  Neuroblastoma is a strongly heterogeneous cancer with very diverse clinical
courses that may vary from spontaneous regression to fatal progression; an
accurate patient's risk estimation at diagnosis is essential to design
appropriate tumor treatment strategies. Neuroblastoma is a paradigm disease
where different diagnostic and prognostic endpoints should be predicted from
common molecular and clinical information, with increasing complexity, as shown
in the FDA MAQC-II study. Here we introduce the novel multiobjective deep
learning architecture CDRP (Concatenated Diagnostic Relapse Prognostic)
composed by 8 layers to obtain a combined diagnostic and prognostic prediction
from high-throughput transcriptomics data. Two distinct loss functions are
optimized for the Event Free Survival (EFS) and Overall Survival (OS)
prognosis, respectively. We use the High-Risk (HR) diagnostic information as an
additional input generated by an autoencoder embedding. The latter is used as
network regulariser, based on a clinical algorithm commonly adopted for
stratifying patients from cancer stage, age at insurgence of disease, and MYCN,
the specific molecular marker. The architecture was applied to Illumina
HiSeq2000 RNA-Seq for 498 neuroblastoma patients (176 at high risk) from the
Sequencing Quality Control (SEQC) study, obtaining state-of-art on the
diagnostic endpoint and improving prediction of prognosis over the HR cohort.
