Combination therapy with Olaratumab/doxorubicin in advanced or metastatic soft tissue sarcoma -a single-Centre experience

BACKGROUND: The antibody targeting platelet-derived growth factor receptor alpha (PDGFRA), olaratumab, was approved in 2016 for metastatic soft tissue sarcoma (STS) in combination with doxorubicin based on promising results of a phase Ib/II trial by the Food and Drug Administration (FDA). However, recently the phase III ANNOUNCE trial could not confirm the additional value of olaratumab in this context. METHODS: Here, in a retrospective analysis we share our single-centre experience with olaratumab/doxorubicin in STS by including n = 32 patients treated with olaratumab/doxorubicin between 2016 and 2019. RESULTS: Median progression-free survival (PFS) in the overall cohort was 3.1 months (range 0.6-16.2). A response [complete remission (CR), partial remission (PR) or stable disease (SD)] was seen in n = 11 (34%) cases, whereas n = 21 (66%) patients showed progressive disease (PD). In n = 9 patients surgery was performed subsequently in an individual therapeutic approach. Out of n = 5 patients receiving additional regional hyperthermia, n = 3 achieved PR or SD. CONCLUSIONS: This single-centre experience does also not support the promising phase Ib/II results for olaratumab/doxorubicin in STS. However, our findings do not preclude that olaratumab combination therapy could be valuable in a neoadjuvant setting. This warrants further exploration also taking into account the heterogeneous nature of STS

The landscape of genetic aberrations in myxofibrosarcoma

Myxofibrosarcoma (MFS) is a rare subtype of sarcoma, whose genetic basis is poorly understood. We analyzed 69 MFS cases using whole-genome (WGS), whole-exome (WES), and/or targeted-sequencing (TS). Newly sequenced genomic data were combined with additional deposited 116 MFS samples. WGS identified a high number of structural variations (SVs) per tumor most frequently affecting the TP53 and RB1 loci, 40% of tumors showed a BRCAness-associated mutation signature, and evidence of chromothripsis was found in all cases. Most frequently mutated /copy number altered genes affected known disease drivers such as TP53 (56.2%), CDKN2A/B (29.7%), RB1 (27.0%), ATRX (19.5%), and HDLBP (18.9%). Several previously unappreciated genetic aberrations including MUC17, FLG, and ZNF780A were identified in more than 20% of patients. Longitudinal analysis of paired diagnosis and relapse time points revealed a 1.2-fold mutation number increase accompanied with substantial changes in clonal composition over time. This study highlights the genetic complexity underlying sarcomagenesis of MFS

Clinical and analytical validation of Ki-67 in 9069 patients from IBCSG VIII + IX, BIG1-98 and GeparTrio trial : systematic modulation of interobserver variance in a comprehensive in silico ring trial

Purpose: Ki-67 has been clinically validated for risk assessment in breast cancer, but the analytical validation and cutpoint-definition remain a challenge. Intraclass correlation coefficients (ICCs) are a statistical parameter for Ki-67 interobserver performance. However, the maximum degree of variance among pathologists allowed for meaningful biomarker results has not been defined. Methods: Different amounts of variance were added to central pathology Ki-67 data (n = 9069) from three cohorts (IBCSGVIII + IX, BIG1-98, GeparTrio) by simulation of 4500 evaluations for each cohort, which were grouped by ICCs, ranging from excellent (ICC = 0.9) to poor concordance (ICC = 0.1). Endpoints were disease-free survival (DFS) and pathological complete response (pCR, GeparTrio). Results: Ki-67 was a significant continuous prognostic marker for DFS over a wide range of cutpoints between 8% and 30% in all three cohorts. In our modelling approach, Ki-67 was a stable prognostic marker despite increased interpathologist variance. Even for a poor ICC of 0.5, one or more significant Ki-67 cutoffs were detected in 86.8% (GeparTrio), 92.4% (IBCSGVIII + IX) and 100% of analyses (BIG1-98). Similarly, in GeparTrio, even with an extremely low ICC of 0.2, 99.6% of analyses were significant for pCR. Conclusions: Our study shows that Ki-67 is a continuous marker which is extremely robust to pathologist variation. Even if only 50% of variance is attributable to true Ki-67-based proliferation (ICC = 0.5), this information is sufficient to obtain statistically significant differences in clinical cohorts. This stable performance explains the observation that many Ki-67 studies achieve significant results despite relevant interobserver variance and points to a high clinical validity of this biomarker. For clinical decisions based on analysis of individual patient data, ongoing efforts to further reduce interobserver variability, including ring trials and standardized guidelines as well as image analysis approaches, should be continued

How to carve up the world: Learning and collaboration for structure recommendation

Structuring is one of the fundamental activities needed to understand data. Human structuring activity lies behind many of the datasets found on the internet that contain grouped instances, such as file or email folders, tags and bookmarks, ontologies and linked data. Understanding the dynamics of large-scale structuring activities is a key prerequisite for theories of individual behaviour in collaborative settings as well as for applications such as recommender systems. One central question is to what extent the "structurer" - be it human or machine - is driven by his/its own prior structures, and to what extent by the structures created by others such as one's communities. In this paper, we propose a method for identifying these dynamics. The method relies on dynamic conceptual clustering, and it simulates an intellectual structuring process operating over an extended period of time. The development of a grouping of dynamically changing items follows a dynamically changing and collectively determined "guiding grouping". The analysis of a real-life dataset of a platform for literature management suggests that even in such a typical "Web 2.0" environment, users are guided somewhat more by their own previous behaviour than by their peers. Furthermore, we also illustrate how the presented method can be used to recommend structure to the user. © Springer-Verlag 2013.status: publishe