Genotyping Cancer-Associated Genes in Chordoma Identifies Mutations in Oncogenes and Areas of Chromosomal Loss Involving CDKN2A, PTEN, and SMARCB1

The molecular mechanisms underlying chordoma pathogenesis are unknown. We therefore sought to identify novel mutations to better understand chordoma biology and to potentially identify therapeutic targets. Given the relatively high costs of whole genome sequencing, we performed a focused genetic analysis using matrix-assisted laser desorption/ionization-time of flight mass spectrometer (Sequenom iPLEX genotyping). We tested 865 hotspot mutations in 111 oncogenes and selected tumor suppressor genes (OncoMap v. 3.0) of 45 human chordoma tumor samples. Of the analyzed samples, seven were identified with at least one mutation. Six of these were from fresh frozen samples, and one was from a paraffin embedded sample. These observations were validated using an independent platform using homogeneous mass extend MALDI-TOF (Sequenom hME Genotyping). These genetic alterations include: ALK (A877S), CTNNB1 (T41A), NRAS (Q61R), PIK3CA (E545K), PTEN (R130), CDKN2A (R58*), and SMARCB1 (R40*). This study reports on the largest comprehensive mutational analysis of chordomas performed to date. To focus on mutations that have the greatest chance of clinical relevance, we tested only oncogenes and tumor suppressor genes that have been previously implicated in the tumorigenesis of more common malignancies. We identified rare genetic changes that may have functional significance to the underlying biology and potential therapeutics for chordomas. Mutations in CDKN2A and PTEN occurred in areas of chromosomal copy loss. When this data is paired with the studies showing 18 of 21 chordoma samples displaying copy loss at the locus for CDKN2A, 17 of 21 chordoma samples displaying copy loss at PTEN, and 3 of 4 chordoma samples displaying deletion at the SMARCB1 locus, we can infer that a loss of heterozygosity at these three loci may play a significant role in chordoma pathogenesis

High-Throughput Genotyping in Metastatic Esophageal Squamous Cell Carcinoma Identifies Phosphoinositide-3-Kinase and BRAF Mutations

Background: Given the high incidence of metastatic esophageal squamous cell carcinoma, especially in Asia, we screened for the presence of somatic mutations using OncoMap platform with the aim of defining subsets of patients who may be potential candidate for targeted therapy. Methods and Materials We analyzed 87 tissue specimens obtained from 80 patients who were pathologically confirmed with esophageal squamous cell carcinoma and received 5-fluoropyrimidine/platinum-based chemotherapy. OncoMap 4.0, a mass-spectrometry based assay, was used to interrogate 471 oncogenic mutations in 41 commonly mutated genes. Tumor specimens were prepared from primary cancer sites in 70 patients and from metastatic sites in 17 patients. In order to test the concordance between primary and metastatic sites from the patient for mutations, we analyzed 7 paired (primary-metastatic) specimens. All specimens were formalin-fixed paraffin embedded tissues and tumor content was >70%. Results: In total, we have detected 20 hotspot mutations out of 80 patients screened. The most frequent mutation was PIK3CA mutation (four E545K, five H1047R and one H1047L) (N = 10, 11.5%) followed by MLH1 V384D (N = 7, 8.0%), TP53 (R306, R175H and R273C) (N = 3, 3.5%), BRAF V600E (N = 1, 1.2%), CTNNB1 D32N (N = 1, 1.2%), and EGFR P733L (N = 1, 1.2%). Distributions of somatic mutations were not different according to anatomic sites of esophageal cancer (cervical/upper, mid, lower). In addition, there was no difference in frequency of mutations between primary-metastasis paired samples. Conclusions: Our study led to the detection of potentially druggable mutations in esophageal SCC which may guide novel therapies in small subsets of esophageal cancer patients

Genomic sequencing of meningiomas identifies oncogenic SMO and AKT1 mutations

Meningiomas are the most common primary nervous system tumor. The tumor suppressor NF2 is disrupted in approximately half of meningiomas1 but the complete spectrum of genetic changes remains undefined. We performed whole-genome or whole-exome sequencing on 17 meningiomas and focused sequencing on an additional 48 tumors to identify and validate somatic genetic alterations. Most meningiomas exhibited simple genomes, with fewer mutations, rearrangements, and copy-number alterations than reported in other adult tumors. However, several meningiomas harbored more complex patterns of copy-number changes and rearrangements including one tumor with chromothripsis. We confirmed focal NF2 inactivation in 43% of tumors and found alterations in epigenetic modifiers among an additional 8% of tumors. A subset of meningiomas lacking NF2 alterations harbored recurrent oncogenic mutations in AKT1 (E17K) and SMO (W535L) and exhibited immunohistochemical evidence of activation of their pathways. These mutations were present in therapeutically challenging tumors of the skull base and higher grade. These results begin to define the spectrum of genetic alterations in meningiomas and identify potential therapeutic targets

High-Throughput Mutation Profiling Identifies Frequent Somatic Mutations in Advanced Gastric Adenocarcinoma

Background: Gastric cancer is one of the leading cancer types in incidence and mortality, especially in Asia. In order to improve survival, identification of a catalogue of molecular alterations underlying gastric cancer is a critical step for developing and designing genome-directed therapies. Methodology/Principal Findings The Center for Cancer Genome Discovery (CCGD) at the Dana-Farber Cancer Institute (DFCI) has adapted a high-throughput genotyping platform to determine the mutation status of a large panel of known cancer genes. The mutation detection platform, termed OncoMap v4, interrogates 474 “hotspot” mutations in 41 genes that are relevant for cancer. We performed OncoMap v4 in formalin-fixed paraffin-embedded (FFPE) tissue specimens from 237 gastric adenocarcinomas. Using OncoMap v4, we found that 34 (14.4%) of 237 gastric cancer patients harbored mutations. Among mutations we screened, PIK3CA mutations were the most frequent (5.1%) followed by p53 (4.6%), APC (2.5%), STK11 (2.1%), CTNNB1 (1.7%), and CDKN2A (0.8%). Six samples harbored concomitant somatic mutations. Mutations of CTNNB1 were significantly more frequent in EBV-associated gastric carcinoma (P = 0.046). Our study led to the detection of potentially druggable mutations in gastric cancer which may guide novel therapies in subsets of gastric cancer patients. Conclusions/Significance: Using high throughput mutation screening platform, we identified that PIK3CA mutations were the most frequently observed target for gastric adenocarcinoma

Colorectal Cancers from Distinct Ancestral Populations Show Variations in BRAF Mutation Frequency

It has been demonstrated for some cancers that the frequency of somatic oncogenic mutations may vary in ancestral populations. To determine whether key driver alterations might occur at different frequencies in colorectal cancer, we applied a high-throughput genotyping platform (OncoMap) to query 385 mutations across 33 known cancer genes in colorectal cancer DNA from 83 Asian, 149 Black and 195 White patients. We found that Asian patients had fewer canonical oncogenic mutations in the genes tested (60% vs Black 79% (P = 0.011) and White 77% (P = 0.015)), and that BRAF mutations occurred at a higher frequency in White patients (17% vs Asian 4% (P = 0.004) and Black 7% (P = 0.014)). These results suggest that the use of genomic approaches to elucidate the different ancestral determinants harbored by patient populations may help to more precisely and effectively treat colorectal cancer

Toward accurate high-throughput SNP genotyping in the presence of inherited copy number variation

<p>Abstract</p> <p>Background</p> <p>The recent discovery of widespread copy number variation in humans has forced a shift away from the assumption of two copies per locus per cell throughout the autosomal genome. In particular, a SNP site can no longer always be accurately assigned one of three genotypes in an individual. In the presence of copy number variability, the individual may theoretically harbor any number of copies of each of the two SNP alleles.</p> <p>Results</p> <p>To address this issue, we have developed a method to infer a "generalized genotype" from raw SNP microarray data. Here we apply our approach to data from 48 individuals and uncover thousands of aberrant SNPs, most in regions that were previously unreported as copy number variants. We show that our allele-specific copy numbers follow Mendelian inheritance patterns that would be obscured in the absence of SNP allele information. The interplay between duplication and point mutation in our data shed light on the relative frequencies of these events in human history, showing that at least some of the duplication events were recurrent.</p> <p>Conclusion</p> <p>This new multi-allelic view of SNPs has a complicated role in disease association studies, and further work will be necessary in order to accurately assess its importance. Software to perform generalized genotyping from SNP array data is freely available online <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>.</p

Whole-exome sequencing and clinical interpretation of FFPE tumor samples to guide precision cancer medicine

Translating whole exome sequencing (WES) for prospective clinical use may impact the care of cancer patients; however, multiple innovations are necessary for clinical implementation. These include: (1) rapid and robust WES from formalin-fixed paraffin embedded (FFPE) tumor tissue, (2) analytical output similar to data from frozen samples, and (3) clinical interpretation of WES data for prospective use. Here, we describe a prospective clinical WES platform for archival FFPE tumor samples. The platform employs computational methods for effective clinical analysis and interpretation of WES data. When applied retrospectively to 511 exomes, the interpretative framework revealed a “long tail” of somatic alterations in clinically important genes. Prospective application of this approach identified clinically relevant alterations in 15/16 patients. In one patient, previously undetected findings guided clinical trial enrollment leading to an objective clinical response. Overall, this methodology may inform the widespread implementation of precision cancer medicine

Profiling Critical Cancer Gene Mutations in Clinical Tumor Samples

Background: Detection of critical cancer gene mutations in clinical tumor specimens may predict patient outcomes and inform treatment options; however, high-throughput mutation profiling remains underdeveloped as a diagnostic approach. We report the implementation of a genotyping and validation algorithm that enables robust tumor mutation profiling in the clinical setting. Methodology: We developed and implemented an optimized mutation profiling platform (“OncoMap”) to interrogate ∼400 mutations in 33 known oncogenes and tumor suppressors, many of which are known to predict response or resistance to targeted therapies. The performance of OncoMap was analyzed using DNA derived from both frozen and FFPE clinical material in a diverse set of cancer types. A subsequent in-depth analysis was conducted on histologically and clinically annotated pediatric gliomas. The sensitivity and specificity of OncoMap were 93.8% and 100% in fresh frozen tissue; and 89.3% and 99.4% in FFPE-derived DNA. We detected known mutations at the expected frequencies in common cancers, as well as novel mutations in adult and pediatric cancers that are likely to predict heightened response or resistance to existing or developmental cancer therapies. OncoMap profiles also support a new molecular stratification of pediatric low-grade gliomas based on BRAF mutations that may have immediate clinical impact. Conclusions: Our results demonstrate the clinical feasibility of high-throughput mutation profiling to query a large panel of “actionable” cancer gene mutations. In the future, this type of approach may be incorporated into both cancer epidemiologic studies and clinical decision making to specify the use of many targeted anticancer agents

Assessing the clinical utility of cancer genomic and proteomic data across tumor types

Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, miRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We found that incorporating molecular data with clinical variables yielded statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2–23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data