Michael Jones

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… Show more

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 differentiate Show less

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… Show more

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 40386 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. Show less