CAR T-CELL THERAPY
Hypophosphatemia due to increased effector cell metabolic activity is associated with neurotoxicity symptoms in CD19-targeted CAR T-cell therapy
A recent study found that the incidence and severity of neurological side effects from CAR T-cell therapy were higher among patients who had hypophosphatemia, or low blood phosphate levels, according to findings recently published in Cancer Immunology Research (2022; https://doi.org/10.1158/2326-6066.CIR-22-0418). Immune effector cell-associated neurotoxicity syndrome (ICANS) is a major complication associated with CAR T-cell therapy, affecting approximately 50 percent of patients. This can present as aphasia, confusion, weakness, somnolence, seizures, and coma. The researchers explored the connection between hypophosphatemia and ICANS incidence. In co-cultured lymphoma cells expressing the CD19 antigen with CD19-targeted CAR-T cells, they found that lymphoma cell killing was correlated with reduced phosphate concentrations in the culture media. Additionally, the researchers reported that CAR-T cells co-cultured with lymphoma cells consumed significantly more phosphate than when cultured alone. Their findings suggest that CAR T-cell-mediated cell killing leads to heightened metabolic demand that could drive hypophosphatemia. The researchers then conducted a retrospective analysis of a clinical cohort of 77 patients with B-cell malignancies treated with CD19-targeted CAR T-cell therapy. They reported that 30 percent of the patients developed ICANS and approximately 60 percent had hypophosphatemia, which was defined as serum phosphate concentrations lower than 2 mg/dL. This cohort of patients demonstrated a significant anti-correlation between serum phosphorus and ICANS incidence and severity, according to the study authors. Additionally, earlier onset of hypophosphatemia after CAR T-cell infusion was more likely to result in neurotoxicity. Significant differences in ICANS severity were not observed when comparing patients who had hypophosphatemia and those who did not. However, data did show that patients with low phosphate levels experienced significantly longer duration of ICANS versus patients with phosphate levels within normal range. The researchers concluded that these findings suggest that phosphorous level monitoring could alert to the development of ICANS in the clinical environment.
AUTHOR COMMENTARY: "The treatment of ICANS is currently limited to supportive care and steroids, which are nonspecific and can have their own side effects," said study author Theodore Scott Nowicki, MD, PhD, Assistant Professor-in-Residence in the Departments of Pediatrics (Hematology/Oncology) and Microbiology, Immunology, & Molecular Genetics at the David Geffen School of Medicine at UCLA. "Therefore, having the ability to predict the onset of ICANS would be a very helpful tool for clinicians."
PROSTATE CANCER
Antagonizing CD105 and androgen receptor to target stromal-epithelial interactions for clinical benefit
Researchers have identified an investigational therapeutic approach that could be effective against treatment-resistant prostate cancer, according to recently published Phase II data (Mol Ther 2022; doi:10.1016/j.ymthe.2022.08.019). Based on this work, a larger, multicenter trial is currently underway. Androgen receptor signaling inhibitors (ARSIs) are the standard of care for advanced prostate cancer. However, eventual resistance to ARSIs can include the expression of androgen receptor (AR) splice variant, AR-V7, the study authors explained. Through the study of human cells and laboratory mice, the researchers found that the cancer cells were signaling to the surrounding supportive cells through a protein called CD105 to make these slice variant proteins. These pre-clinical findings led to an interventional study in prostate cancer patients developing ARSI resistance. The data showed that the combination of carotuximab, a CD105 inhibitor, and ARSI (i.e., enzalutamide or abiraterone) provided disease stabilization in four of nine assessable ARSI-refractory patients. Forty percent of study participants experienced progression-free survival, based on radiographic imaging. "Circulating tumor cell evaluation showed AR-V7 down-regulation in the responsive subjects on combination treatment and revealed a three-gene panel that was predictive of response," the study authors noted. These findings will be validated in a new clinical trial, which will allow future studies to target patients most likely to be helped by this intervention, they concluded.
AUTHOR COMMENTARY: "We found that this therapy may be able to, especially in early cancers, resensitize select patients to androgen suppression. This could allow patients to avoid or delay more toxic interventions such as cytotoxic chemotherapy," said co-author Edwin Posadas, MD, Co-Director of the Experimental Therapeutics Program, Medical Director of the Urologic Oncology Program/Center for Uro-Oncology Research Excellence (CURE), and Associate Professor of Medicine at Cedars-Sinai. "We also hope to find ways of predicting which patients are most likely to benefit from this approach by testing blood and tissue samples using next-generation technologies housed at Cedars-Sinai Cancer."
MELANOMA
Prediction of early-stage melanoma recurrence using clinical and histopathologic features
Researchers have developed an artificial intelligence-based method to predict which melanoma patients are most likely to experience a recurrence and are therefore expected to benefit from aggressive treatment. This approach was validated in a recently published study (npj Precis Oncol 2022; https://doi.org/10.1038/s41698-022-00321-4). To address the need for predictive tools for this patient population, the study authors assessed the effectiveness of algorithms based on machine learning that used data from patient electronic health records to predict melanoma recurrence. They collected 1,720 early-stage melanomas-1,172 from the Mass General Brigham health care system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI)-and extracted 36 clinical and pathologic features of these cancers to predict patients' recurrence risk with supervised machine-learning algorithms. Various MGB and DFCI patient sets were used to develop and validate algorithms. Tumor thickness and rate of cancer cell division were identified as the most predictive features, according to the study authors.
AUTHOR COMMENTARY: "Our comprehensive risk prediction platform using novel machine learning approaches to determine the risk of early-stage melanoma recurrence reached high levels of classification and time to event prediction accuracy," stated senior author Yevgeniy R. Semenov, MD, an investigator in the Department of Dermatology at Massachusetts General Hospital. "Our results suggest that machine learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients who may benefit from adjuvant immunotherapy."
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