Protein Corona Sensor Array Nanosystem for Detection of Coronary Artery Disease.

Coronary artery disease (CAD) is the most common type of heart disease and represents the leading cause of death in both men and women worldwide. Early detection of CAD is crucial for decreasing mortality, prolonging survival, and improving patient quality of life. Herein, a non-invasive is described, nanoparticle-based diagnostic technology which takes advantages of proteomic changes in the nano-bio interface for CAD detection. Nanoparticles (NPs) exposed to biological fluids adsorb on their surface a layer of proteins, the "protein corona" (PC). Pathological changes that alter the plasma proteome can directly result in changes in the PC. By forming disease-specific PCs on six NPs with varying physicochemical properties, a PC-based sensor array is developed for detection of CAD using specific PC pattern recognition. While the PC of a single NP may not provide the required specificity, it is reasoned that multivariate PCs across NPs with different surface chemistries, can provide the desirable information to selectively discriminate the condition under investigation. The results suggest that such an approach can detect CAD with an accuracy of 92.84%, a sensitivity of 87.5%, and a specificity of 82.5%. These new findings demonstrate the potential of PC-based sensor array detection systems for clinical use.

Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary.

Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its primary site and accounts for 3-5% of all cancers. Established targeted therapies are lacking for CUP, leading to generally poor outcomes. We developed OncoNPC, a machine-learning classifier trained on targeted next-generation sequencing (NGS) data from 36,445 tumors across 22 cancer types from three institutions. Oncology NGS-based primary cancer-type classifier (OncoNPC) achieved a weighted F1 score of 0.942 for high confidence predictions ([Formula: see text]) on held-out tumor samples, which made up 65.2% of all the held-out samples. When applied to 971 CUP tumors collected at the Dana-Farber Cancer Institute, OncoNPC predicted primary cancer types with high confidence in 41.2% of the tumors. OncoNPC also identified CUP subgroups with significantly higher polygenic germline risk for the predicted cancer types and with significantly different survival outcomes. Notably, patients with CUP who received first palliative intent treatments concordant with their OncoNPC-predicted cancers had significantly better outcomes (hazard ratio (HR) = 0.348; 95% confidence interval (CI) = 0.210-0.570; P = [Formula: see text]). Furthermore, OncoNPC enabled a 2.2-fold increase in patients with CUP who could have received genomically guided therapies. OncoNPC thus provides evidence of distinct CUP subgroups and offers the potential for clinical decision support for managing patients with CUP.

Utilizing Electronic Health Records (EHR) and Tumor Panel Sequencing to Demystify Prognosis of Cancer of Unknown Primary (CUP) patients.

Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its original site and accounts for 3-5% of all cancers. It does not have established targeted therapies, leading to poor outcomes. We developed OncoNPC, a machine learning classifier trained on targeted next-generation sequencing data from 34,567 tumors from three institutions. OncoNPC achieved a weighted F1 score of 0.94 for high confidence predictions on known cancer types (65% of held-out samples). When applied to 971 CUP tumors from patients treated at the Dana-Farber Cancer Institute, OncoNPC identified actionable molecular alterations in 23% of the tumors. Furthermore, OncoNPC identified CUP subtypes with significantly higher polygenic germline risk for the predicted cancer type and significantly different survival outcomes, supporting its validity. Importantly, CUP patients who received first palliative intent treatments concordant with their OncoNPC-predicted cancer sites had significantly better outcomes (H.R. 0.348, 95% C.I. 0.210 - 0.570, p-value 2.32 × 10-5). OncoNPC thus provides evidence of distinct CUP subtypes and offers the potential for clinical decision support for managing patients with CUP.