Testing Of Combination Therapies Research Articles

Treating ICB-resistant glioma with anti-CD40 and mitotic spindle checkpoint controller BAL101553 (lisavanbulin).

Glioblastoma is a highly malignant brain tumor with no curative treatment options, and immune checkpoint blockade has not yet shown major impact. We hypothesized that drugs targeting mitosis might affect the tumor microenvironment and sensitize cancer cells to immunotherapy. We used 2 glioblastoma mouse models with different immunogenicity profiles, GL261 and SB28, to test the efficacy of antineoplastic and immunotherapy combinations. The spindle assembly checkpoint activator BAL101553 (lisavanbulin), agonistic anti-CD40 antibody, and double immune checkpoint blockade (anti–programmed cell death 1 and anti–cytotoxic T lymphocyte–associated protein 4; anti–PD-1 and anti–CTLA-4) were evaluated individually or in combination for treating orthotopic GL261 and SB28 tumors. Genomic and immunological analyses were used to predict and interpret therapy responsiveness. BAL101553 monotherapy increased survival in immune checkpoint blockade–resistant SB28 glioblastoma tumors and synergized with anti-CD40 antibody, in a T cell–independent manner. In contrast, the more immunogenic and highly mutated GL261 model responded best to anti–PD-1 and anti–CTLA-4 therapy and more modestly to BAL101553 and anti-CD40 combination. Our results show that BAL101553 is a promising therapeutic agent for glioblastoma and could synergize with innate immune stimulation. Overall, these data strongly support immune profiling of glioblastoma patients and preclinical testing of combination therapies with appropriate models for particular patient groups.

Decoding and targeting the molecular basis of MACC1-driven metastatic spread: Lessons from big data mining and clinical-experimental approaches.

Metastasis remains the key issue impacting cancer patient survival and failure or success of cancer therapies. Metastatic spread is a complex process including dissemination of single cells or collective cell migration, penetration of the blood or lymphatic vessels and seeding at a distant organ site. Hundreds of genes involved in metastasis have been identified in studies across numerous cancer types. Here, we analyzed how the metastasis-associated gene MACC1 cooperates with other genes in metastatic spread and how these coactions could be exploited by combination therapies: We performed (i) a MACC1 correlation analysis across 33 cancer types in the mRNA expression data of TCGA and (ii) a comprehensive literature search on reported MACC1 combinations and regulation mechanisms. The key genes MET, HGF and MMP7 reported together with MACC1 showed significant positive correlations with MACC1 in more than half of the cancer types included in the big data analysis. However, ten other genes also reported together with MACC1 in the literature showed significant positive correlations with MACC1 in only a minority of 5 to 15 cancer types. To uncover transcriptional regulation mechanisms that are activated simultaneously with MACC1, we isolated pan-cancer consensus lists of 1306 positively and 590 negatively MACC1-correlating genes from the TCGA data and analyzed each of these lists for sharing transcription factor binding motifs in the promotor region. In these lists, binding sites for the transcription factors TELF1, ETS2, ETV4, TEAD1, FOXO4, NFE2L1, ELK1, SP1 and NFE2L2 were significantly enriched, but none of them except SP1 was reported in combination with MACC1 in the literature. Thus, while some of the results of the big data analysis were in line with the reported experimental results, hypotheses on new genes involved in MACC1-driven metastasis formation could be generated and warrant experimental validation. Furthermore, the results of the big data analysis can help to prioritize cancer types for experimental studies and testing of combination therapies.

Abstract 4814: Testing personalized therapies for ovarian cancer using mate pair genomic analysis and patient-derived xenografts

Abstract Purpose: The treatment of ovarian cancer poses a challenge since 70% of patients will relapse with incurable platinum resistant disease. Although ovarian cancers lack frequently-occurring driver mutations or amplifications, the study presented herein examines the concept of individualized therapy based on mate pair sequencing and protein expression in ovarian cancer patient-derived xenografts. Experimental Procedure: Macrodissection of patient tumor was followed by genomic DNA isolation and next-generation sequencing using an Illumina Mate Pair protocol to identify structural genomic changes and copy number variations. Secondary analyses were carried out to select potential therapeutic targets among those. The tumors were also propagated intraperitoneally in immunocompromised mice and treated with standard chemotherapy or/and targeted therapy chosen based on genomic analyses. Results: Mate pair analysis revealed that, in general, copy number changes (amplifications, gains and losses) were common in ovarian tumors and included genes potentially targetable. Tumors from early generation patient- derived xenografts showed a landscape of genomic aberrations identical to that of original patient tumor. One PDX model in which high expression of HER2 and copy number gains at RICTOR and AKT genetic loci were revealed, was treated with either chemotherapy and Pertuzumab/trastuzumab (HER2 inhibitors), MK-8669 (mTOR inhibitor) or MK-2206 (AKT inhibitor) or chemotherapy alone. Tumor size was measured by serial abdominal ultrasound and after 28 days, the final tumor size ratio compared to baseline was 0.23, 0.36, 0.55 and 0.56, respectively, indicating that the best response was seen in the combined chemotherapy and Pertuzumab/trastuzumab cohort and that was better than in the chemotherapy only cohort. Conclusions: Patient-derived xenografts models in conjunction with genomic and protein analyses of patient tumors are valuable for testing of combination therapies including specific targeting drugs. This approach also provides actionable information for more tailored treatment of ovarian cancer patients. Citation Format: Konstantinos Leventakos, Faye R. Harris, Lin Yang, Xiaonan Hou, Saravut Weroha, Irina V. Kovtun. Testing personalized therapies for ovarian cancer using mate pair genomic analysis and patient-derived xenografts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4814. doi:10.1158/1538-7445.AM2017-4814

Aspergillosis in hematopoietic stem cell transplant recipients: Risk factors, prophylaxis, and treatment

This article discusses newer antifungal agents, recent randomized controlled trials, and the 2008 guidelines for treatment of aspergillosis in reference to hematopoietic stem cell transplantation (HSCT). Strategies such as reduced-intensity conditioning and agents such as infliximab shed new light on aspergillosis risk. The association between Toll-like receptor polymorphisms and aspergillosis is an exciting development. Posaconazole was evaluated in two randomized prophylaxis trials, and a large, randomized trial established voriconazole's therapeutic superiority to amphotericin. However, many questions remain regarding which patients benefit most from prophylaxis; resistance to newer antifungals; and combination, salvage, and immunomodulatory therapies. Current therapies and strategies have improved the outlook of HSCT recipients with invasive aspergillosis. Future directions include increasingly sophisticated risk stratification, clinical testing of combination therapies, and adjunctive immunomodulatory therapies.

The promise of genetically engineered mice for cancer prevention studies

Sophisticated genetic technologies have led to the development of mouse models of human cancers that recapitulate important features of human oncogenesis. Many of these genetically engineered mouse models promise to be very relevant and relatively rapid systems for determining the efficacy of chemopreventive agents and their mechanisms of action. The validation of such models for chemoprevention will help the selection of appropriate agents for large-scale clinical trials and allow the testing of combination therapies.