Person:

Golub, Todd

Background: Current prostate cancer prognostic models are based on pre-treatment prostate specific antigen (PSA) levels, biopsy Gleason score, and clinical staging but in practice are inadequate to accurately predict disease progression. Hence, we sought to develop a molecular panel for prostate cancer progression by reasoning that molecular profiles might further improve current clinical models. Methods: We analyzed a Swedish Watchful Waiting cohort with up to 30 years of clinical follow up using a novel method for gene expression profiling. This cDNA-mediated annealing, selection, ligation, and extension (DASL) method enabled the use of formalin-fixed paraffin-embedded transurethral resection of prostate (TURP) samples taken at the time of the initial diagnosis. We determined the expression profiles of 6100 genes for 281 men divided in two extreme groups: men who died of prostate cancer and men who survived more than 10 years without metastases (lethals and indolents, respectively). Several statistical and machine learning models using clinical and molecular features were evaluated for their ability to distinguish lethal from indolent cases. Results: Surprisingly, none of the predictive models using molecular profiles significantly improved over models using clinical variables only. Additional computational analysis confirmed that molecular heterogeneity within both the lethal and indolent classes is widespread in prostate cancer as compared to other types of tumors. Conclusions: The determination of the molecularly dominant tumor nodule may be limited by sampling at time of initial diagnosis, may not be present at time of initial diagnosis, or may occur as the disease progresses making the development of molecular biomarkers for prostate cancer progression challenging.

Melanoma is notable for its metastatic propensity, lethality in the advanced setting, and association with ultraviolet (UV) exposure early in life1. To obtain a comprehensive genomic view of melanoma, we sequenced the genomes of 25 metastatic melanomas and matched germline DNA. A wide range of point mutation rates was observed: lowest in melanomas whose primaries arose on non-UV exposed hairless skin of the extremities (3 and 14 per Mb genome), intermediate in those originating from hair-bearing skin of the trunk (range = 5 to 55 per Mb), and highest in a patient with a documented history of chronic sun exposure (111 per Mb). Analysis of whole-genome sequence data identified PREX2 - a PTEN-interacting protein and negative regulator of PTEN in breast cancer2 - as a significantly mutated gene with a mutation frequency of approximately 14% in an independent extension cohort of 107 human melanomas. PREX2 mutations are biologically relevant, as ectopic expression of mutant PREX2 accelerated tumor formation of immortalized human melanocytes in vivo. Thus, whole-genome sequencing of human melanoma tumors revealed genomic evidence of UV pathogenesis and discovered a new recurrently mutated gene in melanoma.

Resistance to BRAFV600E inhibitors is associated with reactivation of mitogen-activated protein kinase (MAPK) signaling at different levels in melanoma. To identify downstream effectors of MAPK signaling that could be used as potential additional therapeutic targets for BRAFV600E inhibitors, we used hTERT/CDK4R24C/p53DD-immortalized primary human melanocytes genetically modified to ectopically express BRAF V600E or NRAS G12D and observed induction of the AP-1 transcription factor family member c-Jun. Using a dominant negative approach, in vitro cell proliferation assays, western blots, and flow cytometry showed that MAPK signaling via BRAFV600E promotes melanoma cell proliferation at G1 through AP-1-mediated negative regulation of the INK4 family member, cyclin-dependent kinase inhibitor 2C (CDKN2C), and the CIP/KIP family member, cyclin-dependent kinase inhibitor 1A (CDKN1A). These effects were antagonized by pharmacological inhibition of CDKN2C and CDKN1A targets CDK2 and CDK4 in vitro. In contrast to BRAF V600E or NRAS G12D-expressing melanocytes, melanoma cells have an inherent resistance to suppression of AP-1 activity by BRAFV600E- or MEK-inhibitors. Here, CDK2/4 inhibition statistically significantly augmented the effects of BRAFV600E- or MEK-inhibitors on melanoma cell viability in vitro and growth in athymic nude Foxn1 nu mice (P = .03 when mean tumor volume at day 13 was compared for BRAFV600E inhibitor vs BRAFV600E inhibitor plus CDK2/4 inhibition; P = .02 when mean tumor volume was compared for MEK inhibitor vs MEK inhibitor plus CDK2/4 inhibition; P values were calculated by a two-sided Welch t test; n = 4–8 mice per group).

The incidence of esophageal adenocarcinoma (EAC) has risen 600% over the last 30 years. With a five-year survival rate of 15%, identification of new therapeutic targets for EAC is greatly important. We analyze the mutation spectra from whole exome sequencing of 149 EAC tumors/normal pairs, 15 of which have also been subjected to whole genome sequencing. We identify a mutational signature defined by a high prevalence of A to C transversions at AA dinucleotides. Statistical analysis of exome data identified significantly mutated 26 genes. Of these genes, four (TP53, CDKN2A, SMAD4, and PIK3CA) have been previously implicated in EAC. The novel significantly mutated genes include chromatin modifying factors and candidate contributors: SPG20, TLR4, ELMO1, and DOCK2. Functional analyses of EAC-derived mutations in ELMO1 reveal increased cellular invasion. Therefore, we suggest a new hypothesis about the potential activation of the RAC1 pathway to be a contributor to EAC tumorigenesis.

Somatic chromosomal deletions in cancer are thought to indicate the location of tumor suppressor genes, whereby complete loss of gene function occurs through biallelic deletion, point mutation, or epigenetic silencing, thus fulfilling Knudson's two-hit hypothesis.1 In many recurrent deletions, however, such biallelic inactivation has not been found. One prominent example is the 5q- syndrome, a subtype of myelodysplastic syndrome (MDS) characterized by a defect in erythroid differentiation.2 Here, we describe an RNA interference (RNAi)-based approach to discovery of the 5q- disease gene. We find that partial loss of function of the ribosomal protein RPS14 phenocopies the disease in normal hematopoietic progenitor cells, and moreover that forced expression of RPS14 rescues the disease phenotype in patient-derived bone marrow cells. In addition, we identified a block in the processing of pre-rRNA in RPS14 deficient cells that is highly analogous to the functional defect in Diamond Blackfan Anemia, linking the molecular pathophysiology of the 5q- syndrome to a congenital bone marrow failure syndrome. These results indicate that the 5q- syndrome is caused by a defect in ribosomal protein function, and suggests that RNAi screening is an effective strategy for identifying causal haploinsufficiency disease genes.

Aneuploidy and copy-number alterations (CNAs) are a hallmark of human cancer. Although genetically engineered mouse models (GEMMs) are commonly used to model human cancer, their chromosomal landscapes remain underexplored. Here we use gene expression profiles to infer CNAs in 3,108 samples from 45 mouse models, providing the first comprehensive catalogue of chromosomal aberrations in cancer GEMMs. Mining this resource, we find that most chromosomal aberrations accumulate late during breast tumorigenesis, and observe marked differences in CNA prevalence between mouse mammary tumours initiated with distinct drivers. Some aberrations are recurrent and unique to specific GEMMs, suggesting distinct driver-dependent routes to tumorigenesis. Synteny-based comparison of mouse and human tumours narrows critical regions in CNAs, thereby identifying candidate driver genes. We experimentally validate that loss of Stratifin (SFN) promotes HER2-induced tumorigenesis in human cells. These results demonstrate the power of GEMM CNA analysis to inform the pathogenesis of human cancer.

Comprehensive analyses of cancer genomes promise to inform prognoses and precise cancer treatments. A major barrier, however, is inaccessibility of metastatic tissue. A potential solution is to characterize circulating tumor cells (CTCs), but this requires overcoming the challenges of isolating rare cells and sequencing low-input material. Here we report an integrated process to isolate, qualify and sequence whole exomes of CTCs with high fidelity, using a census-based sequencing strategy. Power calculations suggest that mapping of >99.995% of the standard exome is possible in CTCs. We validated our process in two prostate cancer patients including one for whom we sequenced CTCs, a lymph node metastasis and nine cores of the primary tumor. Fifty-one of 73 CTC mutations (70%) were observed in matched tissue. Moreover, we identified 10 early-trunk and 56 metastatic-trunk mutations in the non-CTC tumor samples and found 90% and 73% of these, respectively, in CTC exomes. This study establishes a foundation for CTC genomics in the clinic.

Major international projects are now underway aimed at creating a comprehensive catalog of all genes responsible for the initiation and progression of cancer. These studies involve sequencing of matched tumor–normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here, we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false positive findings that overshadow true driver events. Here, we show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumor-normal pairs and discover extraordinary variation in (i) mutation frequency and spectrum within cancer types, which shed light on mutational processes and disease etiology, and (ii) mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and allow true cancer genes to rise to attention.

Activating mutations in the RAS family or BRAF frequently occur in many types of human cancers but are rarely detected in breast tumors. However, activation of the RAS-RAF-MEK-ERK Mitogen-Activated Protein Kinase (MAPK) pathway is commonly observed in human breast cancers, suggesting that other genetic alterations lead to activation of this signaling pathway. To identify breast cancer oncogenes that activate the MAPK pathway, we screened a library of human kinases for their ability to induce anchorage-independent growth in a derivative of immortalized human mammary epithelial cells (HMLE). We identified PAK1 as a kinase that permitted HMLE cells to form anchorage-independent colonies. PAK1 is amplified in several human cancer types, including 33% of breast tumor samples and cancer cell lines. The kinase activity of PAK1 is necessary for PAK1-induced transformation. Moreover, we show that PAK1 simultaneously activates MAPK and MET signaling; the latter via inhibition of Merlin. Disruption of these activities inhibits PAK1-driven anchorage-independent growth. These observations establish PAK1 amplification as an alternative mechanism for MAPK activation in human breast cancer and credential PAK1 as a breast cancer oncogene that coordinately regulates multiple signaling pathways, the cooperation of which leads to malignant transformation.

Computational methods for image-based profiling are under active development, but their success hinges on assays that can capture a wide range of phenotypes. We have developed a multiplex cytological profiling assay that “paints the cell” with as many fluorescent markers as possible without compromising our ability to extract rich, quantitative profiles in high throughput. The assay detects seven major cellular components. In a pilot screen of bioactive compounds, the assay detected a range of cellular phenotypes and it clustered compounds with similar annotated protein targets or chemical structure based on cytological profiles. The results demonstrate that the assay captures subtle patterns in the combination of morphological labels, thereby detecting the effects of chemical compounds even though their targets are not stained directly. This image-based assay provides an unbiased approach to characterize compound- and disease-associated cell states to support future probe discovery.