At last week's AAPM annual meeting in Indianapolis, IN, Suneetha Devpura presented the group's most recent findings. Her work was highlighted in the "Best-in-Physics" session as one of the top 15 abstracts in the scientific programme, reflecting the "highest level of scientific quality and innovation".

Devpura, a physics post-doctoral fellow at Henry Ford Health System, Department of Radiation Oncology (Detroit, MI), first presented the team's work in using Raman spectroscopy for diagnostic applications. This work was done in collaboration with Children's hospital of Michigan and Wayne State University (Department of Physics). Raman spectroscopy, she explained, uses laser light to induce molecular vibrations in tissue samples. The probability of Raman scattering is weak (about 1 per 10 million photons) but results in well-resolved peaks, uniquely associated with different biochemical properties of the tissue.

Devpura and co-workers used Raman spectroscopy to examine pre-invasive and invasive head-and-neck squamous cell carcinoma (HNSCC), prostate cancer and paediatric cancers. They excited formalin-fixed paraffin processed tissue samples with 785 or 514.5 nm laser light, and used principal component analysis and discriminant function analysis to analyse and classify the resulting spectral data. They also examined normal tissues corresponding to the respective anatomic site for baseline analysis.

The researchers found distinct trends associated with cancer progression. For example, in the 401 Raman spectra collected from HNSCC tissues, Raman bands from L-tryptophan increased and keratin bands decreased relative to corresponding normal tissue signals. The accuracies for predicting normal tissue, pre-invasive and invasive cancers relative to pathological diagnosis were 91%, 91% and 89%, respectively.

Similarly, for 1220 Raman spectra recorded from prostate cancer samples, distinguishable spectral changes were seen for benign epithelia, prostatic intraepithelial neoplasia, prostate cancer and the surrounding stroma. In 929 spectra from paediatric cancers, beta-carotene, fat and cholesterol were the key biochemicals differentiating normal tissues from neuroblastoma and ganglioneuroma. Devpura notes that these chemicals were impacted by the paraffin fixing process. Here, Raman spectra could classify each category with greater than 90% accuracy in formalin fixed tissues.

"Certain biochemicals are enhanced or suppressed when tissue transforms from normal to carcinoma," Devpura explained.

Radiotherapy response

In the next stage of their research, Devpura and her colleagues performed Raman spectroscopy on 4T1 mouse breast cancer cells, two days after exposing the cells to 0, 5, 15 or 30 Gy of radiation.

They noted distinct spectral differences between the untreated and treated cells. In particular, the position of the DNA peak shifted from 721 to 718 cm–1 and the intensity of the RNA peak (at 780 cm–1) reduced with increasing dose. The spectra could be used to distinguish untreated from treated (15 or 30 Gy) cells with 96.3% sensitivity and 94.6% specificity.

The researchers now plan to expand their Raman spectroscopy studies to animal models. Preliminary experiments will include a study on 4T1 cells implanted subcutaneously into female mice. After the development of lung metastases, the mice will be irradiated and then sacrificed 1, 2 or 3 days later. The researchers will then perform Raman spectroscopy and pathological analysis of excised tissues.

The aim is to use Raman spectroscopy to identify biochemical markers (such as DNA, RNA, proteins, lipids and carbohydrates) that change differently among patients during radiotherapy. The ultimate goal is to use this technique to help predict tumour and normal tissue responses following radiation therapy.

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