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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Nat Genet. Author manuscript; available in PMC 2017 August 9.
Published in final edited form as:
PMCID: PMC5549141
NIHMSID: NIHMS873163

Clonal Evolution of Chemotherapy-resistant Urothelial Carcinoma

Abstract

Chemotherapy-resistant urothelial carcinoma (UC) has no uniformly curative therapy. Understanding how selective pressure from chemotherapy directs UC’s evolution and shapes its clonal architecture is a central biological question with clinical implications. To address this question, we performed whole-exome sequencing and clonality analysis of 72 UCs including 16 matched sets of primary and advanced tumors prospectively collected before and after chemotherapy. Our analysis provided several insights: (i) chemotherapy-treated UC is characterized by intra-patient mutational heterogeneity and the majority of mutations are not shared, (ii) both branching evolution and metastatic spread are very early events in the natural history of UC; (iii) chemotherapy-treated UC is enriched with clonal mutations involving L1-cell adhesion molecule (L1CAM) and integrin signaling pathways; (iv) APOBEC induced-mutagenesis is clonally-enriched in chemotherapy-treated UC and continues to shape UC’s evolution throughout its lifetime.

Introduction

Urothelial carcinoma (UC) results in 15,000 deaths annually in the United States1. Individuals with metastatic UC are standardly treated with platinum-based chemotherapy25. However, nearly all will progress and develop chemotherapy resistance3,6,7. Ultimately, the majority of patients will die of metastatic chemotherapy-resistant UC2,3,5. Little is known about the clonal architecture of advanced chemotherapy-treated UC or the evolutionary dynamics that lead to metastasis and chemotherapy resistance. Large genomic studies like The Cancer Genome Atlas (TCGA) have focused only on untreated primary tumors8. In particular, the extent to which chemotherapy-treated tumors share the genetic profile of the primary tumor remains unknown.

To understand the relative contributions of different subclones and the effects of chemotherapy as a selective pressure in UC, we performed whole exome sequencing and clonality analysis of matched sets of primary, metastatic and germline samples. Because cancers are genetically heterogeneous, we set out to address two fundamental questions: (i) what is the degree of clonal divergence between primary and metastatic UC? (ii) How does chemotherapy impact the genomic landscape of tumor cell populations in advanced and metastatic UC?

We employed the computational framework of CLONET (CLONality Estimate in Tumors) we developed previously9 (online methods) to adjust genomic events for tumor purity and ploidy, and then determine the relative abundance of tumor cell subpopulations through clonality analysis of genomic lesions. By comparing the frequency and patterns of CLONET-adjusted events between primary and metastatic tumors obtained from different anatomical sites and at different time points over each patient’s clinical course, we were able to reconstruct phylogenetic trees and compare clonal evolutionary patterns across the study cohort. We then proceeded to examine the clonally-enriched genomic signatures and trace the evolutionary footprints of mutagenesis mechanisms including the APOBEC3 family of cytidine-deaminases during each cancer’s evolution.

Results

Clonal mutational heterogeneity in chemotherapy-treated UC

To characterize the clonal architecture of advanced chemotherapy-treated UC, we performed whole exome sequencing of 72 prospectively collected urothelial tumors from 32 patients including 16 matched sets of primary, metastatic UCs and germline samples and two rapid autopsy cases (Fig. 1, Supplementary Table 1). The study was designed to enrich for patients with advanced disease, 28/32 (88%) of patients either presented with or developed metastatic disease during the study period (Fig. 1, Supplementary Table 1). Overall, the most frequent mutations and copy number alterations in our cohort were consistent with the results of the TCGA dataset of untreated UC8 (Supplementary Fig. 1, Supplementary Table 2). We observed no statistically significant difference in the number of SNVs between pre-chemotherapy and post-chemotherapy tumors (Supplementary Fig. 2).

Figure 1
Clinical characteristics of study cohort

To compare the clonal structure of pre-chemotherapy and post-chemotherapy tumors across the study cohort, we investigated the number of private and shared mutations between the pre-chemotherapy and post-chemotherapy tumors within each patient as a fraction of the total mutational burden (Fig. 2a). On average, only 28.4% (range 0.2%–76.4%) of mutations were shared between pre- and post-chemotherapy samples (Fig. 2a). This effect was consistent across primary-primary tumor pairs and primary-metastatic tumor pairs (p=0.17, Wilcoxon test) (Fig. 2a). Surprisingly, even mutations in previously reported driver genes10 including PIK3CA, KMT2D (MLL2), ATM and TP53 were not consistently shared between matched pre-chemotherapy and post-chemotherapy tumors (Fig. 2b). We confirmed these findings with targeted sequencing of 250 common driver genes achieving an average coverage of 400x and an excellent concordance with variant allele frequencies obtained from whole exome sequencing (Pearson correlation = 0.93, P<10−171) (Supplementary Fig. 3). Some post-chemotherapy tumors even evolved to develop different mutations in the same key gene. For example, in patient WCM077, the primary pre-chemotherapy tumor and the pelvic post-chemotherapy lymph node metastasis shared a TP53 p.Y234C mutation while the post-chemotherapy lung metastasis had a separate private TP53 p.G266V mutation that was not shared with the primary tumor (Fig. 2b, Supplementary Fig. 4). Taken together, our results demonstrate significant mutational heterogeneity in tumor samples from the same patient and suggest that chemotherapy is associated with a significant change in the mutational landscape of advanced urothelial carcinoma.

Figure 2
Clonal mutational heterogeneity in chemotherapy-treated UC

Early branching evolution in chemotherapy-treated UC

We conducted a phylogenetic analysis of 21 sets of matched tumors from patients from whom at least two tumor samples were available per patient using the parsimony ratchet method11 (Fig. 3, Supplementary Fig. 5). This analysis revealed a pattern of early branching evolution with several successive waves of clonal expansion occurring early in each patient’s UC. In every reconstructed evolutionary tree, the primary tumor was positioned as a branch indicating that the ancestral clone gave rise to multiple cell populations that evolved in parallel during the early stages of tumor evolution.

Figure 3
Early branching evolution in UC

In order to better understand the pattern of clonal evolution during the course of chemotherapy, we followed another individual with UC from the time of diagnosis through death over a period of 16 months. We collected a total of 12 samples from 8 anatomical sites and at three different time-points: the primary untreated tumor obtained by transurethral resection of bladder tumor (TURBT) acquired at initial diagnosis, four areas of residual primary bladder tumor and one pelvic lymph node obtained by radical cystectomy and lymph node dissection following four cycles of gemcitabine-cisplatin chemotherapy, and six metastases obtained at time of rapid autopsy after docetaxel-ramucirumab therapy (4 distant lymph nodes, 2 liver metastases) (Fig. 4a). We analyzed the genomes of these tumors to characterize the full evolutionary arc of the cancer from the time of diagnosis to death. Metastatic tumors obtained at autopsy harbored high clonal fractions (i.e. the percentages of clonal alterations compared to all alterations in a sample) (mean 83%, range 69%–91%) (Fig. 4b). Allele-specific copy number analysis identified a sub-clonal heterozygous deletion of the tumor suppressor CDKN2A in the primary tumor that evolved into a homozygous clonal deletion in the distant metastatic lymph nodes and liver lesions obtained from the autopsy. This was confirmed by fluorescence in-situ hybridization (FISH) (Fig. 4c) suggesting that CDKN2A loss was selected as the tumor evolved under pressure from chemotherapy. Comparison of clonality-adjusted frequencies of somatic alterations across all tumor samples from this patient revealed substantial heterogeneity. This heterogeneity followed several distinct patterns. Certain subclonal mutations (i.e. RYR2, ANKRD62, NCOA3 and LSS) were present in the primary tumor persisted and became enriched in the chemotherapy-treated metastatic lesions. Other subclonal mutations (i.e. POLD2, FOXP1, FGFR4, TRRAP, and EGFR) present in the primary were not observed in the metastatic lesions and were considered “private” to the primary tumor. We also observed other private mutations, which were present exclusively in the metastatic lesions (Fig. 4d). In fact, each tumor in this patient harbored a unique set of private mutations (mean 26.3, range 5–138) that was not shared with any of the other tumors (Fig. 4d).

Figure 4
Reconstructing the spatio-temporal evolution of UC over time and through different treatments

Using clonally adjusted non-silent mutations from each tumor, we reconstructed the evolutionary tree of this patient’s UC (online methods) (Fig. 4e). This reconstruction revealed a complex branching evolutionary pattern. Early truncal mutations (RYR2, ANKRD62, NCOA3 and LSS) were present in the initial founder clone and shared by all descendent clones. At each clonal divergence node, additional mutations were acquired including mutations in driver genes such as TP53 and TSC1. Surprisingly, at the time of the patient’s initial cancer diagnosis, at least 5 waves of clonal expansion (each represented by a branching node, numbered 1–5) (Fig. 4e) had already occurred from the lowest common ancestor as observed in the mutational analysis of the TURBT tumor. This pattern suggests that branching evolution was a very early event in this tumor’s development. While the untreated TURBT tumor had a high fraction of tumor cells harboring founder mutations (Fig. 4e), it also had the farthest genetic distance from all other tumors. We attributed this long genomic distance to the high number of private mutations in the TURBT primary tumor sample (138 mutations) (Fig. 4d, Supplementary Table 3), many of which involve genes implicated in cellular responses to cisplatin including POLD2 and FOXP11215. After neoadjuvant chemotherapy, these mutations disappeared from the evolutionary record when the cancer cells harboring them were likely eradicated by treatment and thus were not observed subsequently in any of the other tumors (Fig. 4d, 4e).

Interestingly, one of the earliest cancer cell clades had already separated at the first divergence node resulting in a population that metastasized to a pelvic lymph node (Fig. 4e). This lymph node was later dissected and removed at the time of radical cystectomy. Surgical removal of this lymph node possibly eliminated mutations from this particular clade from entering the genetic pool that later contributed to the development of additional distant metastases. On the other hand, by the time cystectomy took place, distant metastatic spread had already occurred originating from a different cancer cell clade that branched at divergence node 5 to give rise to both bladder tumor #2 and all the lymph node and visceral distant metastases that were later collected at the time of the rapid autopsy. This transition from the primary to the metastatic state was marked by the acquisition of a non-silent mutation in tetraspanin8 (TSPAN8), a well-recognized pro-metastatic and angiogenesis-promoting gene1618(Fig. 4d, 4e). This sequence supports a possible role for this mutation as a key driver event underlying the metastatic spread of this patient’s UC. Altogether, our data strongly suggests that both branching evolution and metastatic spread are very early events in the natural history of UC.

Heterogeneity in copy number alterations

To understand how copy number alterations (CNA) evolve throughout the lifetime of UC and during chemotherapy, we conducted a detailed analysis of somatic genomic aberrations. Hierarchical clustering of 44 tumor samples based on allele-specific copy number alterations (online methods) showed two distinct clusters (Fig. 5a). Cluster A was defined by 9p21 (CDKN2A, CDKN2B and MTAP) deletions in the setting of euploid copy number background. Cluster B was characterized by several enriched amplifications including 1q21.1 (SETDB1 and MLLT11) amplifications, (P=0.0002, Fisher’s exact test) and 6p22.3 (E2F3) amplifications, (P=0.001, Fisher’s exact test) (Supplementary Table 4). This cluster was also enriched with TP53 mutations (P=0.0001, Fisher’s exact test). The same CNAs clusters are consistently observed when extending our cohort with TCGA untreated UC10 (Supplementary Fig. 6). We also observed an enrichment of tumors belonging to the TCGA bladder cancer cluster III (‘basal/squamous-like’)10 in our copy number cluster A (Fisher’s exact test P=0.02) (Supplementary Fig. 7). There was no statistically significant differential enrichment in the number of metastatic samples or chemotherapy treated-samples between the two clusters suggesting that these clusters may reflect a relatively stable feature of UC biology that is independent of treatment effects or disease stage. Overall, tumor samples from the same patient tended to cluster in the same group despite the presence of private CNAs. To quantify the degree of intra- and inter-patient heterogeneity, we interrogated CNAs from a panel of more that 30000 genes from the Ensembl catalog19. For each pair of tumor samples, we computed the Hamming Distance (HD) as the ratio between the number of genes that have different discrete copy number and the total number of genes analyzed. We identified a significant difference between intra-patient tumor pairs (median HD=0.20) and inter-patient pairs (median HD=0.53) (P=0.00000003, Wilcoxon test) (Fig. 5b). This limited intra-patient heterogeneity with respect to inter-patient heterogeneity suggests that each patient’s cancer is relatively stable during evolution at the copy number level.

Figure 5
Hierarchical clusters of 44 UC tumor samples by copy-number alterations

We investigated the frequency of combined copy number alterations and mutations constituting the ATM/RB/FANCC signature that was previously associated with chemotherapy response in the neoadjuvant setting20. We identified this signature in 11/15 (73.3%) in our pre-chemotherapy tumors and 11/29 (37.9%) (p=0.05) in post-chemotherapy tumors supporting the hypothesis that clones harboring these molecular alterations are likely to disappear after treatment and are superseded by tumor clones with wild-type ATM/RB/FANCC that eventually progress to metastatic chemotherapy-resistant disease.

Clonal enrichment of mutations in chemotherapy-treated UC

We hypothesized that the evolution of chemotherapy-treated UC would proceed in a direction that ultimately leads to the selection of mutations conferring proliferative or chemotherapy-resistance advantages. Using density analysis of CLONET-adjusted variant allele frequencies between pre-chemotherapy and post-chemotherapy tumors, we observed a significant increase in the number of clonal mutations in the post-chemotherapy samples across the study cohort (P=0.0134, Fisher’s exact test) (Fig. 6a, Supplementary Fig. 8) confirming the association between chemotherapy and increased clonality. To dissect the functional impact of these clonally enriched mutations, we conducted gene set enrichment analysis (GSEA) to identify enriched pathways in post-chemotherapy samples (Fig. 6b). This analysis showed a clonal enrichment of mutations in pathways involved in the trans-membrane transport of small molecules (odds ratio = 1.9, FDR = 0.002) suggesting that mutations in multi-drug resistance genes may play a role in the progression of advanced chemotherapy-treated UC. In addition, GSEA demonstrated a significant enrichment in mutations mediating L1-cell adhesion molecule (L1CAM) (odds ratio = 1.9, FDR = 0.12), and integrin signaling pathways (odds ratio = 2.8, FDR = 0.02). The majority of mutations identified in the L1CAM and integrin signaling pathways (83% and 90% respectively) were missense mutations which can conceivably lead to gain-of-function molecular changes that activate these pathways. These results suggest a prominent role for mutations in L1CAM and integrin signaling pathways in conferring a selective advantage for resistance to chemotherapy in UC. Mutations in these pathways may also provide a potential mechanistic link between metastatic spread, tumor microenvironment, and drug-resistance that cooperate to promote tumor survival2125.

Figure 6
Clonal enrichment of mutations in chemotherapy-treated UC

Mutagenesis mechanisms driving the evolution of UC

To characterize the evolution of mutational signatures in advanced chemotherapy-treated UC, we examined the six possible single-base substitutions (C>A, C>G, C>T, T>A, T>C, and T>G). We identified significant differences in these mutational patterns between chemotherapy-naïve and chemotherapy-treated tumors with a statistically significant enrichment of C>A and C>G changes in the chemotherapy-treated tumors (Fig. 7a).

Figure 7
Mutagenesis in advanced UC is shaped by chemotherapy and APOBECs

To distinguish between potential mutagenic mechanisms responsible for these changes, we matched mutational patterns derived from statistical analysis of nucleotide changes to well-defined signatures of potential mutagens. We observed a significant increase in C>A nucleotide substitutions in tumors treated with cisplatin-based chemotherapy consistent with the specific mutagenesis signature induced in C. elegans genome after cisplatin treatment26,27. Further analysis of context motifs of various base substitutions showed enrichment in the (C> T or G changes at the TCW motifs, W–A or T) (Fig. 7b) which is highly suggestive of APOBEC-induced mutagenesis28,29. To confirm this finding, we compared the signature in our cohort to previously reported Sanger signatures2830 (online methods). We observed 4 distinct signatures in our cohort (Fig. 7c). The first signature was very similar to Sanger signatures 2 and 13, attributed to APOBEC mutagenesis2831. We detected three additional signatures corresponding to previously described mutagenic processes associated with age, smoking and ERCC2 mutations28,30. The low frequency of ERCC2 mutations in our cohort of chemotherapy-treated UC (Supplementary Fig. 1) is consistent with previous reports suggesting that ERCC2 mutations are enriched in responders to cisplatin-based chemotherapy32,33 and likely selected against in tumors that progress through chemotherapy.

Because of the prominence of APOBEC-induced mutagenesis in UC, we focused on understanding how APOBEC-induced mutations evolve during chemotherapy by comparing the frequency of APOBEC-induced mutations in chemotherapy-naïve and chemotherapy-treated tumors. We observed a significant enrichment in the APOBEC3-induced mutagenesis (C > T or G changes at the TCW motifs, W–A or T) in post-chemotherapy tumors (Fig. 7d). To dissect the relative contributions of individual members of APOBEC3 cytosine-deaminases to this enrichment, we examined the preferred motif contexts favored by individual APOBEC enzymes for mutating respective cytosines34,35. APOBEC3A favors YTCA, while APOBEC3B favors RTCA motifs (wherein Y = pyrimidine, R = purine)34. APOBEC3G induces cytosine substitutions in the single stranded DNA overhang strand with a preference for the 5′-CCC-3′ motifs35,36. APOBEC3F preferentially mutates cytosines in the TTC motif (where the underlined C is the mutated nucleotide)37. We detected a significant enrichment in APOBEC3A-induced mutations (P=0.00001, Fisher’s exact test), and a similar enrichment of APOBEC3B mutagenesis (P=0.0395, Fisher’s exact test) in post-chemotherapy tumors. In contrast, APOBEC3G mutagenesis was substantially decreased in post-chemotherapy tumors (Fig. 7d). Furthermore, we observed a corresponding statistically significant increase in the clonality of APOBEC-induced mutations in post-chemotherapy tumors (Fig. 7e). Enrichment analysis of APOBEC-induced mutations highlighted key pathways involved in chemotherapy resistance including the ABC family of proteins (odds ratio=2.7, P=0.038, Fisher’s exact test) and homologous recombination DNA-damage repair (odds ratio=3.8, P=0.033, Fisher’s exact test) (Supplementary Table 5). Our findings suggest that the APOBEC mutational process is not merely a transient event in early UC oncogenesis but that it continues to shape the evolution of advanced UC and may promote clonal expansions of chemotherapy-resistant clones.

Discussion

Advanced chemotherapy-resistant UC remains a formidable clinical challenge with limited therapeutic options38. Whole-exome analysis of matched samples from the same patient from different anatomical sites and at sequential time points offers a unique opportunity to reconstruct the evolutionary dynamics and understand the mutagenic pressures shaping the evolution of primary untreated UC to advanced chemotherapy-treated UC.

Our analyses identified substantial spatial and temporal heterogeneity between tumors separated in time or by anatomical location within the same patient. The majority of mutations in the post-chemotherapy tumors were not shared with primary chemotherapy-naive tumors. Branching evolution was the predominant path from primary chemotherapy-naïve UC to advanced chemotherapy-treated UC. Very early in this path, several clonal waves separate from the original founder clone, many of which metastasize early in the tumor’s lifetime and continue to evolve in parallel with the primary tumor. Our findings shed light on the importance of addressing gaps in the existing knowledge of the clonality of early events in UC oncogenesis including multicentricity and malignant seeding, which could lead to alternative interpretations of the phylogenetic trees. Additionally, our findings suggest that extensive heterogeneity and early branching evolution should be taken into consideration as additional layers of biological complexity that go beyond the traditional two-pathway UC oncogenesis model and potentially eclipse grade and stage classifciations39.

We demonstrate for the first time that chemotherapy-treated UC is significantly clonally enriched in mutations in L1CAM and integrin-signaling pathways. The majority of these mutations were missense mutations that could potentially lead to activation of these pathways but the precise functional impact of these mutations warrants future studies. Our results are consistent with data in pre-clinical models of other tumor types such as cholangiocarcinoma, ovarian carcinoma and pancreatic ductal adenocarcinoma demonstrating that L1CAM plays a key role in cisplatin-resistance and protecting cells from apoptosis4043. L1CAM directly binds to integrin receptors via its RGD-motif in the sixth Ig-domain44,45 and there is considerable cross-talk between the two pathways46,47. Previous studies demonstrated that integrin-signaling plays important roles in overriding chemotherapy-induced cell cycle arrest and apoptosis in small-cell lung cancer cells through activation of the PI3-kinase pathway48,49. Overexpression of Beta 1-integrin in hepatocellular carcinoma cell lines protected them against apoptosis induced by chemotherapeutic agents by activating MAP-kinase signaling50. Stimulating Beta1-integrin with an antibody ligand in leukemia cells prevented procaspase-8-mediated induction of apoptosis in a PI3K-dependent manner51. Collectively, these observations suggest that alterations in L1CAM and integrin signaling pathways potentially play a key role in chemotherapy-resistance in UC and provide a mechanistic intersection between the microenvironment and drug-resistance through cell-adhesion-mediated drug resistance (CAM-DR) phenomenon21,52,53. This phenomenon has been implicated in chemotherapy resistance in several malignancies54,55. L1CAM is potentially targetable with antibodies that have demonstrated efficacy in xenograft animal models of cholangiocarcinomas56,57, ovarian and pancreatic ductal carcinomas58. Focal Adhesion Kinase (FAK) inhibitors, which target integrin signaling, have been shown to profoundly sensitize cancer cells to chemotherapy and novel molecular therapeutics and are currently in early phase clinical trials59,60. Our results suggest that similar therapeutic approaches merit further study in chemotherapy-resistant UC.

We clearly demonstrate an increase in APOBEC signatures in chemotherapy-treated tumors. One possible explanation for this interesting finding is that platinum-based chemotherapy increases the formation of APOBEC mutagenesis-prone single-stranded DNA (ssDNA)61,62. This ssDNA is formed during 5′→3′ resection that occurs at DNA double-strand-breaks during homology-directed repair 63. While allowing for error-free repair of these double-strand-breaks DNA induced by the excision of platin-DNA adducts this process may potentially increase the availability of intermediary ssDNA to APOBEC mutagenesis63. Our results also suggest that APOBEC3A is the main enzyme responsible for mutagenesis in advanced chemotherapy-treated UC. This is in accordance with recent data suggesting that APOBEC3A-mediated mutagenesis is the key mutagenic cytidine deaminase in most tumor types because of its high proficiency in generating DNA breaks64. These findings suggest a potential mechanism by which chemotherapy acts to increase the genomic diversity of chemotherapy-treated tumors that requires future study. Our data demonstrate that the clonal evolution of chemotherapy-treated UC is characterized by a dramatic divergence of the mutational landscape in the face of relative stability at the copy-number level over the lifetime of each tumor. This finding potentially reflects the dominance of APOBEC-induced mutagenesis in UC as a mechanism that preferentially induces single nucleotide changes throughout the tumors’ lifetime and during chemotherapy. An alternative explanation for the marked genomic alterations we observed in chemotherapy-treated samples is that a certain degree of genetic drift occurs over time irrespective of the effect of chemotherapy. However, it is unlikely that genetic drift is the sole mechanism accounting for the genetic heterogeneity we observed because chemotherapy is a potent selective pressure that is expected to alter the evolutionary dynamics affecting the pace and steering the direction of genetic drift. In fact, our results are consistent with evolutionary models suggesting that cancer’s adaptation ensues from the interaction between stochastic processes such as mutation generation and clonal selection, which is a deterministic phenomenon65. Our findings support this evolutionary model by demonstrating a complex and dynamic interplay between mutagenic mechanisms such as APOBEC-induced mutagenesis, and extrinsic selective pressures such as chemotherapy to constantly shape the clonal evolution of UC. As genetic information is passed from parent to progeny clones, each process leaves an evolutionary record of molecular alterations in descendent clones that allows reconstruction of the process. However, it is important to note that this record fully exists only in clones that survive selection. Unfit clones are eliminated from the record and can only be captured by serial sampling of cancer cells throughout the tumor’s lifetime whereas resistant clones are selected to expand and supersede previous clonal waves.

One major strength of our study is that it is the most comprehensive study of the clonal evolution of chemotherapy-resistant urothelial carcinoma. Limitations of our study include a small sample size. Of note, we included muscle invasive tumor samples from patients who were never treated with chemotherapy as controls.

Our findings have several potential clinical implications: First, genomic divergence between untreated and treated clones suggests that clinically actionable molecular targets in metastatic chemotherapy-treated tumors may be missed when relying only on biopsies of untreated primary tumors at the time of diagnosis, and that repeat metastatic biopsies during the course of clinical care would be needed to detect the most recent version of the rapidly changing molecular landscape of a given patient’s UC. Second, further study of the functional role of L1CAM and integrin-signaling in mediating chemotherapy-resistance in UC could lead to a potential strategy for reversing or preventing chemotherapy resistance by targeting these pathways. Third: despite its initial effectiveness in eliminating cancer cells, platinum-based chemotherapy is associated with unintended significant mutagenic editing of the genomic landscape of post-chemotherapy tumors. Our insight into the nature of these edits is crucial towards a complete understanding of the basis of chemotherapy-resistance in advanced UC, which may lay the foundation for the development of rational therapeutic strategies for preventing the emergence or reversing the chemotherapy-resistant state of UC.

In summary, our results demonstrate that advanced chemotherapy-treated UC undergoes extensive and dynamic clonal evolution throughout the lifetime of the tumor with significant genetic editing that continues during and after chemotherapy. Our findings lay the foundation for an evolutionary understanding of advanced chemotherapy-treated UC and present opportunities for advancing cancer precision medicine.

Online Methods

Patient enrollment and tumor procurement

All experimental procedures were carried out in accordance with approved guidelines and were approved by the Institutional Review Board (IRB) at Weill-Cornell Medicine. Patients signed informed consent under (IRB) approved protocol (IRB #1305013903). Clinical information was collected from the chart. Smoking status was collected from self-administered questionnaires. Tumor samples were obtained from patients through surgical resection or core biopsies.

Rapid autopsy procedures

The Englander Institute for Precision Medicine at Weill Cornell Medicine-New York Presbyterian has been established to promote personalized medicine focused on molecular diagnostics and therapeutics. Two patients in our series selected the option to be enrolled in the IRB-approved rapid autopsy program. In addition, patients’ next-of-kin provided written consent before autopsy. The WCM117 and WCM259 rapid autopsies were conducted within 6 hours after death. A systematic autopsy protocol is followed where normal and malignant fresh tissue is collected, allocating samples to be snap frozen or formalin-fixed. The goal is to maximize the amount of tissue collected for research purposes. Once the tissue harvest is complete, the autopsy proceeds in accordance with the protocol established by the WCM Autopsy Service. For our current study, tissue samples from multiple sites were procured from each patient as detailed above. After H&E evaluation and frozen slide annotation, DNA was extracted for WES.

DNA extraction and next generation sequencing

In this study, we used a New York State approved whole exome sequencing assay developed in our CLIA laboratory called, EXaCT-11. After macro-dissection of target lesions, tumor DNA was extracted from FFPE or cored OCT-cryopreserved tumors using the Promega Maxwell 16 MDx (Promega, Madison, WI, USA). Germline DNA was extracted from peripheral blood mononuclear cells using the same method. Pathological review by one of the study pathologists (JMM, BR, MAR) confirmed the diagnosis and determined tumor content. A minimum of 200ng of DNA was used for whole exome sequencing. DNA quality was determined by TapeStation Instrument (Agilent Technologies, Santa Clara, CA) and was confirmed by real-time PCR before sequencing. Sequencing was performed using Illumina HiSeq 2500 (2x100bp). A total of 21,522 genes were analyzed with an average coverage of 85x (range 60–102, Supplementary Table 6) using Agilent HaloPlex Exome (Agilent Technologies, Santa Clara, CA). We developed a targeted sequencing assay of 250 cancer genes (referred to as N250) using hybrid-capture SeqCap EZ Choice Enrichment Kits (Roche Sequencing, Pleasanton, CA) (Supplementary Table 7 and Supplementary Table 8). Sequencing was performed using Illumina HiSeq 2500 (PE 2x75) achieving an average coverage of 400x.

Study sample size definition

Our study includes 72 samples from 32 patients. However, patient WCM117 comprises 12 samples (17% of the total) and to avoid possible statistical biases in the analysis, we utilized WCM117 (12 samples) only in Fig. 4, as a specific case study to better understand how chemotherapy shapes evolution. Finer analysis, as allele specific copy number (Fig. 5a) and SNVs clonality (Fig. 6a) also require an estimate of ploidy and purity. After manual inspection of CLONET outputs, we ended up with 44 samples in 25 patients with reliable ploidy and purity estimates. The exact number of samples and patients used in each figure is reported in Supplementary Notes.

Sequence data processing pipeline

All the study samples data were processed through the computational analysis pipeline of the Institute of Precision Medicine at Weill Cornell/New York Presbyterian Hospital (IPM-Exome-pipeline)1. Raw reads quality has been assessed with FASTQC as described previously1. Multi-sample patient data were tested for genotype distance using SPIA2. Pipeline output includes segment DNA copy number data, somatic copy-number aberrations (SCNAs) (Supplementary Table 9), and putative somatic single nucleotide variants (SNVs) (Supplementary Table 10). Finally, to assess tumor ploidy and purity we applied CLONET3 on segmented data and allelic fraction (AF) of germline heterozygous SNP loci (termed informative SNPs, see Allele specific copy number analysis). Upon visual inspection of CLONET output, 53 of 72 samples data were deemed appropriate for downstream copy number analysis; excluded samples data were not associated with chemotherapy treatment (p=0.4384) or biopsy site (p=1).

We have previously published on how EXaCT-1 was developed and optimized for use with FFPE and Frozen samples- in contrast to many research-grade assays1. To further ensure that our results reflect biological effects rather than technical variability between different sample types, we took special care to account for variations in tumor content for each sample in order to correctly map the clonal evolution of UC. In particular, we did not observe any significant difference (P=0.1, Wilcoxon test) when comparing FFPE and fresh samples purity (Supplementary Fig. 9a). Similarly, we did not detect differences (P=0.137, Wilcoxon test) in the numbers of identified non-silent SNVs between FFPE and fresh samples (Supplementary Fig. 9b).

Allele-specific copy number analysis

Somatic copy number altered regions are defined by the log2 of the ratio between the tumor and normal local coverage normalized by the global tumor and normal coverage ratio (named log2R). CLONET refines copy number data adjusting each log2R to account for both aneuploidy and tumor purity. Combining purified log2R values and AF of informative SNPs, CLONET assigns allele-specific copy number values, represented as a pair (cnA, cnB), to each genomic segment4. Quality filters require at least 10 informative SNPs and a mean coverage of 20 reads to call allele-specific values of a segment. If a segment does not pass filters, adjusted log2R values below −0.4 (above 0.4) were categorized as copy number loss (gain).

Differential copy number analysis between pre- and post-chemotherapy has been performed on a set of 1160 genes selected among putative cancer genes [COSMIC5 and Intogen6] (cancerGenes) and bladder-specific genes (Supplementary Table 11).

Unsupervised clustering analysis was performed on allele-specific copy number of cancer Genes by means of hierarchical clustering. Briefly, each gene gID is represented as a pair of real values corresponding to the allele-specific copy number of the genomic segment comprising gID. Then, the Euclidean distance on allele-specific copy number calls is used as distance; this approach distinguishes between ambiguous cases such as copy number wild type status (allele-specific values (1,1)) and copy number neutral loss (2,0) both corresponding to log2R=0 leading to more informative clusters (Supplementary Table 4). Copy number based analysis identified two clusters that we named WCM_A and WCM_B (Fig. 5, Supplementary Fig. 6), which we compared with the original four TCGA clusters (I, II, III, IV) resulted from integrated analysis of mRNA, miRNA and protein data7 (Supplementary Fig. 7). Given a TCGA cluster X, we tested the null hypothesis that clusters WCM_A and WCM_B contain the same proportion of samples from X using Fisher’s exact test (significance level = 0.05).

Single nucleotide variations analysis

To improve the quality of SNVs calls in targeted exons, we applied an integrated approach. We first ran both MuTect8 and SNVseeqer9 to nominate putative aberrant genomic positions. Then, closely looked at the identified positions by means of ASEQ10 in normal and germline samples executing pileup analysis; for each single nucleotide position identified as putative aberrant, ASEQ returns information about the read count for each of the 4 bases A, C, G, and T. To reduce false positives, we required base quality and read quality above 20. Finally, a genomic position is considered aberrant in a tumor sample, if the read count of the alternative base is 0 in the matched germline and 3 or more in the tumor. This step allows to (i) filter out remaining germline SNPs, that is, positions where the alternative base is present in the control sample; (ii) check for the presence of SNVs with low AFs in patient’s multiple samples data. This step is particularly relevant in this study, because SNVs identification methods are designed to work on a single normal-tumor pair, and they do not consider that samples from the same patient could share the same SNVs because they are not independent samples. Finally, we annotated genomic position with information relevant for cancer analysis with Oncotator11 (v1.8.0.0). We exploited the last Oncotator datasource corpus including annotations about gene/transcript names, functional consequence (e.g. missense or nonsense), the predicted impact on protein function, annotation from cancer specific resources as COSMIC or TCGAscape, and possible published results about the specific mutation (Supplementary Table 10). A full description of the resources used by Oncotator is available in the tool help page. We identified 13 possible functional consequences (Supplementary Fig. 10) described in the Sequence Ontology12. To avoid overestimating divergence among samples from the same patient, we were conservative in defining SNVs that more likely produce a change in the protein, i.e., they affect the phenotype, and we considered non-silent only mutation classified as missense, nonsense, splice_site, nonstop, and start codon. However, for the analysis described in Fig. 7, we included all the identified mutations (Supplementary Fig. 10) as mutational mechanisms also affect mutations with neutral functional effects. To further confirm our findings and to check for possible biases introduced by the definition of silent mutations, we also performed the analysis of Figs. 2, ,3,3, ,4,4, and 6 using both silent and non-silent SNVs obtaining comparable results (Supplementary Fig. 11, 12, 13 and 14).

SNVs enrichment analysis

Gene Set Enrichment Analysis (GSEA) refers to a computational method that identifies set of genes that are statistically enriched for a given observable variable. In this study, we interrogated the REACTOME pathway database to search for pathways showing a significant increase in the number of SNVs. We applied Fisher’s exact test followed by FDR correction by Benjamin-Hochberg (BH) procedure. REACTOME pathways with FDR≤0.2 are reported in Fig. 6b. We also checked if, among significant pathways, there is statistically significant difference between pre- and post-chemotherapy samples. Given a pathway P, Fisher’s exact test determines the probability that the number of SNVs in P is different when considering pre- and post-chemo samples. After FDR correction (BH procedure), we highlighted nodes in Fig.6b with FDR≤0.2.

Phylogenetic analysis

High-quality variants identified in the previous steps were used to re-construct the phylogenetic tree of each patient using the parsimony Ratchet method13 (Fig. 3, Fig. 4e and Supplementary Fig. 5). In this representation, each node models a population of tumor cells. Nodes with no children, named leafs, represent cell populations from a tumor sampling, i.e., a tumor biopsy. Internal nodes model inferred tumor cell populations from observed SNVs. The node named WT represents a hypothetical population of wild type cells (cells with no somatic aberrations). In phylogenetic trees, an edge connects two nodes; the length of an edge is proportional to the number of SNVs. For instance, in Fig. 4e, node 1 corresponds to the least common ancestor inferred from all the available biopsies with the number of mutations proportional to the length of the edge from WT to 1. Node 1 is also connected to the Pelvic LN met and to the inferred cell population 2. A branch represents a time point in the evolution of the tumor where two distinct cell populations emerge; the length of the branches models the number of SNVs that are private to each population. In Fig. 4e, we observed few private mutations in the Pelvic LN met with respect to the number of SNVs shared by all the other samples, as supported by heatmap in Fig. 4d.

Clonality of single nucleotide variants

Original CLONET implementation allows for the computation of the clonality of an SNV with copy number normal genomic segment, i.e. the segment has an allele-specific copy number (1, 1). Here we extended it to allow for SNV clonality estimation independently from the copy number status of the genomic segments in which the SNV lies. Given the tumor purity P, the allele specific copy number (cnA, cnB), and the number of reads nRef and nAlt supporting the reference and the alternative base, respectively, estimating the clonality of a SNV requires to compute expected AF. We observed that AF could assume only a finite number of values given the DNA copy number state. For instance, let’s assume that a locus is aberrant (mutated) in one allele and wild-type (not mutated) in two alleles, i.e. SNV in a copy number aberrant segment (CN=3). In this case, with tumor cellularity equal to 100% and clonal SNV, the VAF would be equal to 1/3. Given the number of allele cnSNV harboring a SNV, the expected VAF is defined as

expVAF=cnSNVP2(1-P)+(cnA+cnB)P

To estimate cnSNV, we followed parsimony approach assuming cnSNV that best explains VAF adjusted by DNA admixture, computed as previously described3. We computed the clonality of SNVs using the distance between the observed and expected VAF as

Cl=(VAF-expVAF)(2(1-P)+(cnA+cnB)P)cnSNVP

Complete proof in Supplementary Notes.

Single nucleotide variants signatures

SNVs are partitioned into six mutation classes (column “Mutation class”, Supplementary Table 10) corresponding to six types of base pair substitution, C>A, C>G, C>T, T>A, T>C, T>G. The null hypothesis that pre- and post-chemotherapy samples are equally likely to harbor SNVs of a specific mutation class is tested with Fisher’s exact test (Fig. 7a). The fingerprint of a SNV includes the two bases immediately 5′ and 3′ to each SNV position (column “Genomic context”, Supplementary Table 10) for a total of 96 possible mutation fingerprints14. Fisher’s exact test adjusted for multiple hypotheses testing with Benjamin-Hochberg procedure returns the likelihood that a mutation fingerprint is enriched in pre- or post-chemotherapy samples (Fig. 7b). As the set of mutation fingerprints of a tumor sample is a proxy for the mutational processes that shape the cancer genome, we studied the mutational signatures of our study samples and compared them the with the Sanger signatures14 applying the same approach recently proposed15. Briefly, the Sanger signatures were obtained from the identification of 30 mutational processes signatures (named Sanger signatures) upon application of the original tool on more than 10,000 samples from 40 distinct human cancer types14. In our dataset, we identify 4 mutational signatures (Fig. 7c); the Sanger signature analysis reveals that APOBEC proteins play a role in the mutational processes shaping UC genomes. We checked for statistically significant differences between pre- and post-chemotherapy of individual members of the APOBEC family using Fisher’s exact test followed by Benjamin-Hochberg FDR correction (Fig. 7d). To test if the clonality of APOBEC induced SNVs is enriched in post-chemotherapy samples, we dichotomized the SNV clonality levels (threshold 0.6) and then we applied Fisher’s exact test (Fig. 7e).

Fluorescence in situ hybridization

Two 4-μm-thick tissue sections from each block were cut for FISH analysis. CDKN2A deletion was determined using FISH probe (BAC clone RP11-149I2) and a reference probe, located at 9p21. At least 100 nuclei were evaluated per sample using a fluorescence microscope (Olympus BX51; Olympus Optical).

Statistical analysis

For statistical tests, two-sided Mann–Whitney–Wilcoxon test (referred to as Wilcoxon test in the main text) was used to check for significant differences between two distributions. The two-sided Fisher’s exact test was applied to determine whether the deviations between the observed and the expected counts were significant. When appropriate p-values were adjusted for multiple hypotheses testing with Benjamin-Hochberg procedure. Boxplot statistics were computed with the function “boxplot” of R programming language. No statistical methods were used to predetermine study sample size.

Supplementary Material

2

3

Acknowledgments

We would like to thank our patients and their families for participation in this study. We would like to thank Barry Sleckman for constructive review of the manuscript. We would also like to acknowledge Douglas Scherr and Christopher Barbieri for contributing samples, our research and clinical pathology fellows Jacqueline Fontugne, Myriam Kossai, Chantal Pauli, Kenneth Hennrick and Kyung Park for their assistance during rapid autopsies, and S.S. Chae and D. Wilkes for technical assistance and constructive comments. We would also like to thank Theresa Y. MacDonald, Jessica Padilla and Tarcisio Fedreizzi for technical assistance. Work was also partially supported by the Translational Research Program at WCMC Pathology and Laboratory Medicine. This work was supported by: Conquer Cancer Foundation John and Elizabeth Leonard Family Foundation Young Investigator Award (B.M.F), NIH/NCATS Grant # KL2TR000458 (B.M.F), Early Detection Research Network US NCI 5U01 CA111275-09 (J.M.M., M.A.R. and F.D.), Damon Runyon Cancer Research Foundation Clinical Investigator Award CI-67-13 (H.B.), H2020 European Research Council ERC-CoG 648670 (F.D.).

Footnotes

URLs.

CLONET, https://bitbucket.org/deid00/clonet/; Oncotator, https://www.broadinstitute.org/oncotator/; Oncotator datasource corpus, http://www.broadinstitute.org/~lichtens/oncobeta/oncotator_v1_ds_Jan262015.tar.gz; REACTOME pathway database, http://www.reactome.org/; Sanger signatures http://cancer.sanger.ac.uk/cosmic/signatures/.

Accession codes. All BAM files and associated sample information are deposited in dbGap phs001087.v1.p1.

Author Contributions. Initiation and design of study: B.M.F, H.B, M.A.R., F.D.; Enrolled subjects and contributed samples and clinical data: H.B, D.M.N., S.T.T., A.M; Statistical and bioinformatics analyses: D.P., O.E., A.S., F.D.; Supervision of research: B.M.F, M.A.R., J.R, F.D; Writing of the first draft of the manuscript: B.M.F, D.P, H.B, M.A.R., F.D.; All authors contributed to the writing and editing of the revised manuscript and approved the manuscript.

Conflict of Interest. The authors have no competing interests. The authors declare no competing financial interests.

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