Cytocapsular tubes and networks function as physical superdefence freeway systems conducting conventional cancer drug pan-resistant tumor metastasis

Cancer drug pan-resistant tumor metastasis (cdp-rtm) is a major source of cancer lethality. Cytocapsular tubes (CCTs) and their networks are physical membrane-enclosed freeway systems for cancer cell dissemination across tissues and organs in vivo. Whether cytocapsular tube superlarge biomembranes function as superdenfence and conduct cdp-rtm is unknown. It is also unknown whether conventional cancer drug development methods, including cancer cell line derived xenograft (CDX) and patient cancer cell derived xenograft (PDX), generate cytocapsular tubes (CCTs). It is also unclear whether xenografts can be created that contain CCTs for efficient cancer drug development. Here, we investigated CCT functions related to cancer drug resistance, CCTs in CDX and PDX and CCT xenograft (CCTX). Using clinical cancer tissues, we discovered that CCTs potently shielded against multiple chemotherapy treatments with diverse conventional cancer drugs. Next, our quantitative analyses show that CCT biomembrane drug barriers significantly increase cancer drug resistance by 6.6-folds to14-folds. We found that conventional CDX and PDX animal models do not generate CCTs in these xenografts. By mimicking in vivo cancer cell environments for cancer patient cancer cell culturing, we have successfully isolated CH-5high/CH-6high subpopulations of patient breast cancer cells and pancreas cancer cells that are propertied with cytocapsular tube generation capacities and engender large quantities of CCTs in mouse xenografts. Biochemical and immunohistochemistry analyses demonstrated that CCTs in these xenografts are similar to those in clinical cancer tissues. In summary, our research has identified that CCTs and networks function as physical superdefence freeway systems conducting conventional cancer drug pan-resistant tumor metastasis, and developed a CCTX platform for highly efficient cancer drug development, which pave avenues for more efficient development of effective and precise cancer drugs for tumor cure at both personal and broad-spectrum levels.

Scalable Optimization Methods for Incorporating Spatiotemporal Fractionation into Intensity-Modulated Radiotherapy Planning

It has been recently shown that an additional therapeutic gain may be achieved if a radiotherapy plan is altered over the treatment course using a new treatment paradigm referred to in the literature as spatiotemporal fractionation. Because of the nonconvex and large-scale nature of the corresponding treatment plan optimization problem, the extent of the potential therapeutic gain that may be achieved from spatiotemporal fractionation has been investigated using stylized cancer cases to circumvent the arising computational challenges. This research aims at developing scalable optimization methods to obtain high-quality spatiotemporally fractionated plans with optimality bounds for clinical cancer cases. In particular, the treatment-planning problem is formulated as a quadratically constrained quadratic program and is solved to local optimality using a constraint-generation approach, in which each subproblem is solved using sequential linear/quadratic programming methods. To obtain optimality bounds, cutting-plane and column-generation methods are combined to solve the Lagrangian relaxation of the formulation. The performance of the developed methods are tested on deidentified clinical liver and prostate cancer cases. Results show that the proposed method is capable of achieving local-optimal spatiotemporally fractionated plans with an optimality gap of around 10%–12% for cancer cases tested in this study. Summary of Contribution: The design of spatiotemporally fractionated radiotherapy plans for clinical cancer cases gives rise to a class of nonconvex and large-scale quadratically constrained quadratic programming (QCQP) problems, the solution of which requires the development of efficient models and solution methods. To address the computational challenges posed by the large-scale and nonconvex nature of the problem, we employ large-scale optimization techniques to develop scalable solution methods that find local-optimal solutions along with optimality bounds. We test the performance of the proposed methods on deidentified clinical cancer cases. The proposed methods in this study can, in principle, be applied to solve other QCQP formulations, which commonly arise in several application domains, including graph theory, power systems, and signal processing.