A Fully Annotated Hepatoblastoma Tumoroid Biobank Details Treatment-Induced Evolution and Clonal Dynamics in Paediatric Cancer Patients

Background: Estrogen receptors are over-expressed in around 70% of breast cancer cases. The genetic changes that occur during aromatase inhibitor (AI) treatment are not well understood and may differ depending upon the patient's response phenotype. Methods: We performed whole genome sequencing (WGS) of matched blood, pre-treatment, and post-treatment biopsy samples from 22 estrogen receptor positive breast cancer patients treated with neoadjuvant aromatase inhibitors. For 5 cases, we performed the whole genome sequencing (WGS) on patients’ matched normal, two pre AI-treatment, and two post AI-treatment DNA isolates from biopsy samples. We validated all putative coding and non-coding somatic mutations using deep sequencing. By comparing the validated somatic mutations from pre- and post- AI treatment biopsy samples, we were able to determine the alterations in the tumor genomes. In every case we defined the clonal architecture of each pair of pre-treatment and post-treatment biopsy samples by comparing the variant allele frequencies from thousands of validated somatic mutations. Results: Comparisons of the two pre AI-treatment biopsy samples from the same patient indicates that the variant allele frequencies of mutations showed high concordances in all 5 cases, 0.74 to 0.95 range of correlation coefficient. Only a small percentage of somatic mutations were detected in one pre-treatment sample and not the other (4.65% overall). In comparing the somatic variations between pre-treatment and matched post-treatment biopsy samples in 22 cases, we found that patients with good clinical response to AI treatment retained known driver mutations only in their pre-treatment tumors. Conversely, those patients with poor clinical response presented new driver mutations in their post-treatment samples. Furthermore, the variant allele frequency for most mutated genes decreased in post AI treatment samples for patients with good AI treatment response; on the contrary, the variant allele frequency increased for patients with poor clinical response. Conclusions: From WGS of matched normal, pre-treatment, and post-treatment biopsy samples, we identified new driver genes mutated in patients with poor clinical response, while patients with good clinical response had lost mutated driver genes in their post-treatment biopsy samples. The genetic landscape revealed by WGS of pre-treatment and post-treatment biopsy samples reveals mutational repertoires are remodeled by AI therapy. This finding suggests deep sequencing of AI treated samples will be necessary to reveal the complete complement of mutations present in a patient's tumor. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-423. doi:1538-7445.AM2012-LB-423

Clinical Impact of Clonal and Subclonal TP53, SF3B1, BIRC3, and ATM Mutations in Chronic Lymphocytic Leukemia

The Impact of Autologous Stem Cell Transplantation on the Genetics of High-Risk Relapsed Multiple Myeloma

Remarkable stability in clonal hematopoiesis involving leukemia-driver genes in patients without underlying myeloid neoplasms.

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A Quantitative Analysis of Subclonal and Clonal Gene Mutations Occurring Pre- and Post-Therapy in 53 Cases of Chronic Lymphocytic Leukemia

Background: Cell-free circulating tumor DNA analysis provides a non-invasive method for obtaining actionable genomic information to guide personalized cancer treatment. Deep sequencing of cell-free DNA (cfDNA) can potentially provide insights into tumor heterogeneity across multiple tumor sites in a patient, including emerging treatment-resistant subclones. However, the increased informational complexity of polyclonal cfDNA in circulation poses analysis challenges, particularly in tumors with abundant copy number alterations. To facilitate interpretation of this added complexity, we developed methods to identify cfDNA copy-number driver alterations and cfDNA clonality, Methods: We analyzed a large clinical sequencing database of somatic point mutations and copy number alterations from targeted cfDNA sequencing of 21,807 consecutive patients across >50 cancer types (Guardant Health, CA). We evaluated a minimal cfDNA clonality model that relies on the relationship between variant allele frequency (VAF) in cfDNA and the level of tumor DNA in circulation (ctDNA level), while accounting for copy number alterations. Results: We found that the initial simple model of cfDNA clonality performed well on >90% of samples, given a relatively small targeted genomic region (70 genes, 150 kb). However, normalizing VAF by copy number is subject to error in some samples due to the effect of ctDNA level on variant detection, variable unique molecule coverage across samples, and non-linearity of VAF at high copy number. Therefore, we developed an improved cfDNA clonality model that incorporated these analytical and biological features, which was then trained on a portion of the large cfDNA data set. Our cfDNA clonality model accurately distinguished subclonal resistance from driver alterations in a test set of over 5,000 lung, colorectal, and breast cancer patients. Although numerous subclonal tumor-derived alterations were apparent in the initial test data set, leading to an apparent departure from mutual exclusivity in treatment-naïve tumors, robust mutual exclusivity was observed among cfDNA clonal driver alterations when our cfDNA clonality analysis method was applied. These results suggest our analytical approach can be used to identify treatment-associated emerging resistance alterations in patients from a single blood draw, including parallel evolution of distinct subclonal alterations. Conclusion: Managing cancer will likely depend on identifying emerging treatment-resistant subclones at or in anticipation of progression. Highly accurate deep sequencing of cfDNA, along with comprehensive models of cfDNA clonality, can elucidate subclonal structure of the tumor and identify emerging treatment resistance. Citation Format: Stephen Fairclough, Oliver Zill, Catalin Barbacioru, Justin Odegaard, Richard B. Lanman, AmirAli Talasaz, Darya Chudova. A method for differentiating clonal driver mutations from subclonal emerging resistance mutations in circulating cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2190.

Background: Accurate detection of copy number alterations (CNAs) and reliable estimation of tumor fraction from liquid biopsies are crucial for effectively triaging patient samples in large clinical trials. This is particularly vital in scenarios where variant allele frequency (VAF) data is unavailable, yet an estimation of tumor fraction remains necessary. We hypothesize that specific characteristics of circulating free DNA (cfDNA) can enhance the performance of algorithms aimed at detecting CNAs and estimating tumor fraction. Methods: We introduce CANARy-TF (Copy number ANomaly Assessment and RecoverY Tumor Fraction), a novel algorithm designed to accurately identify CNAs and estimate tumor fraction from low-pass whole-genome and off-target sequencing. CANARy-TF utilizes sequencing coverage data to pinpoint amplifications and deletions by comparing samples against a panel of normals. Additionally, it incorporates unique cfDNA features, such as fragment size and shifts in the distribution of wildtype/mutant fragments, to improve tumor fraction estimation. Results: The performance of CANARy-TF was evaluated on a cohort of 23 cfDNA samples from patients with non-small cell lung cancer (NSCLC), alongside 149 cfDNA samples from healthy individuals as a background population. For all samples, tumor-informed VAF data were available. Analysis revealed a strong correlation between true VAF and the estimated tumor fraction by CANARy-TF, with a Spearman’s rho of 0.86 (p-value = 1.38e-07). In silico mixtures representing tumor prevalence from 0.001% to 5% were generated, demonstrating a limit of detection for the algorithm at around 1%. Conclusion: CANARy-TF effectively estimates CNAs and tumor fraction from plasma cfDNA samples. Notably, this algorithm is robust when applied to low-pass whole-genome sequencing, offering an efficient solution for triaging samples in both clinical settings and liquid biopsy studies. Citation Format: Diego Almanza, Takeshi Sugio, Emily Hamilton, David Kurtz, Maximilian Diehn, Mohammad Shahrokh Esfahani, Ash A. Alizadeh. CANARy-TF: an improved copy number and tumor fraction estimator for liquid biopsy applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4565.

Phylogenetic Analysis Reveals Distinct Evolutionary Patterns in Multiple Myeloma Patient Derived Xenografts

The use of sequencing-based assays for clinical management of pediatric cancer patients has become increasingly common. However, for many pediatric patients, gene panel based sequencing tests yield few actionable results. Given the complex genomic alterations present in many pediatric cancers, especially high-risk solid tumors, we hypothesized that an unbiased approach might reveal more actionable findings and lead to a more comprehensive understanding of these diseases. To accomplish this, we integrated whole-genome sequencing (WGS) with RNAseq in the analysis of a pediatric oncology cohort, with a focus on longitudinal cases to capture potential tumor evolution in metastatic or treated cases. Our cohort consists of 269 high-risk pediatric oncology patients, including patients with relapsed/refractory disease, metastatic disease at diagnosis, prior cancer history, a rare diagnosis, or an estimated overall survival <50%. Solid tumors, CNS tumors, and leukemia/lymphomas are all represented. In total, 391 samples were characterized using WGS (tumor ~60X; germline ~30X) and/or RNAseq (tumor, polyA selected, ≥20 million reads). For 85 of these patients, multiple samples were collected at different time points (diagnosis, resection, relapse, etc.) to identify changes in the cancer over time. If panel testing was performed as part of their clinical care, a comparison to the integrated WGS/RNA analysis was made. WGS was used to identify variants (SNVs), structural rearrangements (SVs), mutational signatures, and copy-number alterations (CNAs). RNAseq was used to identify gene expression outliers, gene fusions, and confirm the expression of variants identified using WGS. The combination of WGS and RNAseq was then used to identify and prioritize potentially actionable variants for each patient. Our results show that the integration of WGS and RNAseq can provide more and higher-quality actionable information than either modality alone, whilst also capturing the majority of actionable variants detected by panel sequencing. RNAseq identified not only druggable fusions and expression outliers, but also many rare and novel fusions. WGS provided fusion validation but highlighted the limitations of WGS alone in identifying fusions resulting from complex SVs. Conversely, WGS was adept at capturing genome-wide patterns of CNAs and loss of heterozygosity that are missed by gene-centric panels. Further RNAseq integration enabled prioritization of expressed SNVs as well as CNAs and SVs that significantly alter gene expression. We also used WGS to extract mutational signatures and tracked their evolution across longitudinal samples. We found potentially biologically significant differences in therapy-induced mutations caused by platinum and alkylating agents. Our unbiased approach has enabled further discovery that advances our understanding of these rare and highly aggressive malignancies. Citation Format: Henry J. Martell, Avanthi Tayi Shah, Alex G. Lee, Bogdan Tanasa, Stanley G. Leung, Aviv Spillinger, Heng-Yi Liu, Inge Behroozfard, Phuong Dinh, Maria V. Pons Ventura, Florette K. Hazard, Arun Rangaswami, Sheri L. Spunt, Norman J. Lacayo, Tabitha Cooney, Jennifer G. Michlitsch, Anurag K. Agrawal, Marcus R. Breese, E. Alejandro Sweet-Cordero. Integrative analysis of whole-genome and RNA sequencing in high-risk pediatric malignancies [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 54.

Clonal Hematopoiesis: Cell of Origin, Lineage Repartition and Dynamic Evolution during Chemotherapy

Clonal Dynamics and Deterministic Clinical Fate Mapping of Patients with Myelodysplastic Neoplasms and Acute Myeloid Leukemia with TP53 Disruption

Benchmarking of Whole Genome Sequencing (WGS) and Whole Transcriptome Sequencing (WTS) As Diagnostic Tools for the Genetic Characterization of Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) in Adults

11P Prognostic impact of MET mutations in exon 14 and copy number alterations in a series of NSCLC patients

Mutational and Clonal Dynamics in Patient-Derived Xenografts of Acute Myeloid Leukemia