Cancers arising from germline DNA mismatch repair deficiency or polymerase proofreading deficiency (MMRD and PPD) in children harbour the highest mutational and microsatellite insertion–deletion (MS-indel) burden in humans. MMRD and PPD cancers are commonly lethal due to the inherent resistance to chemo-irradiation. Although immune checkpoint inhibitors (ICIs) have failed to benefit children in previous studies, we hypothesized that hypermutation caused by MMRD and PPD will improve outcomes following ICI treatment in these patients. Using an international consortium registry study, we report on the ICI treatment of 45 progressive or recurrent tumors from 38 patients. Durable objective responses were observed in most patients, culminating in a 3 year survival of 41.4%. High mutation burden predicted response for ultra-hypermutant cancers (>100 mutations per Mb) enriched for combined MMRD + PPD, while MS-indels predicted response in MMRD tumors with lower mutation burden (10–100 mutations per Mb). Furthermore, both mechanisms were associated with increased immune infiltration even in ‘immunologically cold’ tumors such as gliomas, contributing to the favorable response. Pseudo-progression (flare) was common and was associated with immune activation in the tumor microenvironment and systemically. Furthermore, patients with flare who continued ICI treatment achieved durable responses. This study demonstrates improved survival for patients with tumors not previously known to respond to ICI treatment, including central nervous system and synchronous cancers, and identifies the dual roles of mutation burden and MS-indels in predicting sustained response to immunotherapy.

POLE mutations are a major cause of hypermutant cancers, yet questions remain regarding mechanisms of tumorigenesis, genotype–phenotype correlation, and therapeutic considerations. In this study, we establish mouse models harboring cancer-associated POLE mutations P286R and S459F, which cause rapid albeit distinct time to cancer initiation in vivo, independent of their exonuclease activity. Mouse and human correlates enabled novel stratification of POLE mutations into three groups based on clinical phenotype and mutagenicity. Cancers driven by these mutations displayed striking resemblance to the human ultrahypermutation and specific signatures. Furthermore, Pole-driven cancers exhibited a continuous and stochastic mutagenesis mechanism, resulting in intertumoral and intratumoral heterogeneity. Checkpoint blockade did not prevent Pole lymphomas, but rather likely promoted lymphomagenesis as observed in humans. These observations provide insights into the carcinogenesis of POLE-driven tumors and valuable information for genetic counseling, surveillance, and immunotherapy for patients. Significance: Two mouse models of polymerase exonuclease deficiency shed light on mechanisms of mutation accumulation and considerations for immunotherapy. See related commentary by Wisdom and Kirsch p. 5459

Pleuropulmonary blastoma (PPB) is a tumor occurring almost exclusively in infants and young children. This is the most common primary-lung malignancy in childhood. There is age-associated progression through a distinctive sequence of pathologic changes, from a purely multicystic lesion type I to a high-grade sarcoma type II and III. While complete resection is the cornerstone treatment for type I PPB, aggressive chemotherapy with a less favorable prognosis is associated with type II and III. DICER1 germline mutation is positive in 70% of children with PPB. Diagnosis is challenging, as it resembles congenital pulmonary airway malformation (CPAM) in imaging. Although PPB is an extremely rare malignancy, over the past five years we have encountered several children diagnosed with PPB in our medical center. Herein, we present some of these children and discuss diagnostic, ethical, and therapeutic challenges.

To date, the understanding of pediatric tumor genomics and how these genetic aberrations correlate with clinical outcome is lacking. Here, we report our experience with the next-generation sequencing (NGS) test program and discuss implications for the inclusion of molecular profiling into clinical pediatric oncology trials. We also aimed to explore studies on NGS in pediatric cancers and to quantify the variability of finding actionable mutations and the clinical implications. We present a retrospective case series of all patients whose tumor tissue underwent NGS tests during treatment in our department. We also reviewed the literature and carried out a meta-analysis to explore studies on NGS in pediatric cancers. In 35/37 (94%) patients, we found at least one genomic alteration (GA); mean number of GAs per patient was 2 (range, 0-67), while 164 GAs were detected. Only 3 (8%) patients received precision medicine due to their GAs for a mean of 9 months (range, 5-14 months). Four studies were included in the meta-analysis. The pooled positive actionable mutation rate was 52% (95% CI 39%-66%), and the pooled rate of children who received precision medicine was 10% (95% CI 3%-20%). In children and young adults with high-risk, recurrent, or refractory malignancies, tumor profiling results have clinical implications, despite barriers to the use of matched precision therapy.

Background: Next-generation sequencing (NGS) has emerged as a transformative tool in precision medicine, offering insights into actionable genomic alterations and informing clinical decision-making in childhood and adolescent/young adult (AYA) solid tumors. Methods: We conducted a systematic review and meta-analysis to assess the utility of NGS in identifying actionable genomic alterations and its impact on clinical decision-making. Studies involving patients aged 0–40 years with solid tumors were included. Data were extracted using Covidence, and pooled estimates were calculated using a random-effects model. Bias was assessed using Begg–Mazumdar, Egger, and Harbord tests. Results: Out of 13,624 references screened, 24 studies met eligibility criteria, comprising 5278 patients and 5359 samples, of which 5207 provided usable data. The pooled proportion of actionable alterations was 57.9% (95% CI: 49.0–66.5%), with minimal evidence of publication bias. Clinical decision-making outcomes were reported in 21 studies, with a pooled proportion of 22.8% (95% CI: 16.4–29.9%). Germline mutation rates, reported in 11 studies, yielded a pooled proportion of 11.2% (95% CI: 8.4–14.3%), consistent with rates typically observed in childhood cancers. Significant heterogeneity was observed across studies due to differences in sequencing methodologies, tumor types, and sampling strategies. Conclusions: NGS demonstrates considerable potential in identifying actionable genomic targets and guiding clinical decision-making in childhood and AYA solid tumors. However, the variability in methodologies underscores the need for standardized protocols and reporting practices to enhance comparability and generalizability. This meta-analysis highlights the promise of genomic medicine while acknowledging challenges posed by heterogeneity in study designs.

Pediatric patients with progressive and refractory solid tumors face a challenging prognosis. Despite advancements in treatments like immunotherapy and targeted therapy, survival rates remain low for certain tumor types. Decision-making in these complex cases often necessitates a multidisciplinary approach, integrating risk-based management, precision medicine, and access to clinical trials. Artificial intelligence (AI) technologies, particularly large language models (LLMs), hold promise for improving clinical reasoning and decision support in pediatric oncology. This study evaluated the decision-making capabilities of five AI tools-ChatGPT, Gemini, Claude, Perplexity, and OpenEvidence-in six hypothetical cases of refractory or progressive pediatric solid tumors. Each AI tool was presented with two sequential queries: a request to generate potential treatment options and then a request to identify and justify the most appropriate option from its initial list. The AI tools generated a total of 124 treatment recommendations, with an average of 24.8 per tool. Clinical trial enrollment was the most frequently selected "best option," accounting for 55.2% of cases. Other notable recommendations included targeted therapy (17.2%), surgery (10.3%), chemotherapy (10.3%), best supportive care (10.3%), and immunotherapy (3.4%). Notably, the AI tools exhibited distinct tendencies in their decision-making approaches, with some favoring aggressive interventions and others emphasizing supportive or palliative care. AI tools demonstrate potential for assisting with complex treatment decisions in pediatric oncology, particularly by identifying clinical trial options. However, the observed variability in recommendations underscores the need for careful human oversight to ensure that AI-generated suggestions align with clinical evidence, patient and family preferences, and the overall goals of care. Future research should explore how AI tools can be further refined to incorporate nuanced patient-specific information and address the emotional and psychological impact of AI-assisted decision-making.