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Patient-derived xenografts (PDXs) have clinical value but are time-, cost-, and labor-intensive and thus ill-suited for large-scale experiments. Here, we present a protocol to convert PDX tumors into PDxOs for long-term cultures amenable to moderate-throughput drug screens, including in-depth PDxO validation. We describe steps for PDxO preparation and mouse cell removal. We then detail PDxO validation and characterization and drug response assay. Our PDxO drug screening platform can predict therapy response in vivo and inform functional precision oncology for patients. For complete details on the use and execution of this protocol, please refer to Guillen et al.1.

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Quantitative phase imaging (QPI) measures the growth rate of individual cells by quantifying changes in mass versus time. Here, we use the breast cancer cell lines MCF-7, BT-474, and MDA-MB-231 to validate QPI as a multiparametric approach for determining response to single-agent therapies. Our method allows for rapid determination of drug sensitivity, cytotoxicity, heterogeneity, and time of response for up to 100,000 individual cells or small clusters in a single experiment. We find that QPI EC50 values are concordant with CellTiter-Glo (CTG), a gold standard metabolic endpoint assay. In addition, we apply multiparametric QPI to characterize cytostatic/cytotoxic and rapid/slow responses and track the emergence of resistant subpopulations. Thus, QPI reveals dynamic changes in response heterogeneity in addition to average population responses, a key advantage over endpoint viability or metabolic assays. Overall, multiparametric QPI reveals a rich picture of cell growth by capturing the dynamics of single-cell responses to candidate therapies.

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Models that recapitulate the complexity of human tumors are urgently needed to develop more effective cancer therapies. We report a bank of human patient-derived xenografts (PDXs) and matched organoid cultures from tumors that represent the greatest unmet need: endocrine-resistant, treatment-refractory and metastatic breast cancers. We leverage matched PDXs and PDX-derived organoids (PDxO) for drug screening that is feasible and cost-effective with in vivo validation. Moreover, we demonstrate the feasibility of using these models for precision oncology in real time with clinical care in a case of triple-negative breast cancer (TNBC) with early metastatic recurrence. Our results uncovered a Food and Drug Administration (FDA)-approved drug with high efficacy against the models. Treatment with this therapy resulted in a complete response for the individual and a progression-free survival (PFS) period more than three times longer than their previous therapies. This work provides valuable methods and resources for functional precision medicine and drug development for human breast cancer.

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BACKGROUND: Protein synthesis is regulated by the availability of amino acids, the engagement of growth factor signaling pathways, and adenosine triphosphate (ATP) levels sufficient to support translation. Crosstalk between these inputs is extensive, yet other regulatory mechanisms remain to be characterized. For example, the translation initiation inhibitor rocaglamide A (RocA) induces thioredoxin-interacting protein (TXNIP). TXNIP is a negative regulator of glucose uptake; thus, its induction by RocA links translation to the availability of glucose. MondoA is the principal regulator of glucose-induced transcription, and its activity is triggered by the glycolytic intermediate, glucose 6-phosphate (G6P). MondoA responds to G6P generated by cytoplasmic glucose and mitochondrial ATP (mtATP), suggesting a critical role in the cellular response to these energy sources. TXNIP expression is entirely dependent on MondoA; therefore, we investigated how protein synthesis inhibitors impact its transcriptional activity. METHODS: We investigated how translation regulates MondoA activity using cell line models and loss-of-function approaches. We examined how protein synthesis inhibitors effect gene expression and metabolism using RNA-sequencing and metabolomics, respectively. The biological impact of RocA was evaluated using cell lines and patient-derived xenograft organoid (PDxO) models. RESULTS: We discovered that multiple protein synthesis inhibitors, including RocA, increase TXNIP expression in a manner that depends on MondoA, a functional electron transport chain and mtATP synthesis. Furthermore, RocA and cycloheximide increase mtATP and G6P levels, respectively, and TXNIP induction depends on interactions between the voltage-dependent anion channel (VDAC) and hexokinase (HK), which generates G6P. RocA treatment impacts the regulation of ~ 1200 genes, and ~ 250 of those genes are MondoA-dependent. RocA treatment is cytotoxic to triple negative breast cancer (TNBC) cell lines and shows preferential cytotoxicity against estrogen receptor negative (ER-) PDxO breast cancer models. Finally, RocA-driven cytotoxicity is partially dependent on MondoA or TXNIP. CONCLUSIONS: Our data suggest that protein synthesis inhibitors rewire metabolism, resulting in an increase in mtATP and G6P, the latter driving MondoA-dependent transcriptional activity. Further, MondoA is a critical component of the cellular transcriptional response to RocA. Our functional assays suggest that RocA or similar translation inhibitors may show efficacy against ER- breast tumors and that the levels of MondoA and TXNIP should be considered when exploring these potential treatment options.

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Histone deacetylase (HDAC) inhibition has sporadic clinical efficacy in urothelial carcinoma; the genomic basis for clinical response is not known. In two separate phase I clinical trials testing pharmacokinetic aspects of HDAC inhibitors in advanced solid tumors, we identified one patient with advanced urothelial carcinoma who had a complete response to belinostat, and one patient with advanced urothelial carcinoma who had a partial response to panobinostat. The archived tumors of the responders were genomically characterized in comparison to others with urothelial carcinoma on the trials. Urothelial carcinoma cell lines treated with panobinostat and belinostat were studied to elucidate the mechanisms of benefit. Notably, the urothelial carcinoma tumors that responded to HDAC inhibition had ARID1A mutations. ARID1A mutations were also noted in the tumors of three patients who had stable disease as their best response to HDAC inhibition. Corroborating the basis of sensitivity, transcriptional profiling of platinum-resistant ARID1A-mutated HT1197 cells treated with panobinostat reveals negative enrichment for both cyto-proliferative (MYC and E2F targets) and DNA repair gene sets, and positive enrichment for TP53 and inflammatory gene sets. Our study identifies ARID1A loss as a basis for clinical response to pan HDAC inhibition and offers avenues for potential rational therapeutic combinations with HDAC inhibitors in advanced urothelial carcinoma.

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Microtubules are essential for various cell processes [1] and are nucleated by multi-protein γ-tubulin ring complexes (γ-TuRCs) at various microtubule organizing centers (MTOCs), including centrosomes [2-6]. Recruitment of γ-TuRCs to different MTOCs at different times influences microtubule array formation, but how this is regulated remains an open question. It also remains unclear whether all γ-TuRCs within the same organism have the same composition and how any potential heterogeneity might influence γ-TuRC recruitment. MOZART1 (Mzt1) was recently identified as a γ-TuRC component [7, 8] and is conserved in nearly all eukaryotes [6, 9]. Mzt1 has so far been studied in cultured human cells, yeast, and plants; its absence leads to failures in γ-TuRC recruitment and cell division, resulting in cell death [7, 9-15]. Mzt1 is small (∼8.5 kDa), binds directly to core γ-TuRC components [9, 10, 14, 15], and appears to mediate the interaction between γ-TuRCs and proteins that tether γ-TuRCs to MTOCs [9, 15]. Here, we use Drosophila to investigate the function of Mzt1 in a multicellular animal for the first time. Surprisingly, we find that Drosophila Mzt1 is expressed only in the testes and is present in γ-TuRCs recruited to basal bodies, but not to mitochondria, in developing sperm cells. mzt1 mutants are viable but have defects in basal body positioning and γ-TuRC recruitment to centriole adjuncts; sperm formation is affected and mutants display a rapid age-dependent decline in sperm motility and male fertility. Our results reveal that tissue-specific and MTOC-specific γ-TuRC heterogeneity exist in Drosophila and highlight the complexity of γ-TuRC recruitment in a multicellular animal.

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Lysine-Specific Demethylase 1 (LSD1) over-expression correlates with poorly differentiated neuroblastoma and predicts poor outcome despite multimodal therapy. We have studied the efficacy of reversible and specific LSD1 inhibition with HCI-2509 in neuroblastoma cell lines and particularly the effect of HCI-2509 on the transcriptomic profile in MYCN amplified NGP cells. Cell survival assays show that HCI-2509 is cytotoxic to poorly differentiated neuroblastoma cell lines in low micromole or lower doses. Transcriptional profiling of NGP cells treated with HCI-2509 shows a significant effect on p53, cell cycle, MYCN and hypoxia pathway gene sets. HCI-2509 results in increased histone methyl marks and p53 levels along with cell cycle arrest in the G2/M phase and inhibition of colony formation of NGP cells. Our findings indicate that LSD1 inhibition with HCI-2509 has a multi-target effect in neuroblastoma cell lines, mediated in part via p53. MYCN-amplified neuroblastoma cells have a targeted benefit as HCI-2509 downregulates the MYCN upregulated gene set.

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BACKGROUND: Examining the distribution of variation has proven an extremely profitable technique in the effort to identify sequences of biological significance. Most approaches in the field, however, evaluate only the conserved portions of sequences - ignoring the biological significance of sequence differences. A suite of sophisticated likelihood based statistical models from the field of molecular evolution provides the basis for extracting the information from the full distribution of sequence variation. The number of different problems to which phylogeny-based maximum likelihood calculations can be applied is extensive. Available software packages that can perform likelihood calculations suffer from a lack of flexibility and scalability, or employ error-prone approaches to model parameterisation. RESULTS: Here we describe the implementation of PyEvolve, a toolkit for the application of existing, and development of new, statistical methods for molecular evolution. We present the object architecture and design schema of PyEvolve, which includes an adaptable multi-level parallelisation schema. The approach for defining new methods is illustrated by implementing a novel dinucleotide model of substitution that includes a parameter for mutation of methylated CpG's, which required 8 lines of standard Python code to define. Benchmarking was performed using either a dinucleotide or codon substitution model applied to an alignment of BRCA1 sequences from 20 mammals, or a 10 species subset. Up to five-fold parallel performance gains over serial were recorded. Compared to leading alternative software, PyEvolve exhibited significantly better real world performance for parameter rich models with a large data set, reducing the time required for optimisation from approximately 10 days to approximately 6 hours. CONCLUSION: PyEvolve provides flexible functionality that can be used either for statistical modelling of molecular evolution, or the development of new methods in the field. The toolkit can be used interactively or by writing and executing scripts. The toolkit uses efficient processes for specifying the parameterisation of statistical models, and implements numerous optimisations that make highly parameter rich likelihood functions solvable within hours on multi-cpu hardware. PyEvolve can be readily adapted in response to changing computational demands and hardware configurations to maximise performance. PyEvolve is released under the GPL and can be downloaded from http://cbis.anu.edu.au/software.

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