Distinguishing Second Primary Cancers From Metastases: Statistical Challenges in Testing Clonal Relatedness of Tumors

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1 Distinguishing Second Primary Cancers From Metastases: Statistical Challenges in Testing Clonal Relatedness of Tumors Colin Begg Department of Epidemiology and Biostatistics Memorial Sloan-Kettering Cancer Center Conference in Honor of Jeremy Taylor University of Michigan June 2017

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3 Bishop Jeremy Taylor

4 Admiral Jeremy Taylor

5 Jeremy Taylor Sex Offender Registry

6 From the Jeremy Taylor Official Website

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9 Interests (that I know about) Radiation oncology Surrogate endpoints in clinical trials Cancer genomics / bioinformatics Risk prediction / prostate cancer Key features of his work Stimulated by collaboration Geared towards applicability

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11 Are Pathologic Diagnoses Accurate: Can They Be Improved Using Somatic Tumor Characteristics? Tumors harbor many somatic mutations Comparison of patterns of these mutations has the potential to distinguish independent cancers from those that share a clonal origin Literature has emerged in recent years of clonality studies of this nature Evolving Data Types Candidate markers at sites of common mutations early 2000s Genome-wide analysis using array technology late 2000s Mutation sequencing platforms recent

12 Differential Diagnosis Competing Hypotheses Tumors are biologically independent (multiple primaries) The two tumors developed independently Tumors are clonally related (metastases) The two tumors derive from the same cell Initial pivotal mutations occurred in this single clonal cell

13 Early Technology Assessing Allelic Changes at Candidate Markers Tumors frequently experience allelic losses (often spanning tumor suppressor genes) Comparison of normal versus tumor tissue at candidate heterozygous markers can detect such losses Patterns of LOH can then be compared between two tumors Do the two patterns of losses appear independent?

14 Selected Patients with Breast Cancer [Adapted from Imyanitov et al. (2002)] Case #3 Case #32 Case #22 Locus L R L R L R 1p 1q 3p 5q 6q 8p 11p 11q 13q 16q 17p 17q 18q 22q

15 3p 13q Case #22 COMPETING HYPOTHESES 17q 6q A 1q 6q 17p 18q 3p 17q 13q B 3p 13q A 6q 17q 18q 17p 1q 13q 3p B concordant

16 Outer Table Tumor 1 LOH No LOH LOH e f m 1 Tumor 2 No LOH g h m 2 Null Hypothesis (Independence) J

17 Concordance of Common Mutations Biometrics 2007 = number of loci with LOH on the same allele Distribution of conditional on outer margins

18 Case #3 Case #32 Case #22 Locus 1 L R L R L R 1p 1q 3p 5q 6q 8p 11p 11q 13q 16q 17p 17q 18q 22q Concordant Mutations p < p = 0.02 p = 0.44 Fisher s Exact Test p = p = 0.09 p = 0.17

19 Key Assumptions/Features Markers of LOH occur commonly Each marker has similar probability of occurrence, i.e. occurrences are i.i.d. Test has good statistical properties but limited sensitivity

20 LOH Summary We conducted a study in double primary melanoma using this methodology (Orlow et al. J Inv Derm 2009) We also published a follow-up article in Biometrics (Ostrovnaya et al. 2008) But the technology had moved on and looking solely at LOH is not especially sensitive

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22 Alternative Strategy Comparison of Array CGH Profiles Array data provide a comprehensive genomewide picture of allelic changes Primary advantage Potentially much more information Primary disadvantages Can be messy Data artifacts Other problems

23 CGH Data from Two (Presumed) Independent Primaries (Melanoma Study)

24 Segmented Arrays

25 Synthesizing the Evidence Step 1 Global correlation of gains and losses Chromosome arm is the unit of analysis Step 2 Interrogate similarity of segments within arms Step 3 Aggregate 1 and 2 to synthesize the quantitative evidence

26 Comparison of Specific Within-Arm Changes From Lung Cancer Patient #4 (Later) Are these allelic losses clonal (identical)?

27 Mutational hotspot

28 Comparison of Specific Within-Arm Changes We created an algorithm that quantifies the relative likelihood that the patterns were generated from an identical loss or gain versus allelic changes that are different on the two tumors Method takes into account the selection effect that allelic changes tend to span the same mutational hotspot These contributions obtained only from chromosome arms with common gains or losses Results aggregated with global comparisons of gains and losses

29 Ultimate End-Product Creation of a statistic that determines the odds that the individual common loss or gain was clonal An expression for this evidence that is included in the overall likelihood ratio calculation This is repeated for all concordant gains and losses, leading to aggregate measure of evidence for clonality

30 Study of Molecular Diagnosis of Lung Cancer Girard et al. Clin Cancer Res 2009 Pairs of fresh frozen tissue from MSKCC tissue bank Pathologically diagnosed as independent tumors Clinical data re-evaluated by expert pathologist Genomic profiling with array CGH In addition we conducted mutational profiling of key genes that are known to occasionally exhibit point mutations in lung cancers: EGFR, KRAS, BRAF, P13KCA, TP53, HRAS, NRAS and others

31 Clinico-Pathologic Criteria for Multiple Primary Lung Cancers - Location: - If tumor lesions are in the same segment/lobe: metastases - Histology: - If tumor type is different: multiple primaries - If separate foci of carcinoma in situ: multiple primaries - If carcinoma in common lymphatics: metastases - Tumor extent: - If mediastinal lymph nodes or metastases: metastases - Time interval (for metachronous tumors): - If <2 years: metastases - If >4 years: multiple primaries Martini and Melamed J Thor Cardiovasc Surg 75; ACCP Guidelines 07

32 Available Tissues for Analysis

33 Case #4 Unsegmented

34 Case #4 Segmented

35 Case #23 Unsegmented

36 Case #23 Segmented

37 Case #12 Unsegmented

38 Case #12 Segmented

39 Case #4 Segmented Global correlation: 1.1 x 10 6 to 1 in favor of clonality

40 Case #4 Chromosome 8p Odds: 233 to 1 in favor of clonality

41 Case #4 Chromosome 9p Odds: 789 to 1 in favor of clonality

42 Case #4 Chromosome 12p Odds: 424 to 1 in favor of clonality

43 Case #4 Chromosome 12q Odds: 310 to 1 in favor of clonality

44 Case #4 Chromosome 18q Odds: 143 to 1 in favor of clonality

45 Global correlation Case #4 1.1 x 10 6 to 1 in favor of clonality Individual arm comparisons 3p 653 to 1 in favor of clonality 8p 233 to 1 in favor of clonality 9p 789 to 1 in favor of clonality 12p 424 to 1 in favor of clonality 12q 310 to 1 in favor of clonality 16q 152 to 1 in favor of clonality 18p 2.1 to 1 in favor of clonality 18q 143 to 1 in favor of clonality Overall likelihood ratio 7.9 x in favor of clonality

46 Case #12 Segmented Global correlation: 475 to 1 in favor of independence

47 Case #23 Segmented Global correlation: 1.5 x 10 3 to 1 in favor of independence

48 Case #23 Global correlation 1.5 x 10 3 to 1 in favor of independence Individual arm comparisons 5p 72 to 1 in favor of clonality 17p 19 to 1 in favor of clonality Overall likelihood ratio 0.9 Equivocal Diagnosis

49 Calibration Plot

50 Summary of Results 24 tumor pairs No genomic diagnosis possible for 2 (excessive noise in the array) 8 of 22 diagnosed as either clonal or probably clonal (equivocal) by acgh All 8 had matching point mutations in either BRAF or KRAS none of the other 14 pairs had matching point mutations We conclude that the clinical diagnosis was incorrect in 32% (7/22) of cases.

51 Somatic Mutations

52 Somatic Mutations V600E mutations occur with frequency of about 1% in lung cancers

53 Practical Issues This study used fresh frozen tissue ideal for obtaining high quality arrays Even with good tissue quality arrays can frequently be noisy and uninterpretable Clinical setting paraffin embedded tissue much more common frequently will be old DNA sequencing is the technology of the present

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55 The Current Landscape Mutational Profiles from Sequencing Characteristics Widely different probabilities of occurrence of individual somatic mutations KRAS G12D mutations occur in about 8% of colon cancers PIK3CA H1047R mutations occur in about 13% of breast cancers Most somatic mutations have been observed only once in hundreds or even thousands of cases Random matches at rare loci are much less likely than at common loci We have limited knowledge of these mutation probabilities Sample space of a sequencing platform is only known approximately How do we construct a framework for a statistical test?

56 Example [Adapted from Kunze et al. Histopathology 2014] Case with two synchronous colon tumors On follow-up, tumors in left and right lobes of the lung are identified Cases tested for common KRAS mutations One colon tumor and tumor in lung left lobe are KRAS G12D+ Is the lung tumor a metastasis of the colon primary? How do we quantify the evidence? To further investigate a sequencing panel is performed Results summarized on the next slide How do we adjust the evidence for or against clonality?

57 Observed Mutations Mutation Probability Colon Tumors Lung Tumors T1 T3 Right Left KRAS G12D KRAS G12S XPA G74V PIK3CA Q546P FBXW7 R465C APC R283* APC R499* APC Q1065* TP53 R158H BRAF G596V BAI3 V499L PIC3C2B S314F ETS1 K200N IKZF1 M301I PRKDC R364H ZNF521 L1136V ALK E405* GUCY1A2 V627A ACVR2A A62G 0.004

58 Methodological Issues Conditioning issue Can we ignore the information from the large number of genetic locations where no mutations were observed on either tumor? How do we aggregate the results from loci where mutations were observed? Matches favor clonal relatedness Non-matches favor independence Any viable method depends strongly on the probability of observing a mutation at each observed locus

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60 Likelihood Ratio Test Ostrovnaya et al. Ann Applied Stat 2015 A = {set of loci with matches} B = {set of loci with mutations} p i = probability of a mutation at the i th genetic locus ξ = clonality signal (proportion of observed mutations in the case that are clonal mutations) Likelihood P(A,B ) (1 )p 2(1 )(1 p ) i i i A (1 )(2 p i B i) (1 )(2 p i) Likelihood ratio test of the hypothesis that tumors are independent Test statistic ˆ ˆ 1 1 S p i A i 1 (2 p i E i) 1 1 ˆ 1 ˆ Reference distribution obtained by randomly generating matches over the set of loci with observed mutations

61 Observed Mutations Mutation Probability Colon Tumor Lung Tumor KRAS G12D XPA G74V PIK3CA Q546P FBXW7 R465C APC R283* APC R499* IKZF1 M301I PRKDC R364H ZNF521 L1136V ALK E405* GUCY1A2 V627A ACVR2A A62G Test based solely on KRAS G12D: p=0.04 Global test based on all markers: p=0.07

62 Breast Cancer Study Study of cases with cancers in each breast (bilateral breast cancer) Tissues retrieved from MSK archives 49 cases made it through genotyping pipeline Sequencing panel with 254 cancer genes Questions Any evidence these cancers can represent 1 st site metastases? If so, what proportion of bilateral cases are metastases?

63 Case Somatic Mutations P- Somatic Mutations P- Case Left Right Shared value Left Right Shared value < <

64 Issues Actual numbers of mutations per case are relatively small Why are some cases with matches highly significant and others are not? Can we be sure that cases with no matches are truly independent? p-values are always 1 in these cases Can we estimate the proportion of cases that are clonal? Is 3/49 a reasonable estimate? Can copy number profiling help resolve the uncertainties?

65 Random Effects Mixture Model Mauguen et al. Biometrics, in press Data: matches A and non-matches B Y j (A j,b j) Probability given ξ j : Mixture likelihood j (1 j)p i 2(1 j)(1 p i) P(Y j j) i A j i Bj j (1 j)(2 p i) j (1 j)(2 p i) L j(, j) P(Y j j 0) (1 )P(Y j j 0) Clonality signal distribution in clonal cases, i.e. ξ j >0 : log(1 ) N(, ) j j 2 Marginal Likelihood 2 1 n j1 0 L(,, ) L (, )g( )d j j j j

66 Random Effects Mixture Model Mauguen et al. Biometrics, in press Simulations show that the method works well in moderate and large samples In small samples (like this one) Bias in parameter estimates Boundary MLEs There may well be clonal cases among those cases with 0 matches, especially if few mutations are observed on either tumor

67 Results for Cases with Matches Case Somatic Mutations Left Right Shared p-value Probability Clonal < < ˆ 6%; ˆ 0.01; ˆ 0.52 Interpretations The model thinks only 3 cases are clonal distribution of clonality signals are skewed to high values along with low prior, this suggests that the 3 cases with a single PIK3CA mutation are truly independent.

68 Closing Remarks Applied research in statistical methodology sometimes confronts a moving target Clonality testing technological changes have substantially changed the statistical challenges, even though the fundamental goal has remained the same Key philosophy (which I think I share with Jeremy) Address questions that actually matter to the investigators with whom we collaborate Seek practical solutions Try to be versatile in the areas you seek to research Happy Birthday, Jeremy!

69 Software Available at:-

70 Acknowledgements Biostatistics Kevin Eng Amanda Hummer Adam Olshen Irina Ostrovnaya Venkatraman Seshan Audrey Mauguen Medicine Nicolas Girard William Pao Surgery Tari King Monica Morrow Biology Irene Orlow Epidemiology Lab Diana Tommasi Bradley Bloom Javiar Cotignola Pathology Klaus Busam Bill Travis Richard Scolyer Jorge Reis-Filho

71 Bibliography Begg et al. Biometrics 2007;63: Ostrovnaya et al. Biometrics 2008;64: Ostrovnaya et al. Statistics in Medicine 2010;29: Orlow et al. Journal of Investigative Dermatology 2009;129: Girard et al. Clinical Cancer Research 2009;15: Ostrovnaya/Begg Clinical Cancer Research 2010;16: Ostrovnaya et al. Bioinformatics 2011;27: Andrade et al. Breast Cancer Research 2012;14(4):R103 Ostrovnaya et al. Annals of Applied Statistics 2015;9: Begg et al. Breast Cancer Research 2016;18:66 Mauguen et al. Biometrics 2017, in press