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Objective: Mutant KRAS is a primary driver of pancreatic ductal adenocarcinoma (PDAC), which exhibits marked hypoxia. Despite the strong association of hypoxia and PDAC, the relationship between KRAS and hypoxia is still poorly understood. The oxygen-sensitive histone lysine demethylase, KDM5A, was recently reported to mediate epigenetic responses to hypoxia independent of the hypoxia-inducible factors. KDM5A epigenetically represses transcription via two mechanisms: by its demethylase activity on activating H3K4me3 marks, and through its interaction with deacetylase complexes containing HDAC1/2, which deacetylates activating H3K9ac and H4K16ac histone marks. Under hypoxic conditions, KDM5A loses its activity, leading to restoration of these H3K4me3 marks, thereby activating KDM5A target genes. The purpose of this study is to understand how KDM5A loss of function contributes to pathogenesis of mutant KRAS-driven pancreatic cancer. Methods: Cell lines derived from mice with pancreas-specific p53 deletion and doxycycline-inducible expression of KrasG12D (iKPC). Protein lysates were prepared using RIPA buffer. Histones were purified by acid extraction. Immunoblotting was used to probe for protein levels of Kdm5a, H3K4me3, H3K9ac, and H4K16ac. Genetic ablation of KrasG12D was performed by culturing iKPCs in tetracycline-free medium. Kras was induced by adding doxycycline to the culture medium. Pharmaceutical inhibition of MEK, proteasome, and Kdm5a were performed by treating iKPCs with mirdametinib, MG-132, and CPI-455, respectively. Knockdown of β-TrCP and FBXW7 were performed by stable expression of shRNA in iKPCs. Protein motif scanning was performed using the Eukaryotic Linear Motif (ELM) Prediction online software. Results: We discovered that Kdm5a protein levels were abrogated by induction of KrasG12D and stabilized by genetic ablation of Kras. Pharmaceutical inhibition of MEK or proteasome function stabilized Kdm5a, indicating that Kras induces Kdm5a proteasomal degradation through the MEK/ERK pathway. H3K4me3, H3K9ac, and H4K16ac histone marks were increased in Kras-on iKPC compared to Kras-off iKPC, consistent the expected effect of Kras-induced Kdm5a degradation. Pharmaceutical inhibition Kdm5a in the Kras-off setting restored H3K4me3, suggesting that Kdm5a contributes to demethylation of H3K4me3 in iKPC. We identified two phosphodegron sites corresponding to β-TrCP and FBXW7 of the ubiquitin ligase complex within the Kdm5a protein sequence. Knockdown of either β-TrCP or FBXW7 in iKPC both stabilized Kdm5a despite induction of Kras. Conclusion: We conclude that Kdm5a plays a tumor suppressor role in pancreatic cancer by epigenetically repressing transcriptional programs necessary for KRAS-driven oncogenesis. Citation Format: Jasper R. Chen, Jincheng Han, Cullen M. Taniguchi, Ronald A. DePinho. Loss of KDM5A supports KRAS-driven pancreatic cancer. [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 4757.

Aneuploidy, the phenomenon that genomes acquire or lose chromosomal fragments, has been causally implicated in a wide variety of human cancer. Defining which copy number alterations (CNAs) are pathogenic is an important goal of cancer research. However, data based on bulk samples cannot fully depict tumor heterogeneity and evolution, which occurs in single cells, and thus have limited power to discover CNAs useful for cancer diagnostics and therapeutics. Recent advances in single-cell DNA sequencing have enabled acquisition of single-cell copy number (SCCN) profiles in tens of thousands of cells, which potentiate reconstruction of copy number evolution lineage and discovery of novel cancer genes. However, current analytical approaches that infer clonal lineages are neither accurate, nor scalable to analyzing molecular features, particularly CNA profiles obtained from thousands of cells. Statistical routines have not been established to leverage lineage tracing results towards identifying cancer-related genes. Here, we present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles. In a MEDALT, each node represents a cell, each edge represents a kinship between two cells, arrows point towards younger cells, and the root represents a normal diploid cell. We also present a statistical routine named lineage speciation analysis, which facilitates discovery of fitness-associated alterations (FAAs) and genes from SCCN lineage trees. To evaluate MEDALT, we simulated copy number evolution under various CNA mechanisms such as genome doubling, breakage-fusion-bridge (BFB), etc. and spiked in FAAs. We found that MEDALT substantially improved accuracy of FAA identification over GISTIC and conventional phylogenetics methods such as maximum likelihood (ML), maximal parsimony (MP) and neighbor joining (NJ) trees. We applied our methods on the single-cell DNA-sequencing data acquired from 20 triple-negative breast cancer patients (TNBCs), 4 of which had longitudinal pre-, mid- and post- treatment samples. Most of the TNBC samples appeared to have developed through branched evolution via multiple parallel lineages with distinct mutation rates and DNA damage repair (DDR) loss based on the constructed MEDALT. Using our approaches, we discovered novel genes that are predictive of patient survival in TCGA breast cancer data and are functionally more essential than other control gene sets, based on the CRISPR-cas9 knockout screen data obtained from 27 breast cancer cell lines in the DepMap database. Significant benefits in lineage tracing and cancer gene discovery were also achieved, when applying our approaches on the SCCN profiles derived using InferCNV from the single-cell RNA sequencing data of a cohort of 20 multiple myeloma, head and neck cancer, oral squamous cell carcinoma and ovarian cancer patients. The source code of our study is available at https://github.com/KChen-lab/MEDALT. Citation Format: Fang Wang, Qihan Wang, Vakul Mohanty, Shaoheng Liang, Jinzhuang Dou, Jincheng Han, Darlan Minussi, Ruli Gao, Li Ding, Nicholas Navin, Ken Chen. Single-cell copy number heterogeneity tracing enabling cancer gene discovery [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-017.

Copy number alterations (CNAs) play an important role in cancer development and treatment. CNA profiles in single tumor cells preserve archaeological records of a tumor evolution, enabling retrospective lineage tracing. Although current single-cell sequencing technologies have enabled accurate, high-throughput acquisition of single-cell integer CNA profiles, existing computational methods are neither intelligent, i.e., unaware of CNA evolution mechanisms such as breakage-fusion-bridge, nor scalable to current datasets consisting of thousands of cells. To address this challenge, we developed a novel, efficient computational approach, Medatree (minimal event distance aneuploidy tree) to infer a cell lineage tree from the whole genome CNA profiles of a large number (N>1,000) of cells obtained from patient samples. A tree inferred by Medatree describes the minimal number of single-copy gain or loss events that may have occurred during the evolution, the genealogical relations amongst individual cells, and the significant CNAs associated with lineage expansion. We confirmed through simulation that Medatree substantially improves lineage tracing accuracy (e.g., 0.73 vs. 0.31) over conventional phylogeny methods (e.g., maximum parsimony). We then applied Medatree to single-cell DNA sequencing (scDNA-seq) data obtained from 20 triple-negative breast cancer patients (TNBCs), some of which had longitudinal biopsies through the course of chemotherapy. The trees constructed by Medatree corroborated well with the timing and phenotypes of the biopsies. They also revealed branches with variable CNA rates, likely associated with differentially altered genome instability. We further identified significantly copy-number altered genes through permutation of the single-cell CNA profiles. Compared to results obtained by applying convention methods (phylogenetic trees and GISTIC) on the same datasets, Medatree detected more genes (1.6-fold) that have been functionally associated with breast cancer development and treatment outcome, proving the power of single-cell sequencing. Moreover, it revealed potential convergent CNA (such as gain of IGF1R) occurring in parallel lineages of a tumor sample in 3 out of 20 TNBCs. Similar gains in discovery power were observed when applying Medatree on CNA profiles derived from single-cell RNA sequencing (scRNA-seq) data of 12 multiple myeloma patients. In summary, we developed a first-of-its-kind approach Medatree that enables large-scale retrospective CNA lineage tracing from single-cell copy number profiles obtained from single-cell DNA and RNA-sequencing data of patient samples. Wide application of Medatree is expected to accelerate the study of copy number evolution during tumor development and lead to discovery of novel targets for diagnoses and treatments. The source code of Medatree is available at https://github.com/KChen-lab/Medatree. Citation Format: Fang Wang, Qihan Wang, Jincheng Han, Vakul Mohanty, Shaoheng Liang, Darlan Conterno Minussi, Ruli Gao, Li Ding, Nicholas Navin, Ken Chen. Medatree enabling single cell copy number lineage tracing and functional discovery [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4424.