Integrative analysis of ultra-deep RNA-seq reveals alternative promoter usage as a mechanism of activating oncogenic programmes during prostate cancer progression.

Transcription factor (TF) proteins regulate gene activity by binding to regulatory regions, most importantly at gene promoters. Many genes have alternative promoters (APs) bound by distinct TFs. The role of differential TF activity at APs during tumour development is poorly understood. Here we show, using deep RNA sequencing in 274 biopsies of benign prostate tissue, localized prostate tumours and metastatic castration-resistant prostate cancer, that AP usage increases as tumours progress and APs are responsible for a disproportionate amount of tumour transcriptional activity. Expression of the androgen receptor (AR), the key driver of prostate tumour activity, is correlated with elevated AP usage. We identified AR, FOXA1 and MYC as potential drivers of AP activation. DNA methylation is a likely mechanism for AP activation during tumour progression and lineage plasticity. Our data suggest that prostate tumours activate APs to magnify the transcriptional impact of tumour drivers, including AR and MYC.

Stem-cell states converge in multistage cutaneous squamous cell carcinoma development.

Stem cells play a critical role in cancer development by contributing to cell heterogeneity, lineage plasticity, and drug resistance. We created gene expression networks from hundreds of mouse tissue samples (both normal and tumor) and integrated these with lineage tracing and single-cell RNA-seq, to identify convergence of cell states in premalignant tumor cells expressing markers of lineage plasticity and drug resistance. Two of these cell states representing multilineage plasticity or proliferation were inversely correlated, suggesting a mutually exclusive relationship. Treatment of carcinomas in vivo with chemotherapy repressed the proliferative state and activated multilineage plasticity whereas inhibition of differentiation repressed plasticity and potentiated responses to cell cycle inhibitors. Manipulation of this cell state transition point may provide a source of potential combinatorial targets for cancer therapy.

Single cell-transcriptomic analysis informs the lncRNA landscape in metastatic castration resistant prostate cancer.

Metastatic castration-resistant prostate cancer (mCRPC) is a lethal form of prostate cancer. Although long-noncoding RNAs (lncRNAs) have been implicated in mCRPC, past studies have relied on bulk sequencing methods with low depth and lack of single-cell resolution. Hence, we performed a lncRNA-focused analysis of single-cell RNA-sequencing data (n = 14) from mCRPC biopsies followed by integration with bulk multi-omic datasets. This yielded 389 cell-enriched lncRNAs in prostate cancer cells and the tumor microenvironment (TME). These lncRNAs demonstrated enrichment with regulatory elements and exhibited alterations during prostate cancer progression. Prostate-lncRNAs were correlated with AR mutational status and response to treatment with enzalutamide, while TME-lncRNAs were associated with RB1 deletions and poor prognosis. Finally, lncRNAs identified between prostate adenocarcinomas and neuroendocrine tumors exhibited distinct expression and methylation profiles. Our findings demonstrate the ability of single-cell analysis to refine our understanding of lncRNAs in mCRPC and serve as a resource for future mechanistic studies.

Understanding the impact of ammonium ion substitutions on heterogeneous ice nucleation.

Understanding the mechanisms underpinning heterogeneous ice nucleation in the presence of ionic inclusions is important for fields such as cryopreservation and for improved models of climate and weather prediction. Feldspar and ammonium are both present in significant quantities in the atmosphere, and experimental evidence has shown that feldspar can nucleate ice from ammonium-containing solutions at temperatures warmer than water alone. In recent work, Whale hypothesised that this increase in nucleation temperature is due to an increase in configurational entropy when an ammonium ion is included in the ice hydrogen bond network (T. F. Whale, J. Chem. Phys., 2022, 156, 144503). In this work, we investigate the impact of the inclusion of an ammonium ion on the hydrogen bond network by direct enumeration of the number of structures found using Rick's algorithm. We also determine the energy of these systems and thus compare the effects of enthalpy and entropy to test Whale's hypothesis. We find that the inclusion of an ammonium ion increases the total number of configurations under conditions consistent with a realistic surface charge. We also find that the enthalpic contribution is dominant in determining the location of the ammonium ion within the structure, although we note that this neglects other practicalities of ice nucleation.

Transcriptomic Heterogeneity of Expansile Cribriform and Other Gleason Pattern 4 Prostate Cancer Subtypes.

BACKGROUND: Prostate cancers featuring an expansile cribriform (EC) pattern are associated with worse clinical outcomes following radical prostatectomy (RP). However, studies of the genomic characteristics of Gleason pattern 4 subtypes are limited. OBJECTIVE: To explore transcriptomic characteristics and heterogeneity within Gleason pattern 4 subtypes (fused/poorly formed, glomeruloid, small cribriform, EC/intraductal carcinoma [IDC]) and the association with biochemical recurrence (BCR)-free survival. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective cohort study including 165 men with grade group 2-4 prostate cancer who underwent RP at a single academic institution (2016-2020) and Decipher testing of the RP specimen. Patients with Gleason pattern 5 were excluded. IDC and EC patterns were grouped. Median follow-up was 2.5 yr after RP for patients without BCR. OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSIS: Prompted by heterogeneity within pattern 4 subtypes identified via exploratory analyses, we investigated transcriptomic consensus clusters using partitioning around medoids and hallmark gene set scores. The primary clinical outcome was BCR, defined as two consecutive prostate-specific antigen measurements >0.2 ng/ml at least 8 wk after RP, or any additional treatment. Multivariable Cox proportional-hazards models were used to determine factors associated with BCR-free survival. RESULTS AND LIMITATIONS: In this cohort, 99/165 patients (60%) had EC and 67 experienced BCR. Exploratory analyses and clustering demonstrated transcriptomic heterogeneity within each Gleason pattern 4 subtype. In the multivariable model controlled for pattern 4 subtype, margin status, Cancer of the Prostate Risk Assessment Post-Surgical score, and Decipher score, a newly identified steroid hormone-driven cluster (hazard ratio 2.35 95% confidence interval 1.01-5.47) was associated with worse BCR-free survival. The study is limited by intermediate follow-up, no validation cohort, and lack of accounting for intratumoral and intraprostatic heterogeneity. CONCLUSIONS: Transcriptomic heterogeneity was present within and across each Gleason pattern 4 subtype, demonstrating there is additional biologic diversity not captured by histologic subtypes. This heterogeneity can be used to develop novel signatures and to classify transcriptomic subtypes, which may help in refining risk stratification following RP to further guide decision-making on adjuvant and salvage treatments. PATIENT SUMMARY: We studied prostatectomy specimens and found that tumors with similar microscopic appearance can have genetic differences that may help to predict outcomes after prostatectomy for prostate cancer. Our results demonstrate that further gene expression analysis of prostate cancer subtypes may improve risk stratification after prostatectomy. Future studies are needed to develop novel gene expression signatures and validate these findings in independent sets of patients.

AARDVARK: an automated reversion detector for variants affecting resistance kinetics.

SUMMARY: Resistance to two classes of FDA-approved therapies that target DNA repair-deficient tumors is caused by mutations that restore the tumor cell's DNA repair function. Identifying these "reversion" mutations currently requires manual annotation of patient tumor sequence data. Here we present AARDVARK, an R package that automatically identifies reversion mutations from DNA sequence data. AVAILABILITY AND IMPLEMENTATION: AARDVARK is implemented in R (≥3.5). It is available on GitHub at https://github.com/davidquigley/aardvark. It is licensed under the MIT license.

Interplay of multiple clusters and initial interface positioning for forward flux sampling simulations of crystal nucleation.

Forward flux sampling (FFS) is a path sampling technique widely used in computer simulations of crystal nucleation from the melt. In such studies, the order parameter underpinning the progress of the FFS algorithm is often the size of the largest crystalline nucleus. In this work, we investigate the effects of two computational aspects of FFS simulations, using the prototypical Lennard-Jones liquid as our computational test bed. First, we quantify the impact of the positioning of the liquid basin and first interface in the space of the order parameter. In particular, we demonstrate that these choices are key to ensuring the consistency of the FFS results. Second, we focus on the frequently encountered scenario where the population of crystalline nuclei is such that there are multiple clusters of size comparable to the largest one. We demonstrate the contribution of clusters other than the largest cluster to the initial flux; however, we also show that they can be safely ignored for the purposes of converging a full FFS calculation. We also investigate the impact of different clusters merging, a process that appears to be facilitated by substantial spatial correlations-at least at the supercooling considered here. Importantly, all of our results have been obtained as a function of system size, thus contributing to the ongoing discussion on the impact of finite size effects on simulations of crystal nucleation. Overall, this work either provides or justifies several practical guidelines for performing FFS simulations that can also be applied to more complex and/or computationally expensive models.

The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer.

Systemic targeted therapy in prostate cancer is primarily focused on ablating androgen signaling. Androgen deprivation therapy and second-generation androgen receptor (AR)-targeted therapy selectively favor the development of treatment-resistant subtypes of metastatic castration-resistant prostate cancer (mCRPC), defined by AR and neuroendocrine (NE) markers. Molecular drivers of double-negative (AR-/NE-) mCRPC are poorly defined. In this study, we comprehensively characterized treatment-emergent mCRPC by integrating matched RNA sequencing, whole-genome sequencing, and whole-genome bisulfite sequencing from 210 tumors. AR-/NE- tumors were clinically and molecularly distinct from other mCRPC subtypes, with the shortest survival, amplification of the chromatin remodeler CHD7, and PTEN loss. Methylation changes in CHD7 candidate enhancers were linked to elevated CHD7 expression in AR-/NE+ tumors. Genome-wide methylation analysis nominated Krüppel-like factor 5 (KLF5) as a driver of the AR-/NE- phenotype, and KLF5 activity was linked to RB1 loss. These observations reveal the aggressiveness of AR-/NE- mCRPC and could facilitate the identification of therapeutic targets in this highly aggressive disease. SIGNIFICANCE: Comprehensive characterization of the five subtypes of metastatic castration-resistant prostate cancer identified transcription factors that drive each subtype and showed that the double-negative subtype has the worst prognosis.

Gene networks reveal stem-cell state convergence during preneoplasia and progression to malignancy in multistage skin carcinogenesis.

Adult mammalian stem cells play critical roles in normal tissue homeostasis, as well as in tumor development, by contributing to cell heterogeneity, plasticity, and development of drug resistance. The relationship between different types of normal and cancer stem cells is highly controversial and poorly understood. Here, we carried out gene expression network analysis of normal and tumor samples from genetically heterogeneous mice to create network metagenes for visualization of stem-cell networks, rather than individual stem-cell markers, at the single-cell level during multistage carcinogenesis. We combined this approach with lineage tracing and single-cell RNASeq of stem cells and their progeny, identifying a previously unrecognized hierarchy in which Lgr6+ stem cells from tumors generate progeny that express a range of other stem-cell markers including Sox2, Pitx1, Foxa1, Klf5, and Cd44. Our data identify a convergence of multiple stem-cell and tumor-suppressor pathways in benign tumor cells expressing markers of lineage plasticity and oxidative stress. This same single-cell population expresses network metagenes corresponding to markers of cancer drug resistance in human tumors of the skin, lung and prostate. Treatment of mouse squamous carcinomas in vivo with the chemotherapeutic cis-platin resulted in elevated expression of the genes that mark this cell population. Our data have allowed us to create a simplified model of multistage carcinogenesis that identifies distinct stem-cell states at different stages of tumor progression, thereby identifying networks involved in lineage plasticity, drug resistance, and immune surveillance, providing a rich source of potential targets for cancer therapy.

Reversible epigenetic alterations mediate PSMA expression heterogeneity in advanced metastatic prostate cancer.

Prostate-specific membrane antigen (PSMA) is an important cell surface target in prostate cancer. There are limited data on the heterogeneity of PSMA tissue expression in metastatic castration-resistant prostate cancer (mCRPC). Furthermore, the mechanisms regulating PSMA expression (encoded by the FOLH1 gene) are not well understood. Here, we demonstrate that PSMA expression is heterogeneous across different metastatic sites and molecular subtypes of mCRPC. In a rapid autopsy cohort in which multiple metastatic sites per patient were sampled, we found that 13 of 52 (25%) cases had no detectable PSMA and 23 of 52 (44%) cases showed heterogeneous PSMA expression across individual metastases, with 33 (63%) cases harboring at least 1 PSMA-negative site. PSMA-negative tumors displayed distinct transcriptional profiles with expression of druggable targets such as MUC1. Loss of PSMA was associated with epigenetic changes of the FOLH1 locus, including gain of CpG methylation and loss of histone 3 lysine 27 (H3K27) acetylation. Treatment with histone deacetylase (HDAC) inhibitors reversed this epigenetic repression and restored PSMA expression in vitro and in vivo. Collectively, these data provide insights into the expression patterns and regulation of PSMA in mCRPC and suggest that epigenetic therapies - in particular, HDAC inhibitors - can be used to augment PSMA levels.

Kinetic control of competing nuclei in a dimer lattice-gas model.

Nucleation is a key step in the synthesis of a new material from a solution. The well-established lattice-gas models can be used to gain insight into the basic physics of nucleation pathways involving a single nucleus type. In many situations, a solution is supersaturated with respect to more than one precipitating phase. This can generate a population of both stable and metastable nuclei on similar timescales and, hence, complex nucleation pathways involving a competition between the two. In this study, we introduce a lattice-gas model based on two types of interacting dimers representing the particles in a solution. Each type of dimer nucleates to a specific space-filling structure. Our model is tuned such that stable and metastable phases nucleate on a similar timescale. Either structure may nucleate first, with a probability sensitive to the relative rate at which a solute is replenished from their respective reservoirs. We calculate these nucleation rates via forward flux sampling and demonstrate how the resulting data can be used to infer the nucleation outcome and pathway. Possibilities include direct nucleation of the stable phase, domination of long-lived metastable crystallites, and pathways in which the stable phase nucleates only after multiple post-critical nuclei of the metastable phase have appeared.

Intrinsic Molecular Subtypes of Metastatic Castration-Resistant Prostate Cancer.

PURPOSE: Although numerous biology-driven subtypes have been described previously in metastatic castration-resistant prostate cancer (mCRPC), unsupervised molecular subtyping based on gene expression has been less studied, especially using large cohorts. Thus, we sought to identify the intrinsic molecular subtypes of mCRPC and assess molecular and clinical correlates in the largest combined cohort of mCRPC samples with gene expression data available to date. EXPERIMENTAL DESIGN: We combined and batch-effect corrected gene expression data from four mCRPC cohorts from the Fred Hutchinson Cancer Research Center (N = 157), a small-cell neuroendocrine (NE) prostate cancer (SCNC)-enriched cohort from Weill Cornell Medicine (N = 49), and cohorts from the Stand Up 2 Cancer/Prostate Cancer Foundation East Coast Dream Team (N = 266) and the West Coast Dream Team (N = 162). RESULTS: Hierarchical clustering of RNA-sequencing data from these 634 mCRPC samples identified two distinct adenocarcinoma subtypes, one of which (adeno-immune) was characterized by higher gene expression of immune pathways, higher CIBERSORTx immune scores, diminished ASI benefit, and non-lymph node metastasis tropism compared with an adeno-classic subtype. We also identified two distinct subtypes with enrichment for an NE phenotype, including an NE-liver subgroup characterized by liver metastasis tropism, PTEN loss, and APC and SPOP mutations compared with an NE-classic subgroup. CONCLUSIONS: Our results emphasize the heterogeneity of mCRPC beyond currently accepted molecular phenotypes, and suggest that future studies should consider incorporating transcriptome-wide profiling to better understand how these differences impact treatment responses and outcomes.

Resistance to ATR Inhibitors Is Mediated by Loss of the Nonsense-Mediated Decay Factor UPF2.

Over one million cases of gastric cancer are diagnosed each year globally, and the metastatic disease continues to have a poor prognosis. A significant proportion of gastric tumors have defects in the DNA damage response pathway, creating therapeutic opportunities through synthetic lethal approaches. Several small-molecule inhibitors of ATR, a key regulator of the DNA damage response, are now in clinical development as targeted agents for gastric cancer. Here, we performed a large-scale CRISPR interference screen to discover genetic determinants of response and resistance to ATR inhibitors (ATRi) in gastric cancer cells. Among the top hits identified as mediators of ATRi response were UPF2 and other components of the nonsense-mediated decay (NMD) pathway. Loss of UPF2 caused ATRi resistance across multiple gastric cancer cell lines. Global proteomic, phosphoproteomic, and transcriptional profiling experiments revealed that cell-cycle progression and DNA damage responses were altered in UPF2-mutant cells. Further studies demonstrated that UPF2-depleted cells failed to accumulate in G1 following treatment with ATRi. UPF2 loss also reduced transcription-replication collisions, which has previously been associated with ATRi response, thereby suggesting a possible mechanism of resistance. Our results uncover a novel role for NMD factors in modulating response to ATRi in gastric cancer, highlighting a previously unknown mechanism of resistance that may inform the clinical use of these drugs. SIGNIFICANCE: Loss of NMD proteins promotes resistance to ATR inhibitors in gastric cancer cells, which may provide a combination of therapeutic targets and biomarkers to improve the clinical utility of these drugs.

Transcriptional profiling of matched patient biopsies clarifies molecular determinants of enzalutamide-induced lineage plasticity.

The androgen receptor (AR) signaling inhibitor enzalutamide (enza) is one of the principal treatments for metastatic castration-resistant prostate cancer (CRPC). Several emergent enza clinical resistance mechanisms have been described, including lineage plasticity in which the tumors manifest reduced dependency on the AR. To improve our understanding of enza resistance, herein we analyze the transcriptomes of matched biopsies from men with metastatic CRPC obtained prior to treatment and at progression (n = 21). RNA-sequencing analysis demonstrates that enza does not induce marked, sustained changes in the tumor transcriptome in most patients. However, three patients' progression biopsies show evidence of lineage plasticity. The transcription factor E2F1 and pathways linked to tumor stemness are highly activated in baseline biopsies from patients whose tumors undergo lineage plasticity. We find a gene signature enriched in these baseline biopsies that is strongly associated with poor survival in independent patient cohorts and with risk of castration-induced lineage plasticity in patient-derived xenograft models, suggesting that tumors harboring this gene expression program may be at particular risk for resistance mediated by lineage plasticity and poor outcomes.

Why ARNT Prostate Tumors Responding to Enzalutamide?

SUMMARY: Prostate tumors can develop resistance to androgen receptor (AR)-targeted therapies through treatment-induced changes in transcription factor activity that promote transcriptional and morphologic features of a neuroendocrine lineage. This study identifies an unexpected role for the circadian protein ARNTL in resistance to enzalutamide, a second-generation AR-targeted therapy. See related article by Linder et al., p. 2074 (4).

Deep whole-genome ctDNA chronology of treatment-resistant prostate cancer.

Circulating tumour DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring1. However, owing to past emphasis on targeted and low-resolution profiling approaches, our understanding of the distinct populations that comprise bulk ctDNA is incomplete2-12. Here we perform deep whole-genome sequencing of serial plasma and synchronous metastases in patients with aggressive prostate cancer. We comprehensively assess all classes of genomic alterations and show that ctDNA contains multiple dominant populations, the evolutionary histories of which frequently indicate whole-genome doubling and shifts in mutational processes. Although tissue and ctDNA showed concordant clonally expanded cancer driver alterations, most individual metastases contributed only a minor share of total ctDNA. By comparing serial ctDNA before and after clinical progression on potent inhibitors of the androgen receptor (AR) pathway, we reveal population restructuring converging solely on AR augmentation as the dominant genomic driver of acquired treatment resistance. Finally, we leverage nucleosome footprints in ctDNA to infer mRNA expression in synchronously biopsied metastases, including treatment-induced changes in AR transcription factor signalling activity. Our results provide insights into cancer biology and show that liquid biopsy can be used as a tool for comprehensive multi-omic discovery.

Androgen receptor reprogramming demarcates prognostic, context-dependent gene sets in primary and metastatic prostate cancer.

The androgen receptor (AR) is a prostate master transcription factor. It binds to genetic enhancers, where it regulates gene activity and plays a fundamental role in prostate pathophysiology. Previous work has demonstrated that AR-DNA binding is systematically and consistently reprogrammed during prostate tumorigenesis and disease progression. We charted these reprogrammed AR sites and identified genes proximal to them. We were able to devise gene lists based on AR status within specific histological contexts: normal prostate epithelium, primary prostate tumor, and metastatic prostate cancer. We evaluated expression of the genes in these gene sets in subjects from two distinct clinical cohorts-men treated with surgery for localized prostate cancer and men with metastatic prostate cancer. Among men with localized prostate cancer, expression of genes proximal to AR sites lost in the transition from normal prostate to prostate tumor was associated with clinical outcome. Among men with metastatic disease, expression of genes proximal to AR sites gained in metastatic tumors was associated with clinical outcome. These results are consistent with the notion that AR is fundamental to both maintaining differentiation in normal prostate tissue and driving de-differentiation in advanced prostate cancer. More broadly, the study demonstrates the power of incorporating context-dependent epigenetic data into genetic analyses.

Pulmonary lymphangitis sarcomatosis: a rare cause of severe progressive dyspnoea.

Pulmonary lymphangitis carcinomatosis is a complication of malignancy with a poor prognosis. We describe an unusual case in which it caused ventilatory failure and unfortunately death in a previously well male in his 70s. Abnormal chest imaging led to a wide differential diagnosis with Bronchoscopy confirming malignant cells. MRI of his pelvis and biopsy was done diagnosis of metastatic leiomyosarcoma, a particularly aggressive malignancy. Sarcoma-related lymphangitis carcinomatosis is rarely described in the literature and this is the first case to our knowledge of its association with leiomyosarcoma.

Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures.

We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.