Copy number alteration burden predicts prostate cancer relapse

Machine learning based androgen receptor regulatory gene-related random forest survival model for precise treatment decision in prostate cancer

Background

It has been demonstrated that aberrant androgen receptor (AR) signaling contributes to the pathogenesis of prostate cancer (PCa). To date, the most efficacious strategy for the treatment of PCa remains to target the AR signaling axis. However, numerous PCa patients still face the issue of overtreatment or undertreatment. The establishment of a precise risk prediction model is urgently needed to distinguish patients with high-risk and select appropriate treatment modalities.

Methods

In this study, a consensus AR regulatory gene-related signature (ARS) was developed by integrating a total of 101 algorithm combinations of 10 machine learning algorithms. We evaluated the value of ARS in predicting patient prognosis and the therapeutic effects of the various treatments. Additionally, we conducted a screening of therapeutic targets and agents for high-risk patients, followed by the verification in vitro and in vivo.

Results

ARS was an independent risk factor for biochemical recurrence and distant metastasis in PCa patients. The enhanced and consistent prognostic predictive capability of ARS across various platforms was confirmed when compared with 44 previously published signatures. More importantly, PCa patients in the ARShigh group benefit more from PARP inhibitors and immunotherapy, while chemotherapy, radiotherapy, and AR-targeted therapy are more effective for ARSlow patients. The results of in silico screening suggest that AURKB could potentially serve as a promising therapeutic target for ARShigh patients.

Conclusions

Collectively, this prediction model based on AR regulatory genes holds great clinical translational potential to solve the dilemma of treatment choice and identify potential novel therapeutic targets in PCa.

Tumor evolution metrics predict recurrence beyond 10 years in locally advanced prostate cancer

Cancer evolution lays the groundwork for predictive oncology. Testing evolutionary metrics requires quantitative measurements in controlled clinical trials. We mapped genomic intratumor heterogeneity in locally advanced prostate cancer using 642 samples from 114 individuals enrolled in clinical trials with a 12-year median follow-up. We concomitantly assessed morphological heterogeneity using deep learning in 1,923 histological sections from 250 individuals. Genetic and morphological (Gleason) diversity were independent predictors of recurrence (hazard ratio (HR) = 3.12 and 95% confidence interval (95% CI) = 1.34-7.3; HR = 2.24 and 95% CI = 1.28-3.92). Combined, they identified a group with half the median time to recurrence. Spatial segregation of clones was also an independent marker of recurrence (HR = 2.3 and 95% CI = 1.11-4.8). We identified copy number changes associated with Gleason grade and found that chromosome 6p loss correlated with reduced immune infiltration. Matched profiling of relapse, decades after diagnosis, confirmed that genomic instability is a driving force in prostate cancer progression. This study shows that combining genomics with artificial intelligence-aided histopathology leads to the identification of clinical biomarkers of evolution.

Correlation of syndecan gene amplification with metastatic potential and clinical outcomes in carcinomas

Cell surface receptors play crucial roles in cellular responses to extracellular ligands, helping to modulate the functions of a cell based on information coming from outside the cell. Syndecan refers to a family of cell adhesion receptors that regulate both extracellular and cytosolic events. Alteration of syndecan expression disrupts regulatory mechanisms in a cell type-specific fashion, often leading to serious diseases, notably cancer. Given the multifaceted functions and distinct tissue distributions of syndecan, it will be important to unravel the gene-level intricacies of syndecan expression and thereby further understand its involvement in various carcinogenic processes. Although accumulating evidence indicates that the protein expression patterns of syndecan family members are significantly altered in cancer cells, the underlying gene-level mechanisms remain largely unknown. This review endeavors to explore syndecan gene expression levels across different cancer types by scrutinizing extensive cancer genome datasets using tools such as cBioPortal. Our analysis unveils that somatic mutations in SDC genes are rare occurrences, whereas copy number alterations are frequently observed across diverse cancers, particularly in SDC2 and SDC4. Notably, amplifications of SDC2 and SDC4 correlate with heightened metastatic potential and dismal prognosis. This underscores the recurrent nature of SDC2 and SDC4 amplifications during carcinogenesis and sheds light on their role in promoting cancer activity through augmented protein expression. The identification of these amplifications not only enriches our understanding of carcinogenic mechanisms but also hints at the potential therapeutic avenue of targeting SDC2 and SDC4 to curb cancer cell proliferation and metastasis.

Copy number signatures and CCNE1 amplification reveal the involvement of replication stress in high-grade endometrial tumors oncogenesis

PURPOSE

Managing high-grade endometrial cancer in Martinique poses significant challenges. The diversity of copy number alterations in high-grade endometrial tumors, often associated with a TP53 mutation, is a key factor complicating treatment. Due to the high incidence of high-grade tumors with poor prognosis, our study aimed to characterize the molecular signature of these tumors within a cohort of 25 high-grade endometrial cases.

METHODS

We conducted a comprehensive pangenomic analysis to categorize the copy number alterations involved in these tumors. Whole-Exome Sequencing (WES) and Homologous Recombination (HR) analysis were performed. The alterations obtained from the WES were classified into various signatures using the Copy Number Signatures tool available in COSMIC.

RESULTS

We identified several signatures that correlated with tumor stage and disctinct prognoses. These signatures all seem to be linked to replication stress, with CCNE1 amplification identified as the primary driver of oncogenesis in over 70% of tumors analyzed.

CONCLUSION

The identification of CCNE1 amplification, which is currently being explored as a therapeutic target in clinical trials, suggests new treatment strategies for high-grade endometrial cancer. This finding holds particular significance for Martinique, where access to care is challenging.

Dual inhibition of ATR and DNA-PKcs radiosensitizes ATM-mutant prostate cancer

In advanced castration resistant prostate cancer (CRPC), mutations in the DNA damage response (DDR) gene ataxia telangiectasia mutated ( ATM ) are common. While poly(ADP-ribose) polymerase inhibitors are approved in this context, their clinical efficacy remains limited. Thus, there is a compelling need to identify alternative therapeutic avenues for ATM mutant prostate cancer patients. Here, we generated matched ATM-proficient and ATM-deficient CRPC lines to elucidate the impact of ATM loss on DDR in response to DNA damage via irradiation. Through unbiased phosphoproteomic screening, we unveiled that ATM-deficient CRPC lines maintain dependence on downstream ATM targets through activation of ATR and DNA-PKcs kinases. Dual inhibition of ATR and DNA-PKcs effectively inhibited downstream γH2AX foci formation in response to irradiation and radiosensitized ATM-deficient lines to a greater extent than either ATM-proficient controls or single drug treatment. Further, dual inhibition abrogated residual downstream ATM pathway signaling and impaired replication fork dynamics. To circumvent potential toxicity, we leveraged the RUVBL1/2 ATPase inhibitor Compound B, which leads to the degradation of both ATR and DNA-PKcs kinases. Compound B effectively radiosensitized ATM-deficient CRPC in vitro and in vivo , and impacted replication fork dynamics. Overall, dual targeting of both ATR and DNA-PKcs is necessary to block DDR in ATM-deficient CRPC, and Compound B could be utilized as a novel therapy in combination with irradiation in these patients.

The yin and yang of chromosomal instability in prostate cancer

Metastatic prostate cancer remains an incurable lethal disease. Studies indicate that prostate cancer accumulates genomic changes during disease progression and displays the highest levels of chromosomal instability (CIN) across all types of metastatic tumours. CIN, which refers to ongoing chromosomal DNA gain or loss during mitosis, and derived aneuploidy, are known to be associated with increased tumour heterogeneity, metastasis and therapy resistance in many tumour types. Paradoxically, high CIN levels are also proposed to be detrimental to tumour cell survival, suggesting that cancer cells must develop adaptive mechanisms to ensure their survival. In the context of prostate cancer, studies indicate that CIN has a key role in disease progression and might also offer a therapeutic vulnerability that can be pharmacologically targeted. Thus, a comprehensive evaluation of the causes and consequences of CIN in prostate cancer, its contribution to aggressive advanced disease and a better understanding of the acquired CIN tolerance mechanisms can translate into new tumour classifications, biomarker development and therapeutic strategies.

Optimal systemic therapy in men with low-volume prostate cancer

PURPOSE OF REVIEW

Low-volume prostate cancer is an established prognostic category of metastatic hormone-sensitive prostate cancer. However, the term is often loosely used to reflect the low burden of disease across different prostate cancer states. This review explores the definitions of low-volume prostate cancer, biology, and current evidence for treatment. We also explore future directions, including the impact of advanced imaging modalities, particularly prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scans, on refining patient subgroups and treatment strategies for patients with low-volume prostate cancer.

RECENT FINDINGS

Recent investigations have attempted to redefine low-volume disease, incorporating factors beyond metastatic burden. Advanced imaging, especially PSMA PET, offers enhanced accuracy in detecting metastases, potentially challenging the conventional definition of low volume. The prognosis and treatment of low-volume prostate cancer may vary by the timing of metastatic presentation. Biomarker-directed consolidative therapy, metastases-directed therapy, and de-escalation of systemic therapies will be increasingly important, especially in patients with metachronous low-volume disease.

SUMMARY

In the absence of validated biomarkers, the management of low-volume prostate cancer as defined by CHAARTED criteria may be guided by the timing of metastatic presentation. For metachronous low-volume disease, we recommend novel hormonal therapy (NHT) doublets with or without consolidative metastasis-directed therapy (MDT), and for synchronous low-volume disease, NHT doublets with or without consolidative MDT and prostate-directed radiation. Docetaxel triplets may be a reasonable alternative in some patients with synchronous presentation. There is no clear role of docetaxel doublets in patients with low-volume disease. In the future, a small subset of low-volume diseases with oligometastases selected by genomics and advanced imaging like PSMA PET may achieve long-term remission with MDT with no systemic therapy.

ProstaMine: a bioinformatics tool for identifying subtype-specific co-alterations associated with aggressiveness in prostate cancer

Background

Prostate cancer is a leading cause of cancer-related deaths among men, marked by heterogeneous clinical and molecular characteristics. The complexity of the molecular landscape necessitates tools for identifying multi-gene co-alteration patterns that are associated with aggressive disease. The identification of such gene sets will allow for deeper characterization of the processes underlying prostate cancer progression and potentially lead to novel strategies for treatment.

Methods

We developed ProstaMine to systematically identify co-alterations associated with aggressiveness in prostate cancer molecular subtypes defined by high-fidelity alterations in primary prostate cancer. ProstaMine integrates genomic, transcriptomic, and clinical data from five primary and one metastatic prostate cancer cohorts to prioritize co-alterations enriched in metastatic disease and associated with disease progression.

Results

Integrated analysis of primary tumors defined a set of 17 prostate cancer alterations associated with aggressive characteristics. We applied ProstaMine to NKX3-1-loss and RB1-loss tumors and identified subtype-specific co-alterations associated with metastasis and biochemical relapse in these molecular subtypes. In NKX3-1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations known to regulate prostate cancer signaling pathways including MAPK, NF-kB, p53, PI3K, and Sonic hedgehog. In RB1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations involved in p53, STAT6, and MHC class I antigen presentation. Co-alterations impacting autophagy were noted in both molecular subtypes.

Conclusion

ProstaMine is a method to systematically identify novel subtype-specific co-alterations associated with aggressive characteristics in prostate cancer. The results from ProstaMine provide insights into potential subtype-specific mechanisms of prostate cancer progression which can be formed into testable experimental hypotheses. ProstaMine is publicly available at: https://bioinformatics.cuanschutz.edu/prostamine.

Predicting prostate cancer recurrence: Introducing PCRPS, an advanced online web server

Background

Prostate cancer (PCa) is one of the leading causes of cancer death in men. About 30% of PCa will develop a biochemical recurrence (BCR) following initial treatment, which significantly contributes to prostate cancer-related deaths. In clinical practice, accurate prediction of PCa recurrence is crucial for making informed treatment decisions. However, the development of reliable models and biomarkers for predicting PCa recurrence remains a challenge. In this study, the aim is to establish an effective and reliable tool for predicting the recurrence of PCa.

Methods

We systematically screened and analyzed potential datasets to predict PCa recurrence. Through quality control analysis, low-quality datasets were removed. Using meta-analysis, differential expression analysis, and feature selection, we identified key genes associated with recurrence. We also evaluated 22 previously published signatures for PCa recurrence prediction. To assess prediction performance, we employed nine machine learning algorithms. We compared the predictive capabilities of models constructed using clinical variables, expression data, and their combinations. Subsequently, we implemented these machine learning models into a user-friendly web server freely accessible to all researchers.

Results

Based on transcriptomic data derived from eight multicenter studies consisting of 733 PCa patients, we screened 23 highly influential genes for predicting prostate cancer recurrence. These genes were used to construct the Prostate Cancer Recurrence Prediction Signature (PCRPS). By comparing with 22 published signatures and four important clinicopathological features, the PCRPS exhibited a robust and significantly improved predictive capability. Among the tested algorithms, Random Forest demonstrated the highest AUC value of 0.72 in predicting PCa recurrence in the testing dataset. To facilitate access and usage of these machine learning models by all researchers and clinicians, we also developed an online web server (https://urology1926.shinyapps.io/PCRPS/) where the PCRPS model can be freely utilized. The tool can also be used to (1) predict the PCa recurrence by clinical information or expression data with high accuracy. (2) provide the possibility of PCa recurrence by nine machine learning algorithms. Furthermore, using the PCRPS scores, we predicted the sensitivity of 22 drugs from GDSC2 and 95 drugs from CTRP2 to the samples. These predictions provide valuable insights into potential drug sensitivities related to the PCRPS score groups.

Conclusion

Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment for PCa.

From complexity to clarity: unravelling tumor heterogeneity through the lens of tumor microenvironment for innovative cancer therapy

Despite the tremendous clinical successes recorded in the landscape of cancer therapy, tumor heterogeneity remains a formidable challenge to successful cancer treatment. In recent years, the emergence of high-throughput technologies has advanced our understanding of the variables influencing tumor heterogeneity beyond intrinsic tumor characteristics. Emerging knowledge shows that drivers of tumor heterogeneity are not only intrinsic to cancer cells but can also emanate from their microenvironment, which significantly favors tumor progression and impairs therapeutic response. Although much has been explored to understand the fundamentals of the influence of innate tumor factors on cancer diversity, the roles of the tumor microenvironment (TME) are often undervalued. It is therefore imperative that a clear understanding of the interactions between the TME and other tumor intrinsic factors underlying the plastic molecular behaviors of cancers be identified to develop patient-specific treatment strategies. This review highlights the roles of the TME as an emerging factor in tumor heterogeneity. More particularly, we discuss the role of the TME in the context of tumor heterogeneity and explore the cutting-edge diagnostic and therapeutic approaches that could be used to resolve this recurring clinical conundrum. We conclude by speculating on exciting research questions that can advance our understanding of tumor heterogeneity with the goal of developing customized therapeutic solutions.

GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network

MOTIVATION

Studying the molecular heterogeneity of cancer is essential for achieving personalized therapy. At the same time, understanding the biological processes that drive cancer development can lead to the identification of valuable therapeutic targets. Therefore, achieving accurate and interpretable clinical predictions requires paramount attention to thoroughly characterizing patients at both the molecular and biological pathway levels.

RESULTS

Here, we present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements the pathway-pathway interaction network. We train GraphPath to classify the cancer status of patients with prostate cancer based on their multi-omics profiling. Experiment results show that our method outperforms P-NET and other baseline methods. Besides, two external cohorts are used to validate that the model can be generalized to unseen samples with adequate predictive performance. We reduce the dimensionality of latent pathway embeddings and visualize corresponding classes to further demonstrate the optimal performance of the model. Additionally, since GraphPath's predictions are interpretable, we identify target cancer-associated pathways that significantly contribute to the model's predictions. Such a robust and interpretable model has the potential to greatly enhance our understanding of cancer's biological mechanisms and accelerate the development of targeted therapies.

AVAILABILITY AND IMPLEMENTATION

https://github.com/amazingma/GraphPath.

The genomic and evolutionary landscapes of anaplastic thyroid carcinoma

Anaplastic thyroid carcinoma is arguably the most lethal human malignancy. It often co-occurs with differentiated thyroid cancers, yet the molecular origins of its aggressivity are unknown. We sequenced tumor DNA from 329 regions of thyroid cancer, including 213 from patients with primary anaplastic thyroid carcinomas. We also whole genome sequenced 9 patients using multi-region sequencing of both differentiated and anaplastic thyroid cancer components. Using these data, we demonstrate thatanaplastic thyroid carcinomas have a higher burden of mutations than other thyroid cancers, with distinct mutational signatures and molecular subtypes. Further, different cancer driver genes are mutated in anaplastic and differentiated thyroid carcinomas, even those arising in a single patient. Finally, we unambiguously demonstrate that anaplastic thyroid carcinomas share a genomic origin with co-occurring differentiated carcinomas and emerge from a common malignant field through acquisition of characteristic clonal driver mutations.

Genomic evolution shapes prostate cancer disease type

The development of cancer is an evolutionary process involving the sequential acquisition of genetic alterations that disrupt normal biological processes, enabling tumor cells to rapidly proliferate and eventually invade and metastasize to other tissues. We investigated the genomic evolution of prostate cancer through the application of three separate classification methods, each designed to investigate a different aspect of tumor evolution. Integrating the results revealed the existence of two distinct types of prostate cancer that arise from divergent evolutionary trajectories, designated as the Canonical and Alternative evolutionary disease types. We therefore propose the evotype model for prostate cancer evolution wherein Alternative-evotype tumors diverge from those of the Canonical-evotype through the stochastic accumulation of genetic alterations associated with disruptions to androgen receptor DNA binding. Our model unifies many previous molecular observations, providing a powerful new framework to investigate prostate cancer disease progression.

Machine learning-based extrachromosomal DNA identification in large-scale cohorts reveals its clinical implications in cancer

The clinical implications of extrachromosomal DNA (ecDNA) in cancer therapy remain largely elusive. Here, we present a comprehensive analysis of ecDNA amplification spectra and their association with clinical and molecular features in multiple cohorts comprising over 13,000 pan-cancer patients. Using our developed computational framework, GCAP, and validating it with multifaceted approaches, we reveal a consistent pan-cancer pattern of mutual exclusivity between ecDNA amplification and microsatellite instability (MSI). In addition, we establish the role of ecDNA amplification as a risk factor and refine genomic subtypes in a cohort from 1015 colorectal cancer patients. Importantly, our investigation incorporates data from four clinical trials focused on anti-PD-1 immunotherapy, demonstrating the pivotal role of ecDNA amplification as a biomarker for guiding checkpoint blockade immunotherapy in gastrointestinal cancer. This finding represents clinical evidence linking ecDNA amplification to the effectiveness of immunotherapeutic interventions. Overall, our study provides a proof-of-concept of identifying ecDNA amplification from cancer whole-exome sequencing (WES) data, highlighting the potential of ecDNA amplification as a valuable biomarker for facilitating personalized cancer treatment.

Acquired copy number variation in prostate tumours: a review of common somatic copy number alterations, how they are formed and their clinical utility

Prostate cancer is one of the most commonly diagnosed cancers in men and unfortunately, disease will progress in up to a third of patients despite primary treatment. Currently, there is a significant lack of prognostic tests that accurately predict disease course; however, the acquisition of somatic chromosomal variation in the form of DNA copy number variants may help understand disease progression. Notably, studies have found that a higher burden of somatic copy number alterations (SCNA) correlates with more aggressive disease, recurrence after surgery and metastasis. Here we will review the literature surrounding SCNA formation, including the roles of key tumour suppressors and oncogenes (PTEN, BRCA2, NKX3.1, ERG and AR), and their potential to inform diagnostic and prognostic clinical testing to improve predictive value. Ultimately, SCNAs, or inherited germline alterations that predispose to SCNAs, could have significant clinical utility in diagnostic and prognostic tests, in addition to guiding therapeutic selection.

Association between copy number alterations estimated using low-pass whole genome sequencing of formalin-fixed paraffin-embedded prostate tumor tissue and cancer-specific clinical parameters

Copy number alterations (CNAs) are frequently observed in early-stage prostate cancer and are associated with disease recurrence and tumor aggressiveness. Cost-effective assessment of CNAs could enhance clinical utility of CNAs. Here, we combined the cost-effectiveness of low-pass (low coverage) whole genome sequencing (LPWGS) and the routine availability of formalin-fixed paraffin-embedded (FFPE) tumor tissue for assessing CNAs in a cohort of 187 men with early-stage localised prostate cancer. We detected well known CNAs in 8p, 8q, 13q and 16q and recurrent gains of the oncogene MYC and losses of the tumor suppressor genes NKX3-1, PTEN and RB1, indicating assay reliability. The estimated burden of CNAs was significantly associated with Gleason score, pathological T stage, surgical margin status and biochemical recurrence. Further, genomic losses or gains in specific chromosomal arms were significantly associated with worse BCR-free survival. Copy number signatures extracted from the LPWGS data showed potential for risk stratifying patients, where signatures S1 and S2 showed significant association to worse BCR-free survival compared to S3. Our study provides clinical validation of the associations between CNAs and tumor aggressiveness in an independent and representative RP cohort, while demonstrating the feasibility of performing LPWGS of FFPE tumor tissue for cost-effective assessment of CNAs.

Hyper-Dependence on NHEJ Enables Synergy between DNA-PK Inhibitors and Low-Dose Doxorubicin in Leiomyosarcoma

PURPOSE

Leiomyosarcoma (LMS) is an aggressive sarcoma for which standard chemotherapies achieve response rates under 30%. There are no effective targeted therapies against LMS. Most LMS are characterized by chromosomal instability (CIN), resulting in part from TP53 and RB1 co-inactivation and DNA damage repair defects. We sought to identify therapeutic targets that could exacerbate intrinsic CIN and DNA damage in LMS, inducing lethal genotoxicity.

EXPERIMENTAL DESIGN

We performed clinical targeted sequencing in 287 LMS and genome-wide loss-of-function screens in 3 patient-derived LMS cell lines, to identify LMS-specific dependencies. We validated candidate targets by biochemical and cell-response assays in vitro and in seven mouse models.

RESULTS

Clinical targeted sequencing revealed a high burden of somatic copy-number alterations (median fraction of the genome altered =0.62) and demonstrated homologous recombination deficiency signatures in 35% of LMS. Genome-wide short hairpin RNA screens demonstrated PRKDC (DNA-PKcs) and RPA2 essentiality, consistent with compensatory nonhomologous end joining (NHEJ) hyper-dependence. DNA-PK inhibitor combinations with unconventionally low-dose doxorubicin had synergistic activity in LMS in vitro models. Combination therapy with peposertib and low-dose doxorubicin (standard or liposomal formulations) inhibited growth of 5 of 7 LMS mouse models without toxicity.

CONCLUSIONS

Combinations of DNA-PK inhibitors with unconventionally low, sensitizing, doxorubicin dosing showed synergistic effects in LMS in vitro and in vivo models, without discernable toxicity. These findings underscore the relevance of DNA damage repair alterations in LMS pathogenesis and identify dependence on NHEJ as a clinically actionable vulnerability in LMS.

Fascin-1 expression is associated with neuroendocrine prostate cancer and directly suppressed by androgen receptor

BACKGROUND

Neuroendocrine prostate cancer (NEPC) is an aggressive form of prostate cancer, arising from resistance to androgen-deprivation therapies. However, the molecular mechanisms associated with NEPC development and invasiveness are still poorly understood. Here we investigated the expression and functional significance of Fascin-1 (FSCN1), a pro-metastasis actin-bundling protein associated with poor prognosis of several cancers, in neuroendocrine differentiation of prostate cancer.

METHODS

Differential expression analyses using Genome Expression Omnibus (GEO) database, clinical samples and cell lines were performed. Androgen or antagonist's cellular treatments and knockdown experiments were used to detect changes in cell morphology, molecular markers, migration properties and in vivo tumour growth. Chromatin immunoprecipitation-sequencing (ChIP-Seq) data and ChIP assays were analysed to decipher androgen receptor (AR) binding.

RESULTS

We demonstrated that FSCN1 is upregulated during neuroendocrine differentiation of prostate cancer in vitro, leading to phenotypic changes and NEPC marker expression. In human prostate cancer samples, FSCN1 expression is restricted to NEPC tumours. We showed that the androgen-activated AR downregulates FSCN1 expression and works as a transcriptional repressor to directly suppress FSCN1 expression. AR antagonists alleviate this repression. In addition, FSCN1 silencing further impairs in vivo tumour growth.

CONCLUSION

Collectively, our findings identify FSCN1 as an AR-repressed gene. Particularly, it is involved in NEPC aggressiveness. Our results provide the rationale for the future clinical development of FSCN1 inhibitors in NEPC patients.

Comparative and integrative analysis of transcriptomic and epigenomic-wide DNA methylation changes in African American prostate cancer

African American (AA) men have the highest incidence and mortality rate from Prostate cancer (PCa) than any other racial/ethnic group. To date, PCa genomic studies have largely under-represented tumour samples from AA men. We measured genome-wide DNA methylation in benign and tumor prostate tissues from AA men using the Illumina Infunium 850 K EPIC array. mRNA expression database from a subset of the AA biospecimen were used to assess correlation of transcriptome and methylation datasets. Genome-wide methylation analysis identified 11,460 probes that were significant (p < 0.01) and differentially methylated in AA PCa compared to normal prostate tissues and showed significant (p < 0.01) inverse-correlation with mRNA expression. Ingenuity pathway analysis and Gene Ontology analysis in our AA dataset compared with TCGA dataset showed similarities in methylation patterns: top candidate genes with significant hypermethylation and corresponding down-regulated gene expression were associated with biological pathways in hemidesmosome assembly, mammary gland development, epidermis development, hormone biosynthesis, and cell communication. In addition, top candidate genes with significant hypomethylation and corresponding up-regulated gene expression were associated with biological pathways in macrophage differentiation, cAMP-dependent protein kinase activity, protein destabilization, transcription co-repression, and fatty acid biosynthesis. In contrast, differences in genome-wide methylation in our AA dataset compared with TCGA dataset were enriched for genes in steroid signalling, immune signalling, chromatin structure remodelling and RNA processing. Overall, differential methylation of AMIGO3, IER3, UPB1, GRM7, TFAP2C, TOX2, PLSCR2, ZNF292, ESR2, MIXL1, BOLL, and FGF6 were significant and uniquely associated with PCa progression in our AA cohort.

Regulation of EZH2 Expression by INPP4B in Normal Prostate and Primary Prostate Cancer

The phosphatases INPP4B and PTEN are tumor suppressors that are lost in nearly half of advanced metastatic cancers. The loss of PTEN in prostate epithelium initially leads to an upregulation of several tumor suppressors that slow the progression of prostate cancer in mouse models. We tested whether the loss of INPP4B elicits a similar compensatory response in prostate tissue and whether this response is distinct from the one caused by the loss of PTEN. Knockdown of INPP4B but not PTEN in human prostate cancer cell lines caused a decrease in EZH2 expression. In Inpp4b-/- mouse prostate epithelium, EZH2 levels were decreased, as were methylation levels of histone H3. In contrast, Ezh2 levels were increased in the prostates of Pten-/- male mice. Contrary to PTEN, there was a positive correlation between INPP4B and EZH2 expression in normal human prostates and early-stage prostate tumors. Analysis of single-cell transcriptomic data demonstrated that a subset of EZH2-positive cells expresses INPP4B or PTEN, but rarely both, consistent with their opposing correlation with EZH2 expression. Unlike PTEN, INPP4B did not affect the levels of SMAD4 protein expression or Pml mRNA expression. Like PTEN, p53 protein expression and phosphorylation of Akt in Inpp4b-/- murine prostates were elevated. Taken together, the loss of INPP4B in the prostate leads to overlapping and distinct changes in tumor suppressor and oncogenic downstream signaling.

Analysis of genetic biomarkers, polymorphisms in ADME-related genes and their impact on pharmacotherapy for prostate cancer

Prostate cancer (PCa) is a non-cutaneous malignancy in males with wide variation in incidence rates across the globe. It is the second most reported cause of cancer death. Its etiology may have been linked to genetic polymorphisms, which are not only dominating cause of malignancy casualties but also exerts significant effects on pharmacotherapy outcomes. Although many therapeutic options are available, but suitable candidates identified by useful biomarkers can exhibit maximum therapeutic efficacy. The single-nucleotide polymorphisms (SNPs) reported in androgen receptor signaling genes influence the effectiveness of androgen receptor pathway inhibitors and androgen deprivation therapy. Furthermore, SNPs located in genes involved in transport, drug metabolism, and efflux pumps also influence the efficacy of pharmacotherapy. Hence, SNPs biomarkers provide the basis for individualized pharmacotherapy. The pharmacotherapeutic options for PCa include hormonal therapy, chemotherapy (Docetaxel, Mitoxantrone, Cabazitaxel, and Estramustine, etc.), and radiotherapy. Here, we overview the impact of SNPs reported in various genes on the pharmacotherapy for PCa and evaluate current genetic biomarkers with an emphasis on early diagnosis and individualized treatment strategy in PCa.

Cancer origin tracing and timing in two high-risk prostate cancers using multisample whole genome analysis: prospects for personalized medicine

BACKGROUND

Prostate cancer (PrCa) genomic heterogeneity causes resistance to therapies such as androgen deprivation. Such heterogeneity can be deciphered in the context of evolutionary principles, but current clinical trials do not include evolution as an essential feature. Whether or not analysis of genomic data in an evolutionary context in primary prostate cancer can provide unique added value in the research and clinical domains remains an open question.

METHODS

We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high-risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones.

RESULTS

In patient GP5, CDK12 inactivation was among the first mutations, leading to a PrCa tandem duplicator phenotype and initiating the cancer around age 50, followed by rapid cancer evolution after age 57, and metastasis around age 59, 5 years prior to prostatectomy. In patient GP12, accelerated cancer progression was detected after age 54, and metastasis occurred around age 56, 3 years prior to prostatectomy. Multiple metastasis-originating events were identified in each patient and tracked anatomically. Metastasis from prostate to lymph nodes occurred strictly ipsilaterally in all 12 detected events. In this pilot, metastatic subclone content analysis appears to substantially enhance the identification of key drivers. Evolutionary analysis' potential impact on therapy selection appears positive in these pilot cases.

CONCLUSIONS

PrCa evolutionary analysis allows tracking of anatomic site of origin, timing of cancer origin and spread, and distinction of metastatic-capable from non-metastatic subclones. This enables better identification of actionable targets for therapy. If extended to larger cohorts, it appears likely that similar analyses could add substantial biological insight and clinically relevant value.

Prospective clinical sequencing of 1016 Chinese prostate cancer patients: uncovering genomic characterization and race disparity

Although there is a well-known disparity in prostate cancer (PC) incidence and mortality between Chinese and Western patients, the underlying genomic differences have been investigated only sparsely. This clinicogenomic study was conducted to reveal the genomic mutations contributing to the PC disparity across ethnicities and investigate the mutational profile of Chinese PC patients. A total of 1016 Chinese PC patients were prospectively enrolled and subjected to targeted sequencing, resulting in usable sequencing data for 41 genes from 859 patients. Genomic data retrieved from The Cancer Genome Atlas (TCGA; locoregional PC), Memorial Sloan Kettering Cancer Center [MSKCC; metastatic castration-sensitive PC (mCSPC)], and Stand Up To Cancer [SU2C; metastatic castration-resistant PC (mCRPC)] cohorts were used as comparators representing Western men. Genomic mutations were analyzed using an integrated bioinformatic strategy. A comparison of the disease stages revealed that mutations in tumor protein 53 (TP53), androgen receptor (AR), forkhead box A1 (FOXA1), and genes involved in the cell cycle pathway were enriched in mCRPC. Mutations in adenomatous polyposis coli (APC) gene were found to be more prevalent in patients with visceral metastasis. Genomic differences between Western and Chinese men were mainly observed in castration-sensitive PC, with tumors from Chinese men having more FOXA1 (11.4% vs. 4.2%) but fewer TP53 (4.8% vs. 13%) mutations in locoregional PC and harboring fewer TP53 (11% vs. 29.2%), phosphatase and tensin homolog (PTEN; 2.5% vs. 10.3%), and APC (1.7% vs. 7.4%) mutations in the mCSPC stage than those of Western men. Patients of both ethnicities with mCRPC had similar mutational spectra. Furthermore, FOXA1 class-2 was less common than FOXA1 class-1 and showed no enrichment in metastasis, contrary to the findings in the Western cohort. Our study provides a valuable resource for a better understanding of PC in China and reveals the genomic alterations associated with PC disparity across races.

Chromosomal Copy Number Variation Predicts EGFR-TKI Response and Prognosis for Patients with Non-Small Cell Lung Cancer

Purpose

Chromosomal abnormalities represent genomic signatures linked to cancer prognosis and responses to chemotherapy, immunotherapy, and drug resistance. This study aimed to investigate the impact of chromosome copy number variants (CNVs) on the efficacy of tyrosine kinase inhibitors (TKIs) in EGFR-mutated non-small cell lung cancer (NSCLC) patients, as well as its prognostic implications for progression-free survival (PFS) and overall survival (OS) in EGFR wild-type patients.

Methods

A total of 110 patients with advanced NSCLC were enrolled in this study and categorized into EGFR-mutated and wild-type groups. Utilizing next-generation sequencing (NGS) technology, we assessed 24 genes and chromosome CNVs associated with lung cancer pathways in patients' tissue samples.

Results

Within the EGFR-mutated group, patients with a gain in Chr 1p13.3-p13.1 exhibited poor TKI responses, a high relapse rate, and shortened PFS (P = 0.002). Conversely, EGFR-mutated patients with a gain in 14q31.1-q31.3 demonstrated favorable TKI responses and relatively extended PFS (P = 0.005). Among EGFR wild-type patients, the presence of 7q31.1-q31.31 CNV emerged as an independent factor influencing both PFS and OS (P = 0.013, P = 0.004). Notably, patients with a gain in 7q31.1-q31.31 exhibited prolonged PFS and OS. Additionally, independent prognostic significance for OS in EGFR wild-type patients was observed for CNVs in 9q21.31-q22.2 and 11p11.11-q12.1 regions (P = 0.001). Patients with gains in these regions experienced extended OS, while losses were predictive of poorer outcomes.

Conclusion

Our results suggested that chromosomal copy number variation is a practical indicator for predicting the response of EGFR-targeted therapy and prognosis for NSCLC patients.

Copy number architectures define treatment-mediated selection of lethal prostate cancer clones

Despite initial responses to hormone treatment, metastatic prostate cancer invariably evolves to a lethal state. To characterize the intra-patient evolutionary relationships of metastases that evade treatment, we perform genome-wide copy number profiling and bespoke approaches targeting the androgen receptor (AR) on 167 metastatic regions from 11 organs harvested post-mortem from 10 men who died from prostate cancer. We identify diverse and patient-unique alterations clustering around the AR in metastases from every patient with evidence of independent acquisition of related genomic changes within an individual and, in some patients, the co-existence of AR-neutral clones. Using the genomic boundaries of pan-autosome copy number changes, we confirm a common clone of origin across metastases and diagnostic biopsies, and identified in individual patients, clusters of metastases occupied by dominant clones with diverged autosomal copy number alterations. These autosome-defined clusters are characterized by cluster-specific AR gene architectures, and in two index cases are topologically more congruent than by chance (p-values 3.07 × 10-8 and 6.4 × 10-4). Integration with anatomical sites suggests patterns of spread and points of genomic divergence. Here, we show that copy number boundaries identify treatment-selected clones with putatively distinct lethal trajectories.

GATA2 co-opts TGFβ1/SMAD4 oncogenic signaling and inherited variants at 6q22 to modulate prostate cancer progression

BACKGROUND

Aberrant somatic genomic alteration including copy number amplification is a hallmark of cancer genomes. We previously profiled genomic landscapes of prostate cancer (PCa), yet the underlying causal genes with prognostic potential has not been defined. It remains unclear how a somatic genomic event cooperates with inherited germline variants contribute to cancer predisposition and progression.

METHODS

We applied integrated genomic and clinical data, experimental models and bioinformatic analysis to identify GATA2 as a highly prevalent metastasis-associated genomic amplification in PCa. Biological roles of GATA2 in PCa metastasis was determined in vitro and in vivo. Global chromatin co-occupancy and co-regulation of GATA2 and SMAD4 was investigated by coimmunoprecipitation, ChIP-seq and RNA-seq assays. Tumor cellular assays, qRT-PCR, western blot, ChIP, luciferase assays and CRISPR-Cas9 editing methods were performed to mechanistically understand the cooperation of GATA2 with SMAD4 in promoting TGFβ1 and AR signaling and mediating inherited PCa risk and progression.

RESULTS

In this study, by integrated genomics and experimental analysis, we identified GATA2 as a prevalent metastasis-associated genomic amplification to transcriptionally augment its own expression in PCa. Functional experiments demonstrated that GATA2 physically interacted and cooperated with SMAD4 for genome-wide chromatin co-occupancy and co-regulation of PCa genes and metastasis pathways like TGFβ signaling. Mechanistically, GATA2 was cooperative with SMAD4 to enhance TGFβ and AR signaling pathways, and activated the expression of TGFβ1 via directly binding to a distal enhancer of TGFβ1. Strinkingly, GATA2 and SMAD4 globally mediated inherited PCa risk and formed a transcriptional complex with HOXB13 at the PCa risk-associated rs339331/6q22 enhancer, leading to increased expression of the PCa susceptibility gene RFX6.

CONCLUSIONS

Our study prioritizes causal genomic amplification genes with prognostic values in PCa and reveals the pivotal roles of GATA2 in transcriptionally activating the expression of its own and TGFβ1, thereby co-opting to TGFβ1/SMAD4 signaling and RFX6 at 6q22 to modulate PCa predisposition and progression.

A harmonized resource of integrated prostate cancer clinical, -omic, and signature features

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or  not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

Whole-exome sequencing of Indian prostate cancer reveals a novel therapeutic target: POLQ

PURPOSE

Prostate cancer is the second most common cancer diagnosed worldwide and the third most common cancer among men in India. This study's objective was to characterise the mutational landscape of Indian prostate cancer using whole-exome sequencing to identify population-specific polymorphisms.

METHODS

Whole-exome sequencing was performed of 58 treatment-naive primary prostate tumors of Indian origin. Multiple computational and statistical analyses were used to profile the known common mutations, other deleterious mutations, driver genes, prognostic biomarkers, and gene signatures unique to each clinical parameter. Cox analysis was performed to validate survival-associated genes. McNemar test identified genes significant to recurrence and receiver-operating characteristic (ROC) analysis was conducted to determine its accuracy. OncodriveCLUSTL algorithm was used to deduce driver genes. The druggable target identified was modeled with its known inhibitor using Autodock.

RESULTS

TP53 was the most commonly mutated gene in our cohort. Three novel deleterious variants unique to the Indian prostate cancer subtype were identified: POLQ, FTHL17, and OR8G1. COX regression analysis identified ACSM5, a mitochondrial gene responsible for survival. CYLC1 gene, which encodes for sperm head cytoskeletal protein, was identified as an unfavorable prognostic biomarker indicative of recurrence. The novel POLQ mutant, also identified as a driver gene, was evaluated as the druggable target in this study. POLQ, a DNA repair enzyme implicated in various cancer types, is overexpressed and is associated with a poor prognosis. The mutant POLQ was subjected to structural analysis and modeled with its known inhibitor novobiocin resulting in decreased binding efficiency necessitating the development of a better drug.

CONCLUSION

In this pilot study, the molecular profiling using multiple computational and statistical analyses revealed distinct polymorphisms in the Indian prostate cancer cohort. The mutational signatures identified provide a valuable resource for prognostic stratification and targeted treatment strategies for Indian prostate cancer patients. The DNA repair enzyme, POLQ, was identified as the druggable target in this study.

A DNA copy number alteration classifier as a prognostic tool for prostate cancer patients

BACKGROUND

Distinguishing between true indolent and potentially life-threatening prostate cancer is challenging in tumours displaying clinicopathologic features associated with low or intermediate risk of relapse. Several somatic DNA copy number alterations (CNAs) have been identified as potential prognostic biomarkers, but the standard cytogenetic method to assess them has a limited multiplexing capability.

METHODS

Multiplex ligation-dependent probe amplification (MLPA) targeting 14 genes was optimised to survey 448 tumours of patients with low or intermediate risk (Grade Group 1-3, Gleason score ≤7) who underwent radical prostatectomy. A 6-gene CNA classifier was developed using random survival forest and Cox proportional hazard modelling to predict biochemical recurrence.

RESULTS

The classifier score was significantly associated with biochemical recurrence after adjusting for standard clinicopathologic variables and the known prognostic index CAPRA-S score with a hazard ratio of 2.17 and 1.80, respectively (n = 406, P < 0.01). The prognostic value of this classifier was externally validated in published CNA data from three radical prostatectomy cohorts and one radiation therapy pre-treatment biopsy cohort.

CONCLUSION

The 6-gene CNA classifier generated by a single MLPA assay compatible with the small quantities of DNA extracted from formalin-fixed paraffin-embedded (FFPE) tissue specimens has the potential to improve the clinical management of patients with low or intermediate risk disease.

The Role of Immunotherapy or Immuno-Chemotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review

Many new treatment modalities for non-small-cell carcinoma (NSCLC) have been described in the last two decades. Surgical resections remain the gold standard for early stages and may be considered for locally advanced tumors. Medical treatment has changed drastically in recent years, especially for advanced stages, for which the development of immunotherapy and molecular targeted therapy significantly increased survival and quality of life. The addition of radical surgical resection following immunotherapy or immuno-chemotherapy is feasible and safe with low surgical-related mortality and morbidity in selected patients with initially unresectable NSCLC. However, data from multiple ongoing trials with overall survival as the primary endpoint should be awaited before this strategy is introduced into the standard of care.

Prognostic Values of Gene Copy Number Alterations in Prostate Cancer

Whilst risk prediction for individual prostate cancer (PCa) cases is of a high priority, the current risk stratification indices for PCa management have severe limitations. This study aimed to identify gene copy number alterations (CNAs) with prognostic values and to determine if any combination of gene CNAs could have risk stratification potentials. Clinical and genomic data of 500 PCa cases from the Cancer Genome Atlas stable were retrieved from the Genomic Data Commons and cBioPortal databases. The CNA statuses of a total of 52 genetic markers, including 21 novel markers and 31 previously identified potential prognostic markers, were tested for prognostic significance. The CNA statuses of a total of 51/52 genetic markers were significantly associated with advanced disease at an odds ratio threshold of ≥1.5 or ≤0.667. Moreover, a Kaplan-Meier test identified 27/52 marker CNAs which correlated with disease progression. A Cox Regression analysis showed that the amplification of MIR602 and deletions of MIR602, ZNF267, MROH1, PARP8, and HCN1 correlated with a progression-free survival independent of the disease stage and Gleason prognostic group grade. Furthermore, a binary logistic regression analysis identified twenty-two panels of markers with risk stratification potentials. The best model of 7/52 genetic CNAs, which included the SPOP alteration, SPP1 alteration, CCND1 amplification, PTEN deletion, CDKN1B deletion, PARP8 deletion, and NKX3.1 deletion, stratified the PCa cases into a localised and advanced disease with an accuracy of 70.0%, sensitivity of 85.4%, specificity of 44.9%, positive predictive value of 71.67%, and negative predictive value of 65.35%. This study validated prognostic gene level CNAs identified in previous studies, as well as identified new genetic markers with CNAs that could potentially impact risk stratification in PCa.

The repertoire of copy number alteration signatures in human cancer

Copy number alterations (CNAs) are a predominant source of genetic alterations in human cancer and play an important role in cancer progression. However comprehensive understanding of the mutational processes and signatures of CNA is still lacking. Here we developed a mechanism-agnostic method to categorize CNA based on various fragment properties, which reflect the consequences of mutagenic processes and can be extracted from different types of data, including whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) array. The 14 signatures of CNA have been extracted from 2778 pan-cancer analysis of whole genomes WGS samples, and further validated with 10 851 the cancer genome atlas SNP array dataset. Novel patterns of CNA have been revealed through this study. The activities of some CNA signatures consistently predict cancer patients' prognosis. This study provides a repertoire for understanding the signatures of CNA in cancer, with potential implications for cancer prognosis, evolution and etiology.

Preclinical models of prostate cancer - modelling androgen dependency and castration resistance in vitro, ex vivo and in vivo

Prostate cancer is well known to be dependent on the androgen receptor (AR) for growth and survival. Thus, AR is the main pharmacological target to treat this disease. However, after an initially positive response to AR-targeting therapies, prostate cancer will eventually evolve to castration-resistant prostate cancer, which is often lethal. Tumour growth was initially thought to become androgen-independent following treatments; however, results from molecular studies have shown that most resistance mechanisms involve the reactivation of AR. Consequently, tumour cells become resistant to castration - the blockade of testicular androgens - and not independent of AR per se. However, confusion still remains on how to properly define preclinical models of prostate cancer, including cell lines. Most cell lines were isolated from patients for cell culture after evolution of the tumour to castration-resistant prostate cancer, but not all of these cell lines are described as castration resistant. Moreover, castration refers to the blockade of testosterone production by the testes; thus, even the concept of "castration" in vitro is questionable. To ensure maximal transfer of knowledge from scientific research to the clinic, understanding the limitations and advantages of preclinical models, as well as how these models recapitulate cancer cell androgen dependency and can be used to study castration resistance mechanisms, is essential.

Prognostic Value of Low-Pass Whole Genome Sequencing of Circulating Tumor DNA in Metastatic Castration-Resistant Prostate Cancer

BACKGROUND

Multiple treatments are available for metastatic castration-resistant prostate cancer (mCRPC), including androgen receptor signaling inhibitors (ARSI) enzalutamide and abiraterone, but therapy resistance remains a major clinical obstacle. We examined the clinical utility of low-pass whole-genome sequencing (LPWGS) of circulating tumor DNA (ctDNA) for prognostication in mCRPC.

METHODS

A total of 200 plasma samples from 143 mCRPC patients collected at the start of first-line ARSI treatment (baseline) and at treatment termination (n = 57, matched) were analyzed by LPWGS (median: 0.50X) to access ctDNA% and copy number alteration (CNA) patterns. The best confirmed prostate specific antigen (PSA) response (≥50% decline [PSA50]), PSA progression-free survival (PFS), and overall survival (OS) were used as endpoints. For external validation, we used plasma LPWGS data from an independent cohort of 70 mCRPC patients receiving first-line ARSI.

RESULTS

Baseline ctDNA% ranged from ≤3.0% to 73% (median: 6.6%) and CNA burden from 0% to 82% (median: 13.1%) in the discovery cohort. High ctDNA% and high CNA burden at baseline was associated with poor PSA50 response (P = 0.0123/0.0081), poor PFS (P < 0.0001), and poor OS (P < 0.0001). ctDNA% and CNA burden was higher at PSA progression than at baseline in 32.7% and 42.3% of the patients. High ctDNA% and high CNA burden at baseline was also associated with poor PFS and OS (P ≤ 0.0272) in the validation cohort.

CONCLUSIONS

LPWGS of ctDNA provides clinically relevant information about the tumor genome in mCRPC patients. Using LPWGS data, we show that high ctDNA% and CNA burden at baseline is associated with short PFS and OS in 2 independent cohorts.

Association of self-identified race and genetic ancestry with the immunogenomic landscape of primary prostate cancer

The genomic and immune landscapes of prostate cancer differ by self-identified race. However, few studies have examined the genome-wide copy number landscape and immune content of matched cohorts with genetic ancestry data and clinical outcomes. Here, we assessed prostate cancer somatic copy number alterations (sCNA) and tumor immune content of a grade-matched, surgically treated cohort of 145 self-identified Black (BL) and 145 self-identified White (WH) patients with genetic ancestry estimation. A generalized linear model adjusted with age, preoperative prostate-specific antigen (PSA), and Gleason Grade Group and filtered for germline copy number variations (gCNV) identified 143 loci where copy number varied significantly by percent African ancestry, clustering on chromosomes 6p, 10q, 11p, 12p, and 17p. Multivariable Cox regression models adjusted for age, preoperative PSA levels, and Gleason Grade Group revealed that chromosome 8q gains (including MYC) were significantly associated with biochemical recurrence and metastasis, independent of genetic ancestry. Finally, Treg density in BL and WH patients was significantly correlated with percent genome altered, and these findings were validated in the TCGA cohort. Taken together, our findings identify specific sCNA linked to genetic ancestry and outcome in primary prostate cancer and demonstrate that Treg infiltration varies by global sCNA burden in primary disease.

Adjuvant immunotherapy in early-stage resectable non-small cell lung cancer: A new milestone

Currently, chemotherapy is the standard adjuvant treatment for early-stage non-small cell lung cancer (NSCLC). However, adjuvant cisplatin-based chemotherapy after surgery has been shown to improve 5-year survival rates by only 4-5%. Immunotherapy using immune checkpoint inhibitors (ICIs) has revolutionized the treatment of advanced NSCLC, there is a growing interest in the role of immunotherapy in early-stage NSCLC. Here, we summarize the rationale for adjuvant immunotherapy, including the postoperative immunosuppressive environment and immunological effects of platinum chemotherapy. Many ongoing clinical trials and the related progress in adjuvant immunotherapy in early-stage resectable NSCLC are discussed. Furthermore, we highlight several unresolved challenges, including markers predictive of treatment benefit, the efficacy of treatment for some oncogene-addicted tumors, the optimal combination therapy, the duration of adjuvant immunotherapy, and optimal selection between neoadjuvant and adjuvant immunotherapy. Early findings in some clinical trials are promising, and updated overall survival results will be useful for validating the current role of adjuvant immunotherapy, particularly in the context of perioperative strategy.

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or unstandardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

Candidate targets of copy number deletion events across 17 cancer types

Genome variation is the direct cause of cancer and driver of its clonal evolution. While the impact of many point mutations can be evaluated through their modification of individual genomic elements, even a single copy number aberration (CNA) may encompass hundreds of genes and therefore pose challenges to untangle potentially complex functional effects. However, consistent, recurring and disease-specific patterns in the genome-wide CNA landscape imply that particular CNA may promote cancer-type-specific characteristics. Discerning essential cancer-promoting alterations from the inherent co-dependency in CNA would improve the understanding of mechanisms of CNA and provide new insights into cancer biology and potential therapeutic targets. Here we implement a model using segmental breakpoints to discover non-random gene coverage by copy number deletion (CND). With a diverse set of cancer types from multiple resources, this model identified common and cancer-type-specific oncogenes and tumor suppressor genes as well as cancer-promoting functional pathways. Confirmed by differential expression analysis of data from corresponding cancer types, the results show that for most cancer types, despite dissimilarity of their CND landscapes, similar canonical pathways are affected. In 25 analyses of 17 cancer types, we have identified 19 to 169 significant genes by copy deletion, including RB1, PTEN and CDKN2A as the most significantly deleted genes among all cancer types. We have also shown a shared dependence on core pathways for cancer progression in different cancers as well as cancer type separation by genome-wide significance scores. While this work provides a reference for gene specific significance in many cancers, it chiefly contributes a general framework to derive genome-wide significance and molecular insights in CND profiles with a potential for the analysis of rare cancer types as well as non-coding regions.

Complexities of Prostate Cancer

Prostate cancer has a long disease history and a wide variety and uncertainty in individual patients' clinical progress. In recent years, we have seen a revolutionary advance in both prostate cancer patient care and in the research field. The power of deep sequencing has provided cistromic and transcriptomic knowledge of prostate cancer that has not discovered before. Our understanding of prostate cancer biology, from bedside and molecular imaging techniques, has also been greatly advanced. It is important that our current theragnostic schemes, including our diagnostic modalities, therapeutic responses, and the drugs available to target non-AR signaling should be improved. This review article discusses the current progress in the understanding of prostate cancer biology and the recent advances in diagnostic and therapeutic strategies.

The CIC-ERF co-deletion underlies fusion-independent activation of ETS family member, ETV1, to drive prostate cancer progression

Human prostate cancer can result from chromosomal rearrangements that lead to aberrant ETS gene expression. The mechanisms that lead to fusion-independent ETS factor upregulation and prostate oncogenesis remain relatively unknown. Here, we show that two neighboring transcription factors, Capicua (CIC) and ETS2 repressor factor (ERF), which are co-deleted in human prostate tumors can drive prostate oncogenesis. Concurrent CIC and ERF loss commonly occur through focal genomic deletions at chromosome 19q13.2. Mechanistically, CIC and ERF co-bind the proximal regulatory element and mutually repress the ETS transcription factor, ETV1. Targeting ETV1 in CIC and ERF-deficient prostate cancer limits tumor growth. Thus, we have uncovered a fusion-independent mode of ETS transcriptional activation defined by concurrent loss of CIC and ERF.

Patient-Derived Xenografts and Organoids Recapitulate Castration-Resistant Prostate Cancer with Sustained Androgen Receptor Signaling

Castration-resistant prostate cancer (CRPC) remains an incurable and lethal malignancy. The development of new CRPC treatment strategies is strongly impeded by the scarcity of representative, scalable and transferable preclinical models of advanced, androgen receptor (AR)-driven CRPC. Here, we present contemporary patient-derived xenografts (PDXs) and matching PDX-derived organoids (PDXOs) from CRPC patients who had undergone multiple lines of treatment. These models were comprehensively profiled at the morphologic, genomic (n = 8) and transcriptomic levels (n = 81). All are high-grade adenocarcinomas that exhibit copy number alterations and transcriptomic features representative of CRPC patient cohorts. We identified losses of PTEN and RB1, MYC amplifications, as well as genomic alterations in TP53 and in members of clinically actionable pathways such as AR, PI3K and DNA repair pathways. Importantly, the clinically observed continued reliance of CRPC tumors on AR signaling is preserved across the entire set of models, with AR amplification identified in four PDXs. We demonstrate that PDXs and PDXOs faithfully reflect donor tumors and mimic matching patient drug responses. In particular, our models predicted patient responses to subsequent treatments and captured sensitivities to previously received therapies. Collectively, these PDX-PDXO pairs constitute a reliable new resource for in-depth studies of treatment-induced, AR-driven resistance mechanisms. Moreover, PDXOs can be leveraged for large-scale tumor-specific drug response profiling critical for accelerating therapeutic advances in CRPC.

Enrichment of "Cribriform" morphologies (intraductal and cribriform adenocarcinoma) and genomic alterations in radiorecurrent prostate cancer

Locally relapsed prostate cancer (PCa) after radiation therapy (RT) is associated with substantial morbidity and mortality. Morphological and molecular consequences that may contribute to RT resistance and local recurrence remain poorly understood. Locally recurrent PCa tissue from 53 patients with clinically localized PCa who failed with primary RT and subsequently underwent salvage radical prostatectomy (RP) was analyzed for tumor focality, clinicopathological, molecular, and genomic characteristics. Targeted next-generation sequencing with full exon coverage of 1,425 cancer-related genes was performed on 10 representative radiorecurrent PCas exhibiting no RT effect with matched adjacent benign prostate tissue. At RP, 37 (70%) of PCas had no RT effect with the following characteristics: grade group (GG) ≥ 3 (70%), unifocal tumor (75%), extraprostatic disease (78%), lymph node metastasis (8%), and "cribriform" morphologies (84%) [cribriform PCa (78%) or intraductal carcinoma (IDC-P) (61%)] at a median percentage of approximately 80% of tumor volume. In the setting of multifocal tumors (25%) at RP, the cribriform morphologies were restricted to index tumors. Of 32 patients with available pre-RT biopsy information, 16 had GG1 PCa, none had cribriform morphologies at baseline but 81% demonstrated cribriform morphologies at RP. Notable alterations detected in the sequenced tumors included: defects in DNA damage response and repair (DDR) genes (70%) (TP53, BRCA2, PALB2, ATR, POLQ), PTEN loss (50%), loss of 8p (80%), and gain of MYC (70%). The median tumor mutational burden was 4.18 mutations/Mb with a range of 2.16 to 31.86. Our findings suggest that most radiorecurrent PCas are enriched in cribriform morphologies with potentially targetable genomic alterations. Understanding this phenotypic and genotypic diversity of radiorecurrent PCa is critically important to facilitate optimal patient management.

Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers

Structural variations (SVs) in cancer cells often impact large genomic regions with functional consequences. However, identification of SVs under positive selection is a challenging task because little is known about the genomic features related to the background breakpoint distribution in different cancers. We report a method that uses a generalized additive model to investigate the breakpoint proximity curves from 2,382 whole-genomes of 32 cancer types. We find that a multivariate model, which includes linear and nonlinear partial contributions of various tissue-specific features and their interaction terms, can explain up to 57% of the observed deviance of breakpoint proximity. In particular, three-dimensional genomic features such as topologically associating domains (TADs), TAD-boundaries and their interaction with other features show significant contributions. The model is validated by identification of known cancer genes and revealed putative drivers in cancers different than those with previous evidence of positive selection.

The architecture of clonal expansions in morphologically normal tissue from cancerous and non-cancerous prostates

BACKGROUND

Up to 80% of cases of prostate cancer present with multifocal independent tumour lesions leading to the concept of a field effect present in the normal prostate predisposing to cancer development. In the present study we applied Whole Genome DNA Sequencing (WGS) to a group of morphologically normal tissue (n = 51), including benign prostatic hyperplasia (BPH) and non-BPH samples, from men with and men without prostate cancer. We assess whether the observed genetic changes in morphologically normal tissue are linked to the development of cancer in the prostate.

RESULTS

Single nucleotide variants (P = 7.0 × 10-03, Wilcoxon rank sum test) and small insertions and deletions (indels, P = 8.7 × 10-06) were significantly higher in morphologically normal samples, including BPH, from men with prostate cancer compared to those without. The presence of subclonal expansions under selective pressure, supported by a high level of mutations, were significantly associated with samples from men with prostate cancer (P = 0.035, Fisher exact test). The clonal cell fraction of normal clones was always higher than the proportion of the prostate estimated as epithelial (P = 5.94 × 10-05, paired Wilcoxon signed rank test) which, along with analysis of primary fibroblasts prepared from BPH specimens, suggests a stromal origin. Constructed phylogenies revealed lineages associated with benign tissue that were completely distinct from adjacent tumour clones, but a common lineage between BPH and non-BPH morphologically normal tissues was often observed. Compared to tumours, normal samples have significantly less single nucleotide variants (P = 3.72 × 10-09, paired Wilcoxon signed rank test), have very few rearrangements and a complete lack of copy number alterations.

CONCLUSIONS

Cells within regions of morphologically normal tissue (both BPH and non-BPH) can expand under selective pressure by mechanisms that are distinct from those occurring in adjacent cancer, but that are allied to the presence of cancer. Expansions, which are probably stromal in origin, are characterised by lack of recurrent driver mutations, by almost complete absence of structural variants/copy number alterations, and mutational processes similar to malignant tissue. Our findings have implications for treatment (focal therapy) and early detection approaches.

Accumulation of copy number alterations and clinical progression across advanced prostate cancer

BACKGROUND

Genomic copy number alterations commonly occur in prostate cancer and are one measure of genomic instability. The clinical implication of copy number change in advanced prostate cancer, which defines a wide spectrum of disease from high-risk localised to metastatic, is unknown.

METHODS

We performed copy number profiling on 688 tumour regions from 300 patients, who presented with advanced prostate cancer prior to the start of long-term androgen deprivation therapy (ADT), in the control arm of the prospective randomised STAMPEDE trial. Patients were categorised into metastatic states as follows; high-risk non-metastatic with or without local lymph node involvement, or metastatic low/high volume. We followed up patients for a median of 7 years. Univariable and multivariable Cox survival models were fitted to estimate the association between the burden of copy number alteration as a continuous variable and the hazard of death or disease progression.

RESULTS

The burden of copy number alterations positively associated with radiologically evident distant metastases at diagnosis (P=0.00006) and showed a non-linear relationship with clinical outcome on univariable and multivariable analysis, characterised by a sharp increase in the relative risk of progression (P=0.003) and death (P=0.045) for each unit increase, stabilising into more modest increases with higher copy number burdens. This association between copy number burden and outcome was similar in each metastatic state. Copy number loss occurred significantly more frequently than gain at the lowest copy number burden quartile (q=4.1 × 10-6). Loss of segments in chromosome 5q21-22 and gains at 8q21-24, respectively including CHD1 and cMYC occurred more frequently in cases with higher copy number alteration (for either region: Kolmogorov-Smirnov distance, 0.5; adjusted P<0.0001). Copy number alterations showed variability across tumour regions in the same prostate. This variance associated with increased risk of distant metastases (Kruskal-Wallis test P=0.037).

CONCLUSIONS

Copy number alteration in advanced prostate cancer associates with increased risk of metastases at diagnosis. Accumulation of a limited number of copy number alterations associates with most of the increased risk of disease progression and death. The increased likelihood of involvement of specific segments in high copy number alteration burden cancers may suggest an order underlying the accumulation of copy number changes.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00268476 , registered on December 22, 2005. EudraCT  2004-000193-31 , registered on October 4, 2004.

Genomic copy number alterations as biomarkers for triple negative pregnancy-associated breast cancer

Background

PABC, commonly defined as breast cancer diagnosed during or ≤ 1 year after pregnancy, accounts for 7% of all breast cancers in women ≤ 45 years. Compared to age-matched non-PABC patients, PABC is characterized by a particularly aggressive histopathologic profile with poorly differentiated and estrogen- and progesterone receptor negative tumors and associated high mortality rates. This study assessed the genomic background of triple-negative PABC tumors by detection of copy number alterations (CNAs).

Methods

MLPA was used to compare CNAs in breast cancer-associated chromosomal loci between triple-negative PABC- and subtype-matched non-PABC patients. Both CNA patterns were evaluated by cluster analysis; associations between individual gene CNAs, pathological characteristics and survival were explored.

Results

Triple-negative PABC tumors exhibited unique CNAs compared to non-PABC tumors, including enrichment for TOP2A copy number loss, an independent predictor of worse overall survival (HR 8.96, p = 0.020). Cluster analysis based on CNA profiles identified a triple-negative PABC-subgroup with a particularly poor prognosis, characterized by chromosome 8p copy number loss. Individual gene CNAs analysis revealed that FGFR1 copy number loss on chromosome 8p11.23 was an independent predictor of poor outcome in multivariate analysis (HR 3.59, p = 0.053) and predicted the development of distant metastases (p = 0.048).

Conclusion

This study provides novel insights into the biology of triple-negative PABC tumors suggesting that CNAs, particularly 8p loss and TOP2A loss, are involved in the development of breast cancer during pregnancy. FGFR1 loss and TOP2A loss seem to be promising new biomarkers that independently identify subgroups of PABC patients with poor prognosis. These genomic biomarkers may provide clues for personalized therapy.

Methylation Subtypes of Primary Prostate Cancer Predict Poor Prognosis

BACKGROUND

Patients with prostate cancer experience heterogeneous outcomes after radical prostatectomy. Genomic studies including The Cancer Genome Atlas (TCGA) have reported molecular signatures of prostate cancer, but few studies have assessed the prognostic effects of DNA methylation profiles.

METHODS

We conducted the largest methylome subtyping analysis for primary prostate tumors to date, using methylome data from three patient populations: TCGA, a prostate cancer cohort study conducted at the Fred Hutchinson Cancer Research Center (FH; Seattle, WA), and the Canadian International Cancer Genome Consortium (ICGC) cohort. Four subtypes were detected in the TCGA dataset, then independently assigned to FH and ICGC cohort data. The identified methylation subtypes were assessed for association with cancer prognosis in the above three patient populations.

RESULTS

Using a set of hypermethylated CpG sites, four methylation subtypes were identified in TCGA. Compared with subtype 1, subtype 4 had an HR of 2.09 (P = 0.029) for biochemical recurrence (BCR) in TCGA patients. HRs of 2.76 (P = 0.002) for recurrence and 9.73 (P = 0.002) for metastatic-lethal (metastasis or prostate cancer-specific death) outcomes were observed in the FH cohort. A similar pattern of association was noted in the Canadian ICGC cohort, though HRs were not statistically significant.

CONCLUSIONS

A hypermethylated subtype was associated with an increased hazard of recurrence and mortality in three studies with prostate tumor methylome data. Further molecular work is needed to understand the effect of methylation subtypes on cancer prognosis.

IMPACT

This study identified a DNA methylation subtype that was associated with worse prostate cancer prognosis after radical prostatectomy.

Genomic Profiling of Prostate Cancer: An Updated Review

Understanding the genomic profiling of prostate cancer is crucial, owing to the emergence of precision medicine to guide therapeutic approaches. Over the last decade, integrative genomic profiling of prostate tumors has provided insights that improve the understanding and treatment of the disease. Minimally invasive liquid biopsy procedures have emerged to investigate cancer-related molecules with the advantage of detecting heterogeneity as well as acquired resistance in cancer. The metastatic castration-resistant prostate cancer (mCRPC) tumors have a highly complex genomic landscape compared to primary prostate tumors; a number of mCRPC harbor clinically actionable molecular alterations, including DNA damage repair (e.g., BRCA1/2 and ATM) and PTEN/phosphoinositide 3-kinase signaling. Heterogeneity in the genomic landscape of prostate cancer has become apparent and genomic alterations of TP53, RB1, AR, and cell cycle pathway are associated with poor clinical outcomes in patients. Prostate cancer with mutant SPOP shows a distinct pattern of genomic alterations, associating with better clinical outcomes. Several genomic profiling tests, which can be used in the clinic, are approved by the U.S. Food and Drug Administration, including MSK-IMPACT, FoundationOne CDx, and FoundationOne Liquid CDx. Here, we review emerging evidence for genomic profiling of prostate cancer, especially focusing on associations between genomic alteration and clinical outcome, liquid biopsy, and actionable molecular alterations.

Deep Learning-Based Multi-Omics Integration Robustly Predicts Relapse in Prostate Cancer

Objective

Post-operative biochemical relapse (BCR) continues to occur in a significant percentage of patients with localized prostate cancer (PCa). Current stratification methods are not adequate to identify high-risk patients. The present study exploits the ability of deep learning (DL) algorithms using the H2O package to combine multi-omics data to resolve this problem.

Methods

Five-omics data from 417 PCa patients from The Cancer Genome Atlas (TCGA) were used to construct the DL-based, relapse-sensitive model. Among them, 265 (63.5%) individuals experienced BCR. Five additional independent validation sets were applied to assess its predictive robustness. Bioinformatics analyses of two relapse-associated subgroups were then performed for identification of differentially expressed genes (DEGs), enriched pathway analysis, copy number analysis and immune cell infiltration analysis.

Results

The DL-based model, with a significant difference (P = 6e-9) between two subgroups and good concordance index (C-index = 0.767), were proven to be robust by external validation. 1530 DEGs including 678 up- and 852 down-regulated genes were identified in the high-risk subgroup S2 compared with the low-risk subgroup S1. Enrichment analyses found five hallmark gene sets were up-regulated while 13 were down-regulated. Then, we found that DNA damage repair pathways were significantly enriched in the S2 subgroup. CNV analysis showed that 30.18% of genes were significantly up-regulated and gene amplification on chromosomes 7 and 8 was significantly elevated in the S2 subgroup. Moreover, enrichment analysis revealed that some DEGs and pathways were associated with immunity. Three tumor-infiltrating immune cell (TIIC) groups with a higher proportion in the S2 subgroup (p = 1e-05, p = 8.7e-06, p = 0.00014) and one TIIC group with a higher proportion in the S1 subgroup (P = 1.3e-06) were identified.

Conclusion

We developed a novel, robust classification for understanding PCa relapse. This study validated the effectiveness of deep learning technique in prognosis prediction, and the method may benefit patients and prevent relapse by improving early detection and advancing early intervention.