Zhenlin Ju

High Frequency of PIK3R1 and PIK3R2 Mutations in Endometrial Cancer Elucidates a Novel Mechanism for Regulation of PTEN Protein Stability

We demonstrate that phosphatidylinositol 3-kinase (PI3K) pathway aberrations occur in >80% of endometrioid endometrial cancers, with coordinate mutations of multiple PI3K pathway members being more common than predicted by chance. PIK3R1 (p85α) mutations occur at a higher rate in endometrial cancer than in any other tumor lineage, and PIK3R2 (p85β), not previously demonstrated to be a cancer gene, is also frequently mutated. The dominant activation event in the PI3K pathway appears to be PTEN protein loss. However, in tumors with retained PTEN protein, PI3K pathway mutations phenocopy PTEN loss, resulting in pathway activation. KRAS mutations are common in endometrioid tumors activating independent events from PI3K pathway aberrations. Multiple PIK3R1 and PIK3R2 mutations demonstrate gain of function, including disruption of a novel mechanism of pathway regulation wherein p85α dimers bind and stabilize PTEN. Taken together, the PI3K pathway represents a critical driver of endometrial cancer pathogenesis and a novel therapeutic target.

A pan-cancer proteomic perspective on The Cancer Genome Atlas

Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumors. Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects. Here we use reverse-phase protein arrays to analyze 3,467 patient samples from 11 TCGA “Pan-Cancer” diseases, using 181 high-quality antibodies that target 128 total proteins and 53 post-translationally modified proteins. The resultant proteomic data is integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumor lineages. In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumor lineages. This integrative analysis, with an emphasis on pathways and potentially actionable proteins, provides a framework for determining the prognostic, predictive and therapeutic relevance of the functional proteome.

Integrated Molecular and Clinical Analysis of AKT Activation in Metastatic Melanoma

Purpose: Activation of the phosphoinositide 3-kinase (PI3K)-AKT pathway has been implicated in melanoma based primarily on the prevalence of mutations in PTEN and NRAS. To improve our understanding of the regulation and clinical significance of the PI3K-AKT pathway in melanoma, we quantitatively measured the levels of phosphorylated AKT, its substrate GSK3α/β, and its negative regulator PTEN in clinical metastases. Results were compared with mutational status, clinical outcomes, and sites of metastasis. Experimental Design: DNA and protein were isolated from dissected frozen melanoma metastases (n = 96). Activating mutations of BRAF, NRAS, AKT, PIK3CA, and KIT were detected by mass spectroscopy genotyping. Phosphorylated AKT (Ser473 and Thr308), P-GSK3α/β, and PTEN protein expression were measured by reverse-phase protein array. A panel of human melanoma cells lines (n = 58) was analyzed for comparison. Results: BRAF-mutant tumors had higher levels of P-AKT-Ser473 (P = 0.01), P-AKT-Thr308 (P = 0.002), and P-GSK3α/β (P = 0.08) than NRAS-mutant tumors. Analysis of individual tumors showed that almost all tumors with elevated P-AKT had low PTEN levels; NRAS-mutant tumors had normal PTEN and lower P-AKT. Similar results were observed in melanoma cell lines. Stage III melanoma patients did not differ in overall survival based on activation status of the PI3K-AKT pathway. Brain metastases had significantly higher P-AKT and lower PTEN than lung or liver metastases. Conclusions: Quantitative interrogation of the PI3K-AKT pathway in melanoma reveals unexpected significant differences in AKT activation by NRAS mutation and PTEN loss, and hyperactivation of AKT in brain metastases. These findings have implications for the rational development of targeted therapy for this disease. (Clin Cancer Res 2009;15(24):7538–46)

The Genomic Landscape and Clinical Relevance of A-to-I RNA Editing in Human Cancers

Adenosine-to-inosine (A-to-I) RNA editing is a widespread post-transcriptional mechanism, but its genomic landscape and clinical relevance in cancer have not been investigated systematically. We characterized the global A-to-I RNA editing profiles of 6,236 patient samples of 17 cancer types from The Cancer Genome Atlas and revealed a striking diversity of altered RNA-editing patterns in tumors relative to normal tissues. We identified an appreciable number of clinically relevant editing events, many of which are in noncoding regions. We experimentally demonstrated the effects of several cross-tumor nonsynonymous RNA editing events on cell viability and provide the evidence that RNA editing could selectively affect drug sensitivity. These results highlight RNA editing as an exciting theme for investigating cancer mechanisms, biomarkers, and treatments.

TCPA: a resource for cancer functional proteomics data

To the Editor: Functional proteomics represents a powerful approach to understand the pathophysiology and therapy of cancer. However, comprehensive cancer proteomic data have been relatively limited. As a part of The Cancer Genome Atlas (TCGA) Project and other efforts, we have generated protein expression data over a large number of tumor and cell line samples using reverse-phase protein arrays (RPPAs). RPPA is a quantitative, antibody-based technology that can assess multiple protein markers in many samples in a cost-effective, sensitive and highthroughput manner1,2. This technology has been extensively validated for both cell line and patient samples3–5, and its applications range from building reproducible prognostic models6 to generating experimentally verified mechanistic insights7. Our RPPA profiling platform includes extensively validated antibodies to nearly 200 proteins and phosphoproteins (Supplementary Methods and Supplementary Table 1). We are in the process of extending it to 500 independent proteins, covering all major signaling pathways, including PI3K, MAPK, mTOR, TGF-b, WNT, cell cycle, apoptosis, DNA damage, Hippo and Notch pathways. The current data release covers 4,379 tumor samples and consists of three parts (Supplementary Table 2). These are (i) TCGA tumor tissue sample sets: 3,467 samples from 11 cancer types, to be extended to 25 cancer types; (ii) independent tumor tissue sample sets: one endometrial tumor set (244 samples)7 and two ovarian tumor sets (99 and 130 samples, respectively)6, with other independent sets to be added soon; and (iii) tumor cell lines: 439 samples in four cell line sets, including both baseline and drug-treated cell lines. To our knowledge, this represents the largest publicly available collection of cancer functional proteomics data with parallel DNA and RNA data. To facilitate broad access to these RPPA data sets, we developed a user-friendly data portal, The Cancer Proteome Atlas (TCPA; http://bioinformatics.mdanderson.org/main/ TCPA:Overview). TCPA provides six modules: Summary, My Protein, Download, Visualization, Analysis and Cell Line (Fig. 1, i). The Summary module provides an overview of the RPPA data with detailed descriptions of each set (Fig. 1, ii). The Download module allows users to obtain any RPPA data set for analysis through a tree-view interface (Fig. 1, iii). The My Protein module provides detailed information about each RPPA protein: protein name, corresponding gene symbol, antibody status and source for the antibody. Users can examine the expression pattern of a protein of interest across different tumor types (for example, HER2 expression shown in Fig. 1, iv). The Visualization module provides two ways to examine global protein expression patterns in a specific RPPA data set . One is through a “next-generation clustered heat map” (Fig. 1, v), which allows users to zoom, navigate and scrutinize clustering patterns of samples or proteins and link those patterns to relevant biological information sources. The other is through a network view (Fig. 1, vi), which overlays the correlation between any two interacting partners in the protein interaction network (curated in the Human Protein Reference Database8). The Analysis module provides three analysis methods. (i) For correlation analysis, given a user-specified data set, correlations between any pair of proteins are presented in a table (Fig. 1, vii). Users can search the results by protein name, rank correlations or visualize the scatter plot of a correlation of interest (for example, there is a strong correlation between PKC-a and its phosphorylated form PKC-a_pS657 in endometrial cancer, as shown in Fig. 1, vii). (ii) For differential analysis, differentially expressed protein markers between two tumor types or subtypes can be identified. Given user-defined comparison groups, the Krzywinski and Cairo reply: We are in full agreement with the core of Katz’s argument that “distortion,” “embellishment,” “concealment” and “unrepresentative displays” have no place in principled communication of scientific information1. There is no controversy here—Katz extrapolates our storytelling metaphor beyond the intended scope of our column and argues against a position we did not take. The Points of View series offers effective strategies for visual presentation of complex data. The scope of the Storytelling column2 was limited to the construction of multipanel figures, which summarize as much as they support detailed exposition of the text. The column did not address how this text should be composed or the broad subject of motivation and design of scientific experiments. We described an approach to structure the flow of concepts and data across panels in a figure as a way to achieve a narrative, not confabulation. The design of visual communication requires a distinct approach because we organize and interpret images very differently than words (Gestalt principles of perception3). Whereas text is a natural place for nuance and alternative interpretations, multiple lines of argument in a figure can easily interfere with our perception of all its parts. Our suggestion to “leave out detail that does not advance the plot” speaks to controlling the amount of information to avoid an incomprehensible image and deferring it to the text, where it can be more suitably framed. To interpret it as “inconvenient truths are [to be] swept away” is a misrepresentation. Readers often look to the abstract and then the figures to provide them with an initial impression and overview of the findings. These are not the only elements that are reported, merely the first elements to be read. At each step, from abstract to figure to text, the level of detail is expanded to accommodate the preparedness of the reader to assimilate new information. It is often impossible to “do justice to experimental complexities and their myriad of interpretations” with a figure. We support Katz’s position that authors should include all the details necessary to appreciate, understand and reproduce the science through the use of visual and written communication that is clear, concise and thoughtful.

ARID1A Deficiency Impairs the DNA Damage Checkpoint and Sensitizes Cells to PARP Inhibitors

UNLABELLED ARID1A, SWI/SNF chromatin remodeling complex subunit, is a recently identified tumor suppressor that is mutated in a broad spectrum of human cancers. Thus, it is of fundamental clinical importance to understand its molecular functions and determine whether ARID1A deficiency can be exploited therapeutically. In this article, we report a key function of ARID1A in regulating the DNA damage checkpoint. ARID1A is recruited to DNA double-strand breaks (DSB) via its interaction with the upstream DNA damage checkpoint kinase ATR. At the molecular level, ARID1A facilitates efficient processing of DSB to single-strand ends and sustains DNA damage signaling. Importantly, ARID1A deficiency sensitizes cancer cells to PARP inhibitors in vitro and in vivo, providing a potential therapeutic strategy for patients with ARID1A-mutant tumors. SIGNIFICANCE ARID1A has been identified as one of the most frequently mutated genes across human cancers. Our data suggest that clinical utility of PARP inhibitors might be extended beyond patients with BRCA mutations to a larger group of patients with ARID1A-mutant tumors, which may exhibit therapeutic vulnerability to PARP inhibitors.

Integrative Analysis of Proteomic Signatures, Mutations, and Drug Responsiveness in the NCI 60 Cancer Cell Line Set

Aberrations in oncogenes and tumor suppressors frequently affect the activity of critical signal transduction pathways. To analyze systematically the relationship between the activation status of protein networks and other characteristics of cancer cells, we did reverse phase protein array (RPPA) profiling of the NCI60 cell lines for total protein expression and activation-specific markers of critical signaling pathways. To extend the scope of the study, we merged those data with previously published RPPA results for the NCI60. Integrative analysis of the expanded RPPA data set revealed five major clusters of cell lines and five principal proteomic signatures. Comparison of mutations in the NCI60 cell lines with patterns of protein expression showed significant associations for PTEN, PIK3CA, BRAF, and APC mutations with proteomic clusters. PIK3CA and PTEN mutation enrichment were not cell lineage-specific but were associated with dominant yet distinct groups of proteins. The five RPPA-defined clusters were strongly associated with sensitivity to standard anticancer agents. RPPA analysis identified 27 protein features significantly associated with sensitivity to paclitaxel. The functional status of those proteins was interrogated in a paclitaxel whole genome small interfering RNA (siRNA) library synthetic lethality screen and confirmed the predicted associations with drug sensitivity. These studies expand our understanding of the activation status of protein networks in the NCI60 cancer cell lines, demonstrate the importance of the direct study of protein expression and activation, and provide a basis for further studies integrating the information with other molecular and pharmacological characteristics of cancer. Mol Cancer Ther; 9(2); 257–67

Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer

PurposeTo determine whether functional proteomics improves breast cancer classification and prognostication and can predict pathological complete response (pCR) in patients receiving neoadjuvant taxane and anthracycline-taxane-based systemic therapy (NST).MethodsReverse phase protein array (RPPA) using 146 antibodies to proteins relevant to breast cancer was applied to three independent tumor sets. Supervised clustering to identify subgroups and prognosis in surgical excision specimens from a training set (n = 712) was validated on a test set (n = 168) in two cohorts of patients with primary breast cancer. A score was constructed using ordinal logistic regression to quantify the probability of recurrence in the training set and tested in the test set. The score was then evaluated on 132 FNA biopsies of patients treated with NST to determine ability to predict pCR.ResultsSix breast cancer subgroups were identified by a 10-protein biomarker panel in the 712 tumor training set. They were associated with different recurrence-free survival (RFS) (log-rank p = 8.8 E-10). The structure and ability of the six subgroups to predict RFS was confirmed in the test set (log-rank p = 0.0013). A prognosis score constructed using the 10 proteins in the training set was associated with RFS in both training and test sets (p = 3.2E-13, for test set). There was a significant association between the prognostic score and likelihood of pCR to NST in the FNA set (p = 0.0021).ConclusionWe developed a 10-protein biomarker panel that classifies breast cancer into prognostic groups that may have potential utility in the management of patients who receive anthracycline-taxane-based NST.

Functional Proteomic Analysis of Advanced Serous Ovarian Cancer Using Reverse Phase Protein Array: TGF-β Pathway Signaling Indicates Response to Primary Chemotherapy

Purpose: Using reverse phase protein array, we measured protein expression associated with response to primary chemotherapy in patients with advanced-stage, high-grade serous ovarian cancer. Experimental Design: Tumor samples were obtained from 45 patients with advanced high-grade serous cancers from the Gynecology Tumor Bank at the British Columbia Cancer Agency. Treatment consisted of platinum-based chemotherapy following debulking surgery. Protein lysates were prepared from fresh frozen tumor samples, and 80 validated proteins from signaling pathways implicated in ovarian carcinogenesis were measured by reverse phase protein array. Normalization of Ca-125 by the 3rd cycle of chemotherapy was chosen as the primary outcome measure of chemotherapy response. Logistic regression was used for multivariate analysis to identify protein predictors of Ca-125 normalization and Cox regression to test for the association between protein expression and progression-free survival. A significance level of P ≤ 0.05 was used. Results: The mean age at diagnosis was 56.8 years. epidermal growth factor receptor, YKL-40, and several transforming growth factor β (TGF-β) pathway proteins [c-jun–NH2–kinase (JNK), JNK phosphorylated at residues 183 and 185, plasminogen activator inhibitor 1, Smad3, TAZ] showed significant associations with Ca-125 normalization on univariate testing. On multivariate analysis, epidermal growth factor receptor (P < 0.02), JNK (P < 0.01), and Smad3 (P < 0.04) were significantly associated with normalization of Ca-125. Contingency table analysis of pathway-classified proteins revealed that the selection of TGF-β pathway proteins was unlikely because of false discovery (P < 0.007; Bonferroni adjusted). Conclusion: TGF-β pathway signaling likely plays an important role as a marker or mediator of chemoresistance in advanced serous ovarian cancer. On this basis, future studies to develop and validate a useful predictor of treatment failure are warranted. Clin Cancer Res; 16(10); 2852–60. ©2010 AACR.

A comparative analysis of data generated using two different target preparation methods for hybridization to high-density oligonucleotide microarrays

BackgroundTo generate specific transcript profiles, one must isolate homogenous cell populations using techniques that often yield small amounts of RNA, requiring researchers to employ RNA amplification methods. The data generated by using these methods must be extensively evaluated to determine any technique dependent distortion of the expression profiles.ResultsHigh-density oligonucleotide microarrays were used to perform experiments for comparing data generated by using two protocols, an in vitro transcription (IVT) protocol that requires 5 μg of total RNA and a double in vitro transcription (dIVT) protocol that requires 200 ng of total RNA for target preparation from RNA samples extracted from a normal and a cancer cell line. In both cell lines, about 10% more genes were detected with IVT than with dIVT. Genes were filtered to exclude those that were undetected on all arrays. Hierarchical clustering using the 9,482 genes that passed the filter showed that the variation attributable to biological differences between samples was greater than that introduced by differences in the protocols. We analyzed the behavior of these genes separately for each protocol by using a statistical model to estimate the posterior probability of various levels of fold change. At each level, more differentially expressed genes were detected with IVT than with dIVT. When we checked for genes that had a posterior probability greater than 99% of fold change greater than 2, in data generated by IVT but not dIVT, more than 60% of these genes had posterior probabilities greater than 90% in data generated by dIVT. Both protocols identified the same functional gene categories to be differentially expressed. Differential expression of selected genes was confirmed using quantitative real-time PCR.ConclusionUsing nanogram quantities on total RNA, the usage of dIVT protocol identified differentially expressed genes and functional categories consistent with those detected by the IVT protocol. There was a loss in sensitivity of about 10% when detecting differentially expressed genes using the dIVT protocol. However, the lower amount of RNA required for this protocol, as compared to the IVT protocol, renders this methodology a highly desirable one for biological systems where sample amounts are limiting.