Cancers are categorized based on tissue of origin, how they appear under a microscope (histology) and whether they express certain critical biomarkers. For example, breast cancers are categorized based on their expression of the ER, PR, HER2 genes. Treatment decisions are then based on this “typing”. For example, TNBC patients, which lack ER, PR or HER2 expression, receive different therapies than ER+ patients because the biology underlying their disease is different. For example, TNBC patients receive different therapies than ER+ patients because the biology underlying their disease is different. Drugs that treat cancer are identified, validated and approved using clinical trials, which focus on testing in large patient populations, one drug at a time. Yet, as is every patient unique, every tumor is unique, even every tumor of a particular type. That is, while TNBC tumors chare a common core genetic architecture, each patients TNBC tumor has a unique genetic architecture and underlying biology. Due to this uniqueness, response rates for drugs that are qualified based on performance in larger populations are highly variable and unpredictable from patient to patient. The result of this “one pill fits all” drug development model is modest response rates, weak response magnitudes and duration, and relatively meager gains in overall survival.
A new cutting edge standard is emerging. We have recently learned that most cancer types can more meaningfully be broken into genomic subtypes with certain genetic architectures (e.g. “basal like” TNBC). The new standard is to tailor therapy to the subtype of breast cancer, based on how human cell lines and tumor models for these various subtypes behave in laboratory models. The early results are more than a little encouraging; our weapons against breast cancer are evolving from cluster to GPS guided bombs and we are literally on the doorstep of a new Precision Oncology age in treating breast cancer.
Yet we can do much better. The next step towards Precision Oncology is to fully personalize therapy by assessing not only cancer type, and genomic subtype, but actual patient disease cells when selecting therapy1. The idea is to culture patient tumor cells and screen them against various therapies to find the most effective. Only an empirical approach such as this can accommodate patient to patient variability in tumor cell type heterogeneity and cellular sub-subtypes, which cannot with current technology be knowable. (https://doi.org/10.1007/s00109-017-1620-7)
In other words, if the patient could be administered a personally tailored drug selected based on demonstrated effectiveness with the patient’s particular disease (e.g. their unique sub-subtype signature), then they will be given, by definition, a more effective therapy than even the best performing drug selected from average performance in entire populations of patients with their disease subtype and sub-subtype. This is important because every patient’s breast cancer is unique.
Recent methods and tools for this ultimate personalization of therapy have been described but they are impractical and ineffective due to the time it takes to grow the tumor cells (in mouse models), and the fact that these cells drift from the patient’s disease character during this time.
For example, PDX mouse models are the state-of-the-art for recreating patient tumors ex-vivo. Resected or biopsied patient cells are implanted in mice and grown into replicate tumors. However, the tumor cells, once taken from their host environment, begin to grow differently due to the fact that their stromal cellular, interstitial fluid and endocrine/exocrine environments have changed from human to mouse. Gene expression patterns change, genomes and proteomes change, and cell behaviors change. Tumors are heterogeneous masses of different clonal lineages in their hosts, and in mouse models the rate at which the various lineages grows changes over time resulting in loss of heterogeneity. In short, the tumors quickly drift from the host disease, and due to the long time period required for PDX tumor growth in mice, drug screening results that are obtained months later are often no longer relevant to the patient’s disease.
GLG is pioneering a laser-focused Precision Oncology approach driven by a proprietary, revolutionary platform called Gx-C3TM. With Gx-C3 we capture hyper-proliferative patient cells from patient biopsy, resection or blood samples, culture and expand them outside of the body in a manner that minimizes drift and preserves ultrastructural and cellular identity, and systematically screen them against libraries of drugs to identify the most appropriate compound or compound combinations for each patient’s disease. The system essentially fools the removed cancer cells into believing they are still inside the body’s environment
A portion of resected, biopsied or blood-drawn cancer cell sample is screened against an extensive peptide library in order to identify peptide sequences that bind to the cancer cells. These are sequenced and screened against larger cell samples for the ability to adhere the cells to a support medium (e.g., capture the cells) and promote the identity observed in vivo such as cell division rate, gene expression pattern, specific biomarkers etc. These are determined to be biomimetic peptides, and once identified, they are mass produced and used to capture larger numbers of cells for culture in a unique 3D environment that, with other factors, provides a tumor micro-environment as close to the original in vivo environment as possible for expansion of the cells. After expansion of the cultured cells in this biomimetic environment, they are screened against a compound library. This library could be composed of presently approved therapies, to assist the oncologist in selecting the proper treatment, or could be comprised of pathway targeted therapies such as GLG’s novel STAT3 inhibitors alone or in combination with presently approved therapies, or could even be assumption-free, such as with a very large random new chemical entity library for drug discovery purposes.
The entire process requires a fraction of the cost and time as the patient-derived xenograft (PDx) model, and due to the minimization of drift and preservation of identity, provides results that are more relevant for the patient. We always assess disease subtype and confirm preservation of genomic and proteomic signature integrity between host tissues and ex vivo cells over time.
In essence, GX-C3 solves the PDX mouse problem by systematically identifying, from a comprehensive peptide library, the comprehensive set of peptide sequences the patient’s tumors were reliant on. These peptides are representative of the ECM proteins, growth factors, hormones etc., the host’s non-tumor cells were using in their interactions with the tumor cells. Using an innovative 3-D platform within which to grow ex-vivo tumor cells biomimetically, using these peptides and other proprietary components, we are able to recreate the tumor microenvironment better than PDX models can. Tumor cells can be expanded more rapidly, with far less drift.
Gx-C3 ex-vivo cell culture methodology faithfully replicates host tumor anatomy (hypoxic, necrotic centers, hyperproliferative surfaces), and enables more rapid cell expansion relative to the PDX mouse platform.
Unpublished work using microarrays show substantially improved retention of original genetic architecture compared to PDX. Not only is genotype/phenotype drift ameliorated, but clonal heterogeneity is better preserved.