A HIF1alpha regulatory loop links hypoxia and mitochondrial signals in pheochromocytomas.

Pheochromocytomas are neural crest-derived tumors that arise from inherited or sporadic mutations in at least six independent genes. The proteins encoded by these multiple genes regulate distinct functions. We show here a functional link between tumors with VHL mutations and those with disruption of the genes encoding for succinate dehydrogenase (SDH) subunits B (SDHB) and D (SDHD). A transcription profile of reduced oxidoreductase is detected in all three of these tumor types, together with an angiogenesis/hypoxia profile typical of VHL dysfunction. The oxidoreductase defect, not previously detected in VHL-null tumors, is explained by suppression of the SDHB protein, a component of mitochondrial complex II. The decrease in SDHB is also noted in tumors with SDHD mutations. Gain-of-function and loss-of-function analyses show that the link between hypoxia signals (via VHL) and mitochondrial signals (via SDH) is mediated by HIF1alpha. These findings explain the shared features of pheochromocytomas with VHL and SDH mutations and suggest an additional mechanism for increased HIF1alpha activity in tumors.

A molecular signature of metastasis in primary solid tumors.

Metastasis is the principal event leading to death in individuals with cancer, yet its molecular basis is poorly understood. To explore the molecular differences between human primary tumors and metastases, we compared the gene-expression profiles of adenocarcinoma metastases of multiple tumor types to unmatched primary adenocarcinomas. We found a gene-expression signature that distinguished primary from metastatic adenocarcinomas. More notably, we found that a subset of primary tumors resembled metastatic tumors with respect to this gene-expression signature. We confirmed this finding by applying the expression signature to data on 279 primary solid tumors of diverse types. We found that solid tumors carrying the gene-expression signature were most likely to be associated with metastasis and poor clinical outcome (P < 0.03). These results suggest that the metastatic potential of human tumors is encoded in the bulk of a primary tumor, thus challenging the notion that metastases arise from rare cells within a primary tumor that have the ability to metastasize.

Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning.

Diffuse large B-cell lymphoma (DLBCL), the most common lymphoid malignancy in adults, is curable in less than 50% of patients. Prognostic models based on pre-treatment characteristics, such as the International Prognostic Index (IPI), are currently used to predict outcome in DLBCL. However, clinical outcome models identify neither the molecular basis of clinical heterogeneity, nor specific therapeutic targets. We analyzed the expression of 6,817 genes in diagnostic tumor specimens from DLBCL patients who received cyclophosphamide, adriamycin, vincristine and prednisone (CHOP)-based chemotherapy, and applied a supervised learning prediction method to identify cured versus fatal or refractory disease. The algorithm classified two categories of patients with very different five-year overall survival rates (70% versus 12%). The model also effectively delineated patients within specific IPI risk categories who were likely to be cured or to die of their disease. Genes implicated in DLBCL outcome included some that regulate responses to B-cell-receptor signaling, critical serine/threonine phosphorylation pathways and apoptosis. Our data indicate that supervised learning classification techniques can predict outcome in DLBCL and identify rational targets for intervention.