Prediction of Early Response to Immune Checkpoint Inhibition Using FDG-PET/CT in Melanoma Patients

Artificial neural network identified a 20-gene panel in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in glioblastoma patients

BACKGROUND

Immune checkpoint inhibitors (ICIs) are a promising immunotherapy approach, but glioblastoma clinical trials have not yielded satisfactory results.

OBJECTIVE

To screen glioblastoma patients who may benefit from immunotherapy.

METHODS

Eighty-one patients receiving anti-PD1/PD-L1 treatment from a large-scale clinical trial and 364 patients without immunotherapy from The Cancer Genome Atlas (TCGA) were included. Patients in the ICI-treated cohort were divided into responders and nonresponders according to overall survival (OS), and the most critical responder-relevant features were screened using random forest (RF). We constructed an artificial neural network (ANN) model and verified its predictive value with immunotherapy response and OS.

RESULTS

We defined two groups of ICI-treated glioblastoma patients with large differences in survival benefits as nonresponders (OS ≤6 months, n = 18) and responders (OS ≥17 months, n = 8). No differentially mutated genes were observed between responders and nonresponders. We performed RF analysis to select the most critical responder-relevant features and developed an ANN with 20 input variables, five hidden neurons and one output neuron. Receiver operating characteristic analysis and the DeLong test demonstrated that the ANN had the best performance in predicting responders, with an AUC of 0.97. Survival analysis indicated that ANN-predicted responders had significantly better OS rates than nonresponders.

CONCLUSION

The 20-gene panel developed by the ANN could be a promising biomarker for predicting immunotherapy response and prognostic benefits in ICI-treated GBM patients and may guide oncologists to accurately select potential responders for the preferential use of ICIs.

An Innovative Non-Linear Prediction Model for Clinical Benefit in Women with Newly Diagnosed Breast Cancer Using Baseline FDG-PET/CT and Clinical Data

Objectives: We aimed to develop a novel non-linear statistical model integrating primary tumor features on baseline [18F]-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT), molecular subtype, and clinical data for treatment benefit prediction in women with newly diagnosed breast cancer using innovative statistical techniques, as opposed to conventional methodological approaches. Methods: In this single-center retrospective study, we conducted a comprehensive assessment of women newly diagnosed with breast cancer who had undergone a FDG-PET/CT scan for staging prior to treatment. Primary tumor (PT) volume, maximum and mean standardized uptake value (SUVmax and SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured on PET/CT. Clinical data including clinical staging (TNM) but also PT anatomical site, histology, receptor status, proliferation index, and molecular subtype were obtained from the medical records. Overall survival (OS), progression-free survival (PFS), and clinical benefit (CB) were assessed as endpoints. A logistic generalized additive model was chosen as the statistical approach to assess the impact of all listed variables on CB. Results: 70 women with newly diagnosed breast cancer (mean age 63.3 ± 15.4 years) were included. The most common location of breast cancer was the upper outer quadrant (40.0%) in the left breast (52.9%). An invasive ductal adenocarcinoma (88.6%) with a high tumor proliferation index (mean ki-67 expression 35.1 ± 24.5%) and molecular subtype B (51.4%) was by far the most detected breast tumor. Most PTs displayed on hybrid imaging a greater volume (12.8 ± 30.4 cm3) with hypermetabolism (mean ± SD of PT maximum SUVmax, SUVmean, MTV, and TLG, respectively: 8.1 ± 7.2, 4.9 ± 4.4, 12.7 ± 30.4, and 47.4 ± 80.2). Higher PT volume (p < 0.01), SUVmax (p = 0.04), SUVmean (p = 0.03), and MTV (<0.01) significantly compromised CB. A considerable majority of patients survived throughout this period (92.8%), while five women died (7.2%). In fact, the OS was 31.7 ± 14.2 months and PFS was 30.2 ± 14.1 months. A multivariate prediction model for CB with excellent accuracy could be developed using age, body mass index (BMI), T, M, PT TLG, and PT volume as predictive parameters. PT volume and PT TLG demonstrated a significant influence on CB in lower ranges; however, beyond a specific cutoff value (respectively, 29.52 cm3 for PT volume and 161.95 cm3 for PT TLG), their impact on CB only reached negligible levels. Ultimately, the absence of distant metastasis M displayed a strong positive impact on CB far ahead of the tumor size T (standardized average estimate 0.88 vs. 0.4). Conclusions: Our results emphasized the pivotal role played by FDG-PET/CT prior to treatment in forecasting treatment outcomes in women newly diagnosed with breast cancer. Nevertheless, careful consideration is required when selecting the methodological approach, as our innovative statistical techniques unveiled non-linear influences of predictive biomarkers on treatment benefit, highlighting also the importance of early breast cancer diagnosis.

PET-CT underestimates the true pathological extent of disease at lymphadenectomy for melanoma patients after systemic therapy

INTRODUCTION

Modern systemic therapy has revolutionized the treatment of melanoma. Currently, patients with clinically involved lymph nodes require lymphadenectomy with associated morbidities. Positron Emission Tomography - Computed Tomography (PET-CT) has demonstrated accuracy in melanoma detection and response to therapy. We aimed to identify whether a PET-CT directed lymphatic resection after systemic therapy is oncologically sound.

MATERIALS AND METHODS

Retrospective review of patients who underwent lymphadenectomy after systemic therapy for melanoma with a preoperative PET-CT. Examined demographic, clinical, and perioperative parameters including extent of disease, systemic therapy and response, and PET-CT findings compared to pathological outcomes. We compared patients with "as or less than expected" outcomes on pathology against those with "more than expected" pathological outcomes.

RESULTS

Thirty-nine patients met inclusion criteria. In 28 (71.8%), pathological outcomes were "as or less than expected" by PET-CT, and in 11 (28.2%) pathological outcome were "more than expected". "More than expected" occurred more frequently with advanced disease at presentation with 75% presenting with regional/metastatic disease versus only 42.9% in the "as or less than expected" group (p = 0.015). Poor response to therapy also trended towards the "more than expected" group with only 27.3% favorable response versus 53.6% favorable response in the "as or less than expected" group, not statistically significant. Extent of disease on imaging failed to predict pathological concordance.

CONCLUSION

PET-CT underestimates pathological extent of disease in the lymphatic basin in 30% of patients after systemic therapy. We failed to identify predictors of more extensive disease and warn against limited PET-CT directed lymphatic resections.

Plasma Methylated DNA Markers for Melanoma Surveillance

PURPOSE

Surveillance after primary melanoma treatment aims to detect early signs of low-volume systemic disease. The current standard of care, surveillance imaging, is costly and difficult to access. We therefore sought to develop methylated DNA markers (MDMs) as promising alternatives for disease surveillance.

METHODS

We used reduced representation bisulfite sequencing (RRBS) to identify MDMs in DNA samples obtained from metastatic melanoma, benign nevi, and normal skin tissues. The identified MDMs underwent validation in an independent cohort of tissue and buffy coat DNA samples. Subsequently, we tested the validated MDMs in the plasma DNA of patients with metastatic melanoma undergoing surveillance with total body imaging and compared them with cancer-free controls. To estimate the overall predictive accuracy of the MDMs, we used random forest modeling with bootstrap cross-validation.

RESULTS

Forty MDMs demonstrated discrimination between melanoma cases and controls consisting of benign nevi and normal skin. Nine MDMs passing biological validation in tissue were run on 77 plasma samples from individuals with a history of metastatic melanoma, 49 of whom had evidence of disease detected by imaging at the time of blood draw, and 100 cancer-free controls. The cross-validated sensitivity of the panel for imaging-positive disease was 80% with a specificity of 100% in cancer-free controls, resulting in an overall AUC of 0.88 (95% CI, 0.81 to 0.96). The survival estimates for patients with melanoma who tested positive for the panel at 6 months and 1 year were 67% and 56%, respectively, while those who tested negative had survival rates of 100% and 92%.

CONCLUSION

MDMs identified by RRBS demonstrate a high degree of concordance with imaging results in the plasma of patients with metastatic melanoma. Further prospective studies in larger intended use cohorts are needed to confirm these findings.

More than Just Skin-Deep: A Review of Imaging's Role in Guiding CAR T-Cell Therapy for Advanced Melanoma

Advanced melanoma is one of the deadliest cancers, owing to its invasiveness and its propensity to develop resistance to therapy. Surgery remains the first-line treatment for early-stage tumors but is often not an option for advanced-stage melanoma. Chemotherapy carries a poor prognosis, and despite advances in targeted therapy, the cancer can develop resistance. CAR T-cell therapy has demonstrated great success against hematological cancers, and clinical trials are deploying it against advanced melanoma. Though melanoma remains a challenging disease to treat, radiology will play an increasing role in monitoring both the CAR T-cells and response to therapy. We review the current imaging techniques for advanced melanoma, as well as novel PET tracers and radiomics, in order to guide CAR T-cell therapy and manage potential adverse events.

Predictive Value of Baseline FDG-PET/CT for the Durable Response to Immune Checkpoint Inhibition in NSCLC Patients Using the Morphological and Metabolic Features of Primary Tumors

Objectives: We aimed to investigate the predictive value of baseline 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography (FDG-PET/CT) for durable responses to immune checkpoint inhibitors (ICIs) by linking the morphological and metabolic features of primary tumors (PTs) in nonsmall cell lung cancer (NSCLC) patients. Methods: For the purpose of this single-center study, the imaging data of the patients with a first diagnosis of NSCLC and an available baseline FDG-PET/CT between 2020 and 2021 were retrospectively assessed. The baseline characteristics were collected based on clinical reports and interdisciplinary tumor board documentation. The metabolic (such as standardized uptake value SUV maximum and mean (SUVmax, SUV mean), metabolic tumor volume (MTV), total lesion glycolysis (TLG)) and morphological (such as volume, morphology, margin, and presence of lymphangiosis through imaging) features of all the PTs were retrospectively assessed using FDG-PET/CT. Overall survival (OS), progression-free survival (PFS), clinical benefit (CB) and mortality rate were used as endpoints to define the long-term response to therapy. A backward, stepwise logistic regression analysis was performed in order to define the best model for predicting lasting responses to treatment. Statistical significance was assumed at p < 0.05. Results: A total of 125 patients (median age ± standard deviation (SD) 72.0 ± 9.5 years) were enrolled: 64 men (51.2%) and 61 women (48.8%). Adenocarcinoma was by far the most common histological subtype of NSCLC (47.2%). At the initial diagnosis, the vast majority of all the included patients showed either locally advanced disease (34.4%) or metastatic disease (36.8%). Fifty patients were treated with ICIs either as a first-line (20%) or second-line (20%) therapy, while 75 patients did not receive ICIs. The median values ± SD of PT SUVmax, mean, MTV, and TLG were respectively 10.1 ± 6.0, 6.1 ± 3.5, 13.5 ± 30.7, and 71.4 ± 247.7. The median volume of PT ± SD was 13.7 ± 30.7 cm3. The PTs were most frequently solid (86.4%) with irregular margins (76.8%). Furthermore, in one out of five cases, the morphological evidence of lymphangiosis was seen through imaging (n = 25). The median follow-up ± SD was 18.93 ± 6.98 months. The median values ± SD of OS and PFS were, respectively, 14.80 ± 8.68 months and 14.03 ± 9.02 months. Age, PT volume, SUVmax, TLG, the presence of lymphangiosis features through imaging, and clinical stage IV were very strong long-term outcome predictors of patients treated with ICIs, while no significant outcome predictors could be found for the cohort with no ICI treatment. The optimal cut-off values were determined for PT volume (26.94 cm3) and SUVmax (15.05). Finally, 58% of NSCLC patients treated with ICIs had a CB vs. 78.7% of patients in the cohort with no ICI treatment. However, almost all patients treated with ICIs and with disease progression over time died (mortality in the case of disease progression 95% vs. 62.5% in the cohort without ICIs). Conclusion: Baseline FDG-PET/CT could be used to predict a durable response to ICIs in NSCLC patients. Age, clinical stage IV, lymphangiosis features through imaging, PT volume (thus PT MTV due to a previously demonstrated linear correlation), PT SUVmax, and TLG were very strong long-term outcome predictors. Our results highlight the importance of linking clinical data, as much as morphological features, to the metabolic parameters of primary tumors in a multivariate outcome-predicting model using baseline FDG-PET/CT.

Sex-Related Differences in Metastatic Melanoma Patients Treated with Immune Checkpoint Inhibition

Objectives: We aimed to investigate sex-related differences in patients with advanced melanoma treated with ICI by linking the assessment of inflammatory response in peripheral blood, onset of immune-related adverse events IRAEs during therapy and treatment response in short- and long-term. Methods: For the purpose of this single-center retrospective study metastatic melanoma patients treated with ICI were included. Baseline patient characteristics, blood sample tests and the onset of immune-related adverse events IRAEs were documented based on clinical records. The short-term treatment response was assessed with 18F-2-Fluor-2-desoxy-D-glucose Positron Emission Tomography/Computed Tomography FDG-PET/CT scans performed six months after initiation of ICI. The overall survival OS and progression-free survival PFS were used as endpoints to assess the long-term response to immunotherapy. Results: In total, 103 patients with advanced melanoma (mean age 68 ± 13.83 years) were included, 29 women (mean age 60.41 ± 14.57 years) and 74 men (mean age 65.66 ± 13.34 years). The primary tumor was located on a lower extremity in one out of three women and on the head/neck in one out of three men (p < 0.001). While the superficial spreading (41%) and nodular (36%) melanoma subtypes represented together 77% of the cases in male population, women showed a more heterogenous distribution of melanoma subtypes with the superficial spreading (35%), nodular (23%), acral lentiginous (19%) and mucosal (12%) melanoma subtypes being most frequent in female population (p < 0.001). Most differences between women and men with regards to inflammatory parameters were observed six months after initiation of ICI with a higher median NLR (p = 0.038), lower counts of lymphocytes (p = 0.004) and thrombocytes (p = 0.089) in addition to lower counts of erythrocytes (p < 0.001) and monocytes (p < 0.001) in women towards men. IRAEs were more frequent in women towards men (p = 0.013). Women were more likely to display endocrinological IRAEs, such as thyroiditis being the most frequent adverse event in women. Interestingly IRAEs of the gastrointestinal tract were the most frequent ones in men. Finally, men with advanced melanoma showed a significantly better response to immunotherapy in short- (p = 0.015) and long-term (OS p = 0.015 and PFS p < 0.001) than women. In fact, every fourth man died during the course of the disease, while every second woman did not survive. (p = 0.001). Conclusion: Men with advanced melanoma showed a significantly better response to immunotherapy in short- and long-term than women. Higher immune activation in peripheral blood before and after initiation ICI might be linked to favorable treatment response during and after ICI in favor of men and decoupled from the onset of IRAEs. Given the significantly higher immunotoxicity and worse outcome experienced by women compared to men the use of ICI should be chosen carefully in women with advanced melanoma.

Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review

BACKGROUND

Checkpoint inhibition has radically improved the perspective for patients with metastatic cancer, but predicting who will not respond with high certainty remains difficult. Imaging-derived biomarkers may be able to provide additional insights into the heterogeneity in tumour response between patients. In this systematic review, we aimed to summarise and qualitatively assess the current evidence on imaging biomarkers that predict response and survival in patients treated with checkpoint inhibitors in all cancer types.

METHODS

PubMed and Embase were searched from database inception to 29th November 2021. Articles eligible for inclusion described baseline imaging predictive factors, radiomics and/or imaging machine learning models for predicting response and survival in patients with any kind of malignancy treated with checkpoint inhibitors. Risk of bias was assessed using the QUIPS and PROBAST tools and data was extracted.

RESULTS

In total, 119 studies including 15,580 patients were selected. Of these studies, 73 investigated simple imaging factors. 45 studies investigated radiomic features or deep learning models. Predictors of worse survival were (i) higher tumour burden, (ii) presence of liver metastases, (iii) less subcutaneous adipose tissue, (iv) less dense muscle and (v) presence of symptomatic brain metastases. Hazard rate ratios did not exceed 2.00 for any predictor in the larger and higher quality studies. The added value of baseline fluorodeoxyglucose positron emission tomography parameters in predicting response to treatment was limited. Pilot studies of radioactive drug tracer imaging showed promising results. Reports on radiomics were almost unanimously positive, but numerous methodological concerns exist.

CONCLUSIONS

There is well-supported evidence for several imaging biomarkers that can be used in clinical decision making. Further research, however, is needed into biomarkers that can more accurately identify which patients who will not benefit from checkpoint inhibition. Radiomics and radioactive drug labelling appear to be promising approaches for this purpose.

Development of a Hybrid-Imaging-Based Prognostic Index for Metastasized-Melanoma Patients in Whole-Body 18F-FDG PET/CT and PET/MRI Data

Besides tremendous treatment success in advanced melanoma patients, the rapid development of oncologic treatment options comes with increasingly high costs and can cause severe life-threatening side effects. For this purpose, predictive baseline biomarkers are becoming increasingly important for risk stratification and personalized treatment planning. Thus, the aim of this pilot study was the development of a prognostic tool for the risk stratification of the treatment response and mortality based on PET/MRI and PET/CT, including a convolutional neural network (CNN) for metastasized-melanoma patients before systemic-treatment initiation. The evaluation was based on 37 patients (19 f, 62 ± 13 y/o) with unresectable metastasized melanomas who underwent whole-body 18F-FDG PET/MRI and PET/CT scans on the same day before the initiation of therapy with checkpoint inhibitors and/or BRAF/MEK inhibitors. The overall survival (OS), therapy response, metastatically involved organs, number of lesions, total lesion glycolysis, total metabolic tumor volume (TMTV), peak standardized uptake value (SULpeak), diameter (Dmlesion) and mean apparent diffusion coefficient (ADCmean) were assessed. For each marker, a Kaplan−Meier analysis and the statistical significance (Wilcoxon test, paired t-test and Bonferroni correction) were assessed. Patients were divided into high- and low-risk groups depending on the OS and treatment response. The CNN segmentation and prediction utilized multimodality imaging data for a complementary in-depth risk analysis per patient. The following parameters correlated with longer OS: a TMTV < 50 mL; no metastases in the brain, bone, liver, spleen or pleura; ≤4 affected organ regions; no metastases; a Dmlesion > 37 mm or SULpeak < 1.3; a range of the ADCmean < 600 mm2/s. However, none of the parameters correlated significantly with the stratification of the patients into the high- or low-risk groups. For the CNN, the sensitivity, specificity, PPV and accuracy were 92%, 96%, 92% and 95%, respectively. Imaging biomarkers such as the metastatic involvement of specific organs, a high tumor burden, the presence of at least one large lesion or a high range of intermetastatic diffusivity were negative predictors for the OS, but the identification of high-risk patients was not feasible with the handcrafted parameters. In contrast, the proposed CNN supplied risk stratification with high specificity and sensitivity.

Potential Reasons for Unresponsiveness to Anti-PD1 Immunotherapy in Young Patients with Advanced Melanoma

The impact of age on the clinical benefit of anti-PD1 immunotherapy in advanced melanoma patients has been evolving recently. Due to a reduced immune function in elderly patients, young patients with a robust immune system are theoretically expected to benefit more from the treatment approach. However, in contrast to this hypothesis, recent studies in patients with metastatic melanoma have demonstrated that immunotherapy, especially with anti-PD1 treatment, is less effective in patients below 65 years, on average, with significantly lower responses and reduced overall survival compared to patients above 65 years of age. Besides, data on young patients are even more sparse. Hence, in this review, we will focus on age-dependent differences in the previously described resistance mechanisms to the treatment and discuss the development of potential combination treatment strategies for enhancing the anti-tumor efficacy of anti-PD1 or PDL1 treatment in young melanoma patients.