Superresolution Optical Fluctuation Imaging (SOFI) as initially demonstrated allows for a resolution enhancement in imaging by a factor of square-root of two. Here, we demonstrate how to increase the resolution of SOFI images by re-weighting the Optical Transfer Function (OTF). Furthermore, we demonstrate how cross-cumulants can be exploited to obtain a fair approximation of the underlying Point-Spread Function. We show a two-fold increase of resolution (over the diffraction limit) of near-infrared quantum dot labeled tubulin-network of 3T3 fibroblasts.

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Aims

Lipoprotein(a) concentration is associated with first cardiovascular events in clinical trials. It is unknown if this relationship holds for total (first and subsequent) events. In the ODYSSEY OUTCOMES trial in patients with recent acute coronary syndrome (ACS), the proprotein convertase subtilisin/kexin type 9 inhibitor alirocumab reduced lipoprotein(a), low-density lipoprotein cholesterol (LDL-C), and cardiovascular events compared with placebo. This post hoc analysis determined whether baseline levels and alirocumab-induced changes in lipoprotein(a) and LDL-C [corrected for lipoprotein(a) cholesterol] independently predicted total cardiovascular events.

Methods and results

Cardiovascular events included cardiovascular death, non-fatal myocardial infarction, stroke, hospitalization for unstable angina or heart failure, ischaemia-driven coronary revascularization, peripheral artery disease events, and venous thromboembolism. Proportional hazards models estimated relationships between baseline lipoprotein(a) and total cardiovascular events in the placebo group, effects of alirocumab treatment on total cardiovascular events by baseline lipoprotein(a), and relationships between lipoprotein(a) reduction with alirocumab and subsequent risk of total cardiovascular events. Baseline lipoprotein(a) predicted total cardiovascular events with placebo, while higher baseline lipoprotein(a) levels were associated with greater reduction in total cardiovascular events with alirocumab (hazard ratio Ptrend = 0.045). Alirocumab-induced reductions in lipoprotein(a) (median -5.0 [-13.6, 0] mg/dL) and corrected LDL-C (median -51.3 [-67.1, -34.0] mg/dL) independently predicted lower risk of total cardiovascular events. Each 5-mg/dL reduction in lipoprotein(a) predicted a 2.5% relative reduction in cardiovascular events.

Conclusion

Baseline lipoprotein(a) predicted the risk of total cardiovascular events and risk reduction by alirocumab. Lipoprotein(a) lowering contributed independently to cardiovascular event reduction, supporting the concept of lipoprotein(a) as a treatment target after ACS.

Background

Lipoprotein(a) concentration is associated with cardiovascular events. Alirocumab, a proprotein convertase subtilisin/kexin type 9 inhibitor, lowers lipoprotein(a) and low-density lipoprotein cholesterol (LDL-C).

Objectives

A pre-specified analysis of the placebo-controlled ODYSSEY Outcomes trial in patients with recent acute coronary syndrome (ACS) determined whether alirocumab-induced changes in lipoprotein(a) and LDL-C independently predicted major adverse cardiovascular events (MACE).

Methods

One to 12 months after ACS, 18,924 patients on high-intensity statin therapy were randomized to alirocumab or placebo and followed for 2.8 years (median). Lipoprotein(a) was measured at randomization and 4 and 12 months thereafter. The primary MACE outcome was coronary heart disease death, nonfatal myocardial infarction, ischemic stroke, or hospitalization for unstable angina.

Results

Baseline lipoprotein(a) levels (median: 21.2 mg/dl; interquartile range [IQR]: 6.7 to 59.6 mg/dl) and LDL-C [corrected for cholesterol content in lipoprotein(a)] predicted MACE. Alirocumab reduced lipoprotein(a) by 5.0 mg/dl (IQR: 0 to 13.5 mg/dl), corrected LDL-C by 51.1 mg/dl (IQR: 33.7 to 67.2 mg/dl), and reduced the risk of MACE (hazard ratio [HR]: 0.85; 95% confidence interval [CI]: 0.78 to 0.93). Alirocumab-induced reductions of lipoprotein(a) and corrected LDL-C independently predicted lower risk of MACE, after adjustment for baseline concentrations of both lipoproteins and demographic and clinical characteristics. A 1-mg/dl reduction in lipoprotein(a) with alirocumab was associated with a HR of 0.994 (95% CI: 0.990 to 0.999; p = 0.0081).

Conclusions

Baseline lipoprotein(a) and corrected LDL-C levels and their reductions by alirocumab predicted the risk of MACE after recent ACS. Lipoprotein(a) lowering by alirocumab is an independent contributor to MACE reduction, which suggests that lipoprotein(a) should be an independent treatment target after ACS. (ODYSSEY Outcomes: Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab; NCT01663402).