Purpose
We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in ...subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms.
Methods
TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUV
mean
, SUV
max
, and lean body mass-normalized SUL
peak
measures.
Results
Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUV
mean
, SUV
max
, and SUL
peak
measures.
Conclusions
We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial.
Triple-negative breast cancers (TNBC) remain clinically challenging with a lack of options for targeted therapy. In this study, we report the development of a second-generation BET protein degrader, ...BETd-246, which exhibits superior selectivity, potency, and antitumor activity. In human TNBC cells, BETd-246 induced degradation of BET proteins at low nanomolar concentrations within 1 hour of exposure, resulting in robust growth inhibition and apoptosis. BETd-246 was more potent and effective in TNBC cells than its parental BET inhibitor compound BETi-211. RNA-seq analysis revealed predominant downregulation of a large number of genes involved in proliferation and apoptosis in cells treated with BETd-246, as compared with BETi-211 treatment that upregulated and downregulated a similar number of genes. Functional investigations identified the
gene as a critical downstream effector for BET degraders, which synergized with small-molecule inhibitors of BCL-xL in triggering apoptosis. In multiple murine xenograft models of human breast cancer, BETd-246 and a further optimized analogue BETd-260 effectively depleted BET proteins in tumors and exhibited strong antitumor activities at well-tolerated dosing schedules. Overall, our findings show that targeting BET proteins for degradation represents an effective therapeutic strategy for TNBC treatment.
.
Resistance to endocrine treatment occurs in ~30% of ER
breast cancer patients resulting in ~40,000 deaths/year in the USA. Preclinical studies strongly implicate activation of growth factor receptor, ...HER2 in endocrine treatment resistance. However, clinical trials of pan-HER inhibitors in ER
/HER2
patients have disappointed, likely due to a lack of predictive biomarkers. Here we demonstrate that loss of mismatch repair activates HER2 after endocrine treatment in ER
/HER2
breast cancer cells by protecting HER2 from protein trafficking. Additionally, HER2 activation is indispensable for endocrine treatment resistance in MutL
cells. Consequently, inhibiting HER2 restores sensitivity to endocrine treatment. Patient data from multiple clinical datasets supports an association between MutL loss, HER2 upregulation, and sensitivity to HER inhibitors in ER
/HER2
patients. These results provide strong rationale for MutL loss as a first-in-class predictive marker of sensitivity to combinatorial treatment with endocrine intervention and HER inhibitors in endocrine treatment-resistant ER
/HER2
breast cancer patients.
Data from 8 breast cancer genome-sequencing projects identified 25 patients with HER2 somatic mutations in cancers lacking HER2 gene amplification. To determine the phenotype of these mutations, we ...functionally characterized 13 HER2 mutations using in vitro kinase assays, protein structure analysis, cell culture, and xenograft experiments. Seven of these mutations are activating mutations, including G309A, D769H, D769Y, V777L, P780ins, V842I, and R896C. HER2 in-frame deletion 755-759, which is homologous to EGF receptor (EGFR) exon 19 in-frame deletions, had a neomorphic phenotype with increased phosphorylation of EGFR or HER3. L755S produced lapatinib resistance, but was not an activating mutation in our experimental systems. All of these mutations were sensitive to the irreversible kinase inhibitor, neratinib. These findings show that HER2 somatic mutation is an alternative mechanism to activate HER2 in breast cancer and they validate HER2 somatic mutations as drug targets for breast cancer treatment.
We show that the majority of HER2 somatic mutations in breast cancer patients are activating mutations that likely drive tumorigenesis. Several patients had mutations that are resistant to the reversible HER2 inhibitor lapatinib, but are sensitive to the irreversible HER2 inhibitor, neratinib. Our results suggest that patients with HER2 mutation–positive breast cancers could benefit from existing HER2-targeted drugs.
Triple-negative breast cancers (TNBCs) are a heterogeneous set of cancers that are defined by the absence of hormone receptor expression and HER2 amplification. Here, we found that inducible IκB ...kinase-related (IKK-related) kinase IKBKE expression and JAK/STAT pathway activation compose a cytokine signaling network in the immune-activated subset of TNBC. We found that treatment of cultured IKBKE-driven breast cancer cells with CYT387, a potent inhibitor of TBK1/IKBKE and JAK signaling, impairs proliferation, while inhibition of JAK alone does not. CYT387 treatment inhibited activation of both NF-κB and STAT and disrupted expression of the protumorigenic cytokines CCL5 and IL-6 in these IKBKE-driven breast cancer cells. Moreover, in 3D culture models, the addition of CCL5 and IL-6 to the media not only promoted tumor spheroid dispersal but also stimulated proliferation and migration of endothelial cells. Interruption of cytokine signaling by CYT387 in vivo impaired the growth of an IKBKE-driven TNBC cell line and patient-derived xenografts (PDXs). A combination of CYT387 therapy with a MEK inhibitor was particularly effective, abrogating tumor growth and angiogenesis in an aggressive PDX model of TNBC. Together, these findings reveal that IKBKE-associated cytokine signaling promotes tumorigenicity of immune-driven TNBC and identify a potential therapeutic strategy using clinically available compounds.
Recent advances in mass spectrometry (MS) have enabled extensive analysis of cancer proteomes. Here, we employed quantitative proteomics to profile protein expression across 24 breast cancer ...patient-derived xenograft (PDX) models. Integrated proteogenomic analysis shows positive correlation between expression measurements from transcriptomic and proteomic analyses; further, gene expression-based intrinsic subtypes are largely re-capitulated using non-stromal protein markers. Proteogenomic analysis also validates a number of predicted genomic targets in multiple receptor tyrosine kinases. However, several protein/phosphoprotein events such as overexpression of AKT proteins and ARAF, BRAF, HSP90AB1 phosphosites are not readily explainable by genomic analysis, suggesting that druggable translational and/or post-translational regulatory events may be uniquely diagnosed by MS. Drug treatment experiments targeting HER2 and components of the PI3K pathway supported proteogenomic response predictions in seven xenograft models. Our study demonstrates that MS-based proteomics can identify therapeutic targets and highlights the potential of PDX drug response evaluation to annotate MS-based pathway activities.
In order to gain insight into the influence of grain size on precipitation thermodynamics, bulk materials of coarse-grained (CG), ultrafine-grained (UFG) (with or without dislocations), and ...nanocrystalline (NC) 7075 Al alloy have been fabricated by solid solution treatment, equal-channel angular pressing (ECAP), or high-pressure torsion (HPT) processes. The precipitation behavior and the corresponding thermal phenomenon were studied by transmission electron microscopy (TEM) and differential scanning calorimetry (DSC) heating. The results indicated that there are significant differences in precipitation thermodynamics among the four bulk materials. In the CG and UFG materials without dislocations, homogeneous nucleation is the primary precipitation mechanism. However, the nucleation of the GP zones is suppressed at lower temperatures due to a reduction in the number of residual vacancies and the supersaturation in the UFG interiors. This is attributed to the absorption of vacancies and solute atoms by a greater volume of grain boundaries. It can be observed that the greater the excess of vacancies remaining in grain interiors, the lower the temperature at which nucleation of GP zones occurs. Defect-assisted heterogeneous nucleation was identified as the predominant precipitation mechanism in the UFG materials with dislocations and the NC materials. These defects encompass dislocations, lattice distortions, and grain boundaries. The decomposition processes of solid solutions were found to be almost complete at a lower temperature. The presence of dislocations, lattice distortions, and grain boundaries enables solute atoms to diffuse at a much faster rate, significantly enhancing the precipitation rate and reducing the nucleation and formation energies of various precipitate phases.
This paper analyzes rural revitalization under the goal of common prosperity. Firstly, on the basis of making clear what common prosperity is in the new era along with its connotation, the ...connotation of rural revitalization under the goal of common prosperity is systematically delved into six dimensions: subject, motivation, content, path, process, and goal. Secondly, the intrinsic relationship between common prosperity and rural revitalization is examined from the perspectives of rural revitalization, common prosperity, and development. Thirdly, grounded on theoretical analysis, this paper outlines strategic key points for rural revitalization under the goal of common prosperity upon applying designing principles to practice.
Preclinical magnetic resonance imaging (MRI) is a critical component in a co-clinical research pipeline. Importantly, segmentation of tumors in MRI is a necessary step in tumor phenotyping and ...assessment of response to therapy. However, manual segmentation is time-intensive and suffers from inter- and intra- observer variability and lack of reproducibility. This study aimed to develop an automated pipeline for accurate localization and delineation of TNBC PDX tumors from preclinical T1w and T2w MR images using a deep learning (DL) algorithm and to assess the sensitivity of radiomic features to tumor boundaries. We tested five network architectures including U-Net, dense U-Net, Res-Net, recurrent residual UNet (R2UNet), and dense R2U-Net (D-R2UNet), which were compared against manual delineation by experts. To mitigate bias among multiple experts, the simultaneous truth and performance level estimation (STAPLE) algorithm was applied to create consensus maps. Performance metrics (F1-Score, recall, precision, and AUC) were used to assess the performance of the networks. Multi-contrast D-R2UNet performed best with F1-score = 0.948; however, all networks scored within 1–3% of each other. Radiomic features extracted from D-R2UNet were highly corelated to STAPLE-derived features with 67.13% of T1w and 53.15% of T2w exhibiting correlation ρ ≥ 0.9 (p ≤ 0.05). D-R2UNet-extracted features exhibited better reproducibility relative to STAPLE with 86.71% of T1w and 69.93% of T2w features found to be highly reproducible (CCC ≥ 0.9, p ≤ 0.05). Finally, 39.16% T1w and 13.9% T2w features were identified as insensitive to tumor boundary perturbations (Spearman correlation (−0.4 ≤ ρ ≤ 0.4). We developed a highly reproducible DL algorithm to circumvent manual segmentation of T1w and T2w MR images and identified sensitivity of radiomic features to tumor boundaries.
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