A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data

Abstract

In this paper, we introduce QProteoML, a new quantum machine learning (QML) framework for predicting drug sensitivity in Multiple Myeloma (MM) using high-dimensional proteomic data. MM, an extremely heterogeneous condition, displays often mixed responses to treatment, with a large number of patients showing drug resistance to proteasome inhibitors and immune modulatory agents. However, the methods previously used for genomic and proteomic data analysis techniques are plagued by issues of high dimensionality, imbalanced class distribution and feature redundancy, which work against the accurate predictability and generalizability of such methods. These are compounded by the so-called "curse of dimensionality", with dimensions far outnumbering samples, hence classical model overfitting. In this work, we present QProteoML as an integration of quantum techniques purposefully developed to deal with high-dimensional, imbalanced and redundant data. The framework integrates a combination of Quantum Support Vector Machine (QSVM), Quantum Principal Component Analysis (qPCA), Quantum Annealing (QA) for feature selection and Quantum Generative Adversarial Networks (QGANs) for data augmentation. These quantum algorithms exploit certain quantum phenomena (superposition and entanglement) to perform modelling of nonlinear relationships, dimensionality reduction, and class-imbalance issues. QSVM employs quantum kernels to map data into a higher-dimensional Hilbert space, so that the model can detect complex patterns in MM drug resistance. qPCA reduces dimensionality without loss of important variance, and thus improves computation efficiency. In addition, Quantum Annealing successfully extracts the most informative biomarkers with low redundancy. QProteoML was experimentally tested by comparing accuracy, F1 score and AUC ROC between classical machine learning models such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbors (KNN). Our results demonstrate that QProteoML performs better than classical models, particularly in identifying the drug resistant minority class of patients. Additionally, the model is interpretable and stresses important biomarkers of drug sensitivity in MM. This research opens the possibility of quantum machine learning in personalised medicine for Multiple Myeloma. It demonstrates that quantum algorithms can perform complex biological data suggesting more reliable and accurate drug sensitivity predictions. Future research will be directed toward clinical validation of the given system with larger and more diverse cohorts of MM patients; the integration of quantum hardware for practical applications.

Keywords: Biomarker discovery; Drug sensitivity prediction; Multiple myeloma; Proteomics data; QSVM; Quantum machine learning (QML).

Fig. 1

QProteoML architecture diagram.

Fig. 2

Accuracy comparison between classical and quantum models.

Fig. 3

F1-score comparison between classical and QProteoML model.

Fig. 4

AUC-ROC curve comparison.

Fig. 5

Model performance on specific drug predictions.

Fig. 6

Heatmap of proteomic data.

Fig. 7

Hierarchical clustering of patients based on proteomics.

Fig. 8

Quantum Kernel mapping (Simplified).

Fig. 9

Feature importance from quantum annealing.

Fig. 10

PCA of proteomic data.

Fig. 11

Pairwise correlation Heatmap of proteins.

Fig. 12

Quantum Vs. classical model performance on specific drug predictions.

Fig. 13

ROC curves for each drug.