Ovarian cancer chemokines may not be a significant barrier during whole tumor antigen dendritic-cell vaccine and adoptive T-cell immunotherapy

The construction of a lymphoma cell-based, DC-targeted vaccine, and its application in lymphoma prevention and cure

In recent years, Non-Hodgkin lymphoma (NHL) has been one of the most fast-growing malignant tumor diseases. NHL poses severe damages to physical health and a heavy burden to patients. Traditional therapies (chemotherapy or radiotherapy) bring some benefit to patients, but have severe adverse effects and do not prevent relapse. The relevance of emerging immunotherapy options (immune-checkpoint blockers or adoptive cellular methods) for NHL remains uncertain, and more intensive evaluations are needed. In this work, inspired by the idea of vaccination to promote an immune response to destroy tumors, we used a biomaterial-based strategy to improve a tumor cell-based vaccine and constructed a novel vaccine named Man-EG7/CH@CpG with antitumor properties. In this vaccine, natural tumor cells are used as a vector to load CpG-ODN, and following lethal irradiation, the formulations were decorated with mannose. The study of the characterization of the double-improved vaccine evidenced the enhanced ability of DCs targeting and improved immunocompetence, which displayed an antitumor function. In the lymphoma prevention model, the Man-EG7/CH@CpG vaccine restrained tumor formation with high efficiency. Furthermore, unlike the non-improved vaccine, the double-improved vaccine elicited an enhanced antitumor effect in the lymphoma treatment model. Next, to improve the moderate therapeutic effect of the mono-treatment method, we incorporated a chemotherapeutic drug (doxorubicin, DOX) into the process of vaccination and devised a combination regimen. Fortunately, a tumor inhibition rate of ~85% was achieved via the combination therapy, which could not be achieved by mono-chemotherapy or mono-immunotherapy. In summary, the strategy presented here may provide a novel direction in the establishment of a tumor vaccine and is the basis for a prioritization scheme of immuno-chemotherapy in enhancing the therapeutic effect on NHL.

Immunotherapy Advances for Epithelial Ovarian Cancer

Simple Summary

The overall five-year survival rate in epithelial ovarian cancer is 44% and has only marginally improved in the past two decades. Despite an initial response to standard treatment consisting of chemotherapy and surgical removal of tumor, the lesions invariably recur, and patients ultimately die of chemotherapy resistant disease. New treatment modalities are needed in order to improve the prognosis of women diagnosed with ovarian cancer. One such modality is immunotherapy, which aims to boost the capacity of the patient’s immune system to recognize and attack the tumor cells. We performed a retrospective study to identify some of the most promising immune therapies for epithelial ovarian cancer. Special emphasis was given to immuno-oncology clinical trials.

Abstract

New treatment modalities are needed in order to improve the prognosis of women diagnosed with epithelial ovarian cancer (EOC), the most aggressive gynecologic cancer type. Most ovarian tumors are infiltrated by immune effector cells, providing the rationale for targeted approaches that boost the existing or trigger new anti-tumor immune mechanisms. The field of immuno-oncology has experienced remarkable progress in recent years, although the results seen with single agent immunotherapies in several categories of solid tumors have yet to extend to ovarian cancer. The challenge remains to determine what treatment combinations are most suitable for this disease and which patients are likely to benefit and to identify how immunotherapy should be incorporated into EOC standard of care. We review here some of the most promising immune therapies for EOC and focus on those currently tested in clinical trials.

Transimmunization restores immune surveillance and prevents recurrence in a syngeneic mouse model of ovarian cancer

Ovarian cancer accounts for most deaths from gynecologic malignancies. Although more than 80% of patients respond to first-line standard of care, most of these responders present with recurrence and eventually succumb to carcinomatosis and chemotherapy-resistant disease. To improve patient survival, new modalities must, therefore, target or prevent recurrent disease. Here we describe for the first time a novel syngeneic mouse model of recurrent high-grade serous ovarian cancer (HGSOC), which allows immunotherapeutic interventions in a time course relevant to human carcinomatosis and disease course. Using this model, we demonstrate the efficacy of Transimmunization (TI), a dendritic cell (DC) vaccination strategy that uses autologous and physiologically derived DC loaded with autologous whole tumor antigens. TI has been proven successful in the treatment of human cutaneous T cell lymphoma and we report for the first time its in vivo efficacy against an intra-peritoneal solid tumor. Given as a single therapy, TI is able to elicit an effective anti-tumor immune response and inhibit immune-suppressive crosstalks with sufficient power to curtail tumor progression and establishment of carcinomatosis and recurrent disease. Specifically, TI is able to inhibit the expansion of tumor-associated macrophages as well as myeloid-derived suppressive cells consequently restoring T cell immune-surveillance. These results demonstrate the possible value of TI in the management of ovarian cancer and other intra-peritoneal tumors.

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.