Cancer is a highly complex system characterized by inter-patient variability and intra-tumor heterogeneity. These characteristics make it challenging for research and drug development to identify the right patient population most likely to respond to investigational therapies of interest.
For many cancer treatments, it is well known which part of the cell cycle they target. However, without the tools to characterize cell cycle dysfunction at the individual patient level, it is difficult to predict which patients will most likely respond to treatment.
4D Path has created the QPOR™ platform to directly measure and quantify cell cycle deregulations and tumor microenvironment dynamics to precisely predict a patient's response to therapy.
4D Path can partner with you to develop a customized treatment prediction signature for your therapy of interest, and, potentially, change the trajectory of your program, increase the probability of clinical trial success, and help you deliver this product to patients in need.
Oncologists are faced with the difficult decision of whether to use neoadjuvant chemotherapy in treating breast cancer. Clinical success is approximately 40%, however, it is not known which patients are the likeliest to response. The stakes are high as non-response can worsen the patient’s prognosis and chemotherapy treatment often comes with significant toxicity and adverse effects.
4D Path was asked to develop a customized treatment prediction signature to distinguish patients with a good response to neoadjuvant chemotherapy (RCB 0,1) from those with a poor response (RCB 2,3) in patients who are germline BRCA carriers with HER2-negative breast cancer.
4D Path developed a customized treatment prediction signature, CmbINAC , combining the immune heterogeneity (IHI) , proliferative (PI) , and cell cycle G1S deregulation (G1SI) indices. (See slide 5). In collaboration with the Translational Breast Cancer Research Consortium (TBCRC) and Dana Farber Cancer Institute (DFCI), 88 cases of pretreatment core needle biopsies (CNBs) were evaluated using the 4D Path Q-Plasia OncoReader (QPOR™) generated image-based signatures, CmbINAC.