Cancer Evolution Research Articles

Von Hippel Lindau tumor suppressor controls m6A-dependent gene expression in renal tumorigenesis.

N6-Methyladenosine (m6A) is the most abundant posttranscriptional modification, and its contribution to cancer evolution has recently been appreciated. Renal cancer is the most common adult genitourinary cancer, approximately 85% of which is accounted for by the clear cell renal cell carcinoma (ccRCC) subtype characterized by VHL loss. However, it is unclear whether VHL loss in ccRCC affects m6A patterns. In this study, we demonstrate that VHL binds and promotes METTL3/METTL14 complex formation while VHL depletion suppresses m6A modification, which is distinctive from its canonical E3 ligase role. m6A RNA immunoprecipitation sequencing (RIP-Seq) coupled with RNA-Seq allows us to identify a selection of genes whose expression may be regulated by VHL-m6A signaling. Specifically, PIK3R3 is identified to be a critical gene whose mRNA stability is regulated by VHL in a m6A-dependent but HIF-independent manner. Functionally, PIK3R3 depletion promotes renal cancer cell growth and orthotopic tumor growth while its overexpression leads to decreased tumorigenesis. Mechanistically, the VHL-m6A-regulated PIK3R3 suppresses tumor growth by restraining PI3K/AKT activity. Taken together, we propose a mechanism by which VHL regulates m6A through modulation of METTL3/METTL14 complex formation, thereby promoting PIK3R3 mRNA stability and protein levels that are critical for regulating ccRCC tumorigenesis.

How mutation accumulation depends on the structure of the cell lineage tree.

All the cells of a multicellular organism are the product of cell divisions that trace out a single binary tree, the so-called cell lineage tree. Because cell divisions are accompanied by replication errors, the shape of the cell lineage tree is a key determinant of how somatic evolution, which can potentially lead to cancer, proceeds. Carcinogenesis requires the accumulation of a certain number of driver mutations. By mapping the accumulation of mutations into a graph theoretical problem, we present an exact numerical method to calculate the probability of collecting a given number of mutations and show that for low mutation rates it can be approximated with a simple analytical formula, which depends only on the distribution of the lineage lengths, and is dominated by the longest lineages. Our results are crucial in understanding how natural selection can shape the cell lineage trees of multicellular organisms and curtail somatic evolution.

Abstract SY36-01: Somatic evolution, cancer, and our inevitable decline with age: Inextricably linked

Abstract Why do we get cancer? Why is cancer highly associated with old age? Aging is associated with the accumulation of more mutations; some of which can contribute to cancer phenotypes. However, we now understand that carcinogenesis is much more complex than originally appreciated. In particular, there are tissue environmental forces that both impede and promote cancer evolution. Just as organismal evolution is known to be driven by environmental changes, cellular (somatic) evolution in our bodies is similarly driven by changes in tissue environments, whether caused by the normal process of aging, by lifestyle choices, or by extrinsic exposures. Environmental change promotes selection for new phenotypes that are adaptive to the new context. In our tissues, aging or insult-driven alterations in tissues drives selection for adaptive mutations, and some of these mutations can confer malignant phenotypes. To better understand the evolutionary forces that control somatic cell evolution, we used mouse models of cancer initiation, mathematical models of cellular evolution, and analyses of human tissue samples. We have shown that aging- and inflammation-dependent changes in stem cells and their tissue environments dramatically dictate whether cancer-causing mutations are advantageous to stem cells in our tissues, starting the cells down the path to cancer and deepening our insight into cancer risk. Recent studies from many labs have shown how as we age our tissues become dominated by clones bearing mutations in known cancer-associated genes. I will present studies from our labs exploring how cigarette smoking and aging can alter mutational landscapes in the non-malignant lung. To observe somatic mutations in lung biopsies from individuals with different smoking histories, we used a rare-mutation detection technique called Duplex Sequencing to analyze somatic variants from over 200 lung samples, including from subjects from the BDRE (Colorado), PEACE, and TRACERx cohorts. Using this method, we have identified large numbers of mutations in each brushing or biopsy, many of which are cataloged in the Catalogue Of Somatic Mutations In Cancer (COSMIC), and most of these are predicted to disrupt protein function. We also observe pervasive positive selection acting on mutations in many but not all of the cancer-associated genes, with different patterns of selection in the lungs of ever-smokers and never-smokers. Ongoing studies are using primary human and mouse lung epithelial cultures and mouse models to study how these mutations interact with smoking and age to alter associated clonal expansions, tissue integrity, and malignant progression. We propose a model whereby aging and microenvironmental changes induced through lifestyle choices like smoking can promote selection for cells with adaptive mutations, which can contribute to not only cancer risk but also tissue aging. Thus, while young tissues impede selection for such adaptive mutations, old age is associated with a feed-forward loop of aging tissue-mediated selection for mutant clones that then increase tissue aging. Thus, understanding the forces controlling clonal selection as we age and due to lifetime exposures could be critical for controlling multiple diseases of old age. [EJE, EL and FM contributed equally.These studies were led by CS and JD.] Citation Format: Edward J. Evans, Emilia Lim, Fabio Marongiu, Shi Biao Chia, William Hill, Clare Weeden, Oriol Pich, Andrew Rowan, Faiz Jabbar, Cristina Naceur-Lombardelli, Raju Veeriah, Moumita Ghosh, Daniel Merrick, York E. Miller, Robert Keith, Mariam Jamal-Hanjani, Charles Swanton, James V. DeGregori. Somatic evolution, cancer, and our inevitable decline with age: Inextricably linked [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY36-01.

Abstract NG01: Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity

Abstract NG01: Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity

Abstract LB416: IncreasedTP53somatic evolution in the fallopian tubes of individuals at high risk of ovarian cancer

Abstract High-grade serous carcinoma (HGSC), the most common and pervasive subtype of ovarian cancer, originates in the fallopian tube epithelium from precursor lesions carrying somatic TP53 mutations. These lesions may evolve into carcinomas, eventually spreading to the ovary and peritoneum. Recent research in normal tissues and gynecologic liquid biopsies of individuals without HGSC has demonstrated that somatic TP53 mutations accumulate and clonally expand with age, suggesting that TP53 somatic evolution is part of the normal aging process​. To date, however, TP53 somatic evolution in the normal fallopian tube epithelium has not been characterized and it is unknown whether TP53mutations are more abundant in individuals with germline mutations in BRCA1 or BRCA2 (BRCA carriers) who are at a higher risk of developing HGSC​. In this study, we aimed to characterize age-related TP53 somatic mutations in the normal fallopian tube epithelium across the human lifespan and to determine whether the TP53 mutational landscape is altered in BRCA carriers. Our cohort consisted of 46 autopsy cases (age: 0-91 years) from individuals at low-risk of developing ovarian cancer and 48 prophylactic surgery cases (age: 32-69 years) from BRCA carriers. Fallopian tube tissues were collected, frozen, and macrodissected using a 1mm biopsy punch, enriching for the fallopian tube epithelium. DNA was extracted from each sample and sequenced for TP53 using ultra-deep duplex sequencing (mean depth: ~12,000x). Pathogenicity was determined using a machine learning algorithm (AlphaMissense) and a well-established predictor of TP53 pathogenicity (Seshat). Preliminary analyses in the normal fallopian tube epithelium collected across human lifespan demonstrated that the mutational profile (e.g. indels, splice, nonsense, missense, and silent mutations) in the aging fallopian tubes mirrored the mutational profile of HGSC. We also observed that the number of unique TP53 mutations and pathogenic clones increased with age, revealing TP53 somatic evolution. Additionally, the number of large TP53 mutant clones (>2 duplex mutant reads) increased exponentially with age, staring at age 30. When comparing the fallopian tubes of BRCA carriers to age-matched normal fallopian tubes, the percentage of pathogenic large clones in BRCA carriers was 22.8% vs. 6.0% in low-risk individuals (Fisher exact test, p < 0.0001), suggesting increased TP53 somatic evolution in the fallopian tubes of BRCA carriers. Altogether, our research demonstrates that TP53 somatic evolution is a normal aging phenotype in the fallopian tube, with more and larger pathogenic clones occurring later in life. Preliminary analyses in BRCA carriers indicate that somatic evolution is more prominent in these individuals, which may provide a biological explanation to the increased risk of HGSC and open new venues for better cancer prediction and prevention. Citation Format: CoohleenAnn Coombes, Brendan F. Kohrn, Jeanne U. Fredrickson, Elena Latorre-Esteves, Emma Hazard, Shreya Suresh, Marc R. Radke, Melanie Dillon, Anh P. Vo, Katherine Lai, Ronit Katz, Barbara M. Norquist, Elizabeth M. Swisher, Elizabeth U. Parker, Rosana Risques. IncreasedTP53somatic evolution in the fallopian tubes of individuals at high risk of ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB416.

Abstract NG02: Distinct genomic and immunologic tumor evolution in germline TP53-driven breast cancers

Abstract NG02: Distinct genomic and immunologic tumor evolution in germline TP53-driven breast cancers

Abstract LB226: Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition

Abstract Lung cancer is the leading cause of cancer-related mortality worldwide. Understanding lung cancer evolutionary dynamics can help identify tools to intercept its growth and suggest strategies for treatment. Multiple factors can impact the tumors’ natural history and distinctly affect growth rate. However, research on the evolutionary trajectories of lung cancer across demographic or exposure scenarios remains inadequate. Additionally, the roles of mutational processes and complex genomic alterations on the evolution of lung cancer are still largely unexplored. In the largest genomic study of lung cancer to date, we analyzed deep whole-genome sequencing (~ 81x) and other omics data of 1217 lung cancers from the Sherlock-Lung study. To ensure adequate statistical power for identifying subclone architectures and constructing lung cancer evolutionary histories, we utilized a metric known as NRPCC (number of reads per tumor chromosomal copy) to select 542 lung adenocarcinoma (LUAD) samples for clonal evolution analyses, including 186 and 181 samples from never-smoker subjects of European and Asian ancestry, respectively, and 121 samples from smokers of European ancestry. We found that major driver genes and exogenous mutations contribute to tumor initiation, while copy number gains and endogenous processes appear later in tumor evolution. Tumors harboring EGFR mutations in never-smoker females of European descent show long latency, while tumors with KRAS mutations have shorter latency regardless of ancestry and sex. Notably, tumors harboring the mutational signature ID2 have short latency and aggressive phenotype, accompanied by increased genomic instability, elevated hypoxia scores, high CpG methylator phenotype, low neoantigen burden, and propensity to develop metastasis. We show that LINE-1 retrotransposition-induced mutagenesis contributes to the origin of ID2 mutations. The transcriptional factor ZNF695, a member of the KZFP family, up-regulated in LUAD, appears to contribute to LINE-1 retrotransposition through a dominant-negative effect and LINE-1 promoter demethylation. In a multivariate analysis of genomics, exposures and demographic factors, LUAD latency was most significantly associated with ID2, followed by EGFR mutations, KRAS mutations, and sex, suggesting an independent impact of these factors on LUAD evolution. Our findings underscore the complex interplay of ancestry, sex, exogenous mutagenesis, epigenetic regulation, and LINE-1 retrotransposition in shaping LUAD evolutionary trajectories, paving the way for potential targeted therapeutic interventions. Citation Format: Tongwu Zhang, Wei Zhao, Christopher Wirth, Marcos Díaz-Gay, Jinhu Yin, Phuc H. Hoang, Jian Sang, John McElderry, Alyssa Klein, Azhar Khandekar, Caleb Hartman, Jennifer Rosenbaum, Frank Colon-Matos, Kristine M. Jones, Neil E. Caporaso, Robert Homer, Angela C. Pesatori, Dario Consonni, Lixing Yang, Bin Zhu, Jianxin Shi, Kevin Brown, Nathaniel Rothman, Stephen J. Chanock, Ludmil B. Alexandrov, Jiyeon Choi, Maurizio Cardelli, Qing Lan, Martin A. Nowak, David C. Wedge, Maria Teresa Landi. Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB226.

Abstract SY02-03: Stochastic modeling of clonal evolution in carcinogenesis

Abstract In order to develop strategies that can detect cancer earlier, we need to better understand the dynamics of cancer evolution during the human lifetime. Starting from gestation, this includes measuring both the timing of early somatic mutations that lead to clonal mosaicism as well as the growth rates of mutated clones that carry higher potential for malignant transformation. Continuous observation of this evolving clonal composition across a lifetime is not feasible, therefore mathematical models are needed to integrate data from longitudinal and cross-sectional samples and infer these dynamics. Stochastic models of clonal evolution offer such a framework and provide a means for predicting individual clone trajectories into the future. Furthermore, if measurements of evolution are to be utilized clinically for early detection and patient risk stratification, faster methods to apply these models to data are required. To this end, we derived methods using coalescent theory for single-cell DNA sequencing data that enable instantaneous estimation of the growth rate of a clone along with its confidence intervals. Our tool is available in an open source R package, cloneRate, and provides the first method for constructing valid confidence intervals of growth rates analytically without having to rely on Bayesian inference techniques and complex simulations. When applying our methods to recent datasets derived from normal and neoplastic human blood cells, we quantified increased fitness effects of multi-hit driver mutations and found that higher initial clone growth rates led to decreased time to cancer diagnosis in patients. We can also use these models to estimate other important biological parameters that are difficult to measure in vivo such as bounds for total number of hematopoietic stem cells and expansion rates in early development. Ultimately, the ability to easily and quickly parameterize clonal dynamics in individual patients will benefit future early detection efforts and screening strategies for many cancer types. Citation Format: Kit Curtius. Stochastic modeling of clonal evolution in carcinogenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY02-03.

Concomitant medication, comorbidity and survival in patients with breast cancer

Between 30% and 70% of patients with breast cancer have pre-existing chronic conditions, and more than half are on long-term non-cancer medication at the time of diagnosis. Preliminary epidemiological evidence suggests that some non-cancer medications may affect breast cancer risk, recurrence, and survival. In this nationwide cohort study, we assessed the association between medication use at breast cancer diagnosis and survival. We included 235,368 French women with newly diagnosed non-metastatic breast cancer. In analyzes of 288 medications, we identified eight medications positively associated with either overall survival or disease-free survival: rabeprazole, alverine, atenolol, simvastatin, rosuvastatin, estriol (vaginal or transmucosal), nomegestrol, and hypromellose; and eight medications negatively associated with overall survival or disease-free survival: ferrous fumarate, prednisolone, carbimazole, pristinamycin, oxazepam, alprazolam, hydroxyzine, and mianserin. Full results are available online from an interactive platform (https://adrenaline.curie.fr). This resource provides hypotheses for drugs that may naturally influence breast cancer evolution.

A glycolytic metabolite bypasses “two-hit” tumor suppression by BRCA2

Knudson's "two-hit" paradigm posits that carcinogenesis requires inactivation of both copies of an autosomal tumor suppressor gene. Here, we report that the glycolytic metabolite methylglyoxal (MGO) transiently bypasses Knudson's paradigm by inactivating the breast cancer suppressor protein BRCA2 to elicit a cancer-associated, mutational single-base substitution (SBS) signature in nonmalignant mammary cells or patient-derived organoids. Germline monoallelic BRCA2 mutations predispose to these changes. An analogous SBS signature, again without biallelic BRCA2 inactivation, accompanies MGO accumulation and DNA damage in Kras-driven, Brca2-mutant murine pancreatic cancers and human breast cancers. MGO triggers BRCA2 proteolysis, temporarily disabling BRCA2's tumor suppressive functions in DNA repair and replication, causing functional haploinsufficiency. Intermittent MGO exposure incites episodic SBS mutations without permanent BRCA2 inactivation. Thus, a metabolic mechanism wherein MGO-induced BRCA2 haploinsufficiency transiently bypasses Knudson's two-hit requirement could link glycolysis activation by oncogenes, metabolic disorders, or dietary challenges to mutational signatures implicated in cancer evolution.

Identification of Lower Grade Glioma Antigens Based on Ferroptosis Status for mRNA Vaccine Development.

mRNA vaccines represent a promising and innovative strategy within the realm of cancer immunotherapy. However, their efficacy in treating lower-grade glioma (LGG) requires evaluation. Ferroptosis exhibits close associations with the initiation, evolution, and suppression of cancer. In this study, we explored the landscape of the ferroptosis-associated tumor microenvironment to facilitate the development of mRNA vaccines for LGG patients. Genomic and clinical data of the LGG patients was obtained from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Ferroptosis-related tumor antigens were identified based on differential expression, mutation status, correlation with antigen-presenting cells, and prognosis, relevance to immunogenic cell death (ICD). Antigen expression levels in LGG specimens and cell lines were validated using real time-polymerase chain reaction (RT-PCR). Consensus clustering was employed for patient classification. The immune landscapes of ferroptosis subtypes were further characterized, including immune responses, prognostic ability, tumor microenvironment, and tumor-related signatures. Five tumor antigens, namely, HOTAIR, IDO1, KIF20A, NR5A2, and RRM2 were identified in LGG. RT-PCR demonstrated higher expression of these genes in LGG compared to the control. Twelve gene modules and four ferroptosis subtypes (FS1-FS4) of LGG were defined. FS2 and FS4, characterized as "cold" tumors due to their decreased tumor mutation burden (TMB) and immune checkpoint proteins (ICPs), were deemed appropriate candidates for the mRNA vaccine. HOTAIR, IDO1, KIF20A, NR5A2, and RRM2 were identified as promising candidate antigens for the development of an LGG mRNA vaccine, particularly offering potential benefits to FS2 and FS4 patients.

Protein Interaction Map of APOBEC3 Enzyme Family Reveals Deamination-Independent Role in Cellular Function

Human APOBEC3 enzymes are a family of single-stranded (ss)DNA and RNA cytidine deaminases that act as part of the intrinsic immunity against viruses and retroelements. These enzymes deaminate cytosine to form uracil which can functionally inactivate or cause degradation of viral or retroelement genomes. In addition, APOBEC3s have deamination-independent antiviral activity through protein and nucleic acid interactions. If expression levels are misregulated, some APOBEC3 enzymes can access the human genome leading to deamination and mutagenesis, contributing to cancer initiation and evolution. While APOBEC3 enzymes are known to interact with large ribonucleoprotein complexes, the function and RNA dependence are not entirely understood. To further understand their cellular roles, we determined by affinity purification mass spectrometry (AP-MS) the protein interaction network for the human APOBEC3 enzymes and mapped a diverse set of protein–protein and protein–RNA mediated interactions. Our analysis identified novel RNA-mediated interactions between APOBEC3C, APOBEC3H Haplotype I and II, and APOBEC3G with spliceosome proteins, and APOBEC3G and APOBEC3H Haplotype I with proteins involved in tRNA methylation and ncRNA export from the nucleus. In addition, we identified RNA-independent protein-protein interactions with APOBEC3B, APOBEC3D, and APOBEC3F and the prefoldin family of protein-folding chaperones. Interaction between prefoldin 5 (PFD5) and APOBEC3B disrupted the ability of PFD5 to induce degradation of the oncogene cMyc, implicating the APOBEC3B protein interaction network in cancer. Altogether, the results uncover novel functions and interactions of the APOBEC3 family and suggest they may have fundamental roles in cellular RNA biology, their protein–protein interactions are not redundant, and there are protein-protein interactions with tumor suppressors, suggesting a role in cancer biology. Data are available via ProteomeXchange with the identifier PXD044275.

Abstract 6801: Cellular senescence involves spatial evolution in colorectal cancer

Abstract Cancer evolution is a continuous process involving cellular changes that promote progression and metastasis. Recent studies demonstrate that cancer evolution is spatially associated: a phenomenon called “the spatial evolution of cancer.” In our data, colorectal cancer cells gradually evolve from an adenoid to a collective invasion morphology, ultimately developing a partial epithelial-mesenchymal transition phenotype as they form an invasive front. Trajectory analysis showed that cancer cells at the invasive front of a tumor exhibit a totally different gene expression pattern from those at the center, and that cellular senescence plays a crucial role in this transition. Epigenetic changes in the promoter/exon1 region of the CDKN2A gene caused by increased reactive oxygen species level are crucial for expression of p16INK4A and induction of senescence in the cancer cells. Two types of senescent tumor cells (type I and type II) were observed, and they played different roles in cancer progression due to different gene expression. Type II senescent tumor cells (p16INK4A+/LAMC2+/MMP7+), a final evolved form of cancer cells, are strongly associated with local invasion and lymph node metastasis of colorectal cancer and worsen the prognosis for patients. Citation Format: Tae Jun Park, Seok Yun Kang, Jang Hee Kim, Hyun Woo Lee, Yong Won Choi, Soon Sang Park, Young-Kyoung Lee. Cellular senescence involves spatial evolution in colorectal cancer [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 6801.

Abstract 1627: Persistence and dynamics of mutation, selection, and epistasis during the somatic evolution of low-risk, high-risk, and metastatic prostate cancer

Abstract Driver genes and mutations responsible for prostate cancer tumorigenesis have been identified, but research has not previously quantified the effects of prostate cancer driver mutations, or how the magnitude of those effects vary with cancer stage and epistatic interactions among genes. Using 2699 prostate cancer tumor exomes, genomes, and panel sequences (1648 primary tumors and 1051 metastatic samples), we calculated the cancer effect size (CES)—the each mutation confers to lower-risk (Grade Group 1/2) and higher-risk (Grade Group 3/4/5) tumorigenesis and metastasis. The lower-risk, higher-risk, and metastatic tumors exhibited similar neutral rates at which mutations were introduced into DNA, with these underlying gene mutation rates generally increasing from lower-risk to higher risk to metastatic tumors. However, lower risk, higher risk, and metastatic tumors noticeably differed in the genes and specific somatic variants that were most highly selected for within each group. Therefore, to assess whether differences in natural selection that we observed among low-risk, high-risk, and metastatic tumor types were at least partly due to epistatic interactions, we applied a model of pairwise epistasis that quantified the effect of the presence or absence of a mutation in one gene on the selection for mutations in another gene. We found significant evidence of epistatic effects among driver genes. Our results indicate a very early role for SPOP in the development of prostate cancer: strength of selection for SPOP mutations is tightly associated with the degree to which the mutation ablates contact with the BRD3, and these disruptive mutations in turn increase the selection for mutations in several other tumor suppressors and oncogenes such as RHOA. Previously identified gene-gene interactions such as that between SPOP and PIK3CA were supported by this analysis, and we also identified novel gene-gene interactions such as those between AR and MUC16 occurring during metastasis. In conclusion, we have demonstrated that rates of mutation and strengths of selection on these driver mutations vary dynamically along the trajectories to lower-risk, higher-risk, and metastatic prostate cancer, and that powerful selective epistasis among driver genes provides an explanation for these differences. Because precision therapeutic targeting of somatic variant genes is fundamentally targeting the selective benefit obtained from that variant at the time of treatment, understanding these changes in stage-specific and somatic genotype-specific selective strengths will enable better therapeutic targeting in translational and clinical science. Citation Format: Moein Rajaei, Alexander Yang, Christopher N. Cross, J Nick Fisk, Elizabeth B. Perry, Jeffrey D. Mandell, Stephen G. Gaffney, Takafumi N. Yamaguchi, Julie Livingstone, Vincent L. Cannataro, Peter Humphrey, Jose Costa, Paul C. Boutros, Jeffrey P. Townsend. Persistence and dynamics of mutation, selection, and epistasis during the somatic evolution of low-risk, high-risk, and metastatic prostate cancer [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 1627.

Abstract 6925: Integrative single-cell tracking of genome evolution and tumor cell plasticity in small cell lung cancer (SCLC)

Abstract SCLC is the deadliest type of lung cancer, which is mostly diagnosed at late stages and accounts for 15% of pulmonary tumors. Even though chemotherapy is initially effective, patients with SCLC relapse quickly and develop resistant tumors. Genome studies revealed a universal loss of TP53 and RB1; recent transcriptional studies categorized SCLC based on the expression of 4 lineage transcription factors (TFs). We aimed to dissect how genomic cues impact transcriptional phenotypes, tumor cell plasticity and the dynamics of tumor and immune cell interactions, to thus decipher molecular mechanisms of phenotypic divergence and therapy resistance in SCLC. We performed genome sequencing and single cell transcriptome profiling of 55 tumors from patients with SCLC, including primary tumors, local and distant metastases and of paired relapsing tumors acquired after chemotherapy. Single cell sequencing was performed with the 10x Genomics platform and yielded > 200.000 cells. Transcriptome profiles at single cell levels revealed co-expression of at least two of the lineage TFs ASCL1, NEUROD1 or POU2F3 in each tumor. Copy number (cn) estimation for transcriptome data pointed to genomic events impacting transcriptional heterogeneity within the tumor. Furthermore, our data pointed to a marked intra- and interpatient, as well as to inter-metastatic heterogeneity. To specifically investigate how underlying genetic alterations affect molecular phenotypes, integrative studies across all patients were performed. This revealed shared transcriptional programs in tumors with amplifications of MYC family members; trajectory inference for cases with low and high-level copy gains of MYCN pointed to distinct transcriptional states. We performed multi-regional and longitudinal studies of matched patient cases to reconstruct the genomic patterns of clonal evolution, and projected genomic subsets to transcriptional profiles at single cell level, thus analyzing the effect of genome diversity on the transcriptional landscape. Additionally, tumor intrinsic profiles of heterogeneity were mapped to single cell data of patient tumors to elucidate the effects on tumor-immune cell interactions. All together our data points to a concerted regulation of a multitude of lineage factors and transcriptional programs in each SCLC tumor and provides a first comprehensive framework for the study of underlying genetic alterations that shape the transcriptional landscape and phenotypic plasticity in SCLC. Citation Format: Marcel Schmiel, Laura Kaiser, Maria Cartolano, Martin Peifer, Roman K. Thomas, Julie George. Integrative single-cell tracking of genome evolution and tumor cell plasticity in small cell lung cancer (SCLC) [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 6925.

Abstract 1610: Elucidating tumor evolution and heterogeneity in metastatic bladder cancer from rapid autopsies

Abstract Background: Bladder Cancer is a highly prevalent cancer with staggering mortality of about 20%. Metastatic BLCA (mBLCA) with variant histologies are aggressive and have a poor prognosis. The extent and impact of tumor heterogeneity in these variant subtypes are not fully understood. This work explores the intricate characterization of intra-patient and inter-patient tumor heterogeneity through genomic and transcriptomic analyses, drawing insights from a unique rapid autopsy cohort comprising 20 patients. The resulting revelations provide critical perspectives on heterogeneity and clonal evolution in mBLCA, underscoring the essential role of understanding these dynamics for the development of personalized treatment strategies. Approach: Our research focuses on the genetic and molecular characteristics of these tumors, including the vast landscape of tumor heterogeneity, clonal evolution, genomic alterations, mutational signatures, and structural variants. Within the tumor microenvironment, we identified diverse cell populations with distinct genetic profiles. A detailed examination of mutational signatures sheds light on the specific processes underlying genetic alterations, while the investigation of structural variants elucidates their consequential role in cancer development and progression. Advanced bioinformatics tools are deployed to process and interpret the vast genomic and transcriptomic datasets, facilitating the identification of clonal populations, their genetic features, and the trajectory of clonal evolution. Furthermore, statistical models are employed to discern correlations between mutational signatures, treatment responses, and clinical outcomes. Summary: The study unveils substantial inter-patient heterogeneity and discernible differences in mutational signatures, revealing intriguing correlations with treatment responses. These variations are intricately connected to underlying genomic alterations, copy number alterations, and gene expression patterns, offer a detailed perspective on the molecular landscape of mBLCA. Conclusion: This multi-omics exploration stands as the most extensive study of its kind, leveraging rapid autopsy samples of metastatic variant histology BLCA. The research provides unprecedented resolution of genomic alterations and intra- and inter-patient tumor heterogeneity. The exploration of clonal evolution enhances our appreciation of the temporal dynamics of tumor progression, establishing a robust foundation for future therapeutic interventions precisely targeting evolving tumor clones. As we advance into an era of personalized medicine, these findings pave the way for tailored therapeutic strategies in the challenging landscape of metastatic variant histology Bladder Cancer. Citation Format: Pushpa Itagi, Sonali Arora, Thomas Persse, Michael Yang, Patricia Galipeau, Yixin Lin, Funda Vakar-Lopez, John K. Lee, Petros Grivas, Robert B. Montgomery, Jonathan L. Wright, Hung-Ming Lam, Andrew Hsieh, Gavin Ha. Elucidating tumor evolution and heterogeneity in metastatic bladder cancer from rapid autopsies [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 1610.

Abstract 2326: Integrating single nucleotide variants (SNVs), copy number alterations (CNAs), and structural variants (SVs) into single-cell clonal lineage inference

Abstract Background: We extend algorithms for clonal lineage inference (``tumor phylogenetics'') from single-cell DNA sequence (scDNA-seq) data to accommodate broader classes of genetic variants, including single nucleotide variants (SNVs), copy number alterations (CNAs), and structural variants (SVs). Methods: We develop a computational method based on constrained optimization, posing a problem of inferring cellular lineage trees consistent with a given scDNA-seq data set constrained to follow a Dollo parsimony model for SNVs and SVs. We then seek an optimal tree consistent with these constraints so as to optimize for a CNA minimum evolution model balanced against measure of consistency of the different data types with one another relative to the reconstructed set of clones. We pose this problem as an integer linear program (ILP) that we solve heuristically via a coordinate descent algorithm. We tested the resulting method on a combination of synthetic scDNA-seq data and publicly available real data. Results: Results on synthetic data show the method to be effective at constructing lineage trees under a variety of parameter domains, including variations in noise levels, clone numbers, single cells, and mutation rates. We further show it to yield improved accuracy over widely used prior models that operate on only subsets of these variant types. Application to previously published scDNA-seq data demonstrate the method to be effective at inferring clonal lineage trees incorporating all three variant types. The results further show how the different kinds of variants can contribute in tandem to progression along single cell lineages or to the emergence of clones exhibiting hallmarks of distinct mechanisms of progression. Conclusion: The results support the effectiveness of our method in resolving accurate trees from scDNA-seq data including diverse variant types, as well as the value of considering these mutation types collectively in developing a comprehensive understanding of how various forms of somatic mutability together shape clonal evolution in cancers. Citation Format: Nishat Bristy, Xuecong Fu, Russell Schwartz. Integrating single nucleotide variants (SNVs), copy number alterations (CNAs), and structural variants (SVs) into single-cell clonal lineage inference [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 2326.

Abstract 3781: Tracking triple negative breast cancer (TNBC) evolution in the molecular residual disease (MRD) setting in the c-TRAK TN clinical trial

Abstract Background: Circulating tumor DNA (ctDNA) to track early tumor clones and to identify the point of origin of clones in relapsed disease, could provide novel insights into the impact of heterogeneity on the development of MRD and relapse in TNBC. Methods: c-TRAK TN recruited 161 patients (pts) with early TNBC into prospective ctDNA surveillance after curative therapy. Two distinct early tissue samples were provided, and a subset provided a relapse biopsy. Tissue was whole exome sequenced (WES) and relapse plasma DNA underwent error-corrected WES. Clonal evolution analysis was performed for pts who developed MRD with a minimum of 2 samples with WES (n=44), and non-relapse pts with samples before and after neoadjuvant chemotherapy (NAC) (n=16). Clonal structures were inferred with Pyclone; the founding clone as the cluster with highest cellular prevalence (CP). Clones were validated and tracked using the RaDaR ctDNA assay, supplemented with a median of 9 (range 6-45) variants specific to each clone. Cluster hierarchy and phylogenetic tree structure was derived using ClonEvol. Results: A median of 3 (range 1-5) clones per pt validated. Non-validated clones (12.9%) and pts with only 1 clone validated were removed, resulting in 56 pts for analysis. To assess the impact of spatial heterogeneity, contemporaneous paired early tissue was available from 17 pts who developed MRD. Primary tumor subclones were detected in the MRD or relapse in 88.9% (8/9) pts with a subclone detected in all regions, versus 37.5% (3/8) pts with a subclone unique to 1 region (p = 0.049). To assess the impact of NAC, paired tissue before and after NAC, was available in 32 pts. Distinct sub-clonal evolutionary patterns were identified. Subclones increased in CP or were first detected after NAC in 75% (24/32) pts. Subclones present prior to NAC were reduced or lost in 62% (20/32) pts. Sub-clonal persistence was observed in 46.8% (15/32) pts. Subclones first detected following NAC were detected in the MRD in 60% (6/10) pts. Following NAC, subclones reduced or lost re-emerged in the MRD in 30% (3/10) pts, versus subclones that persisted which were detected in the MRD in 100% (9/9) pts (p=0.003). In 21 pts, relapse samples were available. Relapse-unique subclones were identified in 57% (12/21) pts, with 2 new subclones in 1 pt. Relapse-unique subclones were not detected in MRD in 41.6% (5/12) pts, and were detected in the MRD in 66.7% (8/12) of pts. Conclusion: Polyclonal metastasis is a common mode of metastasis in TNBC, with frequent detection of primary tumor subclones in the metastasis. NAC provides an evolutionary bottleneck, with subclones detected post NAC being the most likely to persist into the MRD and subsequent relapse. Relapsed cancer frequently had subclones not detected in the primary cancer, with approximately half of these potentially late arising, and half detectable in MRD suggesting early establishment of diversity. Citation Format: Maria Coakley, Rosalind J. Cutts, Prithika Sritharan, Sarah Hrebien, Claire Swift, Kathryn Dunne, Lucy Kilburn, Katie Goddard, Guillermo Villacampa, Patricia Rojas, Warren Emmett, Christodoulos Pipinikas, Peter Hall, Catherine Harper-Wynne, Tamas Hickish, Iain Macpherson, Alicia Okines, Andrew Wardley, Duncan Wheatley, Simon Waters, Judith M. Bliss, Isaac Garcia-Murillas, Nicholas C. Turner. Tracking triple negative breast cancer (TNBC) evolution in the molecular residual disease (MRD) setting in the c-TRAK TN clinical trial [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 3781.

Abstract 6244: MultiTME: A Bayesian method for integration and analysis of multimodal, multi-patient spatial profiling data

Abstract The role of the tumor microenvironment (TME) in cancer evolution and progression is complex and multi-faceted. Multiple cellular phenotypes and patterns of cell-cell communication have been implicated as drivers of drug resistance and metastasis. The spatial architecture of the TME is emerging as particularly important in characterizing the trajectory of tumor growth and response to therapy. Accordingly, there has been a rapid rise in technologies to spatially profile the TME. Each technology focuses on a different modality (e.g. transcriptomics, proteomics, metabolomics) and operates at a different granularity, from coarse-grained tissue spots aggregating dozens of cells to fine-grained subcellular resolution. There is currently no computational method capable of drawing unified inferences across modalities. We introduce MultiTME, a single, holistic generative model for jointly modeling multimodal data across multiple samples in a cohort. MultiTME addresses these challenges through a two-dimensional integration of both samples and multimodal data. MultiTME incorporates a cohort of tissue samples, enabling the joint modeling of different samples within a single analytical framework. This integration allows for a comprehensive analysis of the spatial distribution of cell types across multiple samples, enhancing the understanding of TME variability and the identification of robust biomarkers. MultiTME also integrates multimodal data with varying resolutions, such as Spatial Transcriptomics (ST), single-cell RNA sequencing (scRNA-seq), and Imaging Mass Cytometry (IMC), and provides a multi-layered understanding of the TME. This integration enables the modeling of TME at a finer scale and the discovery of cell types and subtypes that are not distinguishable using single-modal approaches. It also provides concurrent expression profiles across different modalities, leading to the identification of novel biomarkers. We demonstrate MultiTME's efficiency and scalability with a dataset of coupled ST, scRNA-seq, and IMC from a cohort of 8 lung cancer patients. MultiTME's capability to handle both intra-cohort and inter-modality variability offers a panoramic view of TME and helps identify biomarkers and therapeutic targets for cancer screening and treatment. MultiTME is open source. Citation Format: Haoran Zhang, Jeff Quinn, Liron Yoffe, Vivek Mittal, Wesley Tansey. MultiTME: A Bayesian method for integration and analysis of multimodal, multi-patient spatial profiling data [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 6244.

Abstract 4254: Genetically engineered human lung organoid models for lung cancer evolution study

Abstract Initial cellular events in a transitional period from normal to cancer cells, such as transcriptomic or epigenetic dynamics, are poorly understood. Observing these is more challenging than in cancer progression, especially for humans. Mapping this phenomenon in early cancer evolution is crucial, as we can identify targetable molecules to inhibit cancer development at the precancerous stage. Immortalized human cancer cell lines hardly capture these appropriately as they have lost usual cellular characteristics. Normal organoids harbor stem cells and their differentiated cells, reflecting the epithelial physiology of tissues, and are more suitable models for cancer evolution studies. To establish a human cancer development model in vitro, we genetically engineered normal human airway organoids derived from adult stem cells into small cell lung cancer (SCLC). We used CRISPR-Cas9 systems to induce non-homologous end-joining in both TP53 and RB1, the most frequently mutated genes of SCLC. First, we knocked out TP53 of the airway organoids, selected by Nutlin-3a, an MDM2 antagonist, for 3-4 weeks, demonstrating 88-89% editing efficiencies calculated from Sanger sequencing results. Most mutations were one base pair deletion or insertion, which induced frameshift mutation in TP53. They did not show a significant difference from wild-type organoids morphologically at bright-field microscopy. Subsequently, we targeted RB1 on TP53-/- airway organoids to induce SCLC. We used Palbociclib, a CDK4/6 inhibitor, to enrich the mutated cells. They had 67.1% efficiency, and 20.4% had a 7-base pair deletion mutation. In the initial period, they seemed to have similar shapes to TP53-/- organoids. Unexpectedly and interestingly, however, we noticed more rapidly proliferating and disorganized organoids disrupting their original round shapes appeared in 4-5 weeks. The cells were indiscriminately piled up on the airway organoids' apical and basolateral surfaces, indicating their malignant traits. Their chaotic proliferation represented the rapidly progressing clinical feature of SCLC with a median survival of 7-16 months with treatment. This is the first work constructing the SCLC development model from the normal human airway organoid. By sequentially single-cell sequencing this organoid model changing to tumorous condition, we can capture the multi-omics landscape during the initial evolution of SCLC. Citation Format: Sungji Moon, Rokhyun Kim, Dakyung Lee, Kyunghyuk Park, Woochan Lee, Seyoon Lee, Young Jun Park, Jongil Kim. Genetically engineered human lung organoid models for lung cancer evolution study [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 4254.