Cancer Clonal Evolution Research Articles

Liquid biopsy detection of structural variant breakpoints to monitor ovarian cancer clonal evolution.

e17538 Background: Chemotherapy is effective at inducing remission for most patients with high grade serous ovarian cancer (HGSOC), yet relapse is frequent. Methods to interrogate the clonal composition and molecular properties of minimal residual disease are needed to better understand the mechanisms of HGSOC relapse. To address this unmet need, we developed and validated a novel cfDNA-based approach for tracking on-treatment clonal evolution using tumor-informed structural variants (SVs). Methods: Genomic DNA (gDNA) was isolated from cryopreserve multisite pre-treatment HGSOC biopsies and matched germline samples and whole genome sequencing (WGS) was performed. cfDNA was isolated from contemporary pre-treatment plasma. High confidence SVs were identified from multisite WGS using a consensus method incorporating five published SV calling algorithms. Tumor-informed bespoke quantitative PCR primer/probe sets were designed to interrogate SVs breakpoints in tumor gDNA and matched cfDNA. Results: Optimization of the methodology was first performed using synthetic cfDNA generated by ultrasonication of DNA from ovarian cancer cell lines. WGS was used to identify SVs and SV breakpoint-spanning PCR assay exhibited 90% sensitivity and 89% specificity in detection of WGS-informed SVs. 11 validated SVs breakpoint-spanning PCR assays had specific amplifications in synthetic cfDNA. 8 SVs breakpoint-spanning quantitative PCR primer/probe sets were synthesized for qPCR (real-time PCR) and ddPCR (digital droplet), the custom TaqMan probe assays had significant increased sensitivity and superior specificity. When cutoff value of negative was set to “undetected” in qPCR and 0 copies/ul in ddPCR, the sensitivity and specificity of custom TaqMan probe assays can still be 100%. . After screening 18 SVs by tumor-informed SV breakpoint-spanning PCR assays, 8 SVs custom TaqMan probe qPCR assays demonstrated 100 % sensitivity and specificity in multisite biopsy samples. 3 shared and 3 private SVs custom TaqMan probe ddPCR assays were selected for absolute quantification, none of these assays had false positive or false negative when 0 copies/ul was used for cutoff value. 1 shared SV custom TaqMan probe ddPCR assay measured 0.947 copies/ul in cfDNA and 0 copies/ul in matched germline control. Altogether, our multi-layer screening approach yields the “perfect”- high sensitivity and specificity- SV breakpoint-spanning assays that are ready to apply for SVs detection in cfDNA. Conclusions: Detection of SVs from pre-treatment cfDNA using tumor-informed breakpoint-spanning ddPCR is feasible and may enable a novel and sensitive method for monitoring on-treatment disease burden. Work is ongoing to extend this method to private SVs with a goal of tracking treatment-induced tumor clonal dynamics using cfDNA.

Longitudinal Comparative Analysis of Circulating Tumor DNA and Matched Tumor Tissue DNA in Patients with Metastatic Colorectal Cancer Receiving Palliative First-Line Systemic Anti-Cancer Therapy.

This study aimed to compare tumor tissue DNA (ttDNA) and circulating tumor DNA (ctDNA) to explore the clinical applicability of ctDNA and to better understand clonal evolution in patients with metastatic colorectal cancer undergoing palliative first-line systemic therapy. We performed targeted sequencing analysis of 88 cancer-associated genes using germline DNA, ctDNA at baseline (baseline-ctDNA), and ctDNA at progressive disease (PD-ctDNA). The results were compared with ttDNA data. Among 208 consecutively enrolled patients, we selected 84 (41 males; median age, 59 years; range, 35 to 90 years) with all four sample types available. A total of 202 driver mutations were found in 34 genes. ttDNA exhibited the highest mutation frequency (n=232), followed by baseline-ctDNA (n=155) and PD-ctDNA (n=117). Sequencing ctDNA alongside ttDNA revealed additional mutations in 40 patients (47.6%). PD-ctDNA detected 13 novel mutations in 10 patients (11.9%) compared to ttDNA and baseline-ctDNA. Notably, seven mutations in five patients (6.0%) were missense or nonsense mutations in APC, TP53, SMAD4, and CDH1 genes. In baseline-ctDNA, higher maximal variant allele frequency (VAF) values (p=0.010) and higher VAF values of APC (p=0.012), TP53 (p=0.012), and KRAS (p=0.005) mutations were significantly associated with worse overall survival. While ttDNA remains more sensitive than ctDNA, our ctDNA platform demonstrated validity and potential value when ttDNA was unavailable. Post-treatment analysis of PD-ctDNA unveiled new pathogenic mutations, signifying cancer's clonal evolution. Additionally, baseline-ctDNA's VAF values were prognostic after treatment.

Abstract 966: Longitudinal comparative analysis of circulating tumor DNA and matched tumor tissue DNA in patients with metastatic colorectal cancer receiving palliative first-line systemic anti-cancer therapy

Abstract Background: Plasma circulating tumor DNA (ctDNA) analysis has become an attractive method for precision oncology. This study compared mutational landscapes of ctDNA with matched tumor tissue DNA (ttDNA) to explore clinical applicability and to better understand clonal evolution in patients with metastatic colorectal cancer receiving palliative first-line systemic anti-cancer therapy (SACT). Methods: We performed unique molecular identifier-based targeted sequencing of 88 cancer-associated genes using the following samples: ttDNA, germline DNA, ctDNA at baseline (baseline-ctDNA), and ctDNA at progressive disease (PD-ctDNA). Results: Of the 208 consecutively enrolled patients, 84 were selected (41 males; median age 59, ranging from 35 to 90) with access to all four samples. Excluding duplicated mutations, a total of 202 driver mutations were discovered in 34 genes. ttDNA exhibited the highest occurrence of mutations (N=232), followed by baseline-ctDNA (N=155) and PD-ctDNA (N=117). Genotype concordance for the ten most mutated genes was highest between baseline-ctDNA and PD-ctDNA, at 94.3%, while the concordance rates for ttDNA were 85.5% with baseline-ctDNA and 83.6% with PD-ctDNA. Overall, 40 of the 84 patients (47.6%) benefited from sequencing ctDNA in addition to ttDNA as it identified additional mutations. Compared to ttDNA and baseline-ctDNA, PD-ctDNA identified 13 novel mutations in ten patients (11.9%). Notably, 7 mutations in five patients (6.0%) were missense or nonsense mutations in APC, TP53, SMAD4, and CDH1 genes. In baseline-ctDNA, patients with maximal variant allele frequency (VAF) values of >0.059 showed significantly worse progression-free survival (median 10.5 vs. 17.1 months, P=0.036) and overall survival (median 18.1 vs. 40.3 months, P=0.010). Moreover, overall survival was significantly worse in those with higher VAF values of APC (median 18.1 vs. 36.8 months, P=0.012), TP53 (median 24.7 vs. 40.3 months, P=0.012), and KRAS (median 13.7 vs. 27.6 months, P=0.005) mutations. Conclusions: While ttDNA remains more sensitive than ctDNA, our unique molecular identifier-based ctDNA platform demonstrated the validity and potential value when ttDNA was unavailable. Furthermore, post-SACT analysis of PD-ctDNA revealed new pathogenic mutations, indicating the clonal evolution of cancer. In addition, the VAF values of baseline-ctDNA predicted prognosis after SACT. Citation Format: Ji-Won Kim, Seung-been Lee, Hong-Geun Kim, Sung-Hyun Hwang, Kui-Jin Kim, Ju Hyun Lee, Jeongmin Seo, Minsu Kang, Eun Hee Jung, Koung Jin Suh, Se Hyun Kim, Jin Won Kim, Yu Jung Kim, Jee Hyun Kim, Nak-Jung Kwon, Keun-Wook Lee. Longitudinal comparative analysis of circulating tumor DNA and matched tumor tissue DNA in patients with metastatic colorectal cancer receiving palliative first-line systemic anti-cancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 966.

Abstract 1613: Evolution of mutations in the antigen presentation machinery across cancers

Abstract Immune escape is a critical hallmark of cancer progression. Under immunosurveillance, a subset of cancer-associated antigens or neoantigens are presented to the cancer cell surface via the antigen presentation machinery (APM) for recognition by antigen-specific T cells. During clonal evolution, cancer cells can evade such immunosurveillance by suppressing or ablating the APM. The defects in APM can further cause resistance to cancer immunotherapies and is associated with worse prognosis. Understanding how the defects in APM emerge and further shape the evolution of cancers is critical for devising strategies to overcome such immune escape mechanisms. However, it is not known when mutations in the APM occur along the history of cancer clonal evolution. We utilized a molecular archeology of cancer approach to reconstruct the evolution history of mutations in APM across 38 cancer types using whole-genome sequencing data from PCAWG. Genetic alterations in the APM occur in nearly 30% of patients, with varying frequencies and associations with immune infiltration across cancer types. Employing a League model to compare the timing of genetic events, we found that APM mutations can be early clonal, late clonal, or subclonal along cancer development. Further examination of individual tumors identified driver mutations that can happen before or after APM mutations along cancer development. Our study reveals the evolution of APM mutations in different cancers in a genetic context-aware manner. This information can contribute to optimizing personalized immunotherapy. Citation Format: Wenjie Chen, Toby Baker, Zhihui Zhang, Peter Van Loo, Shengqing Stan Gu. Evolution of mutations in the antigen presentation machinery across cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1613.

Of Phylogenies and Tumors

When students think of evolution, they might imagine T. rex, or perhaps an abiotic scene of sizzling electrical storms and harsh reducing atmospheres, an Earth that looks like a lunar landscape. Natural selection automatically elicits responses that include “survival of the fittest,” and “descent with modification,” and with these historical biological catch phrases, one conjures up images of large animals battling it out on the Mesozoic plane. Rarely do teachers or students apply these same ideas to cancer and the evolution of somatic cells, which have accrued mutations and epigenetic imprinting and relentlessly survive and proliferate. Our questions in this paper include the following: Can cancer become an important teaching model for students to explore fundamental hypotheses about evolutionary process? Can the multi- step somatic cancer model encourage visualizations that enable students to revisit and reenter previous primary concepts in general biology such as the cell, mitosis, chromosomes, genetic diversity, ecological diversity, immune function, and of course evolution, continually integrating their biology knowledge into process and pattern knowledge? Can the somatic cancer model expose similar patterns and protagonists, linking Darwinian observations of the natural world to our body? And, can the cancer clone model excite critical thinking and student hypotheses about what cancer is as a biological process? Does this visually simple model assist students in recognizing patterns, connecting their biological curriculum dots into a more coherent learning experience? These biological dynamics and intercepting aptitudes of cells are amplified through the cancer model and can help shape the way biology students begin to appreciate the interrelatedness of all biological systems while they continue to explore pivotal points of biological fuzziness, such as the microbiome, limitations of models, and the complex coordination of genomic networks required for the function of even a single cell and the realization of phenotypes. In this paper we use clonal evolution of cancer as a model experience for students to recreate how a single, non-germline cell appears to shadow the classic pattern of natural selection in body cells that have gone awry. With authentic STEAM activities students can easily crossover and revisit previous biological topics and the ubiquitous nature of natural selection as seen in the example of somatic cells that result in a metastasizing tumor, giving students insight into natural selection’s accommodating and tractable patterns throughout the planet.

Abstract A027: Detecting and modeling frequency-dependent evolution in serially passaged primary triple negative breast cancer patient derived xenografts

Abstract Tumor evolution is a contributing factor to most failed cancer treatments. The intratumor heterogeneity of cellular phenotypes found in the tumor microenvironment acts as an evolutionary substate for cancer cells during tumorigenesis, subsequent growth, and after the patient is in the clinic. Selective forces imposed by certain medications can cause the emergence of a tumor with a resistant phenotype. Chemotherapies that target rapidly dividing cells will select for slower dividing ones which are less sensitive to such agents. However, many selective pressures acting on the tumor microenvironment are yet to be elucidated. For example, the effect one cancer cell has on another’s growth through competitive (i.e., the struggle for resources) or cooperative (e.g., exchange of growth factors) interactions remains poorly understood. Our central hypothesis is that cancer cells with distinct karyotypes will have a varying growth rate influenced by the karyotype landscape of the entire tumor. That is, we expect the fitness of a cancer cell to be largely determined by its karyotype and the karyotypes of its neighbors. Thus, we predict that frequency-dependent selection has a significant impact on clonal evolution in cancer. To test this, we have developed two novel methods for respectively identifying likely instances of frequency-dependent selection and modeling the long-term evolutionary trajectory in such cases. To detect frequency-dependent effects, we investigate the relationship between the frequency of one clone and the growth rate of all others (including itself). If a statistically significant correlation can be found, we proceed to modeling clonal evolution. To do this, we use a penalized least squares method and find the best-fitting parameterization of the replicator equation to the clonally resolved frequency data. This provides us with a payoff matrix describing the intensity of competition between each pair of clones. Furthermore, it allows us to model the long-term evolutionary dynamics of the tumor and to make predictions regarding co-existence of clones or clonal sweeps. Recently, investigators serially passaged primary triple negative breast cancer (TNBC) patient derived xenograft (PDX) tumors in NRG mice for over a year, inferring clonal frequencies at multiple timepoints using single cell whole genome (scWGS) and RNA sequencing (scRNA-seq). From this data, we were able to detect statistically significant frequency-dependent effects in the majority of serially passaged PDX tumors. Of these, most are predicted to result in a clonal sweep. Three of the PDX tumors were treated with cisplatin which revealed clone-specific drug response. This presents an opportunity for adaptive therapy scheduling which takes advantage of fitness tradeoffs under different contexts. A greater understanding of the effects frequency-dependent selection has on tumor evolution will contribute to the design of therapies intended to preclude or delay the emergence of resistance to treatment. Citation Format: Thomas A. Veith, Richard Beck, Noemi Andor. Detecting and modeling frequency-dependent evolution in serially passaged primary triple negative breast cancer patient derived xenografts [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A027.

Modeling and predicting cancer clonal evolution with reinforcement learning.

Cancer results from an evolutionary process that typically yields multiple clones with varying sets of mutations within the same tumor. Accurately modeling this process is key to understanding and predicting cancer evolution. Here, we introduce clone to mutation (CloMu), a flexible and low-parameter tree generative model of cancer evolution. CloMu uses a two-layer neural network trained via reinforcement learning to determine the probability of new mutations based on the existing mutations on a clone. CloMu supports several prediction tasks, including the determination of evolutionary trajectories, tree selection, causality and interchangeability between mutations, and mutation fitness. Importantly, previous methods support only some of these tasks, and many suffer from overfitting on data sets with a large number of mutations. Using simulations, we show that CloMu either matches or outperforms current methods on a wide variety of prediction tasks. In particular, for simulated data with interchangeable mutations, current methods are unable to uncover causal relationships as effectively as CloMu. On breast cancer and leukemia cohorts, we show that CloMu determines similarities and causal relationships between mutations as well as the fitness of mutations. We validate CloMu's inferred mutation fitness values for the leukemia cohort by comparing them to clonal proportion data not used during training, showing high concordance. In summary, CloMu's low-parameter model facilitates a wide range of prediction tasks regarding cancer evolution on increasingly available cohort-level data sets.

Diagnostic value of liquid biopsy in the era of precision medicine: 10 years of clinical evidence in cancer.

Liquid biopsy is a diagnostic repeatable test, which in last years has emerged as a powerful tool for profiling cancer genomes in real-time with minimal invasiveness and tailoring oncological decision-making. It analyzes different blood-circulating biomarkers and circulating tumor DNA (ctDNA) is the preferred one. Nevertheless, tissue biopsy remains the gold standard for molecular evaluation of solid tumors whereas liquid biopsy is a complementary tool in many different clinical settings, such as treatment selection, monitoring treatment response, cancer clonal evolution, prognostic evaluation, as well as the detection of early disease and minimal residual disease (MRD). A wide number of technologies have been developed with the aim of increasing their sensitivity and specificity with acceptable costs. Moreover, several preclinical and clinical studies have been conducted to better understand liquid biopsy clinical utility. Anyway, several issues are still a limitation of its use such as false positive and negative results, results interpretation, and standardization of the panel tests. Although there has been rapid development of the research in these fields and recent advances in the clinical setting, many clinical trials and studies are still needed to make liquid biopsy an instrument of clinical routine. This review provides an overview of the current and future clinical applications and opening questions of liquid biopsy in different oncological settings, with particular attention to ctDNA liquid biopsy.

EPCO-07. NON-GENOMIC DETERMINANTS OF TUMOR CELL PHENOTYPES IN ADULT GLIOBLASTOMA

Abstract Non-genomic determinates of oncogenic cellular phenotypes is an emerging concept that expanded our model of tumor hallmarks. We hypothesized that oncogenic programs in adult glioblastoma (GBM) such as angiogenesis, proliferation, DNA repair, epithelial to mesenchymal transition and quiescent states are achieved independently of mutational background or clonal linage. We therefore explored the assortations of large-scale chromosomal rearrangements and canonical driver mutations with oncogenic programs in single cell RNA sequencing (scRNA-seq) of over 3000 tumor cells in 4 adult GBM using open data. We find recurring patterns where tumor cells from diverse mutational background and clonal linage converge upon oncogenic tumor phenotypes. We validate this observation in 9 tumors comprising over 16,000 cells and in xenograft animal models of GBM pre- and post temozolamide treatment. We finally explore the epigenetic associations of oncogenic phenotypes via computational label transfer from scRNA-seq to chromatic accessibility data from single cell ATAC sequencing (scATAC-seq). We find open genomic regions associated with canonical regulators, such as the highly oncogenic mesenchymal phenotype driven by TWIST1. Our results suggest a paradigm shift towards non-genetic determinants of oncogenic phenotypes in GBM complementing the conventional concept of cancer clonal evolution.

Abstract 6085: Clonal evolution of mammary epithelial cells into breast cancers

Abstract [Introduction] Previous studies suggested that breast cancers (BCs) and some adjacent atypical proliferative lesions evolved from the same ancestral cell. However, the clonal structure of normal epithelial cells and their clonal history during evolution to cancer are poorly understood. In this study, we analyzed genetic profiles of normal epithelia and proliferative lesions in the cancer-borne breast to illustrate the clonal evolution of cancer from a normal epithelial cell. [Methods] Single cell-derived normal epithelial organoids (n=47) were established from breast milk of 4 healthy women aged 22-36 and surgical specimens of 15 BC patients aged 29-83 to evaluate somatic mutation rate. Multiple normal lobules or proliferative lesions together with cancer cells were collected using laser-capture micro-dissection (LCM) from fresh frozen (n=3) or formalin-fixed paraffin-embedded (n=6) surgical specimens in 9 premenopausal BC patients. Somatic mutations and copy number alterations were evaluated using whole-genome sequencing. [Results] On the basis of single cell-derived mammary gland-derived organoids, somatic mutations increased in mammary gland cells at a constant rate of 19.4/genome/year before menopause, which then decreased to 6.9/genome/year after menopause. Parity was negatively associated with mutation number (-49.3 per life birth). In total, we analyzed 113 LCM samples, including those from 66 normal lobules, 28 proliferative lesions, and 14 non-invasive and 5 invasive cancer samples. Phylogenetic analysis showed that most of the neighboring normal lobules (53/66) from 3 BC patients shared no somatic mutations with each other and therefore were clonally independent. In other 13 normal lobules, shared mutations including a PIK3CA mutation were observed across multiple adjacent samples, although they were still confined to <10 mm regions. By contrast, we found a large expansion of proliferative lesions sharing a substantial number of somatic mutations with cancer in 5 out of the 6 remaining BC cases. They were expanded over a distance of 25-85 mm, sharing tens to hundreds of mutations including those in BC-related driver genes, such as PIK3CA, AKT1, GATA3, CBFB and PTEN. Of interest, Tumors in 4 out of these 5 cases was Luminal-A type and characterized in common by the presence of 1q gain and 16q loss (1q+/16q-) in both cancer and proliferative lesions. Phylogenetic analysis adapted with the mutation rate in normal cells predicted that 1q+/16q- had been acquired during adolescence to mid-20s, where the clonal expansion had occurred years to decades before the onset of cancer. [Conclusions] Our results suggest that in some BC cases, particularly in those with 1q+/16q-, the clones with the founder driver mutations expanded macroscopically long before the onset of cancer, in which further clonal evolutions recursively occur multi-focally, giving rise to multiple proliferative lesions and ultimately, invasive cancers. Citation Format: Tomomi Nishimura, Nobuyuki Kakiuchi, Kenichi Yoshida, Yasuhide Takeuchi, Hirona Maeda, Yusuke Shiozawa, Masahiro M. Nakagawa, Ryunosuke Saiki, Yotaro Ochi, Tomonori Hirano, Yukiko Kawata, Kosuke Aoki, Masahiro Hirata, Tatsuki R. Kataoka, Takaki Sakurai, Satoko Baba, Yuichi Shiraishi, Kenichi Chiba, Kengo Takeuchi, Hironori Haga, Satoru Miyano, Masakazu Toi, Seishi Ogawa. Clonal evolution of mammary epithelial cells into breast cancers [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 6085.

Abstract 833: Linking phenotype and genotype: Multimodal analysis of surface proteins, intracellular proteins, and SNV in single cells

Abstract Cancers evolve via processes of clonal expansion, selection, and somatic variation, which contribute to tumor heterogeneity. While bulk analysis has improved our understanding of cancer, the heterogeneity of a tumor is masked with the average readout provided by a bulk measurement. This problem could be overcome by employing single-cell technologies. One of the most common single-cell technologies is single-cell RNA sequencing (scRNA-seq). Transcriptomic data collected by scRNA-seq are used to imply activity of gene products based on RNA levels. Signaling pathways related to cancer initiation and progression have been mapped solely based on scRNA-seq results. This approach, however, is not an accurate measurement of proteins that are the functional molecules responsible for key events in cancer. Recent breakthroughs of multimodal approaches such as CITE-seq and REAP-seq partially addressed this problem by allowing simultaneous analysis of transcriptomes and surface protein expression levels. These methods still lack a couple components that are required to fully dissect the biology of cancer. First, the methods are limited to the measurements of cell surface proteins, whereas proteins involved in cancer-related signal transduction and transcriptional pathways are mostly localized in the intracellular compartments. Second, the methods do not allow direct analysis of DNA sequences, hence unable to provide an accurate readout of genotypic information such as single-nucleotide variants and copy number variations. Here we describe a technology that could overcome these hurdles. To achieve this, cells are first treated with oligonucleotide-barcoded antibodies targeting surface proteins. Cells are then fixed and permeabilized, followed by incubation with barcoded antibodies for intracellular proteins. The resulting cells are processed on the Mission Bio TapestriⓇ platform, a device that enables encapsulation of single cells in droplets. Targeted DNA sequencing libraries are generated from the single cells using a multiplex panel of primers targeting regions of interest. Protein sequencing libraries are separately generated from the oligonucleotides off the antibodies. The inclusion of intracellular protein detection enables measurement of pivotal proteins in cancer mechanisms, including apoptosis (BCL2 family proteins), transcription factors (GATA3), tumor suppressors (TP53), as well as phosphorylated proteins involved in cell growth signaling pathways (phosphorylated ERK and STAT proteins). This is the first ever method that provides a solution to effectively link surface and intracellular protein measurement with targeted DNA analysis. With this approach, single-cell readout of genotypic and phenotypic information can be collected together, allowing concurrent complex analyses of cancer clonal evolution and driver protein expression. Citation Format: Prithvi Singh, Saurabh Parikh, Tim Lai, Dalia Dhingra, Aik Ooi. Linking phenotype and genotype: Multimodal analysis of surface proteins, intracellular proteins, and SNV in single cells [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 833.

Discriminating Epithelial to Mesenchymal Transition Phenotypes in Circulating Tumor Cells Isolated from Advanced Gastrointestinal Cancer Patients.

Gastrointestinal (GI) cancers constitute a group of highest morbidity worldwide, with colorectal cancer (CRC) and gastric cancer being among the most frequently diagnosed. The majority of gastrointestinal cancer patients already present metastasis by the time of diagnosis, which is widely associated with cancer-related death. Accumulating evidence suggests that epithelial-to-mesenchymal transition (EMT) in cancer promotes circulating tumor cell (CTCs) formation, which ultimately drives metastasis development. These cells have emerged as a fundamental tool for cancer diagnosis and monitoring, as they reflect tumor heterogeneity and the clonal evolution of cancer in real-time. In particular, EMT phenotypes are commonly associated with therapy resistance. Thus, capturing these CTCs is expected to reveal important clinical information. However, currently available CTC isolation approaches are suboptimal and are often targeted to capture epithelial CTCs, leading to the loss of EMT or mesenchymal CTCs. Here, we describe size-based CTCs isolation using the RUBYchip™, a label-free microfluidic device, aiming to detect EMT biomarkers in CTCs from whole blood samples of GI cancer patients. We found that, for most cases, the mesenchymal phenotype was predominant, and in fact a considerable fraction of isolated CTCs did not express epithelial markers. The RUBYchip™ can overcome the limitations of label-dependent technologies and improve the identification of CTC subpopulations that may be related to different clinical outcomes.

Comparison of Two Blood-Based Genotyping Tests to Investigate the KRAS G12C Mutation in Patients with Non-Small-Cell Lung Cancer at Failure of First-Line Treatments.

Although molecular profiling at diagnosis has traditionally relied on direct sampling of neoplastic tissue, cancer clonal evolution represents a critical obstacle to use primary tissue biopsies to guide clinical decision-making at the time of progressive disease. Liquid biopsies might offer enormous advantages over tissue biopsies, tracking in real-time temporal-based tumor dynamics following each line of treatment. Here, we compared two liquid biopsy assays, specifically real-time polymerase chain reaction and next-generation sequencing, to track the KRAS G12C mutation at onset of progression from previous lines of therapy. The KRAS G12C mutation was acquired at the time of progressive disease in 24% of patients. Furthermore, all patients with KRAS G12C mutation-positive tissue became negative in ctDNA at progressive disease. The presence of other somatic mutations in all these samples confirmed the tumor origin of the circulating DNA. This pilot study suggests that in the assessment of the plasma KRAS G12C mutation as a druggable target, real-time PCR assay Idylla might be a suitable approach to better match patients to interventional biomarker-targeted therapies.

Somatic variant analysis suite: copy number variation clonal visualization online platform for large-scale single-cell genomics.

The recent advance of single-cell copy number variation (CNV) analysis plays an essential role in addressing intratumor heterogeneity, identifying tumor subgroups and restoring tumor-evolving trajectories at single-cell scale. Informative visualization of copy number analysis results boosts productive scientific exploration, validation and sharing. Several single-cell analysis figures have the effectiveness of visualizations for understanding single-cell genomics in published articles and software packages. However, they almost lack real-time interaction, and it is hard to reproduce them. Moreover, existing tools are time-consuming and memory-intensive when they reach large-scale single-cell throughputs. We present an online visualization platform, single-cell Somatic Variant Analysis Suite (scSVAS), for real-time interactive single-cell genomics data visualization. scSVAS is specifically designed for large-scale single-cell genomic analysis that provides an arsenal of unique functionalities. After uploading the specified input files, scSVAS deploys the online interactive visualization automatically. Users may conduct scientific discoveries, share interactive visualizations and download high-quality publication-ready figures. scSVAS provides versatile utilities for managing, investigating, sharing and publishing single-cell CNV profiles. We envision this online platform will expedite the biological understanding of cancer clonal evolution in single-cell resolution. All visualizations are publicly hosted at https://sc.deepomics.org.

Loss of the wild-type KRAS allele promotes pancreatic cancer progression through functional activation of YAP1.

Human pancreatic ductal adenocarcinoma (PDAC) harboring one KRAS mutant allele often displays increasing genomic loss of the remaining wild-type (WT) allele (known as LOH at KRAS) as tumors progress to metastasis, yet the molecular ramification of this WT allelic loss is unknown. In this study, we showed that the restoration of WT KRAS expression in human PDAC cell lines with LOH at KRAS significantly attenuated the malignancy of PDAC cells both in vitro and in vivo, demonstrating a tumor suppressive role of the WT KRAS allele. Through RNA-Seq, we identified the HIPPO signaling pathway to be positively regulated by WT KRAS in PDAC cells. In accordance with this observation, PDAC cells with LOH at KRAS exhibited increased nuclear localization and activation of transcriptional coactivator YAP1. Mechanistically, we discovered that WT KRAS expression sequestered YAP1 from the nucleus, through enhanced 14-3-3zeta interaction with phosphorylated YAP1 at S127. Consistently, expression of a constitutively-active YAP1 mutant in PDAC cells bypassed the growth inhibitory effects of WT KRAS. In patient samples, we found that the YAP1-activation genes were significantly upregulated in tumors with LOH at KRAS, and YAP1 nuclear localization predicted poor survival for PDAC patients. Collectively, our results reveal that the WT allelic loss leads to functional activation of YAP1 and enhanced tumor malignancy, which explains the selection advantage of the tumor cells with LOH at KRAS during pancreatic cancer clonal evolution and progression to metastasis, and should be taken into consideration in future therapeutic strategies targeting KRAS.

Clonal dynamics of circulating tumor DNA during immune checkpoint blockade therapy for melanoma.

Assessment of treatment efficacy of immune checkpoint inhibitors in melanoma patients is difficult as the response to these therapies varies among patients or lesions. The clonal evolution of cancer during immune checkpoint blockade therapy could cause treatment resistance. We investigated the potential of liquid biopsy in monitoring the mutational profiles of metastatic melanoma during immunotherapy. Plasma samples collected from 21 Japanese metastatic melanoma patients before immune checkpoint blockade therapy were subjected to whole‐exome sequencing (WES). Furthermore, 14 Japanese patients with melanoma were enrolled for longitudinal analysis of circulating tumor DNA (ctDNA). Plasma samples were collected prospectively before and during therapy and sequenced. WES of the pretreatment plasma from Japanese melanoma patients showed detectable ctDNA levels with wide ranges of variant allele frequencies within a sample, suggesting clonal and subclonal mutations in ctDNA. In targeted sequencing using longitudinal samples, ctDNA levels correlated with increased tumor size, while ctDNA content immediately decreased after a surge in a patient exhibiting pseudo‐progression, suggesting the potential of ctDNA analysis in discriminating between pseudo‐ and true progression. Mutant ctDNA levels showed different patterns within the clinical course of specific patients, suggesting that these mutations were derived from different tumor clones with distinct therapeutic responses. During further investigation, WES of plasma samples from 1 patient showed marked differences in the mutational profiles of ctDNA, including expansive tumor evolution during an acute exacerbation. Immunotherapy may induce characteristic clonal evolutions of tumors; longitudinal analysis of ctDNA has the potential of determining these tumor evolution patterns and therapeutic responses.

Whole exome and transcriptome sequencing reveal clonal evolution and exhibit immune-related features in metastatic colorectal tumors

Liver is the most common site where metastatic lesions of colorectal cancer (CRC) arise. Although researches have shown mutations in driver genes, copy number variations (CNV) and alterations in relevant signaling pathways promoted the tumor evolution and immune escape during colorectal liver metastasis (CLM), the underlying mechanism remains largely elusive. Tumor and matched metastatic tissues were collected from 16 patients diagnosed with colorectal cancer and subjected to whole-exome sequencing (WES) and RNA sequencing (RNA-seq) for studying colorectal cancer clonal evolution and immune escape during CLM. Shared somatic mutations between primary and metastatic tissues with a commonly observed subclonal-clonal (S-C) changing pattern indicated a common clonal origin between two lesions. The recurrent mutations with S-C changing pattern included those in KRAS, SYNE1, CACNA1H, PCLO, FBXL2, and DNAH11. The main CNV events underwent clonal-clonal evolution (20q amplification (amp), 17p deletion (del), 18q del and 8p del), subclonal-clonal evolution (8q amp, 13q amp, 8p del) and metastasis-specific evolution (8q amp) during the process of CLM. In addition, we revealed a potential mechanism of tumor cell immune escape by analyzing human leukocytes antigens (HLA) related clonal neoantigens and immune cell components in CLM. Our study proposed a novel liver metastasis-related evolutionary process in colorectal cancer and emphasized the theory of neo-immune escape in colorectal liver metastasis.

Abstract 92: Clonal evolution of proliferative lesions into breast cancers

Abstract Introduction: Proliferative lesions in the breast have been implicated in the development of invasive breast cancer. Previous studies showed that adjacent atypical proliferative lesions and breast cancers shared common genetic alterations, suggesting that these evolved from the same ancestral cell. However, the clonal structure of atypical proliferative lesions and their clonal dynamics during progression to cancer are poorly understood. In this study, we compared genetic profiles of proliferative lesions and cancers from the same patients to illustrate the clonal evolution of cancer from a non-malignant epithelial cell. Methods: Multiple samples were collected from various lesions with different disease-level located in the cancer-borne breast, using micro-dissection from formalin-fixed, paraffin-embedded surgical specimens. Somatic mutations and copy number alterations (CNAs) were evaluated by whole exome sequencing. Results: A total of 39 samples from 7 premenopausal females carrying estrogen receptor-positive cancers were analyzed, where the samples were obtained from proliferative lesions (without/with atypia) (N = 3 and 10, respectively), and non-invasive (N = 22) and invasive (N = 4) cancers. The number of somatic mutations per sample increased with disease progression (ranging from 7 to 311). Two cases with bilateral cancers had a pathogenic germline mutation of either BRCA2 or TP53, where no somatic mutations or CNAs were shared by individual proliferative lesions, suggesting multifocal independent cancerous evolutions. By contrast, in the remaining five unilateral cases, no pathogenic germline mutations were detected, but all proliferative lesions, which were separated by a distance of 7 - 70 mm, shared one or more driver alterations, such as an AKT1 mutation, a GATA3 mutation, a CBFB mutation, a PTEN mutation and concurrent 1q gain and 16q loss, while harboring private mutations/CNAs of their own. The analysis of phylogenic trees based on the number of shared mutations predicted an early origin of these founder mutations, which frequently predated decades before the onset of cancer. Conclusions: Our results suggest that early breast cancer development is shaped by the evolution of multiple precancerous clones. In hereditary cases, this process is thought to be substantially promoted multi-focally within the entire breasts, due to innate genomic instability in each mammary epithelial cell for pathogenic germline mutations. By contrast, in sporadic cases, the ancestral cell which has acquired a founder genetic alteration expands macroscopically long before the onset of cancer. These expanded clones work as the niche predisposed to additional mutations, in which branching evolution occurs multi-focally and raises up multiple proliferative lesions, from which invasive cancer finally evolves. Our findings provide unique insight into the early development of breast cancer. Citation Format: Tomomi Nishimura, Nobuyuki Kakiuchi, Kenichi Yoshida, Yasuhide Takeuchi, Hirona Maeda, Yusuke Shiozawa, Masahiro M. Nakagawa, Yotaro Ochi, Yukiko Kawata, Kosuke Aoki, Masahiro Hirata, Tatsuki R. Kataoka, Takaki Sakurai, Satoko Baba, Yuichi Shiraishi, Kenichi Chiba, Kengo Takeuchi, Hironori Haga, Satoru Miyano, Masakazu Toi, Seishi Ogawa. Clonal evolution of proliferative lesions into breast cancers [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 92.

Abstract 3529: Detection of genomic structural variations associated with drug sensitivities and resistance in AML using novel whole genome imaging

Abstract Acute myeloid leukemia (AML) is a particularly devastating collection of hematological cancers and whilst somewhat rare, the patient survival rate is abysmal without bone marrow transplantation. Traditional chemotherapies administered to AML patients cause significant and devastating side effects. Additionally, more than 30% of patients fail to respond to these initial treatments and most patients that do respond will eventually relapse within 5 years. As such, understanding the evolution of AML to identify novel targets, and therefore drug treatment regimens, is a significant medical need. Genomic rearrangements and other Structural Variations (SVs) have long been known to be causative and pathogenic in multiple cancers, including leukemias. Indeed the discovery of the “Philadelphia chromosome” (eventually identified as a BCR-ABL translocation) as causative in Chronic Myeloid Leukemia has prompted much research into SVs in cancers, including the development of targeted therapeutics against oncogenic proteins resulting from genomic rearrangements. These SVs may be involved in cancer initiation, progression, clonal evolution, and drug resistance, and a better understanding of SVs from individual AML patients may help guide therapeutic options. Here we show utilization of an innovative whole genome imaging technology to detect known, and novel, SVs in AML patients' samples. Importantly, this new technology provides an unprecedented level of granularity and quantitation unavailable to other current techniques and it allows an unbiased detection of novel SVs, which may be relevant for disease pathogenesis and/ or drug resistance. Coupled with standard gene expression analyses we have also assessed the chemosensitivities of these samples to 120 FDA approved oncology drugs and 335 epigenetic modulating agents. Here we show how integrative analysis of these diverse datasets is used to associate the detected genomic rearrangements with drug sensitivity profiles, potentially identifying novel therapeutic targets for individual AML patients. Citation Format: Darren Finlay, Rabi Murad, Karl Hong, Joyce Lee, Andy Pang, Chi-Yu Lai, Carol Burian, James Mason, Alex Hastie, Jun Yin, Kristiina Vuori. Detection of genomic structural variations associated with drug sensitivities and resistance in AML using novel whole genome imaging [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3529.

Genomic Lesions Unrepaired at Mitosis Affect Clonal Evolution in Cancer

Mutagen-induced DNA lesions were not all resolved prior to mitosis, yielding mutational strand bias.