Radiation treatment response and hypoxia biomarkers revealed by machine learning assisted Raman spectroscopy in tumour cells and xenograft tissues

• Published in: Analyst (London) Vol. 147; no. 22; pp. 591 - 514
• Main Authors: Deng, Xinchen, Milligan, Kirsty, Brolo, Alexandre, Lum, Julian J, Andrews, Jeffrey L, Jirasek, Andrew
• Format: Journal Article
• Language: English
• Published: London Royal Society of Chemistry 07.11.2022
• Subjects: Alanine; Amino acids; Biochemistry; Biomarkers; Correlation; Glycogens; Hypoxia; Lipids; Machine learning; Medical imaging; Radiation; Radiation dosage; Radiation therapy; Raman spectroscopy; Spectrum analysis; Tumors; Xenotransplantation
• ISSN: 0003-2654; 1364-5528
• DOI: 10.1039/d2an01222g

Recent advancements in anatomical imaging of tumours as treatment targets have led to improvements in RT. However, it is unlikely that improved anatomical imaging alone will be the sole driver for new advances in personalised RT. Biochemically based radiobiological information is likely to be required for next-generation improvements in the personalisation of radiotherapy dose prescriptions to individual patients. In this paper, we use Raman spectroscopy (RS), an optical technique, to monitor individual biochemical response to radiation within a tumour microenvironment. We spatially correlate individual biochemical responses to augmentatively derived hypoxic maps within the tumour microenvironment. Furthermore, we pair RS with a data analytical framework combining (i) group and basis restricted non-negative matrix factorization (GBR-NMF), (ii) a random forest (RF) classifier, (iii) and a feature metric importance calculation method, Shapley Additive exPlanations (SHAP), in order to ascertain the relative importance of individual biochemicals in describing the overall biological response as observed with RS. The current study found that the GBR-NMF-RF-SHAP model helped identify a wide range of radiation response biomarkers and hypoxia indicators ( e.g. , glycogen, lipids, DNA, amino acids) in H460 human lung cancer cells and H460 xenografts. Correlations between the hypoxic regions and Raman chemical biomarkers ( e.g. , glycogen, alanine, and arginine) were also identified in H460 xenografts. To summarize, GBR-NMF-RF-SHAP combined with RS can be applied to monitor the RT-induced biochemical response within cellular and tissue environments. Individual biochemicals were identified that (i) contributed to overall biological response to radiation, and (ii) spatially correlated with hypoxic regions of the tumour. RS combined with our analytical pipeline shows promise for further understanding of individual biochemical dynamics in radiation response for use in cancer therapy. GBR-NMF-RF-SHAP model helped identify a wide range of radiation response biomarkers and hypoxia indicators ( e.g. , glycogen, lipids, DNA, amino acids) in Raman spectra acquired from H460 human lung cancer cells and H460 xenografts.