Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories.

To investigate the fundamental question of how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we focused on nuclear growth in hiPS cell colonies as a model system. We generated a 3D timelapse dataset of thousands of nuclei over multiple days, and developed open-source tools for image and data analysis and an interactive timelapse viewer for exploring quantitative features of nuclear size and shape. We performed a data-driven analysis of nuclear growth variations across timescales. We found that individual nuclear volume growth trajectories arise from short timescale variations attributable to their spatiotemporal context within the colony. We identified a strikingly time-invariant volume compensation relationship between nuclear growth duration and starting volume across the population. Notably, we discovered that inheritance plays a crucial role in determining these two key nuclear growth features while other growth features are determined by their spatiotemporal context and are not inherited.

Automated human induced pluripotent stem cell culture and sample preparation for 3D live-cell microscopy.

To produce abundant cell culture samples to generate large, standardized image datasets of human induced pluripotent stem (hiPS) cells, we developed an automated workflow on a Hamilton STAR liquid handler system. This was developed specifically for culturing hiPS cell lines expressing fluorescently tagged proteins, which we have used to study the principles by which cells establish and maintain robust dynamic localization of cellular structures. This protocol includes all details for the maintenance, passage and seeding of cells, as well as Matrigel coating of 6-well plastic plates and 96-well optical-grade, glass plates. We also developed an automated image-based hiPS cell colony segmentation and feature extraction pipeline to streamline the process of predicting cell count and selecting wells with consistent morphology for high-resolution three-dimensional (3D) microscopy. The imaging samples produced with this protocol have been used to study the integrated intracellular organization and cell-to-cell variability of hiPS cells to train and develop deep learning-based label-free predictions from transmitted-light microscopy images and to develop deep learning-based generative models of single-cell organization. This protocol requires some experience with robotic equipment. However, we provide details and source code to facilitate implementation by biologists less experienced with robotics. The protocol is completed in less than 10 h with minimal human interaction. Overall, automation of our cell culture procedures increased our imaging samples' standardization, reproducibility, scalability and consistency. It also reduced the need for stringent culturist training and eliminated culturist-to-culturist variability, both of which were previous pain points of our original manual pipeline workflow.

Multidrug Analyses in Patients Distinguish Efficacious Cancer Agents Based on Both Tumor Cell Killing and Immunomodulation.

The vision of a precision medicine-guided approach to novel cancer drug development is challenged by high intratumor heterogeneity and interpatient diversity. This complexity is rarely modeled accurately during preclinical drug development, hampering predictions of clinical drug efficacy. To address this issue, we developed Comparative In Vivo Oncology (CIVO) arrayed microinjection technology to test tumor responsiveness to simultaneous microdoses of multiple drugs directly in a patient's tumor. Here, in a study of 18 canine patients with soft tissue sarcoma (STS), CIVO captured complex, patient-specific tumor responses encompassing both cancer cells and multiple immune infiltrates following localized exposure to different chemotherapy agents. CIVO also classified patient-specific tumor resistance to the most effective agent, doxorubicin, and further enabled assessment of a preclinical autophagy inhibitor, PS-1001, to reverse doxorubicin resistance. In a CIVO-identified subset of doxorubicin-resistant tumors, PS-1001 resulted in enhanced antitumor activity, increased infiltration of macrophages, and skewed this infiltrate toward M1 polarization. The ability to evaluate and cross-compare multiple drugs and drug combinations simultaneously in living tumors and across a diverse immunocompetent patient population may provide a foundation from which to make informed drug development decisions. This method also represents a viable functional approach to complement current precision oncology strategies. Cancer Res; 77(11); 2869-80. ©2017 AACR.

A Platform for Rapid, Quantitative Assessment of Multiple Drug Combinations Simultaneously in Solid Tumors In Vivo.

While advances in high-throughput screening have resulted in increased ability to identify synergistic anti-cancer drug combinations, validation of drug synergy in the in vivo setting and prioritization of combinations for clinical development remain low-throughput and resource intensive. Furthermore, there is currently no viable method for prospectively assessing drug synergy directly in human patients in order to potentially tailor therapies. To address these issues we have employed the previously described CIVO platform and developed a quantitative approach for investigating multiple combination hypotheses simultaneously in single living tumors. This platform provides a rapid, quantitative and cost effective approach to compare and prioritize drug combinations based on evidence of synergistic tumor cell killing in the live tumor context. Using a gemcitabine resistant model of pancreatic cancer, we efficiently investigated nine rationally selected Abraxane-based combinations employing only 19 xenografted mice. Among the drugs tested, the BCL2/BCLxL inhibitor ABT-263 was identified as the one agent that synergized with Abraxane® to enhance acute induction of localized apoptosis in this model of human pancreatic cancer. Importantly, results obtained with CIVO accurately predicted the outcome of systemic dosing studies in the same model where superior tumor regression induced by the Abraxane/ABT-263 combination was observed compared to that induced by either single agent. This supports expanded use of CIVO as an in vivo platform for expedited in vivo drug combination validation and sets the stage for performing toxicity-sparing drug combination studies directly in cancer patients with solid malignancies.

Minimally invasive in tumor multidrug comparative analysis with contrast-enhanced MRI in mice.

PURPOSE: To facilitate decision making in the oncology clinic, technologies have recently been developed to independently inject and assess multiple anticancer agents directly in a patient's tumor. To increase the flexibility of this approach beyond histological readouts of response, contrast-enhanced MRI was evaluated for the detection of cell death in living tumors after injection. METHODS: A six-needle arrayed microinjection device designed to provide head-to-head comparisons of chemotherapy responses in living tumors was used. Xenografted non-Hodgkin lymphoma tumors in athymic Nude-Foxn1(nu) mice were injected either with different doses of vincristine or with one needle each of vincristine, doxorubicin, bendamustine, prednisolone, mafosfamide, and a vehicle control. To assess drug responses, measurements of enhancement by T1-weighted contrast-enhanced MRI were made for individual sites at 24, 48, and 72 h after injection. For comparison, histological evaluations of cell death were obtained after tumor resection. RESULTS: Measurements of MRI enhancement at injection sites showed a significant (P < 0.001) positive regression slope as a function of vincristine dose. Average MRI measurements were closely correlated with cell death by hematoxylin and eosin staining (R = 0.81; P = 0.001). CONCLUSION: Contrast-enhanced MRI has the potential to replace or augment histological analyses of tumor responses to microinjected doses of chemotherapy agents with potential application in selecting optimal chemotherapy regimens. Magn Reson Med 76:946-952, 2016. © 2015 Wiley Periodicals, Inc.

A technology platform to assess multiple cancer agents simultaneously within a patient's tumor.

A fundamental problem in cancer drug development is that antitumor efficacy in preclinical cancer models does not translate faithfully to patient outcomes. Much of early cancer drug discovery is performed under in vitro conditions in cell-based models that poorly represent actual malignancies. To address this inconsistency, we have developed a technology platform called CIVO, which enables simultaneous assessment of up to eight drugs or drug combinations within a single solid tumor in vivo. The platform is currently designed for use in animal models of cancer and patients with superficial tumors but can be modified for investigation of deeper-seated malignancies. In xenograft lymphoma models, CIVO microinjection of well-characterized anticancer agents (vincristine, doxorubicin, mafosfamide, and prednisolone) induced spatially defined cellular changes around sites of drug exposure, specific to the known mechanisms of action of each drug. The observed localized responses predicted responses to systemically delivered drugs in animals. In pair-matched lymphoma models, CIVO correctly demonstrated tumor resistance to doxorubicin and vincristine and an unexpected enhanced sensitivity to mafosfamide in multidrug-resistant lymphomas compared with chemotherapy-naïve lymphomas. A CIVO-enabled in vivo screen of 97 approved oncology agents revealed a novel mTOR (mammalian target of rapamycin) pathway inhibitor that exhibits significantly increased tumor-killing activity in the drug-resistant setting compared with chemotherapy-naïve tumors. Finally, feasibility studies to assess the use of CIVO in human and canine patients demonstrated that microinjection of drugs is toxicity-sparing while inducing robust, easily tracked, drug-specific responses in autochthonous tumors, setting the stage for further application of this technology in clinical trials.