Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics.

BACKGROUND: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. METHODS: We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. FINDINGS: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions. INTERPRETATION: Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer. FUNDING: This work was supported by Cancer Research UK (grant no. C8221/A29017).

Exploring biomarkers in trigeminal neuralgia patients operated with microvascular decompression: A comparison with multiple sclerosis patients and non-neurological controls.

BACKGROUND: Trigeminal neuralgia (TN) is a severe facial pain condition often associated with a neurovascular conflict. However, neuroinflammation has also been implicated in TN, as it frequently co-occurs with multiple sclerosis (MS). METHODS: We analysed protein expression levels of TN patients compared to MS patients and controls. Proximity Extension Assay technology was used to analyse the levels of 92 proteins with the Multiplex Neuro-Exploratory panel provided by SciLifeLab, Uppsala, Sweden. Serum and CSF samples were collected from TN patients before (n = 33 and n = 27, respectively) and after (n = 28 and n = 8, respectively) microvascular decompression surgery. Additionally, we included samples from MS patients (n = 20) and controls (n = 20) for comparison. RESULTS: In both serum and CSF, several proteins were found increased in TN patients compared to either MS patients, controls, or both, including EIF4B, PTPN1, EREG, TBCB, PMVK, FKBP5, CD63, CRADD, BST2, CD302, CRIP2, CCL27, PPP3R1, WWP2, KLB, PLA2G10, TDGF1, SMOC1, RBKS, LTBP3, CLSTN1, NXPH1, SFRP1, HMOX2, and GGT5. The overall expression of the 92 proteins in postoperative TN samples seems to shift towards the levels of MS patients and controls in both serum and CSF, as compared to preoperative samples. Interestingly, there was no difference in protein levels between MS patients and controls. CONCLUSIONS: We conclude that TN patients showed increased serum and CSF levels of specific proteins and that successful surgery normalizes these protein levels, highlighting its potential as an effective treatment. However, the similarity between MS and controls challenges the idea of shared pathophysiology with TN, suggesting distinct underlying mechanisms in these conditions. SIGNIFICANCE: This study advances our understanding of trigeminal neuralgia (TN) and its association with multiple sclerosis (MS). By analysing 92 protein biomarkers, we identified distinctive molecular profiles in TN patients, shedding light on potential pathophysiological mechanisms. The observation that successful surgery normalizes many protein levels suggests a promising avenue for TN treatment. Furthermore, the contrasting protein patterns between TN and MS challenge prevailing assumptions of similarity between the two conditions and point to distinct pathophysiological mechanisms.

Evaluation of circulating plasma proteins in breast cancer using Mendelian randomisation.

Biomarkers for early detection of breast cancer may complement population screening approaches to enable earlier and more precise treatment. The blood proteome is an important source for biomarker discovery but so far, few proteins have been identified with breast cancer risk. Here, we measure 2929 unique proteins in plasma from 598 women selected from the Karolinska Mammography Project to explore the association between protein levels, clinical characteristics, and gene variants, and to identify proteins with a causal role in breast cancer. We present 812 cis-acting protein quantitative trait loci for 737 proteins which are used as instruments in Mendelian randomisation analyses of breast cancer risk. Of those, we present five proteins (CD160, DNPH1, LAYN, LRRC37A2 and TLR1) that show a potential causal role in breast cancer risk with confirmatory results in independent cohorts. Our study suggests that these proteins should be further explored as biomarkers and potential drug targets in breast cancer.

Identifying proteomic risk factors for overall, aggressive and early onset prostate cancer using Mendelian randomization and tumor spatial transcriptomics.

Background: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. Methods: We investigated the association of 2,002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomization (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumor tissue to assess their role in tumor aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. Results: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which a majority were novel and replicated. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirm an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also find an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk mapped to existing therapeutic interventions. Conclusion: Our findings emphasize the importance of proteomics for improving our understanding of prostate cancer etiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumors. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

Neoantigen Vaccines; Clinical Trials, Classes, Indications, Adjuvants and Combinatorial Treatments.

Personalized neoantigen vaccines are a highly specific cancer treatment designed to induce a robust cytotoxic T-cell attack against a patient's cancer antigens. In this study, we searched ClinicalTrials.gov for neoantigen vaccine clinical trials and systematically analyzed them, a total of 147 trials. Peptide vaccines are the largest neoantigen vaccine type, comprising up to 41% of the clinical trials. However, mRNA vaccines are a growing neoantigen vaccine group, especially in the most recent clinical trials. The most common cancer types in the clinical trials are glioma, lung cancer, and malignant melanoma, being seen in more than half of the clinical trials. Small-cell lung cancer and non-small-cell lung cancer are the largest individual cancer types. According to the results from the clinical trials, neoantigen vaccines work best when combined with other cancer treatments, and popular combination treatments include immune checkpoint inhibitors, chemotherapy, and radiation therapy. Additionally, half of the clinical trials combined neoantigen vaccines with an adjuvant to boost the immune effects, with poly-ICLC being the most recurrent adjuvant choice. This study clarifies the rapid clinical trial development of personalized neoantigen vaccines as an emerging class of cancer treatment with increasingly diversified opportunities in classes, indications, and combinatorial treatments.

Brain Cancer Drug Discovery: Clinical Trials, Drug Classes, Targets, and Combinatorial Therapies.

Brain cancer is a formidable challenge for drug development, and drugs derived from many cutting-edge technologies are being tested in clinical trials. We manually characterized 981 clinical trials on brain tumors that were registered in ClinicalTrials.gov from 2010 to 2020. We identified 582 unique therapeutic entities targeting 581 unique drug targets and 557 unique treatment combinations involving drugs. We performed the classification of both the drugs and drug targets based on pharmacological and structural classifications. Our analysis demonstrates a large diversity of agents and targets. Currently, we identified 32 different pharmacological directions for therapies that are based on 42 structural classes of agents. Our analysis shows that kinase inhibitors, chemotherapeutic agents, and cancer vaccines are the three most common classes of agents identified in trials. Agents in clinical trials demonstrated uneven distribution in combination approaches; chemotherapy agents, proteasome inhibitors, and immune modulators frequently appeared in combinations, whereas kinase inhibitors, modified immune effector cells did not as was shown by combination networks and descriptive statistics. This analysis provides an extensive overview of the drug discovery field in brain cancer, shifts that have been happening in recent years, and challenges that are likely to come. SIGNIFICANCE STATEMENT: This review provides comprehensive quantitative analysis and discussion of the brain cancer drug discovery field, including classification of drug, targets, and therapies.

Trends in kinase drug discovery: targets, indications and inhibitor design.

The FDA approval of imatinib in 2001 was a breakthrough in molecularly targeted cancer therapy and heralded the emergence of kinase inhibitors as a key drug class in the oncology area and beyond. Twenty years on, this article analyses the landscape of approved and investigational therapies that target kinases and trends within it, including the most popular targets of kinase inhibitors and their expanding range of indications. There are currently 71 small-molecule kinase inhibitors (SMKIs) approved by the FDA and an additional 16 SMKIs approved by other regulatory agencies. Although oncology is still the predominant area for their application, there have been important approvals for indications such as rheumatoid arthritis, and one-third of the SMKIs in clinical development address disorders beyond oncology. Information on clinical trials of SMKIs reveals that approximately 110 novel kinases are currently being explored as targets, which together with the approximately 45 targets of approved kinase inhibitors represent only about 30% of the human kinome, indicating that there are still substantial unexplored opportunities for this drug class. We also discuss trends in kinase inhibitor design, including the development of allosteric and covalent inhibitors, bifunctional inhibitors and chemical degraders.