Early detection of treatment response in myelofibrosis by polarization-sensitive mid-infrared spectroscopic imaging - PROJECT SUMMARY Myelofibrosis (MF) is a severe disease with a low median survival rate of six years and a declining quality of life. JAK2 inhibitors have revolutionized treatment for patients with MF; however, only 30%-40% of patients respond strongly to this therapy. Identifying non-responders early is vital to clinically recommend higher-risk by effective alternative therapies that are not suggested as first-line treatments. Histopathologic examination of bone marrow biopsies is the current standard for identifying non-responders. However, this process is failing patients as it takes three years to reach a definitive conclusion. We aim to reduce this assessment period to six months, thereby improving health outcomes for non-responders and increasing their chances of recovery before the disease causes irreparable damage. This research is essential because non-responders comprise most treated MF patients (60%-70%). To accurately monitor treatment response, it is necessary to identify hematopoietic BM areas and recognize abnormal reticulin-fibers patterns within these areas. However, current methods are inadequate, as they can only identify non-responders reliably after three years. Therefore, we propose a new technology that addresses all the challenges with MF treatment assessment in a single instrument. This technology is observer- independent, label-free, and quantitative, filling a critical gap in the state-of-the-art. Although histopathology is the gold-standard method, it is susceptible to high inter-observer variability in MF due to the qualitative nature of visual inspections. This variability is because of the difficulty in recognizing fiber-pattern trends from immunohistochemical staining data. Our technology overcomes these limitations by providing a comprehensive and quantitative evaluation of the evolution of fibrosis in MF. Our proposed method using mid-infrared spectroscopic imaging (MIRSI) technologies and machine learning addresses an unmet clinical need in assessing the treatment response of MF, benefiting thousands of patients. Our preliminary studies show that MIRSI can distinguish between different tissue subtypes, including hematopoietic tissue, with high accuracy and create detailed maps of reticulin fiber disorganization within hematopoietic areas, providing a precise visualization of fibrosis progression. We propose using a MIRSI-based treatment response score to identify early trends in disease progression. We aim to validate our approach using 678 samples spanning multiple time points for each patient. This strategy will provide a robust numerical tracking of fibrosis progression, facilitate early identification of non-responders, allow them to transfer to novel therapies with disease-modifying activity, and improve the current treatment paradigm.
