This proof-of-concept analysis showed that circulating tumor DNA is an informative, inherently specific, and highly sensitive biomarker of metastatic breast cancer. (Funded by Cancer Research UK and others.).

csaunders@illumina.com

SummaryThe Tasmanian devil (Sarcophilus harrisii), the largest marsupial carnivore, is endangered due to a transmissible facial cancer spread by direct transfer of living cancer cells through biting. Here we describe the sequencing, assembly, and annotation of the Tasmanian devil genome and whole-genome sequences for two geographically distant subclones of the cancer. Genomic analysis suggests that the cancer first arose from a female Tasmanian devil and that the clone has subsequently genetically diverged during its spread across Tasmania. The devil cancer genome contains more than 17,000 somatic base substitution mutations and bears the imprint of a distinct mutational process. Genotyping of somatic mutations in 104 geographically and temporally distributed Tasmanian devil tumors reveals the pattern of evolution and spread of this parasitic clonal lineage, with evidence of a selective sweep in one geographical area and persistence of parallel lineages in other populations.PaperClip

Chronic lymphocytic leukemia is characterized by relapse after treatment and chemotherapy resistance. Similarly, in other malignancies leukemia cells accumulate mutations during growth, forming heterogeneous cell populations that are subject to Darwinian selection and may respond differentially to treatment. There is therefore a clinical need to monitor changes in the subclonal composition of cancers during disease progression. Here, we use whole-genome sequencing to track subclonal heterogeneity in 3 chronic lymphocytic leukemia patients subjected to repeated cycles of therapy. We reveal different somatic mutation profiles in each patient and use these to establish probable hierarchical patterns of subclonal evolution, to identify subclones that decline or expand over time, and to detect founder mutations. We show that clonal evolution patterns are heterogeneous in individual patients. We conclude that genome sequencing is a powerful and sensitive approach to monitor disease progression repeatedly at the molecular level. IntroductionDespite significant progress in the management of lymphomas and leukemias, relapse remains the major cause of death. Increased use of expensive targeted therapies and toxic chemotherapies (especially in the elderly) confronts us with an urgent need to improve response prediction for all cancer patients to reduce side effects and costs from ineffective treatment. Current diagnostic approaches to treatment selection, response monitoring, and relapse prediction are limited to single genes and apply only to a minority of hematologic cancers. This is at odds with modern concepts of tumor propagation and maintenance, which propose that every cell in an individual cancer is characterized by a combination of mutation events that comprise tumorigenic (driver) mutations, passive (passenger) mutations, and possibly predisposing germline risk variants. Cancer cells propagate and diversify during tumor growth, resulting in a heterogeneous population of genotypically and phenotypically distinct subclones that are related in a hierarchical lineage. As the composition of the local environment changes, for example as a consequence of drug treatment, tumor cell populations adapt and evolve by Darwinian selection. [1][2][3] Whole-genome sequencing (WGS) of a single tumor sample can be used to generate a comprehensive catalog of variants that provides a snapshot of the cell population en masse at a particular time point. 2,4-6 However, over time and with continued evolution of the cancer, this snapshot becomes progressively less representative of the disease. Recent reports have described whole-tumor genomes from single patients or cohorts of individuals mostly at single time points and irrespective of treatment. [7][8][9][10] This approach has enabled identification of mutations representative and in some cases highly predictive of histologic cancer type, outcome, and/or treatment response. [11][12][13][14][15] Comparison of sequence data from primary and metastatic tumor samples, or from multiple lo...

To assess factors influencing the success of whole genome sequencing for mainstream clinical diagnosis, we sequenced 217 individuals from 156 independent cases across a broad spectrum of disorders in whom prior screening had identified no pathogenic variants. We quantified the number of candidate variants identified using different strategies for variant calling, filtering, annotation and prioritisation. We found that jointly calling variants across samples, filtering against both local and external databases, deploying multiple annotation tools and using familial transmission above biological plausibility contributed to accuracy. Overall, we identified disease causing variants in 21% of cases, rising to 34% (23/68) for Mendelian disorders and 57% (8/14) in trios. We also discovered 32 potentially clinically actionable variants in 18 genes unrelated to the referral disorder, though only four were ultimately considered reportable. Our results demonstrate the value of genome sequencing for routine clinical diagnosis, but also highlight many outstanding challenges.Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use

The molecular genetic relationship between esophageal adenocarcinoma (EAC) and its precursor lesion, Barrett’s esophagus, is poorly understood. Using whole-genome sequencing on 23 paired Barrett’s esophagus and EAC samples, together with one in-depth Barrett’s esophagus case-study sampled over time and space, we have provided new insights on the following aspects: i) Barrett’s esophagus is polyclonal and highly mutated even in the absence of dysplasia; ii) when cancer develops, copy number increases and heterogeneity persists such that the spectrum of mutations often shows surprisingly little overlap between EAC and adjacent Barrett’s esophagus; and iii) despite differences in specific coding mutations the mutational context suggests a common causative insult underlying these two conditions. From a clinical perspective, the histopathological assessment of dysplasia appears to be a poor reflection of the molecular disarray within the Barrett’s epithelium and a molecular Cytosponge™ technique overcomes sampling bias and has capacity to reflect the entire clonal architecture.

BackgroundBiological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions.MethodsTo overcome these limitations, we propose here a new statistical modelling framework, the ARTIVA formalism (Auto Regressive TIme VArying models), and an associated inferential procedure that allows us to learn temporally varying gene-regulation networks from biological time-course expression data. ARTIVA simultaneously infers the topology of a regulatory network and how it changes over time. It allows us to recover the chronology of regulatory associations for individual genes involved in a specific biological process (development, stress response, etc.).ResultsWe demonstrate that the ARTIVA approach generates detailed insights into the function and dynamics of complex biological systems and exploits efficiently time-course data in systems biology. In particular, two biological scenarios are analyzed: the developmental stages of Drosophila melanogaster and the response of Saccharomyces cerevisiae to benomyl poisoning.ConclusionsARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail.

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