A Platform for Validating Colorectal Cancer Driver Genes Using Mouse Organoids

Systematic approaches for functionally validating cancer genes are needed since numerous genes mutated in cancer tissues have been identified from cancer genome sequencing. The mouse organoid culture system has been extensively used in the field of cancer research since mouse organoids can faithfully recapitulate the physiological behavior of the cells. Taking advantage of this, we recently described a platform for functionally validating colorectal cancer (CRC) driver genes that utilized CRISPR-Cas9 in mouse intestinal tumor organoids. In this review, we will describe how mouse organoids have been applied to CRC research and focus on how CRC genes can be validated using mouse organoids.

HER2 G776S Mutation Promotes Oncogenic Potential in Colorectal Cancer Cells when Accompanied by Loss of APC Function

Background Clinical cancer genome sequencing detects oncogenic variants that are potential targets for cancer treatment, but it also detects variants of unknown significance that may interact and affect the pathophysiology of the tumor; however, these interactions are not fully understood. In this study, we examined the interactions of a minor HER2 mutation (G776S) and APC mutations, which were detected by cancer genome sequencing of samples from a patient with colorectal cancer. Methods We transfected HER2-G776S mutant- or HER2 wild type- expressing vectors into several cell lines, HeLa, FHC, CACO-2 and COLO-320, to evaluate their effects on HER2 phosphorylation and kinase activity, HER2 downstream signaling (phosphorylation of AKT and MAPK), and anchorage-independent growth ability. APC- knockout cells and APC overexpressing cells were established to investigate the effect of APC function on the HER2 signaling pathway. We also evaluated the efficacy of a HER2 tyrosine kinase inhibitor on xenograft tumors derived from HER2-G776S transfected cells. Results HER2 G776S mutation increased the kinase activity and phosphorylation of HER2 protein, but these effects were weaker than those of the other HER2 driver mutation. HER2 G776S did not activate HER2-downstream signal pathways, such as ERK and AKT phosphorylation, in cells with wild-type APC (HeLa and FHC cells). By contrast, HER2 G776S increased the activation of HER2 downstream signaling, especially ERK phosphorylation, and anchorage-independent cell growth in cells with an APC mutation (CACO-2 and COLO-320) and APC-knockout HeLa cells. Wild-type APC overexpression in HER2 G776S-transfected COLO-320 cells neutralized ERK phosphorylation. Loss of APC function increased Wnt pathway activity but also increased RAS–GTP, which increased ERK phosphorylation triggered by HER2 G776S transfection. Afatinib, a pan-HER tyrosine kinase inhibitor, inhibited tumor growth of HER2 G776S-transfected COLO-320 xenografts Conclusions HER2 G776S mutation acts as a weak oncogenic driver, but it also increases HER2–ERK signaling activity by increasing RAS–GTP production when APC function is simultaneously impaired. These results suggest that even weakly active mutations may be therapeutic targets, and the use of this strategy may contribute to the development of HER2-targeted therapy for colorectal cancer.

DIVIS: Integrated and Customizable Pipeline for Cancer Genome Sequencing Analysis and Interpretation

Next-generation sequencing (NGS) has drastically enhanced human cancer research, but diverse sequencing strategies, complicated open-source software, and the identification of massive numbers of mutations have limited the clinical application of NGS. Here, we first presented GPyFlow, a lightweight tool that flexibly customizes, executes, and shares workflows. We then introduced DIVIS, a customizable pipeline based on GPyFlow that integrates read preprocessing, alignment, variant detection, and annotation of whole-genome sequencing, whole-exome sequencing, and gene-panel sequencing. By default, DIVIS screens variants from multiple callers and generates a standard variant-detection format list containing caller evidence for each sample, which is compatible with advanced analyses. Lastly, DIVIS generates a statistical report, including command lines, parameters, quality-control indicators, and mutation summary. DIVIS substantially facilitates complex cancer genome sequencing analyses by means of a single powerful and easy-to-use command. The DIVIS code is freely available at https://github.com/niu-lab/DIVIS, and the docker image can be downloaded from https://hub.docker.com/repository/docker/sunshinerain/divis.

Optimizing Nanopore sequencing-based detection of structural variants enables individualized circulating tumor DNA-based disease monitoring in cancer patients

Here, we describe a novel approach for rapid discovery of a set of tumor-specific genomic structural variants (SVs), based on a combination of low coverage cancer genome sequencing using Oxford Nanopore with an SV calling and filtering pipeline. We applied the method to tumor samples of high-grade ovarian and prostate cancer patients and validated on average ten somatic SVs per patient with breakpoint-spanning PCR mini-amplicons. These SVs could be quantified in ctDNA samples of patients with metastatic prostate cancer using a digital PCR assay. The results suggest that SV dynamics correlate with and may improve existing treatment-response biomarkers such as PSA.https://github.com/UMCUGenetics/SHARC.

Identification of pathogenic variants in cancer genes using base editing screens with editing efficiency correction

BackgroundMillions of nucleotide variants are identified through cancer genome sequencing and it is clinically important to identify the pathogenic variants among them. By introducing base substitutions at guide RNA target regions in the genome, CRISPR-Cas9-based base editors provide the possibility for evaluating a large number of variants in their genomic context. However, the variability in editing efficiency and the complexity of outcome mapping are two existing problems for assigning guide RNA effects to variants in base editing screens.ResultsTo improve the identification of pathogenic variants, we develop a framework to combine base editing screens with sgRNA efficiency and outcome mapping. We apply the method to evaluate more than 9000 variants across all the exons ofBRCA1andBRCA2genes. Our efficiency-corrected scoring model identifies 910 loss-of-function variants forBRCA1/2, including 151 variants in the noncoding part of the genes such as the 5′ untranslated regions. Many of them are identified in cancer patients and are reported as “benign/likely benign” or “variants of uncertain significance” by clinicians. Our data suggest a need to re-evaluate their clinical significance, which may be helpful for risk assessment and treatment of breast and ovarian cancer.ConclusionsOur results suggest that base editing screens with efficiency correction is a powerful strategy to identify pathogenic variants in a high-throughput manner. Applying this strategy to assess variants in both coding and noncoding regions of the genome could have a direct impact on the interpretation of cancer variants.

MAESTRO affords ‘breadth and depth’ for mutation testing

The ability to assay large numbers of low-abundance mutations is crucial in biomedicine. Yet, the technical hurdles of sequencing multiple mutations at extremely high depth and accuracy remain daunting. For sequencing low-level mutations, it’s either ‘depth or breadth’ but not both. Here, we report a simple and powerful approach to accurately track thousands of distinct mutations with minimal reads. Our technique called MAESTRO (minor allele enriched sequencing through recognition oligonucleotides) employs massively-parallel mutation enrichment to empower duplex sequencing—one of the most accurate methods—to track up to 10,000 low-frequency mutations with up to 100-fold less sequencing. In example use cases, we show that MAESTRO could enable mutation validation from cancer genome sequencing studies. We also show that it could track thousands of mutations from a patient’s tumor in cell-free DNA, which may improve detection of minimal residual disease from liquid biopsies. In all, MAESTRO improves the breadth, depth, accuracy, and efficiency of mutation testing.

Diagnosis of molecular subtypes of colorectal cancer using immunohistochemistry

Colorectal cancer ranks third in the morbidity structure among all malignant tumors and includes sporadic and hereditary neoplasms. Cancer genome sequencing has revealed numerous mutation variants that determine the ways colorectal carcinoma progresses. The course, prognosis, and management strategy of the disease vary greatly depending on the subtype of a molecular tumor. This literature review discusses the latest data on the variants of colorectal cancer oncogenesis and presents the phenotypic model classification based on them. Immunohistochemistry (IHC) is suggested for determining the individual tumor characteristics. The article also clarifies the Bethesda panel used to detect microsatellite instability, markers for Lynch syndrome, and a list of IHC markers for determining the phenotypic model of colorectal carcinoma. Keywords: colorectal cancer, phenotypic models, consensus molecular subtypes (CMS), immunohisto-chemistry, Bethesda panel, Lynch syndrome

Epigenetics and Cancer

Alterations in chromatin function and epigenetic mechanisms are a hallmark of cancer. The disruption of epigenetic processes has been linked to altered gene expression and to cancer initiation and progression. Recent cancer genome sequencing projects revealed that numerous epigenetic regulators are frequently mutated in various cancers. This information has not only started to be utilized as prognostic and predictive markers to guide treatment decisions but also provided important information for the understanding of the molecular mechanisms of epigenetic regulation in both physiological and pathological conditions. Furthermore, the reversible nature of epigenetic aberrations has led to the emergence of the promising field of epigenetic therapy that has already provided new therapeutic options for patients with malignancies characterized by epigenetic alterations, laying the basis for new and personalized medicine.

Measuring evolutionary cancer dynamics from genome sequencing, one patient at a time

Cancers progress through the accumulation of somatic mutations which accrue during tumour evolution, allowing some cells to proliferate in an uncontrolled fashion. This growth process is intimately related to latent evolutionary forces moulding the genetic and epigenetic composition of tumour subpopulations. Understanding cancer requires therefore the understanding of these selective pressures. The adoption of widespread next-generation sequencing technologies opens up for the possibility of measuring molecular profiles of cancers at multiple resolutions, across one or multiple patients. In this review we discuss how cancer genome sequencing data from a single tumour can be used to understand these evolutionary forces, overviewing mathematical models and inferential methods adopted in field of Cancer Evolution.