Durable Responses to Anti-PD1 and Anti-CTLA4 in a Preclinical Model of Melanoma Displaying Key Immunotherapy Response Biomarkers

Circulating immune landscape in melanoma patients undergoing anti-PD1 therapy reveals key immune features according to clinical response to treatment

Introduction

Immune checkpoint blockers (ICB) bring unprecedented clinical success, yet many patients endure immune mediated adverse effects and/or fail to respond. Predictive signatures of response to ICB and mechanisms of clinical efficacy or failure remain understudied. DC subsets, in network with conventional αβ T (Tconv), NK, γδ T and iNKT cells, harbor pivotal roles in tumor control, yet their involvement in response to ICB remained underexplored.

Methods

We performed an extensive longitudinal monitoring of circulating immune cells from melanoma patients treated with first-line anti-PD1, before (T0) and during treatment. We assessed the phenotypic and functional features of DC and effector cells' subsets by multi-parametric flow cytometry and ProcartaPlex® dosages.

Results

We revealed differences according to response to treatment and modulations of patterns during treatment, highlighting a strong link between the immune landscape and the outcome of anti-PD1 therapy. Responders exhibited higher frequencies of circulating cDC1s, CD8+ T cells, and γδ2+ T cells in central memory (CM) stage. Notably, we observed a distinct remodeling of ICP expression profile, activation status and natural cytotoxicity receptor patterns of immune subsets during treatment. Anti-PD1 modulated DCs' functionality and triggered deep changes in the functional orientation of Tconv and γδT cells.

Discussion

Overall, our work provides new insights into the immunological landscape sustaining favorable clinical responses or resistance to first-line anti-PD1 therapy in melanoma patients. Such exploration participates in uncovering the mechanism of action of anti-PD1, discovering innovative predictive signatures of response, and paves the way to design pertinent combination strategies to improve patient clinical benefits in the future.

European validation of an image-derived AI-based short-term risk model for individualized breast cancer screening-a nested case-control study

Background

Image-derived artificial intelligence (AI)-based risk models for breast cancer have shown high discriminatory performances compared with clinical risk models based on family history and lifestyle factors. However, little is known about their generalizability across European screening settings. We therefore investigated the discriminatory performances of an AI-based risk model in European screening settings.

Methods

Using four European screening populations in three countries (Italy, Spain, Germany) screened between 2009 and 2020 for women aged 45-69, we performed a nested case-control study to assess the predictive performance of an AI-based risk model. In total, 739 women with incident breast cancers were included together with 7812 controls matched on year of study-entry. Mammographic features (density, microcalcifications, masses, left-right breast asymmetries of these features) were extracted using AI from negative digital mammograms at study-entry. Two-year absolute risks of breast cancer were predicted and assessed after two years of follow-up. Adjusted risk stratification performance metrics were reported per clinical guidelines.

Findings

The overall adjusted Area Under the receiver operating characteristic Curve (aAUC) of the AI risk model was 0.72 (95% CI 0.70-0.75) for breast cancers developed in four screening populations. In the 6.2% [529/8551] of women at high risk using the National Institute of Health and Care Excellence (NICE) guidelines thresholds, cancers were more likely diagnosed after 2 years follow-up, risk-ratio (RR) 6.7 (95% CI 5.6-8.0), compared with the 69% [5907/8551] of women classified at general risk by the model. Similar risk-ratios were observed across levels of mammographic density.

Interpretation

The AI risk model showed generalizable discriminatory performances across European populations and, predicted ∼30% of clinically relevant stage 2 and higher breast cancers in ∼6% of high-risk women who were sent home with a negative mammogram. Similar results were seen in women with fatty and dense breasts.

Funding

Swedish Research Council.

Local and distant response to intratumoral immunotherapy assessed by immunoPET in mice

BACKGROUND

Despite the promising efficacy of immune checkpoint blockers (ICB), tumor resistance and immune-related adverse events hinder their success in cancer treatment. To address these challenges, intratumoral delivery of immunotherapies has emerged as a potential solution, aiming to mitigate side effects through reduced systemic exposure while increasing effectiveness by enhancing local bioavailability. However, a comprehensive understanding of the local and systemic distribution of ICBs following intratumoral administration, as well as their impact on distant tumors, remains crucial for optimizing their therapeutic potential.To comprehensively investigate the distribution patterns following the intratumoral and intravenous administration of radiolabeled anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and to assess its corresponding efficacy in both injected and non-injected tumors, we conducted an immunoPET imaging study.

METHODS

CT26 and MC38 syngeneic colorectal tumor cells were implanted subcutaneously on both flanks of Balb/c and C57Bl/6 mice, respectively. Hamster anti-mouse CTLA-4 antibody (9H10) labeled with zirconium-89 ([89Zr]9H10) was intratumorally or intravenously administered. Whole-body distribution of the antibody was monitored by immunoPET imaging (n=12 CT26 Balb/c mice, n=10 MC38 C57Bl/6 mice). Tumorous responses to injected doses (1-10 mg/kg) were correlated with specific uptake of [89Zr]9H10 (n=24). Impacts on the tumor microenvironment were assessed by immunofluorescence and flow cytometry.

RESULTS

Half of the dose was cleared into the blood 1 hour after intratumoral administration. Despite this, 7 days post-injection, 6-8% of the dose remained in the intratumoral-injected tumors. CT26 tumors with prolonged ICB exposure demonstrated complete responses. Seven days post-injection, the contralateral non-injected tumor uptake of the ICB was comparable to the one achieved through intravenous administration (7.5±1.7% ID.cm-3 and 7.6±2.1% ID.cm-3, respectively) at the same dose in the CT26 model. This observation was confirmed in the MC38 model. Consistent intratumoral pharmacodynamic effects were observed in both intratumoral and intravenous treatment groups, as evidenced by a notable increase in CD8+T cells within the CT26 tumors following treatment.

CONCLUSIONS

ImmunoPET-derived pharmacokinetics supports intratumoral injection of ICBs to decrease systemic exposure while maintaining efficacy compared with intravenous. Intratumoral-ICBs lead to high local drug exposure while maintaining significant therapeutic exposure in non-injected tumors. This immunoPET approach is applicable for clinical practice to support evidence-based drug development.