Periodic Reporting for period 1 - CANPLAST (Cancer cell plasticity on targeted therapy)
Reporting period: 2022-09-01 to 2025-02-28
Novel cancer molecular-targeted therapies allow to significantly prolong the survival of cancer patients. However, the inevitable acquisition of resistance mechanisms limits the clinical benefit of these treatments. To fully understand how resistance develops, it is crucial to better integrate tumor heterogeneity, cancer cell plasticity and microenvironment changes by applying cutting-edge single cell technologies directly on sequentially sampled biopsies from cancer patients.
Additionally, to achieve deeper and longer-lasting clinical responses for cancer patients, we will need to target the rare drug-tolerant persister cancer cells. Samples collected before, during and after treatment will be used to fully describe the characteristics of the cells that are the source of genetic resistant variants that ultimately give rise to tumor relapses. By combining the establishment of patient-derived models and transcriptomic and epigenetic characterization of persister cells we will aim to highlight their vulnerabilities.
Overall, by applying new technological breakthrough at the single cell level on patient biopsies and digging into the intrinsic nature of persister cells we will identify innovative treatment strategies to avoid the emergence of resistance in patients.
Additionally, to achieve deeper and longer-lasting clinical responses for cancer patients, we will need to target the rare drug-tolerant persister cancer cells. Samples collected before, during and after treatment will be used to fully describe the characteristics of the cells that are the source of genetic resistant variants that ultimately give rise to tumor relapses. By combining the establishment of patient-derived models and transcriptomic and epigenetic characterization of persister cells we will aim to highlight their vulnerabilities.
Overall, by applying new technological breakthrough at the single cell level on patient biopsies and digging into the intrinsic nature of persister cells we will identify innovative treatment strategies to avoid the emergence of resistance in patients.
Resistance to FGFR Inhibitors in Urothelial Cancer: In our study published in Cancer Discovery, we examined the challenges of FGFR3 gene mutations in urothelial cancers. We analyzed 21 tumors from patients who relapsed after FGFR3 inhibitor treatment and found that one-third had developed new mutations that made the inhibitors ineffective. Testing various FGFR inhibitors in lab models revealed that erdafitinib and futibatinib were effective against cases resistant to pemigatinib. We also noted changes in the PI3K–mTOR pathway in over half of the tumors, which could be targeted with combination therapies. These findings enhance our understanding of FGFR3-mutated urothelial cancer and suggest new treatment strategies.
Understanding Resistance in FGFR2-Driven Cancers: In our article in Clinical Cancer Research, we explored resistance mechanisms in cancers driven by FGFR2. By analyzing ctDNA and tissue samples from 36 patients, we distinguished between reversible and irreversible inhibitors. We found specific mutations in the FGFR2 kinase domain linked to resistance. Irreversible inhibitors showed better effectiveness against these mutations, particularly lirafugratinib, which was active against resistant forms. This research highlights the significant molecular diversity in patients, especially in cholangiocarcinoma, emphasizing the need for tailored treatment approaches.
MATCH-R Study on Resistance Mechanisms: Our MATCH-R study, published in Molecular Cancer, involved analyzing 1,120 biopsies from 857 patients to identify resistance mechanisms. We discovered that 30.9% of patients had targetable genetic alterations, such as in EGFR and KRAS. We identified resistance mechanisms in 57% of patients receiving targeted therapies. Additionally, we successfully implanted 341 biopsies in mice, creating 136 patient-derived xenograft (PDX) models that accurately reflected the original tumors. These models are valuable for testing new treatment strategies. The MATCH-R study demonstrates the potential of personalized therapies to improve outcomes in patients with advanced cancer.
Understanding Resistance in FGFR2-Driven Cancers: In our article in Clinical Cancer Research, we explored resistance mechanisms in cancers driven by FGFR2. By analyzing ctDNA and tissue samples from 36 patients, we distinguished between reversible and irreversible inhibitors. We found specific mutations in the FGFR2 kinase domain linked to resistance. Irreversible inhibitors showed better effectiveness against these mutations, particularly lirafugratinib, which was active against resistant forms. This research highlights the significant molecular diversity in patients, especially in cholangiocarcinoma, emphasizing the need for tailored treatment approaches.
MATCH-R Study on Resistance Mechanisms: Our MATCH-R study, published in Molecular Cancer, involved analyzing 1,120 biopsies from 857 patients to identify resistance mechanisms. We discovered that 30.9% of patients had targetable genetic alterations, such as in EGFR and KRAS. We identified resistance mechanisms in 57% of patients receiving targeted therapies. Additionally, we successfully implanted 341 biopsies in mice, creating 136 patient-derived xenograft (PDX) models that accurately reflected the original tumors. These models are valuable for testing new treatment strategies. The MATCH-R study demonstrates the potential of personalized therapies to improve outcomes in patients with advanced cancer.
One success of our research was the direct application of our findings in clinical practice. Initially, my project aimed to uncover new resistance mechanisms to innovative cancer treatments using clinical samples. However, it ended up significantly impacting patient survival. Among 100 patients who received targeted therapies and developed resistance, we were able to adjust their treatments based on molecular profiling for 45% of them. This personalized approach led to an average extension of 11 months in their clinical benefit, a level of impact we hadn't anticipated.
Additionally, our project has resulted in a valuable collection of high-throughput sequencing data that is now available to the scientific community. From our studies, we have uploaded several datasets to the European Genome-phenome Archive (EGA) database. For example, in the MATCH-R study, we shared 679 whole exome sequencing (WES) and 544 RNA sequencing (RNAseq) datasets from patient biopsies. We also included data from patient-derived xenograft (PDX) models, which mimic patient tumors in the lab.
Our work has also led to the creation of a unique collection of molecularly characterized PDX models. We developed 188 PDX models from patients resistant to new treatments. To support future research and advancements in personalized medicine, we launched a website that lists all available models: https://pdx.gustaveroussy.fr/(opens in new window).
In summary, our research not only improved treatment strategies for cancer patients but also contributed to a significant repository of data and models that can aid in future cancer research.
Additionally, our project has resulted in a valuable collection of high-throughput sequencing data that is now available to the scientific community. From our studies, we have uploaded several datasets to the European Genome-phenome Archive (EGA) database. For example, in the MATCH-R study, we shared 679 whole exome sequencing (WES) and 544 RNA sequencing (RNAseq) datasets from patient biopsies. We also included data from patient-derived xenograft (PDX) models, which mimic patient tumors in the lab.
Our work has also led to the creation of a unique collection of molecularly characterized PDX models. We developed 188 PDX models from patients resistant to new treatments. To support future research and advancements in personalized medicine, we launched a website that lists all available models: https://pdx.gustaveroussy.fr/(opens in new window).
In summary, our research not only improved treatment strategies for cancer patients but also contributed to a significant repository of data and models that can aid in future cancer research.