Pareja, Timothy Hoang, Pier Selenica, Etta Hanlon, Thais Basili, Higinio Dopeso, Nadeem Abu-Rustum, Jorge Sergio Reis-Filho, Britta Weigelt. Single-cell DNA sequencing from frozen endometrial tumors to address clonal evolution of somatic mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1692.

Characterization of Genetic Heterogeneity in Recurrent Metastases of Renal Cell Carcinoma.

Simple SummarySurvival rates in metastatic renal cell carcinoma (RCC) are still low despite novel therapies available. Thus, knowledge of molecular characteristics of distant metastases is important for personalized treatment strategies. Therefore, we investigated the genetic landscape of metastases, including synchronous and/or recurrent metastases to elucidate potential drug target genes and clinically relevant mutations. Furthermore, differences in mutational composition in different metastatic sites and over the course of the disease and treatment will demonstrate the importance of somatic profiling for precision medicine in RCC, thereby improving disease management in the future.Metastatic renal cell carcinoma (RCC) exhibits poor prognosis. Better knowledge of distant metastases is crucial to foster personalized treatment strategies. Here, we aimed to investigate the genetic landscape of metastases, including synchronous and/or recurrent metastases to elucidate potential drug target genes and clinically relevant mutations in a real-world setting of patients. We assessed 81 metastases from 56 RCC patients, including synchronous and/or recurrent metastases of 19 patients. Samples were analysed through next-generation sequencing with a high coverage (~1000× mean coverage). We therefore established a novel sequencing panel comprising 32 genes with impact on RCC development. We observed a high frequency of mutations in known RCC driver genes (e.g., >40% carriers of VHL and PBRM1 mutations) in metastases irrespective of the metastatic site. The somatic mutational composition was significantly associated with cancer-specific survival (p(logrank) = 0.03). Moreover, we identified in 34 patients at least one drug target gene as well as clinically relevant mutations listed in the VICC Meta-Knowledgebase in 7%. In addition to significantly higher mutational burden in recurrent metastases compared to earlier ones, synchronous and/or recurrent metastases of individual patients, even after a time-period >2 yrs, shared a high proportion of somatic events. Our data demonstrate the importance of somatic profiling in metastases for precision medicine in RCC.

Characterization of genetic heterogeneity within accessions in the USDA soybean germplasm collection.

Soybean breeding relies on the use of wild (Glycine soja Sieb. and Zucc.) and domesticated [Glycine max (L.) Merr.] germplasm for trait improvement. Soybeans are self-pollinating and accessions can be maintained as pure lines, however within-accession genetic variation has been observed in previous studies of some landraces and elite cultivars. The objective of this study was to characterize within-line variation in the accessions housed in the USDA Soybean Germplasm Collection. This collection includes over 20,000 accessions, each previously genotyped using the SoySNP50K Chip. Each SoySNP50K genotype was developed by pooling approximately three individuals per accession. Therefore, clusters of SNPs called as heterozygous within an accession can be inferred to represent putative regions of heterogeneity between the three individuals sampled. In this study, we found high-probability intervals of heterogeneity in 4% of the collection, representing 870 accessions. Heterogeneous loci were found on every chromosome and, collectively, covered 98.4% of the soybean genome and 99% of the gene models. Sanger sequencing confirmed regions of genomic heterogeneity among a subset of ten accessions. This dataset provides useful information and considerations for users of crop germplasm seed banks. Furthermore, the heterogeneous accessions and/or loci represent a unique genetic resource that is immediately available for forward and reverse genetics studies.

Abstract 3004: Single-cell mutational profiling of clonal evolution in myelodysplastic syndromes (MDS) during therapy and disease progression

Abstract Myelodysplastic syndromes (MDS) are a collection of clonal diseases with dysfunctional hematopoietic stem cells, characterized by ineffective hematopoiesis, cytopenias, and dysplasia. MDS is increasingly being recognized as a molecularly heterogeneous disease with variability in clinical phenotype, prognosis, and response to treatment. The clonal evolution in response to therapy remains a challenge in the management of MDS. However, resolution of complex clonal architectures is difficult with current bulk sequencing approaches. High-throughput single-cell genomic profiling enables the resolution of tumor heterogeneity, and may improve clinical diagnosis and treatment monitoring by allowing for characterization and early identification of resistant subclonal populations. To enable the characterization of genetic heterogeneity in tumor cell populations, we developed a novel microfluidic approach that barcodes amplified genomic DNA from thousands of individual cells confined to droplets. The barcodes were then used to reassemble the genetic profiles of cells from next-generation sequencing data. We applied this approach to sequential clinical MDS samples, genotyping the most clinically relevant loci across more than 15,000 individual cells. Additionally, to study effects of subclonal mutations on drug sensitivity, ex vivo functional testing was performed on red blood cell-lysed peripheral blood and/or bone marrow aspirate patient samples. Targeted single-cell sequencing was able to recapitulate bulk sequencing data from both peripheral blood and bone marrow aspirate samples. Furthermore, the single-cell nature of our approach enabled definitive determination of mutational co-occurrence within the same cell. For examples in one sample, bulk sequencing identified mutations in JAK2 and NRAS both at 3% variant allele frequency (VAF), where single-cell analysis suggested that these mutations were mutually exclusive, each defining a distinct subclone. Single-cell sequencing allowed for serial monitoring of clonal evolution, with analysis of sequential samples from this same patient showing increase of NRAS clone from 2.5% to 24.7% at time of disease progression after hypomethylating agent therapy. Furthermore, single-cell analysis was able to identify a distinct subclone characterized by a KRAS mutation (0.4% at initiation of therapy and 6.7% at relapse), missed by serial bulk sequencing. Taken together, our results suggest a greater degree of heterogeneity in MDS samples than suggested with bulk sequencing methods alone, and demonstrate utility of single-cell sequencing for sequential monitoring and identification of resistant clones prior to therapy initiation. We show here that this approach is a feasible, scalable, and effective way to identify and track heterogeneous populations of cells in MDS. Citation Format: Alexey Aleshin, Peter L. Greenberg, Bruno C. Medeiros, Diane Heiser, Marianne Santaguida, Sacha Prashad, Robert Durruthy- Durruthy, Dennis J. Eastburn. Single-cell mutational profiling of clonal evolution in myelodysplastic syndromes (MDS) during therapy and disease progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3004.

Leukemia blast cell identification

Acute leukemia is a heterogeneous group of disorders, characterized by different laboratory and prognostic features. An adequate diagnosis of acute leukemia, based on the identification of a leukemic cell population and the hematopoietic lineage assessment, is the first goal toward defining correct risk stratification of patients and tailoring specific therapies to patients. Diagnosis of acute leukemia underwent an important change since the 1970s when the only available diagnostic tools were cytomorphology and cytochemistry. Nowadays, the development of modern techniques and their combined use in a multimodal approach lead to a better identification and sub-classification of acute leukemia. Thus, blast identification in acute leukemia required a comprehensive global approach, by combining cytomorphology, cytochemistry, multiparameter flow cytometry MFC, cytogenetic, fluorescence in situ hybridization FISH and molecular genetic methods. At least, new sequencing technologies, such as gene expression profiling GEP and whole-genome sequencing, DNA methylation arrays, and comparative genomic hybridization array, could represent the new frontiers in the characterization of genetic heterogeneity in acute leukemia. WIREs Data Mining Knowl Discov 2015, 5:74-85. doi: 10.1002/widm.1146

Application of mass spectrometry to molecular diagnostics of viral infections

Mass spectrometry (MS) has found numerous applications in life sciences. It has high accuracy, sensitivity and wide dynamic range in addition to medium- to high-throughput capabilities. These features make MS a superior platform for analysis of various biomolecules including proteins, lipids, nucleic acids and carbohydrates. Until recently, MS was applied for protein detection and characterization. During the last decade, however, MS has successfully been used for molecular diagnostics of microbial and viral infections with the most notable applications being identification of pathogens, genomic sequencing, mutation detection, DNA methylation analysis, tracking of transmissions, and characterization of genetic heterogeneity. These new developments vastly expand the MS application from experimental research to public health and clinical fields. Matching of molecular techniques with specific requirements of the major MS platforms has produced powerful technologies for molecular diagnostics, which will further benefit from coupling with computational tools for extracting clinical information from MS-derived data.