Single Cell-Derived Primary Rectal Carcinoma Cell Lines Reflect Intratumor Heterogeneity Associated with Treatment Response

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Author Manuscript Published OnlineFirst on April 6, 2020; DOI: 10.1158/1078-0432.CCR-19-1984 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Single cell-derived primary rectal carcinoma cell lines reflect intratumor heterogeneity associated with treatment response

Rüdiger Braun1, Lena Anthuber1*, Daniela Hirsch1*, Darawalee Wangsa1, Justin Lack2,3, Nicole E. McNeil1, Kerstin Heselmeyer-Haddad1, Irianna Torres1, Danny Wangsa1, Markus Brown1, Anthony Tubbs4, Noam Auslander5, E. Michael Gertz5, Philip R. Brauer1, Margaret C. Cam6, Dan L. Sackett7, Jens K. Habermann8, Andre Nussenzweig4, Eytan Ruppin5, Zhongqiu Zhang9, Daniel W. Rosenberg10 and Thomas Ried1

1 Section of Cancer Genomics, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 2 NIAID Collaborative Bioinformatics Resource (NCBR), National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 3 Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD 4 Laboratory of Genome Integrity, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 5 Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 6 Office of Science and Technology Resources, Center for Cancer Research, National Cancer Institute, NIH, Bethesda 7 Eunice Kennedy Shriver National Institute of Child Health & Human Development, NIH, Bethesda, MD 8 Section of Translational Surgical Oncology and Biobanking, Department of Surgery, University of Lübeck and University Medical Center Schleswig-Holstein, Campus Lübeck, Germany 9 Department of Surgery, Waterbury Hospital, University of Connecticut School of Medicine, CT 10 Center for Molecular Oncology, University of Connecticut Health, Farmington, CT

* contributed equally

Running title: SCDCLs reflect treatment response in rectal cancer

Corresponding Author: Dr. Thomas Ried Section of Cancer Genomics National Cancer Institute, Center for Cancer Research, NIH 50 South Drive, Rm. 1408 Bethesda, MD, 20892, USA Phone: +1 240-760-6383 E-mail: [email protected]

Conflict of interest: The authors declare no potential conflicts of interest.

Downloaded from clincancerres.aacrjournals.org on September 26, 2021. © 2020 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 6, 2020; DOI: 10.1158/1078-0432.CCR-19-1984 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

ABSTRACT Purpose: The standard treatment of patients with locally advanced rectal cancer consists of preoperative chemoradiotherapy (CRT) followed by surgery. However, the response of individual tumors to CRT is extremely diverse, presenting a clinical dilemma. This broad variability in treatment response is likely attributable to intratumor heterogeneity (ITH).

Experimental Design: We addressed the impact of ITH on response to CRT by establishing single cell-derived cell lines (SCDCLs) from a treatment-naïve rectal cancer biopsy after xenografting.

Results: Individual SCDCLs derived from the same tumor responded profoundly different to CRT in vitro. Clonal reconstruction of the tumor and derived cell lines based on whole exome sequencing revealed 9 separate clusters with distinct proportions in the SCDCLs. Missense mutations in SV2A and ZWINT were clonal in the resistant SCDCL, but not detected in the sensitive SCDCL. Single cell genetic analysis by multiplex FISH revealed the expansion of a clone with a loss of PIK3CA in the resistant SCDCL. Gene expression profiling by total RNA-sequencing identified the activation of the Wnt-, Akt- and Hedgehog signaling pathways in the resistant SCDCLs. Wnt pathway activation in the resistant SCDCLs was confirmed using a reporter assay.

Conclusions: Our model system of patient-derived SCDCLs provides evidence for the critical role of ITH for treatment response in patients with rectal cancer and shows that distinct genetic aberration profiles are associated with treatment response. We identified specific pathways as the molecular basis of treatment response of individual clones, which could be targeted in resistant subclones of a heterogenous tumor.

STATEMENT OF TRANSLATIONAL RELEVANCE The response of rectal cancers to preoperative chemoradiotherapy is extremely diverse. We hypothesize that treatment response is strongly impacted by intratumor heterogeneity and that distinct tumor clones determine clinical response of the individual patient. Our findings show that single cell-derived cell lines (SCDCLs) from the same tumor exhibit profoundly different treatment responses. Resistant clones were characterized by mutations in SV2A and ZWINT, expansion of a subclone with a PIK3CA loss and activation of Wnt-, Akt- and Hedgehog signaling pathways. We demonstrate the critical role of ITH for treatment response in patients with rectal cancer. In conclusion, SCDCLs provide a unique opportunity to identify distinct genetic and transcriptomic aberration profiles associated with treatment response of individual clones. These can be further explored to identify therapeutic strategies specifically targeting resistant clones and potentially predict response based on single cell profiling.

INTRODUCTION Colorectal cancer (CRC) is the third most commonly diagnosed cancer in males and the second in females worldwide (1). About 30% of these carcinomas are located in the rectum (2). Preoperative chemoradiotherapy (CRT) followed by radical surgical resection represents the current standard of care for locally advanced rectal cancer (UICC II and III) (3). The response of individual tumors to preoperative CRT, however, is extremely diverse. It ranges from complete response without any residual viable tumor cells detectable on histopathological examination of the surgical specimen to the complete absence of local tumor regression. Approximately 25% of patients show primary resistance, i.e., no clinical response (4). This poses a clinical dilemma, because patients with a priori resistant tumors could either be spared exposure to CRT and its severe side effects, treated by alternative, intensified protocols, or selected to undergo immediate surgery. On the other hand, patients for which a complete response could be definitively predicted might not need extensive surgery (5) and could be spared from surgery- associated morbidity and mortality (6). However, identifying reliable response predictors from bulk pretreatment tumor biopsies has proven to be very challenging (7). There has been emerging evidence over the last few years that intratumor heterogeneity (ITH) is a key determinant of tumor biology, treatment response and ultimately patient survival (8). Genomic instability is one of the hallmarks of tumorigenesis and the basis of ITH (9). It results in the continuous acquisition of genetic aberrations in tumor cell populations, the generation of subclones with distinct aberration profiles and the activation of specific signaling pathways; this results in a disparate susceptibility to therapeutic interventions (8). Comprehensive deconvolution of the clonal composition of primary tumors can be achieved by single-cell sequencing (10). In fact, single-cell RNA sequencing of CRCs revealed remarkable transcriptional heterogeneity previously unappreciated by bulk tumor sequencing (11). Recent studies using multi-region DNA sequencing revealed that ITH in CRC is likely to be generated by branching evolutionary processes of tumor clones (12). We hypothesize that the genetic diversity of rectal carcinomas, i.e., ITH, is a major determinant for treatment resistance and resistant subclones. Subclones that acquire resistance under treatment and clonally expand may already be present in treatment-naïve tumors. To explore the impact of ITH and the presence of distinct subclones on treatment response, we generated an in vitro model for clonal analysis of

both, genetic compostion and response. We established single cell-derived cell lines (SCDCLs) from a treatment-naïve biopsy of a rectal carcinoma from a patient with poor clinical response to subsequent CRT.

MATERIALS AND METHODS Tissue collection and xenografting in athymic NCR nu/nu mice Tissue was collected after approval by the ethics committee of the University of Connecticut (IRB Number: IE-10-240-3) and xenografted in athymic NCR nu/nu mice (Charles River Laboratories). For details see Supplementary Data S1.

Cell line establishment, tissue culture and establishment of growth curves After explantation, the xenograft was dissociated and cell lines were established as described in detail in Supplementary Data S2. Cells were checked for mycoplasma contamination by PCR (MycoScope PCR Detection Kit, Genlantis, San Diego, CA). Cell line authentication was verified by short tandem repeat (STR) profiling using the AmpFLSTRTM IdentifilerTM PCR Amplification Kit (Thermo Fisher) according to the manufacturer’s instructions.

Chemoradiotherapy and measurement of cell survival To measure the sensitivity to -radiation, cells growing in log phase were seeded as single cells in 6-well plates and irradiated 24 hours after seeding with a single dose of 4 Gy of -radiation using a 137Cs irradiator (Mark 1 irradiator, JL Shepherd & Associates). For CRT, cells growing in log phase were seeded as single cell suspensions in 6-well plates and 5-FU (Sigma-Aldrich, Steinheim, Germany) was added to a final concentration of 3 M 8 hours after cell seeding. Sixteen hours later, cells were irradiated with a single dose of 2 to 8 Gy of -radiation. Treatment with 5-FU was stopped 8 hours after irradiation by a medium exchange and cell survival was evaluated by a standard colony formation assay and subsequent calculation of the respective survival fraction (SF). Non-irradiated cultures with or without treatment with 5-FU were used for normalization. Cells were grown for 8 days after treatment, fixed with 70% ethanol, dried and stained with Hemalaun (Merck, Darmstadt, Germany). Colonies containing > 50 cells were scored as survivors. Each experiment was performed in triplicate and repeated three times. Statistical analyses were performed with an unpaired Student t-test for irradiation experiments with 4 Gy and two-way ANOVA for dose- response experiments using GraphPad Prism. To measure the sensitivity to 5-FU alone, 1,500 cells growing in log phase were seeded per well cells in 96-well plates and 5-FU (Sigma-Aldrich, Steinheim, Germany) was added 24 hours after seeding to a final concentration in a range of 0 - 150 µM. After

72 hours cell metabolism was measured by CellTiter-Blue (Promega, Madison, WI, USA). Each experiment was performed in triplicates and repeated three times.

Wnt Reporter Assay Cells were transiently transfected with 100 ng of the appropriate dual-luciferase assay plasmid from the TCF/LEF Reporter Assay Kit (Qiagen) using Lipofectamine 3000 (Invitrogen) delivered in Opti-MEM Reduced Serum Medium (Gibco) according to the provided TCF/LEF Reporter Assay protocol (Qiagen). Cells were transfected for 72 hours. Cell lysates were prepared for measurement using the Dual-Luciferase Reporter Assay System (Promega), following the provided instructions. Measurements were taken with a Centro XS3 LB 960 Microplate Luminometer (Berthold Technologies). Fold- change was calculated according to a technical note from Promega.

Histology and immunohistochemistry Immunohistochemistry was performed using the following primary antibodies: monoclonal anti-cytokeratin 20 antibody, clone Ks20.8 (1:200, # M7019, Dako) and monoclonal anti-vimentin antibody, clone SP20 (1:400, # RM-9120-s, Thermo Fisher). For antigen retrieval, sections were incubated in a water bath at 95 °C in either Target Retrieval Solution, pH 9.0 (Tris/EDTA, 1:10; # S2367, Dako) for 20 min (cytokeratin 20) or Target Retrieval Solution, pH 6.1 (citrate, 1:10; S1699, Dako) for 40 min (vimentin). Detection was done using the EnVision Detection System, Peroxidase/DAB, Rabbit/Mouse (# K5007, Dako) according to the manufacturer’s instructions. For Periodic Acid Schiff (PAS) reaction tissue sections were deparaffinized, rehydrated and incubated in periodic acid (1% w/v, freshly prepared; # 10450-60-9, Merck) for 5 minutes followed by incubation in Schiff’s reagent (cat # 3952016, Sigma) for 5 minutes. Counterstaining was done with hematoxylin.

Immunofluorescence Cells were grown on coverslips, washed twice with PBS and fixed with ice cold methanol for 15 mins at -20 °C. Cells were washed with PBS, blocked with 5% goat serum and 0.3% Triton X-100 in PBS for 60 mins at RT and incubated with the primary antibody (1:400, cytokeratin 20, #13063, Cell Signaling) in PBS supplemented with 1% BSA and 0.3% Triton X-100 at 4 °C overnight. Cells were washed with PBS and incubated for 2 hrs with the flourochrome-conjugated secondary antibody (1:500, Alexa Flour 568 goat

anti-rabbit, # A1011, Invitrogen). After washing with PBS, coverslips were mounted with ProLong® Gold antifade reagent with DAPI (Life Technologies).

Spectral karyotyping (SKY) Preparation of metaphase chromosome suspension, slide pretreatment, slide denaturation, hybridization, detection and imaging were conducted as described previously (13).

Multiplex fluorescence in situ hybridization (miFISH) Bacterial artificial chromosome (BAC) contig assembly, DNA extraction, nick translation and precipitation as well as sequential hybridization, detection, imaging and signal counting were performed as reported previously (14). A total of 500 nuclei were analyzed for the parental cell population and 651 for the tumor biopsy. 300 nuclei were analyzed for each SCDCL.

Array based comparative genomic hybridization (array-CGH) DNA was extracted using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) as instructed by the manufacturer. One µg sample DNA and sex-matched reference DNA was labeled using the SureTag DNA Labeling Kit (Agilent, Santa Clara, CA) according to the manufacturer’s instructions (protocol version 7.3). Labeled DNA was subsequently hybridized on SurePrint G3 human 4x 180K CGH arrays (Agilent, Santa Clara, CA) for 24 hours. Slides were processed on an Agilent G2565BA scanner. Agilent Technologies’ Feature Extraction (version 10.7.3.1) was used for data quality control and extraction. Data were visualized and analyzed using Nexus 7.5.

Total RNA sequencing RNA was extracted from each cell line using the RNeasy Mini Kit and on-column DNAse treatment (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. RNA integrity was checked using the 2100 Bioanalyzer (Agilent, Santa Clara, CA). Sequencing libraries targeting mature mRNA and lncRNA were generated using the TruSeq RNA kit (Illumina, San Diego, CA, USA). Samples were sequenced on a single lane of the Illumina HiSeq 2500, with a target depth of > 25 million reads per sample. RNAseq analysis was conducted using the CCBR RNASeq pipeline (https://github.com/CCBR/Pipeliner). For RNAseq gene expression analysis,

contaminating adapter sequences were removed using Trimmomatic v0.36 (15). Trimmed reads were mapped to the GRCh37 human reference genome using STAR v2.5.3 (16) run in 2-pass mode. RSEM v1.3.0 (17) was then used for gene-level expression quantification, and DESeq2 v1.18.1 (18) was used to assess differential expression with false discovery rate (FDR) correction using the Benjamin Hochberg method for multiple tests. Only genes with a counts per million (CPM) of at least 0.5 in a minimum of 3 samples were carried forward for differential expression analysis. Read- and alignment-level quality was assessed using MultiQC v0.9 (http://multiqc.info/) to aggregate QC metrics from FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/), FastQ Screen (https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/), Picard, RSeQC (http://rseqc.sourceforge.net/) and trimmomatic.

Pathway and Gene Set Analyses Pathway enrichment analysis of the top 1000 genes based on p-value for each pairwise differential comparison was performed using ToppGene Suite (http://toppgene.cchmc.org) (19) and the Ingenuity Pathway Analysis tool (IPA; Qiagen). Gene set enrichment analysis was performed using pre-ranked GSEA (20) with default settings and the MsigDB (20) canonical pathway (CP) genesets. For this analysis, genes were ranked using a t-statistic to produce a list sorted from the most significant upregulated gene down to the most significant down-regulated gene. For each pre-ranked GSEA analysis, we used the Enrichment Map (21) plugin in Cytoscape v3.6.1 (22) to collapse pathways based on shared gene membership and functional overlap. All pathway and geneset enrichment p-values were FDR corrected.

Whole exome sequencing DNA was extracted (i) from the tumor biopsy using the DNeasy Blood and Tissue kit (Qiagen, Hilden, Germany), (ii) from the FFPE fixed adjacent tissue as reported elsewhere (23) and (iii) from the cell lines using the Gentra Puregene kit according to the manufacturer’s instructions (Qiagen). cDNA libraries were prepared using the Agilent SureSelectXT Human All Exon V5 plus UTR target enrichment kit. These samples were pooled and sequenced on an Illumina HiSeq2500 with TruSeq V4 chemistry. Alignment and Variant Calling was performed following the CCBR pipeliner workflow (https://github.com/CCBR/Pipeliner). Reads were trimmed using Trimmomatic

v0.36 (15) and mapped to the hs37d5 version of the human reference genome (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembl y_sequence/hs37d5.fa.gz) using BWA-mem v07.15 (24). BAM files were processed using Samtools v1.3 (http://www.htslib.org/) and duplicates were marked using Picard v2.1.1 (http://broadinstitute.github.io/picard/). GATK v3.5.0 (25) was used to perform indel realignment and base recalibration. Read- and alignment-level quality control visualization was performed using MultiQC v1.1 (http://multiqc.info/) to aggregate QC metrics from FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/), FastQ Screen (https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/), Picard, bamtools stats (http://github.org/pezmaster31/bamtools) and Trimmomatic (15). Variant calling was performed in paired mode with MuTect2 (26) and variants were annotated using Oncotator v1.9.1.0 (http://portals.broadinstitute.org/oncotator/).

Copy Number and Cellular Fraction Analysis Copy number was estimated from the exome sequencing data using Sequenza v2.1.2 (27) and FREEC v11.5 (28) jointly. First, Sequenza is run to estimate ploidy and purity, and then these ploidy and purity estimates are used as fixed values in the FREEC analysis to call absolute copy number of all genomic segments. Ploidy and purity estimates from Sequenza were then used as priors to estimate cellular fraction and subclone clustering for each SNP and INDEL using Pyclone v0.13.1 (29). Clusters containing <3 mutations were excluded from further analysis and discussion because they had wide 95% confidence intervals and/or contained no non-silent mutations.

RESULTS Establishment of a treatment-naïve rectal cancer patient-derived xenograft We collected treatment-naïve tumor biopsies and adjacent normal tissue prior to CRT from a 59-year old patient with an adenocarcinoma of the lower rectum (uT3,uN1,M0); the patient responded partially to preoperative CRT (regression grade 2 according to Becker (30)) but rapidly developed liver metastases and local recurrence. The clinical history is summarized in Figure 1A and Supplementary Data S3. To establish an in vitro model that recapitulates ITH in the primary tumor, we aimed to propagate single cell- derived cell lines (SCDCLs). The experimental work-flow is summarized in Figure 1B. First, we generated a patient-derived xenograft (PDX). After subcutaneous implantation of a tumor biopsy into an athymic nude mouse, substantial tumor growth was observed after 25 days. The tumor was explanted 16 weeks after initial engraftment at a size of 544 mm3 and examined by H&E staining, immunohistochemistry and Periodic Acid Schiff (PAS) reaction (Figure 1 C-E). The primary tumor presented as a moderately differentiated colorectal adenocarcinoma with some gland formation and focal PAS- positive mucin production, while the PDX showed a solid growth pattern without gland formation. By immunohistochemistry, both the primary tumor and PDX were positive for CK20 as a characteristic marker for colorectal epithelial cells and negative for the stromal cell marker vimentin. The PDX was subsequently re-implanted into a second athymic nude mouse to enable further growth.

Conversion of the PDX to a cell line and establishment of single cell-derived cell lines (SCDCLs) The secondary PDX was explanted after having reached 608 mm3 at 7 weeks and converted to a cell line employing the Rho-associated kinase (ROCK) inhibitor Y-27632, as described for the propagation of epithelial cells by Schlegel and colleagues (31). Contaminating murine cells were depleted. The patient-derived rectal cancer cell line, termed WHrec1, showed a typical epithelial morphology (Figure 1F) and, in line with the PDX, expressed moderate to high levels of CK20 (Figure 1G). The short tandem repeat (STR) profile is provided in Supplementary Data S4. We analyzed the karyotype of the parental cell population by spectral karyotyping (SKY) and array based comparative genomic hybridization (aCGH) which revealed a near-triploid karyotype with numerous chromosomal imbalances typical for CRC (Figure

1H). aCGH confirmed the loss of chromosomes 1p, 3p, 6, 8, 10, 15, 17p, 18, 21 and Y, and gains of 1q, 19p, 20 and X (Supplementary Figure S1A). We subsequently performed single cell cloning by limiting dilution and established 23 SCDCLs from the parental cell population. The flow-chart in Supplementary Figure S1B summarizes the analytical procedures. The SCDCLs revealed distinct growth patterns and kinetics (Supplementary Figure S2A&B). After single cell cloning, the time until each SCDCL reached confluency in a T25 tissue culture flask ranged from 33 to 64 days.

SCDCLs respond differently to CRT We now tested the central hypothesis that individual clones of the primary tumor respond differently to CRT. To accomplish this, we selected 16 SCDCLs that represent the spectrum of disparate growth kinetics after single cell cloning (Supplementary Figure S2B) and the parental cell population and exposed them each to 4 Gy of -radiation. The survival fraction (SF) was measured by colony formation assays. We observed a profoundly different sensitivity to radiation across the various SCDCLs (Figure 2A). The most sensitive SCDCL (2F4) had a SF4 of 0.07, whereas the most resistant SCDCL (1G10) had a SF4 of 0.18 (p=0.0012). The parental cell population was between these two outermost SF4 values (0.12). Next, we established dose-response curves of the parental cell population as well as the most resistant and most sensitive SCDCL, which revealed significant differences of the SF between SCDCL1 G10 and 2F4 over a dose range of 2 to 8 Gy (p<0.0001) (Figure 2B). The addition of 3 M 5-FU revealed comparable results (p=0.0003) (Figure 2C). Treatment with 5-FU alone did not reveal significant differences between the SCDCLs 1G10 (radio-resistant; IC50 7.4 µM, 95% CI 6.9 – 8.1) and 2F4 (radio-sensitive; IC50 7.9 µM, 95% CI 7.2 – 8.3) in the IC50 value (Figure 2D). To determine whether the proliferation rates of the SCDCLs influence response to radiation, we measured their respective growth rates. No differences in growth rates were found between SCDCLs 1G10 and 2F4 and the parental cell population (Figure 2E). There was also no statistically significant correlation between growth kinetics after single cell cloning and the SF4 (Supplementary Figure S2C). We conclude that the observed differences in response are therefore a reflection of the idiosyncrasies of the individual SCDCLs that are independent of their intrinsic proliferation rates. Assuming that DNA repair capacity might be involved in the profoundly different sensitivity of the

SCDCLs to irradiation, we used the alkaline Comet Assay to quantify the level of DNA damage repair. Even though we observed a trend towards slower DNA damage repair in the sensitive SCDCL (2F4) compared to the resistant SCDCL (1G10), the differences did not reach statistical significance (Supplementary Figure S2D). Hence, we used comprehensive genome and transcriptome profiling to elucidate the molecular basis of response of SCDCLs to CRT.

Whole exome sequencing of SCDCLs reveals resistance-associated subclones To determine whether the distinct treatment phenotypes of the SCDCLs are related to genetic heterogeneity, we performed whole exome sequencing (WES) (Supplementary Data S5). WES showed TP53 (p.G245S) and KRAS (p.G12S) missense mutations and a 32 bp deletion in CTNNB1 (p.VSHWQQQSYLDS22del) in the tumor biopsy, the parental cell population and both SCDCLs at a cellular fraction of 1.0. No mutations were detected in BRAF and APC. The large in-frame CTNNB1 deletion occurred in a hotspot region (Supplementary Figure S2E), that has been described in various tumor entities (32). To infer clonal population structure from WES data, we used PyClone (29). PyClone analysis identified a total of 12 clusters characterized by distinct sets of mutations; three of the clusters had fewer than three mutations and were excluded from further analysis (Figure 3A and Supplementary Data S6).The remaining 9 clusters were present at different frequencies across the samples (Figure 3A). Cluster 0 was detected at high cellular fractions across all samples, and contained the clonal TP53, KRAS and CTNNB1 mutations. Of particular interest is cluster 7, which is characterized by mutations in ZWINT and SV2A. This cluster was detected at very low cellular fractions in the tumor biopsy and the sensitive SCDCL (2% and 0.5%, respectively), but present at intermediate cellular fraction (46%) in the parental cell population and an elevated cellular fraction (63%) in the resistant SCDCL. This suggests a role of this cluster and its associated mutations in treatment resistance. The complete results for the PyClone analysis, including all mutations and inferred clusters, are summarized in Supplementary Data S6. To identify mutations associated with treatment response, we next looked for differences in mutation prevalence in the resistant versus sensitive SCDCLs by comparing cellular fractions of mutations in the resistant versus sensitive SCDCLs along with the tumor biopsy and parental cell population. Mutations in 16 genes were found at

a higher cellular fraction in the resistant SCDCL (1G10), but at reduced cellular fraction in the sensitive SCDCL (2F4), relative to the parental cell population, respectively. Again, this analysis revealed that mutations in SV2A (p.E675A) and ZWINT (p.K74E), which define the resistant enriched PyClone cluster 7, were detected only in the parental cell population and the resistant SCDCL. In fact, the cellular fractions of both mutated genes increased in the resistant SCDCL (Figure 3B). Mutations in ARID3B and PDS5A, both part of PyClone cluster 8, showed an inverse pattern with elevated cellular fraction in the sensitive SCDCL (2F4) and were absent from the resistant SCDCL (1G10) (Figure 3B). Mutations in typical CRC driver genes such as KRAS, TP53 and CTNNB1 were clonal and not associated with the respective response level of the SCDCLs. In contrast to gene mutations, we did not detect any structural variants from the WES analysis that were enriched in either SCDCL. Copy number variants (CNV) determined from WES data based on b-allele frequencies showed very similar gain and loss patterns in the tumor biopsy and the derived cell lines (Supplementary Figure S3). However, the rather low purity of the tumor biopsy (< 40% purity) makes high-resolution CNV calls challenging. We identified 67 genes that differed in the absolute copy number between the resistant and sensitive SCDCL, and also showed significant differential expression (FDR<0.1) in the same fold-change direction as the copy number difference (Supplementary Data S7).

Single cell genetic analysis reveals subclonal composition of the tumor biopsy, the derived parental cell population and SCDCLs To decipher patterns of chromosomal heterogeneity on a single cell level, we performed single-cell genomic analyses using multiplex interphase FISH (miFISH) of 14 gene loci within each nucleus (14). We designed a probe panel targeting the CRC genes COX2 (1q31.1), PIK3CA (3q26.32), APC (5q22.2), CLIC1 (6p21.32), EGFR (7p11.2), MYC (8q24.21), CCND1 (11q13.3), CDX2 (13q12.2), CDH1 (16q22.1), HER2 (7q12), TP53 (17p13.1), SMAD7 (18q21.1) SMAD4 (18q21.2) and ZNF217 (20q13.2). The aneuploid cell population of the tumor biopsy consisted of a major clone in 39.8% of cells, and three minor clones (7.9%, 2.8% and 2.8%) that showed copy number alterations (CNAs) of one gene from the major clone. The remaining cells (46.9%) were very heterogeneous. The chromosomal instability index (CII) was 43.3. The gain and loss patterns and the trajectory of the evolution of cell populations are shown in Figure

4A.

The analysis of the derived parental cell population revealed a major clone in 54.6% of the population, which differed in one gene from the major clone of the biopsy. PIK3CA had no CNA in comparison to the major clone detected in the tumor biopsy which had a loss of PIK3CA. We additionally detected seven minor clones ranging from 2.4% to 5.6% of the population that differed in one gene from the major population, suggesting a single chromosome segregation error. The remaining cells (20.2%) were dominated by a non-clonal distribution of genomic imbalances (Figure 4B). The CII was 19.8. The rectal cancer specific distribution of genomic imbalances of the tumor biopsy was maintained in the derived cell population and in the SCDCLs. In addition, we applied a dedicated algorithm developed for the reconstruction of phylogenetic trees from miFISH data (33). The FISHtrees reveal that the resistant SCDCL 1G10, the sensitive SCDCL 2F4 as well as the parental cell population have a related evolutionary trajectory,

yet develop clones specific to each of the cell lines (Supplementary Figure S4). Single cell genetic analysis of the most resistant SCDCL (1G10) and the most sensitive SCDCL (2F4) (Figure 4C&D) revealed that the major clone of the parental cell population (54.6%) was present in an equal percentage (55.2%) in the resistant SCDCL, but in 81% of the cells of the sensitive SCDCL. Of note, the major clone of the tumor biopsy (39.8%) was observed in 4.6% of the cells in the parental cell population, expanded to 24.8% of the cells in the resistant SCDCL, but was absent in the sensitive SCDCL. This clone differed from the major clone by a loss of one copy of PIK3CA. This change could not detected by WES because a ~20% shift in cellularity for a single copy change is beyond the resolution of WES (Supplementary Figure S5). The CII was lower in the sensitive SCDCL (12.58) compared to the resistant SCDCL (19.39). Our results are consistent with the interpretation that the evolution of SCDCLs, despite ongoing chromosomal instability, is dominated by the selection of rectal cancer specific genomic imbalances.

RNA sequencing reveals activation of Wnt signaling in the resistant SCDCL We subsequently analyzed gene expression profiles in the parental cell population, the three most resistant SCDCLs (1G10, 2D2 and 2G4) and in the three most sensitive SCDCLs (2F4, 1D2 and 2F1) by total RNA sequencing (Supplementary Data S8). Global gene expression profiles separated the parental cell population and the resistant and the sensitive SCDCLs, displayed as a principle component analysis (PCA) in Figure 5A. The differences in gene expression profiles between the three resistant SCDCLs and the

pooled three most sensitive SCDCLs compared to the parental population are visualized as Volcano plots in Supplementary Figure S6. To determine the functional space of these differentially expressed genes, we conducted pathway enrichment analyses using Ingenuity Pathway Analysis (IPA) and ToppFun (ToppGene tool suite; (19)). We found a significant enrichment for genes involved in Wnt-mediated -catenin signaling, PI3K/AKT signaling, and Hedgehog signaling in the resistant SCDCLs (Figure 5B). Activation z- score analysis implemented in IPA was used to identify pathways with differential activity between the resistant and the sensitive SCDCLs, relative to the parental cell population (e.g., candidate pathways will have increased activation in the resistant SCDCLs relative to the parental cell population, but reduced activation in the sensitive SCDCLs relative to the parental, or vice versa). This analysis confirmed both Wnt-mediated -catenin signaling and PI3K/AKT signaling as having increased inferred activity in all three resistant SCDCLs relative to the parental cell population, but reduced inferred activity in the sensitive SCDCLs relative to the parental cell population. TCF7L2 was expressed highest in the most resistant SCDCL (1G10), and mRNA levels decrease with increasing sensitivity to radiation (Figure 5C). The activation of the Wnt-mediated -catenin signaling was confirmed independently using a Wnt reporter assay. As shown in Figure 5D, SCDCL 1G10 has increased activity when compared to both the sensitive SCDCL 2F4 and the parental population (p = 0.05). As an alternative to standard pathway enrichment, we also conducted pre-ranked GSEA analysis using the MsigDB v6.2 canonical pathway collection. Genes were ranked using a t-statistic to produce a list sorted from the most significant up-regulated gene down to the most significant down-regulated gene for each paired comparison (each resistant SCDCL and the sensitive SCDCLs vs the parental cell population). The Enrichment Map (21) plugin in Cytoscape v3.6.1 (22) was used to collapse pathways based on shared gene membership and functional overlap. For an initial comparison, we sought to identify any canonical pathway significantly up- or down-regulated relative to the parental cell population. Clustered enrichment maps for the resistant SCDCL and sensitive SCDCLs are shown in Supplementary Figure S7 illustrating the considerable differences in the significantly enriched pathways for each of these comparisons (red nodes are upregulated relative to the parental cell population, blue nodes are downregulated). For the most resistant SCDCL (1G10; Supplementary Figure S7A), both Wnt-mediated -catenin signaling and Hedgehog signaling were upregulated, while in

the sensitive SCDCLs, there was clear downregulation of cell cycle-related pathways, metabolic pathways, and DNA repair (Supplemetary Figure S7B). Seven pathways exhibited normalized enrichment scores that were significant in at least one GSEA comparison, and correlated with the resistance phenotype (Figure 5E). The most strongly correlated pathway is the Integrin-linked signaling pathway from the Pathway Interaction Database. This pathway plays a prominent role in cancer progression, and is implicated in all of the pathways described above (Wnt, Hedgehog, and PI3K/AKT signaling) that are more active in the resistant vs. the sensitive SCDCL. Integrin signaling is also involved in mitotic spindle organization (34), similarly to ZWINT, which is mutated in the resistant SCDCL. In summary, we established SCDCLs from a treatment naïve rectal cancer biopsy. We found profound differences between individual SCDCLs in response to CRT. Genome and transcriptome profiling identified distinct gene mutations and signaling pathways associated with response to treatment.

DISCUSSION Recent studies using multi-region sequencing of CRCs revealed a considerable degree of ITH (12,35). These studies also suggest that ITH results in the activation of tumor promoting signaling pathways in different regions of the tumors; arguably, that would require tailoring therapeutic interventions taking the heterogeneity of individual tumors into consideration. In fact, studies in breast cancer provided compelling evidence that the selective pressure imposed by specific therapies can result in the expansion of resistant subclones, or subclones that utilize different pathways for survival (36). These studies underline the importance of ITH in therapy response. However, the functional role of individual clones, originating from single cells, in therapeutic resistance cannot be elucidated by such approaches. Clevers and coworkers recently reported the characterization of CRC organoids derived from single cells which showed substantial differences in IC50 valus for chemotherapeutic agents (37). We hypothesized that the establishment of SCDCLs from a primary rectal tumor would provide suitable in vitro models to decipher the role of individual clones vis a vis response to CRT. Analyzing individual SCDCLs could allow identifying gene mutations, chromosomal aberrations and genomic imbalances as well as different gene expression profiles in resistant compared to sensitive clones that are responsible for different therapeutic outcomes. Targeting specific pathways that mediate resistance in distinct subclones of a tumor could increase the proportion of patients who respond favorably to preoperative CRT; this would improve overall prognosis. We established SCDCLs from a treatment naïve biopsy of a patient with locally advanced rectal cancer, reflecting the genetic composition of the unperturbed tumor. Efficient in vitro expansion of human primary rectal tissue remains technically challenging; therefore, murine xenografting is commonly used to propagate primary colorectal tumor tissue ex vivo (38). PDX mouse models show close resemblance to the aberration profile of primary tumors (39) and maintain their intratumoral clonal heterogeneity (40,41). We acknowledge that our approach provides only a snapshot of the ITH in the primary tumor. We did not attempt, nor would it be feasible, to reconstruct the complete clonal architecture of the primary tumor by SCDCLs. We realize that by establishing cell lines through PDX and subsequent single cell cloning, a selective pressure is applied. The approach of establishing cell lines from a tumor biopsy, however, provides a model that allows to analyze heterogeneity from a functional point of view.

The model of SCDCLs allowed us to test the hypothesis that the genetic make- up of individual clones determines the degree of response to CRT of such distinct clones within a rectal carcinoma. Exposure of the SCDCLs to CRT in vitro revealed a gradient of different responses of individual SCDCLs. Of note, the SF of the parental cell population was intermediate, which likely indicates that the parental population is comprised of a mixture of clones with varying degrees of sensitivity to CRT. We conclude that our approach of establishing SCDCLs from a patient sample generates genuine in vitro models of response, and thus allows integration of function, phenotype and genotype that could not be addressed by (multi-region) sequencing of tumor biopsies alone. Using WES and miFISH, we could show that the clonal composition of the tumor biopsy was reflected in the derived parental cell population. Notably, we found that individual subclones present in the parental cell population were propagated in the SCDCLs, which is consistent with our previous results on single cell cloning of CRC cell lines (42). We could recently show that the clonal composition of the parental population was in most cases re-established after single cell cloning maintaining specific genomic imbalances despite ongoing chromosomal instability. The clonal dynamics observed in our SCDCLs in this study is reminiscent of this phenomenon, suggesting that karyotype evolution is driven by the necessity to arrive at and maintain a specific plateau of chromosomal copy numbers as the drivers of carcinogenesis. To further decipher genetic changes associated with treatment resistance, we performed WES-based clonal reconstruction. This revealed distinct patterns for the resistant versus sensitive SCDCLs with respect to subclones. Most striking was the enrichment of mutations in ZWINT and SV2A in the resistanct SCDCL (cellular fraction of 49% and 46%) compared to both parental cell population (26% and 24%), tumor biopsy (both 2%) and sensitive SCDCL (both 0.9%), respectively. ZWINT is a kinetochore-associated protein that interacts with the kinetochore checkpoint protein ZW10 and is required for recruitment of ZW10 to the kinetochore (43). Silencing of ZWINT results in aberrant chromosome segregation (44). In addition, ZWINT expression has been implicated in restistance to multiple anticancer drugs (45). The potential functional role of missense mutation p.K74E remains elusive. However, the coincidence of this mutation and a higher CII in the parental cell population and resistant SCDCL, along with it’s enrichment in the resistant SCDCL might indicate functional relevance. Furthermore, gene-set enrichment analysis identified multiple pathways involved in

mitotic spindle formation as being upregulated in the resistant SCDCL and downregulated in the sensitive SCDCL, both relative to the parental cell population. Integrin-linked kinase signaling was the pathway most strongly correlated with the resistance phenotype; integrin signaling is required for accurate chromosome segregation (46). Synaptic vesicle protein 2A (SV2A) is a transmembrane protein that is critical for regulatetd exocytosis and neurotransmission (47). It is expressed in, e.g., gastrointestinal stroma tumors (48). The potential role of SV2A with respect to sensitivity to CRT requires further exploration. Mainly three signaling pathways were differentially regulated between the parental cell population and the resistant and sensitive SCDCLs: (i) Wnt-, (ii) PI3K/AKT- and (iii) Hedgehog-signaling. Aberrant Wnt signaling is a defining feature of CRCs (49). The Wnt signaling pathway was activated in the resistant SCDCLs as exemplified by pathway enrichment analysis and a subsequent Wnt reporter assay. The Wnt/-catenin signaling pathway is tightly associated with resistance to CRT (3,49). Enhanced Wnt/- catenin signaling in the resistant SCDCL cannot be explained by the detected mutation in CTNNB1, which is clonal and not enriched in either, the resistant or the sensitive SCDCL. However, we found that TCF7L2, one of the key transcription factors of the Wnt/ß-catenin pathway, was overexpressed in resistant SCDCLs (50). We previously found overexpression of this transcription factor in patients that did not respond to CRT (7) and CRC cells were sensitized to CRT upon silencing of TCF7L2 (51). In the tumor from our patient, activation of Wnt/-catenin signaling is most likely independent of the - catenin mutation status. Besides, a heterogenous intratumoral distribution of -catenin accumulated in the nucleus, a surrogate for Wnt/-catenin pathway activation, has been described in CRCs (52,53). The second up-regulated pathway in the more resistant SCDCLs was the PI3K/AKT signaling pathway, which is involved in cell metabolism, growth, proliferation and survival (54). Folkvord and colleagues reported that rectal cancer patients with a poor response to CRT had significantly elevated PI3K/AKT signaling measured by kinase activity compared to well-responding patients (55). Activation of PI3K/AKT signaling has been described as a major contributor to mediation of radioresistance in various cancers such as, e.g., prostate and lung cancer (56,57). Hedgehog signaling was the third pathway that was found to be up-regulated in the more resistant SCDCLs. Hedghog pathway activation has been described to be associated with poor outcome after (chemo)-radiation in cervical cancer (58).

In conclusion, we show that patient-derived SCDCLs are representative models for deciphering the functional role of individual clones of a heterogenous rectal tumor in response to CRT. This approach allows to identify specific gene mutations, chromosomal aberrations, gene expression profiles and signaling pathways in resistant compared to sensitive SCDCLs that likely determine different therapeutic outcomes of heterogenous primary tumors. Future emphasis should be directed to the reconstruction of the clonal architecture of rectal cancers which is the basis for specific targeting of pathways that mediate resistance in distinct subclones. This could increase the proportion of patients who respond favorably to preoperative CRT, improving overall prognosis.

ACKNOWLEDGMENTS R. Braun and D. Hirsch were supported by Mildred Scheel postdoctoral scholarships of the German Cancer Aid (Deutsche Krebshilfe). L. Anthuber received a stipend of the University of Lübeck, Germany. D.W. Rosenberg was funded by NCI grant 5R01CA159976. We thank Jadwiga Jerman (Waterbury Hospital, Waterbury, CT) for clinical data and sample collection and Casey Dagnall (Cancer Genomics Research Laboratory, NCI, Gaithersburg, MD) for STR-profiling. We are grateful to Xiaolin Wu and Bao Tran (Frederick National Laboratory for Cancer Research, NCI, Frederick, MD) for sequencing. We thank Dr. Dali Zong and Dr. Alison McBride for help with irradiation of cells. The authors are grateful to Dr. Reinhard Ebner for critical comments, and Buddy Chen for editorial assistance. This research was supported in part by the Intramural Research Program of the National Institutes of Health, NCI.

AUTHOR CONTRIBUTIONS Conception and design: R. Braun, M.C. Cam, D.W. Rosenberg, T. Ried Development of methodology: R. Braun, D. Hirsch, D. Wangsa, K. Heselmeyer- Haddad, M.C. Cam, A. Nussenzweig, T. Ried Acquisition of data: R. Braun, L. Anthuber, D. Hirsch, D. Wangsa, N.E. McNeil, I. Torres, D. Wangsa, M. Brown, A. Tubbs, P.R. Brauer Analysis and interpretation of data: R. Braun, L. Anthuber, D. Hirsch, J. Lack, K. Heselmeyer-Haddad, N. Auslander, E.M. Gertz, D.L. Sackett, E. Ruppin, T. Ried Writing, review and/or revision of the manuscript: R. Braun, L. Anthuber, D. Hirsch, J. Lack, K. Heselmeyer-Haddad, D.L. Sackett, D.W. Rosenberg, T. Ried Administrative, technical, or material support: R. Braun, J. Lack, J.K. Habermann, Z. Zhang Study supervision: R. Braun, T. Ried

REFERENCES

1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 2018;68(6):394- 424 doi 10.3322/caac.21492. 2. Siegel R, Desantis C, Jemal A. Colorectal cancer statistics, 2014. CA: a cancer journal for clinicians 2014;64(2):104-17 doi 10.3322/caac.21220. 3. Cunningham D, Atkin W, Lenz HJ, Lynch HT, Minsky B, Nordlinger B, et al. Colorectal cancer. Lancet 2010;375(9719):1030-47 doi 10.1016/S0140- 6736(10)60353-4. 4. Rodel C, Martus P, Papadoupolos T, Fuzesi L, Klimpfinger M, Fietkau R, et al. Prognostic significance of tumor regression after preoperative chemoradiotherapy for rectal cancer. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2005;23(34):8688-96 doi 10.1200/JCO.2005.02.1329. 5. O'Neill BD, Brown G, Heald RJ, Cunningham D, Tait DM. Non-operative treatment after neoadjuvant chemoradiotherapy for rectal cancer. The Lancet Oncology 2007;8(7):625-33 doi 10.1016/S1470-2045(07)70202-4. 6. Emmertsen KJ, Laurberg S, Rectal Cancer Function Study G. Impact of bowel dysfunction on quality of life after sphincter-preserving resection for rectal cancer. The British journal of surgery 2013;100(10):1377-87 doi 10.1002/bjs.9223. 7. Ghadimi BM, Grade M, Difilippantonio MJ, Varma S, Simon R, Montagna C, et al. Effectiveness of gene expression profiling for response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2005;23(9):1826-38 doi 10.1200/JCO.2005.00.406. 8. McGranahan N, Swanton C. Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. Cell 2017;168(4):613-28 doi 10.1016/j.cell.2017.01.018. 9. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144(5):646-74 doi S0092-8674(11)00127-9 [pii] 10.1016/j.cell.2011.02.013.

10. Tirosh I, Venteicher AS, Hebert C, Escalante LE, Patel AP, Yizhak K, et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 2016;539(7628):309-13 doi 10.1038/nature20123. 11. Li H, Courtois ET, Sengupta D, Tan Y, Chen KH, Goh JJL, et al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nature Genetics 2017;49(5):708-18 doi 10.1038/ng.3818. 12. Uchi R, Takahashi Y, Niida A, Shimamura T, Hirata H, Sugimachi K, et al. Integrated Multiregional Analysis Proposing a New Model of Colorectal Cancer Evolution. PLoS Genet 2016;12(2):e1005778 doi 10.1371/journal.pgen.1005778. 13. Padilla-Nash HM, Barenboim-Stapleton L, Difilippantonio MJ, Ried T. Spectral karyotyping analysis of human and mouse chromosomes. Nature protocols 2006;1(6):3129-42 doi nprot.2006.358 [pii]10.1038/nprot.2006.358. 14. Fiedler D, Heselmeyer-Haddad K, Hirsch D, Hernandez LS, Torres I, Wangsa D, et al. Single-cell genetic analysis of clonal dynamics in colorectal adenomas indicates CDX2 gain as a predictor of recurrence. International journal of cancer Journal international du cancer 2019;144(7):1561-73 doi 10.1002/ijc.31869. 15. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30(15):2114-20 doi 10.1093/bioinformatics/btu170. 16. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013;29(1):15-21 doi 10.1093/bioinformatics/bts635. 17. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC bioinformatics 2011;12:323 doi 10.1186/1471-2105-12-323. 18. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology 2014;15(12):550 doi 10.1186/s13059-014-0550-8. 19. Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic acids research 2009;37(Web Server issue):W305-11 doi 10.1093/nar/gkp427. 20. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting

genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 2005;102(43):15545-50 doi 10.1073/pnas.0506580102. 21. Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network- based method for gene-set enrichment visualization and interpretation. PloS one 2010;5(11):e13984 doi 10.1371/journal.pone.0013984. 22. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research 2003;13(11):2498-504 doi 10.1101/gr.1239303. 23. Hirsch D, Wangsa D, Zhu YJ, Hu Y, Edelman DC, Meltzer PS, et al. Dynamics of Genome Alterations in Crohn's Disease-Associated Colorectal Carcinogenesis. Clinical cancer research : an official journal of the American Association for Cancer Research 2018;24(20):4997-5011 doi 10.1158/1078-0432.CCR-18-0630. 24. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-ME. ARXIV 2013;1303. 25. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next- generation DNA sequencing data. Genome Research 2010;20(9):1297-303 doi 10.1101/gr.107524.110. 26. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature biotechnology 2013;31(3):213-9 doi 10.1038/nbt.2514. 27. Favero F, Joshi T, Marquard AM, Birkbak NJ, Krzystanek M, Li Q, et al. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Annals of oncology : official journal of the European Society for Medical Oncology / ESMO 2015;26(1):64-70 doi 10.1093/annonc/mdu479. 28. Boeva V, Popova T, Bleakley K, Chiche P, Cappo J, Schleiermacher G, et al. Control-FREEC: a tool for assessing copy number and allelic content using next- generation sequencing data. Bioinformatics 2012;28(3):423-5 doi 10.1093/bioinformatics/btr670. 29. Roth A, Khattra J, Yap D, Wan A, Laks E, Biele J, et al. PyClone: statistical inference of clonal population structure in cancer. Nature methods 2014;11(4):396-8 doi 10.1038/nmeth.2883.

30. Becker K, Mueller JD, Schulmacher C, Ott K, Fink U, Busch R, et al. Histomorphology and grading of regression in gastric carcinoma treated with neoadjuvant chemotherapy. Cancer 2003;98(7):1521-30 doi 10.1002/cncr.11660. 31. Liu X, Krawczyk E, Suprynowicz FA, Palechor-Ceron N, Yuan H, Dakic A, et al. Conditional reprogramming and long-term expansion of normal and tumor cells from human biospecimens. Nature protocols 2017;12(2):439-51 doi 10.1038/nprot.2016.174. 32. Chang MT, Asthana S, Gao SP, Lee BH, Chapman JS, Kandoth C, et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nature Biotechnology 2016;34(2):155-63 doi 10.1038/nbt.3391. 33. Chowdhury SA, Gertz EM, Wangsa D, Heselmeyer-Haddad K, Ried T, Schaffer AA, et al. Inferring models of multiscale copy number evolution for single-tumor phylogenetics. Bioinformatics 2015;31(12):i258-67 doi 10.1093/bioinformatics/btv233. 34. Fielding AB, Dobreva I, McDonald PC, Foster LJ, Dedhar S. Integrin-linked kinase localizes to the centrosome and regulates mitotic spindle organization. J Cell Biol 2008;180(4):681-9 doi 10.1083/jcb.200710074. 35. Sottoriva A, Kang H, Ma Z, Graham TA, Salomon MP, Zhao J, et al. A Big Bang model of human colorectal tumor growth. Nature Genetics 2015;47(3):209-16 doi 10.1038/ng.3214. 36. Brady SW, McQuerry JA, Qiao Y, Piccolo SR, Shrestha G, Jenkins DF, et al. Combating subclonal evolution of resistant cancer phenotypes. Nature communications 2017;8(1):1231 doi 10.1038/s41467-017-01174-3. 37. Roerink SF, Sasaki N, Lee-Six H, Young MD, Alexandrov LB, Behjati S, et al. Intra-tumour diversification in colorectal cancer at the single-cell level. Nature 2018;556(7702):457-62 doi 10.1038/s41586-018-0024-3. 38. Ashley N, Jones M, Ouaret D, Wilding J, Bodmer WF. Rapidly derived colorectal cancer cultures recapitulate parental cancer characteristics and enable personalized therapeutic assays. The Journal of pathology 2014;234(1):34-45 doi 10.1002/path.4371. 39. Fichtner I, Rolff J, Soong R, Hoffmann J, Hammer S, Sommer A, et al. Establishment of patient-derived non-small cell lung cancer xenografts as models for the identification of predictive biomarkers. Clinical cancer research : an official

journal of the American Association for Cancer Research 2008;14(20):6456-68 doi 10.1158/1078-0432.CCR-08-0138. 40. Guenot D, Guerin E, Aguillon-Romain S, Pencreach E, Schneider A, Neuville A, et al. Primary tumour genetic alterations and intra-tumoral heterogeneity are maintained in xenografts of human colon cancers showing chromosome instability. The Journal of pathology 2006;208(5):643-52 doi 10.1002/path.1936. 41. Eirew P, Steif A, Khattra J, Ha G, Yap D, Farahani H, et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 2015;518(7539):422-6 doi 10.1038/nature13952. 42. Wangsa D, Braun R, Schiefer M, Gertz EM, Bronder D, Quintanilla I, et al. The Evolution of Single Cell-derived Colorectal Cancer Cell Lines is Dominated by the Continued Selection of Tumor Specific Genomic Imbalances, Despite Random Chromosomal Instability. Carcinogenesis 2018 doi 10.1093/carcin/bgy068. 43. Wang H, Hu X, Ding X, Dou Z, Yang Z, Shaw AW, et al. Human Zwint-1 specifies localization of Zeste White 10 to kinetochores and is essential for mitotic checkpoint signaling. The Journal of biological chemistry 2004;279(52):54590-8 doi 10.1074/jbc.M407588200. 44. Woo Seo D, Yeop You S, Chung WJ, Cho DH, Kim JS, Su Oh J. Zwint-1 is required for spindle assembly checkpoint function and kinetochore-microtubule attachment during oocyte meiosis. Sci Rep 2015;5:15431 doi 10.1038/srep15431. 45. Kang HC, Kim IJ, Park JH, Shin Y, Ku JL, Jung MS, et al. Identification of genes with differential expression in acquired drug-resistant gastric cancer cells using high-density oligonucleotide microarrays. Clinical cancer research : an official journal of the American Association for Cancer Research 2004;10(1 Pt 1):272- 84. 46. Knouse KA, Lopez KE, Bachofner M, Amon A. Chromosome Segregation Fidelity in Epithelia Requires Tissue Architecture. Cell 2018;175(1):200-11 e13 doi 10.1016/j.cell.2018.07.042. 47. Vogl C, Tanifuji S, Danis B, Daniels V, Foerch P, Wolff C, et al. Synaptic vesicle glycoprotein 2A modulates vesicular release and calcium channel function at peripheral sympathetic synapses. Eur J Neurosci 2015;41(4):398-409 doi 10.1111/ejn.12799.

48. Bumming P, Nilsson O, Ahlman H, Welbencer A, Andersson MK, Sjolund K, et al. Gastrointestinal stromal tumors regularly express synaptic vesicle proteins: evidence of a neuroendocrine phenotype. Endocrine-related cancer 2007;14(3):853-63 doi 10.1677/ERC-06-0014. 49. Markowitz SD, Bertagnolli MM. Molecular origins of cancer: Molecular basis of colorectal cancer. The New England Journal of Medicine 2009;361(25):2449-60 doi 10.1056/NEJMra0804588. 50. Kendziorra E, Ahlborn K, Spitzner M, Rave-Frank M, Emons G, Gaedcke J, et al. Silencing of the Wnt transcription factor TCF4 sensitizes colorectal cancer cells to (chemo-) radiotherapy. Carcinogenesis 2011;32(12):1824-31 doi 10.1093/carcin/bgr222. 51. Emons G, Spitzner M, Reineke S, Moller J, Auslander N, Kramer F, et al. Chemoradiotherapy Resistance in Colorectal Cancer Cells is Mediated by Wnt/beta-catenin Signaling. Molecular cancer research : MCR 2017;15(11):1481- 90 doi 10.1158/1541-7786.MCR-17-0205. 52. Brabletz T, Jung A, Hermann K, Gunther K, Hohenberger W, Kirchner T. Nuclear overexpression of the oncoprotein beta-catenin in colorectal cancer is localized predominantly at the invasion front. Pathology, research and practice 1998;194(10):701-4. 53. Horst D, Reu S, Kriegl L, Engel J, Kirchner T, Jung A. The intratumoral distribution of nuclear beta-catenin is a prognostic marker in colon cancer. Cancer 2009;115(10):2063-70 doi 10.1002/cncr.24254. 54. Manning BD, Cantley LC. AKT/PKB signaling: navigating downstream. Cell 2007;129(7):1261-74 doi 10.1016/j.cell.2007.06.009. 55. Folkvord S, Flatmark K, Dueland S, de Wijn R, Groholt KK, Hole KH, et al. Prediction of response to preoperative chemoradiotherapy in rectal cancer by multiplex kinase activity profiling. International journal of radiation oncology, biology, physics 2010;78(2):555-62 doi 10.1016/j.ijrobp.2010.04.036. 56. Chang L, Graham PH, Ni J, Hao J, Bucci J, Cozzi PJ, et al. Targeting PI3K/Akt/mTOR signaling pathway in the treatment of prostate cancer radioresistance. Crit Rev Oncol Hematol 2015;96(3):507-17 doi 10.1016/j.critrevonc.2015.07.005. 57. Blackhall FH, Pintilie M, Michael M, Leighl N, Feld R, Tsao MS, et al. Expression and prognostic significance of kit, protein kinase B, and mitogen-activated protein

kinase in patients with small cell lung cancer. Clinical cancer research : an official journal of the American Association for Cancer Research 2003;9(6):2241-7. 58. Chaudary N, Pintilie M, Hedley D, Fyles AW, Milosevic M, Clarke B, et al. Hedgehog pathway signaling in cervical carcinoma and outcome after chemoradiation. Cancer 2012;118(12):3105-15 doi 10.1002/cncr.26635.

FIGURE LEGENDS

FIGURE 1: Tumor biopsy, clinical history and cell line establishment. (A) Clinical history of the rectal cancer study patient. (B) Experimental workflow for establishment of SCDCLs. (C) H&E staining of representative sections of the primary tumor biopsy (left panel) and the derived xenograft (right panel). (D) CK20 and vimentin immunohistochemical staining of representative sections of the primary tumor biopsy (left panel) and the derived xenograft (right panel). (E) PAS staining of representative sections of the primary tumor biopsy (left panel) and the derived xenograft (right panel). (F) Morphology of the derived parental cell population. (G) CK20 immunofluorescence of the established parental cell population. (H) SKY analysis which is representative of the cytogenetic aberration profile of the parental population. Note the relative copy number loss of chromosome 18 and chromosome arm 17p and copy number increases of chromosome 20.

FIGURE 2: Treatment response of SCDCLs. (A) Survival fraction after irradiation with 4 Gy (SF4) of the parental cell population (passages 16 and 19) and 16 individual SCDCLs. (B) Dose response of the parental cell population, the most resistant (1G10) and the most sensitive (2F4) SCDCL to irradiation alone. ****, p<0.0001, two-way ANOVA (C) Dose response of the parental cell population, the most resistant (1G10) and the most sensitive (2F4) SCDCL to irradiation in combination with 3 µM 5-FU. ***, p=0.0003, two-way ANOVA (D) Dose response of the parental cell population, the most resistant (1G10) and the most sensitive (2F4) SCDCL to 5-FU alone. (E) Growth curves of the parental cell line, SCDCL 1G10 and SCDCL 2F4. Each experiment was performed in triplicates and repeated three times.

FIGURE 3: PyClone analysis of the tumor biopsy and derived cell lines. (A) Violin plots showing the cellular prevalence of the identified PyClone clusters and selected associated mutations for the tumor biopsy, parental cell population and most sensitive (2F4) and resistant (1G10) SCDCL. Cluster 0 is present at high frequency in all samples, while cluster 7 is absent from the sensitive SCDCL 2F4 and highly enriched in the resistant SCDCL 1G10 compared to both parental cell population and tumor biopsy. (B) Cellular fractions of missense and nonsense mutations in the tumor biopsy, the parental

cell population and SCDCLs 1G10 and 2F4 that are most prevalent in the resistant SCDCL 1G10 or sensitive SCDCL 2F4, respectively.

FIGURE 4: miFISH analysis of the tumor biopsy and the derived cell lines. (Left) Color charts of clonal imbalances based on relative gains and losses of the primary tumor biopsy (A), parental cell population (B), resistant SCDCL 1G10 (C) and sensitive SCDCL 2F4 (D). The color scheme is as follows: green, gains; red, losses; blue, unchanged relative to ploidy. The ‘Locus’ column depicts the target gene for each probe. Each vertical line discerns specific signal patterns of the clones and indicates their prevalence in the population. (Right) Patterns of clonal evolution of the primary tumor biopsy (A), parental cell population (B), resistant SCDCL 1G10 (C) and sensitive SCDCL 2F4 (D). We show the percentage of dominant imbalance clones and their derivations with the most diploid clones left and progressive aneuploidy to the right. Clones derived by a single gain or loss change are connected by arrows.

FIGURE 5: Total RNA sequencing of the parental cell population and SCDCLs. (A) PCA analysis of total RNA seq data showing a distinct clustering of the parental cell population (black), the three most resistant SCDCLs 1G10, 2D2 and 2G4 (each in triplicates; red) and the three most sensitive SCDCLs (2F4, 1D2 and 2F1; green). (B) Heatmap of signaling pathway enrichment in the three most resistant SCDCLs 1G10, 2D2 and 2G4 compared to the three most sensitive SCDCLs (2F4, 1D2 and 2F1). (C) RNA expression of TCF7L2 in the three most resistant SCDCLs 1G10, 2D2 and 2G4 and the three most sensitive SCDCLs (2F4, 1D2 and 2F1). (D) Wnt reporter assay in the resistant (1G10) and sensitive (2F4) SCDCL and the parental cell population. (E) GSEA normalized enrichment scores (NESs) that correlate with resistance. Seven pathways exhibited NESs that were significant in at least one GSEA comparison, and correlated with the resistance phenotype.

Single cell-derived primary rectal carcinoma cell lines reflect intratumor heterogeneity associated with treatment response

Rüdiger Braun, Lena Anthuber, Daniela Hirsch, et al.

Clin Cancer Res Published OnlineFirst April 6, 2020.

Updated version Access the most recent version of this article at: doi:10.1158/1078-0432.CCR-19-1984

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Author Author manuscripts have been peer reviewed and accepted for publication but have not yet been Manuscript edited.

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