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Although the treatment of acute myeloid leukemia (AML) has improved substantially in the past three decades, more than half of all patients develop disease that is refractory to intensive chemotherapy (1), (2). Functional genomics approaches offer a means to discover specific molecules mediating the aberrant growth and survival of cancer cells (3-8). Thus, using a loss-of-function RNA interference genomic screen, we identified the aberrant expression of hepatocyte growth factor (HGF) as a crucial element in AML pathogenesis. We found HGF expression leading to autocrine activation of its receptor tyrosine kinase, MET, in nearly half of the AML cell lines and clinical samples we studied. Genetic depletion of HGF or MET potently inhibited the growth and survival of HGF-expressing AML cells. However, leukemic cells treated with the specific MET kinase inhibitor crizotinib developed resistance resulting from compensatory upregulation of HGF expression, leading to the restoration of MET signaling. In cases of AML where MET is coactivated with other tyrosine kinases, such as fibroblast growth factor receptor 1 (FGFR1) (9), concomitant inhibition of FGFR1 and MET blocked this compensatory HGF upregulation, resulting in sustained logarithmic cell killing both in vitro and in xenograft models in vivo. Our results show a widespread dependence of AML cells on autocrine activation of MET, as well as the key role of compensatory upregulation of HGF expression in maintaining leukemogenic signaling by this receptor. We anticipate that these findings will lead to the design of additional strategies to block adaptive cellular responses that drive compensatory ligand expression as an essential component of the targeted inhibition of oncogenic receptors in human cancers., We used a doxycycline-inducible retroviral RNAi library of 5,087 barcoded shRNAs targeting 1,740 human genes to screen for functional pathway dependence in OCI-AML2 cells derived from a patient with complex-karyotype [...]

Background: Multispectral microscopy and multiple staining can be used to identify cells with distinct immunohistochemical (IHC) characteristics. We present here a method called hypothesized interaction distribution (HID) analysis for characterizing the statistical distribution of pair wise spatial relationships between cells with particular IHC characteristics and apply it to clinical data. Materials and Methods: We retrospectively analyzed data from a study of 26 follicular lymphoma patients in which sections were stained for CD20 and YY1. HID analysis, using leave one out validation, was used to assign patients to one of two groups. We tested the null hypothesis of no difference in Kaplan-Meier survival curves between the groups. Results: Shannon entropy of HIDs assigned patients to groups that had significantly different survival curves (median survival was 7.70 versus 110 months, P = 0.00750). Hypothesized interactions between pairs of cells positive for both CD20 and YY1 were associated with poor survival. Conclusions: HID analysis provides quantitative inferences about possible interactions between spatially proximal cells with particular IHC characteristics. In follicular lymphoma, HID analysis was able to distinguish between patients with poor versus good survival, and it may have diagnostic and prognostic utility in this and other diseases. [ABSTRACT FROM AUTHOR]

The transcription factor Yin Yang (YY) 1 controls many divergent cellular processes, including cell proliferation and apoptosis. These are key to cancer development, as a consequence of which its expression has been studied in an increasingly wide range of human cancers, including lymphoma, breast, prostate, colon, ovarian, cervical, and brain cancers, osteosarcoma, rhabdomyosarcoma, and leukemia. It is a regulatory transcription factor for a wide range of genes, including genes involved in control of the cell cycle and apoptosis, and it can act either to upregulate or downregulate downstream gene expression, depending on the cellular environment, cofactors, and the gene targeted. Its expression has been associated with development of a malignant phenotype in some human cancers; tumor progression, including metastasis; and survival. However, as data on its prognostic significance has become available for more human cancers, its role in tumor progression has become controversial; there is conflicting data on its association with outcome, with some studies showing a favorable and others an unfavorable association. This is probably because of the many different roles YY1 plays in control of proliferation and apoptosis, one or the other of which may be more prominent in any given tumor. These studies are reviewed to give an overview of the increasingly recognized importance of YY1 in human tumorigenesis.

Microarray gene expression profiling has identified gene signatures or "Indicator" genes predictive of outcome in many cancer types including lymphoma, and more recently pancreatic cancer. This has identified novel and powerful diagnostic and prognostic and generically applicable markers, promising more specific diagnosis and treatment, together with improved understanding of pathobiology. There is now an urgent need to translate these signatures to clinical use. However, gene microarrays rely on relatively large amounts of fresh starting tissue obviating measurement of Indicator genes in routine practice, and there is a need for development of another, simple, robust, relatively inexpensive and sensitive method for their translation to clinical use. We have piloted the use of real-time PCR measurement of specific prognostic genes, so called "Indicator" genes, in globally amplified polyA cDNA for this purpose. Poly(A) PCR coordinately amplifies cDNA copies of all polyadenylated mRNAs, thereby generating a PCR product (polyA cDNA) whose composition reflects the relative abundance of all expressed genes in the starting sample. Poly(A) PCR enables global mRNA amplification from picogram amounts of RNA and has been routinely used to analyse expression in small samples including single cells. The poly(A) cDNA pool generated is also indefinitely renewable and as such represents a "molecular block". Real-time PCR measurement, using gene-specific primers and probes, of the expression levels of specific Indicator genes then allows gene signatures to be detected within the poly(A) cDNA, thereby enabling expression profiling of very small amounts of starting material. This chapter details this method as applied to fresh and paraffin embedded tissue and to pancreatic juice. In this chapter, we have concentrated on application of the method to pancreatic cancer, but the generic nature of the method renders it applicable to any cancer type, thereby representing a novel platform for cancer diagnosis across all tumour types.