High score of LDH plus dNLR predicts poor survival in patients with HER2-positive advanced breast cancer treated with trastuzumab emtansine

Antibody-Drug Conjugates to Promote Immune Surveillance: Lessons Learned from Breast Cancer

Antibody-drug conjugates (ADCs) represent an effective class of agents for the treatment of several tumor types, including breast cancer (BC), featuring approved molecules such as trastuzumab-emtansine, trastuzumab-deruxtecan, and sacituzumab-govitecan. Immune-checkpoint inhibitors (ICIs) also showed activity in selected BC subtypes, and two agents, pembrolizumab and atezolizumab, are currently approved for the treatment of triple-negative BC patients. The potential synergy between ADCs and immunotherapy in BC remains an area of active investigation. Preclinical studies suggest that ADCs promote immune surveillance, modulating tumor microenvironment, inducing immunogenic cell death, and enhancing antitumor immunity. Translational evidence has shown potential predictive biomarkers for ADCs alone or in combination with immunotherapy, including expression of target antigen, oncogenic pathways, tumor-infiltrating lymphocytes, and neutrophil-to-lymphocyte ratio. Given this background, several clinical trials evaluated ADC-ICI combinations in BC patients, demonstrating promising outcomes with an overall manageable toxicity profile, and many studies are currently ongoing to confirm the efficacy and feasibility of this therapeutic approach. In the present review, we summarized the available evidence about the integration of ADCs and immunotherapy for the management of BC, emphasizing the need for further translational and clinical investigations to optimize this treatment strategy and elucidate predictive biomarkers, eventually improving patient outcomes.

The Gustave Roussy immune score as a novel scoring system for predicting platinum resistance in advanced high-grade serous ovarian cancer

OBJECTIVE

This study was designed to investigate the relationship between the Gustave-Roussy immune score (GRIm-score) and platinum resistance in patients with advanced high-grade serous ovarian cancer (HGSOC).

METHODS

We conducted a retrospective study of patients diagnosed with advanced HGSOC between January 2017 and December 2020. A nomogram was developed to predict the risk of platinum resistance. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to validate the nomogram. Bootstrap analysis was utilized for internal validation. Additionally, we analyzed the risk factors for platinum resistance in patients who received neoadjuvant chemotherapy (NACT).

RESULTS

A total of 232 patients with advanced HGSOC were included, 52 (22.4 %) of whom experienced relapse with platinum resistance. Multivariate logistic regression analysis revealed that high GRIm-score (OR = 4.174, P < 0.001), NACT (OR = 2.706, P = 0.017), PLT > 260 (OR = 2.233, P = 0.037) and non-R0 (OR = 2.526, P = 0.012) were independent risk factors for platinum resistance. The area under the curve (AUC) of the model was 0.802 (95 % CI 0.736-0.868), and the internally validated AUC of 1000 bootstrap samples was 0.798 (95 % CI 0.725-0.862). In NACT-treated patients, univariate and multivariate logistic regression analyses revealed that a low KELIM score (OR = 10.405, P = 0.001) and PLT > 260 (OR = 4.611, P = 0.014) were independent risk factors for platinum resistance.

CONCLUSION

The GRIm-score and PLT count are important prognostic factors in patients with HGSOC. For precision treatment, the status of partially platinum-sensitive patients should also be considered.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Preoperative lung immune prognostic index predicts survival in patients with pancreatic cancer undergoing radical resection

Background

Lung immune prognostic index (LIPI), a combination of derived neutrophil-to-lymphocyte ratio (dNLR) and lactate dehydrogenase (LDH), is currently attracting considerable interest as a potential prognostic indicator in many malignancies. Our study aimed to investigate the prognostic value of preoperative LIPI in patients with pancreatic ductal adenocarcinoma (PDAC) undergoing radical resection.

Methods

We retrospectively reviewed PDAC patients treated with radical resection from February 2019 to April 2021 at Chinese People's Liberation Army (PLA) general hospital. Based on the cut-off value of dNLR and LDH identified by X-tile, patients were divided into LIPI good and LIPI intermediate/poor group. Kaplan-Meier curve and log-rank test were used to compare the recurrence-free survival (RFS) and overall survival (OS) of the two groups. Univariate and multivariate Cox regression was used to identify the independent prognostic value of LIPI. Subgroup analysis was performed to identify specific population benefited from radical resection.

Results

A total of 205 patients were included and the median RFS and OS was 10.8 and 24.3 months, respectively. Preoperative LIPI intermediate/poor was related to worse RFS and OS (p < 0.05). Preoperative LIPI intermediate/poor, vascular invasion and no adjuvant chemotherapy were indicators of poor OS. Patients with LIPI intermediate/poor had worse OS especially among females and those with adjuvant chemotherapy (p < 0.05). Adjuvant chemotherapy related to better RFS and OS in patients with LIPI good (p < 0.05).

Conclusions

Preoperative LIPI intermediate/poor can be an indicator of poor prognosis in patients with PDAC undergoing radical resection. LIPI good could be an effective marker of benefit from adjuvant chemotherapy. Larger studies are warranted for further validation.