Immune checkpoint therapy—current perspectives and future directions

Predicting the clinical response to chemotherapeutic or targeted treatment in patients with locally advanced or metastatic lung cancer requires an accurate and affordable tool. Tumor organoids are a potential approach in precision medicine for predicting the clinical response to treatment. However, their clinical application in lung cancer has rarely been reported because of the difficulty in generating pure tumor organoids. In this study, we have generated 214 cancer organoids from 107 patients, of which 212 are lung cancer organoids (LCOs), primarily derived from malignant serous effusions. LCO-based drug sensitivity tests (LCO-DSTs) for chemotherapy and targeted therapy have been performed in a real-world study to predict the clinical response to the respective treatment. LCO-DSTs accurately predict the clinical response to treatment in this cohort of patients with advanced lung cancer. In conclusion, LCO-DST is a promising precision medicine tool in treating of advanced lung cancer. 

http://www.cell.com/article/S2666379122004906/pdf

Using patient-derived organoids to predict locally advanced or metastatic lung cancer tumor response: A real-world study

Functional precision oncology using patient-derived assays: bridging genotype and phenotype

Decades of efforts in engineering in vitro cancer models have advanced drug discovery and the insight into cancer biology. However, the establishment of preclinical models that enable fully recapitulating the tumor microenvironment remains challenging owing to its intrinsic complexity. Recent progress in engineering techniques has allowed the development of a new generation of in vitro preclinical models that can recreate complex in vivo tumor microenvironments and accurately predict drug responses, including spheroids, organoids, and tumor‐on‐a‐chip. These biomimetic 3D tumor models are of particular interest as they pave the way for better understanding of cancer biology and accelerating the development of new anticancer therapeutics with reducing animal use. Here, the recent advances in developing these in vitro platforms for cancer modeling and preclinical drug screening, focusing on incorporating hydrogels are reviewed to reconstitute physiologically relevant microenvironments. The combination of spheroids/organoids with microfluidic technologies is also highlighted to better mimic in vivo tumors and discuss the challenges and future directions in the clinical translation of such models for drug screening and personalized medicine.

3D Biomimetic Models to Reconstitute Tumor Microenvironment In Vitro: Spheroids, Organoids, and Tumor‐on‐a‐Chip

Organoid cultures for cancer modeling.

Patient-derived micro-organospheres enable clinical precision oncology.

149 Background: Patient-derived organoids (PDO) have been shown to have a high degree of similarity to the original patient tumors. PDO have also been used to perform high throughput drug screens and shown to correlate with patient response to therapy. Unfortunately, PDO require too much tissue, take too long to establish and are too inefficient and costly for adoption into the clinic. The ideal assay for clinical use would be one that could be performed in less than14 days from a core biopsy to minimize delay in therapy. We have now circumvented these barriers by leveraging recent technological advances in emulsion microfluidics and droplet generators to develop MicroOrganoSpheres (MOS) that can be established and used to predict drug sensitivity within 14 days of obtaining a biopsy. Methods: 18-gauge core biopsy specimens from patients with colorectal cancer liver metastasis who subsequently received an oxaliplatin based therapy were first obtained. Biopsy specimens were minced, enzymatically digested and mixed with components necessary to generate MOS. The mixture was then processed through a custom fabricated flow-focusing droplet microfluidic chip in our MOS Generator Device to generate MOS. After culturing for 8-10 days, MOS were then used to perform drug screen with oxaliplatin. Results: A total of twelve CRC biopsies from liver metastasis were obtained and processed to generate MOS with a success rate of 12/12 (100%). Furthermore, drug screens with oxaliplatin were performed on all twelve samples with an average time to drug screen of 10.1 days. We next wanted to determine if there was a correlation between MOS drug sensitivity and patient clinical outcome (ie. time on treatment). For the first eight patients, MOS was used to predict sensitivity to oxaliplatin and using a drug sensitivity cut-off of 1 uM, four patients were predicted to be sensitive to oxaliplatin and four patients were predicted to be resistant. Three of the four patients predicted to be sensitive to oxaliplatin continue to be on treatment (> 6 months), whereas 3 of the four patients predicted to be resistant to oxaliplatin progressed on oxaliplatin based therapy within 8 weeks (sensitivity = 80%, specificity = 100%, positive predictive value = 100%, negative predictive value = 75%). Conclusions: MOS can be generated from core biopsies and correlates to time on treatment. Although further studies will need to be conducted, the ability to generate MOS and perform a drug screen in < 14 days will allow for the development of a precision oncology platform that can be rapidly used in the clinic to guide therapy.

Microorganospheres as a novel precision oncology platform in colorectal cancer.

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Patient-derived micro-organospheres enable clinical precision oncology.

Microorganospheres as a novel precision oncology platform in colorectal cancer.