Cox proportional hazards regression in small studies of predictive biomarkers.

Predictive biomarkers are essential for personalized medicine since they select the best treatment for a specific patient. However, of all biomarkers that are evaluated, only few are eventually used in clinical practice. Many promising biomarkers may be erroneously abandoned because they are investigated in small studies using standard statistical techniques which can cause small sample bias or lack of power. The standard technique for failure time endpoints is Cox proportional hazards regression with a multiplicative interaction term between binary variables of biomarker and treatment. Properties of this model in small studies have not been evaluated so far, therefore we performed a simulation study to understand its small sample behavior. As a remedy, we applied a Firth correction to the score function of the Cox model and obtained confidence intervals (CI) using a profile likelihood (PL) approach. These methods are generally recommended for small studies of different design. Our results show that a Cox model estimates the biomarker-treatment interaction term and the treatment effect in one of the biomarker subgroups with bias, and overestimates their standard errors. Bias is however reduced and power is increased with Firth correction and PL CIs. Hence, the modified Cox model and PL CI should be used instead of a standard Cox model with Wald based CI in small studies of predictive biomarkers.

A scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer.

BACKGROUND: Many scientific papers are published each year and substantial resources are spent to develop biomarker-based tests for precision oncology. However, only a handful of tests is currently used in daily clinical practice, since development is challenging. In this situation, the application of adequate statistical methods is essential, but little is known about the scope of methods used. METHODS: A PubMed search identified clinical studies among women with breast cancer comparing at least two different treatment groups, one of which chemotherapy or endocrine treatment, by levels of at least one biomarker. Studies presenting original data published in 2019 in one of 15 selected journals were eligible for this review. Clinical and statistical characteristics were extracted by three reviewers and a selection of characteristics for each study was reported. RESULTS: Of 164 studies identified by the query, 31 were eligible. Over 70 different biomarkers were evaluated. Twenty-two studies (71%) evaluated multiplicative interaction between treatment and biomarker. Twenty-eight studies (90%) evaluated either the treatment effect in biomarker subgroups or the biomarker effect in treatment subgroups. Eight studies (26%) reported results for one predictive biomarker analysis, while the majority performed multiple evaluations, either for several biomarkers, outcomes and/or subpopulations. Twenty-one studies (68%) claimed to have found significant differences in treatment effects by biomarker level. Fourteen studies (45%) mentioned that the study was not designed to evaluate treatment effect heterogeneity. CONCLUSIONS: Most studies evaluated treatment heterogeneity via separate analyses of biomarker-specific treatment effects and/or multiplicative interaction analysis. There is a need for the application of more efficient statistical methods to evaluate treatment heterogeneity in clinical studies.

Impact of off-label use regulations on patient care in dermatology - a prospective study of cost-coverage applications filed by tertiary dermatology clinics throughout Germany.

BACKGROUND: In dermatology, a medical speciality with a relatively high number of rare diseases, physicians often have to resort to off-label treatment options. To avoid claims, physicians in Germany can file a cost-coverage request (off-label application, OL-A). OBJECTIVES: Our aim was to investigate the extent to which the current regulations affect patient care. MATERIAL AND METHODS: Prospective cohort study among tertiary dermatology clinics throughout Germany, consecutively including OL-As (05/2019-09/2020) and assessing the follow-up correspondence. We modelled regressions to assess factors associated with cost-coverage decisions and the time needed by health insurers to process the OL-As. RESULTS: Thirteen clinics provided data on 121 OL-As, two of which applied for on-label treatments. Of the remaining 119 OL-As, 70 (58.8%) were immediately approved and 44 (37.0%) rejected. Including cases with one or more appeals, 87 of 119 OL-As (73.1%) were finally approved and 26 (21.9%) rejected. There was an association of the final approval rate with (1) the class of medication/treatment, with approval rates being significantly lower for JAK inhibitors than for biologics (OR 0.16, 95%-CI: 0.03-0.82); (2) German state, with approval rates being lower in eastern than in western states (OR 0.30, 95%-CI 0.12-0.76); and (3) cost of the intervention (no linear trend). However, none of these predictors was significant in our multiple logistic regression models. The median health insurer's processing time (first response) was 29 days (IQR 22-38). Our analyses showed no evidence of an association with the predictors we assessed. In cases approved, the median time from the decision to file an OL-A to the actual initiation of the treatment was 65.5 days (IQR 51-92). CONCLUSIONS: Our study points to substantial delays and inequalities in the provision of timely health care for dermatological patients with rare diseases, often involving treatments for which there is no adequate approved therapy.

Expression of recombinant surfactant protein SFTA3 in the human kidney cell line HEK 293T.

Pulmonary surfactant is broadly known to keep the lung dry, clean and open by lowering the surface tension of the fluid-film that lines the alveoli. The surfactant's protein component, the so called surfactant proteins (SPs), make up a multifunctional protein family. In addition to the four "classical" surfactant proteins (SP-A, SP-B, SP-C and SP-D), which possess immunologic as well as surfactant regulatory properties, two novel putative surfactant proteins (SFTA2 and SFTA3) have recently been described. Neither of them shows sequential nor structural similarity with the already known surfactant proteins. However, bioinformatic analyses as well as first molecular-biological studies reveal properties that have already been described for known surfactant proteins. In our present work we introduce a technique to synthesize, purify and stabilize recombinant SFTA3 derived from the human embryonic kidney cell line HEK 293T. This will provide investigators with a valuable source of further examination and characterization of this fascinating novel member of the surfactant protein family.

Recombinant expression of surfactant protein H (SFTA3) in Escherichia coli.

Surfactant proteins are broadly understood to be an important structural and functional part of the lung surfactant system. These proteins influence the intra-alveolar surface tension and contribute to innate immunity of the lung. Beside the four already known surfactant proteins SP-A, SP-B, SP-C and SP-D, two novel SPs (SP-G/SFTA2 and SP-H/SFTA3) have recently been described. These show no sequential or structural similarity with the already known SPs. However, bioinformatic prediction tools suggest physicochemical properties, which have already been described for other surfactant proteins. Although it is known that SFTA2 and SFTA3 are expressed in different human tissues, their distinct functional properties remain unclear. Here, we describe the establishment of a stable expression system for recombinant SFTA3 protein synthesis in Escherichia coli. This gives rise to future experiments with the overall aim to further examine and characterize this novel protein in more detail.