Machine-Learning Identifies a Strategy for Differentiation Therapy in Solid Tumors.

BACKGROUND: Although differentiation therapy can cure some hematologic malignancies, its curative potential remains unrealized in solid tumors. This is because conventional computational approaches succumb to the thunderous noise of inter-/intratumoral heterogeneity. Using colorectal cancers (CRCs) as an example, here we outline a machine learning(ML)-based approach to track, differentiate, and selectively target cancer stem cells (CSCs). METHODS: A transcriptomic network was built and validated using healthy colon and CRC tissues in diverse gene expression datasets (~5,000 human and >300 mouse samples). Therapeutic targets and perturbation strategies were prioritized using ML, with the goal of reinstating the expression of a transcriptional identifier of the differentiated colonocyte, CDX2, whose loss in poorly differentiated (CSC-enriched) CRCs doubles the risk of relapse/death. The top candidate target was then engaged with a clinical-grade drug and tested on 3 models: CRC lines in vitro, xenografts in mice, and in a prospective cohort of healthy (n = 3) and CRC (n = 23) patient-derived organoids (PDOs). RESULTS: The drug shifts the network predictably, induces CDX2 and crypt differentiation, and shows cytotoxicity in all 3 models, with a high degree of selectivity towards all CDX2-negative cell lines, xenotransplants, and PDOs. The potential for effective pairing of therapeutic efficacy (IC50) and biomarker (CDX2-low state) is confirmed in PDOs using multivariate analyses. A 50-gene signature of therapeutic response is derived and tested on 9 independent cohorts (~1700 CRCs), revealing the impact of CDX2-reinstatement therapy could translate into a ~50% reduction in the risk of mortality/recurrence. CONCLUSIONS: Findings not only validate the precision of the ML approach in targeting CSCs, and objectively assess its impact on clinical outcome, but also exemplify the use of ML in yielding clinical directive information for enhancing personalized medicine.

Type I regressed pleuropulmonary blastoma in a 10-year-old boy.

Pleuropulmonary blastoma (PPB) is a rare, malignant tumor of the lung and is the most common primary pulmonary malignancy in children. Here, we report a case of a boy who was diagnosed with type I regressed PPB after being mislabeled with congenital pulmonary malformation. A 10-year-old boy presented to our hospital with a history of worsening dyspnea. Since birth, his clinical status and radiographic images were concerning for congenital lobar emphysema that was managed conservatively. A chest computed tomography (CT) scan confirmed the persistence of a large cystic lesion and a diagnostic and therapeutic cystectomy was performed. Microscopic examination confirmed the presence of PPB type Ir. Patient was managed surgically alone with no added chemotherapy, as there was no overall survival benefit. PPB Ir has an overall favorable clinical outcome. Limited follow-up data are available due to the rarity of the lesion and the overlap with other congenital cystic lung malformations.