Improvement strategy for immune checkpoint blockade: A focus on the combination with immunogenic cell death inducers.

Cancer immunotherapies have yielded promising outcomes in various malignant tumors by blocking specific immune checkpoint molecules, such as programmed cell death 1 and cytotoxic T lymphocyte antigen 4. However, only a few patients respond to immune checkpoint blockade therapy because of the poor immunogenicity of tumor cells and immune-suppressive tumor microenvironment. Accumulating evidence suggests that chemotherapeutic agents, including oxaliplatin and doxorubicin, not only mediate direct cytotoxicity in tumor cells but also induce immunogenic cancer cell death to stimulate a powerful anti-cancer immune response in the tumor microenvironment. In this review, we summarize the recent advances in cancer combination therapy based on immune checkpoint inhibitors plus immunogenic cell death inducers. Despite some clinical failures and challenges, immunogenic cell death inducers have displayed great potential when combined with immune checkpoint inhibitors for anti-cancer treatment in both preclinical studies and clinical trials.

Combination therapy for pancreatic cancer: anti-PD-(L)1-based strategy.

Mortality associated with pancreatic cancer is among the highest of all malignancies, with a 5-year overall survival of 5-10%. Immunotherapy, represented by the blocking antibodies against programmed cell death protein 1 or its ligand 1 (anti-PD-(L)1), has achieved remarkable success in a number of malignancies. However, due to the immune-suppressive tumor microenvironment, the therapeutic efficacy of anti-PD-(L)1 in pancreatic cancer is far from expectation. To address such a fundamental issue, chemotherapy, radiotherapy, targeted therapy and even immunotherapy itself, have individually been attempted to combine with anti-PD-(L)1 in preclinical and clinical investigation. This review, with a particular focus on pancreatic cancer therapy, collects current anti-PD-(L)1-based combination strategy, highlights potential adverse effects of accumulative combination, and further points out future direction in optimization of combination, including targeting post-translational modification of PD-(L)1 and improving precision of treatment.

A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma.

Purpose: Hepatocellular carcinoma (HCC) is one of the most prevalent malignant diseases worldwide and has a poor prognosis. Gene-based prognostic models have been reported to predict the overall survival of patients with HCC. Unfortunately, most of the genes used in earlier prognostic models lack prospective validation and, thus, cannot be used in clinical practice. Methods: Candidate genes were selected from GEPIA (Gene Expression Profiling Interactive Analysis), and their associations with patients' survival were confirmed by RT-PCR using cDNA tissue microarrays established from patients with HCC after radical resection. A multivariate Cox proportion model was used to calculate the coefficient of corresponding gene. The expression of seven genes of interest (MKI67, AR, PLG, DNASE1L3, PTTG1, PPP1R1A, and TTR) with two reference genes was defined to calculate a risk score which determined groups of different risks. Results: Our risk scoring efficiently classified patients (n = 129) with HCC into a low-, intermediate-, and high-risk group. The three groups showed meaningful distinction of 3-year overall survival rate, i.e., 88.9, 74.5, and 20.6% for the low-, intermediate-, and high-risk group, respectively. The prognostic prediction model of risk scores was subsequently verified using an independent prospective cohort (n = 77) and showed high accuracy. Conclusion: Our seven-gene signature model performed excellent long-term prediction power and provided crucially guiding therapy for patients who are not a candidate for surgery.