The current immunotherapy strategies for triple negative breast cancer (TNBC) are greatly limited due to the immunosuppressive tumor microenvironment (TME). Immunization with cancer vaccines composed of tumor cell lysates (TCL) can induce an effective antitumor immune response. However, this approach also has the disadvantages of inefficient antigen delivery to the tumor tissues and the limited immune response elicited by single‐antigen vaccines. To overcome these limitations, a pH‐sensitive nanocalcium carbonate (CaCO3) carrier loaded with TCL and immune adjuvant CpG (CpG oligodeoxynucleotide 1826) is herein constructed for TNBC immunotherapy. This tailor‐made nanovaccine, termed CaCO3@TCL/CpG, not only neutralizes the acidic TME through the consumption of lactate by CaCO3, which increases the proportion of the M1/M2 macrophages and promotes infiltration of effector immune cells but also activates the dendritic cells in the tumor tissues and recruits cytotoxic T cells to further kill the tumor cells. In vivo fluorescence imaging study shows that the pegylated nanovaccine could stay longer in the blood circulation and extravasate preferentially into tumor site. Besides, the nanovaccine exhibits high cytotoxicity in 4T1 cells and significantly inhibits tumor growth of tumor‐bearing mice. Overall, this pH‐sensitive nanovaccine is a promising nanoplatform for enhanced immunotherapy of TNBC.

Background A hepatocellular carcinoma (HCC) prediction model (ASAP), including age, sex, and the biomarkers alpha-fetoprotein and prothrombin induced by vitamin K absence-II, showed potential clinical value in the early detection of HCC. We validated and updated the model in a real-world cohort and promoted its transferability to daily clinical practice. Methods This retrospective cohort analysis included 1012 of the 2479 eligible patients aged 35 years or older undergoing surveillance for HCC. The data were extracted from the electronic medical records. Biomarker values within the test-to-diagnosis interval were used to validate the ASAP model. Due to its unsatisfactory calibration, three logistic regression models were constructed to recalibrate and update the model. Their discrimination, calibration, and clinical utility were compared. The performance statistics of the final updated model at several risk thresholds are presented. The outcomes of 855 non-HCC patients were further assessed during a median of 10.2 months of follow-up. Statistical analyses were performed using packages in R software. Results The ASAP model had superior discriminative performance in the validation cohort [C-statistic = 0.982, (95% confidence interval 0.972–0.992)] but significantly overestimated the risk of HCC (intercept − 3.243 and slope 1.192 in the calibration plot), reducing its clinical usefulness. Recalibration-in-the-large, which exhibited performance comparable to that of the refitted model revision, led to the retention of the excellent discrimination and substantial improvements in the calibration and clinical utility, achieving a sensitivity of 100% at the median prediction probability of the absence of HCC (1.3%). The probability threshold of 1.3% and the incidence of HCC in the cohort (15.5%) were used to stratify the patients into low-, medium-, and high-risk groups. The cumulative HCC incidences in the non-HCC patients significantly differed among the risk groups (log-rank test, p-value < 0.001). The 3-month, 6-month and 18-month cumulative incidences in the low-risk group were 0.6%, 0.9% and 0.9%, respectively. Conclusions The ASAP model is an accurate tool for HCC risk estimation that requires recalibration before use in a new region because calibration varies with clinical environments. Additionally, rational risk stratification and risk-based management decision-making, e.g., 3-month follow-up recommendations for targeted individuals, helped improve HCC surveillance, which warrants assessment in larger cohorts.