Hodgkin Lymphoma: From Discovery to Clinical Translation – Robust Gene Expression from FFPET

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Classical Hodgkin Lymphoma (CHL) is the most common form of malignant lymphoma affecting people under the age of 30 in the Western World. Although this disease has been regarded as a model for the success of combined modality, multi-agent chemotherapy and radiotherapy, 25–30% of patients still experience relapse or progressive disease following initial treatment. The vast majority of patients under the age of 65 years that relapse are then treated with dose-intense secondary chemotherapy and autologous stem-cell transplantation. This second-line treatment, expensive in terms of both morbidity and financial cost, results in cure for approximately 50% of patients whose primary therapy fails.
CHL is unique amongst lymphoid cancers in that the malignant cells, the so-called Hodgkin Reed-Sternberg (HRS) cells, make up approximately 1% of the tumor. The microenvironment in CHL is made up of numerous cells, including T cells (CD4+ T cells, regulatory T cells, cytotoxic T cells), benign B cells, mast cells, macrophages, eosinophils and fibroblasts, to name but a few. Although there is a paucity of malignant cells, the HRS cells appear to orchestrate an extensive tumor microenvironment, permitting the tumor cells to attain the full malignant phenotype and evade immune surveillance. We hypothesize that genetic alterations harbored by the HRS cells drive the composition and function of both immune & stromal cells within the microenvironment.
This talk will describe 5 years of research in which a linear approach to understanding the biology of CHL was undertaken using gene expression profiling (GEP) and high-resolution copy number (CN) analyses, both of whole biopsies and microdissected HRS cells. Finally, a low-density GEP approach to outcome prediction will be described using NanoString based on diagnostic formalin fixed paraffin embedded tissues.


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  • I want to thank the organizers of this terrific meeting for inviting me to speak, especially my good friend and colleague Tom Grogan.
  • This is an outline of what I will talk about. The story is really one of clinical translation of an outcome predictor using a robust low-density gene expression platform called NanoString.
  • In Canada and the US we see just over 10,000 new HL cases a year. The disease typically shows a bimodal age distribution, with a sizable proportion of cases found in young patients.
  • These curves highlight the success story of medical oncology that is HL. In the 1960s only about 10% of patients were long term survivors, in contrast to the 1980s and beyond, where upwards of 80% of patients are cured. Little progress has been made in the last 30 years.
  • Just read it.
  • HL is a unique cancer, as the tumor cells make up only 1% of the total infiltrate. These HRS cells can be easily identified in sections using IHC (CD15 and CD30). The background contains an inflammatory infiltrate with numerous lymphocytes, eosinophils, plasma cells, macrophages, etc
  • About 5 years ago we set out to try to understand the molecular correlates of treatment failure. We did this in 3-stages, using a frozen tissue archive of diagnostic biopsies enriched for patients whose primary therapy failed.
  • Genetic aberrations harbored by the malignant HRS cells cultivate this microenvironment. The language is cytokines and chemokines and the ultimate goal is to promote growth and survival and foster immune privilege.
  • The aims of this study was to describe a gene-expression based predictor of Overall Survival in advanced stage Hodgkin Lymphoma treated with standard intensity treatment by developing the model in data from the E2496 trial and then testing the model in an independent cohort uniformly treated with ABVD, with clinical characteristics that represent patients seen in routine Oncology and Haematology practice, with the exception of being enriched for primary treatment failure.
  • Let’s walk through the assay principals and talk about the steps that are processed on each of the nCounter instruments. Start with the four pipeting steps described previously and hybridize overnight. It takes about 5min to set up, and usually this is done before going home for the day.
  • Let’s walk through the assay principals and talk about the steps that are processed on each of the nCounter instruments. Start with the four pipeting steps described previously and hybridize overnight. It takes about 5min to set up, and usually this is done before going home for the day.
  • After the excess capture probes and reporter probes are removed, the tripartite structure is bound to the surface of the sample cartridge. The surface of the sample cartridge is coated with streptavidin and the capture probe is attached to biotin. The tripartite hybridized molecules bind in a random fashion across the surface at one end. They are not fixed to a specific address like microarrays. However, the other end is not oriented, and the code cannot be read at this point.
  • Next, the Prep Station passes electric current across the surface of the slide. This forces the molecules to align in the electric field and get stretched. Another biotinylated sequence is added to anchor the reporter probe end in the stretched out, aligned state. Once the reporters are immobilized, the sample cartridge is stable. The cartridge can be imaged immediately or stored at 4 degrees until imaging can occur.
  • When the cartridge is to be counted, it is placed into the scanner. The scanner basically takes fluorescent pictures of the surface of the slide. This is a cartoon image of the beginning image, with a few barcoded molecules shown. Each string is one tripartite hybrid, with one reporter code and capture probe hybridized to one target nucleic acid.
  • Since we are imaging individual molecules, we are able to count the number of times each code is found present in the sample. For example, this gene has been assigned to this code – since there are three of them seen in that image, the DA returns a count of three. A different gene has been assigned a different code; only one of y was seen, so a count of 1 is tabulated. The barcodes in one image are counted and tabulated, then in another image, then another, etc. A running total is kept. In the end, a report is generated containing a list of the genes queried, and the number of times that gene as seen in your sample.
  • The 23 genes include genes consistent with increased macrophages, a Th1 immune response and increase cytotoxic T cell/NK cells in the pretreatment biopsies of those that died.
  • For the test to be clinically useful, it needs to assign patients to risk groups. X-tile was used to determine a threshold that separated patients into 2 group, shown by the blue dotted line on the graph.
  • … ..with 94% 5 y OS in the low risk group and 75% in the high risk group.
  • This 19% absolute difference in 5 y overall survival is seen in a cohort where the IPS has no prognostic power.
  • Hodgkin Lymphoma: From Discovery to Clinical Translation – Robust Gene Expression from FFPET

    1. 1. Hodgkin Lymphoma: FromDiscovery to Clinical TranslationRandy D. GascoyneBC Cancer AgencyVancouver, CanadaVentana Meeting, Tucson 2013Robust gene expression from FFPET
    2. 2. Outline• Brief history of Hodgkin lymphoma & CHL biology• A 3-stage roadmap to understanding HL biology• Benign macrophages contribute to outcomeprediction• Clinical translation, NanoString and a multi-genepredictor• Future work & take home lessons
    3. 3. 3Epidemiology of Hodgkin lymphoma in Canada & USA78,130 Estimatedlymphoma cases in 201155%Males45%Females78,130NHL 10,060Close to 10,060 in Canada & USA diagnosedwith Hodgkin Lymphoma annually• Approximately 1,450 willdie from the disease• Over 206,000 patientshave a history of HL20 yrs >55 yrs34 yrs32% 28%AgeBimodal DistributionHL PatientsHigher survival rate but an increasedHigher survival rate but an increasedincidence of long-term health complicationsincidence of long-term health complicationsNo standard treatment exists forNo standard treatment exists forolder patients (>60 years old)older patients (>60 years old)
    4. 4. PFS and DSS for classical Hodgkinlymphoma over consecutive eras in BCProportionfailurefreesurvivalTime (years) Time (years)Proportiondisease-specificsurvival1960s1960s1970s1970s1980s1980s1990s1990s2000s2000sProgression-free Survival Disease-Specific SurvivalJoseph Connors, 2012 (unpublished)
    5. 5. Clinical aspects of HL• Common cancer in younger people• In about 20-25%, primary therapy with ABVD willfail to cure the patients• High-dose therapy (autoBMT) salvages ~ 50%• Late toxicities are a problem and thus it would beideal if we could identify those patients who arebeing over-treated, in addition to those patientsdestined to fail their primary treatment• Clinical translation will require that we identifyrobust biomarkers that can predict both treatmentsuccess and treatment failure
    6. 6. Classical Hodgkin lymphomaCD30CD3 CD20
    7. 7. Study design, methods and analysis toolsOne-cycle cRNAlabeling reactionMolecular Machines &Industries (MMI)CellCut Laser microdissectionAffymetrix HG U1332.0 Plus arrayMicro-environment1.Whole genomeamplificationTwo-cycle cRNAlabeling reactionAffymetrix HG U1332.0 Plus arraySubmegabase ResolutionTiling Array (SMRT ARRAY)MicrodissectedHRScells2.CD30Frozen lymph nodeSteidl et al., NEJM 2010, 362: 875-885
    8. 8. Steidl et al., NEJM 2010, 362: 875-885
    9. 9. GenderStageType failureTreatment outcomeSignificantlyup-regulatedgenesintreatmentfailuresSignificantlydown-regulatedgenesintreatmentfailuresCluster A Cluster BHierarchical clustering of 130 pretreatment gene expression profiles
    10. 10. Progression Free Survival (years)20151050CumulativeSurvival1.0.9.8.7.6.5.4.3.2.10.0Median PFS:0%-5%: not reached5-25%: 6.15 years>25%: 2.71 yearsLog rank: p=0.034CumulativeSurvival2520151050CumulativeSurvival1.0.9.8.7.6.5.4.3.2.10.010-year DSS:0%-5%: 88.6%5-25%: 67.4%>25%: 59.6%Log rank: p=0.0027CumulativeSurvivalSteidl et al., NEJM 2010CD68 ImmunohistochemistryCD68CD68
    11. 11. Studies on the prognostic value of tumor-associatedmacrophages in classical Hodgkin lymphomaMarkers used Method # Outcome correlation ReferencePNA Histochemistry 43 Adverse (refractory disease, earlyrelapse)Ree et al, Cancer 1985STAT1, ALDH1A1 GE, IHC 235 Adverse (DSS) Sanchez-Aguilera et al, Blood 2006LYZ, STAT1, ALDH1A1 GE, IHC 194 Adverse (refractory disease, earlyrelapse)Sanchez-Espiridion et al, ClincialCancer Research 2009CD68 IHC 166 Adverse (PFS, DSS) Steidl et al, NEJM 2010LYZ, STAT1 GE 262 Favorable (FFS) Sanchez-Espiridion et al, Blood 2010CD68, CD163 IHC 288 Adverse (EFS, OS) Kamper et al, Haematologica 2010CD68 IHC 59 Adverse (refractory disease) Benedicte et al, Blood 2010 [abstr.]CD68 (in combination withFOXP3)IHC 122 Adverse (FFTF, OS) Greaves et al, Blood 2010 [abstr.]CD68 IHC 144 Adverse (EFS, DSS) Yoon et al, Blood 2010 [abstr.]CD68 IHC 105 Adverse (OS) Tzankov et al [personalcommunication]CD68 IHC 45 Adverse (PFS) Hohaus & Larocca[personal communication]CD68 IHC 153 Adverse (OS, PFS) Farinha et al USCAP 2011 [abstr.]CD68, CD163 IHC (double staining) 82 Adverse (OS) Zaki et al, Virch Arch 2011CD68 IHC 52 Adverse (OS) Jakovic et al, Leuk & Lym 2011CD68, CD163 IHC 265 No survival impact Azambuja et al, Ann Oncol 2012CD68, CD163 IHC 144 Adverse (OS) Yoon et al, Europ J Haematol (in-press)CD68 (PG-M1) IHC 151 Adverse (PFS) & correlation withinterim PETTouati et al, ASH 2011 [abst 1558]CD68, CD163, STAT1, LYZ IHC 266/103 Adverse (DSS) for CD68 Sanchez-Espiridion et al, Haematol 12Modified from Steidl et al, Haematologica 2011
    12. 12. CumulativesurvivalTime (years)FFSTrainingCD68high (n=54)CD68low (n=89)CumulativesurvivalTime (years)FFSTrainingCD68high (n=54)CD68low (n=89)Cumulativesurvivalp<0.01CD68high (n=55)CD68low (n=89)Time (years)OSValidationCumulativesurvivalp<0.01CD68high (n=55)CD68low (n=89)Time (years)OSValidationCumulativesurvivalp=0.04Time (years)FFSValidationCD68high (n=55)CD68low (n=89)Cumulativesurvivalp=0.04Time (years)FFSValidationCD68high (n=55)CD68low (n=89)CumulativesurvivalCD68high (n=54)CD68low (n=89)Time (years)OSTrainingCumulativesurvivalCD68high (n=54)CD68low (n=89)Time (years)OSTrainingAMacrophages predict survival in a randomizedphase III clinical trial (E2496)KL Tan et al, Blood 2012, 120: 3280-7
    13. 13. Cumulativesurvivalp<0.01CD163high (n=66)CD163low (n=78)Time (years)OSValidationCumulativesurvivalp<0.01CD163high (n=66)CD163low (n=78)Time (years)OSValidationCumulativesurvivalp<0.01Time (years)FFSValidationCD163high (n=66)CD163low (n=78)Cumulativesurvivalp<0.01Time (years)FFSValidationCD163high (n=66)CD163low (n=78)CumulativesurvivalCD163high (n=53)CD163low (n=90)Time (years)OSTrainingCumulativesurvivalCD163high (n=53)CD163low (n=90)Time (years)OSTrainingCumulativesurvivalTime (years)FFSTrainingCD163high (n=53)CD163low (n=90)CumulativesurvivalTime (years)FFSTrainingCD163high (n=53)CD163low (n=90)BOS and FFS for E2496 CHL cases based on IHC forCD163 (n = 277)KL Tan et al, Blood 2012, 120: 3280-7
    14. 14. Microenvironment in Hodgkin lymphomaSteidl et al, JCO 2011, 29: 1812-26
    15. 15. Whole genomeamplificationMolecular Machines &Industries (MMI)CellCut Laser microdissectionSubmegabase ResolutionTiling Array (SMRT ARRAY)MicrodissectedHRScells2.Two-cycle cRNAlabeling reactionOne-cycle cRNAlabeling reactionAffymetrix HG U1332.0 Plus arrayAffymetrix HG U1332.0 Plus arrayMicro-environment1.CD30Frozen lymph nodeSteidl et al., Blood, 116: 418-27 2010Study design, methods and analysis tools
    16. 16. Recurrent imbalances in 53 classical Hodgkin lymphomasamples: Treatment outcome correlationsTreatmentfailureTreatmentsuccessChromosome 16ABCC1Progression Free Survival with 16p gainProgression Free Survival (years)242220181614121086420CumulativeSurvival1.0.9.8.7.6.5.4.3.2.10.010-year PFS:16p gain present: 12.8%16p gain absent: 63.0%Log rank: p=0.002Steidl et al., Blood, 116: 418-27 2010
    17. 17. Diagnosis Start Rx End RxCureBeginningof diseaseYears0 1 2 3 4No CR / Progressionduring treatment= primary refractoryEarly relapse(≤6 months afterend of treatment)Late relapse(>6 months afterend of treatment)Frequency of 16p gains and time point of progression/relapseLateSequelae(MDS, AML,carcinoma)Follow-up83.3% 33.3% 25.0%Relative frequency of 16p gains (% of cases)Link to primary drug resistance ?
    18. 18. Two-cycle cRNAlabeling reactionMolecular Machines &Industries (MMI)CellCut Laser microdissectionAffymetrix HG U1332.0 Plus arrayMicrodissectedHRScells3.Whole genomeamplificationOne-cycle cRNAlabeling reactionAffymetrix HG U1332.0 Plus arraySubmegabase ResolutionTiling Array (SMRT ARRAY)Micro-environment1.CD30Frozen lymph nodeStudy design, methods and analysis toolsSteidl et al, Blood 2012, 120: 3530-40
    19. 19. Steidl et al, Blood 2012, 120: 3530-40Gene expression profiling from microdissected HRS cells
    20. 20. REL (2p16.1)10 11 12 13 14 15 16 17 18 19 20 21 227 8 9ChromosomeFrequency(%)40402020TNFAIP3 (6q23.3)FOXO3 (6q21) MLL (11q23.3)SOCS1, TNFRSF17 (16p13.13)01 2 3 4 5 6IL27 (16p11.2)SETDB2 (13q14.3)EID2MAP3K10(19q13.2)SCAF1 (19q13.33)NCOA6 (20q11.22)TGIF2RBL1(20q11.23)Figure 2Copy Number and Gene Expression Correlation0.51.01.52.00.02.53.03.54.0MLLT3TNFAIP3SOCS1MLLIL27CD72FOXO3THRARELCD274IL11RATBX2MAP3K10JMJD2CSCAF1SETDB2EID2PAX5NCOA6TGIF2TNFRSF17WNT3RBL1ZBTB5Chromosome 9p020Frequency(%)MLLT3 (9p21.3)IL11RA, CD72 (9p13.3)CD274, JMJD2C (9p24.1)PAX5ZBTB5(9p13.2)-log10(pval)40Chromosome 17q0102030Frequency(%)THRA (17q21.1)WNT3 (17q21.32)TBX2 (17q23.2)Cis & trans correlations from microdissected HRS cellscomparing gene expression with copy-number alterationsSteidl et al, Blood 2012, 120: 3530-40
    21. 21. 183218322012Can we translate biomarker discovery inHodgkin lymphoma into the clinic?2011
    22. 22. There is a clear clinical need• The only tool to inform on an expectation of survivalin CHL is the International Prognostic Score (IPS)• This is only used to design and interpret clinicaltrials and is NOT used to make treatment decisionsfor individual patients• No biological tool is available to reproducibly assignrisk or be used as a predictive test on which up-front treatment decisions could be made
    23. 23. Hodgkin lymphoma:The current state of play• 10-20% of patients with advanced stage CHLsuccumb to disease• “One size fits all” approach to treatment• Lack of reliable tests at diagnosis that can guidemanagementScott and Gascoyne, 2013
    24. 24. At the heart of the debate• There are differing opinions about the up-frontchemotherapy regimen for advanced-stageclassical HL• To some extent this represents a European vsNorth American bias (ABVD vs escalatedBEACOPP)• ABVD fails to cure a sizable minority of patientswhile escBEACOPP visits unnecessary toxicity ona significant percentage of patients
    25. 25. Study outline• Develop a gene-expression based predictor of OSin advanced stage CHL treated with standardintensity treatment• Train on data from a phase III randomized controlclinical trial (E2496)• Validate in an independent cohort treated withABVD, enriched for primary treatment failure andincluding a weighted analysisLI Gordon et al, JCO 2013, 31: 684-91DW Scott et al, JCO 2013, 31: 692-700
    26. 26. The E2496 intergroup trial• Phase III randomized controlled trial comparingABVD and Stanford V (n ~ 854 patients)• Locally extensive and advanced stage cHL• Identical outcomes between the 2 arms– FFS and OSLI Gordon et al,JCO 2013, 31: 684-91
    27. 27. NanoString® Technologies | Confidential27nCounter AssayHybridizeCodeSetto RNARemoveExcessReportersBindReporter toSurfaceImmobilizeand AlignReporterImageSurfaceCountCodesmRNA Capture and Reporter ProbesGK Geiss et al, Nat Biotech 2008, 26: 317-25
    28. 28. NanoString® Technologies | Confidential28nCounter AssayHybridizeCodeSetto RNARemoveExcessReportersBindReporter toSurfaceImmobilizeand AlignReporterImageSurfaceCountCodesHybridized mRNA Excess ProbesGK Geiss et al, Nat Biotech 2008, 26: 317-25
    29. 29. NanoString® Technologies | Confidential29nCounter AssayHybridizeCodeSetto RNARemoveExcessReportersBindReporter toSurfaceImmobilizeand AlignReporterImageSurfaceCountCodesSurface of cartridge is coated withstreptavidinHybridized Probes Bind to Cartridge29
    30. 30. NanoString® Technologies | Confidential30nCounter AssayHybridizeCodeSetto RNARemoveExcessReportersBindReporter toSurfaceImmobilizeand AlignReporterImageSurfaceCountCodesImmobilize and Align Report for Image Collecting and Barcode Counting30− +
    31. 31. NanoString® Technologies | Confidential31nCounter AssayHybridizeCodeSetto RNARemoveExcessReportersBindReporter toSurfaceImmobilizeand AlignReporterImageSurfaceCountCodesOne Coded Reporter = 1 Nucleic AcidGK Geiss et al, Nat Biotech 2008, 26: 317-25
    32. 32. NanoString® Technologies | Confidential32nCounter AssayHybridizeCodeSetto RNARemoveExcessReportersBindReporter toSurfaceImmobilizeand AlignReporterImageSurfaceCountCodesCodes are Countedand Tabulated-Direct digital readout of mRNA-No enzymes-No amplification-Ideally suited to 100 bpmRNA found in FFPET-Same technology touted inbreast cancer (PAM50)
    33. 33. Scott et al, JCO 2013, 31: 692-700
    34. 34. 259 genesSMLR80 genesMacrophage41 genesHRS22 genesCytotoxic T cell/NK20 genesTregs11 genesB cells9 genesEosinophils/Mast cells7 genesAdipocytes6 genes“Spanish”14 genes“French”10 genesMHC9 genesApoptosis10 genesAngiogenesis7 genesExtracellularMatrix 8 genesOther7 genesHousekeeping6 genesScott et al, JCO 2013, 31: 692-700
    35. 35. Results• 95% of cases yielded quality results• 23 gene model• Signature representative of:– Increased macrophages– Th1 immune response– Increased cytotoxic T/NK cellsin the pretreatment biopsiesof patients that diedScott et al, JCO 2013, 31: 692-700
    36. 36. Genes associated with overall survival
    37. 37. ThresholdPredictorScore•Dichotomize the training cohort into “low-”and “high-risk” groups•Maximize the Х2of the Mantel-Cox testScott et al, JCO 2013, 31: 692-700
    38. 38. Training cohortScott et al, JCO 2013
    39. 39. ProportionoverallsurvivalTime (years)Training cohort94%75%]19%
    40. 40. Training cohort – the IPSProportionoverallsurvivalTime (years)Median follow-up 5.3 yearsp = 0.76IPS score3 to 70 to 2
    41. 41. Validation cohortScott et al, JCO 2013Status at last follow upAliveDead
    42. 42. TRAINING COHORT WEIGHTED VALIDATION COHORTOverall survival by predictor scoreScott et al, JCO 2013, 31: 692-700
    43. 43. Survival curvesCohorts C statistic 5 year OS (%) Hazard Ratio 95% CILog-rank PvalueTraining 0.73Low risk 947.1 3.3 – 15.1High risk 75WeightedValidation0.70Low risk 926.7 2.6 – 17.4 <0.001High risk 63Summary of predictor performanceScott et al, JCO 2013, 31: 692-700
    44. 44. Where does it stand?• An internally validated prognostic test for ABVDtreated patients• Requires external validation• Requires testing in patients treated with otherregimens• The real value is in a predictive testScott and Gascoyne, JCO on-line 2012
    45. 45. ProportionoverallsurvivalTime (years)Clinical utilityAdequately treated- Should we consider de-escalation toreduce morbidity and long termsequelae?
    46. 46. ProportionoverallsurvivalTime (years)Clinical utilityInadequately treated- Can this be overcome by moreaggressive upfront treatment?- Do we require novel agents?Target subjects for biological studies
    47. 47. Other predictors in use• PET scan after 2 cycles– Defines a high-risk group– Studies ongoing to determine predictive value– “Penalty” for not being at diagnosis• Opportunity to correlate the NanoString predictorwith PET results– Intergroup trial (SWOG 0816) (Phase II Trial)– BC Cancer Agency dataScott and Gascoyne, 2013
    48. 48. Take home messages• A fundamental understanding of the biology &genomics of CHL clearly informs on potentialdiagnostic & prognostic strategies• Doing science in the context of a clinicallyrelevant question provides value-addedinformation• Clinical translation using robust technologies thatimpact treatment decision making is the pathforward, even for relatively low incidence cancers
    49. 49. AcknowledgementsCentre for Lymphoid CancerChristian SteidlJoseph ConnorsNathalie JohnsonLaurie SehnKerry SavagePedro FarinhaGraham SlackKing TanSanja RogicMerrill BoyleAdele TeleniusSusana Ben-NeriahBarbara MeissnerBruce WoolcockRobert KridelDavid ScottSuman SinghHolly EelyHeidi CheungJacqueline WongBarbara YuenThe BCCRCFong Chun ChanSohrab ShahECOGBrad KahlSandra HorningLeo GordonFangxin HongECOG cliniciansECOG patientsMike FerriereMegan CreamerIntergroup participantsExternal CollaboratorsLisa RimszaArjan DiepstraAnke van den Burg
    50. 50. The end: Questions?

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