We assessed gene expression profiles in 2,752 twins, using a classic twin design to quantify expression heritability and quantitative trait loci (eQTL) in peripheral blood. The most highly heritable genes (~777) were grouped into distinct expression clusters, enriched in gene-poor regions, associated with specific gene function/ontology classes, and strongly associated with disease designation. The design enabled a comparison of twin-based heritability to estimates based on dizygotic IBD sharing and distant genetic relatedness. Consideration of sampling variation suggests that previous heritability estimates have been upwardly biased. Genotyping of 2,494 twins enabled powerful identification of eQTLs, which were further examined in a replication set of 1,895 unrelated subjects. A large number of local eQTLs (6,988) met replication criteria, while a relatively small number of distant eQTLs (165) met quality control and replication standards. Our results provide an important new resource toward understanding the genetic control of transcription.

Epidermal growth factor receptor (EGFR) mutations typically occur in exons 18–21 and are established driver mutations in non-small cell lung cancer (NSCLC)1–3. Targeted therapies are approved for patients with ‘classical’ mutations and a small number of other mutations4–6. However, effective therapies have not been identified for additional EGFR mutations. Furthermore, the frequency and effects of atypical EGFR mutations on drug sensitivity are unknown1,3,7–10. Here we characterize the mutational landscape in 16,715 patients with EGFR-mutant NSCLC, and establish the structure–function relationship of EGFR mutations on drug sensitivity. We found that EGFR mutations can be separated into four distinct subgroups on the basis of sensitivity and structural changes that retrospectively predict patient outcomes following treatment with EGFR inhibitors better than traditional exon-based groups. Together, these data delineate a structure-based approach for defining functional groups of EGFR mutations that can effectively guide treatment and clinical trial choices for patients with EGFR-mutant NSCLC and suggest that a structure–function-based approach may improve the prediction of drug sensitivity to targeted therapies in oncogenes with diverse mutations.

Human tumor xenograft models do not replicate the human immune system and tumor microenvironment. We developed an improved humanized mouse model, derived from fresh cord blood CD34 þ stem cells (CD34 þ HSC), and combined it with lung cancer cell line-derived human xenografts or patient-derived xenografts (Hu-PDX). Fresh CD34 þ HSCs could reconstitute detectable mature human leukocytes (hCD45 þ ) in mice at four weeks without the onset of graftversus-host disease (GVHD). Repopulated human T cells, B cells, natural killer (NK) cells, dendritic cells (DC), and myeloid-derived suppressor cells (MDSC) increased in peripheral blood, spleen, and bone marrow over time. Although cultured CD34 þ HSCs labeled with luciferase could be detected in mice, the cultured HSCs did not develop into mature human immune cells by four weeks, unlike fresh CD34 þ HSCs. Ex vivo, reconstituted T cells, obtained from the tumor-bearing humanized mice, secreted IFNg upon treatment with phorbol myristate acetate (PMA) or exposure to human A549 lung tumor cells and mediated antigen-specific CTL responses, indicating functional activity. Growth of engrafted PDXs and tumor xenografts was not dependent on the human leukocyte antigen status of the donor. Treatment with the anti-PD-1 checkpoint inhibitors pembrolizumab or nivolumab inhibited tumor growth in humanized mice significantly, and correlated with an increased number of CTLs and decreased MDSCs, regardless of the donor HLA type. In conclusion, fresh CD34 þ HSCs are more effective than their expanded counterparts in humanizing mice, and do so in a shorter time. The Hu-PDX model provides an improved platform for evaluation of immunotherapy.

Supplementary data are available at Bioinformatics online.

Purpose: Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors are currently used in combination with endocrine therapy to treat advanced hormone receptor-positive, HER2-negative breast cancer. Although this treatment doubles time to progression compared with endocrine therapy alone, about 25%-35% of patients do not respond, and almost all patients eventually acquire resistance. Discerning the mechanisms of resistance to CDK4/6 inhibition is crucial in devising alternative treatment strategies.Experimental Design: Palbociclib-resistant cells (MCF-7 and T47D) were generated in a step-wise dose-escalading fashion. Whole-exome sequencing, genome-wide expression analysis, and proteomic analysis were performed in both resistant and parental (sensitive) cells. Pathway alteration was assessed mechanistically and pharmacologically. Biomarkers of altered pathways were examined in tumor samples from patients with palbociclib-treated breast cancer whose disease progressed while on treatment.Results: Palbociclib-resistant cells are cross-resistant to other CDK4/6 inhibitors and are also resistant to endocrine therapy (estrogen receptor downregulation). IL6/STAT3 pathway is induced, whereas DNA repair and estrogen receptor pathways are downregulated in the resistant cells. Combined inhibition of STAT3 and PARP significantly increased cell death in the resistant cells. Matched tumor samples from patients with breast cancer who progressed on palbociclib were examined for deregulation of estrogen receptor, DNA repair, and IL6/STAT3 signaling, and results revealed that these pathways are all altered as compared with the pretreatment tumor samples.Conclusions: Palbociclib resistance induces endocrine resistance, estrogen receptor downregulation, and alteration of IL6/STAT3 and DNA damage response pathways in cell lines and patient samples. Targeting IL6/STAT3 activity and DNA repair deficiency using a specific STAT3 inhibitor combined with a PARP inhibitor could effectively treat acquired resistance to palbociclib.

Background Low-molecular-weight-cyclin E (LMW-E) detected by Western blot, predicts for reduced breast cancer survival, however, it is impractical for clinical use. LMW-E lacks a nuclear localization signal which leads to accumulation in the cytoplasm that can be detected by immunohistochemistry. We tested the hypothesis that cytoplasmic staining of cyclin E can be used as a predictor of poor outcome in different subtypes of breast cancer using patient cohorts with distinct clinical and pathologic features. Methods We evaluated the subcellular localization of cyclin E in breast cancer specimens from 2,494 patients from 4 different cohorts: 303 from a prospective study and 2,191 from retrospective cohorts (National Cancer Institute [NCI], MD Anderson Cancer Center [MDA] and the United Kingdom [UK]). Median follow-up times were 8.0, 10.1, 13.5, and 5.7 years, respectively. Results Subcellular localization of cyclin E on immunohistochemistry was associated with full-length (nuclear) and low molecular weight isoforms (cytoplasmic) of cyclin E on Western blot analysis. In multivariable analysis, cytoplasmic cyclin E staining was associated with the greatest risk of recurrence compared with other prognostic factors across all subtypes in three (NCI, MDA and UK) of the cohorts. In the MDA cohort, cytoplasmic cyclin E staining outperformed Ki67 and all other variables as prognostic factors. Conclusion Cytoplasmic cyclin E, identifies patients with the highest likelihood of recurrence consistently across different patient cohorts and subtypes. These patients may benefit from alternative therapies targeting the oncogenic isoforms of cyclin E.

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Summary Motivated by the problem of construction gene co-expression network, we propose a statistical framework for estimating high-dimensional partial correlation matrix by a three-step approach. We first obtain a penalized estimate of a partial correlation matrix using ridge penalty. Next we select the non-zero entries of the partial correlation matrix by hypothesis testing. Finally we reestimate the partial correlation coefficients at these non-zero entries. In the second step, the null distribution of the test statistics derived from penalized partial correlation estimates has not been established. We address this challenge by estimating the null distribution from the empirical distribution of the test statistics of all the penalized partial correlation estimates. Extensive simulation studies demonstrate the good performance of our method. Application on a yeast cell cycle gene expression data shows that our method delivers better predictions of the protein-protein interactions than the Graphic Lasso.