Fig 1.
DR5Nb1-tetra is selective with responses in multiple tumor lineages.
(A) Schematic diagram of DR5Nb1-tetra Nanobody. (B) Composition of in vitro pan-cancer screen tested for response to DR5Nb1-tetra. (C) DR5Nb1-tetra response in the CLiP, CRXX and Lab screens. Response is shown as Amax relative to IC50. Amax cut-offs for sensitive, intermediate, and insensitive classes are drawn. (D) Consistency of Amax values across the three screens. Amax values for each screen (CLIP, CRXX and Lab) are shown as a heatmap colored to represent sensitive (red), intermediate (yellow) and insensitive (blue) categories defined using the same thresholds for Amax across the three screens. Missing values are shown in gray. (E) Response rates (% sensitives) are plotted for each of the lineages (#cell lines ≥10). Lineages with significant (p<0.05 using Fisher’s exact test) enrichment are denoted by *.
Fig 2.
DR5 and CASP8 expression are top ranking features correlated with sensitivity and together improve predictions.
(A) Differential association of gene expression using p-values to assess the significance of gene expression features correlated with DR5Nb1-tetra sensitivity. Inset shows the top 20 genes (dotted line = FDR < 0.05). Colors are based on association with sensitives (red) and insensitives (blue). (B) Comparison of relative surface protein levels of DR5 to mRNA expression in 25 pancreatic cancer cell lines. Points are colored based on sensitivity to DR5Nb1-tetra: sensitives (red), intermediates (orange) and insensitives (blue). (C) Induction of Casp8 activity compared to DR5 gene expression in 27 pancreatic cell lines. (D) DR5+Casp8 expression compared to sensitivity. DR5 and Casp8 are individually significantly associated with response, as shown in the marginal histograms for sensitives (red), insensitives (blue), with overlap shown in purple (DR5: p = 10−12, Casp8: p = 10−9). Scatterplot of DR5 and Casp8 shows that cell lines with high expression of both genes (marked by red box) are enriched in sensitives (PPV = 44%) compared to high DR5 (PPV = 34%) and unselected (PPV = 34%).
Fig 3.
Gene expression ratio predictor (GREP).
(A) Comparison of DR5 versus cFLIP expression in sensitive (red) and insensitive (blue) pancreatic cell lines shows their opposing effects on response. DR5>cFLIP explains sensitivity better than high DR5 alone. (B) Flowchart of analysis steps used for our GREPDR5 analysis. CLIP, CRXX and Lab indicate independent in vitro screens described in text. (C) Comparison of GREP performance in cross-validation (CV) between cell lines from a screen (training set) and tested in a completely independent screen (Test), using the CLiP and combined CRXX+Lab screens. Each chart shows the reciever-operator curves for prediction fidelity, for CV and Test runs, first using CLiP and then using CRXX+Lab as the training set. To preserve complete independence for the “Test” analyses, all lines common to the two screen sets were removed from the test set. (D) GREPDR5 prediction probability across sensitive (red), intermediate (yellow) and insensitive (blue) cell lines defined by a prediction threshold of 0.5.
Fig 4.
GREPDR5 accurately translates across model systems (in vitro to in vivo) and platforms (microarray to RNA-seq).
GREPDR5 predictions trained on cell lines, has been applied to pancreatic primary tumor xenograft (PTX) samples using microarray and RNA-seq data. (A) Performance of DR5+Casp8 predictor on 11 PTX models results in a PPV = 50%. (B) GREPDR5 prediction probability accurately predicts %T/C in 11 pancreatic PTX models (PPV = 100%). Additionally, GREPDR5 predictions correlates linearly with the anti-tumor activity (%T/C or %Regression) (Pearson’s R = -0.91, p = 10−5). (C) GREPDR5 predictions between the microarray and RNA-seq platforms are highly correlated (R = 0.87), showing that GREP can be readily used for translation to another platform. It should be noted that the data was not transformed (e.g. scaled or batch corrected) before applying the predictions.
Fig 5.
GREP improves predictions and translatability over standard approaches for feature selection and classification.
(A) GREPDR5 compared to ratio classifiers with random gene sets (N = 100) of the same size as the hypothesis gene set. 90% of the models were not significantly better than random (Fisher’s test p-value>0.05). Examination of some random models that performed significantly showed that they included DR5-related genes. (B) Challenging the GREP modeling assumptions in predicting in vitro response. GREPDR5 was compared to four models, each built without one or more of its key assumptions (error bars show 95% confidence from cross-validation). GREPDR5 outperforms random and 2-gene classifiers, but a standard gene expression predictor that used ratios for feature selection performed just as well in cell lines. (C) Challenging the GREP modeling assumptions in predicting in vivo response. Validation of the classifier predictions in pancreatic patient-derived tumor xenograft models (PTX) compared to classifiers built with single genes. Assuming a 30% margin of error on the PPV calculation for 11 samples (95% confidence), GREP outperforms both the 2-gene classifier and a standard gene expression predictor that used ratios for feature selection. Vertical dotted line denotes AUC of random classifier (0.5).
Fig 6.
GREP reveals informative relationships between genes.
(A) Network representation of ratios that significantly differentiate response identified by GREPDR5. Genes are connected if they are involved in a ratio, sized based on the number of ratios in which they appear, and colored based on their positivity (%times they appear in the numerator of ratios; ratios were ordered so that they are positively correlated with sensitivity). Red indicates positive, while blue indicates negative. Ratios used in the classifier are shown as bold connections. (B) Importance of individual genes in GREPDR5. Importance of individual features, each assessed using the receiver operator characteristics area under curve (AUCROC) accuracy measure between the full GREPDR5 and one built with that feature excluded.