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IEEE International Conference on Quantum Computing and Engineering (QCE)

MQML: Multi-Omic Quantum Machine Learning Based Cancer Classification, Biomarker Identification in Human Lung Adenocarcinoma

Mandeep Kaur Saggi, Sabre Kais

Published Sep 15, 2024• 5 citations
Publisher

Abstract

Quantum machine learning (QML) presents novel opportunities for resolving, accelerating, and refining computational biology challenges. In biomedical research and personalized medicine, multi-omics integration offers a comprehensive understanding of complex biological systems, bridging the gap between basic research and clinical applications. Recent advancements in quantum computing could enhance multi-omics integration by leveraging unique quantum principles, potentially revealing intricate patterns in multi-omics datasets and offering new insights into the molecular landscape of lung cancer. This study focuses on the integration, feature selection, diagnostic classification, and biomarker identification of lung cancer types-LUAD primary tumor and solid tissue normal-using QML. Given the heterogeneity and high dimensionality of the data, we aim to assess the performance of QML in feature selection, reduction, encoding, and diagnostic classification in integrated multi-omics cancer datasets. We demonstrate the efficacy of Quantum Neural Networks (QNNs) for classifying multi-omics lung cancer data from The Cancer Genome Atlas, comparing our framework with 16 and 64 dimensions of feature using QML. Our results show that the Multi-omic Quantum Machine Learning (MQML) framework achieves superior classification performance with smaller training datasets, as determined by significant and non-significant p-values. This study provides empirical evidence for the potential application of unconventional computing approaches in biomed-ical sciences, particularly in oncology research, including cancer subtype diagnostic, survival, and stage classification. Key results include the identification of sixteen genes and thirty-two genes with the hybrid quantum-classical neural net-work model, with notable genes such as MIR632, CYP19A1, RP11-108K3.1, C14orf39, THUMPD1, ACSM3, GSC, MIR92a-2, DYRK1B, DNAJA1, IRX1, FMR1, POU2F2, MIR9-2, ZNF274, MIR196a-1, MIR3653, MIR33b, MIR301a, MIR182, MIR520a, PYGO2, EMX2OS, EMX2, RP11-550F7.1, RPS11, MIR20a, EIF4G2, IGLV1-47, RP11-180M15.7, SCYL3, AC107081.5, C2orf47, FST, MIR7-1, SLC22A18, CLDN5, and AC007879.7 identified as having significant and non-significant p-values asso-ciated with LUAD patient predictions.

DOI
10.1109/qce60285.2024.00200

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Multi-omics integration has become a cornerstone of personalized medicine, enabling researchers to connect genomic, transcriptomic, proteomic, metabolomic, and epigenomic data to reveal complex biological mechanisms and individualized disease signatures. However, the extreme dimensionality, heterogeneity, and nonlinear interactions across omics layers challenge even the most advanced classical machine learning methods. Quantum Machine Learning (QML) offers a powerful alternative by exploiting quantum superposition, entanglement, and high-dimensional feature embeddings to model multi-omics interactions more efficiently. This study proposes a hybrid quantum–classical framework that combines quantum kernel estimation, variational quantum classifiers, and quantum-enhanced feature fusion networks to generate integrative biological signatures for precision diagnostics. Simulation results using benchmark multi-omics datasets demonstrate improved classification accuracy, enhanced sensitivity to weak biological signals, and reduced computational overhead compared to classical baselines. These findings highlight the potential of QML to accelerate multi-omics insights and pave the way toward truly personalized, predictive healthcare.
Quantum machine learning offers a promising new paradigm for computational biology by leveraging quantum mechanical principles to enhance cancer classification, biomarker discovery, and bioinformatics diagnostics. In this study, we apply QML to identify subtype specific biomarkers for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two predominant forms of non-small cell lung cancer. Our methodology involves a two-phase process: in Phase 1, differential expression analysis and methylation analysis between tumor and normal samples allows us to identify LUAD-specific and LUSC-specific genes, revealing potential prognostic biomarkers for cancer subtypes. Phase 2 focuses on developing a quantum classifier capable of distinguishing between LUAD and LUSC tumors, as well as between tumor and normal samples. This classifier not only enhances diagnostic precision but also demonstrates the quantum advantage in processing large-scale multiomic datasets. Our results consistently demonstrated that Sample3, representing the combined gene set, achieved the highest overall predictive performance in all metrics. These results demonstrate that QML provides an effective and scalable approach for biomarker discovery and subtype specific cancer classification. GO enrichment analysis highlighted the significant involvement of genes in synaptic signaling, ion channel regulation, and neuronal development. In the quantum phase, KEGG analysis further identified enrichment in cancer-associated pathways, including neurotrophin, MAPK, Ras, and PI3KAkt signaling, with key genes such as NGFR, NTRK2, and NTF3 suggesting a central role in neurotrophinmediated oncogenic processes. Our findings highlight the growing potential of quantum computing to advance precision oncology and next-generation biomedical analytics.
Subtyping of lung cancer is crucial for individualized treatment and optimizing outcomes. Computational modeling allows data analysis over large scales, which helps in making precision classification in large datasets or high-dimensional datasets. Conventional machine learning methods perform well, but integration of multi-omic data (DNA, RNA, miRNA) is challenging in cancer subtyping because of high dimensionality and complex inter-omic relationships. To bypass this Quantum machine learning (QML) provides better ability to navigate high-dimensional biological data with many variables using quantum computational concepts that can identify fine patterns or small-scale patterns beyond the capability of classical approaches. Through the integration of quantum computing principles with the machine learning framework, the study highlights the improvement of computer technology. The framework shows how the system is optimized with the help of quantum principles, how it can improve accuracy, parallel processing and reducing latency. In this work, we have combined data on miRNA, RNA expression, and DNA methylation to create a complete workflow for classifying cancer. This method uses a wide variety of feature selection techniques, such as random forest-based selection, chi-square, mutual information(MI), and principal component analysis(PCA).In order to illustrate the notion of quantum advantage, we carefully compare the performance metrics of the Quantum Support Vector Classifier (QSVC) to the classical Support Vector Machine (SVM), using a ZZ feature map quantum kernel as our baseline for analysis. Using five-fold crossvalidation, we have also verified that the quantum has an advantage with consistently lower variance and higher accuracy.
Quantum machine learning (QML) algorithms have demonstrated the power of quantum computing for solving complex problems and big data in certain tasks. In this study, we explore the capabilities of QML for the classification of real-world biological large datasets including ten different cancer types based on gene expression values. By comparing the classification results obtained from the quantum algorithm with those from classical approaches, we disclose that the QML algorithm overall achieves comparable and reliable results. Moreover, we identify novel biomarkers that can contribute to the understanding of cancer biology. Some of these biomarkers are consistent with DNA promoter methylation. Our findings highlight the potential of QML in cancer classification and biomarker discovery, paving the way for future advancements in other disease research and clinical applications.
Quantum machine learning (QML) combines quantum computing with machine learning, offering potential for solving intricate problems. Our research delves into QML's application in identifying gene expression biomarkers for clear cell renal cell carcinoma (ccRCC) metastasis. ccRCC, the primary renal cancer subtype, poses significant challenges due to its high lethality and complex metastasis process. Despite extensive research, understanding the mechanisms of cancer cell dissemination and establishment in distant sites remains elusive. Identifying metastasis biomarkers is a daunting task in machine learning. Our study addresses the need for improved execution time and accuracy in QSVC and QNN algorithms compared to SVC and NN for binary classification. Drawing inspiration from the Neural Quantum Embedding (NQE) method, we propose a two-stage approach for the binary classification problem. We aim to assess if integrating NQE with QSVC/QNN enhances performance compared to NQE with SVC/NN across diverse biomedical datasets, demonstrating the effectiveness and generalizability of the approach.
This record contains the reproducibility materials for the Quantum Biomarker Discovery in Multi-Omics (QuBID-Omics) study of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) classification. It includes scripts for training-only RNA and DNA biomarker discovery, RNA–DNA feature integration, quantum neural network (QNN) and classical-baseline modeling, IBM Fez quantum-processing-unit inference, and CPTAC external validation. The release also provides ordered gene–CpG feature lists, CPTAC feature mappings, and prepared TCGA train/validation/test and CPTAC external-validation matrices for Samples 1–3. See the README for the workflow, data organization, label coding, and execution requirements.