Citations

The NCCN Guidelines for Melanoma: Cutaneous provide strategies for the diagnosis, treatment, and follow-up of patients with cutaneous melanoma, which is among the most common cancers in the United States. The outcome of cutaneous melanoma is significantly influenced by stage at initial presentation. Generally, localized disease and smaller primary tumors have been associated with more favorable prognosis. Thus, the select content featured in this issue focuses on risk factors associated with cutaneous melanoma development and which should be considered during an initial assessment. These include (1) phenotypic predispositions, (2) personal medical history or comorbidities, (3) genetic predispositions, and (4) environmental factors.

Background Immune-related adverse events (irAEs) remain largely unpredictable, potentially affecting multiple organ systems and occurring at almost any point during and even occasionally after immune checkpoint inhibitor (ICI) treatment. To identify populations at risk for these immune-mediated toxicities, we analyzed genetic characteristics and immune markers associated with clinically significant irAEs. Methods We carried out a genome-wide association study on 373 white patients receiving ICI treatment. We identified single nucleotide polymorphisms associated with irAEs. Blood cytokine profiling and peripheral blood mononuclear cell RNA sequencing were performed at pretreatment baseline and 6–8 weeks after ICI initiation. Findings were validated in two external cohorts. Results We identified genetic haplotypes in VWA8 (Von Willebrand Factor A Domain Containing 8) , OSBPL6 (Oxysterol Binding Protein Like 6), and ADAMTS9-AS2 (ADAM Metallopeptidase With Thrombospondin Type 1 Motif 9 Antisense RNA 2) associated with grade ≥2 irAEs. Patients carrying risk haplotypes for one or more genes exhibited significantly greater rates of grade ≥2 (OR 3.02; 95% CI 1.83 to 5.02; p<0.001), grade ≥3 (OR 3.59; 95% CI 1.93 to 6.64; p<0.001), and multiple type irAE (OR 2.60; 95% CI 1.53 to 4.39; p<0.001). Serum CCL3 levels were significantly elevated in individuals carrying risk haplotypes (p=0.03). Gene expression analysis demonstrated activated autoimmune and inflammatory pathways in the genetic risk group. Conclusions Novel polymorphisms in VWA8, OSBPL6, and ADAMTS9-AS2 may impact immune pathways, promote inflammation, potentiate autoimmune phenotypes, and convey risk of irAE in ICI-treated patients.

Abstract Background: Immune checkpoint inhibitors (ICIs) have significantly improved outcomes for patients with metastatic non-small cell lung cancer (NSCLC). However, clinical management is challenging. The PD-L1 biomarker does not optimally guide the choice between ICI monotherapy and combination ICI-chemotherapy, and there are currently no biomarkers to predict immune-related adverse events (irAEs). Here, we describe a versatile platform based on plasma proteomics and machine learning for generating predictive tools to support treatment decisions. Methods: Pre-treatment plasma samples were collected from 616 NSCLC patients receiving ICI-based therapy (NCT04056247). Clinical benefit (CB) data and irAEs were recorded. Proteomic profiling of plasma samples was performed using the SomaScan® assay, covering ~7000 proteins per sample. Proteins associated with Clinical Benefit (progression-free survival at 12 months) were classified as resistance-associated proteins (RAPs). Proteins associated with grade ≥3 irAEs occurring within the first 100 days of treatment were classified as toxicity-associated proteins (TAPs). RAPs and TAPs were integrated into two separate machine learning-based models, yielding CB and irAE probabilities, respectively. The CB model used a CB probability threshold to assign patients a PROphet-positive or PROphet-negative result. Bioinformatic analysis was performed on RAPs and TAPs. Results: CB and irAE models displayed strong predictive performance with a high correlation between predicted probability and observed CB or irAE rate (CB model: R2=0.98; p-value&lt;0.0001; irAE model: R2= 0.92; p-value &lt;0.0001). In the CB model, patients classified as PROphet-positive achieved significantly longer overall survival (OS) than PROphet-negative patients, with a median OS of 25.9 vs. 10.8 months (hazard ratio, HR = 0.51; p-value&lt;0.001). Furthermore, patients with PD-L1 ≥50% tumors and a PROphet-negative result had significantly longer OS with ICI-chemotherapy in comparison to ICI monotherapy (HR=0.23, p-value = 0.0003), suggesting that combination therapy is preferable for such patients despite high PD-L1 levels. Bioinformatic analysis showed that RAPs related to immune regulation, angiogenesis, chemo-resistance, and other potential resistance mechanisms displayed higher expression levels in patients who did not benefit from treatment. Neutrophil- and inflammation-related proteins were significantly enriched in patients who experienced irAEs. Conclusions: We describe two machine-learning based predictive tools that may be used separately or in combination to inform treatment decisions based on the patient’s likelihood to benefit from ICI therapy and to develop serious irAEs. Our findings also provide biological insights related to treatment resistance and immune-related toxicity. Citation Format: Michal Harel, Petros Christopoulos, Igor Puzanov, Jair Bar, Iris Kamer, Niels Reinmuth, Ina Koch, Mor Moskovitz, Adva Levy-Barda, Alona Zer, Michal Lotem, David Farrugia, Rivka Katzenelson, Gillian Price, Abed Agbarya, Helen Cheley, Adam Hassani, Mahmoud Abu-Amna, Tom Geldart, Anirban Chatterjee, Andreas Polychronis, Maya Gottfried, Yanyan Lou, Tatiana Harkovsky, Alison Brewster, Ido Wolf, Ella Tepper, Ben Yellin, Itamar Sela, Coren Lahav, Yehonatan Elon, Raya Leibowitz, Adam P. Dicker, Young Kwang Chae, Ryan J. Sullivan, David Carbone, David Gandara, Jarushka Naidoo. Plasma proteomics-based models for predicting therapeutic benefit and immune-related adverse events in non-small cell lung cancer patients treated with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1208.

PURPOSE Current guidelines for the management of metastatic non–small cell lung cancer (NSCLC) without driver mutations recommend checkpoint immunotherapy with PD-1/PD-L1 inhibitors, either alone or in combination with chemotherapy. This approach fails to account for individual patient variability and host immune factors and often results in less-than-ideal outcomes. To address the limitations of the current guidelines, we developed and subsequently blindly validated a machine learning algorithm using pretreatment plasma proteomic profiles for personalized treatment decisions. PATIENTS AND METHODS We conducted a multicenter observational trial (ClinicalTrials.gov identifier: NCT04056247 ) of patients undergoing PD-1/PD-L1 inhibitor–based therapy (n = 540) and an additional patient cohort receiving chemotherapy (n = 85) who consented to pretreatment plasma and clinical data collection. Plasma proteome profiling was performed using SomaScan Assay v4.1. RESULTS Our test demonstrates a strong association between model output and clinical benefit (CB) from PD-1/PD-L1 inhibitor–based treatments, evidenced by high concordance between predicted and observed CB ( R 2 = 0.98, P < .001). The test categorizes patients as either PROphet-positive or PROphet-negative and further stratifies patient outcomes beyond PD-L1 expression levels. The test successfully differentiates between PROphet-negative patients exhibiting high tumor PD-L1 levels (≥50%) who have enhanced overall survival when treated with a combination of immunotherapy and chemotherapy compared with immunotherapy alone (hazard ratio [HR], 0.23 [95% CI, 0.1 to 0.51], P = .0003). By contrast, PROphet-positive patients show comparable outcomes when treated with immunotherapy alone or in combination with chemotherapy (HR, 0.78 [95% CI, 0.42 to 1.44], P = .424). CONCLUSION Plasma proteome–based testing of individual patients, in combination with standard PD-L1 testing, distinguishes patient subsets with distinct differences in outcomes from PD-1/PD-L1 inhibitor–based therapies. These data suggest that this approach can improve the precision of first-line treatment for metastatic NSCLC.

The NCCN Guidelines for Merkel Cell Carcinoma (MCC) provide recommendations for diagnostic workup, clinical stage, and treatment options for patients. The panel meets annually to discuss updates to the guidelines based on comments from expert review from panel members, institutional review, as well as submissions from within NCCN and external organizations. These NCCN Guidelines Insights focus on the introduction of a new page for locally advanced disease in the setting of clinical node negative status, entitled “Clinical N0 Disease, Locally Advanced MCC.” This new algorithm page addresses locally advanced disease, and the panel clarifies the meaning behind the term “nonsurgical” by further defining locally advanced disease. In addition, the guideline includes the management of in-transit disease and updates to the systemic therapy options.

Basal cell carcinoma (BCC) is the most common form of skin cancer in the United States. Due to the high frequency, BCC occurrences are not typically recorded, and annual rates of incidence can only be estimated. Current estimated rates are 2 million Americans affected annually, and this continues to rise. Exposure to radiation, from either sunlight or previous medical therapy, is a key player in BCC development. BCC is not as aggressive as other skin cancers because it is less likely to metastasize. However, surgery and radiation are prevalent treatment options, therefore disfigurement and limitation of function are significant considerations. The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) outline an updated risk stratification and treatment options available for BCC.

ImportanceSystematic data on the association between anticancer therapies and thromboembolic events (TEEs) in patients with COVID-19 are lacking.ObjectiveTo assess the association between anticancer therapy exposure within 3 months prior to COVID-19 and TEEs following COVID-19 diagnosis in patients with cancer.Design, Setting, and ParticipantsThis registry-based retrospective cohort study included patients who were hospitalized and had active cancer and laboratory-confirmed SARS-CoV-2 infection. Data were accrued from March 2020 to December 2021 and analyzed from December 2021 to October 2022.ExposureTreatments of interest (TOIs) (endocrine therapy, vascular endothelial growth factor inhibitors/tyrosine kinase inhibitors [VEGFis/TKIs], immunomodulators [IMiDs], immune checkpoint inhibitors [ICIs], chemotherapy) vs reference (no systemic therapy) in 3 months prior to COVID-19.Main Outcomes and MeasuresMain outcomes were (1) venous thromboembolism (VTE) and (2) arterial thromboembolism (ATE). Secondary outcome was severity of COVID-19 (rates of intensive care unit admission, mechanical ventilation, 30-day all-cause mortality following TEEs in TOI vs reference group) at 30-day follow-up.ResultsOf 4988 hospitalized patients with cancer (median [IQR] age, 69 [59-78] years; 2608 [52%] male), 1869 had received 1 or more TOIs. Incidence of VTE was higher in all TOI groups: endocrine therapy, 7%; VEGFis/TKIs, 10%; IMiDs, 8%; ICIs, 12%; and chemotherapy, 10%, compared with patients not receiving systemic therapies (6%). In multivariable log-binomial regression analyses, relative risk of VTE (adjusted risk ratio [aRR], 1.33; 95% CI, 1.04-1.69) but not ATE (aRR, 0.81; 95% CI, 0.56-1.16) was significantly higher in those exposed to all TOIs pooled together vs those with no exposure. Among individual drugs, ICIs were significantly associated with VTE (aRR, 1.45; 95% CI, 1.01-2.07). Also noted were significant associations between VTE and active and progressing cancer (aRR, 1.43; 95% CI, 1.01-2.03), history of VTE (aRR, 3.10; 95% CI, 2.38-4.04), and high-risk site of cancer (aRR, 1.42; 95% CI, 1.14-1.75). Black patients had a higher risk of TEEs (aRR, 1.24; 95% CI, 1.03-1.50) than White patients. Patients with TEEs had high intensive care unit admission (46%) and mechanical ventilation (31%) rates. Relative risk of death in patients with TEEs was higher in those exposed to TOIs vs not (aRR, 1.12; 95% CI, 0.91-1.38) and was significantly associated with poor performance status (aRR, 1.77; 95% CI, 1.30-2.40) and active/progressing cancer (aRR, 1.55; 95% CI, 1.13-2.13).Conclusions and RelevanceIn this cohort study, relative risk of developing VTE was high among patients receiving TOIs and varied by the type of therapy, underlying risk factors, and demographics, such as race and ethnicity. These findings highlight the need for close monitoring and perhaps personalized thromboprophylaxis to prevent morbidity and mortality associated with COVID-19–related thromboembolism in patients with cancer.

9122 Background: Initial treatment selection for advanced NSCLC patients without driver mutations is mainly based on evaluating PD-L1 expression levels in the tumor tissue. However, PD-L1 assays are only moderately predictive. In addition, the guidelines for the PD-L1≥50% subpopulation are not definitive, enabling the usage of immune checkpoint inhibitors (ICI) either as a monotherapy or combined with chemotherapy. Here we aim to provide a decision-making tool for the first-line treatment of advanced NSCLC patients based on plasma derived biomarkers obtained before treatment initiation. Methods: Pre-treatment plasma samples were collected from 545 NSCLC patients receiving ICI-based therapy. Clinical benefit was evaluated based on progression-free survival (PFS) at 12 months as a threshold. Deep plasma proteomic profiling was performed using aptamer technology. Based on the proteomic profiles, a computational model, termed PROphet, was developed and evaluated in a blinded manner on the validation subset (n = 272). The model output, clinical benefit probability, was used to stratify the patients into two groups, PROphet-positive or -negative. Results: The model displayed good performance with a high correlation between the predicted clinical benefit and the observed clinical benefit rate (R2= 0.97), outperforming a PD-L1-based prediction model (R2= 0.35). Patients classified as PROphet-positive achieved significantly longer overall survival (OS) than PROphet-negative patients, with a median OS of 25.9 versus 10.8 months (hazard ratio, HR = 0.51; 95% confidence interval, CI = 0.37-0.70; p-value < 0.001). Next, we examined the clinical utility of combining the PROphet output with PD-L1 levels by comparing different treatment modalities. Focusing on PD-L1≥50%, patients with PROphet-positive results displayed similar OS and PFS when receiving ICI monotherapy or ICI-chemotherapy (OS HR = 0.77; CI = 0.42-1.43; p-value = 0.4096), suggesting that these patients may consider monotherapy, thus avoiding chemotherapy-related toxicity. Conversely, PD-L1≥50% patients with a PROphet-negative result displayed a significantly longer OS and PFS when receiving ICI-chemotherapy in comparison to ICI monotherapy, with a median OS that was not reached versus 11.10 months in the combination therapy and monotherapy groups, respectively (HR = 0.29; CI = 0.14-0.59; p-value < 0.001). These patients should consider combination therapy, avoiding a treatment that is suboptimal for them. Results for PD-L1 1-49% and < 1% provide additional clinical information for each subgroup. Conclusions: Plasma proteomic profiling can provide biomarkers that accurately predict the benefit of ICI-based treatment. The model output complements tissue PD-L1 expression levels as a tool to assist therapeutic decisions.

Talimogene laherparepvec (T-VEC) plus ipilimumab has demonstrated greater antitumor activity versus ipilimumab alone, without additional toxicity, in patients with advanced melanoma. Here, we report the 5-year outcomes from a randomized phase II study. These data provide the longest efficacy and safety follow-up for patients with melanoma treated with a combination of an oncolytic virus and a checkpoint inhibitor. Eligible patients with unresectable stage IIIB‒IV melanoma were randomized 1:1 to receive T-VEC plus ipilimumab or ipilimumab alone. T-VEC was administered intralesionally at 10 6 plaque-forming units (PFU)/mL in week 1, followed by 10 8 PFU/mL in week 4 and every 2 weeks thereafter. Ipilimumab (3 mg/kg every 3 weeks; ≤4 doses) was administered intravenously starting at week 1 in the ipilimumab arm and week 6 in the combination arm. The primary end point was investigator-assessed objective response rate (ORR) per immune-related response criteria; key secondary end points included durable response rate (DRR), duration of response (DOR), progression-free survival (PFS), overall survival (OS), and safety. Overall, 198 patients were randomized to receive the combination (n=98) or ipilimumab (n=100). The combination improved the ORR versus ipilimumab (35.7% vs 16.0%; OR 2.9; 95% CI 1.5 to 5.7; p=0.003). DRR was 33.7% and 13.0% (unadjusted OR 3.4; 95% CI 1.7 to 7.0; descriptive p=0.001), respectively. Among the objective responders, the median DOR was 69.2 months (95% CI 38.5 to not estimable) with the combination and was not reached with ipilimumab. Median PFS was 13.5 months with the combination and 6.4 months with ipilimumab (HR 0.78; 95% CI 0.55 to 1.09; descriptive p=0.14). Estimated 5-year OS was 54.7% (95% CI 43.9 to 64.2) in the combination arm and 48.4% (95% CI 37.9 to 58.1) in the ipilimumab arm. Forty-seven (48.0%) and 65 (65.0%) patients in the combination and ipilimumab arms, respectively, received subsequent therapies. No new safety signals were reported. At the 5-year follow-up, the improved response rates observed with T-VEC plus ipilimumab were durable. This is the first randomized controlled study of the combination of an oncolytic virus and a checkpoint inhibitor that meets its primary end point.Trial registration number: NCT01740297 .

Abstract Purpose: Tilsotolimod is an investigational synthetic Toll-like receptor 9 (TLR9) agonist that has demonstrated antitumor activity in preclinical models. The ILLUMINATE-101 phase I study explored the safety, dose, efficacy, and immune effects of intratumoral (it) tilsotolimod monotherapy in multiple solid tumors. Patients and Methods: Patients with a diagnosis of refractory cancer not amenable to curative therapies received tilsotolimod in doses escalating from 8 to 32 mg into a single lesion at weeks 1, 2, 3, 5, 8, and 11. Additional patients with advanced malignant melanoma were enrolled into an expansion cohort at the 8 mg dose. Objectives included characterizing the safety, establishing the dose, efficacy, and immunologic assessment. Blood samples and tumor biopsies of injected and noninjected lesions were obtained at baseline and 24 hours after treatment for immune analyses. Results: Thirty-eight and 16 patients were enrolled into the dose escalation and melanoma expansion cohorts, respectively. Deep visceral injections were conducted in 91% of patients. No dose-limiting toxicities (DLT) or grade 4 treatment-related adverse events were observed. Biopsies 24 hours after treatment demonstrated an increased IFN pathway signature and dendritic cell maturation. Immunologic profiling revealed upregulation of IFN-signaling genes and modulation of genes for checkpoint proteins. In the dose escalation cohort, 12 (34%) of 35 evaluable patients achieved a best overall response rate (ORR) of stable disease (SD), whereas 3 (19%) of 16 evaluable patients in the melanoma cohort achieved stable disease. Conclusions: Overall, tilsotolimod monotherapy was generally well tolerated and induced rapid, robust alterations in the tumor microenvironment. See related commentary by Punekar and Weber, p. 5007