presented at 96th seminar in clinical genetics

1. Personalized Medicine in
Diagnosis and Treatment of
Cancer
Application of NGS
96th Seminar in Clinical Genetics
SR Ghaffari MSc MD PhD
M Rafati MD PhD

2. Genetics in Cancer
 Somatic mutations
 Germline mutations
 Hereditary cancer
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3. Hereditary Cancer
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4. Family 1
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5. Pedigree
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6. Gene
Chromosomal
location
NM-No
Variant
Location
Detected
Mutation
Genotype Classification
Sanger
Verification
TP53 chr17:7577022 NM_001126113.2 EX8
c.916C>T
(p.Arg306Ter)
Het
Pathogenic
(Clinvar)
Confirmed
NGS Analysis
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8. Li-Fraumeni syndrome (LFS)
 Germline P53 pathogenic variants are associated with dominantly inherited Li-
Fraumeni syndrome (LFS), which features early-onset sarcomas of bone and soft
tissues, carcinomas of the breast and adrenal cortex, brain tumors, and acute
leukemias.
 Carriers of germline P53 mutations may also be at increased risk of other cancers.
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9. Family 2
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11. NGS analysis
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13. Cancer Functional Events
 Point mutations
 NGS
 Focal Recurrently Aberrant Copy Number Segments (RACSs):
 Amplifications
 Deletions
 Detected by SNP Array
 Promoter hypermethylation
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14. Next Generation Sequencing
Platforms
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15. Illumina Genome Analyzer
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16. Illumina Sequencing pipeline
 1- Sample Preparation
 2- cluster generation
 3- sequencing and imaging
 4- data analysis
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17. Attach DNA to flow cell
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18. Bridge Amplification
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19. Cluster Generation Clonal
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20. Clonal Single molecule Array
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21. Repeat Sequencing By Synthesis (SBS)
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22. Reversible terminator chemistry
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23. Base Calling
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24. Semiconductor sequencing (Ion Torrent)
 Highly uniform genome coverage
 Rapidly improving per base accuracy
 Low-cost reagents and detection
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25. Basics
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26. Basics
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27. Basics
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28. Basics
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29. Basics
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30. Exome Sequencing
workflow
Hope Generation Foundation
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31. Library Preparation
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32. Library Preparation
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33. Library Preparation
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34. Template Preparation
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35. Template Preparation
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36. Template Preparation
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37. Enrichment
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38. Sequencing
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39. Sequencing, Proton
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40. Sequencing, PGM
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41. Nanopore Oxford
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42. Ion Torrent Platform
Cancer Panels
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43. Cancer Panels
 Ion AmpliSeq™ Comprehensive Cancer Panel: 409 genes
 Ion AmpliSeq™ Cancer Hotspot Panel: 2800 known targets
 Ion AmpliSeq™ BRCA1 and BRCA2 Panel
 Ion AmpliSeq™ Colon and Lung Panel: 22 genes implicated in colon and lung cancers
 Ion AmpliSeq™ TP53 Panel
 Ion AmpliSeq™ RNA Fusion Lung Cancer Panel: a set of known fusion transcripts as
well as expression imbalances between the 3’ and 5’ regions of the genes
 Ion AmpliSeq™ AML h Panel: 19 genes implicated in acute myeloid leukemia.
 Ion AmpliSeq™ RNAApoptosis Panel: 267 genes involved in the cellular apoptosis
pathway
 Ion AmpliSeq™ RNA Cancer Panel
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45. Cancer Hotspot Panel
 2856 known mutation
 207 amplicons
 50 genes
 100% coverage
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46. Ion AmpliSeq™ Cancer Hotspot Panel v2
 Investigation of genomic "hot spot" regions that are frequently mutated in human
cancer genes.
 Compatibility with FFPE samples while expanding mutational content for broader
coverage of additional genes and "hot spot" mutations
 Extremely uniform coverage for more efficient sequencing and cost savings
 Detection of Copy Number Variantions (indel sensitivity)
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48. Comprehensive cancer panel
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49. Comprehensive Cancer Panel
 409 genes
 15992 amplicons
 100% coverage
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51. Colon and lung cancer panel
 Analyse hotspot and targeted regions of 22 genes implicated in colon and lung cancers
(KRAS, EGFR, BRAF, PIK3CA, AKT1, ERBB2, PTEN, NRAS, STK11, MAP2K1,
ALK, DDR2, CTNNB1, MET, TP53, SMAD4, FBXW7, FGFR3, NOTCH1, ERBB4,
FGFR1, FGFR2)
 100% coverage
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53. Ion AmpliSeq™ Pharmacogenomics Panel
 Interrogate SNP, indels and copy number variations (CNV) in the Drug Metabolism
Enzyme (DME) genes.
 The panel focuses on 136 well documented SNP and indel variants and captures
CYP2D6 copy number variations at both the gene level and for exon 9 rearrangement
enabling the screening of broad selection of haplotypes including *36.
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54. Recent Studies
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56. Objectives
 The impact of a biomarker-based (personalized) cancer treatment strategy in the setting
of phase 1 clinical trials was analyzed.
 Objective To compare patient outcomes in phase 1 studies that used a biomarker
selection strategy with those that did not.
 Data Sources PubMed search of phase 1 cancer drug trials (January 1, 2011, through
December 31, 2013).
 Study Selection Studies included trials that evaluated single agents, and reported
efficacy end points (at least response rate [RR]).
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57. Results
 Response rate and progression-free survival (PFS) were compared for arms that used
a personalized strategy (biomarker selection) vs those that did not. Overall survival was
not analyzed owing to insufficient data.
 A total of 346 studies published in the designated 3-year time period were included
in the analysis. Multivariable analysis (meta-regression and weighted multiple
regression models) demonstrated that:
 The personalized approach independently correlated with a significantly higher
median RR (30.6% vs 4.9%) and a longer median PFS (5.7 vs 2.95 months)
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58. Results
 In this meta-analysis, most phase 1 trials of targeted agents did not use a biomarker-
based selection strategy. However, use of a biomarker-based approach was associated
with significantly improved outcomes (RR and PFS).
 Response rates were significantly higher with genomic vs protein biomarkers. Studies
that used targeted agents without a biomarker had negligible response rates.
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59. Conclusion
 Personalized arms using a “genomic (DNA) biomarker” had
higher median RR than those using a “protein biomarker”
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61. Objectives
 Purpose The impact of a personalized cancer treatment strategy (ie, matching patients
with drugs based on specific biomarkers) is still a matter of debate.
 Methods We reviewed phase II single-agent studies (570 studies; 32,149 patients)
published between January 1, 2010, and December 31, 2012 .
 Response rate (RR), progression-free survival (PFS), and overall survival (OS) were
compared for arms that used a personalized strategy versus those that did not.
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62. Results
 The personalized approach, compared with a nonpersonalized approach, consistently and
independently correlated with higher median RR (31% v 10.5%, and prolonged median
PFS (5.9 v 2.7 months, respectively; P < .001) and OS (13.7 v 8.9 months, respectively; P <
.001).
 Nonpersonalized targeted arms had poorer outcomes compared with either personalized
targeted therapy or cytotoxics, with median RR of 4%, 30%, and 11.9%, respectively;
median PFS of 2.6, 6.9, and 3.3 months, respectively (all P < .001); and median OS of 8.7,
15.9, and 9.4 months, respectively (all P < .05).
 Personalized arms using a genomic biomarker had higher median RR and prolonged
median PFS and OS (all P ≤ .05) compared with personalized arms using a protein
biomarker. A personalized strategy was associated with a lower treatment-related death rate
than a nonpersonalized strategy (median, 1.5% v 2.3%, respectively; P < .001).
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63. Conclusion
 Comprehensive analysis of phase II, single-agent arms revealed that, across
malignancies, a personalized strategy was an independent predictor of better outcomes
and fewer toxic deaths. In addition, nonpersonalized targeted therapies were
associated with significantly poorer outcomes than cytotoxic agents, which in turn were
worse than personalized targeted therapy.
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65. Objectives
 Recent studies have provided a detailed census of genes that are mutated in acute
myeloid leukemia (AML).
 Next challenge is to understand how this genetic diversity defines the pathophysiology
of AML and informs clinical practice.
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66. Methods
 Enrollment of a total of 1540 patients in three prospective trials of intensive
therapy.
 Combining driver mutations in 111 cancer genes with cytogenetic and clinical
data, we defined AML genomic subgroups and their relevance to clinical
outcomes.
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67. Results
 Identification of 5234 driver mutations across 76 genes or genomic regions, with 2 or
more drivers identified in 86% of the patients.
 Patterns of co-mutation compartmentalized the cohort into 11 classes, each with distinct
diagnostic features and clinical outcomes.
 In addition to currently defined AML subgroups, three heterogeneous genomic
categories emerged:
 AML with mutations in genes encoding chromatin, RNAsplicing regulators, or both (in 18%
of patients);
 AML with TP53 mutations, chromosomal aneuploidies, or both (in 13%);
 AML with IDH2R172 mutations (in 1%).
 Patients with chromatin–spliceosome and TP53–aneuploidy AML had poor outcomes,
with the various class-defining mutations contributing independently and additively to
the outcome.
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68. Results
 In addition to class-defining lesions, other co-occurring driver mutations also had a
substantial effect on overall survival.
 The prognostic effects of individual mutations were often significantly altered by the
presence or absence of other driver mutations. Such gene–gene interactions were
especially pronounced for NPM1-mutated AML, in which patterns of co-mutation
identified groups with a favorable or adverse prognosis.
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71. Conclusion
 The driver landscape in AML reveals distinct molecular
subgroups that reflect discrete paths in the evolution of AML,
informing disease classification and prognostic stratification.
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74. Genetic events
 Cancers arise because of the acquisition of somatic alterations in their genomes that
alter the function of key cancer genes
 Studies from The Cancer Genome Atlas (TCGA) and the International Cancer Genome
Consortium (ICGC) have generated comprehensive catalogs of the cancer genes
involved in tumorigenesis across a broad range of cancer types
 The emerging landscape of oncogenic alterations in cancer points to a hierarchy of
likely functional processes and pathways that may guide the future treatment of patients
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75. Cancer cell lines
 Human cancer cell lines are a facile experimental model and are widely used for drug
development. Large-scale drug sensitivity screens in cancer cell lines have been used to
identify clinically meaningful gene-drug interactions
 In the past, such screens have labored under the limitation of an imperfect
understanding of the landscape of cancer driver genes, but it is now possible to view
drug sensitivity in such models through the lens of clinically relevant oncogenic
alterations.
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76. Objectives
 Here, we analyzed somatic mutations, copy number alterations, and hypermethylation
across a total of 11,289 tumor samples from 29 tumor types to define a clinically
relevant catalog of recurrent mutated cancer genes, focal amplifications/deletions, and
methylated gene promoters
 These oncogenic alterations were investigated as possible predictors of differential drug
sensitivity across 1,001 cancer cell lines screened with 265 anti-cancer compounds
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77. Cancer functional events
 The WES dataset consisted of somatic variant calls from 48 studies of matched tumor-
normal samples, comprising 6,815 samples and spanning 28 cancer types
 RACSs were identified using ADMIRE for the analysis of 8,239 copy number arrays
spanning 27 cancer types
 iCpGs were identified using DNA methylation array data for 6,166 tumor samples
spanning 21 cancer types
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80. Concordance
 Of the 1,273 pan-cancer CFEs identified in patient tumors, 1,063 (84%) occurred in at
least one cell line, and 1,002 (79%) occurred in at least three
 This concordance was greatest for the RACSs (100% of 425), followed by iCpGs (338
of 378, 89%) and CGs (300 of 470, 64%)
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82. Drug Sensitivity Profiling
 Cell lines underwent extensive drug sensitivity profiling, screening 265 drugs across
990 cancer cell lines and generating 212,774 dose response curves
 Screened compounds included cytotoxics (n = 19) and targeted agents (n = 242)
selected against 20 key pathways and cellular processes in cancer biology
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85. New classification
 A previous hierarchical classification of ∼3,000 tumors identified two major subclasses:
M and C class (dominated by mutations and copy number alterations, respectively).
 We expanded this analysis by including methylation data and by jointly analyzing cell
lines and tumor samples.
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86. New classification
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88. Conclusion
 Among the individual CFE-drug associations, we identified many well-described
pharmacogenomics relationships. These included clinically relevant associations
between alterations in BRAF, ERBB2, EGFR, and the BCR-ABLfusion gene and
sensitivity to clinically approved drugs in defined tumor types, as well as associations
between KRAS, PDGFR, PIK3CA, PTEN, CDKN2A, NRAS,TP53, and FLT3 with drugs
that target their respective protein products or pathways
 Pharmacogenomic screens in cancer cell lines are an unbiased discovery approach for
putative markers of drug sensitivity.
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89. Conclusion
 These findings showed a median of 50% of primary tumor
samples harbor at least one CFE, or logic combination of
CFEs, associated with increased drug response; ranging from
0.63% (OV) to 83.61% (COAD/READ)
 This suggests that there are likely to be a number of molecular subtypes within many
cancers that, following appropriate validation, could be tested in the clinical trial setting
using these stratifications for treatment selection.
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93. Thank you
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