Discrimination between human epidermal growth factor receptor 2 (HER2)-low-expressing and HER2-overexpressing breast cancers: a comparative study of four MRI diffusion models.

OBJECTIVES: To determine the value of conventional DWI, continuous-time random walk (CTRW), fractional order calculus (FROC), and stretched exponential model (SEM) in discriminating human epidermal growth factor receptor 2 (HER2) status of breast cancer (BC). METHODS: This prospective study included 158 women who underwent DWI, CTRW, FROC, and SEM and were pathologically categorized into the HER2-zero-expressing group (n = 10), HER2-low-expressing group (n = 86), and HER2-overexpressing group (n = 62). Nine diffusion parameters, namely ADC, αCTRW, ßCTRW, DCTRW, ßFROC, DFROC, µFROC, αSEM, and DDCSEM of the primary tumor, were derived from four diffusion models. These diffusion metrics and clinicopathologic features were compared between groups. Logistic regression was used to determine the optimal diffusion metrics and clinicopathologic variables for classifying the HER2-expressing statuses. Receiver operating characteristic (ROC) curves were used to evaluate their discriminative ability. RESULTS: The estrogen receptor (ER) status, progesterone receptor (PR) status, and tumor size differed between HER2-low-expressing and HER2-overexpressing groups (p < 0.001 to p = 0.009). The αCTRW, DCTRW, ßFROC, DFROC, µFROC, αSEM, and DDCSEM were significantly lower in HER2-low-expressing BCs than those in HER2-overexpressing BCs (p < 0.001 to p = 0.01). Further multivariable logistic regression analysis showed that the αCTRW was the single best discriminative metric, with an area under the curve (AUC) being higher than that of ADC (0.802 vs. 0.610, p < 0.05); the addition of ER status, PR status, and tumor size to the αCTRW improved the AUC to 0.877. CONCLUSIONS: The αCTRW could help discriminate the HER2-low-expressing and HER2-overexpressing BCs. CLINICAL RELEVANCE STATEMENT: Human epidermal growth factor receptor 2 (HER2)-low-expressing breast cancer (BC) might also benefit from the HER2-targeted therapy. Prediction of HER2-low-expressing BC or HER2-overexpressing BC is crucial for appropriate management. Advanced continuous-time random walk diffusion MRI offers a solution to this clinical issue. KEY POINTS: • Human epidermal receptor 2 (HER2)-low-expressing BC had lower αCTRW, DCTRW, ßFROC, DFROC, µFROC, αSEM, and DDCSEM values compared with HER2-overexpressing breast cancer. • The αCTRW was the single best diffusion metric (AUC = 0.802) for discrimination between the HER2-low-expressing and HER2-overexpressing breast cancers. • The addition of αCTRW to the clinicopathologic features (estrogen receptor status, progesterone receptor status, and tumor size) further improved the discriminative ability.

Programmable Bispecific Nano-immunoengager That Captures T Cells and Reprograms Tumor Microenvironment.

Immune checkpoint blockade (ICB) therapy has revolutionized clinical oncology. However, the efficacy of ICB therapy is limited by the ineffective infiltration of T effector (Teff) cells to tumors and the immunosuppressive tumor microenvironment (TME). Here, we report a programmable tumor cells/Teff cells bispecific nano-immunoengager (NIE) that can circumvent these limitations to improve ICB therapy. The peptidic nanoparticles (NIE-NPs) bind tumor cell surface α3ß1 integrin and undergo in situ transformation into nanofibrillar network nanofibers (NIE-NFs). The prolonged retained nanofibrillar network at the TME captures Teff cells via the activatable α4ß1 integrin ligand and allows sustained release of resiquimod for immunomodulation. This bispecific NIE eliminates syngeneic 4T1 breast cancer and Lewis lung cancer models in mice, when given together with anti-PD-1 antibody. The in vivo structural transformation-based supramolecular bispecific NIE represents an innovative class of programmable receptor-mediated targeted immunotherapeutics to greatly enhance ICB therapy against cancers.

Cerebral schistosomiasis: diffusion-weighted imaging helps to differentiate from brain glioma and metastasis.

Background Diffusion-weighted imaging (DWI) was introduced into clinical use some years ago. However, its use in the diagnosis of cerebral schistosomiasis has not been reported. Purpose To investigate the ability of the apparent diffusion coefficient (ADC) value of DWI in the diagnosis of cerebral schistosomiasis, and to differentiate it from brain high-grade gliomas and metastasis. Material and Methods Conventional brain MRI with pre-contrast, post-contrast, and DWI was performed on 50 cases of cerebral schistosomiasis, high-grade glioma, and brain metastasis. The ADC values of the three lesions, the proximal and the distal perifocal edema were measured. In order to remove the individual difference effect of ADC values, relative ADC (rADC) values were calculated through dividing the ADC value of the lesion area by that of the contralateral normal white matter. rADC values were used to evaluate the differences among cerebral schistosomiasis, brain high-grade gliomas, and metastasis. Results rADC of cerebral schistosomiasis was significantly lower than rADC of brain metastasis ( P < 0.05), without any significant differences when compared with high-grade gliomas. rADC of proximal perifocal edema in cerebral schistosomiasis was significantly higher than in high-grade gliomas ( P < 0.010), but not different compared with brain metastasis. Conclusion DWI examination with ADC values of lesions and proximal perifocal edema might be helpful in the exact diagnosis of cerebral schistosomiasis.

Quantitative Parameters of Diffusion Spectrum Imaging: HER2 Status Prediction in Patients With Breast Cancer.

OBJECTIVE: To explore the value of quantitative parameters derived from diffusion spectrum imaging (DSI) in preoperatively predicting human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer. METHODS: In this prospective study, 114 and 56 female patients with invasive ductal carcinoma were consecutively included in a derivation cohort and an independent validation cohort, respectively. Each patient was categorized into HER2-positive or HER2-negative groups based on the pathologic result. All patients underwent DSI and conventional MRI including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). The tumor size, type of the time-signal intensity curve (TIC) from DCE-MRI, apparent diffusion coefficient (ADC) from DWI, and quantitative parameters derived from DSI, including diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) of primary tumors, were measured and compared between the HER2-positive and HER2-negative groups in the derivation cohort. Univariable and multivariable logistic regression analyses were used to determine the potential independent predictors of HER2 status. The discriminative ability of quantitative parameters was assessed by receiver operating characteristic (ROC) curve analyses and validated in the independent cohort. RESULTS: In the derivation cohort, the tumor size, TIC type, and ADC values did not differ between the HER2-positive and HER2-negative groups (p = 0.126-0.961). DSI quantitative parameters including axial kurtosis of DKI (DKI_AK), non-Gaussianity (MAP_NG), axial non-Gaussianity (MAP_NGAx), radial non-Gaussianity (MAP_NGRad), return-to-origin probability (MAP_RTOP), return-to-axis probability of MAP (MAP_RTAP), and intracellular volume fraction of NODDI (NODDI_ICVF) were lower in the HER2-positive group than in the HER2-negative group (p ≤ 0.001-0.035). DSI quantitative parameters including radial diffusivity (DTI_RD), mean diffusivity of DTI (DTI_MD), mean squared diffusion (MAP_MSD), and q-space inverse variance of MAP (MAP_QIV) were higher in the HER2-positive group than in the HER2-negative group (p = 0.016-0.049). The ROC analysis showed that the area under the curve (AUC) of ADC was 0.632 and 0.568, respectively, in the derivation and validation cohorts. The AUC values of DSI quantitative parameters ranged from 0.628 to 0.700 and from 0.673 to 0.721, respectively, in the derivation and validation cohorts. Logistic regression analysis showed that only NODDI_ICVF was an independent predictor of HER2 status (p = 0.001), with an AUC of 0.700 and 0.721, respectively, in the derivation and validation cohorts. CONCLUSIONS: DSI could be helpful for preoperative prediction of HER2, but DSI alone may not be sufficient in predicting HER2 status preoperatively in patients with breast cancer.

Nanoradiosensitizer with good tissue penetration and enhances oral cancer radiotherapeutic effect.

Low dose non-toxic disulfide cross-linked micelle (DCM) encapsulated paclitaxel (PTX) was found to be highly efficacious as a radiosensitizer against oral cancer preclinical model. Intensity-modulated radiation therapy was locally administered for three consecutive days 24 h after intravascular injection of DCM-[PTX] at 5 mg/kg PTX. DCM-[PTX] NPs combined with conventional radiotherapy (2 Gy) resulted in a 1.7-fold improvement in therapeutic efficacy compared to conventional PTX plus radiotherapy. Interestingly, we found that radiotherapy can decrease tight junctions and increase the accumulation of DCM-[PTX] in tumor sites. Stereotactic body radiotherapy (SBRT) given at 6 Gy was used to further investigate the synergistic anti-tumor effect. Tumor tissues were collected to analyze the relationship between the time interval after SBRT and the biodistribution of the nanomaterials. Compared to combination DCM-[PTX] with conventional radiation dose, combination DCM-PTX with SBRT was found to be more efficacious in inhibiting tumor growth.