Discovering a qualitative transcriptional signature of homologous recombination defectiveness for prostate cancer

Individualized detection of TMPRSS2-ERG fusion status in prostate cancer: a rank-based qualitative transcriptome signature

BACKGROUND

TMPRSS2-ERG (T2E) fusion is highly related to aggressive clinical features in prostate cancer (PC), which guides individual therapy. However, current fusion prediction tools lacked enough accuracy and biomarkers were unable to be applied to individuals across different platforms due to their quantitative nature. This study aims to identify a transcriptome signature to detect the T2E fusion status of PC at the individual level.

METHODS

Based on 272 high-throughput mRNA expression profiles from the Sboner dataset, we developed a rank-based algorithm to identify a qualitative signature to detect T2E fusion in PC. The signature was validated in 1223 samples from three external datasets (Setlur, Clarissa, and TCGA).

RESULTS

A signature, composed of five mRNAs coupled to ERG (five ERG-mRNA pairs, 5-ERG-mRPs), was developed to distinguish T2E fusion status in PC. 5-ERG-mRPs reached 84.56% accuracy in Sboner dataset, which was verified in Setlur dataset (n = 455, accuracy = 82.20%) and Clarissa dataset (n = 118, accuracy = 81.36%). Besides, for 495 samples from TCGA, two subtypes classified by 5-ERG-mRPs showed a higher level of significance in various T2E fusion features than subtypes obtained through current fusion prediction tools, such as STAR-Fusion.

CONCLUSIONS

Overall, 5-ERG-mRPs can robustly detect T2E fusion in PC at the individual level, which can be used on any gene measurement platform without specific normalization procedures. Hence, 5-ERG-mRPs may serve as an auxiliary tool for PC patient management.

Determining magnitude of benefit from poly(ADP-ribose) polymerase inhibitors in prostate cancer

The treatment landscape for castration-resistant prostate cancer (mCRPC) is undergoing significant advancements, particularly with the emergence of poly(ADP-ribose) polymerase inhibitors and their recent US FDA authorizations. The combination of olaparib with abiraterone and prednisone/prednisolone has gained approval for mCRPC patients harboring confirmed BRCA mutations. Subsequently, talazoparib in combination with enzalutamide was approved for patients with mutations in homologous recombination repair genes. Nevertheless, emerging evidence suggests that these treatments may confer benefits irrespective of specific biomarkers. While the understanding of biomarkers in therapy selection for mCRPC is expanding, further data are warranted to provide comprehensive elucidation for guiding clinical practice.

RNA-Based Homologous Recombination Deficiency Signature Detects Homologous Recombination Deficiency-RNA+ Patients With and Without Homologous Recombination Repair Gene Pathogenic Alterations in Men With Prostate Cancer

PURPOSE

Homologous recombination deficiency (HRD) is a well-described phenotype of some prostate cancers; however, current biomarkers for HRD are imperfect and rely on detection of single gene alterations in the homologous recombination repair (HRR) pathway, which may not capture the complexity of HRD biology. RNA signature-based methods of HRD identification present a potentially dynamic assessment of the HRD phenotype; however, its relationship with HRR gene alterations is not well characterized in prostate cancer.

METHODS

A HRD assay on the basis of an RNA signature associated with biallelic BRCA1/2 loss was applied to a retrospective cohort study of 985 men with prostate cancer analyzed on the Tempus xT platform. HRD status was defined by a binary threshold on a continuous scale.

RESULTS

In this cohort, of the 126 (13%) patients found to be HRD+ by RNA signature (HRD-RNA+), 100 (79%) had no coexisting HRR gene alteration. Among samples with biallelic BRCA1/2 loss, 78% (7/9) were classified as HRD-RNA+, while 8% (2/25) of samples with BRCA1/2 monoallelic loss were HRD-RNA+. Biallelic and monoallelic ATM loss exhibited HRD-RNA+ at a lower prevalence: 6.7% (1/15) and 7.1% (1/14), respectively, compared with HRD-RNA+ prevalence among samples without any HRR gene loss (13%; 100/782). HRD-RNA+ was associated with a significantly higher prevalence of TP53 and AR gene alterations relative to HRD-RNA- after correction for multiple comparisons, 59% versus 39% (q = 0.003) and 23% versus 12% (q = 0.024), respectively.

CONCLUSION

Use of an RNA-based HRD signature significantly expands the fraction of patients with prostate cancer who may derive benefit from poly (ADP-ribose) polymerase inhibitors (PARPis) compared with using HRR gene mutations alone. Further studies are needed to evaluate functional HRD significance and inform future usage as a predictive biomarker for PARPi selection.

The functional implication of ATF6α in castration-resistant prostate cancer cells

Stress in the endoplasmic reticulum (ER) may perturb proteostasis and activates the unfolded protein response (UPR). UPR activation is frequently observed in cancer cells and is believed to fuel cancer progression. Here, we report that one of the three UPR sensors, ATF6α, was associated with prostate cancer (PCa) development, while both genetic and pharmacological inhibition of ATF6α impaired the survival of castration-resistance PCa (CRPC) cells. Transcriptomic analyses identified the molecular pathways deregulated upon ATF6α depletion, and also discovered considerable disparity in global gene expression between ATF6α knockdown and Ceapin-A7 treatment. In addition, combined analyses of human CRPC bulk RNA-seq and single-cell RNA-seq (scRNA-seq) public datasets confirmed that CRPC tumors with higher ATF6α activity displayed higher androgen receptor (AR) activity, proliferative and neuroendocrine (NE) like phenotypes, as well as immunosuppressive features. Lastly, we identified a 14-gene set as ATF6α NE gene signature with encouraging prognostic power. In conclusion, our results indicate that ATF6α is correlated with PCa progression and is functionally relevant to CRPC cell survival. Both specificity and efficacy of ATF6α inhibitors require further refinement and evaluation.

Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice

The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).

A Risk Model Developed Based on Homologous Recombination Deficiency Predicts Overall Survival in Patients With Lower Grade Glioma

The role of homologous recombination deficiency (HRD) in lower grade glioma (LGG) has not been elucidated, and accurate prognostic prediction is also important for the treatment and management of LGG. The aim of this study was to construct an HRD-based risk model and to explore the immunological and molecular characteristics of this risk model. The HRD score threshold = 10 was determined from 506 LGG samples in The Cancer Genome Atlas cohort using the best cut-off value, and patients with high HRD scores had worse overall survival. A total of 251 HRD-related genes were identified by analyzing differentially expressed genes, 182 of which were associated with survival. A risk score model based on HRD-related genes was constructed using univariate Cox regression, least absolute shrinkage and selection operator regression, and stepwise regression, and patients were divided into high- and low-risk groups using the median risk score. High-risk patients had significantly worse overall survival than low-risk patients. The risk model had excellent predictive performance for overall survival in LGG and was found to be an independent risk factor. The prognostic value of the risk model was validated using an independent cohort. In addition, the risk score was associated with tumor mutation burden and immune cell infiltration in LGG. High-risk patients had higher HRD scores and “hot” tumor immune microenvironment, which could benefit from poly-ADP-ribose polymerase inhibitors and immune checkpoint inhibitors. Overall, this big data study determined the threshold of HRD score in LGG, identified HRD-related genes, developed a risk model based on HRD-related genes, and determined the molecular and immunological characteristics of the risk model. This provides potential new targets for future targeted therapies and facilitates the development of individualized immunotherapy to improve prognosis.

Management of Patients with Advanced Prostate Cancer: Report from the Advanced Prostate Cancer Consensus Conference 2021

BACKGROUND

Innovations in treatments, imaging, and molecular characterisation in advanced prostate cancer have improved outcomes, but various areas of management still lack high-level evidence to inform clinical practice. The 2021 Advanced Prostate Cancer Consensus Conference (APCCC) addressed some of these questions to supplement guidelines that are based on level 1 evidence.

OBJECTIVE

To present the voting results from APCCC 2021.

DESIGN, SETTING, AND PARTICIPANTS

The experts identified three major areas of controversy related to management of advanced prostate cancer: newly diagnosed metastatic hormone-sensitive prostate cancer (mHSPC), the use of prostate-specific membrane antigen ligands in diagnostics and therapy, and molecular characterisation of tissue and blood. A panel of 86 international prostate cancer experts developed the programme and the consensus questions.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The panel voted publicly but anonymously on 107 pre-defined questions, which were developed by both voting and non-voting panel members prior to the conference following a modified Delphi process.

RESULTS AND LIMITATIONS

The voting reflected the opinions of panellists and did not incorporate a standard literature review or formal meta-analysis. The answer options for the consensus questions received varying degrees of support from panellists, as reflected in this article and the detailed voting results reported in the Supplementary material.

CONCLUSIONS

These voting results from a panel of experts in advanced prostate cancer can help clinicians and patients to navigate controversial areas of management for which high-level evidence is scant. However, diagnostic and treatment decisions should always be individualised according to patient characteristics, such as the extent and location of disease, prior treatment(s), comorbidities, patient preferences, and treatment recommendations, and should also incorporate current and emerging clinical evidence and logistic and economic constraints. Enrolment in clinical trials should be strongly encouraged. Importantly, APCCC 2021 once again identified salient questions that merit evaluation in specifically designed trials.

PATIENT SUMMARY

The Advanced Prostate Cancer Consensus Conference is a forum for discussing current diagnosis and treatment options for patients with advanced prostate cancer. An expert panel votes on predefined questions focused on the most clinically relevant areas for treatment of advanced prostate cancer for which there are gaps in knowledge. The voting results provide a practical guide to help clinicians in discussing treatment options with patients as part of shared decision-making.

Validation of genomic and transcriptomic models of homologous recombination deficiency in a real-world pan-cancer cohort

Background

With the introduction of DNA-damaging therapies into standard of care cancer treatment, there is a growing need for predictive diagnostics assessing homologous recombination deficiency (HRD) status across tumor types. Following the strong clinical evidence for the utility of DNA-sequencing-based HRD testing in ovarian cancer, and growing evidence in breast cancer, we present analytical validation of the Tempus HRD-DNA test. We further developed, validated, and explored the Tempus HRD-RNA model, which uses gene expression data from 16,750 RNA-seq samples to predict HRD status from formalin-fixed paraffin-embedded tumor samples across numerous cancer types.

Methods

Genomic and transcriptomic profiling was performed using next-generation sequencing from Tempus xT, Tempus xO, Tempus xE, Tempus RS, and Tempus RS.v2 assays on 48,843 samples. Samples were labeled based on their BRCA1, BRCA2 and selected Homologous Recombination Repair pathway gene (CDK12, PALB2, RAD51B, RAD51C, RAD51D) mutational status to train and validate HRD-DNA, a genome-wide loss-of-heterozygosity biomarker, and HRD-RNA, a logistic regression model trained on gene expression.

Results

In a sample of 2058 breast and 1216 ovarian tumors, BRCA status was predicted by HRD-DNA with F1-scores of 0.98 and 0.96, respectively. Across an independent set of 1363 samples across solid tumor types, the HRD-RNA model was predictive of BRCA status in prostate, pancreatic, and non-small cell lung cancer, with F1-scores of 0.88, 0.69, and 0.62, respectively.

Conclusions

We predict HRD-positive patients across many cancer types and believe both HRD models may generalize to other mechanisms of HRD outside of BRCA loss. HRD-RNA complements DNA-based HRD detection methods, especially for indications with low prevalence of BRCA alterations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09669-z.