Author Correction: Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Woo, Xing Yi; Giordano, Jessica; Srivastava, Anuj; Zhao, Zi-Ming; Lloyd, Michael W.; Bruijn, Roebi de; Suh, Yun-Suhk; Patidar, Rajesh; Chen, Li; Scherer, Sandra D.; Bailey, Matthew H.; Yang, Chieh-Hsiang; Cortes-Sanchez, Emilio; Xi, Yuanxin; Wang, Jing; Wickramasinghe, Jayamanna; Kossenkov, Andrew V.; Rebecca, Vito W.; Sun, Hua; Mashl, R. Jay; Davies, Sherri R.; Jeon, Ryan; Frech, Christian; Randjelović, Jelena; Rosains, Jacqueline; Galimi, Francesco; Bertotti, Andrea; Lafferty, Adam; O’Farrell, Alice C.; Modave, Elodie; Lambrechts, Diether; Brugge, Petra ter; Serra, Violeta; Marangoni, Elisabetta; Botty, Rania El; Kim, Hyunsoo; Kim, Jong‐Il; Yang, Han‐Kwang; Lee, Charles; Dean, Dennis A.; Davis‐Dusenbery, Brandi N.; Doroshow, James H.; Welm, Alana L.; Welm, Bryan E.; Lewis, Michael T.; Fang, Bingliang; Roth, Jack A.; Meric‐Bernstam, Funda; Herlyn, Meenhard; Davies, Michael A.; Li, Ding; Li, Shunqiang; Govindan, Ramaswamy; Isella, Claudio; Moscow, Jeffrey A.; Trusolino, Livio; Byrne, Annette T.; Jonkers, Jos; Bult, Carol J.; Medico, Enzo; Chuang, Jeffrey H.

doi:10.1038/s41588-021-00811-4

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A low cutoff of |log2 copy ratio| in calling CNA events might introduce noise (false positives), whereas a high cutoff might result in missing true events (false negatives). There are a number of parameters that may affect the optimal cutoff, e.g., cancer type, tumor purity, and the platform used for CNA calling (e.g., whole-exome sequencing, single nucleotide polymorphism arrays, and shallow whole genome sequencing) 10 – 14 . The variance of these parameters in different cancer types is likely to explain why AS and FGA scores have very different predictive power in distinct cancer types.…”

Section: Resultsmentioning

confidence: 99%

Optimizing cancer immunotherapy response prediction by tumor aneuploidy score and fraction of copy number alterations

et al. 2023

View full text Add to dashboard Cite

Identifying patients that are likely to respond to cancer immunotherapy is an important, yet highly challenging clinical need. Using 3139 patients across 17 different cancer types, we comprehensively studied the ability of two common copy-number alteration (CNA) scores—the tumor aneuploidy score (AS) and the fraction of genome single nucleotide polymorphism encompassed by copy-number alterations (FGA)—to predict survival following immunotherapy in both pan-cancer and individual cancer types. First, we show that choice of cutoff during CNA calling significantly influences the predictive power of AS and FGA for patient survival following immunotherapy. Remarkably, by using proper cutoff during CNA calling, AS and FGA can predict pan-cancer survival following immunotherapy for both high-TMB and low-TMB patients. However, at the individual cancer level, our data suggest that the use of AS and FGA for predicting immunotherapy response is currently limited to only a few cancer types. Therefore, larger sample sizes are needed to evaluate the clinical utility of these measures for patient stratification in other cancer types. Finally, we propose a simple, non-parameterized, elbow-point-based method to help determine the cutoff used for calling CNAs.

show abstract

Section: Resultsmentioning

confidence: 99%

Optimizing cancer immunotherapy response prediction by tumor aneuploidy score and fraction of copy number alterations

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Tumor-specific characteristics are maintained through multiple mouse-to-mouse passages [ 16 , 18 ]. In addition, PDXs recapitulate the dynamics of tumor evolution and patient’s response to therapy with high fidelity [ 18 , 21 , 22 ]. PDX models have been used to evaluate new therapeutic approaches and to perform pre-clinical drug testing and biomarker identification [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Patient-derived tumor models are attractive tools to repurpose drugs for ovarian cancer treatment: Pre-clinical updates

Cybula

Bieniasz

2022

Oncotarget

View full text Add to dashboard Cite

Despite advances in understanding of ovarian cancer biology, the progress in translation of research findings into new therapies is still slow. It is associated in part with limitations of commonly used cancer models such as cell lines and genetically engineered mouse models that lack proper representation of diversity and complexity of actual human tumors. In addition, the development of de novo anticancer drugs is a lengthy and expensive process. A promising alternative to new drug development is repurposing existing FDA-approved drugs without primary oncological purpose. These approved agents have known pharmacokinetics, pharmacodynamics, and toxicology and could be approved as anticancer drugs quicker and at lower cost. To successfully translate repurposed drugs to clinical application, an intermediate step of pre-clinical animal studies is required. To address challenges associated with reliability of tumor models for pre-clinical studies, there has been an increase in development of patient-derived xenografts (PDXs), which retain key characteristics of the original patient’s tumor, including histologic, biologic, and genetic features. The expansion and utilization of clinically and molecularly annotated PDX models derived from different ovarian cancer subtypes could substantially aid development of new therapies or rapid approval of repurposed drugs to improve treatment options for ovarian cancer patients.

show abstract

“…Patient-derived xenografts (PDXs) represent in vivo models that closely recapitulate many important features of patients’ tumors. 4 , 5 These models have been deeply exploited for studying the biology of the disease, and for developing new treatments. In the last few years, generation of PDXs collections have been reported for many different types of solid tumors.…”

Section: Introductionmentioning

confidence: 99%

Establishment of 6 pediatric rhabdomyosarcoma patient’s derived xenograft models closely recapitulating patients’ tumor characteristics

Gasparini

Casanova

Centonze

et al. 2022

Tumori

View full text Add to dashboard Cite

Introduction: The prognosis for patients with metastatic and recurrent pediatric rhabdomyosarcoma (RMS) remains poor. The availability of preclinical models is essential to identify promising treatments We established a series of pediatric RMS patient derived xenografts (PDXs), all faithfully mirroring primary tumor characteristics and representing a unique tool for clarifying the biological processes underlying RMS progression and relapse. Methods: Fresh tumor samples from 12 RMS patients were implanted subcutaneously in both flanks of immunocompromised mice. PDXs were considered as grafted after accomplishing three passages in mice. Characterization of tumor tissues and models was performed by comparing both morphology and immunoistochemical and fluorescence in situ hybridization (FISH) characteristics. Results: Six PDXs were established, with a successful take rate of 50%. All models closely mirrored parental tumor characteristics. An increased grafting rate for tumors derived from patients with worse outcome (p = 0.006) was detected. For 50% PDXs grafting occurred when the corresponding patient was still alive. Conclusion: Our findings increase the number of available RMS PDX models and strengthen the role of PDXs as useful preclinical tools for patients with unmet medical needs and to develop personalized therapies.

show abstract

Author Correction: Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Abstract: A Correction to this paper has been published: https://doi.org/10.1038/s41588-021-00811-4.

Cited by 3 publications

References 0 publications

Optimizing cancer immunotherapy response prediction by tumor aneuploidy score and fraction of copy number alterations

Optimizing cancer immunotherapy response prediction by tumor aneuploidy score and fraction of copy number alterations

Patient-derived tumor models are attractive tools to repurpose drugs for ovarian cancer treatment: Pre-clinical updates

Establishment of 6 pediatric rhabdomyosarcoma patient’s derived xenograft models closely recapitulating patients’ tumor characteristics

Contact Info

Product

Resources

About