Lisa Kann

Personalized genomic analyses for cancer mutation discovery and interpretation

Analysis of matched tumor and normal DNA from the same patient improves accuracy of identification of actionable mutations, allowing better targeting of potential treatments. Will the real mutation please stand up? When a patient is diagnosed with cancer, a sample of the tumor is often analyzed to look for mutations that might guide the approach to targeted treatment of the disease. Jones et al. analyzed samples from more than 800 patients with 15 different cancer types and showed that this standard approach is not necessarily accurate without also analyzing a matched sample of normal DNA from the same patient. The authors found that, compared to analysis of paired samples, the standard tumor-only sequencing approach frequently identified mutations that were present in the patient’s normal tissues and were therefore not suitable for targeted therapy or, conversely, missed useful new mutations in the tumor. Massively parallel sequencing approaches are beginning to be used clinically to characterize individual patient tumors and to select therapies based on the identified mutations. A major question in these analyses is the extent to which these methods identify clinically actionable alterations and whether the examination of the tumor tissue alone is sufficient or whether matched normal DNA should also be analyzed to accurately identify tumor-specific (somatic) alterations. To address these issues, we comprehensively evaluated 815 tumor-normal paired samples from patients of 15 tumor types. We identified genomic alterations using next-generation sequencing of whole exomes or 111 targeted genes that were validated with sensitivities >95% and >99%, respectively, and specificities >99.99%. These analyses revealed an average of 140 and 4.3 somatic mutations per exome and targeted analysis, respectively. More than 75% of cases had somatic alterations in genes associated with known therapies or current clinical trials. Analyses of matched normal DNA identified germline alterations in cancer-predisposing genes in 3% of patients with apparently sporadic cancers. In contrast, a tumor-only sequencing approach could not definitively identify germline changes in cancer-predisposing genes and led to additional false-positive findings comprising 31% and 65% of alterations identified in targeted and exome analyses, respectively, including in potentially actionable genes. These data suggest that matched tumor-normal sequencing analyses are essential for precise identification and interpretation of somatic and germline alterations and have important implications for the diagnostic and therapeutic management of cancer patients.

Integrated Next Generation Sequencing and Avatar Mouse Models for Personalized Cancer Treatment

Background: Current technology permits an unbiased massive analysis of somatic genetic alterations from tumor DNA as well as the generation of individualized mouse xenografts (Avatar models). This work aimed to evaluate our experience integrating these two strategies to personalize the treatment of patients with cancer. Methods: We performed whole-exome sequencing analysis of 25 patients with advanced solid tumors to identify putatively actionable tumor-specific genomic alterations. Avatar models were used as an in vivo platform to test proposed treatment strategies. Results: Successful exome sequencing analyses have been obtained for 23 patients. Tumor-specific mutations and copy-number variations were identified. All samples profiled contained relevant genomic alterations. Tumor was implanted to create an Avatar model from 14 patients and 10 succeeded. Occasionally, actionable alterations such as mutations in NF1, PI3KA, and DDR2 failed to provide any benefit when a targeted drug was tested in the Avatar and, accordingly, treatment of the patients with these drugs was not effective. To date, 13 patients have received a personalized treatment and 6 achieved durable partial remissions. Prior testing of candidate treatments in Avatar models correlated with clinical response and helped to select empirical treatments in some patients with no actionable mutations. Conclusion: The use of full genomic analysis for cancer care is encouraging but presents important challenges that will need to be solved for broad clinical application. Avatar models are a promising investigational platform for therapeutic decision making. While limitations still exist, this strategy should be further tested. Clin Cancer Res; 20(9); 2476–84. ©2014 AACR.

Genomic analyses of gynaecologic carcinosarcomas reveal frequent mutations in chromatin remodelling genes

Malignant mixed Müllerian tumours, also known as carcinosarcomas, are rare tumours of gynaecological origin. Here we perform whole-exome analyses of 22 tumours using massively parallel sequencing to determine the mutational landscape of this tumour type. On average, we identify 43 mutations per tumour, excluding four cases with a mutator phenotype that harboured inactivating mutations in mismatch repair genes. In addition to mutations in TP53 and KRAS, we identify genetic alterations in chromatin remodelling genes, ARID1A and ARID1B, in histone methyltransferase MLL3, in histone deacetylase modifier SPOP and in chromatin assembly factor BAZ1A, in nearly two thirds of cases. Alterations in genes with potential clinical utility are observed in more than three quarters of the cases and included members of the PI3-kinase and homologous DNA repair pathways. These findings highlight the importance of the dysregulation of chromatin remodelling in carcinosarcoma tumorigenesis and suggest new avenues for personalized therapy.

Abstract A039: Accurate identification and prioritization of candidate neoantigens from cancer exome sequencing

Somatic, nonsynonymous genetic alterations present in cancer can lead to the formation of novel protein sequences and thus production of immunogenic “non-self” neoantigens. Those neoantigens with sufficient expression will be processed and presented on MHC molecules, subsequently inducing a tumor-specific T cell response. Emerging data from clinical studies suggest that neoantigen load and composition is associated with the efficacy of some immunotherapies. As neoantigens play central a role in the cancer-immunity cycle, it is critical to identify the most potent immunogenic neoantigens effectively and accurately. Here we have leveraged highly accurate cancer whole exome (WES) analyses from FFPE tumor tissue with a state-of-the-art analysis protocol to identify and prioritize candidate neoantigens most likely to promote an immune response. ImmunoSelect-R utilizes somatic variants from WES to ensure detection of true somatic peptides and minimize false positives, and provides accurate HLA typing from whole exome sequencing data with >99.9% sensitivity and specificity. When applied to a set of experimentally validated neoantigens, ImmunoSelect correctly classified 18 out of 19 as strong neoantigen candidates, suggesting a sensitivity of greater than 90%. Moreover, in a small set of 10 patients, ImmunoSelect consistently ranked experimentally validated neoantigens within top 20% of all neoantigen candidates derived from whole exome sequencing. In summary, ImmunoSelect is able to identify and prioritize candidate neoantigens from cancer exome sequencing results effectively and accurately, enabling personalized cancer vaccine development, adoptive T-cell transfer, and prediction of response to checkpoint inhibitors Citation Format: James White, John Simmons, Sam Angiuoli, Mark Sausen, Sian Jones, Lisa Kann, Manish Shukla, Maria Sevdali, Victor Velculescu, Luis Diaz, Theresa Zhang. Accurate identification and prioritization of candidate neoantigens from cancer exome sequencing [abstract]. In: Proceedings of the Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; 2016 Sept 25-28; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(11 Suppl):Abstract nr A039.

Abstract 528: Identify and prioritize candidate neoantigens from cancer exome sequencing with unmatched accuracy

Somatic, nonsynonymous genetic alterations present in cancer can lead to the formation of novel protein sequences and thus production of immunogenic “non-self” neoantigens. Some of those neoantigens will be processed, presented on MHC molecules, and induce tumor-specific T cell responses. Because neoantigens play central roles in the cancer-immunity cycle, it is critical to identify the most potent immunogenic neoantigens effectively and accurately. Combining PGDx9s highly accurate cancer exome analyses (CancerXome™) with in silico neoantigen prediction, we have launched ImmunoSelect-R™ that identifies and prioritizes the most relevant mutation-derived neoantigens. To ensure detection of true somatic mutations and prevent false positive mutations from confounding neoantigen identification, ImmunoSelect utilizes CancerXome that delivers unparalleled cancer whole exome sequencing accuracy, achieving 95% sensitivity and 97% positive predictive value at 10% mutant allele frequency with 150x coverage. ImmunoSelect also provides accurate HLA typing from whole exome sequencing with >99.9% sensitivity and specificity. Once exome-based mutations and novel open-reading-frames are identified and HLA genotypes defined, ImmunoSelect utilizes state of art bioinformatics pipelines for prediction and prioritization of the most relevant neoantigens. When applied to a set of experimentally validated neoantigens, ImmunoSelect identified 18 out of 19 of them as being strong neoantigen candidates, suggesting a sensitivity of greater than 90%. Moreover, ImmunoSelect consistently ranked experimentally validated neoantigens within top 20% of all neoantigen candidates derived from whole exome sequencing. In summary, ImmunoSelect is able to identify and prioritize candidate neoantigens from cancer exome sequencing results effectively and accurately, enabling personalized cancer vaccine development, adoptive T-cell transfer, and prediction of response to checkpoint inhibitors Citation Format: James White, Sam Angiuoli, Mark Sausen, Sian Jones, Lisa Kann, Manish Shukla, Maria Sevdali, Victor Velculescu, Luis Diaz, Theresa Zhang. Identify and prioritize candidate neoantigens from cancer exome sequencing with unmatched accuracy. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 528.

Abstract 3894: The importance of matched tumor and normal DNA for somatic mutation discovery and clinical interpretation

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Massively parallel sequencing approaches are beginning to be used clinically to characterize individual patient tumors and to identify targeted therapies based on mutations identified. A major question in these analyses is the extent to which these methods identify clinically actionable alterations in patients and whether the examination of the tumor tissue alone is sufficient or whether matched normal DNA should also be analyzed to accurately identify tumor-specific (somatic) alterations. To address these issues, we comprehensively evaluated tumor-normal paired samples from 753 cancers from fourteen tumor types. We analyzed somatic alterations using whole exome or targeted next generation sequencing approaches that were validated with high sensitivity and specificity. These analyses revealed an average of 140 and 4.34 somatic changes per exome and targeted analyses, respectively. Approximately 65% of cases had somatic alterations in genes associated with known therapies or current clinical trials. In contrast, a tumor-only sequencing approach followed by bioinformatic removal of common germline variants from existing databases led to a 36% and 58% false discovery rate in alterations identified in targeted and exome analyses, respectively, including in potentially actionable genes. These data suggest that matched tumor-normal sequencing analyses are essential for precise identification and interpretation of somatic alterations and have important implications for the diagnostic and therapeutic management of cancer patients. Citation Format: Siân Jones, Mark Sausen, Valsamo Anagnostou, Samuel V. Angiuoli, Bryan Chesnick, Kevin Galens, Maura Kadan, Lisa Kann, Karli Lytle, Derek Murphy, Monica Nesselbush, Eniko Papp, Sonya Parpart-Li, David Riley, Manish Shukla, Theresa Zhang, Luis A. Diaz, Victor E. Velculescu. The importance of matched tumor and normal DNA for somatic mutation discovery and clinical interpretation. [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 3894. doi:10.1158/1538-7445.AM2015-3894

Abstract 2205: Integrated next generation sequencing and patient-derived xenografts to personalized cancer treatment.

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC Background: The knowledge of actionable somatic genomic alterations present in human tumors is enabling the new era of personalized cancer treatment. The great intellectual challenge lies in linking confirmed mutations to protein function. Personalized tumor graft models (Avatars) can aid in the process of genomic analyses interpretation to ultimately move from molecular profile to medication. Methods: Using massive parallel sequencing we performed whole exome sequencing analysis of tumor and matched normal blood samples of 23 patients (pts) with advanced solid tumors (7 lung cancer, 7 pancreatic cancer, 1 neuroendocrine tumor, 2 glioblastoma, 1 uveal melanoma, 2 melanomas and 3 colon cancer) to identify putatively actionable tumor-specific genomic alterations. Avatar models generated by direct engraftment of tumor samples from the pts into immunocompromised mice were used as an in vivo platform to test proposed treatment strategies. Results: Successful exome sequencing analyses has been obtained for 21 pts (1 patient died prematurely, 1 sample was insufficient). Tumor specific mutations (Muts) and copy number variations were identified ranging from 5 to 952 and 0 to 36 respectively. All samples profiled contained clinically meaningful genomic alterations. A successful Avatar model was generated for 10 out of 17 pts. Two engraftment failures corresponded to EGFR mutant lung tumors resected while pts were receiving erlotinib, which initially grew but then regressed. Some of the most relevant drugabble alterations were: KRAS, CHEK1, FGFR2, IGF1R, MET, BRCA1, XPC, NOTCH, CREB3LB, GNA11, SMAD4 and EGFR. In occasions druggable alterations such as muts in NF1, PTPRC, PI3KA and DDR2 failed to provide any benefit when a targeted drug was tested in the Avatar and accordingly treatment of the pts with these drugs was not effective. In one case, loss of STK11 lead to testing of mTOR and SRC inhibitors resulting in tumor regression both in the Avatar and in the clinic. At present time 10 pts have received a personalized treatment: 2 pts, as expected based on the Avatar model, did not response; 4 pts resulted in durable partial remissions and 4 pts are currently on treatment with disease stabilization. In one of the EGFR mutant lung pts the genomic analysis revealed traces of an acquired mutation and allowed decision making at an earlier time point, prior to relapse. Overall, there was a remarkable correlation between drug activity in the Avatar and clinical outcome in the pts, in terms of drug resistance and sensitivity. Conclusion: The detection of actionable tumor-specific genomic alterations in the clinical setting is feasible. However predicting treatment response to known oncogenes is complex and requires detailed information of how different genetic backgrounds function. Avatar models are a powerful investigational platform for therapeutic decision making and help to guide cancer treatment in the clinic. Citation Format: Elena Garralda, Keren Paz, Pedro P. Lopez-Casas, Siân Jones, Amanda Katz, Lisa M. Kann, Fernando Lopez-Rios, Francesca Sarno, Fatima Al-Shahrour, David Vasquez, Elizabeth Bruckheimer, Samuel V. Angiuoli, Luis A. Diaz, Alfonso Valencia, Victor E. Velculescu, David Sidransky, Manuel Hidalgo. Integrated next generation sequencing and patient-derived xenografts to personalized cancer treatment. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2205. doi:10.1158/1538-7445.AM2013-2205