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Intratumor Heterogeneity (ITH) is a functionally important property of tumor tissue and may be involved in drug resistance mechanisms. Although descriptions of ITH can be traced back to very early reports about cancer tissue, mechanistic investigations are still limited by the precision of analysis methods and access to relevant tissue sources. PDX models have provided a reproducible source of tissue with at least a partial representation of naturally occurring ITH. We investigated the properties of phenotypically distinct cell populations by Fluorescence activated cell sorting (FACS) tissue derived cells from multiple tumors from a triple negative breast cancer patient derived xenograft (PDX) model. We subsequently subjected each population to in depth gene expression analysis. Our findings suggest that process related gene expression changes (caused by tissue dissociation and FACS sorting) are restricted to Immediate Early Genes (IEGs). This allowed us to discover highly reproducible gene expression profiles of distinct cellular compartments identifiable by cell surface markers in this particular tumor model. Within the context of data from a previously published model our work suggests that gene expression profiles associated with hypoxia, stemness and drug resistance may reside in tumor subpopulations predictably growing in PDX models. This approach provides a novel opportunity for prospective mechanistic studies of ITH.

In a variety of cells, the Ca 2+ signalling process is mediated by the endoplasmic-reticulum-membrane-associated Ca 2+ release channel, inositol 1,4,5-trisphosphate (InsP 3 ) receptor (InsP 3 R) 1 . Being ubiquitous and present in organisms ranging from humans to Caenorhabditis elegans , InsP 3 R has a vital role in the control of cellular and physiological processes as diverse as cell division, cell proliferation, apoptosis, fertilization, development, behaviour, memory and learning 2 . Mouse type I InsP 3 R (InsP 3 R1), found in high abundance in cerebellar Purkinje cells, is a polypeptide with three major functionally distinct regions: the amino-terminal InsP 3 -binding region, the central modulatory region and the carboxy-terminal channel region 2 . Here we present a 2.2-Å crystal structure of the InsP 3 -binding core of mouse InsP 3 R1 in complex with InsP 3 . The asymmetric, boomerang-like structure consists of an N-terminal β-trefoil domain and a C-terminal α-helical domain containing an ‘armadillo repeat’-like fold. The cleft formed by the two domains exposes a cluster of arginine and lysine residues that coordinate the three phosphoryl groups of InsP 3 . Putative Ca 2+ -binding sites are identified in two separate locations within the InsP 3 -binding core.

Genes subject to monoallelic expression are expressed from only one of the two alleles either selected at random (random monoallelic genes) or in a parent-of-origin specific manner (imprinted genes). Because high densities of long interspersed nuclear element (LINE)-1 transposon sequence have been implicated in X-inactivation, we asked whether monoallelically expressed autosomal genes are also flanked by high densities of LINE-1 sequence. A statistical analysis of repeat content in the regions surrounding monoallelically and biallelically expressed genes revealed that random monoallelic genes were flanked by significantly higher densities of LINE-1 sequence, evolutionarily more recent and less truncated LINE-1 elements, fewer CpG islands, and fewer base-pairs of short interspersed nuclear elements (SINEs) sequence than biallelically expressed genes. Random monoallelic and imprinted genes were pooled and subjected to a clustering analysis algorithm, which found two clusters on the basis of aforementioned sequence characteristics. Interestingly, these clusters did not follow the random monoallelic vs. imprinted classifications. We infer that chromosomal sequence context plays a role in monoallelic gene expression and may involve the recognition of long repeats or other features. The sequence characteristics that distinguished the high-LINE-1 category were used to identify more than 1,000 additional genes from the human and mouse genomes as candidate genes for monoallelic expression.

Comprehensive molecular analysis of rare circulating tumor cells (CTCs) and cell clusters is often hampered by low throughput and purity, as well as cell loss. To address this, we developed a fully integrated platform for flow cytometry-based isolation of CTCs and clusters from blood that can be combined with whole transcriptome analysis or targeted RNA transcript quantification. Downstream molecular signature can be linked to cell phenotype through index sorting. This newly developed platform utilizes in-line magnetic particle-based leukocyte depletion, and acoustic cell focusing and washing to achieve >98% reduction of blood cells and non-cellular debris, along with >1.5 log-fold enrichment of spiked tumor cells. We could also detect 1 spiked-in tumor cell in 1 million WBCs in 4/7 replicates. Importantly, the use of a large 200μm nozzle and low sheath pressure (3.5 psi) minimized shear forces, thereby maintaining cell viability and integrity while allowing for simultaneous recovery of single cells and clusters from blood. As proof of principle, we isolated and transcriptionally characterized 63 single CTCs from a genetically engineered pancreatic cancer mouse model (n = 12 mice) and, using index sorting, were able to identify distinct epithelial and mesenchymal sub-populations based on linked single cell protein and gene expression.

Porous silicon nanoparficles （pSiNPs） are a promising nanocarrier system for drug delivery owing to their biocompatibility, biodegradability, and non-inflammatory nature. Here, we investigate the fabrication and characterization of thermally hydrocarbonized pSiNPs （THCpSiNPs） and chitosan-coated THCpSiNPs for therapeutic oligonucleotide delivery. Chitosan coating after oligonucleotide loading significantly improves sustained oligonucleotide release and suppresses burst release effects. Moreover, cellular uptake, endocytosis, and cytotoxicity of oligonucleotide-loaded THCpSiNPs have been evaluated in vitro. Standard cell viability assays demonstrate that cells incubated with the NPs at a concentration of 0.1 mg/mL are 95% viable. In addition, chitosan coating significantly enhances the uptake of oligonucleotide-loaded THCpSiNPs across the cell membrane. Moreover, histopathological analysis of liver, kidney, spleen, and skin tissue collected from mice receiving NPs further demonstrates the biocompatible and non-inflammatory properties of the NPs as a gene delivery vehicle for intravenous and subcutaneous administration in vivo. Taken together, these results suggest that THCpSiNPs provide a versatile platform that could be used as efficient vehicles for the intracellular delivery of oligonucleotides for gene therapy.

Intratumor Heterogeneity (ITH) is a functionally important property of tumor tissue and may be involved in drug resistance mechanisms. Although descriptions of ITH can be traced back to very early reports about cancer tissue, mechanistic investigations are still limited by the precision of analysis methods and access to relevant tissue sources. PDX models have provided a reproducible source of tissue with at least a partial representation of naturally occurring ITH. We investigated the properties of phenotypically distinct cell populations by Fluorescence activated cell sorting (FACS) tissue derived cells from multiple tumors from a triple negative breast cancer patient derived xenograft (PDX) model. We subsequently subjected each population to in depth gene expression analysis. Our findings suggest that process related gene expression changes (caused by tissue dissociation and FACS sorting) are restricted to Immediate Early Genes (IEGs). This allowed us to discover highly reproducible gene expression profiles of distinct cellular compartments identifiable by cell surface markers in this particular tumor model. Within the context of data from a previously published model our work suggests that gene expression profiles associated with hypoxia, stemness and drug resistance may reside in tumor subpopulations predictably growing in PDX models. This approach provides a novel opportunity for prospective mechanistic studies of ITH.

Breast cancers are comprised of molecularly distinct subtypes that may respond differently to pathway-targeted therapies now under development. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways, and drug response. In a test of 77 therapeutic compounds, nearly all drugs showed differential responses across these cell lines, and approximately one third showed subtype-, pathway-, and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance and may inform efforts to develop molecular assays that predict clinical response.

Dose-response assays are a common and increasingly high throughput method of assessing the toxicity of potential drug targets on test populations of cells. Such assays typically involve serial dilutions of the compounds in question applied to cell samples to determine the level of cell activity across a broad range of concentrations. Another factor in such experiments may be the change in activity under different enzyme combinations and the use of controls to adjust for interassay variations. Typically, the decreasing number in the population of cells due to increasing concentrations of the drug can be modeled with a logistic curve. Since the appropriate range of concentrations of the drug to test in order to see these reactions cannot be predetermined fully, frequently the data available for a given experimental unit may not be enough to fit such curves on their own successfully. Instead, the assay data as a whole can be successfully analyzed with methods such as constrained fitting and mixed effects models, where each set can borrow strength from each other in order to be fitted while still taking into account individual significances of the specific experiment. This dissertation illustrates variations of such methods on three major datasets from the Joe Gray lab at Lawrence Berkeley National Laboratory, the Douglas Clark lab at the Department of Chemical Engineering at UC Berkeley, and Bionovo, Inc. of Emeryville, California. The first dataset from breast cancer cell line testing involves the estimation of the National Cancer Institute concentration parameter called GI50, the concentration of the drug at which it inhibits the growth of the population of cells by half. We develop a method utilizing replicate data to estimate this parameter. The second dataset involves the estimation of a more commonly used concentration parameter called the IC50, which doesn't take into account the initial cell population, on special assays that mimic the liver metabolism in the body. The methods involve mixed effects models that incorporate the specific enzyme conditions and types of cells important to the experiment. The third dataset, involving different effects of plant compounds on an osteosarcoma cell line, illustrates the usage of negative and positive controls to appropriately adjust the observations for interassay variation.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths worldwide. Only a fraction of NSCLC harbor actionable driver mutations and there is an urgent need for patient-derived model systems that will enable the development of new targeted therapies. NSCLC and other cancers display profound proteome remodeling compared to normal tissue that is not predicted by DNA or RNA analyses. Here, we generate 137 NSCLC patient-derived xenografts (PDXs) that recapitulate the histology and molecular features of primary NSCLC. Proteome analysis of the PDX models reveals 3 adenocarcinoma and 2 squamous cell carcinoma proteotypes that are associated with different patient outcomes, protein-phosphotyrosine profiles, signatures of activated pathways and candidate targets, and in adenocarcinoma, stromal immune features. These findings portend proteome-based NSCLC classification and treatment and support the PDX resource as a viable model for the development of new targeted therapies. With non-small cell lung cancer (NSCLC) being the leading cause of cancer deaths worldwide, the development of targeted therapies remains crucial. Here, the generation and multi-omics characterization of 137 NSCLC patient-derived xenografts provides a resource for potential classifications and targets.