Genomic Profiling Identifies Putative Pathogenic Alterations in NSCLC Brain Metastases

Background: Studies by The Cancer Genome Atlas (TCGA) and others have identified regions of somatic copy number alteration (SCNA) in cervical squamous cell carcinoma (CESC), head and neck squamous cell carcinoma (HNSC), and lung squamous cell carcinoma (LUSC). These three tumor types exhibit frequent SCNAs in chromosome 11, including focal gains in 11q13 and 11q22, as well as broad losses in 11p and 11q22-qter. Because expression levels are affected by underlying genomic events, we hypothesized that an integrated analysis of multiple genomic data types would provide increased ability to identify target genes in SCNA regions when compared to methods based on copy number alone while providing insight into mechanisms regulating expression. Examination of multiple tumor types provides a method to validate findings. Techniques: Gene expression (GE), DNA copy number (CN), DNA methylation (ME), and microRNA expression (miR) data were obtained from the TCGA studies of CESC, HNSC, and LUSC. For each tumor type, univariate and multivariate linear models were constructed on a gene-by-gene basis to investigate the effect of changes in CN, ME, and miR on GE. Analysis of model output provided an approach to identify target genes in SCNA regions and assess the effect of genomic alterations on expression. Results: Genome-wide GE, CN, ME, and miR data were available for n = 191 (CESC), n = 453 (HNSC), and n = 326 (LUSC) tumor samples. Univariate modeling detected a strong overall association between GE and CN, as measured by the coefficient of determination (model R2). Underlying CN events produced high model R2 for widely recognized genes, e.g. FADD in HNSC and LUSC, and less well-known genes. For example, DCUN1D5 (11q22) exhibits high model R2 in all three tumor types. Univariate analyses suggest that ALDH3B1 (11q13), a predicted target of the tumor suppressor miR-205, is epigenetically regulated. Most samples exhibit high expression of miR-205 and moderate expression of ALDH3B1, but increased ALDH3B1 levels are seen when miR-205 levels are low. This is notable in light of the striking negative association between miR-205 expression and methylation levels, particularly in CESC and LUSC. Fanconi anemia family member FANCF lies in a broad region of CN loss in 11p that contains no clear driver gene. Whereas univariate associations between FANCF GE and CN were moderate, multivariate analyses highlighted this gene because of the additional effect of ME on GE, which was pronounced in CESC and LUSC. Conclusion: Linear modeling techniques provide a flexible and powerful basis for performing integrated analysis of genomic data. Our approach produces predicted results when analyzing known cancer genes, highlights lesser known genes for future study, provides insight into gene regulation, and draws attention to genes relevant in multiple tumor types. Citation Format: Vonn Walter, Ying Du, Xiaoying Yin, Wei Sun, Matthew D. Wilkerson, Michele C. Hayward, Ashley H. Salazar, Charles M. Perou, David N. Hayes. Integrated genomic analysis of chromosome 11 in squamous tumors. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4801. doi:10.1158/1538-7445.AM2015-4801

Introduction: The genomic landscape of pre-invasive lung disease, including AAH/AIS/MIA (atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive carcinoma) and squamous CIS (carcinoma in situ) which are considered to be precursors to lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), has been profiled to a limited degree, due to the lack of matched samples within the same patient. Using TRACERx study we investigated the phylogenetic evolutionary patterns between matched pre-invasive and invasive lesions. Methods: Post-operative pathology reports for 746 primary invasive tumors collected within TRACERx were screened to identify pre-invasive lesions that had been resected at the time of primary surgery. 50 pre-invasive lesions (21 AAH, 7 AIS, 6 MIA, 16 CIS) from 36 pts were subjected to whole exome sequencing with 119 matched tumor regions from 22 LUAD, 13 LUSC and 1 pleomorphic carcinoma. Phylogenetic relationships between pre-invasive and invasive lesions, timing of clonal divergence, mutational processes were investigated. Results: Two major types of phylogenetic relationships were identified based on whether pre-invasive and invasive lesions shared a somatic common ancestor (SCA group; 7/22 LUAD, 11/13 LUSC) or not (non-SCA group; 15/22 LUAD, 2/13 LUSC, 1/1 pleomorphic carcinoma). Within SCA group, two evolutionary patterns were identified based on whether only clonal mutations or clonal and subclonal mutations were shared between pre-invasive and invasive lesions. 3/16 SCA pre-invasive lesions from ex-smokers were found to diverge from the invasive tumor years prior to diagnosis. Compared with non-SCA, SCA pre-invasive lesions harbored elevated mutational burden, APOBEC mutagenesis, chromosomal instability featured by increased loss of heterozygosity and somatic copy number alterations (SCNAs). Whole genome doubling was only detected in SCA pre-invasive lesions. In SCA LUAD, 3/7 exhibited clonal TP53 mutations, 3/7 exhibited clonal co-mutations of KEAP1 and STK11. In 5/15 non-SCA LUADs harboring a KRAS mutation, convergent KRAS mutation was detected in 4 matched pre-invasive and invasive lesions. Clonal focal gains on 3q21-29 (containing PIK3CA, ATR, SOX2) were detected in 10/11 SCA LUSC, suggesting that 3q gains may represent early events in tumor initiation. In the non-SCA group, convergent SCNAs encompassing oncogenes (PIK3CA, SOX2, FGFR3) or tumor suppressor genes (STK11, KEAP1, MGA) were detected. Conclusions: We demonstrate that matched pre-invasive and invasive lesions fall in two distinct phylogenetic evolutionary groups: those that have a shared somatic common ancestor and those that do not. SCA pre-invasive lesions exhibit elevated APOBEC mutagenesis and chromosomal instability, shedding some light on the process of malignant transformation. Citation Format: Haoran Zhai, Clare Puttick, David A. Moore, Michelle Dietzen, Sophia Ward, Maise Al Bakir, Thomas Watkins, Andrew Rowan, Selvaraju Veeriah, Robert Hynds, Oriol Pich, Nnennaya Kanu, TRACERx Consortium, Nicholas McGranahan, Charles Swanton, Mariam Jamal-Hanjani. Genomic evolutionary patterns in matched pre-invasive and invasive lung disease in TRACERx [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3626.

Acquired copy number alterations of miRNA genes in acute myeloid leukemia are uncommon

To analyze the differentially expressed genes (DEGs) between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) with bioinformatics analysis and search for potential biomarkers for clinical diagnosis of nonsmall cell lung cancer (NSCLC). The gene expression profiling datasets of LUAD and LUSC were acquired. The transcriptome differences between LUAD and LUSC were identified using R language processing and t-test analysis. The differential expressions of the genes were shown by Venn diagram. The DEGs identified by GEO2R were analyzed with DAVID and Ingenuity Pathway Analysis (IPA) to identify the signaling pathways and biomarkers that could be used for differential diagnosis of LUAD and LUSC. The TCGA data and the biomarker expression data from clinical lung cancer samples were used to verify the differential expressions of the Osteoarthritis pathway and LXR/RXR between LUAD and LUSC. We further examined the differential expressions of miR-181 and its two target genes, WNT5A and MBD2, in 23 clinical specimens of lung squamous cell carcinoma and the paired adjacent tissues. GEO data analysis identified 851 DEGs (including 276 up-regulated and 575 down-regulated genes) in LUAD and 885 DEGs (including 406 up-regulated and 479 down-regulated genes) in LUSC. DAVID and IPA analysis revealed that leukocyte migration and inflammatory responses were more abundant in LUAD than in LUSC. Osteoarthritis pathway was inhibited in LUAD and activated in LUSC. IPA analysis showed that transcription factors (GATA4, RELA, YBX1, TP63 and MBD2), cytokines (WNT5A and IL1A) and microRNAs (miR-34a, miR-181b and miR-15a) differed significantly between LUAD and LUSC. miR-34a with IL-1A, miR-15a with YBX1, and miR-181b with WNT5A and MBD2 could serve as the paired microRNA and mRNA targets for differential diagnosis of NSCLC subtypes. Analysis of the clinical samples showed an increased expression of miR-181b-5p and the down-regulation of WNT5A, which could be used as molecular markers for the diagnosis of LUSC. Through transcriptome analysis, we identified candidate genes, paired microRNAs and pathways for differentiating LUAD and LUSC, and they can provide novel differential diagnosis and therapeutic strategies for LUAD and LUSC.

INTRODUCTION Meningiomas are the most prevalent primary intracranial tumors in adults, with clinical management traditionally guided by the World Health Organization (WHO) grading system. However, discrepancies between histological grade and clinical behavior highlight the limitations of this system. Somatic copy number alterations (SCNAs) have been implicated in meningioma pathogenesis, correlating with tumor aggressiveness and recurrence risk. Their integration into prognostic models may enhance outcomes. This study investigates the influence of SCNAs on meningioma recurrence, focusing on their patterns of mutual exclusivity and co-occurrence. METHODS We analyzed 334 primary meningiomas, noting 23 recurrences (6.9%). Clinical data, including age, sex, WHO grade, and tumor characteristics, were collected. SCNAs and mutations were derived from whole-exome sequencing of tumor and matched blood samples. We employed univariate and multivariate Cox proportional hazards regression analyses to identify SCNAs associated with recurrence, fitting the regression model with the cumulative number of SCNAs as a factor covariate. The Kaplan-Meier estimator was utilized for visualizing survival curves. RESULTS Five SCNAs were significantly associated with recurrence risk (adjusted P < 0.05): 10qLOSS, 11pLOSS, 2pLOSS, 14qLOSS, and 18qLOSS. Each SCNA independently increased recurrence risk; a single SCNA tripled this risk, while two co-occurring SCNAs increased it more than ninefold (P = 0.00003). Notably, meningiomas with four co-occurring SCNAs exhibited a greater than 22-fold increase in recurrence risk. Importantly, all identified SCNAs contributed to chromosomal instability, suggesting a synergistic effect that enhances tumor recurrence potential. CONCLUSION The accumulation and specific combinations of these five SCNAs significantly enhance the likelihood of meningioma recurrence. Although chr1pLOSS did not independently predict recurrence, it promotes chromosomal instability that facilitates the accumulation of these risk SCNAs. Our findings underscore the potential of SCNAs in predicting meningioma recurrence across all grades, contributing to improved patient management strategies.

Non-small cell lung cancer (NSCLC) remains the most frequent malignancy worldwide, and lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) represent two major subtypes. LINC00628 has been demonstrated to promote LUAD progression; however, its clinical role in NSCLC remains elusive. The aim of the present study was to analyze the expression of long intergenic non-protein coding RNA 628 (LINC00628) in NSCLC, including in the LUAD and LUSC subtypes. In addition, its roles in NSCLC development and prognosis were also examined. Data from The Cancer Genome Atlas (TCGA) database were first used to assess the expression and prognostic potential in both LUAD and LUSC, then LINC00628 expression in 128 NSCLC tissues was measured using reverse transcription-quantitative PCR. A receiver operating characteristic curve was used to assess the ability of LINC00628 to discriminate between patients with LUAD and LUSC. Kaplan-Meier curves were used to analyze the relationship between LINC00628 expression and the overall survival of patients. Cox regression analysis was used to explore the potential prognostic factors that might be independently associated with NSCLC overall survival. Both in silico and tissue analysis data indicated that the expression of LINC00628 was significantly upregulated in NSCLC tissue compared with matched normal controls (P<0.001). LINC00628 expression levels were also significantly higher in LUAD cases than in patients with LUSC (P<0.001). In addition, LINC00628 could discriminate LUAD from LUSC cases. The expression of LINC00628 was significantly associated with tumor size (P=0.013), histological type (P=0.009), lymph node metastasis (P=0.021) and TNM stage (P=0.008). Survival analysis based on data from both TCGA and patients included in the present study identified an association between LINC00628 and overall survival in LUAD, but this relationship was not observed in LUSC for TCGA data. Cox regression analysis demonstrated that high LINC00628 expression was associated with poor overall survival in patients with LUAD (P=0.001), but not in patients with LUSC (P=0.088). In conclusion, LINC00628 expression was upregulated in NSCLC and associated with patient prognosis. Patients with LUAD had higher LINC00628 expression levels than those with LUSC, and increased LINC00628 served as an independent prognostic factor in LUAD, but not LUSC.

BackgroundMost cancers exhibit somatic copy number alterations (SCNAs)—gains and losses of variable regions of DNA. SCNAs play a key role in cancer adaptation through modulation of gene expression, deletion of tumor suppressor genes, or amplification of oncogenes. Systematic analysis of SCNAs is now a routine task in both the clinic and research and can help identify novel cancer genes, improve our understanding of cancer gene regulation, and enable us to accurately reconstruct cancer phylogenies. However, to conduct such analyses, SCNA profiles have to be integrated between samples, patients, and cohorts—often a nontrivial task, for which dedicated toolkits are lacking.ResultsTo fill this gap, we developed CNSistent, a Python package for imputation, filtering, consistent segmentation, feature extraction, and visualization of cancer copy number profiles from heterogeneous datasets. We demonstrate the utility of CNSistent by applying it to the following publicly available cohorts: The Cancer Genome Atlas, Pan-Cancer Analysis of Whole Genomes, and TRAcking Cancer Evolution through therapy (Rx). We compare the effect of sample preprocessing and different segmentation and aggregation strategies on cancer type and subtype classification tasks using various classification models. We also evaluate how well a classifier trained on one cohort generalizes to another. Lastly, we introduce 2 segment-based peak and outlier scores to investigate relationships between segments, between samples, and between cancer types. Using these scores, we investigate non–small cell lung cancer samples, highlighting that SOX2 amplification is the dominant copy number alteration in lung squamous cell carcinoma and the main distinction to lung adenocarcinoma.ConclusionsCNSistent is a general-purpose toolkit for integrated processing of SCNA profiles across many patients and cohorts. It is available at https://bitbucket.org/schwarzlab/cnsistent. The Research Resource Identifier for CNSistent is SCR_027025.

Classification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional subtypes using microarray data, and corresponding signatures were subsequently used to classify RNA-seq data. Cross-platform unsupervised classification facilitates the identification of robust transcriptional subtypes by combining vast amounts of publicly available microarray and RNA-seq data. However, cross-platform classification is challenging because of intrinsic differences in data generated using the two gene expression profiling technologies. In this report, we show that robust gene expression subtypes can be identified in integrated data representing over 3500 normal and tumor lung samples profiled using two widely used platforms, Affymetrix HG-U133 Plus 2.0 Array and Illumina HiSeq RNA sequencing. We tested and analyzed consensus clustering for 384 combinations of data processing methods. The agreement between subtypes identified in single-platform and cross-platform normalized data was then evaluated using a variety of statistics. Results show that unsupervised learning can be achieved with combined microarray and RNA-seq data using selected preprocessing, cross-platform normalization, and unsupervised feature selection methods. Our analysis confirmed three lung adenocarcinoma transcriptional subtypes, but only two consistent subtypes in squamous cell carcinoma, as opposed to four subtypes previously identified. Further analysis showed that tumor subtypes were associated with distinct patterns of genomic alterations in genes coding for therapeutic targets. Importantly, by integrating quantitative proteomics data, we were able to identify tumor subtype biomarkers that effectively classify samples on the basis of both gene and protein expression. This study provides the basis for further integrative data analysis across gene and protein expression profiling platforms.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Triple-negative breast cancer (TNBC) is a highly aggressive subtype that lacks targeted therapies, leading to a poorer prognosis. However, some patients achieve long-term recurrence-free survival (RFS), offering valuable insights into tumor biology and potential treatment strategies. We conducted a comprehensive multi-omics analysis of 132 patients with American Joint Committee on Cancer (AJCC) stage III TNBC, comprising 36 long-term survivors (RFS≥8 years), 62 moderate-term survivors (RFS: 3-8 years), and 34 short-term survivors (RFS<3 years). Analyses investigated clinicopathological factors, whole-exome sequencing, germline mutations, copy number alterations (CNAs), RNA sequences, and metabolomic profiles. Long-term survivors exhibited fewer metastatic regional lymph nodes, along with tumors showing reduced stromal fibrosis and lower Ki67 index. Molecularly, these tumors exhibited multiple alterations in genes related to homologous recombination repair, with higher frequencies of germline mutations and somatic CNAs. Additionally, tumors from long-term survivors demonstrated significant downregulation of the RTK-RAS signaling pathway. Metabolomic profiling revealed decreased levels of lipids and carbohydrate, particularly those involved in glycerophospholipid, fructose, and mannose metabolism, in long-term survival group. Multivariate Cox analysis identified fibrosis [hazard ratio (HR): 12.70, 95% confidence interval (95% CI): 2.19-73.54, P=0.005] and RAC1 copy number loss/deletion (HR: 0.22, 95% CI: 0.06-0.83, P=0.026) as independent predictors of RFS. Higher fructose/mannose metabolism was associated with worse overall survival (HR: 1.30, 95% CI: 1.01-1.68, P=0.045). Our findings emphasize the association between biological determinants and prolonged survival in patients with TNBC. Our study systematically identified the key molecular and metabolic features associated with prolonged survival in AJCC stage III TNBC, suggesting potential therapeutic targets to improve patient outcomes.

Non-small cell lung carcinoma (NSCLC) is a major cancer type whose epigenetic alteration remains unclear. We analyzed open chromatin data with matched whole-genome sequencing and RNA-seq data of 50 primary NSCLC cases. We observed high interpatient heterogeneity of open chromatin profiles and the degree of heterogeneity correlated to several clinical parameters. Lung adenocarcinoma and lung squamous cell carcinoma (LUSC) exhibited distinct open chromatin patterns. Beyond this, we uncovered that the broadest open chromatin peaks indicated key NSCLC genes and led to less stable expression. Furthermore, we found that the open chromatin peaks were gained or lost together with somatic copy number alterations and affected the expression of important NSCLC genes. In addition, we identified 21 joint-quantitative trait loci (joint-QTL) that correlated to both assay for transposase accessible chromatin sequencing peak intensity and gene expression levels. Finally, we identified 87 regulatory risk loci associated with lung cancer-related phenotypes by intersecting the QTLs with genome-wide association study significant loci. In summary, this compendium of multiomics data provides valuable insights and a resource to understand the landscape of open chromatin features and regulatory networks in NSCLC. SIGNIFICANCE: This study utilizes state of the art genomic methods to differentiate lung cancer subtypes.See related commentary by Bowcock, p. 4808.

Introduction: Analyses of bulk RNA sequencing (RNA-seq) data are central to most large-scale tumour sequencing studies. Solid tumors often present a dynamic, heterogeneous environment consisting of both cancer (sub)clones and normal cells. Bulk expression data represent population averages and its interpretation is confounded by both normal cell contamination and somatic copy number alterations. Several computational methods to deconvolve tumour and normal expression profiles from bulk RNA-seq have been developed recently. However, these methods often rely on a set of cell-type-specific reference signatures and ignore the effect of copy number changes. Methods: To address these issues, we have developed a method that formalizes the relationship between allele-specific copy number, expression and sample purity to deconvolve the expression profiles of tumor and normal cells from bulk RNA-seq data in an unbiased manner. Our method was applied to sequencing data produced by the TRACERx consortium, a longitudinal study with multi-region whole-exome and RNA-seq of non-small-cell lung cancers. A total of 414 primary tumor regions and 140 adjacent normal tissue samples from 140 TRACERx patients with matched DNA and RNA sequencing data were processed. Results: Here, we were able to directly deconvolve a median of ~2,000 genes per sample and indirectly infer tumor and normal expression profiles of ~10,000 genes. The accuracy of the deconvolution was validated using in-silico mixtures of patient-derived tumour and normal cells and in regions with loss of heterogeneity (LOH) directly on the bulk sequencing data, where the total fraction of expression attributed to tumor cells can be computed directly using somatic mutations. Our method revealed a strong and constitutive genome-wide overexpression in cancer cells compared to admixed normal cells, this overexpression was more pronounced in lung squamous cell carcinoma than lung adenocarcinoma (p&amp;lt0.001). Multidimensional projection of the purified tumor and normal expression profiles together with the adjacent normal tissue showed clear separation between the purified expression profiles and notably, the deconvolved normal expression was more similar to the normal adjacent profiles than the purified tumor profiles. Conclusion: Overall, these results suggest that our method is able to accurately disentangle the expression of tumour and normal cells from bulk RNA-seq without any previous knowledge. It has potential applications in many studies that include matched RNA-seq and copy number data and can provide new insights functional characterization, the taxonomy of cancer, and tumor evolution. Citation Format: Carla Castignani, Jonas Demeulemeester, Elizabeth Larose Cadieux, Robert E. Hynds, David R. Pearce, Stefan C. Dentro, Peter Van Loo, Charles Swanton, TRACERx Consortium. Allele-specific copy-number based deconvolution of bulk tumour RNA sequencing data from the TRACERx study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1211.

Some reports suggest that Numb is a potential tumor suppressor. However, its role in non-small cell lung cancer remains unclear. Non-small cell lung cancer comprises two major histological subtypes, adenocarcinoma and squamous cell carcinoma. To investigate the role of Numb in tumorigenesis of lung adenocarcinoma and squamous cell carcinoma, we firstly performed loss-of-function and gain-of-function assays. Moreover, Numb expression was investigated in surgically resected lung adenocarcinoma and squamous cell carcinoma tissues by immunohistochemistry and correlations with prognosis were analyzed. Numb suppressed the proliferation, migration, and invasion of adenocarcinoma cells and inhibited Notch signaling and epithelial-mesenchymal transition in vitro. Numb overexpression also inhibited subcutaneous adenocarcinoma tumor growth. In contrast, Numb promoted the proliferation, migration, and invasion of squamous cell carcinoma cells, but did not induce any consistent changes in Notch signaling. High Numb expression was associated with favorable prognosis in patients with lung adenocarcinoma, but not in those with squamous cell carcinoma. Collectively, our data demonstrate that Numb plays distinct roles in lung adenocarcinoma and squamous cell carcinoma. In lung adenocarcinoma, Numb impairs tumor growth and inhibits the Notch pathway and epithelial-mesenchymal transition, whereas in lung squamous cell carcinoma it may promote proliferation.

Transcriptomic Analysis of Clonal Hematopoiesis in Non-Small Cell Lung Carcinoma Unveils a Tumorigenic and Dysregulated Immune Profile

Results from a Phase 2 Study of Induction Docetaxel and Carboplatin Followed by Maintenance Rucaparib in the Treatment of Patients with Metastatic Castration-resistant Prostate Cancer with DNA Homologous Recombination Repair Deficiency.