Live Cell Mass Accumulation Measurement Non-Invasively Predicts Carboplatin Sensitivity in Triple-Negative Breast Cancer Patient-Derived Xenografts

Drug screening at single-organoid resolution via bioprinting and interferometry

High throughput drug screening is an established approach to investigate tumor biology and identify therapeutic leads. Traditional platforms use two-dimensional cultures which do not accurately reflect the biology of human tumors. More clinically relevant model systems such as three-dimensional tumor organoids can be difficult to scale and screen. Manually seeded organoids coupled to destructive endpoint assays allow for the characterization of treatment response, but do not capture transitory changes and intra-sample heterogeneity underlying clinically observed resistance to therapy. We present a pipeline to generate bioprinted tumor organoids linked to label-free, time-resolved imaging via high-speed live cell interferometry (HSLCI) and machine learning-based quantitation of individual organoids. Bioprinting cells gives rise to 3D structures with unaltered tumor histology and gene expression profiles. HSLCI imaging in tandem with machine learning-based segmentation and classification tools enables accurate, label-free parallel mass measurements for thousands of organoids. We demonstrate that this strategy identifies organoids transiently or persistently sensitive or resistant to specific therapies, information that could be used to guide rapid therapy selection.

Discovering Synergistic Compounds with BYL-719 in PI3K Overactivated Basal-like PDXs

Basal-like triple-negative breast cancer (TNBC) tumor cells are difficult to eliminate due to resistance mechanisms that promote survival. While this breast cancer subtype has low PIK3CA mutation rates when compared to estrogen receptor-positive (ER+) breast cancers, most basal-like TNBCs have an overactive PI3K pathway due to gene amplification or high gene expression. BYL-719 is a PIK3CA inhibitor that has been found to have low drug-drug interactions, which increases the likelihood that it could be useful for combinatorial therapy. Alpelisib (BYL-719) with fulvestrant was recently approved for treating ER+ breast cancer patients whose cancer had developed resistance to ER-targeting therapy. In these studies, a set of basal-like patient-derived xenograft (PDX) models was transcriptionally defined with bulk and single-cell RNA-sequencing and clinically actionable mutation profiles defined with Oncomine mutational profiling. This information was overlaid onto therapeutic drug screening results. BYL-719-based, synergistic two-drug combinations were identified with 20 different compounds, including everolimus, afatinib, and dronedarone, which were also found to be effective at minimizing tumor growth. These data support the use of these drug combinations towards cancers with activating PIK3CA mutations/gene amplifications or PTEN deficient/PI3K overactive pathways.

Quantitative Phase Imaging: Recent Advances and Expanding Potential in Biomedicine

Quantitative phase imaging (QPI) is a label-free, wide-field microscopy approach with significant opportunities for biomedical applications. QPI uses the natural phase shift of light as it passes through a transparent object, such as a mammalian cell, to quantify biomass distribution and spatial and temporal changes in biomass. Reported in cell studies more than 60 years ago, ongoing advances in QPI hardware and software are leading to numerous applications in biology, with a dramatic expansion in utility over the past two decades. Today, investigations of cell size, morphology, behavior, cellular viscoelasticity, drug efficacy, biomass accumulation and turnover, and transport mechanics are supporting studies of development, physiology, neural activity, cancer, and additional physiological processes and diseases. Here, we review the field of QPI in biology starting with underlying principles, followed by a discussion of technical approaches currently available or being developed, and end with an examination of the breadth of applications in use or under development. We comment on strengths and shortcomings for the deployment of QPI in key biomedical contexts and conclude with emerging challenges and opportunities based on combining QPI with other methodologies that expand the scope and utility of QPI even further.

Identification of nuclear export inhibitor-based combination therapies in preclinical models of triple-negative breast cancer

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High-throughput drug screening reveals promising therapeutic candidates for TNBC.

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KPT-330, an XPO1 inhibitor, and GSK2126458 exhibit synergism in preclinical models of TNBC.

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XPO1 is overexpressed in basal-like breast tumors.

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XPO1 expression is associated with PIK3CA, MTOR, and MKI67 expression at the single-cell level.

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XPO1 overexpression in basal-like patients is associated with greater rates of metastases.

An estimated 284,000 Americans will be diagnosed with breast cancer in 2021. Of these individuals, 15–20% have basal-like triple-negative breast cancer (TNBC), which is known to be highly metastatic. Chemotherapy is standard of care for TNBC patients, but chemoresistance is a common clinical problem. There is currently a lack of alternative, targeted treatment strategies for TNBC; this study sought to identify novel therapeutic combinations to treat basal-like TNBCs. For these studies, four human basal-like TNBC cell lines were utilized to determine the cytotoxicity profile of 1363 clinically-used drugs. Ten promising therapeutic candidates were identified, and synergism studies were performed in vitro. Two drug combinations that included KPT-330, an XPO1 inhibitor, were synergistic in all four cell lines. In vivo testing of four basal-like patient-derived xenografts (PDX) identified one combination, KPT-330 and GSK2126458 (a PI3K/mTOR inhibitor), that decreased tumor burden in mice significantly more than monotherapy with either single agent. Bulk and single-cell RNA-sequencing, immunohistochemistry, and analysis of published genomic datasets found that XPO1 was abundantly expressed in human basal-like TNBC cell lines, PDXs, and patient tumor samples. Within basal-like PDXs, XPO1 overexpression was associated with increased proliferation at the cellular level. Within patient datasets, XPO1 overexpression was correlated with greater rates of metastasis in patients with basal-like tumors. These studies identify a promising potential new combination therapy for patients with basal-like breast cancer.

Single cell biomass tracking allows identification and isolation of rare targeted therapy-resistant DLBCL cells within a mixed population

Adaptive resistance is a major limitation in the use of targeted therapies for cancer. Using real time biomass tracking, we demonstrate the isolation and identification of rare (1% fraction) diffuse large B cell lymphoma cells resistant to the PI3K inhibitor idelalisib, from an otherwise sensitive population. This technique allows direct study of these rare, drug tolerant cells.

The utility of the "Glowing Head" mouse for breast cancer metastasis research

The expression of cellular reporters to label cancer cells, such as green fluorescent protein (GFP) and luciferase, can stimulate immune responses and effect tumor growth. Recently, a mouse model that expresses GFP and luciferase in the anterior pituitary gland was generated to tolerize mice to these proteins; the "Glowing Head" mouse. Mice were obtained from a commercial vendor, bred, and then used for tumor growth and metastasis studies. The transgene expression of luciferase was assessed within tumor-naïve mice as well as mice with mammary tumors or metastases. Tumor-free mice with white fur, compared to black fur, allowed for stronger luciferase transgene expression to be observed in the pituitary, sternum, and femur. Growth of four different luciferase-expressing mouse cancer cell lines readily occurred in the mammary gland. Though sternum expression of the luciferase transgene occurred in cancer-free mice, growth or death of luciferase positive cancer cells in the lung could be observed. Liver metastases seeded by portal vein injections of luciferase positive cancer cell lines were completely distinct from luciferase transgene expression. Though lung and brain metastasis studies have limitations, the Glowing Head mouse can be useful to inhibit immune system rejection of luciferase or GFP expressing cancer cells. This mouse model is most beneficial for studies of mammary tumors and liver metastases.

Identifying fates of cancer cells exposed to mitotic inhibitors by quantitative phase imaging

Cell cycle deregulation is a cancer hallmark that has stimulated the development of mitotic inhibitors with differing mechanisms of action. Quantitative phase imaging (QPI) is an emerging approach for determining cancer cell sensitivities to chemotherapies in vitro. Cancer cell fates in response to mitotic inhibitors are agent- and dose-dependent. Fates that lead to chromosomal instabilities may result in a survival advantage and drug resistance. Conventional techniques for quantifying cell fates are incompatible with growth inhibition assays that produce binary live/dead results. Therefore, we used QPI to quantify post-mitotic fates of G0/G1 synchronized HeLa cervical adenocarcinoma and M202 melanoma cells during 24 h of escalating-dose exposures to mitotic inhibitors, including microtubule inhibitors paclitaxel and colchicine, and an Aurora kinase A inhibitor, VX-680. QPI determined cell fates by measuring changes in cell biomass, morphology, and mean phase-shift. Cell fates fell into three groups: (1) bipolar division from drug failure; (2) cell death or sustained mitotic arrest; and (3) aberrant endocycling or multipolar division. In this proof-of-concept study, colchicine was most effective in producing desirable outcomes of sustained mitotic arrest or death throughout its dosing range, whereas both paclitaxel and VX-680 yielded dose-dependent multipolar divisions or endocycling, respectively. Furthermore, rapid completion of mitosis associated with bipolar divisions whereas prolonged mitosis associated with multipolar divisions or cell death. Overall, QPI measurement of drug-induced cancer cell fates provides a tool to inform the development of candidate agents by quantifying the dosing ranges over which suboptimal inhibitor choices lead to undesirable, aberrant cancer cell fates.