A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes

Completing a genomic characterisation of microscopic tumour samples with copy number

BACKGROUND

Genomic insights in settings where tumour sample sizes are limited to just hundreds or even tens of cells hold great clinical potential, but also present significant technical challenges. We previously developed the DigiPico sequencing platform to accurately identify somatic mutations from such samples.

RESULTS

Here, we complete this genomic characterisation with copy number. We present a novel protocol, PicoCNV, to call allele-specific somatic copy number alterations from picogram quantities of tumour DNA. We find that PicoCNV provides exactly accurate copy number in 84% of the genome for even the smallest samples, and demonstrate its clinical potential in maintenance therapy.

CONCLUSIONS

PicoCNV complements our existing platform, allowing for accurate and comprehensive genomic characterisations of cancers in settings where only microscopic samples are available.

Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology

With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. In particular, with the announcement of the Precision Medicine Initiative by U.S. President Barack Obama in his State of the Union address at the beginning of 2015, "precision medicine," which aims to divide patients and potential patients into subgroups with respect to disease susceptibility, has become the focus of worldwide attention. The field of oncology is also actively adopting the precision oncology approach, which is based on molecular profiling, such as genomic information, to select the appropriate treatment. However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes and suggest optimal treatments, but this method causes the problem that the number of patients who benefit from it is limited. In order to steadily develop precision oncology, it is necessary to integrate and analyze more detailed omics data, such as whole genome data and epigenome data. On the other hand, with the advancement of analysis technologies such as NGS, the amount of data obtained by omics analysis has become enormous, and artificial intelligence (AI) technologies, mainly machine learning (ML) technologies, are being actively used to make more efficient and accurate predictions. In this review, we will focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.

Promises and challenges of adoptive T-cell therapies for solid tumours

Cancer is a leading cause of death worldwide and, despite new targeted therapies and immunotherapies, many patients with advanced-stage- or high-risk cancers still die, owing to metastatic disease. Adoptive T-cell therapy, involving the autologous or allogeneic transplant of tumour-infiltrating lymphocytes or genetically modified T cells expressing novel T-cell receptors or chimeric antigen receptors, has shown promise in the treatment of cancer patients, leading to durable responses and, in some cases, cure. Technological advances in genomics, computational biology, immunology and cell manufacturing have brought the aspiration of individualised therapies for cancer patients closer to reality. This new era of cell-based individualised therapeutics challenges the traditional standards of therapeutic interventions and provides opportunities for a paradigm shift in our approach to cancer therapy. Invited speakers at a 2020 symposium discussed three areas-cancer genomics, cancer immunology and cell-therapy manufacturing-that are essential to the effective translation of T-cell therapies in the treatment of solid malignancies. Key advances have been made in understanding genetic intratumour heterogeneity, and strategies to accurately identify neoantigens, overcome T-cell exhaustion and circumvent tumour immunosuppression after cell-therapy infusion are being developed. Advances are being made in cell-manufacturing approaches that have the potential to establish cell-therapies as credible therapeutic options. T-cell therapies face many challenges but hold great promise for improving clinical outcomes for patients with solid tumours.

IGF2BP1 is a targetable SRC/MAPK-dependent driver of invasive growth in ovarian cancer

Epithelial-to-mesenchymal transition (EMT) is a hallmark of aggressive, mesenchymal-like high-grade serous ovarian carcinoma (HGSOC). The SRC kinase is a key driver of cancer-associated EMT promoting adherens junction (AJ) disassembly by phosphorylation-driven internalization and degradation of AJ proteins. Here, we show that the IGF2 mRNA-binding protein 1 (IGF2BP1) is up-regulated in mesenchymal-like HGSOC and promotes SRC activation by a previously unknown protein-ligand-induced, but RNA-independent mechanism. IGF2BP1-driven invasive growth of ovarian cancer cells essentially relies on the SRC-dependent disassembly of AJs. Concomitantly, IGF2BP1 enhances ERK2 expression in an RNA-binding dependent manner. Together this reveals a post-transcriptional mechanism of interconnected stimulation of SRC/ERK signalling in ovarian cancer cells. The IGF2BP1-SRC/ERK2 axis is targetable by the SRC-inhibitor saracatinib and MEK-inhibitor selumetinib. However, due to IGF2BP1-directed stimulation, only combinatorial treatment effectively overcomes the IGF2BP1-promoted invasive growth in 3D culture conditions as well as intraperitoneal mouse models. In conclusion, we reveal an unexpected role of IGF2BP1 in enhancing SRC/MAPK-driven invasive growth of ovarian cancer cells. This provides a rationale for the therapeutic benefit of combinatorial SRC/MEK inhibition in mesenchymal-like HGSOC.

Finding the mutations that drive resistance

Mutations that allow tumors to evolve and become resistant to treatment can be readily identified with a new sequencing approach.