ACC Therapy Predictions Show No Significant Progression Benefit

A small retrospective study published in Discover Oncology reports that patients with metastatic adrenocortical carcinoma who received chemotherapy agents predicted to elicit a response had a longer median time to progression than those receiving therapies predicted to be ineffective. However, the researchers emphasized that this difference was not statistically significant, characterizing the findings as hypothesis-generating rather than definitive evidence of clinical benefit. The study, led by Nicole H. Ducich and colleagues, sought to evaluate a rapid, genome-agnostic approach to personalized systemic therapy for a rare and aggressive malignancy where standard treatments have shown limited success.

Adrenocortical carcinoma is described in the study background as a rare, aggressive malignancy with five-year survival rates of less than 15% when treated with standard approaches to systemic therapy. The authors noted that personalized medicine using next-generation sequencing has not been applicable to the vast majority of patients in this population due to the limited number of actionable mutations present. Consequently, the research team aimed to assess an alternative strategy that does not rely on identifying specific genomic mutations to guide treatment selection.

The study involved a retrospective analysis of patients with metastatic adrenocortical carcinoma who underwent surgical intervention between June 2022 and August 2025. The cohort consisted of fifteen patients, encompassing 188 within-patient transition intervals and 73 clinical events. Tumor samples from these patients were evaluated ex vivo using cell mass-based response testing.

Patients were stratified according to whether they received therapies that the ex vivo testing predicted would elicit a response or those predicted to be ineffective. To analyze the data, the researchers employed a non-homogeneous Markov multistate model with transition-specific Cox proportional hazards modeling. This statistical approach was used to assess the effects of receiving a therapy predicted to be efficacious and surgery across different disease states.

Patients treated with chemotherapy agents predicted to elicit a response had a median time to progression of 9.6 months, compared to 4.8 months for those receiving therapies predicted to be ineffective. The hazard ratio for this comparison was 0.437, with a 95% confidence interval ranging from 0.157 to 1.212. Despite the numerical difference in median times, the p-value was 0.10, indicating that the difference was not statistically significant at conventional levels of confidence.

The analysis also revealed that the effect of predicted-response therapy varied depending on the disease state of the patient. The researchers noted that the extreme estimate observed after disease progression, with a hazard ratio less than 0.001 and a p-value less than 0.001, should be considered exploratory. This suggests that while the overall effect was not significant, there may be specific contexts within the disease trajectory where the predictive model performs differently, although this requires further investigation.

The authors identified several limitations that constrain the interpretation of these results. The small number of patients, the presence of repeated observations within patients, potential confounding factors, and sparse clinical events all contribute to the uncertainty surrounding the findings. Because of these factors, the study is positioned as a preliminary step that supports the need for further prospective validation in larger cohorts. The retrospective nature of the analysis also limits the ability to establish causal relationships between the predicted response and the observed clinical outcomes.

This research highlights the challenges of applying personalized medicine to rare cancers where genomic alterations are infrequent. By utilizing ex vivo cell mass-based response testing, the team demonstrated a feasible method for rapidly assessing drug sensitivity without relying on genomic sequencing. While the current data do not show a statistically significant benefit, the approach offers a potential pathway for tailoring systemic therapy in adrenocortical carcinoma. Future studies with larger sample sizes and prospective designs are necessary to determine if this genome-agnostic strategy can improve patient outcomes.

Subject of Research: Evaluation of a genome-agnostic personalized therapy approach for metastatic adrenocortical carcinoma

Article Title: A rapid, genome-agnostic approach to personalized systemic therapy for patients with adrenocortical carcinoma

Article References: Ducich, N. H., Ho, J., Pu, T., Xiao, S., Burke, J. F., Smith, E. C., Smith, K. M., Satterwhite, A. A., Desai, P. P., Remmert, K., Magee, T., Kimmerling, R. J., del Rivero, J., & Hernandez, J. M. (2026). A rapid, genome-agnostic approach to personalized systemic therapy for patients with adrenocortical carcinoma. Discover Oncology. https://doi.org/10.1007/s12672-026-06205-4

Image Credits: AI Generated

DOI: 10.1007/s12672-026-06205-4

Keywords: adrenocortical carcinoma, personalized medicine, systemic therapy, retrospective study, ex vivo testing, cancer research, clinical outcomes, metastatic disease, rapid, genome-agnostic, approach, personalized