Feasibility of Ultra-High-Throughput Functional Screening of Melanoma Biopsies for Discovery of Novel Cancer Drug Combinations

Protocol for high throughput 3D drug screening of patient derived melanoma and renal cell carcinoma

High Throughput Screening (HTS) with 3D cell models is possible thanks to the recent progress and development in 3D cell culture technologies. Results from multiple studies have demonstrated different drug responses between 2D and 3D cell culture. It is now widely accepted that 3D cell models more accurately represent the physiologic conditions of tumors over 2D cell models. However, there is still a need for more accurate tests that are scalable and better imitate the complex conditions in living tissues. Here, we describe ultrahigh throughput 3D methods of drug response profiling in patient derived primary tumors including melanoma as well as renal cell carcinoma that were tested against the NCI oncologic set of FDA approved drugs. We also tested their autologous patient derived cancer associated fibroblasts, varied the in-vitro conditions using matrix vs matrix free methods and completed this in both 3D vs 2D rendered cancer cells. The result indicates a heterologous response to the drugs based on their genetic background, but not on their maintenance condition. Here, we present the methods and supporting results of the HTS efforts using these 3D of organoids derived from patients. This demonstrated the possibility of using patient derived 3D cells for HTS and expands on our screening capabilities for testing other types of cancer using clinically approved anti-cancer agents to find drugs for potential off label use.

Pan-Cancer Pharmacogenomic Analysis of Patient-Derived Tumor Cells Using Clinically Relevant Drug Exposures

As a result of tumor heterogeneity and solid cancers harboring multiple molecular defects, precision medicine platforms in oncology are most effective when both genetic and pharmacologic determinants of a tumor are evaluated. Expandable patient-derived xenograft (PDX) mouse tumor and corresponding PDX culture (PDXC) models recapitulate many of the biological and genetic characteristics of the original patient tumor, allowing for a comprehensive pharmacogenomic analysis. Here, the somatic mutations of 23 matched patient tumor and PDX samples encompassing four cancers were first evaluated using next-generation sequencing (NGS). 19 antitumor agents were evaluated across 78 patient-derived tumor cultures using clinically relevant drug exposures. A binarization threshold sensitivity classification determined in culture (PDXC) was used to identify tumors that best respond to drug in vivo (PDX). Using this sensitivity classification, logic models of DNA mutations were developed for 19 antitumor agents to predict drug response. We determined that the concordance of somatic mutations across patient and corresponding PDX samples increased as variant allele frequency increased. Notable individual PDXC responses to specific drugs, as well as lineage-specific drug responses were identified. Robust responses identified in PDXC were recapitulated in vivo in PDX-bearing mice and logic modeling determined somatic gene mutation(s) defining response to specific antitumor agents. In conclusion, combining NGS of primary patient tumors, high-throughput drug screen using clinically relevant doses, and logic modeling, can provide a platform for understanding response to therapeutic drugs targeting cancer.

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.

Ex Vivo Analysis of Primary Tumor Specimens for Evaluation of Cancer Therapeutics

The use of ex vivo drug sensitivity testing to predict drug activity in individual patients has been actively explored for almost 50 years without delivering a generally useful predictive capability. However, extended failure should not be an indicator of futility. This is especially true in cancer research where ultimate success is often preceded by less successful attempts. For example, both immune- and genetic-based targeted therapies for cancer underwent numerous failed attempts before biological understanding, improved targets, and optimized drug development matured to facilitate an arsenal of transformational drugs. Similarly, the concept of directly assessing drug sensitivity of primary tumor biopsies-and the use of this information to help direct therapeutic approaches-has a long history with a definitive learning curve. In this review, we will survey the history of ex vivo testing as well as the current state of the art for this field. We will present an update on methodologies and approaches, describe the use of these technologies to test cutting-edge drug classes, and describe an increasingly nuanced understanding of tumor types and models for which this strategy is most likely to succeed. We will consider the relative strengths and weaknesses of predicting drug activity across the broad biological context of cancer patients and tumor types. This will include an analysis of the potential for ex vivo drug sensitivity testing to accurately predict drug activity within each of the biological hallmarks of cancer pathogenesis.

Evolution of the Experimental Models of Cholangiocarcinoma

Cholangiocarcinoma (CCA) is a rare, aggressive disease with poor overall survival. In advanced cases, surgery is often not possible or fails; in addition, there is a lack of effective and specific therapies. Multidisciplinary approaches and advanced technologies have improved the knowledge of CCA molecular pathogenesis, highlighting its extreme heterogeneity and high frequency of genetic and molecular aberrations. Effective preclinical models, therefore, should be based on a comparable level of complexity. In the past years, there has been a consistent increase in the number of available CCA models. The exploitation of even more complex CCA models is rising. Examples are the use of CRISPR/Cas9 or stabilized organoids for in vitro studies, as well as patient-derived xenografts or transgenic mouse models for in vivo applications. Here, we examine the available preclinical CCA models exploited to investigate: (i) carcinogenesis processes from initiation to progression; and (ii) tools for personalized therapy and innovative therapeutic approaches, including chemotherapy and immune/targeted therapies. For each model, we describe the potential applications, highlighting both its advantages and limits.

Drug responses are conserved across patient-derived xenograft models of melanoma leading to identification of novel drug combination therapies

BACKGROUND

Patient-derived xenograft (PDX) mouse tumour models can predict response to therapy in patients. Predictions made from PDX cultures (PDXC) would allow for more rapid and comprehensive evaluation of potential treatment options for patients, including drug combinations.

METHODS

We developed a PDX library of BRAF-mutant metastatic melanoma, and a high-throughput drug-screening (HTDS) platform utilising clinically relevant drug exposures. We then evaluated 34 antitumor agents across eight melanoma PDXCs, compared drug response to BRAF and MEK inhibitors alone or in combination with PDXC and the corresponding PDX, and investigated novel drug combinations targeting BRAF inhibitor-resistant melanoma.

RESULTS

The concordance of cancer-driving mutations across patient, matched PDX and subsequent PDX generations increases as variant allele frequency (VAF) increases. There was a high correlation in the magnitude of response to BRAF and MEK inhibitors between PDXCs and corresponding PDXs. PDXCs and corresponding PDXs from metastatic melanoma patients that progressed on standard-of-care therapy demonstrated similar resistance patterns to BRAF and MEK inhibitor therapy. Importantly, HTDS identified novel drug combinations to target BRAF-resistant melanoma.

CONCLUSIONS

The biological consistency observed between PDXCs and PDXs suggests that PDXCs may allow for a rapid and comprehensive identification of treatments for aggressive cancers, including combination therapies.

Molecularly targeted drug combinations demonstrate selective effectiveness for myeloid- and lymphoid-derived hematologic malignancies

Translating the genetic and epigenetic heterogeneity underlying human cancers into therapeutic strategies is an ongoing challenge. Large-scale sequencing efforts have uncovered a spectrum of mutations in many hematologic malignancies, including acute myeloid leukemia (AML), suggesting that combinations of agents will be required to treat these diseases effectively. Combinatorial approaches will also be critical for combating the emergence of genetically heterogeneous subclones, rescue signals in the microenvironment, and tumor-intrinsic feedback pathways that all contribute to disease relapse. To identify novel and effective drug combinations, we performed ex vivo sensitivity profiling of 122 primary patient samples from a variety of hematologic malignancies against a panel of 48 drug combinations. The combinations were designed as drug pairs that target nonoverlapping biological pathways and comprise drugs from different classes, preferably with Food and Drug Administration approval. A combination ratio (CR) was derived for each drug pair, and CRs were evaluated with respect to diagnostic categories as well as against genetic, cytogenetic, and cellular phenotypes of specimens from the two largest disease categories: AML and chronic lymphocytic leukemia (CLL). Nearly all tested combinations involving a BCL2 inhibitor showed additional benefit in patients with myeloid malignancies, whereas select combinations involving PI3K, CSF1R, or bromodomain inhibitors showed preferential benefit in lymphoid malignancies. Expanded analyses of patients with AML and CLL revealed specific patterns of ex vivo drug combination efficacy that were associated with select genetic, cytogenetic, and phenotypic disease subsets, warranting further evaluation. These findings highlight the heuristic value of an integrated functional genomic approach to the identification of novel treatment strategies for hematologic malignancies.