ABSTRACT
Neuroblastoma is the most common extracranial solid tumor of childhood. While MYCN and mutant anaplastic lymphoma kinase (ALKF1174L) cooperate in tumorigenesis, how ALK contributes to tumor formation remains unclear. Here, we used a human stem cell-based model of neuroblastoma. Mis-expression of ALKF1174L and MYCN resulted in shorter latency compared to MYCN alone. MYCN tumors resembled adrenergic, while ALK/MYCN tumors resembled mesenchymal, neuroblastoma. Transcriptomic analysis revealed enrichment in focal adhesion signaling, particularly the extracellular matrix genes POSTN and FN1 in ALK/MYCN tumors. Patients with ALK-mutant tumors similarly demonstrated elevated levels of POSTN and FN1. Knockdown of POSTN, but not FN1, delayed adhesion and suppressed proliferation of ALK/MYCN tumors. Furthermore, loss of POSTN reduced ALK-dependent activation of WNT signaling. Reciprocally, inhibition of the WNT pathway reduced expression of POSTN and growth of ALK/MYCN tumor cells. Thus, ALK drives neuroblastoma in part through a feedforward loop between POSTN and WNT signaling.
Subject(s)
Neuroblastoma , Receptor Protein-Tyrosine Kinases , Humans , Anaplastic Lymphoma Kinase/genetics , Cell Adhesion Molecules , Cell Line, Tumor , N-Myc Proto-Oncogene Protein/genetics , N-Myc Proto-Oncogene Protein/metabolism , Neuroblastoma/pathology , Receptor Protein-Tyrosine Kinases/metabolism , Wnt Signaling PathwayABSTRACT
Disease-modifying treatments have transformed the natural history of spinal muscular atrophy (SMA), but the cellular pathways altered by SMN restoration remain undefined and biomarkers cannot yet precisely predict treatment response. We performed an exploratory cerebrospinal fluid (CSF) proteomic study in a diverse sample of SMA patients treated with nusinersen to elucidate therapeutic pathways and identify predictors of motor improvement. Proteomic analyses were performed on CSF samples collected before treatment (T0) and at 6 months (T6) using an Olink panel to quantify 1113 peptides. A supervised machine learning approach was used to identify proteins that discriminated patients who improved functionally from those who did not after 2 years of treatment. A total of 49 SMA patients were included (10 type 1, 18 type 2, and 21 type 3), ranging in age from 3 months to 65 years. Most proteins showed a decrease in CSF concentration at T6. The machine learning algorithm identified ARSB, ENTPD2, NEFL, and IFI30 as the proteins most predictive of improvement. The machine learning model was able to predict motor improvement at 2 years with 79.6% accuracy. The results highlight the potential application of CSF biomarkers to predict motor improvement following SMA treatment. Validation in larger datasets is needed.
