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  • American Association for Cancer Research (AACR)  (15)
  • 1
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 4277-4277
    Abstract: Background: Tumour proliferation rate is a key phenotypic feature of cancer, with higher rates linked to poorer clinical outcomes. Thus far, proliferation rates have been measured using pathological or experimental techniques on bulk tumour samples. However, while tumours are heterogeneous compositions of distinct clones with varying levels of fitness, measuring the proliferation of individual clones has not been possible to date. We hypothesise that enabling the identification of the most proliferative clones would reveal genomic hallmarks of aggressive clones, or the prediction of their potential phenotype, e.g., metastatic potential. Methods: We have developed SPRINTER (Single-cell Proliferation Rate Inference in Neoplasms Through Evolutionary Routes), a novel computational method to measure proliferation rates in individual tumour clones using single-cell whole-genome DNA sequencing. To assess the accuracy and power of SPRINTER, we have also developed an experimental approach to DNA sequence & gt;18,000 single cells, accurately separated in different DNA-replication phases. We have sequenced and applied SPRINTER to & gt;10,000 non-small cell lung cancer cells from longitudinal and metastatic tumour samples within the TRACERx study and PEACE autopsy programme. We have further analysed published data from & gt;10,000 breast cancer cells. Results: We demonstrate that SPRINTER can accurately identify subpopulations of cells with different proliferation rates using relatively small numbers of cells, in contrast to previous preliminary approaches. While our estimates are concordant with previous bulk experimental studies (5-40%), we importantly have identified clonal heterogeneity in proliferation rates. Using bulk analysis, we have identified patterns of dissemination of tumour clones in non-small cell lung cancer. Integrating this with single-cell data, our results indicate that more widely disseminating tumour clones have higher proliferation rates, suggesting a link between proliferation and dissemination potential. We additionally find that clones are more proliferative in the metastatic versus the primary setting. Furthermore, we have identified high proliferation clones that may have a selective advantage in a breast tumour, and have inferred that they likely arose recently in cancer evolution. Conclusions: We have developed a novel method that enables accurate identification of proliferation rates of individual tumour clones using single-cell DNA sequencing data, allowing the investigation of genomic hallmarks in highly proliferative clones that might lead to higher fitness. Citation Format: Olivia Lucas, Sophie Ward, Rija Zaidi, Mark Hill, Emilia Lim, Haoran Zhai, Abigail Bunkum, Sonya Hessey, Michelle Dietzen, Andrew Rowan, Cristina Naceur-Lombardelli, Nnenna Kanu, Mariam Jamal-Hanjani, Charles Swanton, Simone Zaccaria. Measuring proliferation rates of distinct tumour clones using single-cell DNA sequencing. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4277.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 2
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 3792-3792
    Abstract: Background: Primary lung cancer is the leading cause of cancer-related mortality, with metastatic disease being responsible for the majority of deaths. To gain insight into the lethal process of metastasis, we report on the longitudinal evolutionary analysis of the TRACERx 421 paired primary-metastasis cohort. Methods: 712 tumor samples, of which 485 were primary tumor and 227 were metastatic samples, from 129 metastatic non-small cell lung cancer (NSCLC) patients with detailed clinical annotation were collected from 18 UK hospital sites and whole exome sequenced. Mutations and copy number events were integrated to resolve the evolutionary history of each tumor. Results: We observe that metastases generally diverge relatively late in molecular time, after the majority of mutations in the primary tumor have accumulated, with a substantial minority (33%) diverging prior to the last clonal sweep in the primary tumor. For this minority of cases, divergence is estimated to have occurred at tumor volumes below the limit of computed tomography (CT) detection. Our extensively sampled cohort reveals that sampling bias may result in erroneous inference of metastatic trajectories. 79% of metastases diverging after the last clonal sweep in the primary tumor would be misclassified as diverging prior to the last clonal sweep if only a single region of the primary tumor is considered. Patterns of dissemination range from monoclonal, involving a single metastatic clone (68% - where metastatic potential is likely acquired once in the life-history of the tumor), to polyclonal and polyphyletic (17%), where metastatic potential may have arisen multiple times during lung cancer development, or at a single time point early in the development of the tumor. We find that thoracic lymph node disease resected at surgery was responsible for less than 20% of subsequent disease dissemination, suggesting that lymph nodes likely represent a hallmark of metastatic potential rather than a gateway to further metastases. Furthermore, we observe that clones which seed the metastases are generally dominant within the primary, reflecting positive selection and acquisition of subclonal mutations in specific cancer genes (e.g. RB1, PIK3CA). In squamous cell carcinomas, we find that non-metastatic primary tumors show no significant evidence of positive subclonal selection. We find that 35% of metastases harbor driver mutations not identified in the primary tumor and identify somatic copy number alterations that are enriched in metastases (e.g. CCND1 gains in lung adenocarcinoma). Conclusions: These data highlight the potential to apply evolutionary measures to primary tumors to predict metastatic risk, the limitations to current screening approaches particularly for early tumor divergence, and the importance of future precision adjuvant therapies to target disseminated micro-metastatic clones. Citation Format: Ariana Huebner, Maise Al Bakir, Carlos Martinez Ruiz, Kristiana Grigoriadis, Thomas B. Watkins, Oriol Pich, David A. Moore, Selvaraju Veeriah, Sophia Ward, Joanne Laycock, Diana Johnson, Andrew Rowan, Maryam Razaq, Mita Akther, Cristina Naceur-Lombardelli, Sonya Hessey, Michelle Dietzen, Emma Colliver, Alexander M. Frankell, Emilia Lim, Takahiro Karasaki, Christopher Abbosh, Crispin T. Hiley, Mark S. Hill, Daniel Cook, Gareth Wilson, TRACERx consortium, Allan Hackshaw, Nicolai J. Birkbak, Simone Zaccaria, Mariam Jamal-Hanjani, Charles Swanton, Nicholas McGranahan. TRACERx: Mapping the evolution of metastases in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3792.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 3
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    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 12_Supplement ( 2022-06-15), p. 1699-1699
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 1699-1699
    Abstract: Introduction: The analysis of somatic mutations has identified different mutational signatures that represent different exogenous or endogenous mutagenic processes active during tumor development. Those biological processes represent a main driver of diversity, but little is known about the dynamics of their activity over time. Based on the multi-region sequencing data of the TRACERx study we developed SignaTree, a tool to deconvolve mutational signatures for each subclone within a phylogenetic tree of a tumor to quantify the dynamics of mutational processes over time. Methods: The subclonal architecture of 396 TRACERx tumors with phylogenetic tree output were used to develop SignaTree and investigate mutational signature dynamics over time. Only mutational signatures that were identified in a de novo signature analysis of the TRACERx 421 patients were included for deconstruction. A randomized sampling technique informed by both ancestral and descendent mutations was used to mitigate the uncertainty of signature deconvolution within small subclones. A bootstrapping method was applied to reconstruct the distribution of the mutational signature activity within the ancestral clone to statistically test the change in signature activity for significance between ancestral and descendent clones. Simulations of mutational signatures within different subclones along a phylogenetic tree were used for validation purposes. Results: SignaTree was identified to better predict mutational signature activity in 83% of simulations in comparison to applying a deconstruction tool (deconstructSigs) without a randomized sampling step for small subclones. 77% of the TRACERx tumors showed at least one significant change in signature activity between ancestral and descendent clones, with the majority of tumors exhibiting multiple shifts in mutational processes during their evolutionary history. The most common signature dynamic observed involved a decrease in smoking signature (SBS4) and an increase in APOBEC (SBS2 + SBS13) related activity. 8% of tumors presented evidence for dynamic fluctuations in APOBEC activity over time (episodic APOBEC) which has previously only been described in vitro. Most of them exhibited episodic APOBEC activity only in a small subset of tumor regions highlighting local heterogeneity in addition to timing diversity of the endogenous APOBEC process. Conclusion: SignaTree allows the deconstruction of mutational signatures within subclones and improves the ability to detect signatures in clones with a low number of mutations. Using this approach, it is possible to identify different evolutionary patterns of mutational signature activity during tumor development including episodic APOBEC activity. Citation Format: Michelle Dietzen, Oriol Pich, TRACERx Consortium, Simone Zaccaria, Charles Swanton, Nicholas McGranahan. SignaTree: A tool to identify evolutionary trajectories of the activity of mutational processes in TRACERx [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1699.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 4
    In: Cancer Research, American Association for Cancer Research (AACR), ( 2023-10-09), p. OF1-OF17
    Abstract: Multiple large-scale genomic profiling efforts have been undertaken in osteosarcoma to define the genomic drivers of tumorigenesis, therapeutic response, and disease recurrence. The spatial and temporal intratumor heterogeneity could also play a role in promoting tumor growth and treatment resistance. We conducted longitudinal whole-genome sequencing of 37 tumor samples from 8 patients with relapsed or refractory osteosarcoma. Each patient had at least one sample from a primary site and a metastatic or relapse site. Subclonal copy-number alterations were identified in all patients except one. In 5 patients, subclones from the primary tumor emerged and dominated at subsequent relapses. MYC gain/amplification was enriched in the treatment-resistant clones in 6 of 7 patients with multiple clones. Amplifications in other potential driver genes, such as CCNE1, RAD21, VEGFA, and IGF1R, were also observed in the resistant copy-number clones. A chromosomal duplication timing analysis revealed that complex genomic rearrangements typically occurred prior to diagnosis, supporting a macroevolutionary model of evolution, where a large number of genomic aberrations are acquired over a short period of time followed by clonal selection, as opposed to ongoing evolution. A mutational signature analysis of recurrent tumors revealed that homologous repair deficiency (HRD)-related SBS3 increases at each time point in patients with recurrent disease, suggesting that HRD continues to be an active mutagenic process after diagnosis. Overall, by examining the clonal relationships between temporally and spatially separated samples from patients with relapsed/refractory osteosarcoma, this study sheds light on the intratumor heterogeneity and potential drivers of treatment resistance in this disease. Significance: The chemoresistant population in recurrent osteosarcoma is subclonal at diagnosis, emerges at the time of primary resection due to selective pressure from neoadjuvant chemotherapy, and is characterized by unique oncogenic amplifications.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 5
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    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 12_Supplement ( 2022-06-15), p. 801-801
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 801-801
    Abstract: Osteosarcoma is an aggressive disease that exhibits almost universal p53 inactivation and a high level of genomic complexity. The chaotic genome of most osteosarcomas has led many to assume that this complexity must accumulate under conditions of significant genomic/chromosomal instability. However, such conclusions have been drawn primarily from single-timepoint bulk-tumor analyses. More recent studies have questioned this assumption of chromosomal instability, hypothesizing that genomic complexity could arise from an early catastrophic event followed by faithful propagation of a highly complex, malignancy-promoting genome. To determine whether osteosarcoma tumors show evidence of instability, we performed single-cell whole-genome sequencing of & gt;14,000 osteosarcoma cells obtained from 15 distinct lesions resected from 8 individual patients. Using the CHISEL algorithm, we inferred allele- and haplotype-specific copy numbers from this sequencing data. To evaluate how the SCNA profiles changed during the evolutionary pressures associated with metastatic dissemination and therapy, we compared the single-cell SCNA profiles of paired samples collected from both primary sites and metastases and from pre-treatment and relapsed specimens. We likewise compared profiles of patient tumors propagated orthotopically in mouse tibias to those grown within the lung to evaluate for tissue-dependent emergence of rare subclones. Despite extensive structural variations that give rise to highly complex patterns of SCNA, the cells within each tumor showed remarkably little heterogeneity. We found similar evidence of chromosomal stability when we reconstructed phylogenetic trees to identify the evolutionary relationships of cells collected from metastatic lesions or at relapse. Despite being evolutionarily distant, nearly all variants could be assigned to the trunk of the phylogenetic tree, with only modest changes occurring in the branches. This result suggests that nearly all SCNAs were acquired in an early event, followed by selection of advantageous patterns with subsequent preservation of that particular SCNA pattern. Analysis of bulk whole-genome sequencing from serially collected patient samples supports the preservation of SCNA profiles over time. This work demonstrates the power of combining single-cell DNA sequencing with an allele- and haplotype-specific CNV inference algorithm. Our approach clarifies longstanding questions about the genetics of osteosarcoma initiation and progression, calling into question previous assumptions of genomic instability inferred from single-timepoint bulk sequencing data. These results suggest that an isolated, early catastrophic event, rather than sustained genomic instability, gives rise to the complex genome that characterizes osteosarcoma tumors. Citation Format: Sanjana Rajan, Simone Zaccaria, Matthew Cannon, Maren Cam, Amy Gross, Benjamin Raphael, Ryan D. Roberts. Surprisingly conserved copy numbers from cell to cell within structurally complex tumors challenge the unstable genome hypothesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 801.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 6
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 3620-3620
    Abstract: Introduction: Faithful DNA replication is a key requirement for normal cell division which initiates from replication origins distributed throughout the genome. Not all origins initiate replication at the same time, generating a conserved temporal order of DNA replication (the replication timing program) which is cell and tissue type specific. The extent to which replication timing alters during malignant transformation and the resultant impact on the genomic landscape in cancer remains unclear. Here, we explored the landscape of altered replication timing in lung and breast cancer and the interplay between altered replication timing and genomic cancer evolution. Methods: To characterize replication timing profiles, the Repli-Seq protocol was applied to 4 lung adenocarcinoma, 3 lung squamous cell carcinoma, and 4 breast cancer cell lines, as well as the cell-of-origin of each cancer type. Replication timing was measured as the log2-transformed ratio between early and late replicated reads in 50kb windows. To identify altered replication timing regions the log2-ratio of each cancer cell line was compared to its matched normal. Publicly available WGS and RNA-seq data from patients were used to investigate the relationship between replication timing and genomic alterations. Results: We identified a systematic shift in replication timing from normal to cancer cell lines in 6%-17% of the genome, half of which were classified as late-to-early and half early-to-late replicated. We identified an increase in mutation load in early-to-late replicated regions in comparison to non-altered early replicated regions, whereas the opposite was true for late-to-early replicated regions. We observed a more prevalent shift in the mutational landscape in breast compared to lung cancer, suggesting that most mutations in breast cancer were accumulated after the alteration in replication timing. In addition, we identified known and novel mutational processes active in differently replicated parts of the genome. Furthermore, we observed an enrichment of APOBEC3-associated mutation clusters (omikli events) in early and late-to-early replicated regions. Recurrently gained copy number segments showed an enrichment in not-altered early replicated regions. Also, late-to-early replicated genes exhibited an increase in expression in cancer relative to normal, whereas early-to-late replicated genes exhibited a decrease in expression. Cancer type-specific cancer genes were enriched in late-to-early replicated regions that became up-regulated in cancer, whereas essential genes tended not to change their replication timing during malignant transformation. Conclusion: We identified significant alterations in the replication timing program during malignant transformation which influence the genomic and transcriptomic landscape during tumor evolution. Citation Format: Michelle Dietzen, Haoran Zhai, Olivia Lucas, Sophia Ward, Yanping Guo, Wei Ting-Lu, Oriol Pich, Simone Zaccaria, Charles Swanton, Nicholas McGranahan, Nnennaya Kanu. Investigating the role of altered replication timing during tumor evolution in lung and breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3620.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 7
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 1394-1394
    Abstract: Background: The adaptive immune system plays an important role in tumor evolution. A key source of cytotoxic T cell response in cancer is neoantigens, cancer cell specific mutations that result in mutant peptides that elicit a T cell mediated immune response. However, a mutation can only engender a neoantigen if the associated mutant peptide is presented to T cells by HLA molecules, and, as such, transcriptional repression or loss of the HLA genes can have important implications for immune evasion. Methods: We elucidate allele specific genomic and transcriptomic disruption to class I and II HLA genes. In whole exome sequencing (WES) data, our new tool (LOHHLA2.0) assesses loss of heterozygosity (LOH) status and somatic mutations. In RNA sequencing (RNAseq) data, LOHHLA2.0 quantifies allele specific expression and transcriptional repression referencing matched tumor adjacent normal samples. We applied LOHHLA2.0 to the TRACERx421 dataset, including 1554 WES tumor regions from 421 patients and 941 RNAseq regions from 357 tumor patients, 96 of which also had RNAseq from tumor adjacent normal samples. Results: We find that our method is more accurate than existing tools (RSEM) at calling gene level expression, e.g. in HLA-DPB1, RSEM under-calls HLA expression by a factor of two. 36% of TRACERx421 primary tumors harbored HLA LOH of at least one class I HLA gene, validating our previous findings in the TRACERx100 cohort. Strikingly, we found that 74% (71/96) of primary tumors with matched tumor adjacent normal tissue exhibited transcriptional repression of one or more class I HLA alleles, and 81% (78/96) exhibited class II allele transcriptional repression. Class I HLA transcriptional repression was more likely to occur subclonally than LOH. In a subset of tumors, we observed convergence upon disruption of the same allele through alternative mechanisms; with genomic loss in one tumor region and transcriptional repression in another region of the same tumor. Across the tumor regions, we found a concordance between HLA expression and immune infiltrate levels, with immune hot tumors exhibiting higher HLA class I expression. Conclusions: In this study, we find significant disruption to class I and II HLA expression adding to the diversity of immune evasion processes evident in early stage treatment naive NSCLC. Citation Format: Clare Puttick, Oriol Pich, Michelle Leung, Carlos Martinez-Ruiz, Robert Bentham, Rachel Rosenthal, Sonya Hessey, James R. Black, Emilia L. Lim, Katey Enfield, Emma Colliver, Krijn Dijkstra, Crispin T. Hiley, Takahiro Karasaki, Ariana Huebner, Maise Al Bakir, Thomas B. Watkins, Alexander M. Frankell, Simone Zaccaria, Mariam Jamal-Hanjani, Nicholas McGranahan, Charles Swanton. Pervasive allele specific transcriptional repression of the class I and II HLA genes in TRACERx non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1394.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 8
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 28, No. 18_Supplement ( 2022-09-15), p. B022-B022
    Abstract: Objective: Multiple large-scale tumor genomic profiling efforts have been undertaken in osteosarcoma, however little is known about the spatial and temporal intratumor heterogeneity and how it may drive treatment resistance. Methods: We performed 30-80x whole genome sequencing (WGS) of 37 tumor samples from 8 patients with relapsed or refractory osteosarcoma. Each patient had at least one sample from a primary site and one sample from a metastatic or relapse site. A set of high confidence single nucleotide variants (SNV), copy number alterations (CNA), structural variations (SV) were called for each sample using our pediatric expanded genomics pipeline and an evolutionary analysis was performed using a custom pipeline of computational tools. Results: Of the 8 patients in our cohort, 4 had localized disease at diagnosis (OSCE4, OSCE5, OSCE6, OSCE9) and 4 had metastatic disease at diagnosis (OSCE1, OSCE2, OSCE3, OSCE10). There were 17 samples from primary sites, 7 were pretreatment biopsies, 10 from on therapy primary resections. 20 samples came from metastatic sites, 15 of which were from lung metastases. Driver gene SNV’s were identified in 5 of 8 patients, including TP53 (OSCE1), ATRX (OSCE3, OSCE10), RB1 (OSCE4), and CDKN2A (OSCE9). There were no new driver SNV’s that emerged post-therapy in any patient. HATCHet, an algorithm that infers clone-specific copy number alterations, identified subclonal CNAs in all but one patient (OSCE2). In the 7 patients with subclonal CNAs, 6 had two copy number clones identified, and 1 patient (OSCE10) had three copy number clones identified. In 5 patients (OSCE1, OSCE4, OSCE5, OSCE6, OSCE10) there is a copy number clone that is subclonal in the primary tumor which emerges and dominates at subsequent relapses. The resistant clone in each of these cases had either MYC gain/amplification. Amplifications in CCNE1 (OSCE1), RAD21 (OSCE4, OSCE5, OSCE10), VEGFA (OSCE1, OSCE9), IGF1R (OSCE6) were also identified as potential drivers in the resistant copy number clones. In two of these patients (OSCE1, OSCE6), this treatment-resistant subclone becomes the dominant copy number clone by the time of primary resection. SNV based phylogenies revealed a heterogenous mix of monoclonal and polyclonal seeding of metastases and monophyletic and polyphyletic modes of dissemination. Over half the new mutations acquired in recurrent disease were attributed to HRD or cisplatin mutational signatures. TP53 structural variants were seen in 6/8 patients (OSCE2, OSCE3, OSCE4, OSCE6, OSCE9, OSCE10). New structural variants involving driver genes were only detected in one relapse sample from patient OSCE10 (DMD deletion). Conclusion: Subclonal copy number clones emerge and dominate in relapsed osteosarcoma, with MYC gain/amplification a defining characteristic in our cohort. Selective pressure from neoadjuvant chemotherapy reveals this clone at the time of primary resection, highlighting that genomic profiling at this time point may be more reflective of its metastatic potential. Citation Format: Michael D. Kinnaman, Simone Zaccaria, Alvin Makohon-Moore, Gunes Gundem, Juan E. Arango Ossa, Nancy Bouvier, Filemon S. Dela Cruz, Meera Hameed, Julia Lynne Glade Bender, William D. Tap, Paul Meyers, Elli Papaemmanuil, Andrew Kung, Christine A. Iacobuzio-Donahue. Subclonal somatic copy number alterations emerge and dominate in relapsed/refractory osteosarcoma [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr B022.
    Type of Medium: Online Resource
    ISSN: 1557-3265
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 9
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 81, No. 13_Supplement ( 2021-07-01), p. 3123-3123
    Abstract: Introduction: Studies of cancer evolution have relied on archival tissue surplus to diagnostic requirements from living patients obtained in early stage disease and less commonly relapsed/metastatic disease. PEACE (Posthumous Evaluation of Advanced Cancer Environment) is a national research autopsy study aiming to understand the biological processes driving metastatic disease and cancer evolution. Experimental procedures: Patients recruited into the national TRACERx (TRAcking Cancer Evolution through therapy (Rx)) lung study who subsequently developed metastatic disease were enrolled into PEACE. Here we present a cohort of 15 TRACERx/PEACE patients in whom multi-region tumor sampling was performed at diagnosis +/- relapse and at autopsy. Fresh frozen tissue was subjected to whole-exome sequencing (mean depth 390) and germline DNA from blood was used for reference. Single nucleotide variants (SNVs) were identified using VarScan2 and the subclonal composition of each tumor was inferred using PyClone and used to reconstruct phylogenetic trees. Summary of data: Disease progression was associated with increased genomic complexity. Different patterns of progression occurred; monoclonal/monophyletic, polyclonal/monophyletic and polyclonal/polyphyletic dissemination. Both early and late divergence relative to the last clonal sweep were observed. Putative drivers were found to occur both clonally and subclonally. SNVs and copy number aberrations were used to reconstruct migration patterns. Conclusions: Preliminary analysis in this cohort demonstrates increasing genomic complexity with disease progression and variation in patterns of metastatic dissemination. We are yet to establish the impact of the tumor and immune microenvironment on such patterns as well as clinical presentation and outcome. Ongoing PEACE analysis includes study of the somatic copy number landscape and the characterisation of subclones that seed metastases. Patient & sample characteristicsN (%)Total patients15Median age (IQR)73 (65 - 80)Female sex (%)6 (40%)Histological subtypeAdenocarcinoma7 (47%)Squamous cell carcinoma5 (33%)Large cell carcinoma2 (13%)Adenosquamous carcinoma1 (7%)Treatment receivedRadiotherapy8 (53%)Chemotherapy6 (40%)Immunotherapy5 (33%)Targeted therapy4 (27%)Smoking statusCurrent smoker2 (13%)Ex-smoker12 (80%)Never smoker1 (7%)Total metastases sampled289Median samples per patient (range)19 (5 - 41)Number of samples per patient5-104 (27%)11-205 (33%) & gt;206 (40%)Number of tissue types sampled17 Citation Format: Ariana Huebner, Sonya Hessey, Cristina Naceur-Lombardelli, Mita Akther, Selvaraju Veeriah, Maise Al Bakir, David Moore, Simone Zaccaria, Nicholas McGranahan, Charles Swanton, Mariam Jamal-Hanjani, TRACERx and PEACE consortium. Lung cancer evolutionary trajectories in PEACE [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3123.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
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  • 10
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    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Communications Vol. 3, No. 4 ( 2023-04-12), p. 564-575
    In: Cancer Research Communications, American Association for Cancer Research (AACR), Vol. 3, No. 4 ( 2023-04-12), p. 564-575
    Abstract: Osteosarcoma is an aggressive malignancy characterized by high genomic complexity. Identification of few recurrent mutations in protein coding genes suggests that somatic copy-number aberrations (SCNA) are the genetic drivers of disease. Models around genomic instability conflict—it is unclear whether osteosarcomas result from pervasive ongoing clonal evolution with continuous optimization of the fitness landscape or an early catastrophic event followed by stable maintenance of an abnormal genome. We address this question by investigating SCNAs in & gt;12,000 tumor cells obtained from human osteosarcomas using single-cell DNA sequencing, with a degree of precision and accuracy not possible when inferring single-cell states using bulk sequencing. Using the CHISEL algorithm, we inferred allele- and haplotype-specific SCNAs from this whole-genome single-cell DNA sequencing data. Surprisingly, despite extensive structural complexity, these tumors exhibit a high degree of cell-cell homogeneity with little subclonal diversification. Longitudinal analysis of patient samples obtained at distant therapeutic timepoints (diagnosis, relapse) demonstrated remarkable conservation of SCNA profiles over tumor evolution. Phylogenetic analysis suggests that the majority of SCNAs were acquired early in the oncogenic process, with relatively few structure-altering events arising in response to therapy or during adaptation to growth in metastatic tissues. These data further support the emerging hypothesis that early catastrophic events, rather than sustained genomic instability, give rise to structural complexity, which is then preserved over long periods of tumor developmental time. Significance: Chromosomally complex tumors are often described as genomically unstable. However, determining whether complexity arises from remote time-limited events that give rise to structural alterations or a progressive accumulation of structural events in persistently unstable tumors has implications for diagnosis, biomarker assessment, mechanisms of treatment resistance, and represents a conceptual advance in our understanding of intratumoral heterogeneity and tumor evolution.
    Type of Medium: Online Resource
    ISSN: 2767-9764
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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