FERMI: A Novel Method for Sensitive Detection of Rare Mutations in Somatic Tissue

Mito-SiPE is a sequence-independent and PCR-free mtDNA enrichment method for accurate ultra-deep mitochondrial sequencing

The analysis of somatic variation in the mitochondrial genome requires deep sequencing of mitochondrial DNA. This is ordinarily achieved by selective enrichment methods, such as PCR amplification or probe hybridization. These methods can introduce bias and are prone to contamination by nuclear-mitochondrial sequences (NUMTs), elements that can introduce artefacts into heteroplasmy analysis. We isolated intact mitochondria using differential centrifugation and alkaline lysis and subjected purified mitochondrial DNA to a sequence-independent and PCR-free method to obtain ultra-deep (>80,000X) sequencing coverage of the mitochondrial genome. This methodology avoids false-heteroplasmy calls that occur when long-range PCR amplification is used for mitochondrial DNA enrichment. Previously published methods employing mitochondrial DNA purification did not measure mitochondrial DNA enrichment or utilise high coverage short-read sequencing. Here, we describe a protocol that yields mitochondrial DNA and have quantified the increased level of mitochondrial DNA post-enrichment in 7 different mouse tissues. This method will enable researchers to identify changes in low frequency heteroplasmy without introducing PCR biases or NUMT contamination that are incorrectly identified as heteroplasmy when long-range PCR is used.

Signatures Beyond Oncogenic Mutations in Cell-Free DNA Sequencing for Non-Invasive, Early Detection of Cancer

Early detection of cancer saves lives, but an effective detection strategy in public health settings requires a delicate balance - periodic screening should neither miss rapidly progressing disease nor fail to detect rare tumors at unusual locations; on the other hand, even a modest false positive rate carries risks of over-diagnosis and over-treatment of relatively indolent non-malignant disease. Genomic profiling of cell-free DNA from liquid biopsy using massively parallel sequencing is emerging as an attractive, non-invasive screening platform for sensitive detection of multiple types of cancer in a single assay. Genomic data from cell-free DNA can not only identify oncogenic mutation status, but also additional molecular signatures related to potential tissue of origin, the extent of clonal growth, and malignant disease states. Utilization of the full potential of the molecular signatures from cfDNA sequencing data can guide clinical management strategies for targeted follow-ups using imaging or molecular marker-based diagnostic platforms and treatment options.

A Methodological Framework to Discover Pharmacogenomic Interactions Based on Random Forests

The identification of genomic alterations in tumor tissues, including somatic mutations, deletions, and gene amplifications, produces large amounts of data, which can be correlated with a diversity of therapeutic responses. We aimed to provide a methodological framework to discover pharmacogenomic interactions based on Random Forests. We matched two databases from the Cancer Cell Line Encyclopaedia (CCLE) project, and the Genomics of Drug Sensitivity in Cancer (GDSC) project. For a total of 648 shared cell lines, we considered 48,270 gene alterations from CCLE as input features and the area under the dose-response curve (AUC) for 265 drugs from GDSC as the outcomes. A three-step reduction to 501 alterations was performed, selecting known driver genes and excluding very frequent/infrequent alterations and redundant ones. For each model, we used the concordance correlation coefficient (CCC) for assessing the predictive performance, and permutation importance for assessing the contribution of each alteration. In a reasonable computational time (56 min), we identified 12 compounds whose response was at least fairly sensitive (CCC > 20) to the alteration profiles. Some diversities were found in the sets of influential alterations, providing clues to discover significant drug-gene interactions. The proposed methodological framework can be helpful for mining pharmacogenomic interactions.

Precocious clonal hematopoiesis in Down syndrome is accompanied by immune dysregulation

Children with Down syndrome develop early signs of clonal evolution that resemble traditional clonal hematopoiesis. Children with trisomy 21 who exhibit clonal hematopoiesis display cytokine and gene expression profiles indicative of disrupted immunity.

Clinical Utility of Next-Generation Sequencing in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a genetically heterogeneous disease that, even with current advancements in therapy, continues to have a poor prognosis. Recurrent somatic mutations have been identified in a core set of pathogenic genes including FLT3 (25-30% prevalence), NPM1 (25-30%), DNMT3A (25-30%), IDH1/2 (5-15%), and TET2 (5-15%), with direct diagnostic, prognostic, and targeted therapeutic implications. Advances in the understanding of the complex mechanisms of AML leukemogenesis have led to the development and recent US Food and Drug Administration (FDA) approval of several targeted therapies: midostaurin and gilteritinib targeting activated FLT3, and ivosidenib and enasidenib targeting mutated IDH1/2. Several additional drug candidates targeting other recurrently mutated gene pathways in AML are also being actively developed. Furthermore, outside of the realm of predicting responses to targeted therapies, many other mutated genes, which comprise the so-called long tail of oncogenic drivers in AML, have been shown to provide clinically useful diagnostic and prognostic information for AML patients. Many of these recurrently mutated genes have also been shown to be excellent biomarkers for post-treatment minimal residual disease (MRD) monitoring for assessing treatment response and predicting future relapse. In addition, the identification of germline mutations in a set of genes predisposing to myeloid malignancies may directly inform treatment decisions (particularly stem cell transplantation) and impact other family members. Recent advances in sequencing technology have made it practically and economically feasible to evaluate many genes simultaneously using next-generation sequencing (NGS). Mutation screening with NGS panels has been recommended by national and international professional guidelines as the standard of care for AML patients. NGS-based detection of the heterogeneous genes commonly mutated in AML has practical clinical utility for disease diagnosis, prognosis, prediction of targeted therapy response, and MRD monitoring.

Unraveling Hematopoiesis through the Lens of Genomics

Hematopoiesis has long served as a paradigm of stem cell biology and tissue homeostasis. In the past decade, the genomics revolution has ushered in powerful new methods for investigating the hematopoietic system that have provided transformative insights into its biology. As part of the advances in genomics, increasingly accurate deep sequencing and novel methods of cell tracking have revealed hematopoiesis to be more of a continuous and less of a discrete and punctuated process than originally envisioned. In part, this continuous nature of hematopoiesis is made possible by the emergent outcomes of vast, interconnected regulatory networks that influence cell fates and lineage commitment. It is also becoming clear how these mechanisms are modulated by genetic variation present throughout the population. This review describes how these recently uncovered complexities are reshaping our concept of tissue development and homeostasis while opening up a more comprehensive future understanding of hematopoiesis.

Modeling genetic heterogeneity of drug response and resistance in cancer

Heterogeneity in tumors is recognized as a key contributor to drug resistance and spread of advanced disease, but deep characterization of genetic variation within tumors has only recently been quantifiable with the advancement of next generation sequencing and single cell technologies. These data have been essential in developing molecular models of how tumors develop, evolve, and respond to environmental changes, such as therapeutic intervention. A deeper understanding of tumor evolution has subsequently opened up new research efforts to develop mathematical models that account for evolutionary dynamics with the goal of predicting drug response and resistance in cancer. Here, we describe recent advances and limitations of how models of tumor evolution can impact treatment strategies for cancer patients.