Subclonal Heterogeneity Research Articles

Spatial transcriptomics reveals profound subclonal heterogeneity and T-cell dysfunction in extramedullary myeloma

AbstractExtramedullary disease (EMD) is a high-risk feature of multiple myeloma (MM) and remains a poor prognostic factor, even in the era of novel immunotherapies. Here, we applied spatial transcriptomics (RNA tomography for spatially resolved transcriptomics [tomo-seq] [n = 2] and 10x Visium [n = 12]) and single-cell RNA sequencing (n = 3) to a set of 14 EMD biopsies to dissect the 3-dimensional architecture of tumor cells and their microenvironment. Overall, infiltrating immune and stromal cells showed both intrapatient and interpatient variations, with no uniform distribution over the lesion. We observed substantial heterogeneity at the copy number level within plasma cells, including the emergence of new subclones in circumscribed areas of the tumor, which is consistent with genomic instability. We further identified the spatial expression differences between GPRC5D and TNFRSF17, 2 important antigens for bispecific antibody therapy. EMD masses were infiltrated by various immune cells, including T cells. Notably, exhausted TIM3+/PD-1+ T cells diffusely colocalized with MM cells, whereas functional and activated CD8+ T cells showed a focal infiltration pattern along with M1 macrophages in tumor-free regions. This segregation of fit and exhausted T cells was resolved in the case of response to T-cell–engaging bispecific antibodies. MM and microenvironment cells were embedded in a complex network that influenced immune activation and angiogenesis, and oxidative phosphorylation represented the major metabolic program within EMD lesions. In summary, spatial transcriptomics has revealed a multicellular ecosystem in EMD with checkpoint inhibition and dual targeting as potential new therapeutic avenues.

Abstract PO4-13-11: Epigenetically defined sub-clonal heterogeneity drives therapy resistance in triple-negative breast cancer

Abstract Background Triple-negative breast cancer (TNBC) is a highly aggressive and heterogeneous disease with an average survival of less than 50%. Although immunotherapy is showing promising results in early TNBC, many patients do not respond, and chemotherapy remains the main treatment. High recurrence rates in TNBC are in part due to its inherent molecular heterogeneity and sub-clonal diversity, whereby cells present in minority sub-populations escape therapeutic pressure. Recurrent genomic alterations are not selected for upon therapy resistance in neoadjuvant chemotherapy (NAC) resistant TNBC, however sub-clonal transcriptionally defined cells are “primed” to drive resistance and are pre-existing, suggesting phenotypic heterogeneity is governed by inheritable activation of epigenetically defined regulatory regions. Here, we sought to characterise the sub-clonal and spatial diversity of chemotherapy resistant TNBC to identify the epigenetic hallmarks of lethal sub-populations of cells that may induce cell state transition, therapy resistance and ultimately metastatic progression. Methods Sixteen primary untreated triple-negative breast tumours (eight chemotherapy sensitive (ChemoS) and eight chemotherapy resistant (ChemoR, who died of their disease within three years)) were subjected to cell-matched high-depth multiome single nuclei RNA- and ATAC-sequencing. Driver DNA mutations and copy number alterations (CNA’s) were identified using a clinically validated targeted capture panel. Four tumours were subject to spatial RNA and ATAC profiling. Fourteen TNBC PDX models generated from patients with residual disease post NAC were subjected to single nuclei sequencing and used for validation. Meta-clustering of epithelial cells from the RNA profiles was used to identify sub-populations of cells harbouring distinct epigenetic and transcriptomic features enriched in ChemoR patients. Findings were validated in the BrighTNess TNBC NAC clinical trial and in chemotherapy treated TNBC patients from the SCAN-B study. Results Unsupervised meta-clustering (MC) of epithelial cells in G1 phase of the cell cycle identified nine meta-clusters across the dataset. A distinct immune-like MC was contributed by epithelial G1 cells, suggesting a cross-talk of epithelial cells with spatially proximal immune cells. Two MCs were dominated by cells from ChemoR patients and were defined by pathways related to hypoxia, EMT, and extra-cellular matrix signalling. Using scATAC profiles, we identified that the sub-populations of cells in the ChemoR dominant MCs were underpinned by both promoter and distal differential chromatin accessibility, that mapped to H3K27ac enhancer sites, suggesting a role of distal enhancer chromatin modification in regulating MC transcription. None of the meta-clusters were dominated by tumours with specific genetic mutations or CNA’s. Spatial ATAC and RNA profiling of a subset of tumours identified spatial epigenetically defined regions that showed distinct chromatin profiles, suggesting that the epigenetic regulation of genes that define the MCs are spatially distinct. ChemoR clusters were independently reproduced in a cohort of fourteen residual disease TNBC PDXs. Genes pertaining to the immune-like MC were associated with a good prognosis in independent cohorts of contemporary treated TNBC (SCAN-B) and associated with response to NAC in the BrighTNess trial. Marker genes from ChemoR dominant MCs were associated with poor prognosis and significant levels of residual disease following NAC. Conclusions Our multi-modal integrative analysis reveals unprecedented insight into the role of epigenetically defined spatially distinct, sub-clonal populations that pre-exist in treatment naïve TNBC and predict therapy response. This suggests pre-existing populations of cells have already acquired the epigenetic footprints and hence transcriptomic features to allow them to evade therapy. Citation Format: Fatemeh Ahmadimoghari, Yu Zhang, Ioanna Mavrommati, Shefali Thakur, Chris Starling, Ioannis Roxanis, Syed Haider, Rachael Natrajan. Epigenetically defined sub-clonal heterogeneity drives therapy resistance in triple-negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-13-11.

Immune-tumor interaction dictates spatially directed evolution of esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a poor-prognostic cancer type with extensive intra- and inter-patient heterogeneity in both genomic variations and tumor microenvironment (TME). However, the patterns and drivers of spatial genomic and microenvironmental heterogeneity of ESCC remain largely unknown. Here, we generated a spatial multi-omic atlas by whole-exome, transcriptome, and methylome sequencing of 507 tumor samples from 103 patients. We identified a novel tumor suppressor PREX2, accounting for 22% of ESCCs with frequent somatic mutations or hyper-methylation, which promoted migration and invasion of ESCC cells in vitro. Analysis of the TME and quantification of subclonal expansion indicated that ESCCs undergo spatially directed evolution, where subclones mostly originated from the tumor center but had a biased clonal expansion to the upper direction of the esophagus. Interestingly, we found upper regions of ESCCs often underwent stronger immunoediting with increased selective fitness, suggesting more stringent immune selection. In addition, distinct TMEs were associated with variable genomic and clinical outcomes. Among them, hot TME was associated with high immune evasion and subclonal heterogeneity. We also found that immunoediting, instead of CD8+ T cell abundance, acts as an independent prognostic factor of ESCCs. Importantly, we found significant heterogeneity in previously considered potential therapeutic targets, as well as BRCAness characteristics in a subset of patients, emphasizing the importance of focusing on heterogeneity in ESCC targeted therapy. Collectively, these findings provide novel insights into the mechanisms of the spatial evolution of ESCC and inform precision therapeutic strategies.

Spatial transcriptomics reveals discrete tumour microenvironments and autocrine loops within ovarian cancer subclones

High-grade serous ovarian carcinoma (HGSOC) is genetically unstable and characterised by the presence of subclones with distinct genotypes. Intratumoural heterogeneity is linked to recurrence, chemotherapy resistance, and poor prognosis. Here, we use spatial transcriptomics to identify HGSOC subclones and study their association with infiltrating cell populations. Visium spatial transcriptomics reveals multiple tumour subclones with different copy number alterations present within individual tumour sections. These subclones differentially express various ligands and receptors and are predicted to differentially associate with different stromal and immune cell populations. In one sample, CosMx single molecule imaging reveals subclones differentially associating with immune cell populations, fibroblasts, and endothelial cells. Cell-to-cell communication analysis identifies subclone-specific signalling to stromal and immune cells and multiple subclone-specific autocrine loops. Our study highlights the high degree of subclonal heterogeneity in HGSOC and suggests that subclone-specific ligand and receptor expression patterns likely modulate how HGSOC cells interact with their local microenvironment.

Abstract 6941: Transcriptomic effects of retinoblastoma progression-related somatic copy number alteration (SCNA)

Abstract Retinoblastoma is a pediatric retinal cancer that initiates in response to biallelic RB1 loss or MYCN amplification in a cone precursor cell of origin. After initial emergence, RB1-/- retinoblastomas progress to more aggressive forms by accumulation of single nucleotide variants (SNVs) and highly recurrent somatic copy number alterations including 1q+, 2p+, 6p+ and 16q- (RB SCNAs), resulting in heterogeneous mixtures of subclonal cell populations. SCNAs are enriched in highly evolved and aggressive tumor subtypes and show prognostic value in retinoblastoma liquid biopsies. However, the transcriptomic effects of retinoblastoma SCNAs are poorly understood. Single cell methods may enable comparison of tumor subclones within emerging retinoblastoma tumors and thereby provide insight into tumor progression. We retrieved single cell RNA sequencing data for 25 retinoblastoma tumors and inferred SCNAs and subclone phylogenies from scRNAseq information using a recently published tool, Numbat. We identified tumors with clonal variation in RB SCNAs and excluded samples with poor transcriptome quality or excessive additional SCNAs. We proceeded with analysis of 11 tumors with subclonal heterogeneity for an SCNA of interest, including 10 with 1q+, eight with 2p+, four with 6p+, and six with 16q-. After removal of nontumor cells, we defined cell clusters, cluster marker genes, and cell cycle phase gene signatures, and we compared cell state distributions for subclones with an acquired SCNA relative to their immediate antecedents. These analyses revealed recurrently discriminated transcriptomic clusters corresponding to six discrete cell cycle states (post-mitotic G1, G1, G1/S, S, S/G2, G2/M) and variable cell stress states, including a cluster with hypoxia and heat shock related marker gene expression in all samples. In each tumor, SCNA-defined tumor subclones had a distinct cell state distribution. Tumor subclones with acquired 1q+ were enriched in G2 states and depleted in G1 states. Subclones with 2p+ were enriched in G2 states. Subclones with 6p+ were enriched in S phase states. Subclones with 16q- were diminished in post-mitotic cell states. Tumor clones with 1q+ and 16q- were most strongly enriched for G2 phase cells. Such 1q+/16q- tumor cells were significantly differentially distributed in three tumor replicates. Tumor clones with other RB SCNAs were enriched over diploid clones in proliferating G2 cells without SCNA specific effects. To identify candidate drivers of SCNA-specific transcriptomic effects we measured RB SCNA clonal variation in gene expression within and across transcriptomic states. We identified recurrently differentially expressed in-segment genes in 1q+, 2p+, 6p+, and 16q-, as well as out-of-segment genes. Finally, we propose molecular mechanisms underlying SCNA recurrence that may enable tumor evolution in retinoblastoma. Citation Format: Kevin Stachelek, David E. Cobrinik. Transcriptomic effects of retinoblastoma progression-related somatic copy number alteration (SCNA) [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 6941.

Abstract 4340: Profiling the evolutionary history of giant cancer cells in sarcomas: Hopeful monsters or an evolutionary dead end

Abstract Background: As tumors evolve, they accumulate somatic mutations that serve as an archeological record of their evolutionary past. Undifferentiated pleomorphic sarcomas (USARCs) are malignant soft tissue tumors that often contain scattered giant cancer cells, or potential ‘hopeful monster cells’, histologically. As USARCs frequently undergo whole genome doubling (WGD) and chromothripsis (Steele et al, 2019), both mutational events are likely to contribute to the formation of giant cancer cells, which frequently display atypical nuclear features such as polyploidy, micronuclei and extrachromosomal DNA. Giant cancer cells are also a common histological finding in other sarcoma types, such as dedifferentiated liposarcomas. Aims: This study aims to leverage single cell sequencing approaches to determine the evolutionary history and genomic landscape of giant cancer cells in sarcomas. Methods: 112 giant cancer cells identified in 8 USARCs, 1 dedifferentiated liposarcoma (DDLPS) and 1 myxofibrosarcoma by histology (i.e. nuclear size and shape, as well as multi-nucleation) were isolated via laser capture microdissection thereby retaining important spatial and morphological information. Once isolated, single cell DNA was extracted and amplified using the Ampli1 whole genome amplification (WGA) kit and underwent WGS. DNA was also estimated at a single cell level in one USARC using FACS. Once sorted into different ploidy populations, single nuclei also underwent WGS. Allele specific copy number aberrations (CNAs) were inferred in all single cells. Bulk tumour and normal WGS was also performed. Results: LCM isolated giant cancer cells exhibited extreme sub-clonal heterogeneity with some demonstrating de novo chromothripsis events that was not present in bulk WGS. FACS analysis and copy number inference of single cancer cells in one USARC also revealed the presence of three ploidy populations, including a haploid, a near diploid and a WGD population. Single cell WGS and copy number profiling demonstrated that following near genome scale haploidisation, multiple haploid cells underwent successive rounds of WGD. Conclusions: Chromothripsis and WGD emerges as major contributors to intra-tumor heterogeneity in sarcoma giant cancer cells. Single cell LCM and WGS revealed that some giant cancer cells contained de novo chromothripsis events that were not observed in bulk WGS. Multiple sub-clonal WGD events also permitted one USARC to continue to explore its fitness landscape contributing to ongoing tumor heterogeneity and lethality. Citation Format: Amy Bowes, Tom Lesluyes, Sara Waise, Annelien Verfaillie, Adrienne Flanagan, Jonas Demeulemeester, Maxime Tarabichi, Nischalan Pillay, Peter Van Loo. Profiling the evolutionary history of giant cancer cells in sarcomas: Hopeful monsters or an evolutionary dead end [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 4340.

Abstract 318: Demystifying tumor heterogeneity with a fully automated, high-throughput single-cell DNA-Seq (scDNA-seq) workflow

Abstract Objective: Genetic heterogeneity is a key factor underlying tumor drug resistance and metastatic potential. Understanding this heterogeneity is therefore vital to improving both prognosis and treatment. Next-generation sequencing is a valuable tool for analyzing the genetic makeup of tumors. However, bulk sequencing methods lack the sensitivity to fully resolve tumor heterogeneity. While single cell methods provide a powerful approach to dissect such heterogeneity, to date, these methods have been limited in their throughput. To address this bottleneck, we have developed a fully automated workflow for generating scDNA-seq libraries based on PicoPLEX® whole genome amplification (WGA) technology. This high-throughput method, which has been optimized on our ICELL8® cx Single-Cell System, enables the generation of WGA libraries for >1,000 single cells within one day. Methods: We first sought to demonstrate the ability of this new high-throughput method to generate WGA libraries of comparable quality to the standard PicoPLEX workflow in terms of genome coverage, GC bias, and other typical quality metrics. We additionally assessed copy number variant (CNV) detection sensitivity using two cell lines with well-characterized small segmental CNVs: GM22601 (~25Mb deletion on chromosome 4) and GM05067 (~45Mb gain on chromosome 9). We also analyzed a lymphoblastoid line (K562) that carries a range of chromosomal aneuploidies. A total of 1,288 single-cell WGA libraries were generated and sequenced to a depth of 250K paired-end reads per cell. Data analysis was done using the Ginkgo CNV pipeline with an average bin size of 500 kb. As a final proof of principal, we also generated single cell data using tumor and adjacent normal tissue from two clear cell renal cell carcinoma (ccRCC) samples. Results: Libraries generated using our high-throughput workflow had a high mapping rate, with 94.1% of the reads being uniquely mapped. Additionally, the libraries were comparable to those generated with the standard PicoPLEX workflow in terms of coverage uniformity, GC bias and other metrics. Moreover, the segmental aneuploidies in both GM22601 and GM05067 were reliably detected in >90% of cells at a read depth as low as 250,000 reads per cell. Analysis of the ccRCC samples revealed subclonal heterogeneity with various CNVs common to ccRCC, including deletion of chr. 3p, amplification of chr. 5q, and duplication of chr. 2. Conclusion: By adapting PicoPLEX technology to high throughput using the ICELL8 cx Single-Cell System, we have obtained single-cell WGA libraries from up to 1,200 cells at once and enabled the reliable detection of CNVs and tumor subclones at a shallow sequencing depth. In addition, by leveraging the automated nanoliter-dispensing capabilities of ICELL8 cx system this method provides a significant reduction in reagent use and labor compared to plate-based methods. Citation Format: Xuan Li, Raymond Mendoza, Samantha Leong, Hima Anbunathan, Mike Covington, Mohammad Fallahi, Bryan Bell, Shuwen Chen, Yue Yun, Andrew Farmer. Demystifying tumor heterogeneity with a fully automated, high-throughput single-cell DNA-Seq (scDNA-seq) workflow [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 318.

Abstract B058: Deconvoluting the intra-tumour heterogeneity and subclonal evolution of CDK4/6 inhibitor resistance in ER+ breast cancer

Abstract Subclonal heterogeneity and evolution are characteristics of breast cancer (BC), that play a key role in tumour development, progression, and resistance to current therapies. Preliminary single-cell sequencing studies suggest that subclonal heterogeneity drives therapy response in aggressive BC. Here, we sought to identify whether pre-existing transcriptomically defined cell subpopulations underpin resistance to CDK4/6 inhibitors (CDK4/6i) in ER+ BC, and to identify the distinct or shared resistance mechanisms between different CDK4/6i currently used in the clinic. ER+ BC cell lines, MCF7 and T47D, were infected with a lentiviral barcode library and were long-term exposed to DMSO or CDK4/6i (Abemaciclib or Palbociclib) until drug resistance was achieved. CDK4/6i resistant or DMSO cell lines were then subjected to DNA sequencing for the detection of barcodes coupled with single-cell transcriptomic and barcode sequencing, whole exome sequencing and G1/S cell cycle checkpoint analysis at a protein level. Single-cell lineage tracing identified that resistance to both CDK4/6i in MCF7 cells was stochastic, while in T47D cells, resistance to both CDK4/6i was conserved across all biological replicates. In all cell models, abemaciclib exerted a stronger selective pressure than palbociclib. Detection of barcodes from dead cells highlighted differential selective pressures of the drugs during resistance generation and upon resistance emergence. Single-cell transcriptomic sequencing identified shared and differential mechanisms of resistance between cell lines and specific CDK4/6i, suggestive of different subclonal, non-genetic mechanisms driving resistance. Some of these subclones were pre-defined (i.e. shared transcriptomic features with parental cells), whereas other resistant subclones were demarked by acquisition of pathway up-regulation. For instance, RB1 expression was abolished in the entire palbociclib-resistant T47D population, with copy number loss, indicating RB1 as a genetic driver of resistance. No evidence of RB1 loss was observed in MCF7 palbociclib-resistant clonal population, however a subclonal cluster was identified with loss of RB1 expression. Interestingly, abemaciclib-resistant MCF7 cells had lost RB1 protein expression, suggesting differential non-genetic mechanisms of resistance to CDK4/6i in MCF7 cells. Through single-cell lineage tracing, we have identified that resistance to CDK4/6i can be stochastic or conserved according to the cell model. Additionally, single-cell transcriptomic sequencing identified that in some cell models, cells that have the ability to become resistant are pre-existing, suggesting transcriptomic and epigenomic mechanisms of resistance, where cells exhibit drug-tolerant or drug-induced persistent properties. Citation Format: Ioanna Mavrommati, Fatemeh Ahmadi Moughari, Shefali Thakur, Yu Zhang, Lesley-Ann Martin, Syed Haider, Rachael Natrajan. Deconvoluting the intra-tumour heterogeneity and subclonal evolution of CDK4/6 inhibitor resistance in ER+ breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B058.

Abstract B059: Epigenetically defined sub-clonal heterogeneity drives therapy resistance in triple-negative breast cancer

Abstract Background: Triple-negative breast cancer (TNBC) is a highly aggressive and heterogeneous disease with an average survival of less than 50%. Although immunotherapy is showing promising results, many patients do not respond, and chemotherapy remains the main treatment. High TNBC recurrence rates are in part due to its molecular heterogeneity. Here, we sought to characterise the sub-clonal and spatial diversity of chemotherapy resistant TNBC to identify the epigenetic hallmarks of lethal sub-populations of cells that may induce cell state transition, therapy resistance and ultimately metastatic progression. Methods: Sixteen primary untreated triple-negative breast tumours (eight chemotherapy sensitive (ChemoS) and eight chemotherapy resistant (ChemoR, who died of their disease within three years)) were subjected to cell-matched high-depth multiome single nuclei RNA- and ATAC-sequencing. Four tumours were subject to spatial RNA and ATAC profiling. Fourteen TNBC PDX models generated from patients with residual disease post NAC were subjected to single nuclei sequencing and used for validation. Meta-clustering of epithelial cells from the RNA profiles was used to identify sub-populations of cells harbouring distinct epigenetic and transcriptomic features enriched in ChemoR patients. Findings were validated in the BrighTNess TNBC NAC clinical trial and in chemotherapy treated TNBC patients from the SCAN-B study. Results: Unsupervised meta-clustering (MC) of epithelial cells in G1 phase of the cell cycle identified nine MC’s across the dataset, including a distinct immune-like MC. Two MCs were dominated by cells from ChemoR patients and were defined by hypoxia, EMT, and extra-cellular matrix signalling. Sub-populations of cells in the ChemoR dominant MCs were underpinned by both promoter and distal differential chromatin accessibility, that mapped to H3K27ac enhancer sites, suggesting a role of enhancer regulation of MC transcription. Spatial ATAC and RNA profiling of a subset of tumours identified spatially epigenetically defined regions that showed distinct chromatin profiles, suggesting that the epigenetic regulation of genes that define the MCs are spatially distinct. ChemoR clusters were independently reproduced in a cohort of fourteen residual disease TNBC PDXs. Genes pertaining to the immune-like MC were associated with a good prognosis in independent cohorts of contemporary treated TNBC (SCAN-B) and with response to NAC in the BrighTNess trial. Marker genes from ChemoR dominant MCs were associated with poor prognosis and significant levels of residual disease following NAC. Conclusions: Our multi-modal integrative analysis reveals unprecedented insight into the role of epigenetically defined spatially distinct, sub-clonal populations that pre-exist in treatment naïve TNBC and predict therapy response. This suggests pre-existing populations of cells have already acquired the epigenetic footprints and hence transcriptomic features to allow them to evade therapy. Citation Format: Fatemeh Ahmadimoghari, Yu Zhang, Ioanna Mavrommatis, Shafali Thakur, Chris Starling, Ioannis Roxanis, Syed Haider, Rachael Natrajan. Epigenetically defined sub-clonal heterogeneity drives therapy resistance in triple-negative breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B059.

Abstract IA023: Spatial reconstruction and temporal inference of DNA methylation alterations in primary human colorectal cancer

Abstract Phenotypic plasticity is thought to assist immune evasion, metastasis, and therapeutic resistance in tumor evolution. Epigenetic mechanisms such as DNA methylation provide for a more easily acquired and flexible, yet semi-stable adaptation by the tumor cell than hardwired genetic changes. Cancer-associated gain of DNA methylation at promoter CpG islands or distal regulatory elements is associated with transcriptional silencing, whereas loss of DNA methylation is observed at late-replicating, lamina-attached regions of the genome, tracking with cumulative cell divisions. One of the most well-documented genomic features predisposing to DNA hypermethylation is Polycomb Repressive Complex (PRC) occupancy in precursor cells, consistent with an epigenetic block to cellular differentiation as an early or predisposing event in carcinogenesis. A central question is whether this epigenetic switch from reversible PRC repression to more stable DNA hypermethylation occurs before onset of clonal expansion, or whether it represents an ongoing process during tumor evolution. Clonal expansion is a pivotal characteristic of cancer, and is thought to be initiated by a genetic alteration in a key gatekeeper driver gene. However, not all normal cells appear to be susceptible to malignant transformation following such an event. Multiple lines of evidence suggest that epigenetic heterogeneity among normal cells may affect their cancer-initiating potential. We have developed a tumor processing pipeline to deeply and comprehensively map DNA methylation patterns in primary human colorectal cancer (CRC) and adjacent tissues, while preserving spatial information, enabling inference of the temporal order of molecular alterations. We employ a suite of technologies to map the spatial heterogeneity of DNA methylation patterns in primary human CRC. These include a deep single-cell whole-genome bisulfite sequencing technique that improves genomic coverage several-fold over existing published protocols, providing DNA methylation measurements at the majority of CpGs in the genome in individual tumor cells. We have also calibrated and validated an epigenetic clock that allows us to accurately measure the relative replicative history of individual cells. We infer phylogenetic relationships among subclones, and relate these to their spatial distribution in the primary tumor. We find that some tumors have high proportions of PRC-associated clonal hypermethylation, indicating that these hypermethylation events were already present prior to clonal expansion, possibly contributing to susceptibility to malignant transformation in the cell-of-origin, whereas other tumors appear to have active PRC-associated CpG island hypermethylation ongoing during tumor expansion, generating subclonal heterogeneity, which may influence post-therapeutic recurrence potential. Citation Format: Peter W. Laird, Nathan J. Spix, Hsiao-yun Milliron, Manpreet Kalkat, Emily Eugster, David Sokol, Emily Jung, Paula Nolte, Kelly K. Krzyzanowski, Toshinori Hinoue, Hui Shen. Spatial reconstruction and temporal inference of DNA methylation alterations in primary human colorectal cancer [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 IA023.

Single Cell RNA Sequencing Enables Identification of Mantle Cell Lymphoma Subclones with Drug-Resistant Profiles Already at Diagnosis and May be Used for Therapy Prognostication and Personalization

Mantle cell lymphoma (MCL) is a chronically relapsing B cell lymphoproliferative malignancy characterized by translocation t(11;14)(q13;q32), cyclin D1 overexpression and significant chromosomal instability. One of the main hypotheses of MCL relapses is a clonal evolution driven by therapy selection of subpopulations carrying adverse genetic and epigenetic aberrations. Our previous whole exome sequencing analysis of 25 MCL tumors showed that the majority of the detected single-nucleotide variants and copy number variants (CNV) were already detectable at diagnosis. To further analyze the subclonal structure of MCL at diagnosis and clonal evolution at relapse, we implemented single cell RNA sequencing analysis of five patients with MCL at diagnosis and at the first clinical relapse after failure of standard immunochemotherapy. All analyzed patients achieved at least partial remission. Fresh-frozen tumor cells were sorted to obtain Hoechst − CD45 + population which was processed using single cell 3′ protocol (10X Genomics). The libraries were paired-end sequenced on Novaseq 6000 (Illumina). The reads were aligned to GRCh38 human genome. The MCL cells were distinguished from the normal B cells based on of CCND1 expression and unsupervised clustering. CNVs were predicted using inferCNV package. These CNVs shared a high degree of similarity with the CNVs predicted using available exome sequencing data. The t-SNE representation of the data clearly showed unique positions for the malignant lymphocytes obtained from each of the five analyzed patients ( Figure 1a). In contrast, non-malignant cells (T cells, B cells and monocytes) have grouped together irrespective of the patient or compartment of origin ( Figure 1b). These data suggest two conclusions. First, each patient's malignant cells have unique genetic background. Second, the impact of the microenvironment does not override the impact of the genetic makeup of the non-malignant cells. The data thus suggest comparability of the malignant cells obtained from different compartments. Hierarchical clustering of the per-cell CNV profiles revealed subclonal heterogeneity of the MCL population in all analyzed compartments both at diagnosis and at relapse. In all analyzed patients the clustering identified several large subclones with significant differences (DG L) and at least one minor subclone (DG S) with significant similarities compared to the major clone detectable at relapse (R L). These data suggest that the majority of the diagnostic MCL cells (i.e., DG L clones) were indeed eliminated by therapy, which on clinical level corresponded to achieving remission. On the other hand, the data also suggest that MCL cells with CNV profiles highly similar to the major relapse clone (i.e., R L) were detectable already at diagnosis (i.e., DG S, Figure 1c). To gain a better insight into the biology of the DG S clones with potential drug-resistant phenotype, we analyzed transcriptome differences between DG S and DG L clones. For each patient we identified different sets of significantly upregulated genes in DG S compared to DG L. These included genes coding for important cell surface molecules ( MARCKS, CD52, CD79A, LTB), regulators of cell motility and cytoskeleton dynamics ( STMN1, SWAP70, CFL1, TUBB, TUBA1B, TMSB4X), transcription factor PRDM2, energy metabolism proteins ( MT-ND4, MT-CO1, ATP5MG), protein degradation proteins ( UBA52, PSMC4, UFD1, UBE2L3) and others. Among these genes, significant upregulation of TMSB4X, SWAP70, HLA-A, TUBB4B, RPS26, UBA52, PSMB2, SNRPG, ARPC1B, ATP5MG was detected in three out of five patients. Many of these genes code for oncogenes involved in cell motility and aggressiveness. To further characterize subclonal clusters in MCL population we used marker gene detection and subsequent gene set enrichment analysis, which identified cell adhesion molecules as major category upregulated in four out of five patients in clusters associated with relapse. Our data shows the applicability of the single cell genomic approaches not only to analyze subclonal heterogeneity for the study of disease biology, but also to identify drug-resistant subclones at diagnosis, predict response to various types of therapies thereby enabling therapy personalization. Acknowledgement: Supported by Grant Agency of the Czech Republic GA23-05474S and National Institute for Cancer Research (EXCELES) LX22NPO5102.

Acquisition of 1pLOH-1qGain Is a Frequent Trajectory in Expanding Relapsed/Refractory Myeloma Subclones

Genetic clonal heterogeneity is common in multiple myeloma (MM). However, the specific drivers of subclonal evolution associated with disease progression under therapeutic selection has not been fully characterized with the resource of sequential samples to accurately observe changes in relation to drug exposures. To address this gap, we generated whole-genome sequencing data for 2-4 sequential samples of 50 UK relapsed/refractory MM (rrMM) patients taken before, during and after acquisition of therapy resistance. These patients were exposed to a real-world range of multi-agent therapy combinations between 2011 and 2020 all of which included IMiDs. We undertook high-resolution subclonal heterogeneity analysis using Battenberg and multi-dimensional DPClust as gold-standard methods [Salcedo et al. 2020, PMID: 31919445] and implemented the Plackett-Luce model of partial rankings to time genomic events based on their cancer cell fraction and identify alternative evolutionary trajectories [Ansari-Pour et al. 2021, PMID: 34836952]. This approach enabled the identification and tracking of recurrent genomic aberrations gaining dominance amongst the evolving subclones of the disease in relation to therapeutic exposure and resistance acquisition, and to our knowledge is the first time that has been applied to analyze multi-timepoint myeloma WGS datasets. We observed recurrent selection of subclones harbouring 17p loss of heterozygosity (LOH) or 1pLOH with disease progression and therapy resistance acquisition in tumour phylogenies. In addition, clonal timing analysis of the longitudinal dataset suggested that sequential acquisition of 1pLOH and 1qGain in the same clone was a common selected trajectory (22% of cases). To validate this finding, the same trajectory analysis was undertaken in a much larger IMiD-refractory rrMM WGS as a validation dataset (N=386, Myeloma Genome Project), albeit with single time-point samples and compared with a set of unrelated newly diagnosed WGS dataset (N=198, IFM 2009) [Ansari-Pour et al. 2023, PMID: 36223594]. Strikingly, 1pLOH was found to be the only event that showed evidence for clonal selection in this case-control analysis (timing rank fold-change = 3.7, P<2.2x10 -16). Interestingly, the 1pLOH-1qGain trajectory (27% of cases; co-occurrence OR = 2.3 (95% CI 1.5-3.8), P=2x10 -4) was also identified in the validation dataset, corroborating the potential driverness of the dual chromosome 1 copy number events in subclonal expansion during therapy resistance acquisition. While this WGS-based analysis of selected evolutionary trajectories in relapsed/refractory myeloma patient tumours needs further confirmation with sequential WGS datasets from uniformly treated cohorts, these findings suggest potential collaborative genetic interaction between 1pLOH and 1qGain in the same cell as emerging co-drivers of resistance/disease progression, conferring a greater selective advantage under therapeutic exposure than either event on its own. If confirmed, these biomarkers may define a new ‘double hit’ in relapsed/refractory myeloma, with biological consequences that will require novel and directed therapeutic approaches.

Chronic lymphocytic leukemia patient-derived xenografts recapitulate clonal evolution to Richter transformation

Chronic lymphocytic leukemia (CLL) is a B-cell neoplasm with a heterogeneous clinical behavior. In 5–10% of patients the disease transforms into a diffuse large-B cell lymphoma known as Richter transformation (RT), which is associated with dismal prognosis. Here, we aimed to establish patient-derived xenograft (PDX) models to study the molecular features and evolution of CLL and RT. We generated two PDXs by injecting CLL (PDX12) and RT (PDX19) cells into immunocompromised NSG mice. Both PDXs were morphologically and phenotypically similar to RT. Whole-genome sequencing analysis at different time points of the PDX evolution revealed a genomic landscape similar to RT tumors from both patients and uncovered an unprecedented RT subclonal heterogeneity and clonal evolution during PDX generation. In PDX12, the transformed cells expanded from a very small subclone already present at the CLL stage. Transcriptomic analysis of PDXs showed a high oxidative phosphorylation (OXPHOS) and low B-cell receptor (BCR) signaling similar to the RT in the patients. IACS-010759, an OXPHOS inhibitor, reduced proliferation, and circumvented resistance to venetoclax. In summary, we have generated new RT-PDX models, one of them from CLL cells that mimicked the evolution of CLL to RT uncovering intrinsic features of RT cells of therapeutical value.

Abstract B010: A novel systemic methodology for mapping the clonal architectures of human prostate tumors that develop resistance to androgen receptor (AR)-targeted therapy

Abstract Developing resistance to therapeutics is a major factor leading to the high mortality rate of metastatic prostate cancer. One critical factor that mediates therapeutic resistance is the diversity of genetic mechanisms used by numerous cancer subpopulations that may be geographically co-mingled in the same physical tumor. Nonetheless, our fundamental knowledge about heterogenous mechanisms that globally contribute to drug resistance is lacking. Preliminary studies conducted by our group have shown that preexisting subclonal heterogeneity in patient tumors prior to treatment can impact clinical and pathologic outcomes. Therefore, we aimed to reconstruct tumor phylogenies and model subclonal evolution using genomic alterations identified in human prostate tumors using samples from a recent clinical trial (NCT02430480), in which patients received six months of intense androgen deprivation (ADT plus enzalutamide) prior to surgery. In this study, MRI/ultrasound-targeted biopsies were acquired at baseline, and whole mount prostatectomies were completely mapped to identify residual disease. To achieve our goal, we are performing laser capture microdissection to obtain samples of histologically distinct tumor from every tumor focus, both at baseline and post-treatment. Whole-exome sequencing (WES) is being used to identify genomic variations and then computationally map tumor subclones to the physical tissue. In addition, bulk whole-genome sequencing is being performed to resolve global subclonal architecture, to be further informed by the ground truth clonal exclusivity determined by WES of geographically distinct tumor foci. From these two complementary datasets, clonal relationships between each patient’s primary disease and its treatment-resistant counterpart will be determined and will reveal the genomic origins of drug resistance and their contribution to disease evolution. Further analyses will distinguish the evolutionary determinants of drug sensitivity (n=15 in this cohort) versus those patients with significant residual disease (n=22). To the best of our knowledge, this is the most comprehensive study mapping tumor subclones both temporally and spatially to understand the genetic origins of therapy resistance in human prostate cancer. Uncovering evolutionary tracks that each tumor utilizes for developing advanced diseases holds an unprecedented potential for effectively preventing disease from progressing. We expect that our analysis will uncover both known (AR variants and TMPRSS2:ERG fusion) and novel resistance mechanisms. Histopathological studies are being conducted in parallel for validating the spatial distribution of uncovered genetic drivers in each case. Establishment of this research framework will be essential to determining bona fide causes of various diseases from an evolutionary standpoint, which will be necessary for future precision medicine that is centered on timely and accurate clinical decision-making. This presentation will primarily be about the proposed research methodology and preliminary data will be discussed. Citation Format: Chennan Li, Scott Wilkinson, Shana Y. Trostel, William L. Dahut, Fatima Karzai, Adam G. Sowalsky. A novel systemic methodology for mapping the clonal architectures of human prostate tumors that develop resistance to androgen receptor (AR)-targeted therapy [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr B010.

Single-cell RNA sequencing revealed subclonal heterogeneity and gene signatures of gemcitabine sensitivity in pancreatic cancer.

Introduction: Resistance to gemcitabine is common and critically limits its therapeutic efficacy in pancreatic ductal adenocarcinoma (PDAC). Methods: We constructed 17 patient-derived xenograft (PDX) models from PDAC patient samples and identified the most notable responder to gemcitabine by screening the PDX sets in vivo. To analyze tumor evolution and microenvironmental changes pre- and post-chemotherapy, single-cell RNA sequencing (scRNA-seq) was performed. Results: ScRNA-seq revealed that gemcitabine promoted the expansion of subclones associated with drug resistance and recruited macrophages related to tumor progression and metastasis. We further investigated the particular drug-resistant subclone and established a gemcitabine sensitivity gene panel (GSGP) (SLC46A1, PCSK1N, KRT7, CAV2, and LDHA), dividing PDAC patients into two groups to predict the overall survival (OS) in The Cancer Genome Atlas (TCGA) training dataset. The signature was successfully validated in three independent datasets. We also found that 5-GSGP predicted the sensitivity to gemcitabine in PDAC patients in the TCGA training dataset who were treated with gemcitabine. Discussion and conclusion: Our study provides new insight into the natural selection of tumor cell subclones and remodeling of tumor microenvironment (TME) cells induced by gemcitabine. We revealed a specific drug resistance subclone, and based on the characteristics of this subclone, we constructed a GSGP that can robustly predict gemcitabine sensitivity and prognosis in pancreatic cancer, which provides a theoretical basis for individualized clinical treatment.

Differentially expressed markers between chemoresistant and non-chemoresistant cells in TNB

This study focuses on chemoresistance in triple-negative breast cancer (TNBC). Dataset GSE118389 and paper Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq from Simona Cristea from Dana-Farber Cancer Institute was used to discover genetic markers that are related to chemoresistance in TNBC. Differentially expressed genes between a chemoresistant cluster and non-chemoresistant clusters in various subpopulations of the TNBC samples were identified, compared, and analyzed. 1987 markers that are differentially expressed in the chemoresistant cluster and non- chemoresistant clusters were found.

Abstract 3124: Identification of sub-clonal loss of heterozygosity (LOH) in glioblastomas analyzed with spatial transcriptomics

Abstract We propose a method to leverage spatial transcriptomics and bioinformatic analysis to identify sub-clonal loss of heterozygosity that could otherwise be missed by standard bulk sequencing in glioblastoma tumors. Glioblastomas are highly invasive and aggressive brain tumors with a low five-year survival rate of less than 10%. Current treatment strategies are not sufficient for long term disease outcome, and innovative approaches to analyze glioblastoma are imperative for future improved options. Many factors contribute to difficulty of treatment, though one critical consideration is the diffuse nature and known intratumoral heterogeneity of this cancer. It is important to determine LOH heterogeneity, as this genomic alteration is associated with copy number variation and specific classifications of gliomas. Loss of heterozygosity is an irreversible deletion of DNA that is a common alteration in cancer. Application of spatial transcriptomics can increase our understanding of genomic complexity across a tissue, and unsupervised graph clustering can computationally separate spatial regions with similar gene expression patterns. Our method investigates spatial clusters individually at known heterozygous SNP sites to identify evidence for LOH with Bayesian inference and a hidden Markov model. Our analysis centers on the balance of reference (A) and non-reference (B) spatial transcriptomics read counts at SNPs of interest. B allele frequency is the number of reads aligned to the alternative allele divided by the total number of reads counted at the SNP. At a normal two-copy state at a heterozygous site, B allele frequency is ideally 50 percent. LOH can be identified as this value deviates toward 0 or 100, and copy number alterations impact this frequency. Our method utilizes Bayes factor values, or likelihood ratios between two models, using allele counts to provide support for either LOH or a heterozygous event. For our analysis, spatial transcriptomics BAMs are processed with a graph clustering algorithm and split into cluster-specific BAMs. We calculate allele count coverage for each cluster at pre-selected heterozygous SNP positions identified through germline exome sequencing. Bayes factor values are then calculated at each SNP, resulting in evidence for LOH or a heterozygous event. Chromosomal regions are segmented by a hidden Markov approach. Cumulative metrics for each segment are evaluated to determine a “state” label of heterozygous, LOH, or undefined. We tested our method on three WHO grade 4 IDH wild-type EGFRvIII positive fresh frozen glioblastoma tissue samples. Sub-clonal heterogeneity was identified in two samples and confirmed by bulk exome sequencing, bulk RNA sequencing, and analysis of initial clinical reports. Citation Format: Michelle G. Webb, Frances Chow, Carmel McCullough, Bohan Zhang, Kyle Hurth, John Carpten, Gabriel Zada, David W. Craig. Identification of sub-clonal loss of heterozygosity (LOH) in glioblastomas analyzed with spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3124.

Abstract 111: Characterizing subpopulation response to doxorubicin in a barcode-labeled triple negative breast carcinoma cell line

Abstract Despite recent advances in diagnosis and late-stage treatment, progression-free survival is not achieved for many cancer patients. Triple negative breast cancer (TNBC) poses a particular challenge as an aggressive and heterogeneous disease lacking the benefit of hormonal therapies. We have utilized a novel genetic lineage-tracking system, ClonMapper in TNBC cells to elucidate the pre-existent and de novo cell states that arise from chemotherapeutic intervention. ClonMapper genetic tags were stably integrated into MDA-MB-231 cells using lentiviral transduction and positively labeled cells were isolated with fluorescence-activated cell sorting to generate a starting library of ~500 uniquely barcoded cells. Barcode-labeled MDA-MB-231 were selected with doxorubicin at 250, 400 and 550 nM for 48 hours and allowed to recover. To determine the contribution of transcriptomic state to cellular drug response we carried out single cell RNA-sequencing (scRNA-seq) on 4 populations: a pretreatment cell library, an intermediary taken 30 h into drug selection, and two recovered populations from the 250 nM group. Importantly, we are able to capture and deconvolve ClonMapper lineage tags from scRNA-seq results and overlay them with transcriptomic state. Following treatment with doxorubicin we found a dose-dependent reduction in the number of unique lineages, with all replicates showing >6-fold decrease. Post-treatment replicates were each significantly comprised (90%) by fewer than three individual lineages. Lineage-resolved transcriptomics revealed two distinct lineage-specific subpopulations within the pretreatment cell library. These subpopulations demonstrated unique survivorship trajectories, beginning 30 h into selection and remaining well-separated following recovery from treatment. Comparing these subpopulations directly we observe enrichment for several pathways with markers genes remaining highly expressed post-treatment. Crucially, both subpopulations harbor a unique set of lineages which are found to survive treatment. However, in the absence of treatment we find that one subpopulation displays a faster growth rate and is likely to outcompete the other in culture conditions. The more proliferative subpopulation is marked by enrichment of Myc targets, compared with enrichment of glycolysis and KRAS signaling in the less proliferative subpopulation. This data demonstrates that subclonal heterogeneity inherent within this TNBC cell model, drives differential cell responses to chemotherapy. Citation Format: Daylin Morgan, Amy Brock. Characterizing subpopulation response to doxorubicin in a barcode-labeled triple negative breast carcinoma cell line [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 111.

Abstract 92: Deploying spatial transcriptomics to inform intratumoral heterogeneity in late-stage uveal melanoma leveraging advanced preclinical modeling and clinical samples

Abstract Uveal Melanoma (UM) is a rare tumor characterized by mutually-exclusive activating mutations in GNAQ or GNA11, followed by a mutation event in BAP1, SF3B1 or EIF1AX. Notably, a large subset of patients present with a unique chromosome 3 copy-loss event, which is highly associated with metastatic progression in late-stage disease. To date, the biology underlying the causes and consequences of this unique copy-loss event (Monosomy 3 or M3), and its role in promoting metastases remains largely unexplored. This can be attributed to both the lack of preclinical models and in-depth characterization of paired primary and metastatic tumors in patients. To address this, we derived a preclinical isogenic model of chromosome 3 copy-loss through CRISPR-based centromere targeting in a well characterized Disomy 3 UM cell line. Isogenic Disomy (D3) and Monosomy (M3) clones derived from these efforts have enabled us to develop patient derived xenograft (PDX) models to compare D3 and M3 behavior in paired, in vivo, primary and metastatic settings. This modeling has enabled us to employ spatial transcriptomics in a PDX setting to characterize gene expression signatures across unique D3 and M3 UM clones. Investigation of metastatic UM tumor heterogeneity in our models enables us to characterize unique features of phenotypically transformed clones (e.g. depigmentation and growth advantage). We have also adapted existing methodology to infer chromosomal copy number events from spatial transcriptomics data to our PDX system, where we are able to overcome the lack of same-species microenvironment controls. Our methodology allows for deep characterization of sub-clonal heterogeneity in both D3 and M3 settings and informs on the unique biology underlying invasiveness and outgrowth of M3 tumors. Finally, we complement our preclinical analyses with an investigation of spatial heterogeneity in a patient cohort of paired primary and metastatic tumors. More broadly, comparing these D3 vs. M3 signatures provides an opportunity to expand on our knowledge of metastatic disease drivers and derive prognostic signatures associated with poor survival. Citation Format: Sanjana Srinivasan, Johnathon Rose, Joseph R. Daniele, Michael Peoples, Meng He, Rosalba Minelli, Chiu-Yi Liu, Jason Gay, Melinda Soeung, Khalida Wani, Luigi Perelli, Dmitriy Loza, Ningping Feng, Christopher P. Vellano, Joseph Marszalek, Giulio F. Draetta, P. Andrew Futreal, Scott E. Woodman, Alexander J. Lazar, Timothy Heffernan, Alessandro Carugo, Giannicola Genovese, Virginia Giuliani. Deploying spatial transcriptomics to inform intratumoral heterogeneity in late-stage uveal melanoma leveraging advanced preclinical modeling and clinical samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 92.

Advances in the multi-omics landscape of follicular lymphoma

Follicular lymphoma (FL) is the most common indolent lymphoma originating from germinal center B cells. FL represents a clinically and biologically heterogeneous disease. Most patients have favorable outcomes, but a subset of patients experiences early progression or transformation and has a poor prognosis. Abnormalities in FL cells and tumor microenvironment have been revealed using multi-omics techniques, including genomic, epigenomic, transcriptomic and proteomic analysis. Recurrent somatic gene aberrations mainly involve epigenetic modifiers, transcription factors, oncogenic pathways and microenvironment modulators. Single-cell transcriptomic analysis show marked inter- and intra-patient FL subclone heterogeneity. In addition, a comprehensive profile of microenvironmental components is provided, unveiling the crosstalk between tumor and microenvironment that induce FL progression and facilitate immune escape. Together, these studies provide insights into the mechanisms and biomarkers of high-risk FL populations, as well as the potential targeted and immunotherapy options. Future research should focus on integrating multi-omics aberrations to optimize therapeutic strategies in FL.