Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous backbone upon which to study genetic variants, candidate targets, 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 various lineages and ethnicities. Integrating these data with functional characterizations such as drug-sensitivity data, 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 to accelerate cancer research using model cancer cell lines.

Human monocytes have been grouped into classical (CD14++CD16−), non-classical (CD14dimCD16++), and intermediate (CD14++CD16+) subsets. Documentation of normal function and variation in this complement of subtypes, particularly their differentiation potential to dendritic cells (DC) or macrophages, remains incomplete. We therefore phenotyped monocytes from peripheral blood of healthy subjects and performed functional studies on high-speed sorted subsets. Subset frequencies were found to be tightly controlled over time and across individuals. Subsets were distinct in their secretion of TNFα, IL-6, and IL-1β in response to TLR agonists, with classical monocytes being the most producers and non-classical monocytes the least. Monocytes, particularly those of the non-classical subtype, secreted interferon-α (IFN-α) in response to intracellular TLR3 stimulation. After incubation with IL-4 and GM-CSF, classical monocytes acquired monocyte-derived DC (mo-DC) markers and morphology and stimulated allogeneic T cell proliferation in MLR; intermediate and non-classical monocytes did not. After incubation with IL-3 and Flt3 ligand, no subset differentiated to plasmacytoid DC. After incubation with GM-CSF (M1 induction) or macrophage colony-stimulating factor (M-CSF) (M2 induction), all subsets acquired macrophage morphology, secreted macrophage-associated cytokines, and displayed enhanced phagocytosis. From these studies we conclude that classical monocytes are the principal source of mo-DCs, but all subsets can differentiate to macrophages. We also found that monocytes, in particular the non-classical subset, represent an alternate source of type I IFN secretion in response to virus-associated TLR agonists.

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Introduction The cellular events that contribute to generation of donor-specific anti-HLA antibodies (DSA) post-kidney transplantation (KTx) are not well understood. Characterization of such mechanisms could allow tailoring of immunosuppression to benefit sensitized patients. Methods We prospectively monitored circulating T follicular helper (cT FH ) cells in KTx recipients who received T-cell depleting (thymoglobulin, n = 54) or T-cell nondepleting (basiliximab, n = 20) induction therapy from pre-KTx to 1 year post-KTx and assessed their phenotypic changes due to induction and DSA occurrence, in addition to healthy controls ( n = 13), for a total of 307 blood samples. Results Before KTx, patients displayed comparable levels of resting, central memory cT FH cells with similar polarization to those of healthy controls. Unlike basiliximab induction, thymoglobulin induction significantly depleted cT FH cells, triggered lymphopenia-induced proliferation that skewed cT FH cells toward increased Th1 polarization, effector memory, and elevated programmed cell death protein 1 (PD-1) int/hi expression, resembling activated phenotypes. Regardless of induction, patients who developed DSA post-KTx, harbored pre-KTx donor-reactive memory interleukin (IL)-21 + cT FH cells and showed higher % cT FH and lower % of T regulatory (T REG ) cells post-KTx resulting in elevated cT FH :T REG ratio at DSA occurrence. Conclusion Induction therapy distinctly shapes cT FH cell phenotype post-KTx. Monitoring cT FH cells before and after KTx may help detect those patients prone to DSA generation post-KTx.

While the genomes of normal tissues undergo dynamic changes over time, little is understood about the temporal-spatial dynamics of genomes in premalignant tissues that progress to cancer compared to those that remain cancer-free. Here we use whole genome sequencing to contrast genomic alterations in 427 longitudinal samples from 40 patients with stable Barrett’s esophagus compared to 40 Barrett’s patients who progressed to esophageal adenocarcinoma (ESAD). We show the same somatic mutational processes are active in Barrett’s tissue regardless of outcome, with high levels of mutation, ESAD gene and focal chromosomal alterations, and similar mutational signatures. The critical distinction between stable Barrett’s versus those who progress to cancer is acquisition and expansion of TP53−/− cell populations having complex structural variants and high-level amplifications, which are detectable up to six years prior to a cancer diagnosis. These findings reveal the timing of common somatic genome dynamics in stable Barrett’s esophagus and define key genomic features specific to progression to esophageal adenocarcinoma, both of which are critical for cancer prevention and early detection strategies.

Cancer genomes often harbor hundreds of somatic DNA rearrangement junctions, many of which cannot be easily classified into simple (e.g. deletion, translocation) or complex (e.g. chromothripsis, chromoplexy) structural variant classes. Applying a novel genome graph computational paradigm to analyze the topology of junction copy number (JCN) across 2,833 tumor whole genome sequences (WGS), we introduce three complex rearrangement phenomena: pyrgo, rigma, and tyfonas. Pyrgo are "towers" of low-JCN duplications associated with early replicating regions and superenhancers, and are enriched in breast and ovarian cancers. Rigma comprise "chasms" of low-JCN deletions at late-replicating fragile sites in esophageal and other gastrointestinal (GI) adenocarcinomas. Tyfonas are "typhoons" of high-JCN junctions and fold back inversions that are enriched in acral but not cutaneous melanoma and associated with a previously uncharacterized mutational process of non-APOBEC kataegis. Clustering of tumors according to genome graph-derived features identifies subgroups associated with DNA repair defects and poor prognosis. Cancer genomics | Structural variation | DNA rearrangements | Mutational Processes | Genome graphs | Copy number alterationsCorrespondence: mski@mskilab.org K. Hadi, X.Yao, J. Behr et al. | bioRχiv | November 12, 2019 | 1-8

Following infection with Human immunodeficiency virus 1 (HIV-1) there is a remarkable variation in virus replication and disease progression. Both host and viral factors have been implicated in the observed differences in disease status. Here, we focus on understanding the contribution of HIV-1 viral protein R (Vpr) by evaluating the disease-associated Vpr polymorphism and its biological functions from HIV-1 positive rapid progressor (RP) and long-term nonprogressor (LTNP) subjects. Results presented here show distinct variation in phenotypes of Vpr alleles from LTNP and RP subjects. Most notably, the polymorphism of Vpr at R36W and L68M associated with RP shows higher levels of oligomerization, and increased virus replication, whereas R77Q exhibits poor replication kinetics. Interestingly, we did not observe correlation with cell cycle arrest function. Together these results indicate that polymorphisms in Vpr in part may contribute to altered virus replication kinetics leading to the observed differences in disease progression in LTNP and RP groups.

DK provided samples and clinical information. TW assisted with animal studies. OF performed PacBio sequencing. SS, MB, and KB performed single cell and Visium sequencing. TS provided pathology expertise. RV provided biostatistical expertise. AS and PC supervised the study. AS and SC obtained funding. AW and AS wrote the manuscript with input from other authors.Competing interests: Rocket Pharmaceuticals provided research funding and partial salary support to A.S. for an unrelated project. P.J.C. is a founder, director and consultant for Mu Genomics Ltd. B.S. is a co-inventor of intellectual property related to DCN1 small molecule inhibitors licensed by MSK to Cinsanso. He has rights to receive royalty income as a result of this arrangement. MSK has financial interests related to this intellectual property and Cinsanso as a result of this arrangement. Other authors declare no competing interests.