Spatial topology of organelle is a new breast cancer cell classifier.

Genomics and proteomics have been central to identify tumor cell populations, but more accurate approaches to classify cell subtypes are still lacking. We propose a new methodology to accurately classify cancer cells based on their organelle spatial topology. Herein, we developed an organelle topology-based cell classification pipeline (OTCCP), which integrates artificial intelligence (AI) and imaging quantification to analyze organelle spatial distribution and inter-organelle topology. OTCCP was used to classify a panel of human breast cancer cells, grown as 2D monolayer or 3D tumor spheroids using early endosomes, mitochondria, and their inter-organelle contacts. Organelle topology allows for a highly precise differentiation between cell lines of different subtypes and aggressiveness. These findings lay the groundwork for using organelle topological profiling as a fast and efficient method for phenotyping breast cancer function as well as a discovery tool to advance our understanding of cancer cell biology at the subcellular level.

Biomarkers Predictive of Extubation and Survival of COVID-19 Patients.

Purpose Many patients with COVID-19 who develop acute respiratory distress syndrome (ARDS) require prolonged periods of mechanical ventilation. Mechanical ventilation may amplify ventilator-associated complications and extend resource utilization. A better understanding of prognostic indicators could help in the planning and distribution of resources, particularly in resource-limited areas. We analyzed laboratory studies of intubated COVID-19 patients with the goal of identifying biomarkers that may predict extubation success and survival to discharge. Methods A retrospective chart review was performed on all COVID-19 patients requiring mechanical ventilation between January 3, 2020, and January 7, 2020, in a single academic tertiary care center in Northeastern New York State. The electronic medical record was used to collect 14 laboratory variables at three time points: admission, intubation, and extubation (including terminal extubation) for all intubated intensive care unit (ICU) patients treated for COVID-19. Mean laboratory values were analyzed with the Mann-Whitney U test. Categorical variables were analyzed with the two-sample Wilcoxon rank-sum test. Results Seventy-two patients met the inclusion criteria. Forty-three patients were male. The mean age was 61 years. The overall mortality was 50%. On admission, intubated patients who survived had significantly higher platelet counts (p=0.024), and absolute lymphocyte counts (ALC; p=0.047). Notably, ferritin (p=0.018) and aspartate transaminase (AST; p=0.0045) levels were lower in survivors. At the time of intubation, survivors again had a higher platelet count (p=0.024) and ALC (p=0.037) levels. They had a lower D-dimer (p=0.0014), ferritin (p=0.0015), lactate dehydrogenase (LDH; p=0.0145), and AST (p=0.018) compared to intubated patients who died. At extubation, survivors had higher platelet count (p=0.0002), ALC (p=0.0013), and neutrophil/lymphocyte ratio (NLR; p=0.0024). Survivors had lower d-dimer (p=0.035), ferritin (p=0.0012), CRP (p=0.045), LDH (p=0.002), AST (p<0.001), and ALK (p=0.0048). Conclusions Biomarkers associated with increased risk of mortality include platelet count, ALC, lymphocyte percentage, NLR, D-dimer, ferritin, C-reactive protein (CRP), AST, alanine transaminase (ALT), and alkaline phosphatase (ALK). This study provides additional evidence that these biomarkers have prognostic value in patients with severe COVID-19. The goal is to find objective surrogate markers of disease improvement or success of extubation. When considered within the larger body of data, it is our hope that a mortality risk calculator can be generated for intubated COVID-19 patients.