The Evolutionary Origins of Recurrent Pancreatic Cancer€¦ · 19/03/2020 · 16 is very common in pancreatic cancer and may contribute to the aggressiveness of this disease 17

The Evolutionary Origins of Recurrent Pancreatic Cancer 1

2 3 4

Hitomi Sakamoto1*, Marc A. Attiyeh1*, Jeffrey M. Gerold 9,10*, Alvin P. Makohon-Moore1,6, 5 Akimasa Hayashi1, Jungeui Hong1, Rajya Kappagantula1,2, Lance Zhang1, Jerry P. Melchor1, 6

Johannes G. Reiter11, Alexander Heyde9,10, Craig M. Bielski8, Alexander V. Penson4,6, Mithat 7 Gönen4, Debyani Chakravarti8, Eileen M. O’Reilly3,6, Laura D. Wood12,13, Ralph H. Hruban12,13, 8

Martin A. Nowak9,10, Nicholas D. Socci5,7, Barry S. Taylor4,6,8, Christine A. Iacobuzio-Donahue2,6,7 9 10 1Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, NY NY USA 11 2Department of Pathology, Memorial Sloan Kettering Cancer Center, NY NY USA 12 3Department of Medicine, Memorial Sloan Kettering Cancer Center, NY NY USA 13 4Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, NY NY 14 USA 15 5Bioinformatics Core, Memorial Sloan Kettering Cancer Center, NY NY USA 16 6Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, NY NY 17 USA 18 7David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer 19 Center, NY NY USA 20 8Marie-Josee and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering 21 Cancer Center, New York, NY USA 22 9Program for Evolutionary Dynamics, Harvard University, Cambridge MA USA 23 10Department of Mathematics and Department of Organismic and Evolutionary Biology, 24 Harvard University, Cambridge MA USA 25 11Canary Center for Cancer Early Detection, Department of Radiology, Stanford University, Palo 26 Alto CA USA 27 12Department of Pathology, Johns Hopkins Medical Institutions, Baltimore MD USA 28 13Sol Goldman Pancreatic Cancer Research Center, Baltimore MD USA 29 30 *equal contribution. 31 32 Running Title: Origins of Recurrent Pancreatic Cancer 33 34 Keywords: pancreatic cancer, metastasis, adjuvant therapy, platinum, cisplatin, mutational 35 signature, evolutionary biology 36 37 Financial Support: Supported by NIH/NCI grants R01 CA179991 and R35 CA220508 to C.I.D., 38 2T32 CA160001-06 to A.M.M, the Daiichi-Sankyo Foundation of Life Science Fellowship to A.H, 39 the Mochida Memorial Foundation for Medical and Pharmaceutical Research Fellowship to A.H, 40 R00 CA22999102 to J.G.R., and NIH/NCI P50 CA62924. This work was funded in part by the 41 Marie-Josée and Henry R. Kravis Center for Molecular Oncology and the National Cancer 42 Institute Cancer Center Core Grant No. P30-CA008748. 43

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Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 19, 2020; DOI: 10.1158/2159-8290.CD-19-1508

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Address correspondence to Christine Iacobuzio-Donahue at MSKCC, Zuckerman Research 1 Building, 417 E. 68th St, Rm Z761, New York, NY 10065. Phone: 646-888-2239. email: 2 [email protected]. 3 4 5 Conflicts of Interest: The authors declare no conflicts of interest related to this work. C.I.D. 6 receives research support from Bristol Myers Squibb. 7 8



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3

Abstract (146 words) 1

Surgery is the only curative option for Stage I/II pancreatic cancer, nonetheless most 2

patients will recur after surgery and die of their disease. To identify novel opportunities for 3

management of recurrent pancreatic cancer we performed whole exome or targeted 4

sequencing of 10 resected primary cancers and matched intrapancreatic recurrences or distant 5

metastases. We identified that recurrent disease after adjuvant or first-line platinum therapy 6

corresponds to an increased mutational burden. Recurrent disease is enriched for genetic 7

alterations predicted to activate Mapk/Erk and PI3K/AKT signaling and develops from a 8

monophyletic or polyphyletic origin. Treatment induced genetic bottlenecks lead to a modified 9

genetic landscape and subclonal heterogeneity for driver gene alterations in part due to 10

intermetastatic seeding. In one patient what was believed to be recurrent disease was an 11

independent (second) primary tumor. These findings suggest routine post-treatment sampling 12

may have value in the management of recurrent pancreatic cancer. 13

14 15 16




4

Statement of Significance: The biologic features or clinical vulnerabilities of recurrent 1

pancreatic cancer after pancreaticoduodenectomy are unknown. Using whole exome 2

sequencing we find that recurrent disease has a distinct genomic landscape, intermetastatic 3

genetic heterogeneity, diverse clonal origins and higher mutational burden than found for 4

treatment naïve disease. 5

6 7




5

Introduction 1

Pancreatic ductal adenocarcinoma (PDA) is currently the 3rd leading cause of cancer 2

death in the United States and is projected to become the 2nd cause of cancer death within five 3

years (1). Several reasons account for these statistics, including an inability to diagnose the 4

disease when at a curative stage, late presentation and modest impact of current best available 5

therapies (2). There is a limited understanding of the genetics of recurrent disease which limits 6

targeted therapy opportunities or informed design of clinical trials. 7

Approximately 10-15% of newly diagnosed PDA patients are diagnosed with early-stage 8

disease (Stage I or II). For these patients, surgical resection followed by adjuvant therapy is the 9

only option for cure (2). While long term survival following resection of PDA has been reported 10

(3,4), the majority of patients who undergo resection will recur locally or at distant sites and die 11

of their disease within five years. Several factors have been shown to have predictive or 12

prognostic value for disease free or overall survival in resected PDA patients, including a high 13

ratio of involved to total resected lymph nodes, larger tumor size, high tumor grade, the 14

presence of vascular and perineural invasion, or variably positive margins (5,6). Venous invasion 15

is very common in pancreatic cancer and may contribute to the aggressiveness of this disease 16

(7). Molecular features of PDA have also been attributed to worse outcome after surgery. For 17

example, patients with co-incident TP53 and SMAD4 alterations have shorter disease-free 18

survival than patients whose tumors do not have these genetic alterations (8). Alternatively, the 19

presence of a basal expression signature (9), paucity of an immune signature (10) or microbial 20

dysbiosis (11) have also been associated with worse overall survival. 21

We have previously reported in a small cohort of treatment naïve Stage IV PDA patients 22

that we found no evidence of driver gene heterogeneity among primary and metastatic sites 23

(12). What heterogeneity was found in those patients corresponded to passenger mutations 24

only. Moreover, the extent of passenger gene heterogeneity was far less than may be seen in 25

spatially distinct cells within normal tissues indicating at least one clonal sweep occurred within 26

the primary tumor prior to metastatic dissemination. By contrast, the extent to which 27

metastatic PDA that arises following surgical resection exhibits similar features is unknown. To 28

address this question and to improve our understanding of recurrent PDA following resection 29




6

we performed whole exome and/or targeted sequencing of 10 primary PDAs, matched local 1

(pancreatic resection bed) recurrences and multiple anatomically diverse metastases. We 2

identified that pancreatic cancer recurrences following surgery have an increased mutational 3

burden, distinct subclonal origins and in some instances are characterized by somatic mutations 4

with potential implications for clinical management. 5

6

7




7

Results 1 We screened a collection of more than 160 PDA research autopsies to identify patients 2

for whom a sample of their original surgical pancreatic resection was available, who underwent 3

adjuvant treatment after surgery, and who had histologically confirmed recurrent disease 4

within the pancreatic remnant and one or more metastases to anatomically distinct sites such 5

as the liver, lungs or peritoneum (Fig. 1A,B). We identified nine such patients for study (Table 6

S1). An additional patient was included that did not have metastatic disease at autopsy but did 7

have an aggressive local recurrence with multiple geographically distinct samples of this mass 8

available for profiling. One normal tissue sample from each patient was also used to distinguish 9

somatic from germline variants (Table S2). Histologic review of each tumor sample indicated 10

that in two patients the primary tumor was conventional ductal adenocarcinoma whereas the 11

recurrent disease had squamous features (PAM39) or squamous differentiation (PAM46). In a 12

third patient the primary tumor exhibited classic ductal adenocarcinoma whereas the recurrent 13

disease was anaplastic (PAM37). One patient had a primary small cell carcinoma of the 14

pancreas (PAM41). The recurrent disease was notable for transition to a large cell phenotype 15

(Fig. S1). 16

All samples were analyzed by whole exome sequencing (WES) to a median of 330x 17

coverage (range 135-652x)(see Methods, Table S3). To supplement the breadth of WES 18

sequencing in these samples, we also performed concurrent deep sequencing using a targeted 19

sequencing panel to ensure greater sensitivity for mutations and DNA copy number alterations 20

in 410 cancer associated genes (Table S4) (13). We identified 4864 total somatic single 21

nucleotide variants (SNVs) and small insertions or deletions (indels) with a median of 57 per 22

sample (range 17-203; Fig. 1C, Table S5). We found no evidence of microsatellite instability in 23

these 10 patients, nor were germline or somatic mutations identified in recognized MSI-related 24

or other DNA damage repair genes (14). 25

To better understand the patterns of mutation accumulation in primary versus recurrent 26

disease, we performed mutational signatures analysis of all samples that underwent WES (Fig. 27

1C). Nine signature classes were identified in our cohort: aging (signatures 1 and 5), double 28

strand break repair (DSBR, signature 3), apolipoprotein B mRNA editing enzyme, catalytic 29

polypeptide-like (APOBEC, signatures 2 and 13), mismatch repair defects (signatures 6, 15, 20, 30




8

21 and 26), tobacco (signature 4), somatic hypermutation (signature 9), and the three unknown 1

signatures 8, 17 and 23 (15). A high prevalence of Signature 9 (POLN) was seen in at least one 2

sample in seven of eight patients. This signature is associated with somatic hypermutation by 3

polymerase , a DNA polymerase that plays a role in DNA repair by translesion synthesis (16). 4

As previously reported, we also identified subsets of patients with an abundance of signatures 5

with unknown etiology such as signatures 8 and 17 (10). Patient PAM41 with a primary small 6

cell carcinoma of the pancreas had a remarkable abundance of mutations characteristic of 7

signature 23, a rare type of signature of unknown etiology (15). Comparisons of the prevalence 8

of each signature class specifically in post-treatment recurrent disease versus treatment naïve 9

primary carcinomas revealed a significant increase in the double strand break repair signature 10

only (median 16 somatic mutations per primary tumor versus 92 in recurrent disease, two-11

sided 2-squared test, p<0.0001) (Fig. 1D). Four patients received a platinum agent as part of 12

their adjuvant or first line therapy for recurrent disease (Table S1), thus we determined the 13

extent that this signature was enriched in these patients compared to those who received other 14

regimens. Comparison of these two groups revealed that patients who did not receive a 15

platinum agent had a modest yet significant increase in their recurrent disease with respect to 16

the double strand break repair signature (median 58 somatic mutations per primary versus 532 17

in recurrent disease, Mann-Whitney U test, p<0.002) (Fig. 1E). Moreover, as previously 18

described (17,18) this signature was remarkably more prevalent in the recurrent disease of 19

patients who received one or more platinum agents (median 84.5 somatic mutations per 20

primary versus 1379 in recurrent disease, Mann-Whitney U test, p<0.0001). 21

We next determined the somatic alterations in known cancer genes in each patient. We 22

identified somatic mutations in known PDA driver genes predicted to have functionally 23

deleterious effects such as those in KRAS, CDKN2A, TP53, SMAD4, and ARID1B (Fig. 2A, Table S6 24

and S7). Collectively, the genetic features of the resected PDA samples (sample PT1 from each 25

patient) were consistent with previous sequencing studies of these cancers (9). By contrast, the 26

genomic features of recurrent disease (Fig. 2B, Table S6) were notable for somatic alterations in 27

additional genes, some of which are predicted to or likely to activate Mapk/Erk signaling (G12V 28

mutation in KRAS, I679Dfs*21 mutation in NF1, R111X mutation in PPP6C)(19,20), 29




9

PI3K/Akt/MTOR signaling (D323H hotspot mutation in AKT1, E542K hotspot mutation in PIK3CA, 1

homozygous deletion of STK11)(21), or MYC/MAX regulated gene expression (Y1312X mutation 2

in CHD8, W1004X mutation in MGA, X863_ splicing mutation in NOTCH1, up to 16-fold 3

amplification of MYC) (22,23). Somatic alterations in genes predicted to affect chromatin-4

mediated gene expression (up to 38-fold focal amplification of HIST1H3B, frameshift mutations 5

in KMT2B, KMT2C, KMT2D, c.4471-1N>A splicing mutations in TRIP12) (24–26), nuclear export 6

(E571K hotspot mutation in XPO1) (27) and DNA damage repair (c.497-1N>A splicing mutation 7

in ATM, F134V mutation in TP53) were also found (28,29). Recurrences from two patients 8

contained copy number alterations of genes implicated in innate immunity signaling (PRKCI 38-9

fold focal amplification, TRAF3 homozygous deletion) (30,31). Whole genome duplication 10

(WGD) was present in one or more samples of recurrent disease in eight patients (32) (Table 11

S7). In only two of these eight patients was WGD detected in the primary tumor indicating gains 12

in ploidy may accompany PDA progression. Finally, we determined the extent that alternative 13

mechanisms of increasing mutant KRAS signaling exist in recurrent disease. Allelic imbalance for 14

mutant KRAS was identified in at least one sample of the recurrent disease in all ten patients 15

including both patients for which KRAS mutations were identified in the recurrent disease (Fig. 16

2C and Table S8). In five patients two or more routes to allelic imbalance occurred 17

independently during clonal progression indicating convergent evolution towards increased 18

KRAS signaling (Fig. 2D,2E). Collectively these data identify potential signaling and 19

transcriptional pathways by which recurrent disease develops. 20

We hypothesized that somatic alterations identified in recurrent PDA samples reflect 21

the clonal expansion of pre-existent populations following selective pressure imposed by 22

adjuvant therapy. Pairwise cancer cell fraction (CCF) plots generated for all patients confirmed 23

the enrichment of one or more subclonal populations in the recurrent disease compared to the 24

primary tumor (Fig. 3A). We specifically focused on the AKT1 D323H in PAM40, PIK3CA E542K in 25

PAM39 and NOTCH1 X863_splice site mutation in PAM41 as they represent functionally 26

relevant alterations and in theory may be clinically actionable (http://oncokb.org). The KRAS 27

p.G12V mutation in PAM40 was also of interest given recent reports of subclonal KRAS 28

mutations in PDA (9). Droplet digital PCR confirmed that in all instances these mutations existed 29




10

in the primary tumor at prevalence rates from 0.2-2%, below the level of detection of our WES 1

(Fig.3B-D). 2

To understand the evolutionary origins of recurrent disease in each patient we inferred 3

phylogenies based on high confidence mutations in each patient (Methods). One patient who 4

was clinically thought to have an intrapancreatic recurrence of the resected PDA after 18 5

months, but molecular analysis revealed a second (independent) primary PDA (Fig. 4A). For 6

example, the resected primary tumor (PAM44PT1) had a KRAS p.G12R mutation and 80 private 7

passenger mutations, whereas the “recurrence” samples from this patient (PAM44PT2-PT5) 8

shared 124 mutations not seen in the primary. These latter mutations included a KRAS p.G12D, 9

a TP53 p.F134V and a 29 basepair frameshift deletion in KMT2D (Fig. 4B). A CCF plot of the 10

original primary (PAM44PT1) compared to the “recurrence” samples confirmed the mutual 11

exclusivity of the somatic mutations in each lineage (Fig. 4C). Re-review of the histology of the 12

first primary tumor and associated imaging studies did not suggest the presence of a cystic 13

neoplasm. These findings are highly indicative of two independent primary tumors that arose 14

from distinct precursors (33). 15

In the remaining nine patients the sample of resected primary tumor and all samples of 16

recurrent disease arose from a common ancestor of neoplastic cells containing canonical PDA 17

driver mutations. However, there were two distinct evolutionary trajectories by which 18

recurrent disease arose. For five patients (PAM37, PAM38, PAM40, PAM42, PAM46) the 19

resected primary tumor sample formed the outgroup in the phylogeny (Fig.5A,B and Figs. S2A-20

H) indicating that in these patients the recurrent disease developed from a single residual clonal 21

population, i.e. a monophyletic origin. In the remaining four patients (PAM39, PAM41, PAM43, 22

PAM45) (Fig. 5C,D and Fig. S3A-F) the recurrent disease was inferred to be seeded by multiple 23

ancestral clones and was polyphyletic in origin. For a more objective metric of relatedness 24

among the primary and recurrent disease in each patient we calculated pairwise Jaccard 25

similarity coefficients for all samples within a patient. The average Jaccard indices per patient 26

supported the distinction of monophyletic versus polyphyletic origins of recurrent disease (Fig. 27

5E-G) in that monophyletic recurrences were significantly more distant (divergent) from the 28

primary tumor, whereas polyphyletic recurrences were highly related to the primary tumors. 29




11

Recognizing the sample numbers are low for robust outcomes analysis, exploratory analysis 1

indicates that patients with monophyletic recurrences had a longer disease-free but not overall 2

survival (Fig. 5H,I). From phylogenetic analysis alone, the timing of dissemination to other 3

organs cannot be readily resolved. However, utilizing mathematical modeling and previously 4

measured metastasis doubling times (34), we found that the minimal time required to grow 5

from one to a billion cells (roughly 1 cm3) is 1.82 years, or 21.9 months (90% CI: 1.61 – 2.05 6

years) (Methods). Since clinical metastases occurred much earlier than these required 1.82 7

years after surgery in all patients with distant disease (median metastasis free survival 11.0 8

months, range 6-18 months)(Table S1), at least one of the metastases must have been 9

microscopically seeded before surgery. Patient PAM46 typified these dynamics as they had a 10

grossly positive surgical margin and developed a radiographically evident locoregional 11

recurrence, but no metastases, 17 months after surgery (Table S1). Irrespective of origin, in all 12

nine patients additional subclonal expansions occurred after dissemination, in some cases 13

resulting in spatial heterogeneity for driver gene mutations among the different sites of 14

recurrent disease (Fig. 5 and Figs. S2,S3). 15

We next sought to understand the clonal origins of local recurrences, a major clinical 16

issue for patients who undergo potentially curative resection (35), by inferring the migration 17

patterns of recurrent disease across spatially distinct sites in each patient (Methods). 18

These analyses indicated that the seeding patterns of recurrent disease were diverse, with 19

multiple patterns coexisting in the same patient. Metastasis to metastasis seeding was evident 20

in some patients, typified by PAM37 in whom an omental metastasis seeded three liver 21

metastases (Fig. 6A,6B), in PAM38 in whom a liver metastasis seeded a lung metastasis (Fig. 22

6C,6D), and in PAM45 in whom a perirectal metastasis seeded two abdominal wall metastases 23

(Fig 6E,6F). Local recurrences can also be seeded by the primary tumor or by a metastasis. In 24

PAM41 (Fig. 1B and Fig. 7A,7B) and PAM43 (Fig. 7C,7D), the primary tumor seeded the local 25

recurrence despite both having negative surgical margins at the time of resection. By contrast, 26

PAM38 (Fig. 6C,6D) and PAM42 (Fig. 7E,7F) had local recurrences seeded by a liver or lung 27

metastasis, respectively. Analysis of PAM39 and PAM40 indicated that two or more seeding 28

patterns were equally likely to have occurred for all sites analyzed and thus no dissemination 29




12

events could be confidently inferred. Inter-metastatic seeding was inferred in patients with 1

both monophyletic (PAM37, PAM38, PAM42, Fig. 6) and polyphyletic (PAM41, PAM43, PAM45, 2

Fig. 7) recurrences collectively indicating that migration patterns are unrelated to phylecity of 3

the recurrent disease. While we studied a small cohort, surgical margin status appeared to be 4

unrelated to the origin of a local recurrence. For example, local recurrences seeded by the 5

primary tumor were noted in patients with negative surgical margins (i.e. PAM41, Fig. 7A,7B) 6

whereas local recurrences seeded by a metastasis were noted in patients with a positive 7

surgical margin (i.e PAM38, Fig. 6C,6D). 8

9

10

Discussion 11

To date genomic studies of PDA have primarily relied on resections or biopsies of 12

treatment naïve disease (9,36,37). Herein, we demonstrate that study of post-treatment 13

samples may have value in identifying putative therapeutic vulnerabilities in recurrent disease. 14

Of immediate clinical relevance is the finding that recurrent disease is enriched for somatic 15

alterations in genes associated with Mapk/Erk or PI3K/Akt signaling, some of which are 16

potentially actionable (http://oncokb.org). Importantly, these targets are pre-existent and 17

selected for during adjuvant treatment itself. While our sample size is not sufficient for a robust 18

gene discovery in advanced pancreatic cancer, we identified genes not commonly associated 19

with the PDA landscape such as the nuclear exportin XPO1 (21), the serine-threonine protein 20

phosphatase PPP6C (28), or regulators of innate immunity (PRKCI, TRAF3) (30,31). These 21

findings support the need for prospective and statistically robust efforts to sequence post-22

treatment PDA to better define the genes or pathways repeatedly targeted by somatic 23

alteration. We caution readers that the evolutionary timing of these potentially actionable 24

events is critical to know. For example, in patient PAM40 all recurrent disease analyzed was the 25

result of expansion of a single subclone with a known deleterious AKT1 D323H mutation; in 26

theory all recurrent disease in this patient may have been sensitive to an AKT1 inhibitor. By 27

contrast, in patients such as PAM41 the PIK3CA E542K mutation occurred in a minor subclone 28

of the recurrent disease and hence would not be suitable for targeting. Nonetheless, while our 29




13

sample set is small it indicates that such instances may occur in PDA; such information could 1

arm a clinician with more information for how to manage recurrent disease. 2

Another important finding of this study is that it illustrates a taxonomy by which 3

recurrent PDA may be stratified: monophyletic origin, polyphyletic origin or unique origin (i.e. 4

synchronous/metachronous primaries). In all patients with bona fide recurrent PDA of mono- or 5

polyphyletic origin we find that systemic subclinical dissemination had already occurred at the 6

time of surgery, consistent with prior estimates (38). Questions for future investigation thus 7

relate to methods to identify mono- versus polyphyletic recurrences in real time, the extent to 8

which subclonal diversity develops within each category, and the clinical significance of this 9

finding in the context of ongoing or planned clinical trials. Finally, while second primary 10

carcinomas of the pancreas have been reported, our finding of a metachronous primary PDA in 11

one of 10 otherwise unselected patients with an intrapancreatic mass post resection suggest 12

that this phenomenon may be more common than previously appreciated (39,40). Detailed and 13

formal prospective studies of intrapancreatic masses in patients who have undergone prior 14

resection for PDA will be required to more firmly understand the frequency of second primary 15

tumors and the risk factors associated with their development as has been shown for invasive 16

carcinomas arising in intraductal papillary mucinous neoplasms of the pancreas (41). 17

Finally, we note that the genomic features of these patients, all of whom presented with 18

Stage I/II disease and underwent surgery and therapy, are in stark contrast to those of 19

untreated Stage IV PDA (12). In untreated PDA the genetic features of both the primary and 20

metastases are remarkably uniform, and the genetic heterogeneity seen appears due 21

exclusively to passenger mutations, suggesting at least one clonal sweep occurred prior to 22

metastatic dissemination. By contrast, Stage I/II disease is notable for subclonal heterogeneity 23

for driver genes as reported in other tumor types (42,43). Treatment induced genetic 24

bottlenecks that sculpt the genomic landscape of PDA, together with intermetastatic seeding, 25

likely contribute to subclonal and inter-metastatic heterogeneity for driver gene alterations 26

observed in recurrent disease. Thus, context appears key in the interpretation of heterogeneity 27

in PDA. 28




14

In summary, we identify novel genetic features of PDA in the context of recurrent 1

disease after surgical resection and treatment with potential clinical implications for use of 2

targeted therapies in disease management. In the event that therapeutically targetable gene 3

alterations are validated in recurrent disease, and as more targeted therapies become 4

available, post-treatment sampling is likely to contribute to identification of the mechanisms of 5

resistance and early identification of resistant clones. 6

7

Methods 8 9

Tissue Samples. Patient samples were generously shared by the Gastrointestinal Cancer Rapid 10

Medical Donation Program (GICRMDP) resource at The Johns Hopkins Hospital. Sections were 11

cut from formalin-fixed paraffin embedded (FFPE) or frozen sections available and reviewed to 12

identify those with at least 20% neoplastic cellularity and preserved tissue quality. Samples 13

meeting these criteria were macro-dissected from serial unstained sections before extraction of 14

genomic DNA using DNeasy® Blood & Tissue Kits for frozen samples or QIAamp DNA FFPE 15

Tissue Kits for FFPE materials (Qiagen) following the manufacturer instructions. 16

17

Whole Exome Sequencing. DNA quantification, library preparation and sequencing were 18

performed in the Integrated Genomics Operation and bioinformatics analysis of somatic 19

variants by the Bioinformatics Core at MSKCC. Libraries were created using 20

AgilentExon_51MB_hg19_v3 as bait and sequenced on an Illumina HiSeq 2500. Whole exome 21

sequencing resulted in a median target sequence depth of 317X (min-max) with 83% of the 22

coding regions covered at least 100x and a calculated average tumor cellularity of 35.7% (Table 23

S3). Paired-end sequencing data were aligned to the reference human genome (hg19) using 24

BWA. Read de-duplication, base quality recalibration, and multiple sequence realignment were 25

performed using the Picard Suite and GATK. Point mutations and small insertions and deletions 26

were detected with MuTect and HaplotypeCaller, respectively. Genome-wide total and allele-27

specific DNA copy number was determined using the FACETS algorithm (44). For targeted 28

sequencing of 410 cancer genes, barcoded libraries from patient-matched tumor and normal 29

samples were captured and sequenced using methods previously reported in detail (13). Raw 30




15

sequencing data has been deposited in the European Genome Phenome Archive (EGA) under 1

accession number EGAS00001004097. 2

3

Filtering and Annotation of Variants. For each patient, somatic variants were filtered using the 4

following criteria: patient-matched normal coverage ≥10 reads, variant count in patient-5

matched normal ≤1, patient-matched normal variant frequency <0.02, tumor coverage ≥20 6

reads, and tumor variant allele frequency ≥0.05 in at least one tumor sample. The resulting list 7

of somatic variants were filtered for those present in the coding regions only and subject to 8

further bioinformatic annotation for pathogenicity and germline allele frequencies from healthy 9

populations distributed worldwide using LiFD (45)(Table S5). MSI status was inferred from the 10

sequencing data using a clinically validated algorithm (46), with MSI-H defined as an MSIsensor 11

score ≥10. For all mutations, we inferred the cancer cell fraction (CCF) from the mutant allele 12

fraction, local copy number, and FACETS estimate of tumor purity according to previously 13

described methods (47,48). Confidence intervals for the CCF therefore reflect the 95% binomial 14

confidence interval for the underlying mutant allele fraction. Whole gene copy number 15

alterations and whole genome duplication were inferred by FACETs. Genes with copy number 16

of 8-fold or greater inferred by FACETs were considered amplifications. 17

18

Phylogenetic analysis. We applied Treeomics v1.6.0 to reconstruct the phylogenies of recurrent 19

disease using high quality somatic variants identified by whole exome sequencing. Treeomics 20

uses a Bayesian inference model to account for sequencing errors and low purity and employs 21

Integer Linear Programming to infer a maximum likelihood tree. 22

23

Mathematical modeling. To calculate the minimal required time a metastasis founding cell 24

needs to grow to a detectable lesion of 1 cm3 (~109 cells) we used the smallest previously 25

measured PDA metastasis doubling time of 27 days (49) leading to an exponential growth rate 26

of r=0.026 per day. Assuming a PDA cell division time of 2.3 days (50), the expected minimal 27

time for a tumor conditioned on survival takes to reach 109 cells is 1.82 years (90% CI: 1.61 – 28

2.05 years). 29




16

1

Mutational signatures. Mutational signatures were derived using the methods described by 2

Alexandrov et al (15). To enrich for the most abundant signatures we merged those with similar 3

putative etiologies into a single group. Signature groups present in at least 20% abundance in 4

at least one sample were included for additional study and statistical analysis. 5

6

Droplet Digital PCR. Absolute quantification of mutant alleles was determined using a RainDrop 7

Plus Digital PCR system according to the manufacturer instructions. Predesigned or custom 8

designed TaqMan assays were obtained for variants of interest (Thermo Fisher). Approximately 9

75 ng of gDNA were used per reaction in a 25 ul volume. Each reaction contained 5 million 10

droplets at a target inclusion rate of 10% (500,000 target molecules). 11

12

Migration Pattern Inferences. PyClone and MACHINA were used to infer seeding patterns 13

associated with metastasis and local recurrence. To alleviate long run times associated with the 14

high sample number context we applied specific filters to focus on the most informative loci. 15

These were a) the locus was sequenced to a depth of at least 60 in at least one sample; b) the 16

locus had a copy number profile consistent with a relatively simple genomic history in all 17

samples (the combination of major allele A and minor allele B at the locus was required to be 18

one of AB, AAB, AAAB, or AABB in each sample, though not necessarily the same across 19

samples) and c) each locus was required to be sequenced to nonzero depth in all remaining 20

samples. Samples that did not contain more than 20 variant loci after applying this filter were 21

excluded. In patient PAM41, a large cluster of highly related liver metastases (PT4, 8, 9, 10, 11) 22

were simplified by including only sample PT9 to improve runtime. The resulting mutations and 23

associated major and minor copy numbers were clustered with PyClone using default settings. 24

The PyClone consensus cluster files were used to enumerate evolutionary relationships, then 25

the combination of PyClone cluster frequency estimates and enumerated trees together were 26

used to search for the most parsimonious migration patterns consistent with each tree 27

topology. The resulting solution with the lowest overall migration number, and then the lowest 28




17

comigration number, were determined. No other constraints were applied to the migration 1

plots. 2

3

Statistics. Descriptive data are represented as a mean and standard deviation unless otherwise 4

mentioned. Parametric distributions were compared by a Student’s T-test whereas non-5

parametric distributions were compared by a Mann-Whitney U test. Frequency data were 6

compared using a X2 test. All comparisons were two-sided. Survival curves were plotted 7

according to the methods of Kaplan and Meier and compared using a log-rank test. 8

9




18

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24

25




23

Figure Legends 1

2

Figure 1. Recurrent pancreatic cancer after adjuvant therapy has distinct genetic features 3

compared to the primary tumor. A. Schematic illustrating the temporal and spatially distinct 4

sample types collected for this study. In all patients the primary tumor is designated PT1. The 5

number of samples of recurrent disease varied per patient, ranging from PT2-PT4 up to PT2-6

PT11. B. Pancreatic tail remnant and attached spleen removed at autopsy of patient PAM41. 7

Arrowheads indicate the resection margin and dashed circle indicate the location and 8

dimensions of the recurrent tumor. C. Integration of mutation type, sample location, radiation 9

therapy, chemotherapy, and mutational signature for each sample. D. Comparison of the 10

prevalence of distinct mutational signatures in primary carcinomas (P) versus recurrent disease 11

(R) per mutational signature indicates a statistically significant increase in mutations 12

characteristic of the DNA damage repair signature only (two-sided 2-squared test, p<0.0001). 13

E. Comparison of DNA damage repair signature in patients who did (n=4) and did not (n=6) 14

receive a platinum agent as adjuvant or first line therapy. All comparisons by two-sided Mann-15

Whitney U test. 16

17

Fig. 2. Genetic Features of Recurrent Pancreatic Cancer. A. Oncoprint of somatic mutations 18

and copy number alterations present in the pre and post-treatment samples analyzed for each 19

patient (clonal mutations). B. Oncoprint of somatic mutations and copy number alterations 20

present in one or more samples of the recurrent PDA only, but not in the matched originally 21

resected primary tumor (subclonal mutations). The full list of somatic alterations identified in 22

both the primary and recurrent disease are listed in Tables S6 and S7. C. Frequency of diploid 23

(D), tetraploid (T) and allelic imbalance (AI) at the KRAS locus in primary tumors versus 24

recurrent disease. D. Proportion of genetic events leading to allelic imbalance in recurrent 25

pancreatic cancer. Complete data are shown in Table S8. E. Schematic illustration of convergent 26

genetic events that increase mutant KRAS dosage in the recurrent disease of PAM37. 27

Abbreviations used are WGD, whole genome duplication and CN LOH, copy neutral loss of 28

heterozygosity. 29




24

1

Figure 3: Somatic alterations in recurrent pancreatic cancer alterations reflect expansion of 2

subclones pre-existent in the primary tumor. A. Representative density cloud plots of the 3

primary tumor (PT1) and one matched sample of recurrent disease for nine different patients. 4

Subclonal expansion of cells containing a PIK3CA E524K mutation (PAM39), a KRAS G12V and 5

AKT D323H mutation (PAM40), a NOTCH1 frameshift mutation (PAM41), KMT2C frameshift 6

mutation (PAM45) and CHD8 nonsense and missense mutations (PAM46) in the recurrent 7

disease are seen. The cancer cell fractions of representative clonal driver genes for all cases (i.e. 8

KRAS, TP53, SMAD4 and/or GNAS) are shown for reference. B-D. Droplet digital PCR analysis of 9

mutant allele abundance in the primary tumor and one matched sample of recurrent disease in 10

three different patients. In all three patients subclones were preexistent at low allele 11

frequencies in the primary tumor. Each dot represents one droplet, and color bars at top right 12

of each plot indicate relative intensity of the VIC labeled mutant (x axis) and FAM labeled wild 13

type allele (y axis) fluorescent labels. 14

15 Figure 4: Metachronous pancreatic cancer simulating a local recurrence. A. Clinical timeline of 16

events in this patient. Liver and peritoneal metastases were radiographically evident at 18 17

months and a mass in the pancreatic remnant was noted at autopsy. B. Phylogenetic analysis of 18

whole exome sequencing data generated for this patient indicates the metachronous 19

pancreatic cancer contains a unique repertoire of somatic driver gene alterations including a 20

KRAS-G12D mutation compared to the first primary (PT1) that contains a G12V mutation. C. 21

Representative density cloud plot of the first primary tumor (PT1) and one sample of the 22

metachronous primary tumor (PT3). The majority of somatic alterations in each primary are 23

mutually exclusive from each other. 24

25 Figure 5: Recurrent pancreatic cancer originates from two distinct evolutionary origins. A. 26

Phylogenetic analysis of the relationships of the primary tumor to the local recurrence and liver 27

metastases in patient PAM40. All recurrent disease is the result of clonal expansion of a single 28

pre-existent subclone notable for an AKT1 and KRAS mutations (See Fig 2B). The primary tumor 29

(PT1) is the outgroup in the tree. A subclonal TRIP12 mutation is also seen in a single liver 30




25

metastasis. B. Color code of sample origins in PAM40. C. Phylogenetic analysis of the 1

relationships of the primary tumor to the local recurrence and liver metastases in patient 2

PAM39. In this patient the recurrent disease is the result of more than one clonal expansion. 3

The preexistent PIK3CA mutation has expanded in the lineage that gave rise to samples PT2 and 4

PT9. D. Color code of sample origins in PAM39. E,F. Jaccard indices for each pairwise 5

comparison in PAM40 (monophyletic recurrence) versus PAM39 (polyphyletic recurrence). G. 6

Comparison of the average Jaccard index for primary tumors and their matched recurrences in 7

patients with monophyletic recurrences versus those with polyphyletic recurrences. 8

Monophyletic recurrences are significantly different from their matched primary tumor 9

indicating passage through a genetic bottleneck, whereas no difference is found between the 10

primary tumor and matched recurrences in patients with polyphyletic recurrences. 11

(comparisons by a Student’s two-sided T test). Monophyletic (“common origin”) recurrences 12

are associated with an improved disease-free survival (H) but not overall survival (I) in this small 13

cohort (comparisons by log-rank test of Kaplan Meier survival curves). 14

15 Figure 6: Examples of intermetastatic seeding in recurrent pancreatic cancer. For each patient 16

the previously resected primary tumor is indicated by PT1 to the left of each patient schematic. 17

+M, positive surgical margin; +LN, positive lymph nodes. In the body maps for all three patients 18

(panels A,C,E), red lines reflect migration from the primary tumor and blue lines indicate 19

migration from a site of recurrent disease. Solid lines indicate high confidence migration 20

patterns and dashed lines indicate low confidence migration patterns inferred by MACHINA 21

(panels B,D,F). Each patient had at least one high confidence migration event from one site of 22

recurrent disease to another. In patient PAM45, two migration patterns were equally likely to 23

have occurred (F) although both predict that the primary tumor seeded the local recurrence 24

and the perirectal metastasis seeded the abdominal metastases. 25

26 Figure 7: Origins of local recurrences after surgical resection. For each patient the previously 27

resected primary tumor is indicated by PT1 to the left of each patient schematic. -M, negative 28

surgical margin; +LN, positive lymph nodes, -LN, negative lymph nodes. In the body maps for all 29

three patients (panels A,C,E), red lines reflect migration from the primary tumor and blue lines 30




26

indicate migration from a site of recurrent disease. Solid lines indicate high confidence 1

migration patterns and dashed lines indicate low confidence migration patterns inferred by 2

MACHINA (panels B,D,F). In PAM41 (A,B) and PAM43 (C,D) the local recurrence is seeded by the 3

originally resected primary tumor, whereas in PAM42 (E,F) it is seeded by a lung metastasis. In 4

patients PAM41 (A,B) and PAM42 (E,F) three or more migration patterns were equally likely to 5

have occurred. 6

7 8




Stage I/II

PDA

adjuvant

treatment

anastomosis

locoregional

and distant

recurrences (samples

PT2 up to PT11)

primary tumor

resection

(sample PT1)

A

spleen

Local

recurrence

pancreatic

tail

B

E D

Fig 1

Prim

ary

Rec

urren

ce

Prim

ary

Rec

urren

ce

0

2000

4000

6000

8000

Mu

tati

on

s

No Platinum

(4 patients)

Platinum

(6 patients)

p<0.002

NS

p<0.0001

p<0.02

C

Pe

rce

nt

of

To

tal

PAM37 PAM38 PAM39 PAM40 PAM41 PAM42 PAM43 PAM44 PAM45 PAM46

No

ns

yn

on

ym

ou

s M

uta

tio

ns

Aging

DNA Repair

APOBEC

MMR/MSI

Tobacco

POLN

Sig 8

Sig 23

Other

Sig 17

Signature

Radiation

No

Yes

Chemotherapy

None

Adj/1st Line Platinum

Other

Location

Local Recurrence

Primary

Liver

Lymph Node

Abdominal Cavity

Prostate/Perirectal

Lung

Diaphragm

Pericardium

Missense

Frameshift Deletion

Frameshift Insertion

In frame Deletion

In frame Insertion

Nonsense

Nonstop

Splice Site

Mutation Type




A

B

Primaries

Recurrences

p<

0.0

03

D

T

AI

AI

T

D

C

Gain/Amplification

Heterozygous Loss

Loss and Gain

Loss after WGD

Loss before WGD CN LOH before WGD CN LOH after WGD

Double Loss after WGD

CN LOH before WGD and Loss

D

PT5

PT6

PT4

PT3

CNLOH before

WGD, then Loss

Diploid WGD and amplification

WGD and gain E

Fig 2 Research.

on December 12, 2020. © 2020 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from



PAM41 (PT1 vs PT10)

A

1.44% 51.92%

Primary (PT1)

2.23%

AKT1-D323H

KRAS-G12V KRAS-G12V

55.26%

Liver Metastasis (PT4)

AKT1-D323H

B

C Primary (PT1) Local Recurrence (PT2)

PI3KCA-Q542K PI3KCA-Q542K

0.12% 5.36%

D Primary (PT1) Liver Metastasis (PT11)

0.22% 9.29%

NOTCH1 NOTCH1

PAM37 (PT1 vs PT6) PAM38 (PT1 vs PT2) PAM39 (PT1 vs PT9)

PAM40 (PT1 vs PT6)

PAM45 (PT1 vs PT7) PAM46 (PT1 vs PT9)

PAM42 (PT1 vs PT4)

PAM43 (PT1 vs PT3)

CCF Recurrence

CC

F P

rim

ary

Fig 3 Research.




Death

20 mo S

(+LN,

VI)

Erl,

Rad

18

mo

liver

and peritoneal

metastases

G,Erl

PAM44

Germline

NF1

KMT2D, KRASG12D, TP53

KRASG12R

Scale: 50 SNVs

PT1

PT3

PT2

PT5

PT4

B

A

C

PT

1 C

CF

PT3 CCF

Fig 4 Research.




Primary Tumor

Local Recurrence

Liver Metastases

PAM40

Germline

GNAS

RNF43

AKT1, KRAS

Scale: 100 SNVs

PT1

PT2

PT4

PT8

PT5

PT6

PT7

TRIP12

PT3

A

Primary Tumor

Local Recurrence

Liver Metastasis

Lymph Node Metastasis

Peritoneal Metastasis

Prostate Metastasis

C

PIK3CA

PAM39

Germline

Scale: 100 SNVs PT1

PT6

PT2

PT3

PT9

PT8

PT7

PT5

PT4

KRAS

TP53

G H I

PAM40 (Monophyletic) PAM39 (Polyphyletic) E F

B

D

Fig 5 Research.




PT1

PAM37

PT5

PT3 PT4

PT3

A

+LN

B

PT2

PT3

PT4

PT1

PAM38 C

+M

+LN

D

PAM45

PT2

PT1

PT8

PT3 PT4

PT7

PT5

PT6

E

F

Fig 6 Research.




PAM41

PT2

PT6

PT3

PT7

PT9 PT5

PT1

A

-M

+LN

B

PT4

PT2/3

PT1

PT5

PAM42

PT6

E

-M

-LN

PT4

PT2

PT1

PT5

PAM43

PT6

PT3

-M

+LN

C

D

F

Fig 7 Research.




Published OnlineFirst March 19, 2020.Cancer Discov Hitomi Sakamoto, Marc A Attiyeh, Jeffrey M Gerold, et al. The Evolutionary Origins of Recurrent Pancreatic Cancer

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The Evolutionary Origins of Recurrent Pancreatic Cancer€¦ · 19/03/2020 · 16 is very common in pancreatic cancer and may contribute to the aggressiveness of this disease 17

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