SCIENCE

Nearly seventy percent of diagnostic lab test errors occur due to variability in preanalytical factors. These are the parameters involved with all aspects of tissue processing, starting from the time tissue is collected from the patient in the operating room, until it is received and tested in the laboratory. While there are several protocols for transporting fixed tissue, organs, and liquid biopsies, such protocols are lacking for transport and handling of live solid tumor tissue specimens. There is a critical need to establish preanalytical protocols to reduce variability in biospecimen integrity and improve diagnostics for personalized medicine. Here, we provide a comprehensive protocol for the standard collection, handling, packaging, cold-chain logistics, and receipt of solid tumortissue biospecimens to preserve tissue viability.

This ASCO GI 2024 abstract describes a high-throughput, ex vivo “bioprinted organoid tumor” (BOT) platform designed to capture drug responses in 3D colorectal cancer tissue models beyond what standard monolayer and xenograft assays can predict. Using HT-29 colorectal adenocarcinoma cells embedded in a printable bioink, the team printed BOTs in different geometries and treated them with the proteasome inhibitor bortezomib and the survivin inhibitor YM-155, then assessed response via live/dead immunofluorescence and observed morphologic/phenotypic changes. Both agents produced a dose-dependent response, and importantly, the BOTs showed disrupted self-assembly/phenotypic modulation at and even below “effective” doses; signals that ATP-only assays can miss and that could lead to overstated efficacy when control wells proliferate differently. The authors conclude that functional, high-throughput ex vivo drug-response prediction platforms like BOTs could improve preclinical screening by capturing phenotype alongside viability.

This issued patent strengthens the scientific and engineering foundation of Simple Microchamber Array Technology (SMART) by expanding the foundation for multiplexed, parallel exposure of intact biological samples to many test conditions in a single run. Building on SMART’s biomimetic microchamber array architecture, this patent further supports controlled, organized delivery of multiple fluids across defined regions of a single tissue specimen, enabling efficient side-by-side evaluation while preserving native tissue structure. The result is a practical path to higher-throughput, tissue-sparing workflows that can accelerate comparative compound assessment and iteration in human-relevant ex vivo testing, especially when patient material is limited.

This SABCS 2022 / Cancer Research supplement abstract (P6-01-38) presents a way to expand triple-negative breast cancer (TNBC) tissue into 3D bioprinted organoid tumors (BOTs) that mimic core biopsies for ex vivo chemotherapy sensitivity/resistance testing. The team bioprinted MDA-MB-231 TNBC cells in an alginate-based bioink into biopsy-like geometries, cured/crosslinked the constructs, and then loaded them into a solid tumor “biopsy-on-a-chip” platform for drug exposure with live/dead immunofluorescence readouts. They report an optimized crosslinking/geometry workflow compatible with the chip and demonstrate diffusion of small molecules into the bioprinted tissue to substantial depth, supporting multi-agent testing. Overall, the work argues that BOT-based ex vivo platforms could provide more clinically relevant efficacy signals than conventional in vitro models and help guide personalized chemotherapy strategies.

This abstract argues that preclinical drug testing is often poor at predicting human outcomes because standard static cultures and many animal models don’t capture the human tumor microenvironment, contributing to high clinical failure rates. Here we present a microfluidic ex vivo tissue model built with engineered microporous membranes (soft lithography and electrospinning) and multicellular healthy and tumorous microtissues using pancreatic and colorectal cell lines. Permeability testing showed selective transport supporting barrier/transport function. When “tumorous” tissue was connected upstream of “healthy” vascular and muscle tissue, we observed migration and invasion, and identified invasive subpopulations with more aggressive proliferation than cells that stayed at the original tumor site. Overall, we propose human-relevant, ex vivo models as a path toward faster, more comprehensive pharmacologic testing and more predictive, personalized treatment guidance.

Cancer deals a devastating one-two punch – every year – by claiming 8 million lives worldwide while cratering $2.5 trillion in economic impact. Over 40% of patients wipe out their entire life savings within two years of diagnosis. This is unsustainable.

Next generation cancer supermodels built on CerFlux ChipMux™ - including POET®, PEER®, POETRY, and PROPHET - can deliver a one-two counterpunch by enhancing our understanding of mechanisms of cancer and by deploying this knowledge to combat cancer at every stage: from protective measures to early diagnosis, optimal personalized therapy, and precision post treatment surveillance.

Each category – in vitro, in vivo, ex vivo, in silico – undergirding cancer models is in a state of renaissance resulting in a faster pace of new knowledge across the bench to bedside continuum.

Despite advances in therapeutics, cancer remains the second leading cause of death worldwide, and next-generation cancer models could help change that. However, obtaining enough tissue for ex vivo precision and personalized medicine can be challenging by tumor type and biopsy site. Current expansion approaches such as patient-derived xenografts (slow to establish) and patient-derived organoids (limited by size/scale), have practical constraints. Here we present 3D bioprinted organoid tumors (BOTs) that mimic core needle biopsy tissue, aiming to reduce time, cost, and barriers to evaluating novel therapeutics. We report producing and applying BOT core biopsy tissue for ex vivo testing, validating 3D microarchitecture with high-content fluorescence imaging and custom image analysis, measuring diffusion of stains and mock agents to 200 μm depth, and quantifying spatially distinct drug activity within intact BOT cores using advanced image-processing modules.

Nearly 70% of diagnostic lab test errors stem from variability in preanalytical factors—everything from when tissue is removed from the patient to when it is tested in the lab. Because preanalytical protocols are often not standardized, specimen integrity can be compromised. Establishing clear protocols would help remote collection sites reduce variability in specimen viability and integrity, improving test rigor and reproducibility and supporting innovation in live-tissue diagnostics for personalized medicine. Here we present findings from a study on how cold-chain logistics affect solid tumor specimens, evaluating transit time, container and wet-ice packing, and transport media composition. Overall, standardized preanalytics can strengthen diagnostic reliability and enhance the predictive value of next-generation cancer models for translational research and bedside applications.

Nearly half the world will be diagnosed with cancer, and more than 1.7 million new cases are diagnosed each year in the U.S. Worse, systemic therapy is ineffective in ~70% of patients because the drugs don’t match the patient’s tumor, creating major physical, emotional, and financial burden. Because tumors are heterogeneous, the same treatment can produce very different responses across patients, making personalized approaches - based on testing efficacy directly on a patient’s own tumor tissue - fundamentally better than today’s trial-and-error care.

Here we present findings from our low-cost, ex vivo personalized solid tumor biopsy-on-a-chip platform designed to rapidly evaluate multiple therapeutics on intact core biopsy tissue before treatment. Core biopsies were generated from xenograft and human tumor tissue using 18- and 20-gauge spring-loaded biopsy systems. We observed and quantified differential activity of anticancer agents versus mock drugs using custom image-processing algorithms.