Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Oncology Review An Overview of Publications Featuring Illumina® Technology Table of Contents Introduction.......................................................................................................................... 3 Risk Prediction ...................................................................................................................... 5 Screening .............................................................................................................................. 7 Screening for Cancer DNA in Plasma ......................................................................................................... 8 Screening for RNA Markers in Plasma ........................................................................................................ 9 Diagnosis ............................................................................................................................ 10 Whole-Genome Resequencing................................................................................................................. 12 Whole-Exome Sequencing ...................................................................................................................... 13 Targeted Gene Sequencing..................................................................................................................... 14 Expression Analysis ............................................................................................................................... 16 FFPE Samples ....................................................................................................................................... 17 Response to Therapy........................................................................................................... 19 Metastasis and Recurrence ................................................................................................. 21 Cancer Drug Development .................................................................................................. 23 Clinical Trials ...................................................................................................................... 24 Bibliography........................................................................................................................ 26 Introduction The ideal cancer treatment is a protocol designed to target the specific molecular mechanism of the tumor and is well tolerated by the patient. The challenge has been that the same clinical presentation can represent several different molecular mechanisms and patients can vary widely in their tolerance to cancer therapies. Recent publications offer a glimpse at how the advances in cancer genetics, technology, and therapeutics development can move clinical decisions from heuristic to evidencebased decisions.1 As cancer progresses, it accumulates somatic mutations and genomic rearrangements. The vast majority of these genomic rearrangements are incidental consequences of the dysfunctional genetic machinery in the cancer cell. These mutations have no material impact on the disease progression and are often called passenger mutations. Conversely a small number of mutations are driver mutations that may lead to drug resistance or metastasis, which can profoundly impact a patient’s prognosis.2 There is increasing evidence that driver mutations are informative biomarkers that can assist in making therapeutic decisions to tailor the treatment to the specific patient. Ongoing monitoring during treatment can also gauge the response to treatment and risk of relapse.3 Next-generation sequencing is a highly sensitive tool to determine mutations and it is now accessible, affordable, and robust. The remaining challenge is the need for meticulously conducted clinical trials to determine which mutation profiles correlate with sensitivity or resistance to specific therapies.2 Future studies will have much larger cohorts to provide statistically robust associations and build increasingly complete mutation databases. The ultimate goal is that this accumulated knowledge will help clinicians to provide the right drug to the right patient at the right time. Risk Prediction Screening Diagnosis Response to Therapy Metastasis and Recurrence Cancer is a disease of the genome and next-generation sequencing can potentially impact every step of its management. Reviews Arnedos M., Andre F., Farace F., Lacroix L., Besse B., et al. (2012) The challenge to bring personalized cancer medicine from clinical trials into routine clinical practice: the case of the Institut Gustave Roussy. Mol Oncol 6: 204-210 Blake P. M., Decker D. A., Glennon T. M., Liang Y. M., Losko S., et al. (2011) Toward an integrated knowledge environment to support modern oncology. Cancer J 17: 257-263 Cancer Genome Atlas N. (2012) Comprehensive molecular portraits of human breast tumours. Nature 490: 61-70 Dancey J. E., Bedard P. L., Onetto N. and Hudson T. J. (2012) The genetic basis for cancer treatment decisions. Cell 148: 409-420 1 2 3 Lipson D., Capelletti M., Yelensky R., Otto G., Parker A., et al. (2012) Identification of new ALK and RET gene fusions from colorectal and lung cancer biopsies. Nat Med 18: 382-384 Dancey J. E., Bedard P. L., Onetto N. and Hudson T. J. (2012) The genetic basis for cancer treatment decisions. Cell 148: 409420 Mardis E. R. (2012) Genome sequencing and cancer. Curr Opin Genet Dev 22: 245-250 3 Godley L. A. (2012) Profiles in leukemia. N Engl J Med 366: 1152-1153 Longo D. L. (2012) Tumor heterogeneity and personalized medicine. N Engl J Med 366: 956-957 Mardis E. R. (2012) Genome sequencing and cancer. Curr Opin Genet Dev 22: 245-250 Weigelt B., Pusztai L., Ashworth A. and Reis-Filho J. S. (2012) Challenges translating breast cancer gene signatures into the clinic. Nat Rev Clin Oncol 9: 58-64 Yap T. A., Gerlinger M., Futreal P. A., Pusztai L. and Swanton C. (2012) Intratumor heterogeneity: seeing the wood for the trees. Sci Transl Med 4: 127ps110 Yates L. R. and Campbell P. J. (2012) Evolution of the cancer genome. Nat Rev Genet 13: 795-806 4 Risk Prediction Each tumor cell genome contains an accumulation of unique somatic alterations that range from point mutations to chromosomal translocations over a background of inherited genomic alterations that are known to confer increased susceptibility to cancer development.4 Next-generation sequencing can determine these mutations at base-pair resolution and, as a result, there has been a significant increase in the number of newly discovered markers. Lists of markers and cancer mutations are being compiled in increasingly comprehensive collections by international consortia such as The Cancer Genome Atlas (TCGA),5 the International Cancer Genome Consortium (ICGC)6 and Catalogue Of Somatic Mutations In Cancer (COSMIC)7. Risk markers, or genetic predisposition, are captured in the germline. Germline refers to the genetic material you inherit from your parents and that you in turn will pass on to your children. A predisposition to some cancers can be inherited and these cancers are often characterized by early onset of the disease. Some types of breast cancer8 and colon cancers9 are examples. Array-based genome-wide association studies (GWAS) and linkage studies can be effective in capturing the region where these markers reside. However, sequencing is usually required to find the causal genes.10 De novo mutations can be present in normal karyotypes. A recent study suggests that most of the mutations found in acute myeloid leukemia (AML) genomes are actually random events that occurred in hematopoietic progenitor cells (HSPCs) before they acquired the cancer-causing initiating mutation.11 Prognostic markers are based on somatic mutations that occur in the tumor during disease progression. As a result, an individual may need to be tested several times during treatment to screen for the occurrence of new mutations. In addition, due to the heterogeneity of advanced tumors, markers of good and poor prognosis can be present in different regions of the same tumor.12 Retesting during treatment is particularly useful to determine the potential of relapse and drug resistance. Targeted and whole-genome sequencing have their respective advantages and disadvantages. For more information, see the Diagnosis section in this document. 4 5 6 7 8 9 10 11 12 Mardis E. R. (2012) Genome sequencing and cancer. Curr Opin Genet Dev 22: 245-250 http://cancergenome.nih.gov/ http://www.icgc.org/ http://www.sanger.ac.uk/genetics/CGP/cosmic/ Antoniou A. C., Wang X., Fredericksen Z. S., McGuffog L., Tarrell R., et al. (2010) A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor-negative breast cancer in the general population. Nat Genet 42: 885-892 Pineda M., Gonzalez S., Lazaro C., Blanco I. and Capella G. (2010) Detection of genetic alterations in hereditary colorectal cancer screening. Mutat Res 693: 19-31 Stacey S. N., Sulem P., Jonasdottir A., Masson G., Gudmundsson J., et al. (2011) A germline variant in the TP53 polyadenylation signal confers cancer susceptibility. Nat Genet 43: 1098-1103 Welch J. S., Ley T. J., Link D. C., Miller C. A., Larson D. E., et al. (2012) The origin and evolution of mutations in acute myeloid leukemia. Cell 150: 264-278 Gerlinger M., Rowan A. J., Horswell S., Larkin J., Endesfelder D., et al. (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366: 883-892 5 References Brooks J. D. (2012) Translational genomics: the challenge of developing cancer biomarkers. Genome Res 22: 183-187 The authors discuss the challenges and future of cancer biomarkers. One interesting observation was that many cancers take years or even decades before they progress to lethality. This suggests that there may be a large window of opportunity for detection and eradication of transformed cells. While this window appears to provide ample time for detection, the average size of these cancers at the time of metastasis is 9 mm in diameter. More importantly, to produce a 50% decrease in ovarian cancer mortality, it was estimated that tumors would need to be detected by the time they are 5 mm in diameter. Detection of a protein from a 5 mm tumor diluted in a 5 L blood volume of a 60 kg woman is well beyond the sensitivity of currently available protein detection technologies. Other non-technical factors also play a role. For example, cancer detection in a clinical setting requires large and costly randomized clinical trials to demonstrate improved patient outcomes. 6 Screening Many cancers take years or even decades before they progress to lethality, which presents a large window of opportunity to detect and eradicate the transformed cells.13 The transformed cells are initially characterized by subtle neomorphic changes that are difficult to distinguish by established pathological methods. This lack of a definitive diagnosis may delay treatment.14 It has been shown that next-generation sequencing can accurately detect cancer-specific mutations in very early neoplastic cells before the morphological changes become apparent.15 The accumulation of somatic mutations often precedes changes in the cell morphology. Cells that escape the chemotherapy may go undetected by conventional pathology, but go on to become the dominant clones during relapse. Grey, brown, light brown, and purple represent clones with unique mutations. In some genes the mutations frequently occur in the same location, which may indicate a specific mechanism at work. However in the majority of genes the mutations can appear apparently randomly throughout the gene, which may reflect the failure of the replication and repair mechanisms. Microarrays require prior knowledge of where the mutations will occur and may miss these de novo mutations. Sequencing can detect mutations from both scenarios with equal facility. Two hypothetical genes with two different mutation models are shown. The dark boxes indicate exomes and the red bars indicate locations where mutations occur. Panel A: Recurrent mutations in a specific location may indicate the involvement of a biological mechanism to generate the mutations. Panel B: Scattered mutations occurring throughout the gene, such as P53, may be due to the failure of replication and repair mechanisms. Sequencing can detect mutations from both scenarios. 13 14 15 Brooks J. D. (2012) Translational genomics: the challenge of developing cancer biomarkers. Genome Res 22: 183-187 Godley L. A. (2012) Profiles in leukemia. N Engl J Med 366: 1152-1153 Walter M. J., Shen D., Ding L., Shao J., Koboldt D. C., et al. (2012) Clonal architecture of secondary acute myeloid leukemia. N Engl J Med 366: 1090-1098 7 Screening for Cancer DNA in Plasma The presence of tumor-derived DNA in the plasma of cancer patients offers opportunities for the noninvasive detection and monitoring of cancer.16-17 This would be of particular benefit for tumors that are difficult to detect and access.18 References Chan K. C., Jiang P., Zheng Y. W., Liao G. J., Sun H., et al. (2012) Cancer Genome Scanning in Plasma: Detection of Tumor-Associated Copy Number Aberrations, Single-Nucleotide Variants, and Tumoral Heterogeneity by Massively Parallel Sequencing. Clin Chem The authors describe the genome-wide profiling of copy number aberrations and point mutations in the plasma of cancer patients. The fractional concentrations of tumor-derived DNA in plasma correlated with tumor size and surgical treatment. The potential utility of this approach was demonstrated by the analysis of a patient with 2 synchronous cancers. Illumina Technology: HiSeq® 2000 51 bp or 76 bp paired-end reads with the TruSeq® SBS Kit v2. The adapter-ligated DNA was enriched with a 12-cycle PCR. Four regions from two ovarian tumors were sampled. The fractional concentrations of tumor-derived plasma DNA were estimated based on SNVs associated with each of the ovarian tumoral regions. The calculated fractional concentration of tumor DNA in plasma increases when the estimate is based on SNVs associated with multiple tumor regions. 16 17 18 8 Schwarzenbach H. (2012) Circulating nucleic acids and protease activities in blood of tumor patients. Expert Opin Biol Ther 12 Suppl 1: S163-169 Schwarzenbach H., Hoon D. S. and Pantel K. (2011) Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer 11: 426-437 Brooks J. D. (2012) Translational genomics: the challenge of developing cancer biomarkers. Genome Res 22: 183-187 Forshew T., Murtaza M., Parkinson C., Gale D., Tsui D. W., et al. (2012) Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Sci Transl Med 4: 136ra168 The authors used a cancer panel for tagged-amplicon deep sequencing (TAm-Seq) and screened 5995 genomic bases for low-frequency mutations. They identified cancer mutations present in circulating DNA at allele frequencies as low as 2%, with sensitivity and specificity of >97%. The authors demonstrated the use of TAm-Seq to noninvasively identify the origin of metastatic relapse in a patient with multiple primary tumors. In another case they identified in plasma an EGFR mutation not found in an initial ovarian biopsy. While 2% sensitivity is not high enough for diagnostics, it may allow the non-invasive monitoring of treatment and relapse. In monitoring applications the marker-mutations from the primary tumors are known, which greatly simplifies the data analysis and targeted panel design. Illumina Technology: Genome Analyzer IIx with an average depth of 3250 reads for each of 96 barcoded samples Screening for RNA Markers in Plasma Recent studies have shown that miRNA and mRNA can be isolated from the serum and other body fluids of cancer patients. The distinct miR profiles in the cell free circulating fluids of cancer patients have the potential to become a new class of biomarkers to detect and monitor cancers. References Anindo M. I. and Yaqinuddin A. (2012) Insights into the potential use of microRNAs as biomarker in cancer. Int J Surg 10: 443-449 Mo M. H., Chen L., Fu Y., Wang W. and Fu S. W. (2012) Cell-free Circulating miRNA Biomarkers in Cancer. J Cancer 3: 432-448 Mosig R. A., Lobl M., Senturk E., Shah H., Cohen S., et al. (2012) IGFBP-4 tumor and serum levels are increased across all stages of epithelial ovarian cancer. J Ovarian Res 5: 3 Ramskold D., Luo S., Wang Y. C., Li R., Deng Q., et al. (2012) Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30: 777-782 9 Diagnosis Any tumor section used for genomic DNA isolation will include normal cells such as stromal cells, blood vessels, and immune cells, all of which contribute normal genomic DNA to the DNA from the tumor cells. Based on conventional pathology estimates, most studies focus on tumors with >60% tumor nuclei present.19 In addition to containing normal DNA, the tumor itself may be heterogeneous. This is particularly true in late stage cancers where mutations accumulate to form polyclonal tumors, each with potentially unique drug responses. When tumors are highly heterogeneous it may take several biopsies to represent all the cell types.20 A hypothetical polyclonal tumor in a background of normal tissue. Most tumor samples contain a mixture of tumor and normal cells. The tumor itself may contain several different clonal types, each with a different response to therapy and potential for recurrence. Deep sequencing with next-generation sequencing technology refers to sequencing the same region multiple times, sometimes hundreds of times. Since every sequence was generated from a single DNA molecule, deep sequencing allows the detection of clones comprising as little as 1% of the original sample. Comparing tumor and normal tissue sequences from the same individual make it easy to identify the normal tissue. Currently, a typical recommendation is a minimum of 40-fold coverage for normal genomes and an 80-fold coverage for cancer genomes. The optimal read depth will vary depending on the cancer type and the required sensitivity. A hypothetical example of a tumor with two cancer clones and contaminating adjacent tissue. The sequences produced by the normal cells in the tumor sample (top two sequences in the tumor alignment) can be identified by comparison to the sequence produced by the adjacent normal tissue. The remaining sequences in the tumor sample can be separated into two groups that represent the major and minor tumor clones. Minor clones, if left untreated, may become major components of the tumor upon relapse. In an actual analysis the tumor sample will have at least 40-fold coverage and cover targeted sets of genes, whole exomes, or the whole genome. 19 20 10 Mardis E. R. (2012) Genome sequencing and cancer. Curr Opin Genet Dev 22: 245-250 Gerlinger M., Rowan A. J., Horswell S., Larkin J., Endesfelder D., et al. (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366: 883-892 There are three general approaches to detect somatic mutations in the cancer genome: whole-genome sequencing, whole-exome sequencing, and targeted gene sequencing. The table below contains a brief summary of the advantages and disadvantages of the respective approaches. In the long run, wholegenome sequencing will clearly be the optimal approach as our knowledge of the genome grows and our ability to handle and interpret the large data sets improves. In the immediate future targeted gene sequencing, such as cancer panels, can map drugs already on the market to patients who can derive immediate benefit from them.21-22 Patients who remain undiagnosed after screening with a cancer panel could be screened more extensively with whole-genome or whole-exome sequencing. Approach Advantages Disadvantages Whole-genome sequencing Comprehensive view of whole genome Can detect all types of mutations Standardized processing and analysis for all patients and all tumor types Does not require any prior knowledge of the disease About half the cost of whole-genome sequencing Small data set is easier to interpret Standardized processing and analysis for all patients and all tumor types Will detect indels and SNPs Does not require any prior knowledge of the disease Deep sequencing with good sensitivity for rare clones Cost-effective Results are easy to interpret Findings are actionable for cancerrelevant genes Very deep sequencing with very high sensitivity for rare clones Whole-exome sequencing Targeted gene sequencing 21 22 23 More expensive Large dataset more difficult to interpret Findings may not be actionable Risk of incidental findings Shallow sequencing is less sensitive than targeted approaches Only 1% to 1.5% of the genome is sequenced May miss fusion genes and oncogenes23 Findings may not be actionable Risk of incidental findings Will miss many mutations Requires a prior knowledge of the genes of interest Delay diagnosis of patients with rare tumors that are not represented on the panel Holbrook J. D., Parker J. S., Gallagher K. T., Halsey W. S., Hughes A. M., et al. (2011) Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine. J Transl Med 9: 119 Lipson D., Capelletti M., Yelensky R., Otto G., Parker A., et al. (2012) Identification of new ALK and RET gene fusions from colorectal and lung cancer biopsies. Nat Med 18: 382-384 Chapman M. A., Lawrence M. S., Keats J. J., Cibulskis K., Sougnez C., et al. (2011) Initial genome sequencing and analysis of multiple myeloma. Nature 471: 467-472 11 Whole-Genome Resequencing Whole-genome sequencing of tumor-normal pair samples provides a comprehensive picture of all unique mutations present in the tumor. It has become relatively inexpensive and fast to sequence complete genomes and it is an excellent choice for hypothesis-free discovery applications. References Berger M. F., Hodis E., Heffernan T. P., Deribe Y. L., Lawrence M. S., et al. (2012) Melanoma genome sequencing reveals frequent PREX2 mutations. Nature 485: 502-506 This is an example of using whole-genome next-generation sequencing to find a candidate gene, followed by targeted sequencing of a larger cohort. Illumina Technology: Genome Analyzer IIx and HiSeq 2000 101 bp paired-end reads with 32 to 65 bp haploid coverage Zhang J., Ding L., Holmfeldt L., Wu G., Heatley S. L., et al. (2012) The genetic basis of early Tcell precursor acute lymphoblastic leukaemia. Nature 481: 157-163 Early T-cell precursor acute lymphoblastic leukemia (ETP ALL) is a rare and aggressive malignancy of unknown genetic basis. The authors performed whole-genome sequencing of matched normals and leukemic samples from 12 ETP ALL cases and determined the frequency of somatic mutations in a separate cohort of 52 ETP and 42 non-ETP childhood T-lineage acute lymphoblastic leukemia (T-ALL) cases. The mutational spectrum is similar to myeloid tumors and the global transcriptional profile of ETP ALL was also similar to that of normal and myeloid leukemia hematopoietic stem cells. These findings suggest that addition of myeloid-directed therapies might improve the poor outcome of ETP ALL. In a study such as this, where there are few samples and the genetic alterations are unknown, whole-genome sequencing is an effective tool to find the genetic alterations. Illumina Technology: Genome Analyzer IIx for 101 bp paired-end reads Ding L., Ley T. J., Larson D. E., Miller C. A., Koboldt D. C., et al. (2012) Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481: 506-510 Ellis M. J., Ding L., Shen D., Luo J., Suman V. J., et al. (2012) Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature 486: 353-360 Govindan R., Ding L., Griffith M., Subramanian J., Dees N. D., et al. (2012) Genomic landscape of nonsmall cell lung cancer in smokers and never-smokers. Cell 150: 1121-1134 12 Whole-Exome Sequencing Exome sequencing focuses only on the 1% to 2% of the genome that codes for proteins and is therefore less expensive to run and easier to analyze. There have been many notable successes using this approach on Mendelian diseases.24-25 Although it produces only one-fiftieth of the whole-genome sequence, the cost saving is approximately 50% due to the more expensive and labor-intensive processing of the genetic material.26 In cancer research, where gross genomic rearrangements are common, exome sequencing may miss key mutations.27 References Curtis C., Shah S. P., Chin S. F., Turashvili G., Rueda O. M., et al. (2012) The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486: 346-352 Gerlinger M., Rowan A. J., Horswell S., Larkin J., Endesfelder D., et al. (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366: 883-892 Grasso C. S., Wu Y. M., Robinson D. R., Cao X., Dhanasekaran S. M., et al. (2012) The mutational landscape of lethal castration-resistant prostate cancer. Nature 487: 239-243 Koboldt D. C., Zhang Q., Larson D. E., Shen D., McLellan M. D., et al. (2012) VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 22: 568-576 Leidenroth A., Sorte H. S., Gilfillan G., Ehrlich M., Lyle R., et al. (2012) Diagnosis by sequencing: correction of misdiagnosis from FSHD2 to LGMD2A by whole-exome analysis. Eur J Hum Genet 20: 9991003 Lohr J. G., Stojanov P., Lawrence M. S., Auclair D., Chapuy B., et al. (2012) Discovery and prioritization of somatic mutations in diffuse large B-cell lymphoma (DLBCL) by whole-exome sequencing. Proc Natl Acad Sci U S A 109: 3879-3884 Zang Z. J., Cutcutache I., Poon S. L., Zhang S. L., McPherson J. R., et al. (2012) Exome sequencing of gastric adenocarcinoma identifies recurrent somatic mutations in cell adhesion and chromatin remodeling genes. Nat Genet 44: 570-574 24 25 26 27 Gilissen C., Hoischen A., Brunner H. G. and Veltman J. A. (2012) Disease gene identification strategies for exome sequencing. Eur J Hum Genet 20: 490-497 Majewski J., Schwartzentruber J., Lalonde E., Montpetit A. and Jabado N. (2011) What can exome sequencing do for you? J Med Genet 48: 580-589 Waters H. (2012) New NIH genetics center focuses its lens on exome, despite doubts. Nat Med 18: 8 Chapman M. A., Lawrence M. S., Keats J. J., Cibulskis K., Sougnez C., et al. (2011) Initial genome sequencing and analysis of multiple myeloma. Nature 471: 467-472 13 Targeted Gene Sequencing Targeted resequencing focuses on a restricted set of genes that are selected based on some prior knowledge. By using only cancer-relevant genes, the results are relatively easy to interpret and potentially actionable. A panel that contains the appropriate genes could be used on different cancer types and streamline laboratory processing and data interpretation. Larger studies in the future may show potential stratification of the patients according to disease progression, genetic profile, environmental exposure, or other factors.28-29 Studies to date indicate that this approach may have significant potential as a diagnostic tool. Example of a Cancer Panel30 Genetic Marker Application Drug BCR-ABL BCR-ABL/T315I BRAF V600E BRCA1/2 Ph+ CML; Ph+ ALL Resistance to anti-BCR-ABL agents Metastatic melanoma Metastatic ovarian cancer and breast cancer with BRCA 1/2 mutations Imatinib, dasatinib, nilotinib Imatinib, dasatinib, nilotinib Vemurafenib Olaparib, veliparib, iniparib c-Kit EGFR EGFR T790M Kit (CD117)-positive malignant GIST Locally advanced, unresectable, or metastatic NSCLC Resistance to EGFR tyrosine kinase inhibitors in advanced NSCLC ALK kinase inhibitor for metastatic NSCLC with this fusion gene Imatinib Erlotinib, gefitinib Erlotinib, gefitinib HER2-positive breast cancer or metastatic gastric or gastroesophageal junction adenocarcinoma Resistance to EGFR antibodies in metastatic colorectal cancer Trastuzumab Acute promyelocytic leukemia Deficiency is associated with increased risk of myelotoxicity Homozygosity for UGT1A1*28 is associated with risk of toxicity ATRA, arsenic trioxide Mercaptopurine, azathioprine Deficiency is associated with risk of severe toxicity 5-Fluorouracil EML4-ALK HER2 amplification KRAS PML/RAR TPMT UGT1A1 DPD Crizotinib Cetuximab, panitumumab Irinotecan ATRA, all trans retinoic acid; Ph+, Philadelphia-positive chromosome; DPD, dihydropyrimine dehydrogenase; EGFR, epidermal growth factor receptor; EML4-ALK, echinoderm microtubule-associated protein-like 4 anaplastic lymphoma kinase; HER2, human epidermal growth receptor 2; GIST, gastrointestinal stromal tumors; ALL, acute lymphocytic leukemia; NSCLC, non-small cell lung cancer; TPMT, thiopurine Smethyltransferase. 28 29 30 14 Holbrook J. D., Parker J. S., Gallagher K. T., Halsey W. S., Hughes A. M., et al. (2011) Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine. J Transl Med 9: 119 Govindan R., Ding L., Griffith M., Subramanian J., Dees N. D., et al. (2012) Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 150: 1121-1134 Dancey J. E., Bedard P. L., Onetto N. and Hudson T. J. (2012) The genetic basis for cancer treatment decisions. Cell 148: 409420 References Lipson D., Capelletti M., Yelensky R., Otto G., Parker A., et al. (2012) Identification of new ALK and RET gene fusions from colorectal and lung cancer biopsies. Nat Med 18: 382-384 In this study the authors targeted 145 cancer-relevant genes in 40 colorectal cancer and 24 non–small cell lung cancer (NSCLC) formalin-fixed and paraffin-embedded (FFPE) samples. Of the samples tested, 59% contain mutations represented in this panel. The remaining patients who did not have mutations in the genes represented on the panel would be good candidates for whole-genome sequencing to potentially expand the cancer-relevant gene panel. A small group of genes represent the majority of mutations, but the diversity of the remaining mutations is remarkable. It clearly indicates the benefit of a molecular diagnosis to treat each patient appropriately. Illumina Technology: HiSeq 2000 with 36 bp paired-end reads to an average depth of 229 fold A hypothetical cancer panel. Brown squares indicate mutated genes. Within the same cancer type, different genes are impacted. Harismendy O., Schwab R. B., Bao L., Olson J., Rozenzhak S., et al. (2011) Detection of low prevalence somatic mutations in solid tumors with ultra-deep targeted sequencing. Genome Biol 12: R124 The authors used ultra-deep targeted sequencing to screen 71.1 kb of sequence encompassing the mutational hotspots of 42 cancer genes. In a mixing experiment, the sensitivity and specificity were determined to be >94% and >99%, respectively, for low prevalence mutations. They evaluated the performance and utility of the assay by using it to study the mutational profile of both clinical cancer and mouse xenograft samples. The prevalence of the mutations detected in complex DNA mixtures has been limited to ~20% using Sanger sequencing. The sensitivity of the assay presented here is capable of detecting mutations present at 5% prevalence. This should allow the detection of mutations in heterogeneous or poor-quality samples with rare mutated clones, low cellularity, or contamination with stroma or immune cell infiltration, all of which are commonly seen in clinical samples. Illumina technology: MiSeq® system for 151 bp paired-end reads Gerstung M., Beisel C., Rechsteiner M., Wild P., Schraml P., et al. (2012) Reliable detection of subclonal single-nucleotide variants in tumour cell populations. Nat Commun 3: 811 Govindan R., Ding L., Griffith M., Subramanian J., Dees N. D., et al. (2012) Genomic landscape of nonsmall cell lung cancer in smokers and never-smokers. Cell 150: 1121-1134 15 Pritchard C. C., Smith C., Salipante S. J., Lee M. K., Thornton A. M., et al. (2012) ColoSeq provides comprehensive lynch and polyposis syndrome mutational analysis using massively parallel sequencing. J Mol Diagn 14: 357-366 Wagle N., Berger M. F., Davis M. J., Blumenstiel B., Defelice M., et al. (2012) High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing. Cancer Discov 2: 82-93 Expression Analysis The dynamic nature of gene expression patterns, combined with the instability of RNA during tissue handling has made the development of RNA-based diagnostics a challenge.31 However, there is an increased appreciation of the role that expression analysis can play to improve the interpretation of genetic alterations for cancer diagnostics and treatment. For example: Distinguish between passenger and driver mutations. A mutation in a gene that is not expressed is unlikely to impact the progression of the disease and is probably a passenger mutation. Conversely, a highly expressed aberrant or fusion gene is very likely a driver mutation that will impact the disease progression.32 Distinguish between activating and inactivating mutations. For some gene mutations, the choice of therapeutic can be critically dependent on the activation status of the gene.33-34 A highly expressed target gene would require an inhibitor drug, while the same target gene with a loss-offunction mutation would require and activator drug. References Lee C. H., Ou W. B., Marino-Enriquez A., Zhu M., Mayeda M., et al. (2012) 14-3-3 fusion oncogenes in high-grade endometrial stromal sarcoma. Proc Natl Acad Sci U S A 109: 929-934 The authors identified a transforming 14-3-3 oncoprotein through conventional cytogenetics and wholetranscriptome sequencing. It is a recurrent genetic mechanism in a clinically aggressive form of uterine sarcoma: high-grade endometrial stromal sarcoma (ESS). It proved an excellent demonstration in detecting fusion genes with mRNA-seq. Illumina Technology: Genome Analyzer II Curtis C., Shah S. P., Chin S. F., Turashvili G., Rueda O. M., et al. (2012) The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486: 346-352 Fountzilas E., Markou K., Vlachtsis K., Nikolaou A., Arapantoni-Dadioti P., et al. (2012) Identification and validation of gene expression models that predict clinical outcome in patients with early-stage laryngeal cancer. Ann Oncol 23: 2146-2153 31 32 33 34 16 Rodriguez-Gonzalez F. G., Mustafa D. A., Mostert B. and Sieuwerts A. M. (2012) The challenge of gene expression profiling in heterogeneous clinical samples. Methods Schwartzentruber J., Korshunov A., Liu X. Y., Jones D. T., Pfaff E., et al. (2012) Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482: 226-231 Cancer Genome Atlas N. (2012) Comprehensive molecular portraits of human breast tumours. Nature 490: 61-70 Lynch T. J., Bell D. W., Sordella R., Gurubhagavatula S., Okimoto R. A., et al. (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 350: 2129-2139 Ju Y. S., Lee W. C., Shin J. Y., Lee S., Bleazard T., et al. (2012) A transforming KIF5B and RET gene fusion in lung adenocarcinoma revealed from whole-genome and transcriptome sequencing. Genome Res 22: 436-445 Rudin C. M., Durinck S., Stawiski E. W., Poirier J. T., Modrusan Z., et al. (2012) Comprehensive genomic analysis identifies SOX2 as a frequently amplified gene in small-cell lung cancer. Nat Genet 44: 1111-1116 Scrima M., De Marco C., Fabiani F., Franco R., Pirozzi G., et al. (2012) Signaling networks associated with AKT activation in non-small cell lung cancer (NSCLC): new insights on the role of phosphatydil-inositol-3 kinase. PLoS ONE 7: e30427 FFPE Samples Clinical samples are typically limited in size, particularly in the case of needle biopsies and needle aspirates. When a limited amount of tissue is available for testing, the first priority is to prepare histology slides for pathology. As a result the only material available for sequencing is from FFPE samples.35 Fresh FFPE samples can deliver excellent results when the appropriate care is taken during collection and storage.36 References Wagle N., Berger M. F., Davis M. J., Blumenstiel B., Defelice M., et al. (2012) High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing. Cancer Discov 2: 82-93 The authors describe a sequencing-based approach to identifying genomic alterations in FFPE tumor samples. These studies confirmed the feasibility and clinical utility of targeted sequencing in the oncology. Illumina Technology: HiSeq with 100 bp paired-end reads Adams M. D., Veigl M. L., Wang Z., Molyneux N., Sun S., et al. (2012) Global mutational profiling of formalin-fixed human colon cancers from a pathology archive. Mod Pathol 25: 1599-1608 Corless C. L. and Spellman P. T. (2012) Tackling formalin-fixed, paraffin-embedded tumor tissue with next-generation sequencing. Cancer Discov 2: 23-24 Frankel A. (2012) Formalin fixation in the '-omics' era: a primer for the surgeon-scientist. ANZ J Surg 82: 395-402 Gravendeel L. A., de Rooi J. J., Eilers P. H., van den Bent M. J., Sillevis Smitt P. A., et al. (2012) Gene expression profiles of gliomas in formalin-fixed paraffin-embedded material. Br J Cancer 106: 538-545 How Kit A., Nielsen H. M. and Tost J. (2012) DNA methylation based biomarkers: Practical considerations and applications. Biochimie 94: 2314-2337 Kotorashvili A., Ramnauth A., Liu C., Lin J., Ye K., et al. (2012) Effective DNA/RNA co-extraction for analysis of microRNAs, mRNAs, and genomic DNA from formalin-fixed paraffin-embedded specimens. PLoS ONE 7: e34683 35 36 Corless C. L. and Spellman P. T. (2012) Tackling formalin-fixed, paraffin-embedded tumor tissue with next-generation sequencing. Cancer Discov 2: 23-24 Yost S. E., Smith E. N., Schwab R. B., Bao L., Jung H., et al. (2012) Identification of high-confidence somatic mutations in whole genome sequence of formalin-fixed breast cancer specimens. Nucleic Acids Res 40: e107 17 Ma R., Yan W., Zhang G., Lv H., Liu Z., et al. (2012) Upregulation of miR-196b confers a poor prognosis in glioblastoma patients via inducing a proliferative phenotype. PLoS ONE 7: e38096 Rentoft M., Coates P. J., Laurell G. and Nylander K. (2012) Transcriptional profiling of formalin fixed paraffin embedded tissue: pitfalls and recommendations for identifying biologically relevant changes. PLoS ONE 7: e35276 Sinicropi D., Qu K., Collin F., Crager M., Liu M. L., et al. (2012) Whole transcriptome RNA-Seq analysis of breast cancer recurrence risk using formalin-fixed paraffin-embedded tumor tissue. PLoS ONE 7: e40092 Vui-Kee K., Mohd Dali A. Z., Mohamed Rose I., Ghazali R., Jamal R., et al. (2012) Molecular markers associated with nonepithelial ovarian cancer in formalin-fixed, paraffin-embedded specimens by genome wide expression profiling. Kaohsiung J Med Sci 28: 243-250 Wagle N., Berger M. F., Davis M. J., Blumenstiel B., Defelice M., et al. (2012) High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing. Cancer Discov 2: 82-93 Yost S. E., Smith E. N., Schwab R. B., Bao L., Jung H., et al. (2012) Identification of high-confidence somatic mutations in whole genome sequence of formalin-fixed breast cancer specimens. Nucleic Acids Res 40: e107 18 Response to Therapy The ideal cancer treatment is a protocol designed to target the specific molecular mechanism of the tumor and is well tolerated by the patient. The same clinical presentation can represent several different molecular mechanisms, each with different drug sensitivity, and patients can vary widely in their tolerance to cancer therapies. As the cancer progresses, it accumulates somatic mutations and genomic rearrangements. This can lead to drug resistance or metastasis, which can profoundly impact the patient’s prognosis.37 There is increasing evidence that informative biomarkers can assist in making therapeutic decisions to tailor the treatment to the specific patient. Ongoing monitoring during treatment can also gauge the response to treatment and risk of relapse.38 Intratumor heterogeneity. The progressive accumulation of somatic mutations results in a heterogeneous polyclonal tumor where different clones may respond differently to treatment. References Ding L., Ley T. J., Larson D. E., Miller C. A., Koboldt D. C., et al. (2012) Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481: 506-510 This study addresses the causes of relapse in acute myeloid leukemia (AML). The authors found two general mechanisms: (1) the founding clone in the primary tumor gained mutations and evolved into the relapse clone, and (2) a subclone of the founding clone survived initial therapy, gained additional mutations and expanded at relapse. In one case, a subclone that made up only 5.1% of the primary tumor became the predominant clone after relapse. In all cases chemotherapy failed to eradicate the founding clone. This study underscores the importance of detecting and eradicating small cellular populations after diagnosis and also after the initial treatment. The ability of next-generation sequencing to detect de novo mutations in very small cell populations makes it uniquely suitable to this type of application. Illumina Technology: Genome Analyzer IIx 100 bp paired-end reads 37 38 Caldas C. (2012) Cancer sequencing unravels clonal evolution. Nat Biotechnol 30: 408-410 Dancey J. E., Bedard P. L., Onetto N. and Hudson T. J. (2012) The genetic basis for cancer treatment decisions. Cell 148: 409420 19 Walter M. J., Shen D., Ding L., Shao J., Koboldt D. C., et al. (2012) Clonal architecture of secondary acute myeloid leukemia. N Engl J Med 366: 1090-1098 Secondary acute myeloid leukemia (AML) develops in approximately one in three people with myelodysplastic syndromes. This study’s intent was to identify mutations in myelodysplastic syndromes that may predict progression to AML. The authors performed whole-genome sequencing of seven paired samples of skin and bone marrow in seven subjects with secondary AML, as well as matched bone marrow samples from the antecedent myelodysplastic-syndrome. They found that in all cases the dominant secondary-AML clone was derived from a myelodysplastic-syndrome founding clone. This implies that myelodysplastic syndrome samples contain prognostically important mutations and therapies that target these mutations may improve outcomes. Illumina Technology: Genome Analyzer IIx and HiSeq 2000 with 2 x 75 paired-end reads and 100x coverage Gerlinger M., Rowan A. J., Horswell S., Larkin J., Endesfelder D., et al. (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366: 883-892 The authors used whole-exome sequencing to investigate multiple samples from spatially separated regions from primary renal carcinomas and associated metastatic sites in two patients. They found extensive heterogeneity within the primary tumor and noted that 63–69% of all somatic mutations were not detectable across every tumor region. Gene-expression signatures of good and poor prognosis were also detected in different regions of the same tumor. This underscores the importance of early diagnosis before the mutations accumulate, as well as the need for multiple biopsy sites in larger tumors. The use of multiple samples from the same patient allows the authors to reconstruct the progression of the disease. This is a remarkably powerful approach that indicated not only the trigger events, but also genes that display parallel evolution. Parallel evolution is usually an indication of evolutionary pressure and it implies that those genes would make effective therapeutic targets. Illumina Technology: Genome Analyzer IIx and HiSeq 2000 Yap T. A., Gerlinger M., Futreal P. A., Pusztai L. and Swanton C. (2012) Intratumor heterogeneity: seeing the wood for the trees. Sci Transl Med 4: 127ps110 20 Metastasis and Recurrence Metastasis is a complex process in which cancer cells break away from the primary tumor and circulate through the bloodstream or lymphatic system to other sites in the body. At the new sites the cells continue to multiply and eventually form additional tumors. The ability of tumors, such as pancreatic cancer and uveal cancers, to metastasize contributes greatly to their lethality. Many fundamental questions remain about the clonal structures of metastatic tumors, phylogenetic relationships among metastases, the scale of ongoing parallel evolution in metastatic and primary sites, and how the tumor disseminates. Review Caldas C. (2012) Cancer sequencing unravels clonal evolution. Nat Biotechnol 30: 408-410 Metastases can originate from either a major clone in the primary tumor (metastasis 1), or from minor clones (metastasis 2). Metastases can also undergo clonal evolution, as shown in metastasis 1. 21 References Hsieh A. C., Liu Y., Edlind M. P., Ingolia N. T., Janes M. R., et al. (2012) The translational landscape of mTOR signalling steers cancer initiation and metastasis. Nature 485: 55-61 The authors demonstrated a specialized translation of the prostate cancer genome by oncogenic mTOR signaling, which resulted in a very specific repertoire of genes involved in cell proliferation, metabolism, and invasion. The transcripts were functionally characterized a class of translationally controlled proinvasion messenger RNAs that orchestrate prostate cancer invasion and metastasis. Illumina Technology: Genome Analyzer II for mRNA-Seq and ribo-Seq Gerlinger M., Rowan A. J., Horswell S., Larkin J., Endesfelder D., et al. (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366: 883-892 Nickel G. C., Barnholtz-Sloan J., Gould M. P., McMahon S., Cohen A., et al. (2012) Characterizing mutational heterogeneity in a glioblastoma patient with double recurrence. PLoS ONE 7: e35262 Turajlic S., Furney S. J., Lambros M. B., Mitsopoulos C., Kozarewa I., et al. (2012) Whole genome sequencing of matched primary and metastatic acral melanomas. Genome Res 22: 196-207 Sethi N. and Kang Y. (2011) Unravelling the complexity of metastasis - molecular understanding and targeted therapies. Nat Rev Cancer 11: 735-748 22 Cancer Drug Development From a drug development perspective cancer presents unique challenges.39 The typical tumor sample consists of two genomes: the germline inherited from the parents and the somatic mutations that accumulate during the progression of the disease. The dynamic nature of the tumor genome allows it to rapidly acquire drug resistance in response to therapy. In advanced tumors the accumulated mutations can result in multiple clones, each with its own drug susceptibility or resistance. It is therefore not surprising that next-generation sequencing with its ability to analyze the genome hypothesis-free and at high resolution, in effect an ability to establish molecular phenotypes, is being incorporated into the oncology drug discovery and development process. Review Mills G. B. (2012) An emerging toolkit for targeted cancer therapies. Genome Res 22: 177-182 References Prahallad A., Sun C., Huang S., Di Nicolantonio F., Salazar R., et al. (2012) Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 483: 100103 Inhibition of the BRAF(V600E) oncoprotein by the small-molecule drug PLX4032 (vemurafenib) is highly effective in the treatment of melanoma. However, 8% to 10% of colon cancer patients harboring the same BRAF(V600E) oncogenic lesion have a poor prognosis and show very limited response to this drug. This paper provides evidence that these patients may benefit from combination therapy consisting of BRAF and EGFR inhibitors. Illumina Technology: Genome AnalyzerTM Zhang J., Benavente C. A., McEvoy J., Flores-Otero J., Ding L., et al. (2012) A novel retinoblastoma therapy from genomic and epigenetic analyses. Nature 481: 329-334 By integrating epigenetic and gene expression analyses the authors identified SYK as an important oncogene in retinoblastoma. This is important not only for expanding our understanding of the biology of retinoblastoma but also for advancing immediate therapeutic options that were not previously considered, such as the use of BAY 61-3606 or R406. This study highlights the value of integrating whole-genome sequencing analyses of the genetic and epigenetic features of tumor genomes to find a cure for cancers such as retinoblastoma. Illumina Technology: Genome Analyzer 100 bp paired-end and Human Methylation 27k BeadChip. Astanehe A., Finkbeiner M. R., Krzywinski M., Fotovati A., Dhillon J., et al. (2012) MKNK1 is a YB-1 target gene responsible for imparting trastuzumab resistance and can be blocked by RSK inhibition. Oncogene 31: 4434-4446 Luo Y., Ellis L. Z., Dallaglio K., Takeda M., Robinson W. A., et al. (2012) Side population cells from human melanoma tumors reveal diverse mechanisms for chemoresistance. J Invest Dermatol 132: 2440-2450 39 Woollard P. M., Mehta N. A., Vamathevan J. J., Van Horn S., Bonde B. K., et al. (2011) The application of next-generation sequencing technologies to drug discovery and development. Drug Discov Today 16: 512-519 23 Clinical Trials The emergence of high throughput technologies has allowed the stratification of most common diseases into sub-segments based on their molecular phenotypes. A unique molecular phenotype may account for only 2% to 10% of the total population of the disease. This profound change has led to the development of the concept of stratified medicine, wherein the population targeted by a drug is small and the expected level of efficacy for each drug is very high. Identification of these subgroups and the availability of new agents targeting these infrequent alterations are already affecting the way in which clinical trials are being conducted.40-41 References Arnedos M., Andre F., Farace F., Lacroix L., Besse B., et al. (2012) The challenge to bring personalized cancer medicine from clinical trials into routine clinical practice: the case of the Institut Gustave Roussy. Mol Oncol 6: 204-210 This paper describes the ongoing and future prospective trials at the Institut Gustave Roussy. The longterm goal for these trails is to prepare large studies that will test the benefit of high throughput technologies and personalized medicine. As a first step these studies will select patients presenting specific molecular alterations for phase I and II clinical trials. Ellis M. J., Ding L., Shen D., Luo J., Suman V. J., et al. (2012) Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature 486: 353-360 To correlate the variable clinical features of estrogen-receptor-positive breast cancer with somatic alterations, the authors studied pretreatment tumor biopsies accrued from patients in two studies of neoadjuvant aromatase inhibitor therapy. Distinct phenotypes in estrogen-receptor-positive breast cancer were found to be associated with specific patterns of somatic mutations. These mutation patterns map into cellular pathways linked to tumor biology, but most recurrent mutations are relatively infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing. Illumina Technology: Genome Analyzer 75 or 100 bp paired-end reads Falchook G. S., Lewis K. D., Infante J. R., Gordon M. S., Vogelzang N. J., et al. (2012) Activity of the oral MEK inhibitor trametinib in patients with advanced melanoma: a phase 1 doseescalation trial. Lancet Oncol 13: 782-789 The data presented in this paper show substantial clinical activity of trametinib in melanoma and suggest that MEK is a valid therapeutic target. Differences in response rates according to the mutational profile indicate the importance of mutational analyses in the future. Illumina Technology: GoldenGate® custom genotyping array42 40 41 42 24 Arnedos M., Andre F., Farace F., Lacroix L., Besse B., et al. (2012) The challenge to bring personalized cancer medicine from clinical trials into routine clinical practice: the case of the Institut Gustave Roussy. Mol Oncol 6: 204-210 Van Schaeybroeck S., Allen W. L., Turkington R. C. and Johnston P. G. (2011) Implementing prognostic and predictive biomarkers in CRC clinical trials. Nat Rev Clin Oncol 8: 222-232 Moy C., Aziz M. U., Greshock J., Szabo S., McNeil E., et al. (2011) Mutation and copy number detection in human cancers using a custom genotyping assay. Genomics 98: 296-301 Iyer G., Hanrahan A. J., Milowsky M. I., Al-Ahmadie H., Scott S. N., et al. (2012) Genome sequencing identifies a basis for everolimus sensitivity. Science 338: 221 The authors studied the metastatic bladder cancer genome of a patient with who was enrolled in a phase II clinical trial—ClinicalTrials.gov NCT00805 129. The trial failed to achieve its PFS end-point, but the patient achieved a durable and ongoing (>2 years) complete response to everolimus. Sequencing revealed that the patient's tumor harbored TSC1 somatic mutations that may have enhanced the effectiveness of the treatment. This study demonstrated the feasibility of using whole-genome sequencing in the clinical setting to identify biomarkers of drug sensitivity that can aid in the identification of patients most likely to respond to targeted anticancer drugs. Illumina Technology: HiSeq 100 bp paired-end whole-genome sequences of tumor-normal pairs to 40 fold coverage 25 Bibliography Adams M. D., Veigl M. L., Wang Z., Molyneux N., Sun S., et al. (2012) Global mutational profiling of formalin-fixed human colon cancers from a pathology archive. Mod Pathol 25: 1599-1608 Anindo M. I. and Yaqinuddin A. (2012) Insights into the potential use of microRNAs as biomarker in cancer. Int J Surg 10: 443-449 Antoniou A. C., Wang X., Fredericksen Z. S., McGuffog L., Tarrell R., et al. (2010) A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor-negative breast cancer in the general population. Nat Genet 42: 885892 Arnedos M., Andre F., Farace F., Lacroix L., Besse B., et al. (2012) The challenge to bring personalized cancer medicine from clinical trials into routine clinical practice: the case of the Institut Gustave Roussy. Mol Oncol 6: 204-210 Astanehe A., Finkbeiner M. R., Krzywinski M., Fotovati A., Dhillon J., et al. (2012) MKNK1 is a YB-1 target gene responsible for imparting trastuzumab resistance and can be blocked by RSK inhibition. Oncogene 31: 4434-4446 Berger M. F., Hodis E., Heffernan T. P., Deribe Y. L., Lawrence M. S., et al. (2012) Melanoma genome sequencing reveals frequent PREX2 mutations. Nature 485: 502-506 Blake P. M., Decker D. A., Glennon T. M., Liang Y. M., Losko S., et al. (2011) Toward an integrated knowledge environment to support modern oncology. Cancer J 17: 257-263 Brooks J. D. (2012) Translational genomics: the challenge of developing cancer biomarkers. Genome Res 22: 183-187 26 Caldas C. (2012) Cancer sequencing unravels clonal evolution. Nat Biotechnol 30: 408-410 Cancer Genome Atlas N. (2012) Comprehensive molecular portraits of human breast tumours. Nature 490: 61-70 Chan K. C., Jiang P., Zheng Y. W., Liao G. J., Sun H., et al. (2012) Cancer Genome Scanning in Plasma: Detection of Tumor-Associated Copy Number Aberrations, SingleNucleotide Variants, and Tumoral Heterogeneity by Massively Parallel Sequencing. Clin Chem Chapman M. A., Lawrence M. S., Keats J. J., Cibulskis K., Sougnez C., et al. (2011) Initial genome sequencing and analysis of multiple myeloma. Nature 471: 467-472 Corless C. L. and Spellman P. T. (2012) Tackling formalin-fixed, paraffin-embedded tumor tissue with next-generation sequencing. Cancer Discov 2: 23-24 Curtis C., Shah S. P., Chin S. F., Turashvili G., Rueda O. M., et al. (2012) The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486: 346-352 Dancey J. E., Bedard P. L., Onetto N. and Hudson T. J. (2012) The genetic basis for cancer treatment decisions. Cell 148: 409-420 Ding L., Ley T. J., Larson D. E., Miller C. A., Koboldt D. C., et al. (2012) Clonal evolution in relapsed acute myeloid leukaemia revealed by wholegenome sequencing. Nature 481: 506-510 Ellis M. J., Ding L., Shen D., Luo J., Suman V. J., et al. (2012) Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature 486: 353-360 Falchook G. S., Lewis K. D., Infante J. R., Gordon M. S., Vogelzang N. J., et al. (2012) Activity of the oral MEK inhibitor trametinib in patients with advanced melanoma: a phase 1 dose-escalation trial. Lancet Oncol 13: 782-789 Forshew T., Murtaza M., Parkinson C., Gale D., Tsui D. W., et al. (2012) Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Sci Transl Med 4: 136ra168 Fountzilas E., Markou K., Vlachtsis K., Nikolaou A., Arapantoni-Dadioti P., et al. (2012) Identification and validation of gene expression models that predict clinical outcome in patients with earlystage laryngeal cancer. Ann Oncol 23: 2146-2153 Frankel A. (2012) Formalin fixation in the '-omics' era: a primer for the surgeon-scientist. ANZ J Surg 82: 395-402 Gerlinger M., Rowan A. J., Horswell S., Larkin J., Endesfelder D., et al. (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366: 883-892 Gerstung M., Beisel C., Rechsteiner M., Wild P., Schraml P., et al. (2012) Reliable detection of subclonal singlenucleotide variants in tumour cell populations. Nat Commun 3: 811 Gilissen C., Hoischen A., Brunner H. G. and Veltman J. A. (2012) Disease gene identification strategies for exome sequencing. Eur J Hum Genet 20: 490-497 Godley L. A. (2012) Profiles in leukemia. N Engl J Med 366: 1152-1153 Govindan R., Ding L., Griffith M., Subramanian J., Dees N. D., et al. (2012) Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 150: 1121-1134 Grasso C. S., Wu Y. M., Robinson D. R., Cao X., Dhanasekaran S. M., et al. (2012) The mutational landscape of lethal castrationresistant prostate cancer. Nature 487: 239-243 Gravendeel L. A., de Rooi J. J., Eilers P. H., van den Bent M. J., Sillevis Smitt P. A., et al. (2012) Gene expression profiles of gliomas in formalin-fixed paraffin-embedded material. Br J Cancer 106: 538-545 Harismendy O., Schwab R. B., Bao L., Olson J., Rozenzhak S., et al. (2011) Detection of low prevalence somatic mutations in solid tumors with ultra-deep targeted sequencing. Genome Biol 12: R124 Holbrook J. D., Parker J. S., Gallagher K. T., Halsey W. S., Hughes A. M., et al. (2011) Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine. J Transl Med 9: 119 How Kit A., Nielsen H. M. and Tost J. (2012) DNA methylation based biomarkers: Practical considerations and applications. Biochimie 94: 2314-2337 Hsieh A. C., Liu Y., Edlind M. P., Ingolia N. T., Janes M. R., et al. (2012) The translational landscape of mTOR signalling steers cancer initiation and metastasis. Nature 485: 55-61 Iyer G., Hanrahan A. J., Milowsky M. I., Al-Ahmadie H., Scott S. N., et al. (2012) Genome sequencing identifies a basis for everolimus sensitivity. Science 338: 221 Ju Y. S., Lee W. C., Shin J. Y., Lee S., Bleazard T., et al. (2012) A transforming KIF5B and RET gene fusion in lung adenocarcinoma revealed from whole-genome and transcriptome sequencing. Genome Res 22: 436-445 Koboldt D. C., Zhang Q., Larson D. E., Shen D., McLellan M. D., et al. (2012) VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 22: 568-576 Kotorashvili A., Ramnauth A., Liu C., Lin J., Ye K., et al. (2012) Effective DNA/RNA coextraction for analysis of microRNAs, mRNAs, and genomic DNA from formalinfixed paraffin-embedded specimens. PLoS ONE 7: e34683 Lee C. H., Ou W. B., MarinoEnriquez A., Zhu M., Mayeda M., et al. (2012) 14-3-3 fusion oncogenes in high-grade endometrial stromal sarcoma. Proc Natl Acad Sci U S A 109: 929-934 Leidenroth A., Sorte H. S., Gilfillan G., Ehrlich M., Lyle R., et al. (2012) Diagnosis by sequencing: correction of misdiagnosis from FSHD2 to LGMD2A by whole-exome analysis. Eur J Hum Genet 20: 999-1003 Lipson D., Capelletti M., Yelensky R., Otto G., Parker A., et al. (2012) Identification of new ALK and RET gene fusions from colorectal and lung cancer biopsies. Nat Med 18: 382-384 Lohr J. G., Stojanov P., Lawrence M. S., Auclair D., Chapuy B., et al. (2012) Discovery and prioritization of somatic mutations in diffuse large B-cell lymphoma (DLBCL) by whole-exome sequencing. Proc Natl Acad Sci U S A 109: 3879-3884 Longo D. L. (2012) Tumor heterogeneity and personalized medicine. N Engl J Med 366: 956-957 Luo Y., Ellis L. Z., Dallaglio K., Takeda M., Robinson W. A., et al. (2012) Side population cells from human melanoma tumors reveal diverse mechanisms for chemoresistance. J Invest Dermatol 132: 2440-2450 Lynch T. J., Bell D. W., Sordella R., Gurubhagavatula S., Okimoto R. A., et al. (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 350: 2129-2139 Ma R., Yan W., Zhang G., Lv H., Liu Z., et al. (2012) Upregulation of miR-196b confers a poor prognosis in glioblastoma patients via inducing a proliferative phenotype. PLoS ONE 7: e38096 Majewski J., Schwartzentruber J., Lalonde E., Montpetit A. and Jabado N. (2011) What can exome sequencing do for you? J Med Genet 48: 580-589 Mardis E. R. (2012) Genome sequencing and cancer. Curr Opin Genet Dev 22: 245-250 Mills G. B. (2012) An emerging toolkit for targeted cancer therapies. Genome Res 22: 177-182 Mo M. H., Chen L., Fu Y., Wang W. and Fu S. W. (2012) Cellfree Circulating miRNA Biomarkers in Cancer. J Cancer 3: 432-448 Mosig R. A., Lobl M., Senturk E., Shah H., Cohen S., et al. (2012) IGFBP-4 tumor and serum levels are increased across all stages of epithelial ovarian cancer. J Ovarian Res 5: 3 Moy C., Aziz M. U., Greshock J., Szabo S., McNeil E., et al. (2011) Mutation and copy number detection in human cancers using a custom genotyping assay. Genomics 98: 296-301 27 Nickel G. C., Barnholtz-Sloan J., Gould M. P., McMahon S., Cohen A., et al. (2012) Characterizing mutational heterogeneity in a glioblastoma patient with double recurrence. PLoS ONE 7: e35262 Schwartzentruber J., Korshunov A., Liu X. Y., Jones D. T., Pfaff E., et al. (2012) Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482: 226-231 Van Schaeybroeck S., Allen W. L., Turkington R. C. and Johnston P. G. (2011) Implementing prognostic and predictive biomarkers in CRC clinical trials. Nat Rev Clin Oncol 8: 222-232 Pineda M., Gonzalez S., Lazaro C., Blanco I. and Capella G. (2010) Detection of genetic alterations in hereditary colorectal cancer screening. Mutat Res 693: 19-31 Schwarzenbach H., Hoon D. S. and Pantel K. (2011) Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer 11: 426-437 Vui-Kee K., Mohd Dali A. Z., Mohamed Rose I., Ghazali R., Jamal R., et al. (2012) Molecular markers associated with nonepithelial ovarian cancer in formalin-fixed, paraffin-embedded specimens by genome wide expression profiling. Kaohsiung J Med Sci 28: 243-250 Prahallad A., Sun C., Huang S., Di Nicolantonio F., Salazar R., et al. (2012) Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 483: 100-103 Pritchard C. C., Smith C., Salipante S. J., Lee M. K., Thornton A. M., et al. (2012) ColoSeq provides comprehensive lynch and polyposis syndrome mutational analysis using massively parallel sequencing. J Mol Diagn 14: 357-366 Ramskold D., Luo S., Wang Y. C., Li R., Deng Q., et al. (2012) Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30: 777782 Rentoft M., Coates P. J., Laurell G. and Nylander K. (2012) Transcriptional profiling of formalin fixed paraffin embedded tissue: pitfalls and recommendations for identifying biologically relevant changes. PLoS ONE 7: e35276 Rodriguez-Gonzalez F. G., Mustafa D. A., Mostert B. and Sieuwerts A. M. (2012) The challenge of gene expression profiling in heterogeneous clinical samples. Methods Rudin C. M., Durinck S., Stawiski E. W., Poirier J. T., Modrusan Z., et al. (2012) Comprehensive genomic analysis identifies SOX2 as a frequently amplified gene in small-cell lung cancer. Nat Genet 44: 1111-1116 28 Schwarzenbach H. (2012) Circulating nucleic acids and protease activities in blood of tumor patients. Expert Opin Biol Ther 12 Suppl 1: S163-169 Scrima M., De Marco C., Fabiani F., Franco R., Pirozzi G., et al. (2012) Signaling networks associated with AKT activation in non-small cell lung cancer (NSCLC): new insights on the role of phosphatydil-inositol-3 kinase. PLoS ONE 7: e30427 Sethi N. and Kang Y. (2011) Unravelling the complexity of metastasis - molecular understanding and targeted therapies. Nat Rev Cancer 11: 735-748 Sinicropi D., Qu K., Collin F., Crager M., Liu M. L., et al. (2012) Whole transcriptome RNA-Seq analysis of breast cancer recurrence risk using formalin-fixed paraffinembedded tumor tissue. PLoS ONE 7: e40092 Stacey S. N., Sulem P., Jonasdottir A., Masson G., Gudmundsson J., et al. (2011) A germline variant in the TP53 polyadenylation signal confers cancer susceptibility. Nat Genet 43: 1098-1103 Turajlic S., Furney S. J., Lambros M. B., Mitsopoulos C., Kozarewa I., et al. (2012) Whole genome sequencing of matched primary and metastatic acral melanomas. Genome Res 22: 196-207 Wagle N., Berger M. F., Davis M. J., Blumenstiel B., Defelice M., et al. (2012) High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing. Cancer Discov 2: 82-93 Walter M. J., Shen D., Ding L., Shao J., Koboldt D. C., et al. (2012) Clonal architecture of secondary acute myeloid leukemia. N Engl J Med 366: 1090-1098 Waters H. (2012) New NIH genetics center focuses its lens on exome, despite doubts. Nat Med 18: 8 Weigelt B., Pusztai L., Ashworth A. and Reis-Filho J. S. (2012) Challenges translating breast cancer gene signatures into the clinic. Nat Rev Clin Oncol 9: 5864 Welch J. S., Ley T. J., Link D. C., Miller C. A., Larson D. E., et al. (2012) The origin and evolution of mutations in acute myeloid leukemia. Cell 150: 264-278 Woollard P. M., Mehta N. A., Vamathevan J. J., Van Horn S., Bonde B. K., et al. (2011) The application of next-generation sequencing technologies to drug discovery and development. Drug Discov Today 16: 512-519 Yap T. A., Gerlinger M., Futreal P. A., Pusztai L. and Swanton C. (2012) Intratumor heterogeneity: seeing the wood for the trees. Sci Transl Med 4: 127ps110 Yates L. R. and Campbell P. J. (2012) Evolution of the cancer genome. Nat Rev Genet 13: 795-806 Yost S. E., Smith E. N., Schwab R. B., Bao L., Jung H., et al. (2012) Identification of highconfidence somatic mutations in whole genome sequence of formalin-fixed breast cancer specimens. Nucleic Acids Res 40: e107 Zang Z. J., Cutcutache I., Poon S. L., Zhang S. L., McPherson J. R., et al. (2012) Exome sequencing of gastric adenocarcinoma identifies recurrent somatic mutations in cell adhesion and chromatin remodeling genes. Nat Genet 44: 570-574 Zhang J., Benavente C. A., McEvoy J., Flores-Otero J., Ding L., et al. (2012) A novel retinoblastoma therapy from genomic and epigenetic analyses. Nature 481: 329-334 Zhang J., Ding L., Holmfeldt L., Wu G., Heatley S. L., et al. (2012) The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature 481: 157-163 29 FOR RESEARCH USE ONLY © 2013 Illumina, Inc. All rights reserved. Illumina, IlluminaDx, BaseSpace, BeadArray, BeadXpress, cBot, CSPro, DASL, DesignStudio, Eco, GAIIx, Genetic Energy, Genome Analyzer, GenomeStudio, GoldenGate, HiScan, HiSeq, Infinium, iSelect, MiSeq, Nextera, NuPCR, SeqMonitor, Solexa, TruSeq, TruSight, VeraCode, the pumpkin orange color, and the Genetic Energy streaming bases design are trademarks or registered trademarks of Illumina, Inc. All other brands and names contained herein are the property of their respective owners. Pub. No. 773-2012-007 Current as of 14 January 2013