Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects

Many cancer treatments are associated with serious side effects, while they often only benefit a subset of the patients. Therefore, there is an urgent clinical need for tools that can aid in selecting the right treatment at diagnosis. Here we introduce simulated treatment learning (STL), which enables prediction of a patient’s treatment benefit. STL uses the idea that patients who received different treatments, but have similar genetic tumor profiles, can be used to model their response to the alternative treatment. We apply STL to two multiple myeloma gene expression datasets, containing different treatments (bortezomib and lenalidomide). We find that

The utility of peripheral venous lactate in emergency department patients with normal and higher lactate levels: A prospective observational study

Objective: to assess the utility of peripheral venous lactate (PVL) in Emergency Department patients. Methods: arteriovenous agreement was assessed in three subgroups: PVL <2 mmol/l, PVL ≥ 2 mmol/l to < 4 mmol/l and PVL ≥ 4 mmol/l. The predictive value of PVL to predict arterial lactate (AL) ≥2 mmol/l was assessed at different cut-off values. Results: 74 samples were analysed. The venous-arterial mean difference and 95% limits of agreement for the subgroups were 0.25 mmol/l (-0.18 to 0.68), 0.37 mmol/l (-0.57 to 1.32) and -0.89 mmol/l (-3.75 to 1.97). PVL ≥2 mmol/l predicts AL ≥2 mmol/l with 100% sensitivity. Conclusion: PVL <2 mmol/l rules out arterial hyperlactatemia. As agreement declines in higher levels, arterial sampling should be considered

A multiplex PCR predictor for aCGH success of FFPE samples.

Formalin-fixed, paraffin-embedded (FFPE) tissue archives are the largest and longest time-spanning collections of patient material in pathology archives. Methods to disclose information with molecular techniques, such as array comparative genomic hybridisation (aCGH) have rapidly developed but are still not optimal. Array comparative genomic hybridisation is one efficient method for finding tumour suppressors and oncogenes in solid tumours, and also for classification of tumours. The fastest way of analysing large numbers of tumours is through the use of archival tissue samples with first, the huge advantage of larger median follow-up time of patients studied and second, the advantage of being able to locate and analyse multiple tumours, even across generations, from related individuals (families). Unfortunately, DNA from archival tissues is not always suitable for molecular analysis due to insufficient quality. Until now, this quality remained undefined. We report the optimisation of a genomic-DNA isolation procedure from FFPE pathology archives in combination with a subsequent multiplex PCR-based quality-control that simply identified all samples refractory to further DNA-based analyses

A non-BRCA1/2 hereditary breast cancer sub-group defined by aCGH profiling of genetically related patients

Item does not contain fulltextGermline mutations in BRCA1 and BRCA2 explain approximately 25% of all familial breast cancers. Despite intense efforts to find additional high-risk breast cancer genes (BRCAx) using linkage analysis, none have been reported thus far. Here we explore the hypothesis that BRCAx breast tumors from genetically related patients share a somatic genetic etiology that might be revealed by array comparative genomic hybridization (aCGH) profiling. As BRCA1 and BRCA2 tumors can be identified on the basis of specific genomic profiles, the same may be true for a subset of BRCAx families. Analyses used aCGH to compare 58 non-BRCA1/2 familial breast tumors (designated BRCAx) to sporadic (non-familiar) controls, BRCA1 and BRCA2 tumors. The selection criteria for BRCAx families included at least three cases of breast cancer diagnosed before the age of 60 in the family, and the absence of ovarian or male breast cancer. Hierarchical cluster analysis was performed to determine sub-groups within the BRCAx tumor class and family heterogeneity. Analysis of aCGH profiles of BRCAx tumors indicated that they constitute a heterogeneous class, but are distinct from both sporadic and BRCA1/2 tumors. The BRCAx class could be divided into sub-groups. One subgroup was characterized by a gain of chromosome 22. Tumors from family members were classified within the same sub-group in agreement with the hypothesis that tumors from the same family would harbor a similar genetic background. This approach provides a method to target a sub-group of BRCAx families for further linkage analysis studies