The standard therapy regimen of conventional osteosarcoma includes neoadjuvant chemotherapy followed by surgical resection and postoperative chemotherapy. The percentage of necrotic tissue following induction chemotherapy is assessed by using the Huvos grading system, which classifies patients as “poor responders” (PR) and “good responders” (GR). The aim of this study was to identify molecular markers expressed differentially between good and poor responders to neoadjuvant chemotherapy in order to predict the response to chemotherapy in conventional osteosarcomas before beginning treatment. Suppression Substractive Hybridization (SSH) was performed by using cDNA from frozen biopsy specimens. Expression of selected relevant genes identified by SSH was validated by using QRT-PCR. Immunohistochemistry (IHC) on tissue microarray (TMA) sections of 52 biopsies was performed to investigate protein expression in an independent cohort. ERK1 and STAT3 mRNA level were significantly different between PR and GR in an independent cohort. Phosphorylated STAT3 and ERK1 expressions by IHC on TMA were correlated with poor response to chemotherapy. Our results suggest that ERK1 and STAT3 expression are good predictive markers for chemotherapy response and that inhibitors might be used in combination with common chemotherapeutic drugs in conventional osteosarcomas.

Soft tissue sarcoma (STS) are rare and aggressive tumours. Their classification includes numerous histological subtypes of frequent poor prognosis. Liposarcomas (LPS) are the most frequent type among them, and the aggressiveness and deep localization of dedifferentiated LPS are linked to high levels of recurrence. Current treatments available today lead to five-year overall survival has remained stuck around 60–70% for the past three decades. Here, we highlight a correlation between Aurora kinasa A (AURKA) and AURKB mRNA overexpression and a low metastasis-free survival. AURKA and AURKB expression analysis at genomic and protein level on a 9-STS cell lines panel highlighted STS heterogeneity, especially in LPS subtype. AURKA and AURKB inhibition by RNAi and drug targeting with AMG 900, a pan Aurora Kinase inhibitor, in four LPS cell lines reduces cell survival and clonogenic proliferation, inducing apoptosis and polyploidy. When combined with doxorubicin, the standard treatment in STS, aurora kinases inhibitor can be considered as an enhancer of standard treatment or as an independent drug. Kinome analysis suggested its effect was linked to the inhibition of the MAP-kinase pathway, with differential drug resistance profiles depending on molecular characteristics of the tumor. Aurora Kinase inhibition by AMG 900 could be a promising therapy in STS.

Glioblastoma (GBM) is the most frequent and aggressive primary brain tumor in adults. Recently, we demonstrated that plasma denaturation profiles of glioblastoma patients obtained using Differential Scanning Fluorimetry can be automatically distinguished from healthy controls with the help of Artificial Intelligence (AI). Here, we used a set of machine-learning algorithms to automatically classify plasma denaturation profiles of glioblastoma patients according to their EGFR status. We found that Adaboost AI is able to discriminate EGFR alterations in GBM with an 81.5% accuracy. Our study shows that the use of these plasma denaturation profiles could answer the unmet neuro-oncology need for diagnostic predictive biomarker in combination with brain MRI and clinical data, in order to allow for a rapid orientation of patients for a definitive pathological diagnosis and then treatment. We complete this study by showing that discriminating another mutation, MGMT, seems harder, and that post-surgery monitoring using our approach is not conclusive in the 48 h that follow the surgery.

The biobank of the Assistance Publique Hôpitaux de Marseille (AP-HM) works as a multi-site biobank around one Quality Management System. The biobank works on the collection, preparation, storage and release of biological resources and their associated data. It receives tissue samples for neoplasms, DNA and cells for rare genetic diseases and blood samples for hematological and cardiovascular diseases. These samples are provided and classified by experts of different domains; the samples are under strict technical protocols with appropriate clinical data. This wide range of sample types, diseases and services allows collaborations with public or private laboratories to customize research projects or therapeutic trials. Funding statement: The AP-HM biobank is supported by the French government’s MIG grant, intended to cover part of its annual cost based on its annual activity, valorization of released samples and funds from the AP-HM.