Download Oncology Review: An Overview of Publications Featuring Illumina

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
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