Phagocytosed Polyhedrin-Cytokine Cocrystal Nanoparticles Provide Sustained Secretion of Bioactive Cytokines from Macrophages.

Many cells possess the ability to engulf and incorporate particles by phagocytosis. This active process is characteristic of microorganisms as well as higher order species. In mammals, monocytes, macrophages, and microglia are among the so-called professional phagocytes. In addition, cells such as fibroblast and chondrocytes are classified as nonprofessional phagocytes. Professional phagocytes play important roles in both the innate and adaptive immune responses, wound healing, and tissue homeostasis. Consequently, these cells are increasingly studied as targets and vectors of therapeutic intervention to treat a range of diseases. Professional phagocytes are notoriously difficult to transfect limiting their study and manipulation. Consequently, efforts have shifted towards the development of nanoparticles to deliver a cargo to phagocytic cells via phagocytosis. However, this approach carries significant technical challenges, particularly for protein cargos. We have focused on the development of nanoscale cocrystalline protein depots, known as PODS®, that contain protein cargos, including cytokines. Here, we show that PODS are readily phagocytosed by nonprofessional as well as professional phagocytic cells and have attributes, such as highly sustained release of cargo, that suggest potential utility for the study and exploitation of phagocytic cells for drug delivery. Monocytes and macrophages that ingest PODS retain normal characteristics including a robust chemotactic response. Moreover, the PODS-cytokine cargo is secreted by the loaded cell at a level sufficient to modulate the behavior of surrounding nonphagocytic cells. The results presented here demonstrate the potential of PODS nanoparticles as a novel molecular tool for the study and manipulation of phagocytic cells and for the development of Trojan horse immunotherapy strategies to treat cancer and other diseases.

ASCL1-coexpression profiling but not single gene expression profiling defines lung adenocarcinomas of neuroendocrine nature with poor prognosis.

BACKGROUND: Lung adenocarcinoma is heterogeneous regarding histology, etiology and prognosis. Although there have been several attempts to find a subgroup with poor prognosis, it is unclear whether or not adenocarcinoma with neuroendocrine (NE) nature has unfavorable prognosis. MATERIALS AND METHODS: To elucidate whether a subtype of adenocarcinoma with NE nature has poor prognosis, we performed gene expression profiling by cDNA microarray for 262 Japanese lung cancer and 30 normal lung samples, including 171 adenocarcinomas, 56 squamous cell carcinomas and 35 NE tumors. A co-expression gene set with ASCL1, an NE master gene, was utilized to classify tumors by non-negative matrix factorization, followed by validation using an ASCL1 knock-down gene set in DMS79 cells as well as an independent cohort (n=139) derived from public microarray databases as a test set. RESULTS: The co-expression gene set classified the adenocarcinomas into alveolar cell (AL), squamoid, and NE subtypes. The NE subtype, which clustered together almost all the NE tumors, had significantly poorer prognosis than the AL subtype that clustered with normal lung samples (p=0.0075). The knock-down gene set also classified the 171 adenocarcinomas into three subtypes and this NE subtype also had the poorest prognosis. The co-expression gene set classified the independent database-derived American cohort into two subtypes, with the NE subtype having poorer prognosis. None of the single NE gene expression was found to be linked to survival difference. CONCLUSION: Co-expression gene set with ASCL1, rather than single NE gene expression, successfully identifies an NE subtype of lung adenocarcinoma with poor prognosis.

Cost-effectiveness of active versus conservative colposcopic management of mild dyskaryosis.

Management of mild dyskaryosis remains controversial. In this study, we compared the cost-effectiveness of active versus conservative colposcopic management of women presenting with mild dyskaryosis in two different hospital settings. All women presenting in 2001 with a mild dyskaryotic smear and requiring colposcopy were studied in two different clinical settings (70 women at Darent Valley Hospital (DVH) and 327 at St George's Hospital (SGH)). At DVH, treatment is offered should there be any evidence of cervical intraepithelial neoplasia (CIN). On the other hand, a more conservative approach of cytological and colposcopical follow-up is offered to patients with evidence of low-grade disease at SGH. The outcome of both groups of patients was determined in terms of the number of colposcopy visits per patient, the risk of missing disease as a consequence of patients lost to follow-up and hospital costs as well as costs to patient over a four-year period. The majority (70%) of DVH patients had 1-2 colposcopy visits whereas the majority (60%) of SGH patients had 3-7 visits. At SGH 44% of untreated patients were lost to follow-up and an unknown number of those might have had high-grade disease. Active management is more cost-effective compared with conservative management ( pound323 and pound589 as cost per patient effectively treated in the two hospitals respectively). In conclusion, active management of low-grade disease is associated with lower hospital and patient costs compared with the conservative strategy.

Two subclasses of lung squamous cell carcinoma with different gene expression profiles and prognosis identified by hierarchical clustering and non-negative matrix factorization.

Current clinical and histopathological criteria used to define lung squamous cell carcinomas (SCCs) are insufficient to predict clinical outcome. To make a clinically useful classification by gene expression profiling, we used a 40 386 element cDNA microarray to analyse 48 SCC, nine adenocarcinoma, and 30 normal lung samples. Initial analysis by hierarchical clustering (HC) allowed division of SCCs into two distinct subclasses. An additional independent round of HC induced a similar partition and consensus clustering with the non-negative matrix factorization approach indicated the robustness of this classification. Kaplan-Meier analysis with the log-rank test pointed to a nonsignificant difference in survival (P = 0.071), but the likelihood of survival to 6 years was significantly different between the two groups (40.5 vs 81.8%, P = 0.014, Z-test). Biological process categories characteristic for each subclass were identified statistically and upregulation of cell-proliferation-related genes was evident in the subclass with poor prognosis. In the subclass with better survival, genes involved in differentiated intracellular functions, such as the MAPKKK cascade, ceramide metabolism, or regulation of transcription, were upregulated. This work represents an important step toward the identification of clinically useful classification for lung SCC.

Two prognostically significant subtypes of high-grade lung neuroendocrine tumours independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles.

BACKGROUND: Classification of high-grade neuroendocrine tumours (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40?386 elements to analyse the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumours and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analysed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumour samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analysed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterised markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumours, and provide important insights into their underlying biology.

Integrated classification of lung tumors and cell lines by expression profiling.

The utility of cancer cell lines depends largely on their accurate classification, commonly based on histopathological diagnosis of the cancers from which they were derived. However, because cancer is often heterogeneous, the cell line, which also has the opportunity to alter in vitro, may not be representative. Yet without the overall architecture used in histopathological diagnosis of fresh samples, reclassification of cell lines has been difficult. Gene-expression profiling accurately reproduces histopathological classification and is readily applicable to cell lines. Here, we compare the gene-expression profiles of 41 cell lines with 44 tumors from lung cancer. These profiles were generated after hybridization of samples to four replicate 7,685-element cDNA microarrays. After removal of genes that were uniformly up- or down-regulated in fresh compared with cell-line samples, cluster analysis produced four major branch groups. Within these major branches, fresh tumor samples essentially clustered according to pathological type, and further subclusters were seen for both adenocarcinoma (AC) and small cell lung carcinoma (SCLC). Four of eight squamous cell carcinoma (SCC) cell lines clustered with fresh SCC, and 11 of 13 SCLC cell lines grouped with fresh SCLC. In contrast, although none of the 11 AC cell lines clustered with AC tumors, three clustered with SCC tumors and six with SCLC tumors. Although it is possible that preexisting SCC or SCLC cells are being selected from AC tumors after establishment of cell lines, we propose that, even in situ, AC will ultimately progress toward one of two poorly differentiated phenotypes with expression profiles resembling SCC or SCLC.