Molecular Genetic Approaches and Potential New Therapeutic Strategies for Pediatric Diffuse Intrinsic Pontine Glioma
Bibliographic record
Abstract
Diffuse intrinsic pontine gliomas (DIPG) are tumors that diffusely involve the brainstem and appear almost exclusively during childhood and adolescence. This devastating cancer is the main cause of brain tumor–related death in children, with a median survival time of less than 1 year for the majority of affected patients. Because of the location, surgical resection is not an option for this disease, and diagnosis is currently based on clinical findings and radiologic appearance. Radiation is the mainstay of therapy but is largely palliative, and decades of clinical trials of numerous chemotherapeutic regimens have not led to an improvement in outcome for these patients. DIPGs usually histologically resemble high-grade astrocytic tumors (anaplastic astrocytoma or glioblastoma [GBM], WHO grade 3/4). Thus many pediatric clinical trials over the past several decades have been based on agents with activity in adult GBM, although they have failed to show any benefit in pediatric DIPGs. However, we are now entering an era in which molecular data specific to pediatric DIPG are becoming available, thus potentially overcoming one of the roadblocks to smarter trial design and improved patient outcome. Although still relatively limited when compared with those from large-scale genomic studies of adult cancer, several important conclusions can be drawn from the data available for DIPGs so far that can help inform future clinical trials. First, consistent with previous data, Paugh et al report differences at both the copy number and expression level that distinguish pediatric DIPG from both adult and pediatric supratentorial GBM. This confirms that DIPG must be considered as a separate biologic entity for the purposes of clinical trial design. Second, receptor tyrosine kinases (RTKs) appear to be upregulated at the genomic or expression level (or both) in the majority of pediatric DIPGs. The most common recurrent focal gain in pediatric DIPG encompasses PDGFRA, occurring at the genomic level in at least 30% of DIPGs, with an even larger number showing overexpression at the RNA and protein levels. Gain of EGFR does not appear to be a frequent event in pediatric DIPG. However, two other RTKs are reported by Paugh et al to frequently show gains in DIPGs: MET and IGF1R. Interestingly, most, but not all, of the DIPGs showing gain of these RTKs also show gain of PDGFRA (64% and 88% of tumors showing gain of MET or IGF1R, respectively, have a concomitant gain of PDGFRA). Using fluorescence in situ hybridization (FISH), Paugh et al were able to demonstrate that this represented both cases where the same cells showed amplification of both genes and cases where different clones within the same tumor showed amplification of either PDGFRA or MET. Further, their FISH studies uncovered cases with RTK amplification that were missed by single nucleotide polymorphism (SNP) array analysis and vice versa. This raises important questions related to use of these data for clinical trials: (1) If we are to use targeted agents, what method should be used to identify gain of the target (FISH v SNP array)? (2) Should we be looking for gain/amplification of the target at the genomic level or expression of the target at the protein level? (3) What about tumor heterogeneity? If we use biopsy samples (the only real option for real-time, biology-based stratification for clinical trials), will they be representative of the tumor as a whole? The study by Paugh et al suggests substantial tumor heterogeneity at the genomic level. Will this be the same at the protein level? How many cells within the tumor need to express the RTK before a response to a targeted inhibitor might be anticipated? Can we expect a bystander effect, or will we, as suggested by Paugh et al, simply be allowing outgrowth of tumor cells lacking that particular RTK? In addition to these open biologic questions, the availability of these data raises further clinical questions. Can this information translate into a clinical breakthrough after 30 years of unsuccessful clinical trials? Can molecular biology really help to identify the best drug out of a growing number of targeted therapeutics currently under development? Without any doubt, the collection of material from postmortem samples has generated immense hope in the neuro-oncology community, offering insight into critical pathways involved in DIPG growth. The lack of tissue material has certainly been one of the major limiting factors for the development of innovative clinical trials. However, it is unlikely that this new information alone will be sufficient to change the outcome of this deadly disease. These findings need to be linked to other recent breakthroughs in DIPG research, such as the generation of PDGF-induced brainstem glioma models and orthotopic DIPG xenograft models that can potentially be used as preclinical tools for the testing of novel molecules with or without concomitant radiation. Together, these findings will provide new insights into DIPG pathogenesis and may ultimately result in successful therapeutic avenues to treat DIPG. Some recent trials of biologic modifiers have already identified a subset of patient with JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L S
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".