Back

Comparative analysis of patient-derived organoids and patient-derived xenografts as avatar models for predicting response to anti-cancer therapy

Romero, J. M.; Magrill, J.; Kalashnikov, N.; Luo, Y. Z.; Chen, O. J.; Busque, S.; Ma, R.; Atallah, A.; Lazaratos, A.-M.; Mendelson, D.; Wilson, L.; Deshmukh, S.; Taifour, T.; Sorin, M.; Arthur, I.; Kuasne, H.; Levett, J. Y.; Wang, Y.; Seufferlein, T.; Kleger, A.; Gout, J.; Beutel, A. K.; Brugarolas, J.; Poshusta, Z. S.; Hogenson, T. L.; Fernandez-Zapico, M. E.; Hamada, A.; Yagishita, S.; Nichols, A.; Barrett, J. W.; Papaccio, F.; Castillo, J.; Inoue, M.; Massfelder, T.; Lang, H.; Lindner, V.; Nilsson, J.; Dantes, Z.; Wells, G. A.; Kim, S. H.; Ittmann, M. M.; Villanueva, H.; Lerner, S. P.; Siko

2025-08-12 oncology
10.1101/2025.08.10.25333051
Show abstract

Patient-derived xenografts (PDX) and organoids (PDO) are widely used to model cancer and predict treatment response in matched patients. However, their predictive accuracy has not been systematically studied nor compared. We conducted a systematic review and meta-analysis of studies using PDX or PDO from solid tumors treated with identical anti-cancer agents as the matched patient, identifying 411 patient-model pairs (267 PDX, 144 PDO). Overall concordance in treatment response between patients and matched models was 70%, with no significant differences between PDX and PDO. Sensitivity, specificity, positive and negative predictive value were also comparable. Patients whose matched PDO responded to therapy had prolonged progression-free survival. For PDX, this association held only when analyses were restricted to patient-model pairs with low risk of bias after applying a bias assessment metric. Together, these findings suggest that PDO perform similarly to PDX in predicting matched-patient response, while potentially offering lower financial and ethical burdens. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/25333051v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@b635d7org.highwire.dtl.DTLVardef@88ee03org.highwire.dtl.DTLVardef@1c21ed3org.highwire.dtl.DTLVardef@175beef_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Cancers
based on 57 papers
Top 1%
13.1%
2
Clinical Cancer Research
based on 22 papers
Top 0.3%
10.1%
3
JCO Precision Oncology
based on 11 papers
Top 0.1%
7.5%
4
Frontiers in Oncology
based on 34 papers
Top 1%
7.5%
5
npj Precision Oncology
based on 14 papers
Top 0.1%
7.5%
6
British Journal of Cancer
based on 22 papers
Top 0.8%
5.0%
50% of probability mass above
7
eLife
based on 262 papers
Top 4%
5.0%
8
Nature Communications
based on 483 papers
Top 18%
4.4%
9
Cancer Medicine
based on 17 papers
Top 1%
2.9%
10
JCO Clinical Cancer Informatics
based on 14 papers
Top 0.8%
2.9%
11
BMC Cancer
based on 21 papers
Top 2%
2.8%
12
PLOS ONE
based on 1737 papers
Top 84%
2.4%
13
Scientific Reports
based on 701 papers
Top 63%
2.4%
14
JAMA Network Open
based on 125 papers
Top 10%
1.7%
15
International Journal of Radiation Oncology*Biology*Physics
based on 13 papers
Top 2%
1.7%
16
Journal for ImmunoTherapy of Cancer
based on 14 papers
Top 2%
1.3%
17
Cancer Epidemiology, Biomarkers & Prevention
based on 14 papers
Top 2%
1.3%
18
Breast Cancer Research
based on 11 papers
Top 1.0%
1.3%
19
International Journal of Cancer
based on 18 papers
Top 2%
1.2%
20
JNCI: Journal of the National Cancer Institute
based on 13 papers
Top 2%
0.8%
21
BMJ Open
based on 553 papers
Top 50%
0.8%
22
Radiotherapy and Oncology
based on 11 papers
Top 2%
0.8%
23
Neuro-Oncology Advances
based on 14 papers
Top 2%
0.8%
24
BMJ Health & Care Informatics
based on 13 papers
Top 3%
0.8%
25
Cell Reports
based on 25 papers
Top 2%
0.8%
26
Blood
based on 14 papers
Top 1%
0.7%