The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia.

Although children and adolescents with acute lymphoblastic leukaemia (ALL) have high survival rates, approximately 15-20% of patients relapse. Risk of relapse is routinely estimated at diagnosis by biological factors, including flow cytometry data. This high-dimensional data is typically manually assessed by projecting it onto a subset of biomarkers. Cell density and "empty spaces" in 2D projections of the data, i.e. regions devoid of cells, are then used for qualitative assessment. Here, we use topological data analysis (TDA), which quantifies shapes, including empty spaces, in data, to analyse pre-treatment ALL datasets with known patient outcomes. We combine these fully unsupervised analyses with Machine Learning (ML) to identify significant shape characteristics and demonstrate that they accurately predict risk of relapse, particularly for patients previously classified as 'low risk'. We independently confirm the predictive power of CD10, CD20, CD38, and CD45 as biomarkers for ALL diagnosis. Based on our analyses, we propose three increasingly detailed prognostic pipelines for analysing flow cytometry data from ALL patients depending on technical and technological availability: 1. Visual inspection of specific biological features in biparametric projections of the data; 2. Computation of quantitative topological descriptors of such projections; 3. A combined analysis, using TDA and ML, in the four-parameter space defined by CD10, CD20, CD38 and CD45. Our analyses readily extend to other haematological malignancies.

Plasma levels of mid-regional pro-adrenomedullin in sepsis are associated with risk of death.

BACKGROUND: Identifying Intensive Care Unit (ICU) patients with sepsis and predicting the risk of death are unmet clinical needs. METHODS: Prospective observational single-center study of 120 consecutive ICU patients with suspected severe sepsis at Jerez Hospital. Epidemiological, clinical, laboratory data and MR-proADM, Procalcitonin (PCT) and C-reactive protein (CRP) levels were recorded at ICU admission and follow-up. RESULTS: At ICU discharge, 104 patients were diagnosed with severe sepsis and 39 died. Plasma MR-proADM was highly indicative of sepsis: 4.05 nmol/L vs. of 0.309 nmol/L (P<0.001), with area under the ROC curve (AUC-ROC) was 0.947. At 48 hours following admission, the median MR-proADM levels in surviving sepsis patients fell to 1.65 nmol/L but remained higher in the non-survivors (2.475 nmol/L) (P=0.04). On day 5 the levels fell to 1.36 nmol/L in surviving sepsis patients vs. 3.42 nmol/L in the non-survivors (P<0.001). On day 5 the survivors showed greater MR-proADM clearance (62.7% vs. 21.2%). The AUC-ROC on day 5 was 0.825, PCT 0.725 and CRP 0.700. The AUC-ROC to MR-proADM clearance on day 5 was 0.734. In a multivariable model, MR-proADM levels at 48 hours and on day 5 and clearance on day 5 following admission were statistically significant predictive factors of mortality. CONCLUSIONS: In clinical practice, in ICU patients admitted with SIRS and organ dysfunction, an MR-proADM cut-off point of 1.425 nmol/L helps to identify those with sepsis. An MR-proADM value above 5.626 nmol/L 48 hours after admission was associated with a high risk of death.