Abstract 3898: Exome analysis of an exceptional responder uncovers an activating GNAS mutation that may confer sensitivity to cytotoxic chemotherapy
Bibliographic record
Abstract
Abstract Introduction: The molecular characterization of patients who exhibit a unique response to treatments that are not effective in most patients, so called “exceptional responders”, can shed light on the biological underpinnings of the response and provide rationale for selection of future patients for the same treatment. Here we present the genomic characterization of a 22-year-old female patient who presented with an unclassifiable kidney cancer and diffuse bilateral lung metastases. The primary tumour was resected and platinum plus gemcitabine therapy achieved a complete resolution of the lung metastases. Approximately 2 years later the patient again developed bilateral lung metastases and an additional course of platinum plus gemcitabine once again achieved a complete radiological response. To better understand the genomic contributors to this dramatic and recurrent response, we performed whole exome sequence analysis of the diagnostic formalin-fixed paraffin-embedded tumor tissue and a matched blood sample. Methods and Results: The patient provided informed consent and was profiled through the Princess Margaret Cancer Centre Genomic Investigation of Unusual and Spectacular responders (GENIUS) program. From tumour and matched blood specimens, we isolated coding and untranslated regions followed by deep sequencing (∼250X median coverage) on an Illumina HiSeq 2000. Copy-number analysis using VarScan2 revealed 23 somatic copy-number alterations (12 gains, 11 losses) including loss of 9p and gains of 3q and 8q, consistent with reports on clear cell renal cell carcinomas. Mutational analysis using muTect found 60 somatic, coding mutations, consistent with mutation rates seen in other kidney cancers. Of these variants, only one was a previously reported hotspot mutation, GNAS p.R201H (NM_000516, also known as p.R844H on transcript NM_080425). This mutation causes constitutive activation of the G-protein complex and activates adenylate cyclase to produce cyclic-AMP (cAMP) that can activate oncogenic pathways. However, excess cellular cAMP levels have also been found to promote apoptosis. Furthermore, a germline SNP in GNAS (rs7121), thought to increase GNAS transcript stability, is associated with improved response to gemcitabine plus platinum in non-small-cell lung cancer patients. The current patient was found to be heterozygous for the rs7121 SNP, which may compound the chemosensitivity phenotype observed. Conclusion: We have identified a p.R201H mutation in GNAS that may both drive tumour progression and confer exceptional chemo-sensitivity in a patient with an unclassified kidney cancer. Further analysis is underway in vitro to validate the hypothesis that this mutation confers sensitivity to chemotherapy. Confirmation of this hypothesis may suggest that chemotherapy could be considered for patients harbouring this mutation. Citation Format: Jeff P. Bruce, Arnavaz Danesh, Neil Winegarden, Patrick Yau, Carl Virtanen, Suzanne Kamel-Reid, Michael H. Roehrl, Anthony M. Joshua, Jennifer Knox, Trevor J. Pugh. Exome analysis of an exceptional responder uncovers an activating GNAS mutation that may confer sensitivity to cytotoxic chemotherapy. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3898. doi:10.1158/1538-7445.AM2015-3898
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".