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Liquid biopsy offers a versatile, noninvasive opportunity to diagnose, characterize, and monitor disease in patients with cancer. There are particularly promising applications with which to use liquid biopsies to predict and evaluate response to immunotherapy. Circulating tumor DNA (ctDNA) can reflect the genomic state of a patient's overall disease and, thus, might identify prognostic and predictive biomarkers for immune checkpoint inhibitor therapy. ctDNA might also be a proxy for a patient's overall disease burden, which could be used for early diagnosis and monitoring treatment response. These applications can enable novel trial designs, such as enrollment of early-stage patients with a high risk for relapse, and the evaluation of response patterns unique to immunotherapies. However, barriers to the widespread adoption of ctDNA assessment remain, including the absence of standardized procedures for collecting and processing ctDNA samples and relatively limited data on clinical utility. Identifying and solving these challenges could allow ctDNA to become a powerful clinical and research tool in the era of personalized immunotherapy., (©2019 American Association for Cancer Research.)

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

The original version of this Article contained an error in the author affiliations. The affiliation of Kevin P. White with Tempus Labs, Inc. Chicago, IL, USA was inadvertently omitted. This has now been corrected in both the PDF and HTML versions of the Article.

Racial/ethnic disparities in breast cancer mortality continue to widen but genomic studies rarely interrogate breast cancer in diverse populations. Through genome, exome, and RNA sequencing, we examined the molecular features of breast cancers using 194 patients from Nigeria and 1037 patients from The Cancer Genome Atlas (TCGA). Relative to Black and White cohorts in TCGA, Nigerian HR + /HER2 - tumors are characterized by increased homologous recombination deficiency signature, pervasive TP53 mutations, and greater structural variation-indicating aggressive biology. GATA3 mutations are also more frequent in Nigerians regardless of subtype. Higher proportions of APOBEC-mediated substitutions strongly associate with PIK3CA and CDH1 mutations, which are underrepresented in Nigerians and Blacks. PLK2, KDM6A, and B2M are also identified as previously unreported significantly mutated genes in breast cancer. This dataset provides novel insights into potential molecular mechanisms underlying outcome disparities and lay a foundation for deployment of precision therapeutics in underserved populations.

Background: Matched sequencing of both tumor and normal tissue is routinely used to classify variants of uncertain significance (VUS) into somatic vs. germline. However, assays used in molecular diagnostics focus on known somatic alterations in cancer genes and often only sequence tumors. Therefore, an algorithm that reliably classifies variants would be helpful for retrospective exploratory analyses. Contamination of tumor samples with normal cells results in differences in expected allelic fractions of germline and somatic variants, which can be exploited to accurately infer genotypes after adjusting for local copy number. However, existing algorithms for determining tumor purity, ploidy and copy number are not designed for unmatched short read sequencing data., Results: We describe a methodology and corresponding open source software for estimating tumor purity, copy number, loss of heterozygosity (LOH), and contamination, and for classification of single nucleotide variants (SNVs) by somatic status and clonality. This R package, PureCN, is optimized for targeted short read sequencing data, integrates well with standard somatic variant detection pipelines, and has support for matched and unmatched tumor samples. Accuracy is demonstrated on simulated data and on real whole exome sequencing data., Conclusions: Our algorithm provides accurate estimates of tumor purity and ploidy, even if matched normal samples are not available. This in turn allows accurate classification of SNVs. The software is provided as open source (Artistic License 2.0) R/Bioconductor package PureCN (http://bioconductor.org/packages/PureCN/).

Mantle cell lymphoma (MCL) is an aggressive malignancy that is characterized by poor prognosis. Large-scale pharmacological profiling across more than 100 hematological cell line models identified a subset of MCL cell lines that are highly sensitive to the B cell receptor (BCR) signaling inhibitors ibrutinib and sotrastaurin. Sensitive MCL models exhibited chronic activation of the BCR-driven classical nuclear factor-κB (NF-κB) pathway, whereas insensitive cell lines displayed activation of the alternative NF-κB pathway. Transcriptome sequencing revealed genetic lesions in alternative NF-κB pathway signaling components in ibrutinib-insensitive cell lines, and sequencing of 165 samples from patients with MCL identified recurrent mutations in TRAF2 or BIRC3 in 15% of these individuals. Although they are associated with insensitivity to ibrutinib, lesions in the alternative NF-κB pathway conferred dependence on the protein kinase NIK (also called mitogen-activated protein 3 kinase 14 or MAP3K14) both in vitro and in vivo. Thus, NIK is a new therapeutic target for MCL treatment, particularly for lymphomas that are refractory to BCR pathway inhibitors. Our findings reveal a pattern of mutually exclusive activation of the BCR-NF-κB or NIK-NF-κB pathways in MCL and provide critical insights into patient stratification strategies for NF-κB pathway-targeted agents.

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

Oocytes are powerful local modulators of follicular cell functions and many of the activities of oocytes are attributed to members of the transforming growth factor-beta (TGF-beta) superfamily. Whilst in the mouse it is known that members of this family are able to mimic many of the effects of oocytes on follicular cells, the relative importance of any of these factors is unknown in bovine follicles. The objectives of this study were to determine if bovine oocytes express and secrete TGF-beta and to compare oocyte-secreted factor(s) to TGF-beta in terms of their capacities to stimulate mural granulosa cell (MGC) DNA synthesis. Bovine ovaries were collected from an abattoir and RNA was extracted from isolated MGC, cumulus cells, cumulus-oocyte complexes and denuded oocytes (DO). Using RT-PCR, all cell types were found to express TGF-beta1 and TGF-beta2 mRNA transcripts. However, no TGF-beta bioactivity could be detected from DO using a sensitive (40 pg/ml) and specific mink lung fibroblast cell bioassay. MGC were cultured with various combinations and doses of TGF-beta2 and DO for 18 h, followed by a 6-h pulse of [3H]-thymidine as an indicator of cellular DNA synthesis. MGC DNA synthesis was stimulated by both TGF-beta2 and DO. However in response to increasing doses of TGF-beta2, [3H]-thymidine levels plateaued at <2-fold above control levels, whereas levels continued to increase over the dose range of DO tested (up to 3.4-fold). Addition of a TGF-beta pan-specific neutralising antibody to MGC cultures eliminated the TGF-beta2-stimulatory effects on DNA synthesis and the TGF-beta2-suppressive effects on progesterone production, but the antibody was unable to neutralise the same responses when induced by DO. These results support a role for TGF-beta1, TGF-beta2 and DO in paracrine/autocrine regulation of bovine granulosa cell function, but indicate that neither TGF-beta1 nor TGF-beta2 can account for the actions of bovine oocytes on granulosa cells.

Prions are mammalian proteins (PrPs) with a unique pathogenic property: a nonendogenous isoform PrP(Sc) can catalyze conversion of the endogenous PrP(C) isoform into additional PrP(Sc). In this work, we demonstrate that PrP(C) helix 1 has certain properties (hydrophilicity, charge distribution) that make it unique among all naturally occurring alpha-helices, and which are indicative of a highly specific model of prion infectivity. The beta-nucleation model proposes that PrP(Sc) is an aggregate with a hydrophilic core, consisting of a beta-sheet-like arrangement of constituent helix 1 components. It is suggested by using structural arguments, and confirmed by using CHARMM energy calculations, that aggregate formation from two PrP(C) molecules is highly unfavorable, but the addition of chains to an existing aggregate is favorable. The beta-nucleation model is shown to be consistent with the prion species-barrier, as well as with infectivity data. Sequence analysis of all known protein structures indicates that PrP is uniquely suited to beta-nucleation, in contrast to the many proteins that readily form less favorable (often nonspecific) hydrophobic aggregates.

Background: Methods of model protein design have until now been largely ad hoc, yielding sequences that are foldable only at some seemingly arbitrary simulation temperature. But real proteins exist and must fold within an imposed thermal environment. The need exists for a sequence design method based on statistical-mechanical first principles, thus containing a rigorous treatment of folding temperature., Results: In this work, we report a method of rational sequence design that takes a target structure and a desired optimal folding temperature TZ and generates a sequence that is predicted to be thermodynamically stable with respect to the target structure at a folding temperature TF approximately TZ. This 'cumulant design method' is based on a mean-field high temperature expansion of the molecular partition function. Folding simulations of the designed sequences confirm that sequences designed at TZ do indeed fold optimally when TF approximately TZ., Conclusions: The cumulant method is highly successful in designing model proteins. It also provides some insight into the thermal properties of real proteins, illuminating the features that distinguish thermostable and psychotropic (cold-loving) sequences from their mesophilic counterparts.

Liver transplantation remains the treatment of choice for many forms of end-stage liver disease. In most large series, 5-year actuarial survival is greater than 70%. The majority of the morbidity and mortality occurs in the first 6 months posttransplant; as these figures have improved, so have overall survival rates. Infants under 1 year of age have a survival rate below that of older patients; in addition, a severe organ shortage for these patients continues. The use of reduced grafts has ameliorated the problem to a certain extent; however, further expansion of the donor pool is still necessary. Progress has also been made in the postoperative management of transplant patients. We currently follow AKBR and TNF levels in all patients to aid in the diagnosis of primary nonfunction and acute rejection, respectively. The introduction of additional immunosuppressive agents has instigated several large clinical trials. CsA, however, remains the gold standard to which these drugs must be compared.