The transcription factor SOX2 is important for brain development and for neural stem cells (NSC) maintenance. Sox2-deleted (Sox2-del) NSC from neonatal mouse brain are lost after few passages in culture. Two highly expressed genes, Fos and Socs3, are strongly downregulated in Sox2-del NSC; we previously showed that Fos or Socs3 overexpression by lentiviral transduction fully rescues NSC’s long-term maintenance in culture. Sox2-del NSC are severely defective in neuronal production when induced to differentiate. NSC rescued by Sox2 reintroduction correctly differentiate into neurons. Similarly, Fos transduction rescues normal or even increased numbers of immature neurons expressing beta-tubulinIII, but not more differentiated markers (MAP2). Additionally, many cells with both beta-tubulinIII and GFAP expression appear, indicating that FOS stimulates the initial differentiation of a “mixed” neuronal/glial progenitor. The unexpected rescue by FOS suggested that FOS, a SOX2 transcriptional target, might act on neuronal genes, together with SOX2. CUT&RUN analysis to detect genome-wide binding of SOX2, FOS, and JUN (the AP1 complex) revealed that a high proportion of genes expressed in NSC are bound by both SOX2 and AP1. Downregulated genes in Sox2-del NSC are highly enriched in genes that are also expressed in neurons, and a high proportion of the “neuronal” genes are bound by both SOX2 and AP1.

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By leveraging the ever-increasing availability of cancer omics data and the continuous advances in cancer data science and machine learning, we have discovered the existence of cancer type-specific evolutionary signatures associated with different disease outcomes. These signatures represent "favored trajectories" of acquisition of driver mutations that are repeatedly detected in patients with similar prognosis. In this work, we present a novel framework named ASCETIC (Agony-baSed Cancer EvoluTion InferenCe) that extracts such signatures from NGS experiments generated by different technologies such as bulk and single-cell sequencing data. In our study, we applied ASCETIC to (i) single-cell sequencing data from 146 patients with distinct myeloid malignancies and bulk whole-exome sequencing data from 366 acute myeloid leukemia patients, (ii) multi-region sequencing data from 100 early-stage lung cancer patients from the TRACERx project, (iii) whole-exome/genome sequencing data from more than 10,000 Pan-Cancer Atlas samples, and (iv) targeted bulk sequencing data from more than 25,000 MSK-MET metastatic patients (both datasets including multiple cancer types). As a result, we extracted different cancer (sub)type-specific single-nucleotide variants evolutionary signatures associated with clusters of patients with statistically significant different prognoses. In addition, we conducted several validations using diverse and previously unexplored datasets to evaluate the reliability and applicability of the evolutionary signatures extracted by ASCETIC. Such analyses provided evidence of the robustness and generalizability of the identified evolutionary patterns.