Journal of Nanomedicine & Biotherapeutic Discovery
Open Access

Abstract

Noninvasive risk stratification of patients using predictive biomarker apoptosis index of tumors

Raghu Pandurangi

Statement of the Problem: Human tumors are heterogeneous which evoke different responses from different treatments. Current animal models used in cancer research are xenografts which would not mimic human tumors. A noninvasive imaging modality to assess cell death in target and non-target organs simultaneously may help to overcome the heterogeneity and may identify a biomarker which can be used to predict the efficacy and toxicity of treatments. Apoptosis Index (AI) is the measure of cell death in tumor, the modulation of which reflects how it responds to therapy. For example, we and others have shown that lower the spontaneous AI, lower the response and vice versa from the treatments irrespective of the nature of treatments. We have developed a novel technology “A Priori Activation of Apoptosis Pathways of Tumor” (AAAPT) which raises AI of spontaneous tumors above a threshold level in order to evoke a better response from therapy.

Methodology & Theoretical Orientation: Cancer cells have ability to enhance survival pathways (e.g. NF-kB and PARP) and down regulate the cell death pathways (e.g. CD95, ASK1) for their survival. Hence, we have designed new technology to target these pathways to sensitize those resistant tumor cells using targeted activation technology. We have used clinically oriented SPECT and Ultrasound Imaging techniques to assess AI as a predictive biomarker of efficacy and toxicity of chemotherapy respectively.

Findings: SPECT imaging of Lewis Lung Carcinoma (LLC) showed an enhanced cell death (higher AI) post treatment by Cyclophosphamide while, US imaging reversed the cardiotoxicity by doxorubicin by using AAAPT as a neoadjuvant to Doxorubicin.

Conclusion & Significance: The noninvasive assessment of AI (measure of cell death) by SPECT combined with US imaging can be used to risk stratify patients in terms of who responds to which therapy earlier compared to tumor regression timelines.

Published Date: 2020-11-30; Received Date: 2020-11-20