Unique Spectrum of Activating BRAF Alterations in Prostate Cancer

Roles of CDK12 mutations in PCa development and treatment

Prostate cancer (PCa) is one of the most common cancers in men, and cyclin-dependent kinase 12 (CDK12) is emerging as a novel star player in the PCa tumorigenesis and progression to castration-resistant prostate cancer (CRPC). In PCa, CDK12 alterations are mostly loss-of-function mutations featuring intronic polyadenylation (IPA), focal tandem duplications (FTDs), and R-loops formation and transcription-replication conflicts (TRCs). The occurrence of IPA can result in homologous recombination deficiency (HRD) and androgen receptor (AR) variation. FTDs induce neoantigens and increase the expression of the AR, MYC, and other hotspot- associated genes. R-loops lead to TRCs and influence various cellular processes, including gene expression and genome stability. Due to the poor prognosis of CDK12-mutant PCa patients and the mediocre response to classic standard therapies, HRD and increased neoantigen levels have provided clinicians with new insights into alternative systematic treatments for this novel PCa phenotype. In this review, we summarize the roles of CDK12 mutations in PCa and discuss their clinical value, suggesting that CDK12 potentially represents a target for further research and the development of clinical strategies for PCa.

Defining the genetic profile of prostate cancer

Several studies indicated that prostate cancer has a hereditary component. In particular, a significant risk of prostate cancer has been linked to a tight familial lineage. However, to provide insight into how prostate cancer is inherited, characterising its genetic profile is essential. The current body of research on the analysis of genetic mutations in prostate cancer was reviewed to achieve this. This paper reports on the effects and underlying processes of prostate cancer that have been linked to decreased male fertility. Many research approaches used have resulted in the discovery of unique inheritance patterns and manifest traits, the onset and spread of prostate cancer have also been linked to many genes. Studies have specifically examined Androgen Receptor gene variants about prostate cancer risk and disease progression. Research has shown that genetic and environmental variables are important contributors to prostate cancer, even if the true origins of the disease are not fully recognised or established. Researchers studying the genetics of prostate cancer are using genome-wide association studies more and more because of their outstanding effectiveness in revealing susceptibility loci for prostate cancer. Genome-Wide Association Studies provides a detailed method for identifying the distinct sequence of a gene that is associated with cancer risk. Surgical procedures and radiation treatments are 2 of the treatment options for prostate cancer. Notwithstanding the compelling evidence shown in this work, suggests that more research must be done to detect the gene alterations and the use of genetic variants in the treatment of prostate cancer.

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.

Precision medicine for prostate cancer: An international perspective

Greater personalization of cancer medicine continues to shape therapy development and patient selection accordingly. The treatment of prostate cancer has evolved considerably since the discovery of androgen deprivation therapy. The comprehensive profiling of the prostate cancer genome has mapped the targetable molecular landscape of the disease and identified opportunities for the implementation of novel and combination therapies. In this review, we provide an overview of the molecular biology of prostate cancer and tools developed to aid prognostication and prediction of therapy benefit. Modern treatment of advanced prostate cancer is reviewed as a paradigm of increasing precision-informed approach to patient care, and must be considered on a global scale with respect to the state of science and care delivery.

A consensus-based classification workflow to determine genetically inferred ancestry from comprehensive genomic profiling of patients with solid tumors

Disparities in cancer diagnosis, treatment, and outcomes based on self-identified race and ethnicity (SIRE) are well documented, yet these variables have historically been excluded from clinical research. Without SIRE, genetic ancestry can be inferred using single-nucleotide polymorphisms (SNPs) detected from tumor DNA using comprehensive genomic profiling (CGP). However, factors inherent to CGP of tumor DNA increase the difficulty of identifying ancestry-informative SNPs, and current workflows for inferring genetic ancestry from CGP need improvements in key areas of the ancestry inference process. This study used genomic data from 4274 diverse reference subjects and CGP data from 491 patients with solid tumors and SIRE to develop and validate a workflow to obtain accurate genetically inferred ancestry (GIA) from CGP sequencing results. We use consensus-based classification to derive confident ancestral inferences from an expanded reference dataset covering eight world populations (African, Admixed American, Central Asian/Siberian, European, East Asian, Middle Eastern, Oceania, South Asian). Our GIA calls were highly concordant with SIRE (95%) and aligned well with reference populations of inferred ancestries. Further, our workflow could expand on SIRE by (i) detecting the ancestry of patients that usually lack appropriate racial categories, (ii) determining what patients have mixed ancestry, and (iii) resolving ancestries of patients in heterogeneous racial categories and who had missing SIRE. Accurate GIA provides needed information to enable ancestry-aware biomarker research, ensure the inclusion of underrepresented groups in clinical research, and increase the diverse representation of patient populations eligible for precision medicine therapies and trials.

Targeting the multifaceted BRAF in cancer: New directions

Activating mutations in the mitogen-activated protein kinase (MAPK) pathway represent driver alterations governing tumorigenesis, metastasis, and therapy resistance. MAPK activation predominantly occurs through genomic alterations in RAS and BRAF. BRAF is an effector kinase that functions downstream of RAS and propagates this oncogenic activity through MEK and ERK. Across cancers, BRAF alterations include gain-of-function mutations, copy-number alterations, and structural rearrangements. In cancer patients, BRAF-targeting precision therapeutics are effective against Class I BRAF alterations (p.V600 hotspot mutations) in tumors such as melanomas, thyroid cancers, and colorectal cancers. However, numerous non-Class I BRAF inhibitors are also in development and have been explored in some cancers. Here we discuss the diverse forms of BRAF alterations found in human cancers and the strategies to inhibit them in patients harboring cancers of distinct origins.

Genomic deletions explain the generation of alternative BRAF isoforms conferring resistance to MAPK inhibitors in melanoma

Resistance to MAPK inhibitors (MAPKi), the main cause of relapse in BRAF-mutant melanoma, is associated with the production of alternative BRAF mRNA isoforms (altBRAFs) in up to 30% of patients receiving BRAF inhibitor monotherapy. These altBRAFs have been described as being generated by alternative pre-mRNA splicing, and splicing modulation has been proposed as a therapeutic strategy to overcome resistance. In contrast, we report that altBRAFs are generated through genomic deletions. Using different in vitro models of altBRAF-mediated melanoma resistance, we demonstrate the production of altBRAFs exclusively from the BRAF V600E allele, correlating with corresponding genomic deletions. Genomic deletions are also detected in tumor samples from melanoma and breast cancer patients expressing altBRAFs. Along with the identification of altBRAFs in BRAF wild-type and in MAPKi-naive melanoma samples, our results represent a major shift in our understanding of mechanisms leading to the generation of BRAF transcripts variants associated with resistance in melanoma.

Identification of recurrent BRAF non-V600 mutations in intraductal carcinoma of the prostate in Chinese populations

While BRAF alterations have been established as a driver in various solid malignancies, the characterization of BRAF alterations in prostate cancer (PCa) has not been thoroughly interrogated. By bioinformatics analysis, we first found that BRAF alterations were associated with advanced PCa and exhibited mutually exclusive pattern with ERG alteration across multiple cohorts. Of the most interest, recurrent non-V600 BRAF mutations were found in 3 of 21 (14.3 %) PCa patients demonstrating IDC-P morphology. Furthermore, experimental overexpression of BRAFK601E and BRAFL597R exhibited emergence of oncogenic phenotypes with intensified MAPK signaling in vitro, which could be targeted by MEK inhibitors. Comparison of the incidence of BRAF alterations in IDC-P between western and Chinese ancestry revealed an increased prevalence in the Chinese population. The BRAF mutation may represent important genetic alteration in a subset of IDC-P, highlighting the role of MAPK signaling pathway in this subtype of PCa. To the best of knowledge, this is the first description of non-V600 BRAF mutation in setting of IDC-P, which may in part explain the aggressive phenotype seen in IDC-P and could also bring more treatment options for PCa patients with IDC-P harboring such mutations.

Circulating Tumor DNA Enables Sensitive Detection of Actionable Gene Fusions and Rearrangements Across Cancer Types

PURPOSE

Genomic rearrangements can generate potent oncogenic drivers or disrupt tumor suppressor genes. This study examines the landscape of fusions and rearrangements detected by liquid biopsy (LBx) of circulating tumor DNA (ctDNA) across different cancer types.

EXPERIMENTAL DESIGN

LBx from 53,842 patients with 66 solid tumor types were profiled using FoundationOneLiquid CDx, a hybrid-capture sequencing platform that queries 324 cancer-related genes. Tissue biopsies (TBx) profiled using FoundationOneCDx were used as a comparator.

RESULTS

Among all LBx, 7,377 (14%) had ≥1 pathogenic rearrangement detected. A total of 3,648 (6.8%) LBx had ≥1 gain-of-function (GOF) oncogene rearrangement, and 4,428 (8.2%) LBx had ≥1 loss-of-function rearrangement detected. Cancer types with higher prevalence of GOF rearrangements included those with canonical fusion drivers: prostate cancer (19%), cholangiocarcinoma (6.4%), bladder (5.5%), and non-small cell lung cancer (4.4%). Although the prevalence of driver rearrangements was lower in LBx than TBx overall, the frequency of detection was comparable in LBx with a tumor fraction (TF) ≥1%. Rearrangements in FGFR2, BRAF, RET, and ALK, were detected across cancer types, but tended to be clonal variants in some cancer types and potential acquired resistance variants in others.

CONCLUSIONS

In contrast to some prior literature, this study reports detection of a wide variety of rearrangements in ctDNA. The prevalence of driver rearrangements in tissue and LBx was comparable when TF ≥1%. LBx presents a viable alternative when TBx is not available, and there may be less value in confirmatory testing when TF is sufficient.