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⁺ stem cells (CD34⁺ HSC), and combined it with lung cancer cell line–derived human xenografts or patient-derived xenografts (Hu-PDX). Fresh CD34⁺ HSCs could reconstitute detectable mature human leukocytes (hCD45⁺) in mice at four weeks without the onset of graft-versus-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⁺ 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⁺ HSCs. Ex vivo, reconstituted T cells, obtained from the tumor-bearing humanized mice, secreted IFNγ 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⁺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.

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.

Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multilayered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multilevel genomic networks in human cancers. We implement a Bayesian node-wise selection (BANS) approach based on variable selection techniques that coherently accounts for the multiple types of dependencies in mlGGM; this flexible strategy exploits edge-specific prior knowledge and selects sparse and interpretable models. Through simulated data generated under various scenarios, we demonstrate that BANS outperforms other existing multivariate regression-based methodologies. Our integrative genomic network analysis for key signaling pathways across multiple cancer types highlights commonalities and differences of p53 integrative networks and epigenetic effects of BRCA2 on p53 and its interaction with T68 phosphorylated CHK2, that may have translational utilities of finding biomarkers and therapeutic targets. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Background: The estimation of microbial networks can provide important insight into the ecological relationships among the organisms that comprise the microbiome. However, there are a number of critical statistical challenges in the inference of such networks from high-throughput data. Since the abundances in each sample are constrained to have a fixed sum and there is incomplete overlap in microbial populations across subjects, the data are both compositional and zero-inflated.Results: We propose the COmpositional Zero-Inflated Network Estimation (COZINE) method for inference of microbial networks which addresses these critical aspects of the data while maintaining computational scalability. COZINE relies on the multivariate Hurdle model to infer a sparse set of conditional dependencies which reflect not only relationships among the continuous values, but also among binary indicators of presence or absence and between the binary and continuous representations of the data. Our simulation results show that the proposed method is better able to capture various types of microbial relationships than existing approaches. We demonstrate the utility of the method with an application to understanding the oral microbiome network in a cohort of leukemic patients.Conclusions: Our proposed method addresses important challenges in microbiome network estimation, and can be effectively applied to discover various types of dependence relationships in microbial communities. The procedure we have developed, which we refer to as COZINE, is available online athttps://github.com/MinJinHa/COZINE.