3D genome mapping identifies subgroup-specific chromosome conformations and tumor-dependency genes in ependymoma.

Ependymoma is a tumor of the brain or spinal cord. The two most common and aggressive molecular groups of ependymoma are the supratentorial ZFTA-fusion associated and the posterior fossa ependymoma group A. In both groups, tumors occur mainly in young children and frequently recur after treatment. Although molecular mechanisms underlying these diseases have recently been uncovered, they remain difficult to target and innovative therapeutic approaches are urgently needed. Here, we use genome-wide chromosome conformation capture (Hi-C), complemented with CTCF and H3K27ac ChIP-seq, as well as gene expression and DNA methylation analysis in primary and relapsed ependymoma tumors, to identify chromosomal conformations and regulatory mechanisms associated with aberrant gene expression. In particular, we observe the formation of new topologically associating domains ('neo-TADs') caused by structural variants, group-specific 3D chromatin loops, and the replacement of CTCF insulators by DNA hyper-methylation. Through inhibition experiments, we validate that genes implicated by these 3D genome conformations are essential for the survival of patient-derived ependymoma models in a group-specific manner. Thus, this study extends our ability to reveal tumor-dependency genes by 3D genome conformations even in tumors that lack targetable genetic alterations.

Drug sensitivity profiling of 3D tumor tissue cultures in the pediatric precision oncology program INFORM.

The international precision oncology program INFORM enrolls relapsed/refractory pediatric cancer patients for comprehensive molecular analysis. We report a two-year pilot study implementing ex vivo drug sensitivity profiling (DSP) using a library of 75-78 clinically relevant drugs. We included 132 viable tumor samples from 35 pediatric oncology centers in seven countries. DSP was conducted on multicellular fresh tumor tissue spheroid cultures in 384-well plates with an overall mean processing time of three weeks. In 89 cases (67%), sufficient viable tissue was received; 69 (78%) passed internal quality controls. The DSP results matched the identified molecular targets, including BRAF, ALK, MET, and TP53 status. Drug vulnerabilities were identified in 80% of cases lacking actionable (very) high-evidence molecular events, adding value to the molecular data. Striking parallels between clinical courses and the DSP results were observed in selected patients. Overall, DSP in clinical real-time is feasible in international multicenter precision oncology programs.

Patient-by-Patient Deep Transfer Learning for Drug-Response Profiling Using Confocal Fluorescence Microscopy of Pediatric Patient-Derived Tumor-Cell Spheroids.

Image-based phenotypic drug profiling is receiving increasing attention in drug discovery and precision medicine. Compared to classical end-point measurements quantifying drug response, image-based profiling enables both the quantification of drug response and characterization of disease entities and drug-induced cell-death phenotypes. Here, we aim to quantify image-based drug responses in patient-derived 3D spheroid tumor cell cultures, tackling the challenges of a lack of single-cell-segmentation methods and limited patient-derived material. Therefore, we investigate deep transfer learning with patient-by-patient fine-tuning for cell-viability quantification. We fine-tune a convolutional neural network (pre-trained on ImageNet) with 210 control images specific to a single training cell line and 54 additional screen -specific assay control images. This method of image-based drug profiling is validated on 6 cell lines with known drug sensitivities, and further tested with primary patient-derived samples in a medium-throughput setting. Network outputs at different drug concentrations are used for drug-sensitivity scoring, and dense-layer activations are used in t-distributed stochastic neighbor embeddings of drugs to visualize groups of drugs with similar cell-death phenotypes. Image-based cell-line experiments show strong correlation to metabolic results ( R ≈ 0.7 ) and confirm expected hits, indicating the predictive power of deep learning to identify drug-hit candidates for individual patients. In patient-derived samples, combining drug sensitivity scoring with phenotypic analysis may provide opportunities for complementary combination treatments. Deep transfer learning with patient-by-patient fine-tuning is a promising, segmentation-free image-analysis approach for precision medicine and drug discovery.

Functional Therapeutic Target Validation Using Pediatric Zebrafish Xenograft Models.

The survival rate among children with relapsed tumors remains poor, due to tumor heterogeneity, lack of directly actionable tumor drivers and multidrug resistance. Novel personalized medicine approaches tailored to each tumor are urgently needed to improve cancer treatment. Current pediatric precision oncology platforms, such as the INFORM (INdividualized Therapy FOr Relapsed Malignancies in Childhood) study, reveal that molecular profiling of tumor tissue identifies targets associated with clinical benefit in a subgroup of patients only and should be complemented with functional drug testing. In such an approach, patient-derived tumor cells are exposed to a library of approved oncological drugs in a physiological setting, e.g., in the form of animal avatars injected with patient tumor cells. We used molecularly fully characterized tumor samples from the INFORM study to compare drug screen results of individual patient-derived cell models in functional assays: (i) patient-derived spheroid cultures within a few days after tumor dissociation; (ii) tumor cells reisolated from the corresponding mouse PDX; (iii) corresponding long-term organoid-like cultures and (iv) drug evaluation with the corresponding zebrafish PDX (zPDX) model. Each model had its advantage and complemented the others for drug hit and drug combination selection. Our results provide evidence that in vivo zPDX drug screening is a promising add-on to current functional drug screening in precision medicine platforms.

The Pediatric Precision Oncology INFORM Registry: Clinical Outcome and Benefit for Patients with Very High-Evidence Targets.

INFORM is a prospective, multinational registry gathering clinical and molecular data of relapsed, progressive, or high-risk pediatric patients with cancer. This report describes long-term follow-up of 519 patients in whom molecular alterations were evaluated according to a predefined seven-scale target prioritization algorithm. Mean turnaround time from sample receipt to report was 25.4 days. The highest target priority level was observed in 42 patients (8.1%). Of these, 20 patients received matched targeted treatment with a median progression-free survival of 204 days [95% confidence interval (CI), 99-not applicable], compared with 117 days (95% CI, 106-143; P = 0.011) in all other patients. The respective molecular targets were shown to be predictive for matched treatment response and not prognostic surrogates for improved outcome. Hereditary cancer predisposition syndromes were identified in 7.5% of patients, half of which were newly identified through the study. Integrated molecular analyses resulted in a change or refinement of diagnoses in 8.2% of cases. SIGNIFICANCE: The pediatric precision oncology INFORM registry prospectively tested a target prioritization algorithm in a real-world, multinational setting and identified subgroups of patients benefiting from matched targeted treatment with improved progression-free survival, refinement of diagnosis, and identification of hereditary cancer predisposition syndromes.See related commentary by Eggermont et al., p. 2677.This article is highlighted in the In This Issue feature, p. 2659.

Brainstem biopsy in pediatric diffuse intrinsic pontine glioma in the era of precision medicine: the INFORM study experience.

PURPOSE: Diffuse intrinsic pontine glioma (DIPG) is a highly aggressive paediatric brain tumour with fatal outcome. The Individualised Therapy For Relapsed Malignancies In Childhood (INFORM) registry study offers comprehensive molecular profiling of high-risk tumours to identify target alterations for potential precision therapy. We analysed molecular characteristics and clinical data after brainstem biopsy of all enrolled newly diagnosed DIPGs. PATIENTS AND METHODS: From -February 2015 to February 2018, 21 subsequent primary DIPG cases were enrolled in the nation-wide multicentre INFORM registry study after brainstem biopsy. Whole-genome, whole-exome sequencing and DNA methylation analysis were performed, and RNA-sequencing was added in case of sufficient material. Clinical data were obtained from standardised questionnaires and the INFORM clinical data bank. RESULTS: Tumour material obtained from brainstem biopsy was sufficient for DNA analysis in all cases and RNA analysis in 16 of 21 cases. In 16 of 21 cases (76%), potential targetable alterations were identified including highly relevant MET and NTRK1 fusions as well as an EZH2 alteration not previously described in DIPG. In 5 of 21 cases, molecular information was used for initiation of targeted treatment. The majority of patients (19/21) presented with neurological deficits at diagnosis. Newly arising or worsening of neurological deficits post-biopsy occurred in nine patients. Symptoms were reversible or improved notably in eight cases. CONCLUSION: In this multicentre study setting, brainstem biopsy of DIPG was feasible and yielded sufficient material for comprehensive molecular profiling. Relevant molecular targets were identified impacting clinical management in a substantial subset. Death or severe bleeding occurred in none of the cases. One of 20 patients experienced unilateral paraesthesia possibly related to biopsy.

Molecular Transition of an Adult Low-Grade Brain Tumor to an Atypical Teratoid/Rhabdoid Tumor Over a Time-Course of 14 Years.

Atypical teratoid/rhabdoid tumor (AT/RT) of the central nervous system is a highly malignant, pediatric brain tumor typically arising de novo. Inactivation of SMARCB1 is a defining molecular event. We present here a rare case of an adult (35 years) low-grade SMARCB1-deleted brain tumor with transition into prototypical AT/RT over 14 years. Molecular analysis was performed for 3 tumor presentations including copy number analysis, DNA methylation analysis (450k), and whole exome sequencing. We detected the identical somatic SMARCB1 deletion at all 3 time-points. In an unsupervised hierarchical clustering of methylation data together with 127 reference cases comprising 9 brain tumor classes all 3 manifestations clustered with AT/RT. Exome sequencing revealed an increase of mutational burden over time. The acquired mutations and additional copy number changes did not affect known cancer genes. In conclusion, we demonstrate molecular changes associated with histological and clinical transition of a low-grade brain tumor to an adult AT/RT. Our observation of a stable disease course for nearly 10 years in a tumor with SMARCB1 loss and an AT/RT-like DNA methylation profile indicates that caution may be required in the diagnostic interpretation of such findings in adult patients.

Next-generation personalised medicine for high-risk paediatric cancer patients - The INFORM pilot study.

The 'Individualized Therapy for Relapsed Malignancies in Childhood' (INFORM) precision medicine study is a nationwide German program for children with high-risk relapsed/refractory malignancies, which aims to identify therapeutic targets on an individualised basis. In a pilot phase, reported here, we developed the logistical and analytical pipelines necessary for rapid and comprehensive molecular profiling in a clinical setting. Fifty-seven patients from 20 centers were prospectively recruited. Malignancies investigated included sarcomas (n = 25), brain tumours (n = 23), and others (n = 9). Whole-exome, low-coverage whole-genome, and RNA sequencing were complemented with methylation and expression microarray analyses. Alterations were assessed for potential targetability according to a customised prioritisation algorithm and subsequently discussed in an interdisciplinary molecular tumour board. Next-generation sequencing data were generated for 52 patients, with the full analysis possible in 46 of 52. Turnaround time from sample receipt until first report averaged 28 d. Twenty-six patients (50%) harbored a potentially druggable alteration with a prioritisation score of 'intermediate' or higher (level 4 of 7). Common targets included receptor tyrosine kinases, phosphoinositide 3-kinase-mammalian target of rapamycin pathway, mitogen-activated protein kinase pathway, and cell cycle control. Ten patients received a targeted therapy based on these findings, with responses observed in some previously treatment-refractory tumours. Comparative primary relapse analysis revealed substantial tumour evolution as well as one case of unsuspected secondary malignancy, highlighting the importance of re-biopsy at relapse. This study demonstrates the feasibility of comprehensive, real-time molecular profiling for high-risk paediatric cancer patients. This extended proof-of-concept, with examples of treatment consequences, expands upon previous personalised oncology endeavors, and presents a model with considerable interest and practical relevance in the burgeoning era of personalised medicine.