Development of biomarkers to predict chemotherapy efficacy based on tumor blood flow and metabolism in breast cancer

Abstract

Breast cancer is a prevalent disease in women, with increasing incidence rates worldwide. Early detection of breast cancer is crucial since the survival rate significantly improves when the disease is diagnosed and treated in its initial stages. Prediction of the tumor response to chemotherapy at the earliest stage holds significant importance as it can reduce side effects after chemotherapy and facilitates the clinician in devising an appropriate therapy plan. Thus, disease monitoring progression after treatment is important to assess treatment efficacy and predict the likelihood of future disease progression.

Several modalities are employed for monitoring the response of breast cancer patients to chemotherapy. Digital X-ray mammography is the most commonly used to diagnose and monitor tumor response and treatment effectiveness in breast cancer patients. Ultrasound is a complimentary device usually performed with other modalities and provides solely morphological information and relies heavily on the operator's experience. Positron Emission Spectroscopy, although valuable, is costly and impractical for frequent observations due to using the radionuclides in nuclear medicine procedures. Although conventional techniques like tumor size assessment are frequently used, they may not offer fast insights into treatment response. In contrast, Diffuse Optical Technology (DOT) presents an alternative method for monitoring tumor response. It conducts this by measuring vital information such as metabolism, oxygen saturation, and blood flow, which can effectively depict the metabolic alterations occurring within tumors.

We propose a hypothesis that a change in blood flow information, such as oxygen saturation (StO2), relative blood flow index (rBFI), oxyhemoglobin (HbO2), and deoxyhemoglobin (RHb) obtained from DRS-DCS system can serve as early indicators to monitor treatment efficacy. These changes expect to occur earlier than structural changes in the tumor after chemotherapy. This study aims to assess the viability of utilizing Diffuse Reflectance Spectroscopy (DRS) and Diffuse Correlation Spectroscopy (DCS) as non-invasive optical methods for evaluating tissue oxygen saturation and metabolism in breast tumors. The study also seeks to monitor the response of tumors to chemotherapy using these techniques. Two groups of rats were used in this study: control and chemotherapy (CTX). The baseline data was set as Day 0, and the tissue oxygen saturation (StO2), relative blood flow index (rBFI), relative tissue metabolic rate (rTMRO2), oxyhemoglobin (HbO2) and deoxyhemoglobin (RHb) were measured using DRS and DCS until Day 21. The data was measured every day and chemotherapy using cyclophosphamide was applied at Day 8.

The results indicate that, CTX group, StO2 decreased as the tumor grew in all groups after cell inoculation followed by initially increased one day after chemotherapy and eventually recovering to a level close to the reference data within the first day of the trial while rBFI also increased as the tumor grow in all groups but plateaued and decreased once the tumor volume exceeded 200mm3. Tumor volume started to decrease 2 days after chemotherapy. However, the rBFI showed an initial drop on the day of chemotherapy with a gradual increase until day 6 after chemotherapy. rTMRO2 tended to increase as tumor size and has an initial drop on the day of chemotherapy followed by gradually increasing until day six and reaching the maximum point before declining by remaining value still higher than baseline.

In summary, monitoring tissue saturated oxygen information during chemotherapy does appear to indicate the early response of tumors to chemotherapy whereas monitoring in tumor blood flow information does not appear to indicate the early response of tumors to chemotherapy but may provide valuable insights into vasculature changes during tumor volume regression due to chemotherapy. Further investigations are required to compare the metabolism and rBFI changes between tumors that respond to treatment and those that do not, to determine whether blood flow information can serve as a biomarker for predicting the tumor response to treatment.