M6620, a selective ATP-competitive inhibitor of the ATM and RAD3-related (ATR) kinase, is currently under investigation with radiation in patients with non–small cell lung cancer (NSCLC) brain metastases. We evaluated the DNA damage response (DDR) pathway profile of NSCLC and assessed the radiosensitizing effects of M6620 in a preclinical NSCLC brain metastasis model. Mutation analysis and transcriptome profiling of DDR genes and pathways was performed on NSCLC patient samples. NSCLC cell lines were assessed with proliferation, clonogenic survival, apoptosis, cell cycle, and DNA damage signaling and repair assays. NSCLC brain metastasis patient-derived xenograft models were used to assess intracranial response and overall survival. In vivo IHC was performed to confirm in vitro results. A significant portion of NSCLC patient tumors demonstrated enrichment of DDR pathways. DDR pathways correlated with lung squamous cell histology; and mutations in ATR, ATM, BRCA1, BRCA2, CHEK1, and CHEK2 correlated with enrichment of DDR pathways in lung adenocarcinomas. M6620 reduced colony formation after radiotherapy and resulted in inhibition of DNA DSB repair, abrogation of the radiation-induced G2 cell checkpoint, and formation of dysfunctional micronuclei, leading to enhanced radiation-induced mitotic death. The combination of M6620 and radiation resulted in improved overall survival in mice compared with radiation alone. In vivo IHC revealed inhibition of pChk1 in the radiation plus M6620 group. M6620 enhances the effect of radiation in our preclinical NSCLC brain metastasis models, supporting the ongoing clinical trial (NCT02589522) evaluating M6620 in combination with whole brain irradiation in patients with NSCLC brain metastases.

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Cancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Here, we provide a comprehensive resource of precision combination therapies tailored to oncogenic co-alterations that are recurrent across patient cohorts. To generate the resource, we developed Recurrent Features Leveraged for Combination Therapy (REFLECT), which integrates machine learning and cancer informatics algorithms. Using multi-omic data, the method maps recurrent co-alteration signatures in patient cohorts to combination therapies. We validated the REFLECT pipeline using data from patient-derived xenografts, in vitro drug screens, and a combination therapy clinical trial. These validations demonstrate that REFLECT-selected combination therapies have significantly improved efficacy, synergy, and survival outcomes. In patient cohorts with immunotherapy response markers, DNA repair aberrations, and HER2 activation, we have identified therapeutically actionable and recurrent co-alteration signatures. REFLECT provides a resource and framework to design combination therapies tailored to tumor cohorts in data-driven clinical trials and pre-clinical studies.

Bacterial vaginosis (BV) is the most common cause of vaginal discharge among reproductive-age women. It is associated with multiple adverse health outcomes, including increased risk of acquisition of HIV and other sexually transmitted infections (STIs), in addition to adverse birth outcomes.

Bacterial vaginosis (BV) is the most common vaginal dysbiosis. In this condition, a polymicrobial biofilm develops on vaginal epithelial cells. Accurately quantifying the bacterial burden of the BV biofilm is necessary to further our understanding of BV pathogenesis. Historically, the standard for calculating total bacterial burden of the BV biofilm has been based on quantifying Escherichia coli 16S rRNA gene copy number. However, E. coli is improper for measuring the bacterial burden of this unique micro-environment. Here, we propose a novel qPCR standard to quantify bacterial burden in vaginal microbial communities, from an optimal state to a mature BV biofilm. These standards consist of different combinations of vaginal bacteria including three common BV-associated bacteria (BVAB) Gardnerella spp. (G), Prevotella spp. (P), and Fannyhessea spp. (F) and commensal Lactobacillus spp. (L) using the 16S rRNA gene (G:P:F:L, G:P:F, G:P:L and 1G:9L). We compared these standards to the traditional E. coli (E) reference standard using known quantities of mock vaginal communities and 16 vaginal samples from women. The E standard significantly underestimated the copy numbers of the mock communities, and this underestimation was significantly greater at lower copy numbers of these communities. The G:P:L standard was the most accurate across all mock communities and when compared to other mixed vaginal standards. Mixed vaginal standards were further validated with vaginal samples. This new G:P:L standard can be used in BV pathogenesis research to enhance reproducibility and reliability in quantitative measurements of BVAB, spanning from the optimal to non-optimal (including BV) vaginal microbiota.