Abstract Human cancer tissues are known to release their own DNA into body fluids such as plasma. These DNA molecules derived from tumors, collectively known as circulating tumor DNA (ctDNA), contain genetic information about disease progression at the time of sampling. Importantly, ctDNA has recently been utilized for capturing the whole-body disease profile and when sampled at various time points over the treatment, can inform the dynamic of clonal heterogeneity and its contribution to therapy failures. Metastatic castration-resistant prostate cancer (mCRPC) is a class of cancerous diseases with a high degree of clonal heterogeneity and high mortality which is largely due to the frequent emergence of therapy resistance. Therefore, reconstructing the clonal history for mCRPC diseases using ctDNA has great potential in identification of novel and targetable molecular mechanisms that drive mCRPC progression. To assess this possibility, we hypothesize that tumor clones that are responsible for therapy resistance carry distinct genomic profiles, which are captured in plasma ctDNA and could be computationally resolved through genomic sequencing and clonal reconstruction. To address this question, we obtained plasma cell-free DNA (cfDNA), a mixture of ctDNA and other tissue-derived DNA, from 38 individuals treated with combination PD-L1 and PARP inhibition involved in a recent clinical trial (NCT02484404). Whole-genome sequencing was performed using cfDNA and fragmented buffy coat DNA as germline control. cfDNA samples with a tumor fraction of lower than 10% were excluded. Various computational strategies were used to model the clonal structures of diseases and estimate the temporal order in which small and large structural variants emerged. Through this approach, we observed a negative association between cfDNA tumor fraction and therapy response. Multiple clonal evolutionary patterns leading to therapy failures were observed. For example, both gain and loss of activating androgen receptor mutations were detected in different cases following the treatment, which represents two distinct mechanisms of evading immunotherapy targeting. As a result, changes in the relevant transcriptional activities were also detected through ctDNA fragment-based computational inference. Additionally, clonal persistence was detected in multiple cases that exhibited stable diseases upon the treatment, which involves loss-of-function mutations in p53, ATM, and CDK12. To estimate the potential contribution to plasma ctDNA by metastatic diseases, multi-region whole genome and exome sequencing data of the tumors that are available for some cases prior to the treatment were also analyzed. Clones resolved from analyzing ctDNA were computationally mapped to the primary disease or metastatic tissue with a shared set of mutations in a subset of cases and may explain the patterns of treatment response. Our novel findings from this study will fill in a critical gap of knowledge about the complex genetic mechanisms driving mCRPC immunotherapy resistance. Citation Format: Chennan Li, Anna Baj, Clara C. Y. Seo, Nicholas T. Terrigino, John R. Bright, S. Thomas Hennigan, Isaiah M. King, Scott Wilkinson, Shana Y. Trostel, William D. Figg, William L. Dahut, Jung Min Lee, David Y. Takeda, Fatima Karzai, Adam G. Sowalsky. Tracing the clonal dynamic of metastatic castration-resistant prostate cancer over immunotherapy using circulating tumor DNA [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 PR005.

BackgroundPreclinical models recapitulating the metastatic phenotypes are essential for developing the next-generation therapies for metastatic prostate cancer (mPC). We aimed to establish a cohort of clinically relevant mPC models, particularly androgen receptor positive (AR+) bone metastasis models, from LuCaP patient-derived xenografts (PDX) that reflect the heterogeneity and complexity of mPC.MethodsPDX tumors were dissociated into single cells, modified to express luciferase, and were inoculated into NSG mice via intracardiac injection. The progression of metastases was monitored by bioluminescent imaging. Histological phenotypes of metastases were characterized by immunohistochemistry and immunofluorescence staining. Castration responses were further investigated in two AR-positive models.ResultsOur PDX-derived metastasis (PDM) model collection comprises three AR+ adenocarcinomas (ARPC) and one AR- neuroendocrine carcinoma (NEPC). All ARPC models developed bone metastases with either an osteoblastic, osteolytic, or mixed phenotype, while the NEPC model mainly developed brain metastasis. Different mechanisms of castration resistance were observed in two AR+ PDM models with distinct genotypes, such as combined loss of TP53 and RB1 in one model and expression of AR splice variant 7 (AR-V7) expression in another model. Intriguingly, the castration-resistant tumors displayed inter- and intra-tumor as well as organ-specific heterogeneity in lineage specification.ConclusionGenetically diverse PDM models provide a clinically relevant system for biomarker identification and personalized medicine in metastatic castration-resistant prostate cancer.

ABSTRACT Patients diagnosed with localized high-risk prostate cancer have higher rates of recurrence, and the introduction of neoadjuvant intensive hormonal therapies seeks to treat occult micrometastatic disease by their addition to definitive treatment. Sufficient profiling of baseline disease has remained a challenge in enabling the in-depth assessment of phenotypes associated with exceptional vs. poor pathologic responses after treatment. In this study, we report comprehensive and integrative gene expression profiling of 37 locally advanced prostate tumors prior to six months of androgen deprivation therapy (ADT) plus the androgen receptor (AR) inhibitor enzalutamide prior to radical prostatectomy. A robust transcriptional program associated with HER2 activity was positively associated with poor outcome and opposed AR activity, even after adjusting for common genomic alterations in prostate cancer including PTEN loss and expression of the TMPRSS2:ERG fusion. Patients experiencing exceptional pathologic responses demonstrated lower levels of HER2 and phospho-HER2 by immunohistochemistry of biopsy tissues. The inverse correlation of AR and HER2 activity was found to be a universal feature of all aggressive prostate tumors, validated by transcriptional profiling an external cohort of 121 patients and immunostaining of tumors from 84 additional patients. Importantly, the AR activity-low, HER2 activity-high cells that resist ADT are a pre-existing subset of cells that can be targeted by HER2 inhibition alone or in combination with enzalutamide. In summary, we show that prostate tumors adopt an AR activity-low prior to antiandrogen exposure that can be exploited by treatment with HER2 inhibitors. ClinicalTrials.gov registration: NCT02430480 .

Supplementary Data from Loss of KMT5C Promotes EGFR Inhibitor Resistance in NSCLC via LINC01510-Mediated Upregulation of MET

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.

Circulating tumor DNA (ctDNA) is a class of short tumor-derived DNA molecules that are typically detected in body fluids including plasma. Recent evidence suggests that certain molecular characteristics of ctDNA associated with various aspects of cancer transcriptome and ctDNA abundance can inform disease burden.1 These findings have shown the promise of utilizing liquid biopsy for tracing disease progression and guiding clinical decisions. Despite the increasing advances in ctDNA research and the diverse computational workflows developed to support such research, computational toolsets that tackle multiple critical questions at once are lacking yet are highly needed by the science community. To address this need, Li et al. recently developed a comprehensive toolkit for assessing cancer transcriptomic dynamics using the whole-genome sequencing (WGS) data of cell free DNA (cfDNA), which contains DNA of normal cells and ctDNA.2 This tool, named Integrated analysis toolkit for whole-genome-wide features of cfDNA (INAC), carries out multiple functions including generating whole-genome copy number profiles, estimating gene expression using fragment-based analyses, and identifying disease predictive features using machine learning (see Figure 1). Human cancers often undergo massive chromosomal rearrangements, resulting in aneuploidy that is detected through somatic copy number profiling. Depending on how much ctDNA species are captured (also known as the tumor or ctDNA fraction), the somatic copy number profile of a cfDNA sample can reflect tumor aneuploidy to a certain degree. In cases where multiple diseases co-occur in an individual, a mixture of the copy number alterations of both diseases is likely observed when analyzing cfDNA. Therefore, the copy number profile of a cfDNA sample may inform tumor burden over the time and disease classification, which is evaluated by the INAC_CNV module (see Table 1). In addition, multiple analyses of ctDNA fragmentation patterns that are provided by the toolkit are essential to transcriptomic modeling. The ctDNA species identified in plasma originate from fragmented DNA that remains bound by histones but is released from tumor cells in its nucleosome-bound form.9 Therefore, the distribution of ctDNA reads mapped to a genomic locus can be used to infer chromatin accessibility. In this context, open chromatin is associated with a higher percentage of short ctDNA fragments (or a high short/long fragment ratio) and a lower read abundance at promoters may indicate active gene expression, and vice versa10; see Figure 1). To enable the assessment of expression rate, INAC calculates the long-to-short fragment ratio and nucleosome-depleted region sequence depths at various regions flanking transcriptional start sites (TSSs) using the INAC_TSS module. Additionally, a recently published article described promoter fragmentation entropy (PFE) as another measure of gene expression state.5 The underlying basis is that highly expressed genes are associated with an open chromatin state and thus more diverse histone binding pattern, which is recognized as a greater variety of ctDNA fragment sizes with a high PFE (see Figure 1). The algorithm that assesses this fragmentation feature is also included in the INAC for evaluating expression states as INAC_PFE. Finally, one potential clinical application of ctDNA analysis is using its various molecular characteristics for predicting disease states and clinical outcome. Li et al. showed that ctDNA fragmentation patterns at TSSs could predict cancer and normal tissues better than other tested features using different machine learning (ML) methods. It is possible that additional fragmentation features that can be captured by the toolkit and have not yet been explored may be better associated with cancers, or a specific subtype of a cancer using the provided ML algorithm. Clearly, the accessibility to the toolset reveals new possibilities of identifying potential predictive features for a given disease. For researchers who are new to computational analysis of ctDNA, the toolkit described by Li et al. has particular benefits. The entire script was written in R, which is commonly used by biological scientists and therefore allows for easier troubleshooting and additional customization and code modifications. For instance, users can compare fragments with any sizes of interest and evaluate the fragmentation pattern at a specific set of genomic loci such as enhancers or non-coding RNAs. Gathering this information may foster new hypothesis testing to generate novel research ideas. For example, if a researcher wished to test whether a chromatin state is altered at genomic locations enriched for a transcriptional motif or regulatory sequence over the course of a therapy or a metastatic event, this use can be quickly assessed using INAC. A limitation of this toolset lies in its intentional design to assess various fragmentomic properties of ctDNA and not other recently discovered molecular features, such as base- or motif-level information for additional functional implications.11, 8 Unfortunately, INAC does not support ML modeling for distinguishing various disease types using ctDNA features, which may easily be addressed. Once additional molecular patterns of ctDNA are described that implicate other essential biological functions, further expanding this WGS-based toolkit would be beneficial for the rising ctDNA community. The INAC toolkit as a whole has significant future potential in aiding our understanding of cancer biology via the analysis of ctDNA and its assessment of clinical utility. The authors declare no conflict of interest.

Abstract EGFR inhibitors (EGFRi) are standard-of-care treatments administered to patients with non–small cell lung cancer (NSCLC) that harbor EGFR alterations. However, development of resistance posttreatment remains a major challenge. Multiple mechanisms can promote survival of EGFRi-treated NSCLC cells, including secondary mutations in EGFR and activation of bypass tracks that circumvent the requirement for EGFR signaling. Nevertheless, the mechanisms involved in bypass signaling activation are understudied and require further elucidation. In this study, we identify that loss of an epigenetic factor, lysine methyltransferase 5C (KMT5C), drives resistance of NSCLC to multiple EGFRis, including erlotinib, gefitinib, afatinib, and osimertinib. KMT5C catalyzed trimethylation of histone H4 lysine 20 (H4K20), a modification required for gene repression and maintenance of heterochromatin. Loss of KMT5C led to upregulation of an oncogenic long noncoding RNA, LINC01510, that promoted transcription of the oncogene MET, a component of a major bypass mechanism involved in EGFRi resistance. These findings underscore the loss of KMT5C as a critical event in driving EGFRi resistance by promoting a LINC01510/MET axis, providing mechanistic insights that could help improve NSCLC treatment. Significance: Dysregulation of the epigenetic modifier KMT5C can drive MET-mediated EGFRi resistance, implicating KMT5C loss as a putative biomarker of resistance and H4K20 methylation as a potential target in EGFRi-resistant lung cancer.

Mutated KRAS and TP53 are well recognized drivers of multiple human cancers including lung cancer, the leading cause of cancer-associated deaths. Yet, directly targeting mutated KRAS or restoring wildtype p53 levels in KRAS/p53-mutated tumors still remains a therapeutic challenge. A rational alternative approach involves targeting potentiators of KRAS;p53-driven tumorigenesis, which represents one of the research ambitions pioneered by the National Cancer Institute (NCI) RAS initiative to meet this challenge. Therefore, we aimed to identify genes, that when lost, potentiate cellular transformation of a non-transformed KRAS;TP53-mutated human bronchial epithelial cell line (HBEC-KP). To address this question, a genome-wide selection experiment was performed using CRISPR/Cas9. Anchorage-independent (AI) growth, a hallmark of cancer, was selected as a phenotypic readout for one experiment. Without additional genetic perturbation, wildtype HBEC-KP cells were incapable of AI growth. Only certain mutants were capable of supporting colony formation in soft agar. These clones were isolated and the integrated small guide RNAs (sgRNAs) were identified. Genes that are targeted by the sgRNAs were then individually knocked out for validation. Through this approach, we discovered that loss of ARPC3 is sufficient for promoting AI growth in the HBEC-KP cells. This contribution to AI growth is attributed to the role that ARPC3 plays in regulating Arp2/3 activity, as inactivating the Arp2/3 complex using a small molecule inhibitor, CK666 also promoted colony growth in the HBEC-KP cells. We determined that ARPC3 loss was not sufficient to drive AI – in the absence of KRAS activation and p53 loss colony growth was not observed. This indicates the essential role that mutated KRAS and/or p53 play to support AI. To test if mutated KRAS is essential for the phenotype, the Arp2/3 complex was inactivated in KRAS-mutated HBECs, which supported AI growth. This suggests that mutated KRAS and loss of Arp2/3 activity are sufficient for driving AI growth in HBECs. However, combined loss of p53 and ARPC3 failed to drive the phenotype, implicating that mutated KRAS is required. In addition, the Arp2/3 complex is essential for maintaining lamellipodial shape via actin filament network and cell motility, which was validated in our HBEC-KP-ARPC3 knockout cells. However, this protein complex is also known to promote AI growth and development of various cancers, as opposed to acting as a suppressor for growth. Multiple mechanisms that lead to this growth phenotype are currently being explored. Clinical relevance is also being closely examined to further understand the role of the Arp2/3 complex in cancer. Citation Format: Chennan Li, Andrea L. Kasinski. Loss of Arp2/3 activity promotes anchorage independent growth in KRAS;TP53-mutated human bronchial epithelial cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 838.

Pinpointing the underlying mechanisms that drive tumorigenesis in human patients is a prerequisite for identifying suitable therapeutic targets for precision medicine. In contrast to cell culture systems, mouse models are highly favored for evaluating tumor progression and therapeutic response in a more realistic in vivo context. The past decade has witnessed a dramatic increase in the number of functional genomic studies using diverse mouse models, including in vivo clustered regularly interspaced short palindromic repeats (CRISPR) and RNA interference (RNAi) screens, and these have provided a wealth of knowledge addressing multiple essential questions in translational cancer research. We compare the multiple mouse systems and genomic tools that are commonly used for in vivo screens to illustrate their strengths and limitations. Crucial components of screen design and data analysis are also discussed.

Understanding the ecology of phosphate solubilizing bacteria (PSBs) is critical for developing better strategies to increase crop productivity. In this study, the diversity of PSBs and of the total bacteria in the rhizosphere of eggplant (Solanum melongena L.) cultivated in organic, integrated and conventional farming systems was compared at four developmental stages of its lifecycle. Both selective culture and high-throughput sequencing analysis of 16S rRNA amplicons indicated that Enterobacter with strong or very strong in vivo phosphate solubilization activities was enriched in the rhizosphere during the fruiting stage. The high-throughput sequencing analysis results demonstrated that farming systems explained 23% of total bacterial community variation. Plant development and farming systems synergistically shaped the rhizospheric bacterial community, in which the degree of variation influenced by farming systems decreased over the plant development phase from 56% to 26.3% to 16.3%, and finally to no significant effect as the plant reached at fruiting stage. Pangenome analysis indicated that two-component and transporter systems varied between the rhizosphere and soil PSBs. This study elucidated the complex interactions among farming systems, plant development and rhizosphere microbiomes.

Efforts to search for better treatment options for cancer have been a priority, and due to these efforts, new alternative therapies have emerged. For instance, clinically relevant tumor-suppressive microRNAs that target key oncogenic drivers have been identified as potential anti-cancer therapeutics. MicroRNAs are small non-coding RNAs that negatively regulate gene expression at the posttranscriptional level. Aberrant microRNA expression, through misexpression of microRNA target genes, can have profound cellular effects leading to a variety of diseases, including cancer. While altered microRNA expression contributes to a cancerous state, restoration of microRNA expression has therapeutic benefits. For example, ectopic expression of microRNA-34a (miR-34a), a tumor suppressor gene that is a direct transcriptional target of p53 and thus is reduced in p53 mutant tumors, has clear effects on cell proliferation and survival in murine models of cancer. MicroRNA replacement therapies have recently been tested in combination with other agents, including other microRNAs, to simultaneously target multiple pathways to improve the therapeutic response. Thus, we reasoned that other microRNA combinations could collaborate to further improve treatment. To test this hypothesis miR-34a was used in an unbiased cell-based approach to identify combinatorial microRNA pairs with enhanced efficacy over miR-34a alone. This approach identified a subset of microRNAs that was able to enhance the miR-34a antiproliferative activity. These microRNA combinatorial therapeutics could offer superior tumor-suppressive abilities to suppress oncogenic properties compared to a monotherapeutic approach. Collectively these studies aim to address an unmet need of identifying, characterizing, and therapeutically targeting microRNAs for the treatment of cancer.

Abstract Despite the fact that the death rate continues to drop over the last decades, lung cancer is still by far the leading cause of cancer mortalities due to lack of highly accurate prediction method and effective targeted therapeutics. Thus, this calls for identification of novel biomarkers and therapeutic targets, particularly those targeting critical genes that drive lung cancer development and malignancy. KRAS and TP53 are two of the most commonly mutated genes in non-small cell lung cancer (NSCLC) which represents 85% of all cases in lung cancer. However, aberrant expressions of many other genes that act as drivers of lung cancer are yet unidentified. Particularly, microRNAs which are genome-encoded small RNA molecules are globally downregulated in many cancers, and disrupting microRNA biogenesis has been shown to promote tumor formation. We utilize the power of the CRISPR-Cas9 gene knockout system to screen for critical tumor suppressor genes and microRNAs in the human and mouse genomes that when lost, can drive neoplastic transformation of lung cells. Two non-cancerous mammalian lung model systems are used for this study. (1) The human bronchial epithelial cells that stably express KRAS G12V and TP53-targeting shRNA (HBEC-KP) are used as the baseline in the first approach. Importantly, the HBEC-KP cells are anchorage dependent and incapable of forming tumor in in vivo. We have transiently transfected HBEC-KP tdTomato-expressing cells with Cas9 and transduced the cells with lenti-sgRNA human library (A). Cells were either passaged in two-dimensions for over two-months or were selected for growth in soft agar assays. Resulting cells were harvested to identify sgRNAs enriched in each of the individual condition. Several known tumor suppressor genes and microRNAs (such as BRCA2, let-7a-3, miR-34a) are present among top hits, while MYC and TP53 are highly depleted, which suggests the validity of this data. Top hits will be selected and validated. (2) The second model being used is the KrasLSL-G12D mouse model. Genetic recombination induces lung hyperplasia in these mice, which will serve as the baseline to identify gene and microRNA knockouts that drive advanced tumor progression. The KrasLSL-G12D; RosaLSL-Cas9/LSL-Cas9 (KC) mice have been generated, and will be validated for Cre-induced Cas9 stable expression. KC mice will be intratracheally injected with the mixture of adeno-Cre and lenti-sgRNAs, and eventually sgRNAs that are highly enriched in individual tumors that develop will be identified through deep sequencing and bioinformatic analysis, and targeted genes and microRNAs downregulated in the tumors will be validated in functional assays and for loss in human NSCLC tumor samples. Citation Format: Chennan Li, Sagar M. Utturkar, Andrea L. Kasinski. Identifying genes and microRNAs that when lost, can drive neoplastic transformation of non-cancerous lung cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2349.