Longevity Assurance 5 (LASS5), a member of the LASS/Ceramide Synthases family, synthesizes C16-ceramide and is implicated in tumor biology. However, its precise role is not yet well understood. A yeast two-hybrid screen was performed using a human cDNA library to identify potential LASS5-interaction partners. One identified clone encodes succinate dehydrogenase subunit B (SDHB). Mammalian two-hybrid assays showed that LASS5 interacts with SDHB, and the result was also confirmed by GST pull-down and co-immunoprecipitation assays. The C-terminal fragment of SDHB was required for the interaction. LASS5 and SDHB were co-localized in COS-7 cells. LASS5 and SDHB expressions were found to be up-regulated in neuroglioma tissue. Transfection assays showed that LASS5 or SDHB expression repressed p53 or p21 reporter activity, respectively. Simultaneous LASS5 and SDHB expression resulted in stronger repression of p53 and p21 reporter activity, suggesting that LASS5 and SDHB interaction may synergistically affect transcriptional regulation of p53 and p21. Our data provide new molecular insights into potential roles of LASS5 and SDHB in tumor biology.
Unsupervised clustering and deconvolution analysis identifies a novel subtype of M-CRPC endowed with hybrid epithelial/mesenchymal (E/M) and luminal progenitor-like traits (Mesenchymal and Stem-like PC, MSPC). Analysis of patient datasets and mechanistic studies indicate that MSPC arises as a consequence of therapy-induced lineage plasticity. AR blockade instigates two separate and complementary processes: 1) transcriptional silencing of TP53 and hence acquisition of hybrid E/M and stem-like traits; and 2) inhibition of the BMP signaling, which promotes resistance to the pro-apoptotic and anti-proliferative effects of AR inhibition. The drug-tolerant prostate cancer cells generated through reprogramming are rescued by neuregulin and generate metastases in mice. Combined inhibition of HER2/3 and AR or mTORC1 exhibit efficacy in preclinical models of mixed ARPC/MSPC or MSPC, respectively. These results identify a novel subtype of M-CRPC, trace its origin to therapy-induced lineage plasticity, and reveal its dependency on HER2/3 signaling.
We have proposed a new sampling method (CBS), along with corresponding new techniques including boundary sampling and grid sampling, to improve time and space efficiency of IMRT optimization. A corresponding theory is developed to quantify the error bound. Experimental results have shown that our new methods significantly reduce solution time and memory costs with negligible impact on resulting plan quality.
Purpose: The IMRT optimization problem requires substantial computer time to find optimal dose distributions because of the large number of variables and constraints. Voxel sampling reduces the number of constraints and accelerates the optimization process, but usually deteriorates the quality of the dose distributions to the organs. We propose a novel sampling algorithm that accelerates the IMRT optimization process without significantly deteriorating the quality of the dose distribution. Methods: We included all boundary voxels, as well as a sampled fraction of interior voxels of organs in the optimization. We selected a fraction of interior voxels using a clustering algorithm, that creates clusters of voxels that have similar influence matrix signatures. A few voxels are selected from each cluster based on the pre‐set sampling rate. Results: We ran sampling and no‐sampling IMRT plans for de‐identified head and neck treatment plans. Testing with the different sampling rates, we found that including 10% of inner voxels produced the good dose distributions. For this optimal sampling rate, the algorithm accelerated IMRT optimization by a factor of 2–3 times with a negligible loss of accuracy that was, on average, 0.3% for common dosimetric planning criteria. Conclusion: We demonstrated that a sampling could be developed that reduces optimization time by more than a factor of 2, without significantly degrading the dose quality.
Purpose: Our goal is to validate treatment planning metrics that are computationally efficient yet have inherent radiobiological meaning, either directly or by correlating highly with more explicitly biological metrics. We show how mean‐tail‐dose and the generalized equivalent uniform dose (gEUD) metrics could be used to replace dose‐volume metrics in IMRT treatment planning. Method and Materials: gEUD and MOH/MOCx (mean of the hottest/coldest x% of a structure) are candidates for dose‐volume metric replacements due both to their averaging‐nature (closer to the expected radiobiology), as well as their convexity (and the linearity of MOH/MOCx), which allows for optimal solutions and faster optimization. We used datasets from both 3DCRT planning (219 lung/pneumonitis; 263 lung/esophagitis; 491 prostate) and IMRT (40 prostate; 398 head and neck). We calculated the Spearman's rank‐correlation coefficient between typical clinical dose‐volume metrics and these quasi‐radiobiological metrics with a range of values for the variable parameter (a for gEUD; x for MOH/MOCx). We also calculated the sensitivity and specificity values resulting from using the quasi‐radiobiological metrics to ‘predict’ violation of a clinical dose‐volume threshold. Results: We found high correlations between the proposed quasi‐radiobiological metrics and clinical dose‐volume metrics. Twenty‐seven out of thirty correlations tested had a Spearman correlation coefficient above 0.90 with highly significant p‐values. We also found, for many of the dose‐volume metrics, the associated quasi‐radiobiological metric was an excellent classifier. Conclusion: There is evidence from large datasets including both 3DCRT and IMRT patients that the biological metrics of gEUD and MOH/MOCx are highly correlated to traditional dose‐volume metrics and are fairly accurate for classifying acceptable from non‐acceptable plans. Many of the gEUD or MOH/MOCx metrics could be substituted for traditional dose‐volume constraints with a sensitivity and specificity well above 0.80. Conflict of Interest: Partially supported by a grant from Tomotherapy, Inc., as well as NIH R01 grant CA85181.
Purpose: Prioritized prescription optimization is a method of IMRT treatment planning which solves the optimization problem step‐wise, first by determining the best performance of a high‐priority objective and then converting it to a constraint as lower priority goals are optimized in turn. This system is fully automated; however, there is a variable value we call ‘slip’ factor which determines the amount of degradation allowed after each priority is addressed. The purpose of this work is to investigate an approach to automating selection of the slip factor. Method and Materials: We propose a new version of prioritized prescription optimization that, after each step, estimates the desired value of the slip factor for the next step. We estimate, to first order, the rate of change of the objective function with respect to small perturbations in the slip. We then apply the Sensitivity Theorem to analyze the effect of the slip factor. In summary, after solving the first problem and obtaining the solution to the first step, wI, we plug the value of wI into a closed‐form expression of the Lagrange multiplier vector λ(0) to get λ(0), and then use the last element of λ(0) to compute the first‐order approximation model for the optimal objective value with variable slip. Results: We use the Sensitivity Theorem and Lagrange multipliers to derive a first‐order approximation model for the optimal objective function value for a given slip factor. On this basis, we propose to automatically select slip values based on the expected gain in the objective function. Conclusion: We have developed a mathematical theory and framework which allows us to automate the selection of the slip factor in prioritized prescription optimization, based on a local linear approximation. This proposal is the subject of ongoing tests.
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