Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets

The European Union multi-disciplinary Personalised RNA interference to Enhance the Delivery of Individualised Cytotoxic and Targeted therapeutics (PREDICT) consortium has recently initiated a framework to accelerate the development of predictive biomarkers of individual patient response to anti-cancer agents. The consortium focuses on the identification of reliable predictive biomarkers to approved agents with anti-angiogenic activity for which no reliable predictive biomarkers exist: sunitinib, a multi-targeted tyrosine kinase inhibitor and everolimus, a mammalian target of rapamycin (mTOR) pathway inhibitor. Through the analysis of tumor tissue derived from pre-operative renal cell carcinoma (RCC) clinical trials, the PREDICT consortium will use established and novel methods to integrate comprehensive tumor-derived genomic data with personalized tumor-derived small hairpin RNA and high-throughput small interfering RNA screens to identify and validate functionally important genomic or transcriptomic predictive biomarkers of individual drug response in patients. PREDICT's approach to predictive biomarker discovery differs from conventional associative learning approaches, which can be susceptible to the detection of chance associations that lead to overestimation of true clinical accuracy. These methods will identify molecular pathways important for survival and growth of RCC cells and particular targets suitable for therapeutic development. Importantly, our results may enable individualized treatment of RCC, reducing ineffective therapy in drug-resistant disease, leading to improved quality of life and higher cost efficiency, which in turn should broaden patient access to beneficial therapeutics, thereby enhancing clinical outcome and cancer survival. The consortium will also establish and consolidate a European network providing the technological and clinical platform for large-scale functional genomic biomarker discovery. Here we review our current understanding of molecular mechanisms driving resistance to anti-angiogenesis agents, the current limitations of laboratory and clinical trial strategies and how the PREDICT consortium will endeavor to identify a new generation of predictive biomarkers

MYCN mediates cysteine addiction and sensitizes neuroblastoma to ferroptosis

Aberrant expression of MYC transcription factor family members predicts poor clinical outcome in many human cancers. Oncogenic MYC profoundly alters metabolism and mediates an antioxidant response to maintain redox balance. Here we show that MYCN induces massive lipid peroxidation on depletion of cysteine, the rate-limiting amino acid for glutathione (GSH) biosynthesis, and sensitizes cells to ferroptosis, an oxidative, non-apoptotic and iron-dependent type of cell death. The high cysteine demand of MYCN-amplified childhood neuroblastoma is met by uptake and transsulfuration. When uptake is limited, cysteine usage for protein synthesis is maintained at the expense of GSH triggering ferroptosis and potentially contributing to spontaneous tumor regression in low-risk neuroblastomas. Pharmacological inhibition of both cystine uptake and transsulfuration combined with GPX4 inactivation resulted in tumor remission in an orthotopic MYCN-amplified neuroblastoma model. These findings provide a proof of concept of combining multiple ferroptosis targets as a promising therapeutic strategy for aggressive MYCN-amplified tumors

Deciphering Seed Sequence Based Off-Target Effects in a Large-Scale RNAi Reporter Screen for E-Cadherin Expression

<div><p>Functional RNAi based screening is affected by large numbers of false positive and negative hits due to prevalent sequence based off-target effects. We performed a druggable genome targeting siRNA screen intended to identify novel regulators of E-cadherin (CDH1) expression, a known key player in epithelial mesenchymal transition (EMT). Analysis of primary screening results indicated a large number of false-positive hits. To address these crucial difficulties we developed an analysis method, SENSORS, which, similar to published methods, is a seed enrichment strategy for analyzing siRNA off-targets in RNAi screens. Using our approach, we were able to demonstrate that accounting for seed based off-target effects stratifies primary screening results and enables the discovery of additional screening hits. While traditional hit detection methods are prone to false positive results which are undetected, we were able to identify false positive hits robustly. Transcription factor MYBL1 was identified as a putative novel target required for CDH1 expression and verified experimentally. No siRNA pool targeting MYBL1 was present in the used siRNA library. Instead, MYBL1 was identified as a putative CDH1 regulating target solely based on the SENSORS off-target score, i.e. as a gene that is a cause for off-target effects down regulating E-cadherin expression.</p></div

CDK5R1 false positive prediction and validation.

<p>(<b>A</b>) ZEB1 and KRAS were the most significant off-targets in our screen causing an E-cadherin up regulation while CDH1 and MYBL1 are strong negative off-targets causing a loss of E-cadherin expression. The red dashed line is the hit threshold for primary screening data (shown on the y-axis). Pools that fell within the orange zone (i.e. pools showing a primary score above the primary screen threshold but that have no significant off-target z-score) and that have at least one seed matching into the strong positive off-targets are considered likely false positives (red circles). These pools were deconvoluted and validated experimentally. (<b>B</b>) Common seed analysis for the CDK5R1 pool. While no other siRNAs with the seed sequence GTACCTC exhibited a significant phenotypic score, some of the siRNAs with the seed sequence AACAATG (match in ZEB1 3’UTR) showed a similar phenotype to the CDK5R1 pool (red points). One seed sequence is only present in the CDK5R1 pool. (<b>C</b>) Deconvolution of CDK5R1 siRNAs. The siRNA containing the seed AACAATG (si16899) was the only one showing a significant up regulation of E-cadherin expression, while all other siRNAs targeted against CDK5R1 showed no phenotype. (<b>D</b>) C911 control. The C911 control for si16899 kept the phenotype of the unaltered siRNA, indicating that the observed phenotype is due to a seed sequence-mediated off-target effect. The ZEB1 C911 siRNA showed no phenotype indicating that the ZEB1 phenotype is a true positive (on-target) result.</p

Seed enrichment visualizations for 4 high scoring off-targets.

<p>Density curves show the tendency of high scoring positive (red) and negative (blue) off-targets. The x-axes show the rank of the indicated numbers of seeds while the density of the respective ranks is shown on the y-axes. The difference in trends of high and low scoring off-targets is clearly visible by left- and right-skewed densities, respectively. ZEB1 (top left) and CDH1 (bottom right) were the most significant off-targets observed.</p

Contingency table.

<p>8,977 genes that were classified as expressed or non-expressed by integrating two gene expression data sets for PANC-1 cells (only genes that are absent or present in all data sets were considered) were examined for expression by integrating two PANC-1 expression data sets and assigned with an absent or present expression status by stringent criteria. For genes targeted by siRNA pools exhibiting a significant phenotype (primary screening hits) no significant difference between expressed and non-expressed genes could be detected (p = 0.32).</p><p>Contingency table.</p

MYBL1 is a transcriptional activator of E-cadherin.

<p>For five of the seven siRNA pairs used to validate MYBL1 we observed a significant (marked by asterisks, p < 0.05) down-regulation of the CDH1 gene using on-target siRNAs in comparison with the modified C911 siRNAs. MYBL1 si1 caused a very low expression of CDH1 for both the unaltered siRNA and the C911 control siRNA. Thus, we expected the loss of CDH1 after knock-down of MYBL1 with si1 to be strong, but we observed a non-working C911 control for unknown reasons. The difference between unmodified and C911 siRNA specific control siRNA can be regarded as the observed true on-target effect, while the C911 control is a specific control on a single reagent level.</p

Primary screening results and expression of screened targets.

<p>(<b>A</b>) Overlaid box and violin plot showing the primary screen phenotype distribution. Colored circles show effects of the ZEB1 positive control pool (red), the CDH1 pool (gold) and the CDK5R1 pool (blue), respectively. The dashed grey line indicates the hit threshold. (<b>B</b>) Histogram of log values of primary screening results combined with the expression status for a subset of 8,977 genes. The red dashed line indicates the hit threshold.</p