Fig. 3: CeSta outperforms the state-of-the-art baseline classifier on IRCC-PDX and CR-PDX. | Nature Communications

Fig. 3: CeSta outperforms the state-of-the-art baseline classifier on IRCC-PDX and CR-PDX.

From: Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer patient-derived xenografts

Fig. 3

a Classification performances quantified through F1 scores (harmonic mean of precision and recall) across 50 train/test IRCC-PDX split replicates (x-axis) for the stacked classifier (‘CeSta’, in blue), an elastic net penalised logistic model (‘elNet baseline’, in tan) which uses state-of-the-art clinical features for cetuximab sensitivity in CRC (KRAS, NRAS, BRAF mutational status, right colon tumour location), a rule-based classifier using the KRAS-BRAF-NRAS triple negative clinical signature (tripleNegRule, in orange) as a binary predictor, and another rule-based classifier which uses both the aforementioned triple-negative signature and the ‘right colon’ feature (tripleNegRightRule, in green). b Area under the receiver-operating-characteristic curve (AUROC) values and error bars, obtained via DeLong’s method, indicating 95% confidence intervals69,70 across 50 IRCC-PDX of n = 150 and 81 train/test split replicates replicates (x-axis), for CeSta (in blue) and the elastic net penalised logistic model (‘elNet baseline’, in tan) described in (a). c AUROC (DeLong’s method) computed over the external validation CR-PDX dataset for CeSta (in blue) and the elNet baseline classifier (‘elNet baseline’, in tan) after a single instance of both models is trained and tuned over the entire IRCC-PDX dataset. The shaded area between the CeSta and elNet baseline ROC curves represents the improvement in AUROC. Decision point coordinates correspond to the false-positive and true positive rates obtained from the corresponding classifier’s predictions. Here, rule-based classifier decision points overlap with the elNet baseline’s. d Confusion matrix from a comparison of CeSta classifier outcomes (same validation setup as c) and PDXs actual cetuximab response over the external validation CR-PDX dataset. Correct predictions are on the diagonal highlighted in blue, incorrect predictions off the diagonal are highlighted in purple. e CeSta correct prediction counts (same validation setup as c) over the CR-PDX external validation set grouped by PDX cetuximab sensitivity (x-axis) and PDX KRAS-NRAS-BRAF triple-negative status (y-axis). CeSta correctly predicts additional triple-negative non-responders (3) and triple-positive responders (1), which all baseline classifiers miss. Source data are provided as a Source Data file.

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