Cancer is a leading cause of death and despite many new drugs, a major diagnostic challenge remains knowing which drug will work best for a patient. A new class of drugs called checkpoint inhibitors (CPIs) have revolutionized cancer treatment. However, current diagnostic methods (e.g. PDL1, MSI and TMB) do not accurately predict which patients will respond.~Elephas is developing a diagnostic platform using small 3D Live Tumor Fragments (LTFs) from participants for accurate prediction of drug response with a focus on CPIs such as Pembrolizumab (Keytruda). These LTFs contain both tumor cells and infiltrating immune cells, which are critical in determining response to CPIs and other immunotherapies.~In this observational clinical basket trial, participants will be recruited and their actual clinical response (using RECIST 1.1) to CPIs across five solid tumors (lung, head/neck, bladder, kidney, and skin) will be compared to the platform's predictive Artificial Intelligence (AI) score that is based on RNA, clinical data, and 3D microscopy images. The sensitivity and specificity of the platform's score will be determined and compared to current diagnostic methods for CPIs like PDL1, MSI, and TMB.
The primary objective of this study is to determine the ex-vivo prognostic accuracy of the Cybrid live tumor diagnostic platform across a basket of solid tumors, using in-vivo RECIST 1.1 as the reference method.