Enabling CAR T-cell therapies for HIV-positive lymphoma patients - A call for action

People living with HIV have a higher risk of developing lymphoma. Outcomes for people living with HIV with relapsed or refractory (r/r) lymphoma remain poor. For this group of patients, chimeric antigen receptor (CAR) T-cell therapy represents a new successful treatment strategy. However, people living with HIV were not included in pivotal trials, so data are limited to case reports. We searched the PubMed and Ovid technologies databases for literature until 1 November 2022 using the terms 'HIV and CAR-T', 'HIV and lymphoma' and 'HIV and CAR-T and lymphoma'. Six cases with sufficient information were included in the review. The mean CD4+ T-cell count before CAR T-cell therapy was 221 cells/μL (range 52-629). The viral load was below the limit of detection in four patients. All patients had diffuse large B-cell lymphoma (DLBCL) and were treated with gamma-retroviral-based axicabtagene ciloleucel. Four patients developed cytokine-release syndrome (CRS) grade 2 or less or immune effector-cell-associated neurotoxicity syndrome (ICANs) grade 3-4. Four of six patients responded to CAR T-cell therapy (three complete remissions, one partial remission). In summary, there are no clinical reasons to restrict the use of CAR T-cell therapy in people living with HIV with r/r DLBCL. According to the current data, CAR T-cell therapy was safe and effective. In people who meet the standard criteria for CAR T-cell therapy, this treatment approach could significantly improve the unmet need for more effective treatment options for people living with HIV with r/r lymphoma.

Baseline immune signature score of Tregs × HLA-DR+CD4+ T cells × PD1+CD8+ T cells predicts outcome to immunotherapy in cancer patients

Background

The use of immunotherapy (IT) is rapidly increasing across different tumor entities. PD-L1 expression is primarily used for therapy evaluation. The disadvantages of PD-L1 status are spatial and temporal heterogeneity as well as tumor type-dependent variation of predictive value. To optimize patient selection for IT, new prediction markers for therapy success are needed. Based on the systemic efficacy of IT, we dissected the immune signature of peripheral blood as an easily accessible predictive biomarker for therapeutic success.

Methods

We conducted a retrospective clinical study of 62 cancer patients treated with IT. We assessed peripheral immune cell counts before the start of IT via flow cytometry. The predictive value for therapy response of developed immune signature scores was tested by ROC curve analyses and scores were correlated with time to progression (TTP).

Results

High score values of "Tregs ÷ (CD4⁺/CD8⁺ ratio)" (Score A) and high score values of "Tregs × HLA-DR⁺CD4⁺ T cells × PD1⁺CD8⁺ T cells" (Score B) significantly correlated with response at first staging (p = 0.001; p < 0.001). At the optimal cutoff point, Score A correctly predicted 79.1% and Score B correctly predicted 89.3% of the staging results (sensitivity: 86.2%, 90.0%; specificity: 64.3%, 87.5%). A high Score A and Score B statistically correlated with prolonged median TTP (6.13 vs. 2.17 months, p = 0.025; 6.43 vs. 1.83 months, p = 0.016). Cox regression analyses for TTP showed a risk reduction of 55.7% (HR = 0.44, p = 0.029) for Score A and an adjusted risk reduction of 73.2% (HR = 0.27, p = 0.016) for Score B.

Conclusion

The two identified immune signature scores showed high predictive value for therapy response as well as for prolonged TTP in a pan-cancer patient population. Our scores are easy to determine by using peripheral blood and flow cytometry, apply to different cancer entities, and allow an outcome prediction before the start of IT.

Red blood cell transfusions impact response rates to immunotherapy in patients with solid malignant tumors

Red blood cell (RBC) transfusions have been shown to exert immunosuppressive effects in different diseases. In consequence, RBC transfusions might also negatively influence the response to immunotherapeutic treatment approaches. To address how RBC transfusions impact response rates of antitumor immunotherapy (IT), we conducted a retrolective clinical study of patients with different solid tumors treated with IT (atezolizumab, pembrolizumab, nivolumab and/or ipilimumab). We assessed the number of RBC concentrates received within 30 days before and 60 days after the start of IT. Primary objective was the initial therapy response at first staging, secondary objectives the number of immune related adverse events and infections. 15 of 55 included patients (27.3%) received RBC concentrates. The response rates were 77.5% in the non-transfused (n=40) versus 46.7% in the transfused patient group (n=15) and reached statistical significance (p=0.047). The correlation between therapy response and transfusion was statistically significant (p=0.026) after adjustment for the only identified confounder “line of therapy”. In contrast, transfusion in the interval 30 days before IT showed no significant difference for treatment response (p=0.705). Moreover, no correlation was detected between RBC transfusion and irAE rate (p=0.149) or infection rate (p=0.135). In conclusion, we show for the first time that the administration of RBC transfusions during, but not before initiation of IT treatment, negatively influences the response rates to IT. Our findings suggest a restrictive transfusion management in patients undergoing IT to receive optimal response rates.