Introduction: Translation initiation is activated in cancer through increase in eukaryotic initiation factor 4E (eIF4E), eIF4G, phosphorylated eIF4E-binding protein (p4E-BP1) and phosphorylated ribosomal protein S6 (pS6), and decreased programmed cell death protein 4 (pdcd4), a translational inhibitor. Further, translation elongation is deregulated though alterations in eukaryotic elongation factor 2 (eEF2) and eEF2 kinase (eEF2K). We sought to determine the association of these translational aberrations with clinical-pathologic factors and survival outcomes in hormone receptor-positive breast cancer. Methods: Primary tumors were collected from 190 patients with Stage I to III hormone receptor-positive breast cancer. Expression of eIF4E, eIF4G, 4E-BP1, p4E-BP1 T37/46, p4E-BP1 S65, p4E-BP1 T70, S6, pS6 S235/236, pS6 S240/244, pdcd4, eEF2 and eEF2K was assessed by reverse phase protein arrays. Univariable and multivariable analyses for recurrence-free survival (RFS) and overall survival (OS) were performed. Results: High eEF2, S6, pS6 S240/244, p4E-BP1 T70, and low pdcd4 were significantly associated with node positivity. Median follow-up for living patients was 96 months. High p4E-BP1 T36/47, p4E-BP1 S65, p4E-BP1 T70 and 4E-BP1 were associated with worse RFS. High p4E-BP1 T70 and pS6 S235/236, and low pdcd4, were associated with worse OS. In multivariable analysis, in addition to positive nodes, p4E-BP1 S65 remained a significant predictor of RFS (HR = 1.62, 95% CI = 1.13-2.31; P = 0.008). In addition to age, pS6 S235/236 (HR = 1.73, 95% CI = 1.03-2.90, P = 0.039), eEF2K (HR = 2.19, 95% CI = 1.35-3.56, P = 0.002) and pdcd4 (HR = 0.42, 95% CI = 0.25-0.70, P = 0.001) were associated with OS. Conclusions: Increased pS6, p4E-BP1, eEF2K and decreased pdcd4 are associated with poor prognosis in hormone receptor-positive breast cancer, suggesting their role as prognostic markers and therapeutic targets.

Although patient-derived xenografts (PDX) are commonly used for preclinical modeling in cancer research, a standard approach to in vivo tumor growth analysis and assessment of antitumor activity is lacking, complicating the comparison of different studies and determination of whether a PDX experiment has produced evidence needed to consider a new therapy promising. We present consensus recommendations for assessment of PDX growth and antitumor activity, providing public access to a suite of tools for in vivo growth analyses. We expect that harmonizing PDX study design and analysis and assessing a suite of analytical tools will enhance information exchange and facilitate identification of promising novel therapies and biomarkers for guiding cancer therapy.

Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors were segmented on DCE images of 1 min and 2.5 min post-injection. FTVs were measured using the optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The Mann–Whitney test was used to compare the performance of the FTVs at C2 and C4. Of the 100 patients, 49 (49%) had a pathologic complete response (pCR) and 51 (51%) had a non-pCR. The maximum area under the receiving operating characteristic curve (AUC) for predicting the treatment response was 0.84 (p < 0.001) for FTV at C4 followed by FTV at C2 (AUC = 0.82, p < 0.001). The FTV measured at baseline was not able to discriminate pCR from non-pCR. FTVs measured on DCE MRI at C2, as well as at C4, of NAST can potentially predict pCR and non-pCR in TNBC patients.

Purpose: To develop deep learning models for predicting the pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in patients with triple-negative breast cancer (TNBC) based on pretreatment multiparametric breast MRI and clinicopathological data. Methods: The prospective institutional review board-approved study [NCT02276443] included 282 patients with stage I–III TNBC who had multiparametric breast MRI at baseline and underwent NAST and surgery during 2016–2021. Dynamic contrast-enhanced MRI (DCE), diffusion-weighted imaging (DWI), and clinicopathological data were used for the model development and internal testing. Data from the I-SPY 2 trial (2010–2016) were used for external testing. Four variables with a potential impact on model performance were systematically investigated: 3D model frameworks, tumor volume preprocessing, tumor ROI selection, and data inputs. Results: Forty-eight models with different variable combinations were investigated. The best-performing model in the internal testing dataset used DCE, DWI, and clinicopathological data with the originally contoured tumor volume, the tight bounding box of the tumor mask, and ResNeXt50, and achieved an area under the receiver operating characteristic curve (AUC) of 0.76 (95% CI: 0.60–0.88). The best-performing models in the external testing dataset achieved an AUC of 0.72 (95% CI: 0.57–0.84) using only DCE images (originally contoured tumor volume, enlarged bounding box of tumor mask, and ResNeXt50) and an AUC of 0.72 (95% CI: 0.56–0.86) using only DWI images (originally contoured tumor volume, enlarged bounding box of tumor mask, and ResNet18). Conclusions: We developed 3D deep learning models based on pretreatment data that could predict pCR to NAST in TNBC patients.

Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients’ treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.

We evaluated the cancer preventive efficacy of TAK-242, an inhibitor of Toll-like receptor 4 (TLR4), in a mouse model of hepatocellular carcinoma (HCC) occurring in the context of nonalcoholic steatohepatitis (NASH). We also assessed the cellular events associated with the preventive treatment efficacy. We tested oral administration of TAK-242, at clinically relevant but toxicity-reducing doses and scheduling, in mice with hepatocyte-specific deletion of Pten (HepPten−). The optimal dose and oral gavage formulation of TAK-242 were determined to be 30 mg/kg in 5% DMSO in 30% 2-hydroxypropyl-β-cyclodextrin. Daily oral administration of 30 mg/kg TAK-242 over 18 weeks was well tolerated and resulted in reduced development of tumors (lesions > 7.5 mm3) in HepPten− mice. This effect was accompanied by reduced macrovesicular steatosis and serum levels of alanine aminotransferase. In addition, 30 mg/kg TAK-242 daily treatment of small preexisting adenomas (lesions < 7.5 mm3) over 18 weeks, significantly reduced their progression to HCC. RNA sequencing identified 220 hepatic genes significantly altered upon TAK-242 treatment, that significantly correlated with tumor burden. Finally, cell deconvolution analysis revealed that TAK-242 treatment resulted in reduced hepatic populations of endothelial cells and myeloid-derived immune cells (Kupffer cells, Siglec-H high dendritic cells, and neutrophilic granule protein high neutrophils), while the proportion of mt-Nd4 high hepatocytes significantly increased, suggesting a decrease in hepatic inflammation and concomitant increase in mitochondrial function and oxidative phosphorylation upon TLR4 inhibition. In conclusion, this study identified treatment strategies and novel molecular and cellular mechanisms associated with the prevention of HCC in the context of NASH that merit further investigations. Prevention Relevance: Means to prevent development of HCC or progression of small adenomas to HCC in patients with NASH are urgently needed to reduce the growing mortality due to HCC. We characterized the chemopreventive effect of oral administration of the TLR4 inhibitor TAK-242 in a model of NASH-associated HCC.