Project description:Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Instead, using plasma proteomes quantifying 302 plasma protein groups at 387 timepoints in 57 critically ill patients on invasive mechanical ventilation, we found 14 proteins that showed trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81, n=49). We tested the established predictor on an independent validation cohort (AUROC of 1.0, n=24). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.
Project description:Purpose: Colorectal cancer (CRC) patients with peritoneal metastases (CRPM) have limited treatment options and the lowest CRC survival rates. We aimed to determine whether organoid testing could help guide precision treatment for CRPM patients, as the clinical utility of prospective, functional drug screening including non-standard agents is unknown. Experimental Design: CRPM organoids (peritonoids) isolated from patients underwent parallel next-generation sequencing and medium-throughput drug panel testing ex vivo to identify specific drug sensitivities for each patient. We measured the utility of such a service including: success of peritonoid generation, time to cultivate peritonoids, reproducibility of the medium-throughput drug testing, and documented changes to clinical therapy as a result of the testing. Results: Peritonoids were successfully generated and validated from 68% (19/28) of patients undergoing standard care. Genomic and drug profiling was completed within 8 weeks and a formal report ranking drug sensitivities was provided to the medical oncology team upon failure of standard care treatment. This resulted in a treatment change for 2 patients, one of whom had a partial response despite previously progressing on multiple rounds of standard care chemotherapy. The barrier to implementing this technology in Australia is the need for drug access and funding for off-label indications. Conclusions: Our approach is feasible, reproducible and can guide novel therapeutic choices in this poor prognosis cohort, where new treatment options are urgently needed. This platform is relevant to many solid organ malignancies.
Project description:Septic shock, the excessive host immune response to pathogen infection, poses a major health concern accounting globally for approximately 20% of all deaths. Current methods to establish disease severity are unacceptably slow, unspecific and insensitive, hindering timely and effective treatment and patient care. Aiming to identify easy-to-assay glyco-signatures that may identify and guide the clinical management of the worst affected patients, we applied quantitative glycomics and glycoproteomics to serum longitudinally collected from septic shock survivors (n = 29) and non-survivors (n = 8). Glycomics of all 134 serum samples (2-7 samples/patient, daily sampling until recovery or death) revealed significant N-glycome dynamics in both septic shock survivors and non-survivors. Interestingly, unsupervised clustering of the serum N-glycome upon ICU admission (day 1) indicated survivorship-specific glyco-signatures. We therefore trained a random forest model using the serum N-glycome data from all ICU samples except for day 1 and tested the model using the clinically informative day 1 data. Excitingly, the model accurately classified survivorship outcomes of 35 of 37 patients (specificity 94.6%) and correctly predicted 6 of 8 non-survivors (sensitivity 75%). Targeted N-glycome analysis revealed raised levels of Lewis fucosylation in non-survivors relative to survivors already upon ICU admission. Elevated Lewis fucosylation was recapitulated by comparative glycoproteomics that also identified α-1-acid-glycoprotein as the principal carrier of Lewis fucosylation. Enabled by integrated -omics approaches, this study has laid a foundation for improved clinical management of septic shock patients by uncovering easy-to-assay glyco-signatures that early in the disease course accurately identify individuals with poor survival outcomes.