CXCR2 antagonist navarixin in combination with pembrolizumab in select advanced solid tumors: a phase 2 randomized trial.

C-X-C motif chemokine receptor 2 (CXCR2) has a role in tumor progression, lineage plasticity, and reduction of immune checkpoint inhibitor efficacy. Preclinical evidence suggests potential benefit of CXCR2 inhibition in multiple solid tumors. In this phase 2 study (NCT03473925), adults with previously treated advanced or metastatic castration-resistant prostate cancer (CRPC), microsatellite-stable colorectal cancer (MSS CRC), or non-small-cell lung cancer (NSCLC) were randomized 1:1 to the CXCR2 antagonist navarixin 30 or 100 mg orally once daily plus pembrolizumab 200 mg intravenously every 3 weeks up to 35 cycles. Primary endpoints were investigator-assessed objective response rate (RECIST v1.1) and safety. Of 105 patients (CRPC, n=40; MSS CRC, n=40; NSCLC, n=25), 3 had a partial response (2 CRPC, 1 MSS CRC) for ORRs of 5%, 2.5%, and 0%, respectively. Median progression-free survival was 1.8-2.4 months without evidence of a dose-response relationship, and the study was closed at a prespecified interim analysis for lack of efficacy. Dose-limiting toxicities occurred in 2/48 patients (4%) receiving navarixin 30 mg and 3/48 (6%) receiving navarixin 100 mg; events included grade 4 neutropenia and grade 3 transaminase elevation, hepatitis, and pneumonitis. Treatment-related adverse events occurred in 70/105 patients (67%) and led to treatment discontinuation in 7/105 (7%). Maximal reductions from baseline in absolute neutrophil count were 44.5%-48.2% (cycle 1) and 37.5%-44.2% (cycle 2) and occurred within 6-12 hours postdose in both groups. Navarixin plus pembrolizumab did not demonstrate sufficient efficacy in this study. Safety and tolerability of the combination were manageable. (Trial registration: ClinicalTrials.gov , NCT03473925).

Prevalence of homologous recombination biomarkers in multiple tumor types: an observational study.

Aim: To determine the prevalence of deleterious mutations in BRCA1 and BRCA2 and in 13 genes involved in homologous recombination repair (HRR), the prevalence of genomic loss of heterozygosity and the allelic and hereditary status of BRCA1, BRCA2 and other HRR gene mutations in multiple solid tumor types. Patients & methods: This was a retrospective observational study of patients with an advanced/metastatic diagnosis in one of 15 solid tumor types, who were identified in a real-world clinico-genomic database. Results: Tumor tissue samples from 9457 patients were analyzed, among which 4.7% had known or suspected deleterious BRCA1/2 mutations. The prevalence (range) of mutations in HRR genes was 13.6% (2.4%-26.0%) and genomic loss of heterozygosity ≥16% was 20.6% (2.6-34.4%) across all tumor types. Conclusion: The prevalence of mutations varied significantly depending on the type of tumor.

The integrity of the human genome is maintained via multiple pathways of DNA repair, one of the most important of which is homologous recombination repair (HRR), which uses a sister chromatid as a template for high-fidelity restoration of altered DNA sequences. This study aimed to determine the prevalence of deleterious mutations, i.e., changes in the genetic code that interfere with proper cellular function, in the breast cancer genes BRCA1 and BRCA2 and in 13 other genes involved in HRR in various types of solid tumors in patients with advanced or metastatic cancer. The researchers found that 4.7% of tumor samples had BRCA1/2 mutations, 13.6% had mutations in any of the HRR genes and 20.6% had genomic loss of heterozygosity (gLOH) of at least 16% i.e., loss of sections of chromosomes affecting 16% or more of the genome. BRCA1/2 mutations were most common in ovarian cancer (13.1%), prostate cancer (9.3%), breast cancer (8.2%) and pancreatic cancer (4.9%). Prevalence for mutations in HRR genes ranges from 2.4 to 26.0% and gLOH ≥16% ranged from 2.6 to 34.4% depending on the tumor type. In conclusion, the prevalence of mutations in the BRCA1/2 genes, HRR genes and gLOH ≥16% varied widely across 15 tumor types.

The Prediction of LptA and LptC Protein-Protein Interactions and Virtual Screening for Potential Inhibitors.

Antibiotic resistance in Gram-negative bacteria remains one of the most pressing challenges to global public health. Blocking the transportation of lipopolysaccharides (LPS), a crucial component of the outer membrane of Gram-negative bacteria, is considered a promising strategy for drug discovery. In the transportation process of LPS, two components of the LPS transport (Lpt) complex, LptA and LptC, are responsible for shuttling LPS across the periplasm to the outer membrane, highlighting their potential as targets for antibacterial drug development. In the current study, a protein-protein interaction (PPI) model of LptA and LptC was constructed, and a molecular screening strategy was employed to search a protein-protein interaction compound library. The screening results indicated that compound 18593 exhibits favorable binding free energy with LptA and LptC. In comparison with the molecular dynamics (MD) simulations on currently known inhibitors, compound 18593 shows more stable target binding ability at the same level. The current study suggests that compound 18593 may exhibit an inhibitory effect on the LPS transport process, making it a promising hit compound for further research.

Hybrid Causal Feature Selection for Cancer Biomarker Identification from RNA-seq Data.

The discovery of cancer biomarkers helps to advance medical diagnosis and plays an important role in biomedical applications. Most of the existing data-driven methods identify biomarkers by ranking-based strategies, which generally return a subset or superset of the actual biomarkers, while some other causal-wise feature selection methods are based on Markov Blanket (MB) learning, facing the challenges of high-dimensionality & low-sample. In this work, we propose a novel hybrid causal feature selection method (called CAFES) to support large-scale cancer biomarker discovery from real RNA-seq data. Concretely, CAFES first uses minimal-redundancy & maximal-relevance strategy for dimensionality reduction that returns a set of candidate features. CAFES then learns the causal skeleton w.r.t. those features by CI tests and further obtains an appropriate superset of the MB of the target variable. Finally, CAFES learns the causal structure of this superset by the DAG-GNN algorithm and then obtains the MB of the target variable, which can be treated as the cancer biomarkers. We conduct experiments to evaluate the proposed method on two real well-known RNA-seq datasets that covering both binary and multi-class cases. We compare our method CAFES with seven recent methods including Semi-HITON-MB, STMB, BAMB, FBED, LCS-FS, EEMB, and EAMB. The results show that CAFES can identify dozens of cancer biomarkers, and 1/6  âˆ¼ 1/2 of the discovered biomarkers can be verified by existing works that they are really directly related to the corresponding disease. An advantage of CAFES is that its Recall is significantly higher than those of all the counterparts, indicating that the continuous optimization (DAG-GNN) with the returned causal skeleton after feature selection (that can be treated as a conditional independence-based constraint to the optimization problem) is effective in cancer biomarkers identification under high-dimensional and low-sample RNA-seq data. The source code of CAFES is available at https://github.com/Milkteaww/CFS.

Towards Effective Causal Partitioning by Edge Cutting of Adjoint Graph.

Causal partitioning is an effective approach for causal discovery based on the divide-and-conquer strategy. Up to now, various heuristic methods based on conditional independence (CI) tests have been proposed for causal partitioning. However, most of these methods fail to achieve satisfactory partitioning without violating d-separation, leading to poor inference performance. In this work, we transform causal partitioning into an alternative problem that can be more easily solved. Concretely, we first construct a superstructure G of the true causal graph GT by performing a set of low-order CI tests on the observed data D. Then, we leverage point-line duality to obtain a graph GA adjoint to G. We show that the solution of minimizing edge-cut ratio on GA can lead to a valid causal partitioning with smaller causal-cut ratio on G and without violating d-separation. We design an efficient algorithm to solve this problem. Extensive experiments show that the proposed method can achieve significantly better causal partitioning without violating d-separation than the existing methods. The source code and data are available at https://github.com/hzsiat/CPA.

Biomarker Testing Journey Among Patients with Advanced Solid Tumors and Treatment Patterns by Homologous Recombination Repair Status: A Clinico-Genomic Database Study.

INTRODUCTION: Defects in the homologous recombination repair (HRR) pathway can include mutations in BRCA1 and BRCA2 (BRCAm) and other HRR genes (HRRm). These mutations are associated with a homologous recombination deficiency (HRD) phenotype. We evaluated testing journey and treatment patterns by BRCAm, HRRm, and HRD status in a real-world dataset. METHODS: Deidentified data for patients who had undergone comprehensive genomic profiling using FoundationOne®CDx were collected through December 31, 2020, from a real-world multi-tumor clinico-genomic database (CGDB) capturing data from clinics in the United States. Patients eligible for inclusion in this analysis had a confirmed diagnosis with advanced or metastatic disease between January 1, 2018, and December 31, 2019, for 1 of 15 solid tumor types. Objectives were to evaluate patient treatment patterns by BRCAm, HRRm, and HRD status and to describe the timing of when (throughout disease course) comprehensive genomic profiling was performed. RESULTS: Among 9457 patients included in the overall population with evaluable biomarker status, 7856 (83.1%) received ≥ 1 systemic therapy. Among the 7856 patients who received systemic therapy, 2324 (30.0%) underwent testing before first-line therapy, 4114 (52.4%) were tested after receiving first-line therapy and before receiving subsequent therapy (if any), 970 (12.3%) were tested after second-line therapy and before receiving subsequent therapy (if any), and 447 (5.7%) patients underwent testing after receiving third-line therapy. A higher proportion of patients with BRCAm, HRRm, or HRD-positive status were treated with poly(ADP-ribose) polymerase (PARP) inhibitors across all lines of therapy. There was no evidence of a meaningful difference in the proportion of patients who received other treatment (including chemotherapy and immunotherapy) by BRCAm, HRRm, or HRD status. CONCLUSION: The majority of patients from this real-world dataset underwent FoundationOne®CDx testing after initiation of first-line treatment. Testing appeared to influence treatment patterns, with a higher proportion of patients with BRCAm, HRRm, and HRD-positive disease receiving PARP inhibitors.