Personalized Medicine in Diagnosis and Treatment of

CancerApplication of NGS

96th Seminar in Clinical Genetics

SR Ghaffari MSc MD PhDM Rafati MD PhD

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Genetics in Cancer

Somatic mutations Germline mutations

Hereditary cancer

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Hereditary Cancer

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Family 1

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Pedigree

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Gene Chromosomal location NM-No Variant

LocationDetected 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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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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Family 2

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NGS analysis

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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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Next Generation SequencingPlatforms

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Illumina Genome Analyzer

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Illumina Sequencing pipeline

1- Sample Preparation 2- cluster generation 3- sequencing and imaging 4- data analysis

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Attach DNA to flow cell

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Bridge Amplification

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Cluster Generation Clonal

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Clonal Single molecule Array

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Repeat Sequencing By Synthesis (SBS)

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Reversible terminator chemistry

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Base Calling

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Semiconductor sequencing (Ion Torrent)

Highly uniform genome coverage Rapidly improving per base accuracy Low-cost reagents and detection

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Basics

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Basics

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Basics

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Basics

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Basics

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Exome Sequencingworkflow

Hope Generation Foundation

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Library Preparation

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Library Preparation

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Library Preparation

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Template Preparation

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Template Preparation

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Template Preparation

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Enrichment

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Sequencing

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Sequencing, Proton

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Sequencing, PGM

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Nanopore Oxford

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Ion Torrent PlatformCancer Panels

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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™ RNA Apoptosis Panel: 267 genes involved in the cellular apoptosis pathway Ion AmpliSeq™ RNA Cancer Panel

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Cancer Hotspot Panel

2856 known mutation 207 amplicons 50 genes 100% coverage

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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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Comprehensive cancer panel

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Comprehensive Cancer Panel

409 genes 15992 amplicons 100% coverage

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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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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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Recent Studies

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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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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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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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Conclusion

Personalized arms using a “genomic (DNA) biomarker” had higher median RR than those using a “protein biomarker”

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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New classification

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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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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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Thank you