Reverse Clinical Engineering^®

Using patient-derived 3D tumor models as in vitro diagnostics for modern precision oncology

ASC Oncology has developed Reverse Clinical Engineering^® (RCE), a test procedure to determine in individual patients with malignant solid tumors which anticancer treatments are most likely to be effective and which are not. The RCE is an in vitro diagnostic test based on patient-derived 3D (PD3D^®) cell cultures, so-called tumor organoids, obtained from a patient’s vital tumor sample. The test provides the treating physician with additional information, thus making it easier for both the physician and the patient to decide on an optimal therapy. This enables a personalized therapy with presumably higher likelihood of survival and a better quality of life to be selected as early as possible, and treatments that are probably ineffective, including potential undesired side effects, to be avoided.

Medical need

The Reverse Clinical Engineering^® test procedure is a valuable complementary diagnostic tool for deciding on the optimal therapy

Tumors exhibit significant heterogeneity, which presents a challenge for treatment (Burrell et al., 2013): while many tumors respond to recommended standard treatment regimens, there is a substantial proportion of non-responders for whom established therapeutic concepts do not have the desired effect. By providing complementary data for medical treatment decisions, RCE testing helps physicians to offer their patients the most appropriate therapy with the highest chances of success. 

Reverse Clinical Engineering^® + clinical trial data = more insights

The test procedure is an important supplement to randomized controlled trials (RCTs). Similarly, to an n = 1 study, it can determine the efficacy of drug therapies for each individual patient prior to commencing therapy. In contrast, RCTs can evaluate a therapy’s overall efficacy and safety. However, as individual responses can only be derived to a limited extent from the average values obtained in large cohort studies, the informative value for the individual patient is limited.

On the way to improved individual therapy decisions with Reverse Clinical Engineering^®

Personalized therapy concepts and stratification according to molecular tumor subtypes are becoming increasingly common in clinical practice. However, precision medicine is still in its infancy: studies have found that sequencing identifies therapeutically actionable driver mutations in less than half of analyzed patients, and only 0.8-3% of patients subsequently benefit from matched targeted therapy (Tannock & Hickman, 2019).

There is growing scientific evidence that tumors exhibit heterogeneity and/ or plasticity that extends beyond the genetic level (Brock & Huang, 2017). Cell connectivity and the microenvironment seem to influence how the tumor responds to treatment and whether resistance develops (Huang, 2021). While the impact of plasticity on the tumor’s therapy response cannot yet be adequately mapped by genome sequencing or other forms of biomarker testing, some of it can be replicated in patient-derived 3D tumor models (Walter et al., 2021, Yao 2020, Yang et al., 2022) – which is why the RCE is a valuable addition to standard diagnostics.

Reverse Clinical Engineering^® to reduce the risk of further lines of therapy

The patient’s prognosis worsens with each additional line of therapy, because the likelihood of a therapy response can decrease considerably (Kessous et al., 2020). RCE testing helps to identify the best possible treatment option early on, which can minimize the risk of ineffective treatment and subsequent further lines of therapy. In addition, the procedure can help identify the best possible therapeutic option in a timely manner when guideline recommendations are only available with a low level of evidence, as in certain rare tumors or advanced tumors for which there are no further curative options. 

How the test procedure works

The RCE test procedure is based on PD3D^® tumor models, so-called organoids. Together with partners from pharmaceutical research, our scientists have developed an automated platform which enables the screening of organoids in a high-throughput, parallelized and standardized manner (Boehnke et al., 2016). The procedure has replaced the use of the NCI60 panel for pre-clinical drug testing at the National Cancer Institute (NCI) and has been selected by the NCI as a “best practice approach” (Ledford, 2016, Boehnke et al., 2016, NCI, 2019).

From a fresh tissue sample to the patient-derived 3D tumor model

PD3D^® tumor models can be generated from fresh tissue samples, such as resects or biopsies. The sample is, first, mechanochemically processed in the laboratory and living tumor cells are then cultivated in a matrix. The matrix resembles the human extracellular basement membrane and thus mimics the tumor’s complex environment. In addition, the matrix forms a hydrogel that allows the cells to grow three-dimensionally. Each individual tumor entity is cultured in a specifically optimized medium that favors the tumor cells’ growth and limits the proliferation of other normal non-cancerous cells in the sample. Under these conditions, the tumor cells develop into organoids, which are composed of organ-specific cell types and spatially organized in a manner similar to the tumor’s parent organ. 

The organoids exhibit genomic, molecular, and phenotypic characteristics of the patient’s tumor that can be stably maintained over multiple passages in long-term cultures (Sachs et al.,2018). As accurate and clinically relevant copies, they can also replicate different subtypes and the intratumoral heterogeneity of the patient’s tumor (Kopper et al., 2019, Sharick et al., 2020).

From testing to the chemosensitivity profile

Once PD3D^® models have been created and amplified, the drugs in question can be tested. For this purpose, the organoids are cultured in 96- or 384-well plates, treated with the drugs in an automated and parallelized manner, and the cell viability is then measured and evaluated.

To obtain clinically relevant results, the active substances are assayed at different concentrations, which are based on the clinically measurable maximum concentration (C_max) of the respective active substance in blood plasma. Cell viability is determined using an ATP-dependent luminescence assay. This quantifies the ATP content of the treated organoids, which is directly proportional to the number of metabolically active viable cells (Crouch et al., 1993).

The data is used to determine the IC₅₀ value, which indicates the drug concentration at which growth of the tumor cells is half-maximally inhibited. If the IC₅₀ value is above the C_max value (IC₅₀ > C_max), it is unlikely that therapy will result in an effective tumor response in the patient. A lower IC_50, below C_max (IC₅₀ < C_max), is more likely to inhibit tumor cell growth and result in a treatment response.

Finally, within on average of 28 days after receiving the sample, we prepare a report containing a detailed evaluation and graphical presentation of the data as well as clear recommendations for the subsequent therapy decision. NB: The results should always be interpreted in the context of other medical data and understood as a valuable, complementary aid in decision-making and therapy planning with the patient. 

Performance

The use of tumor organoids for improved personalized therapy has been demonstrated in international co-clinical studies. The procedure has also been recommended by the NCI as a “best practice approach” for drug testing since 2017 and is already established in the preclinical setting (Boehnke et al., 2016, NCI, 2019). ASC Oncology is preparing a multicenter international phase IV study during first-line therapy of difficult-to-treat tumor diseases with the Medical University of Innsbruck as the lead investigational site. 

Certification

The laboratory in which the RCE procedure is performed is compliant with the requirements of ISO standards ISO 13485 and ISO 15189, GxP, FDA CFR 21 Part 11 and the standard data security safeguards and policies of the HIPAA in data documentation and data processing. A corresponding ISO certification is in preparation.

Medical Statistics

A meta-analysis of 17 co-clinical studies (in colorectal cancer, rectal cancer, gastric cancer, pancreatic cancer, ovarian cancer, and breast cancer; Wensink et al., 2021) found the following characteristics for the test procedure using tumor organoids:

Diagnostic sensitivity:        81% (95% CI: 0.69 – 0.89)

Diagnostic specificity:        74% (95% CI: 0.64 – 0.82)

For individual tumor entities, co-clinical studies revealed the following values:

Efficacy – higher chance of improved treatment response

Significantly prolonged progression-free survival (PFS) following patient-derived tumor cell assays

In patients with advanced colorectal cancer, the efficacy of adjuvant chemotherapy after tumor resection could be predicted using organoid-based assays. Accordingly, patients whose tumor organoids in the test were sensitive to chemotherapy had a significantly longer PFS of 16.0 months than patients whose tumor organoids in the test were resistant to chemotherapy and who had a shorter PFS of 9.0 months (Wang et al., 2023).

In patients with lung cancer, the use of patient-derived tumor cell testing has significantly prolonged the PFS by more than twofold, from 8.5 to 18 months (Chen et al., 2018).

Prolonged relapse-free survival (RFS) in predicted responders according to tests with patient-derived tumor organoids

Patients with pancreatic cancer who were classified as responders to adjuvant therapy based on in vitro testing of individual tumor organoids had an RFS >12 months after surgical resection, whereas disease recurred within one year on adjuvant therapy in patients classified as non-responders based on in vitro data (Sharick et al., 2020).  

Quality of life – Improved patient satisfaction

Increased adherence and avoidance of potential undesired side effects from less promising therapies through precision medicine

Tailored treatment plans with minimal side effects and patient participation in treatment decisions can benefit adherence, trust, quality of care, and cancer treatment outcomes (Shingler et al., 2014, Kehl et al., 2015).

Added value for the healthcare system

Requirements and limitations

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