Abstract IA06: The role of hypermutation and replication repair deficiency in response of childhood cancers to immune checkpoint inhibitors
Bibliographic record
Abstract
Abstract Cancers exert mutations on the genome that are used to both drive cancers and cause resistance to common therapies but also can be used to study the history of the specific tumor and find its Achilles’ heel. Although cancer uses alterations such as copy number alterations, fusion genes, and other types of mutations, single nucleotide variations (SNV) are a common cause of cancer initiation and progression. Most studies define specific mutations and their driving force in cancer, but few address the role of mutational load in cancer. Recent data suggest that high mutational load stems from specific mutagenic processes that can be related to external processes, including genotoxic therapies, and internal processes, including genetic predisposition to cancer. Indeed, inherited replication repair deficiency (RRD) caused by mutations in the RRD genes results in a cancer syndrome characterized by the highest mutational load among human cancers. It is important to recognize and study the impact of hypermutation in cancer since these tumors may be resistant to current therapies and may respond to immune checkpoint inhibition. In order to study the above issues, we compiled data from >80,000 tumors from multiple organs in children and adults. We define hypermutation as >10mut/MB and ultrahypermutation as >100mut/MB. This nomenclature is important in order to compare and stratify tumors for clinical trials. A portion (about 5%) of childhood cancers are hypermutant and are enriched for RRD. Importantly, hypermutation exist in many types of childhood cancers and define an important clinical subgroup. Furthermore, all ultrahypermutant childhood cancers will have driver mutations in RRD genes. Similarly, 17% of adult tumors have hypermutations that are enriched for RRD throughout all cancer types and are also enriched for RRD. Hypermutation can be used to study the tumor history and biologic behavior. Using mutational load, mutational signatures, and allelic frequency, one can determine the true drivers among many mutations in specific oncogenes. Mutational signatures stratify these cancers into 8 clusters regardless of tissue of origin. These clusters include novel ones that provide data on the initial driver events and secondary ones related to resistance to chemotherapy. Finally, using the above nomenclature one can decipher potential early mutations that could be traced to the germline. These are so specific that by contacting physicians who sent tumor for sequencing and suggesting germline testing we uncovered RRD syndromes in all patients. These resulted in installation of surveillance protocols and therapy adjustment, including introduction of immune checkpoint inhibition. Current data collected on patients with RRD treated with immune checkpoint inhibition reveal 33% objective responses and >60% survival at 1 year for relapsed/progressive hypermutant childhood tumors. Together, these data suggest that hypermutation is a common event in human cancers spanning most tumor types. These cancers are driven by common mechanisms and provide information on the origins of these cancers, resistance to common therapies, and potential responses to immunotherapy. Finally, in childhood cancers, a significant proportion of hypermutant cancers stem from germline mutations. Recognition of these will improve survival for these patients and family members. Citation Format: Uri Tabori. The role of hypermutation and replication repair deficiency in response of childhood cancers to immune checkpoint inhibitors [abstract]. In: Proceedings of the AACR Special Conference: Pediatric Cancer Research: From Basic Science to the Clinic; 2017 Dec 3-6; Atlanta, Georgia. Philadelphia (PA): AACR; Cancer Res 2018;78(19 Suppl):Abstract nr IA06.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".