Citations

ABSTRACT Reversion mutations (REVs) restore homologous recombination repair (HRR) and confer resistance to PARP inhibitors (PARPi) in HR-deficient cancers. Yet, their prevalence, mechanisms, and biological constraints remain undefined. We analyzed genomic profiling of 609,464 tissue and liquid biopsy samples across multiple cancer types to delineate the pan-cancer landscape of REVs. REVs were identified in eight HRR genes, most frequently BRCA2 and BRCA1 and notably never in ATM or CHEK2 . REVs exclusively impacted truncating pathogenic variants, predominantly through large in-frame and exon-level deletions, associated with repetitive sequences. Conserved functional domains are relatively depleted of REVs. Structural modeling and functional studies support that exon-level deletions preserve critical domain architecture and confer PARPi resistance. The study establishes HRR reversion as a structurally permissive, yet evolutionarily constrained, resistance mechanism with implications on response, monitoring, and therapeutic strategy. One Sentence Summary Homologous recombination reversion evolves through structurally tolerated, microhomology-driven alterations that restore DNA repair under therapeutic selection across cancers.

OBJECTIVE In many individuals, type 2 diabetes (T2D) remains poorly controlled despite taking multiple glucose-lowering therapies. Several studies have demonstrated that endogenous hypercortisolism is prevalent among these individuals. We tested whether cortisol-directed therapy improves their glycemic control. RESEARCH DESIGN AND METHODS In this prospective, multicenter, double-blind study, 136 individuals with T2D (hemoglobin A1c [HbA1c] 7.5%–11.5% [58–102 mmol/mol] on multiple medications) and hypercortisolism (by dexamethasone suppression test) were randomized 2:1 to the glucocorticoid receptor antagonist mifepristone (300–900 mg once daily; n = 91) or placebo (n = 45) for 24 weeks, with stratification by presence/absence of an adrenal imaging abnormality. The primary end point was the change in HbA1c. Secondary end points included changes in glucose-lowering medications, weight, and waist circumference and safety. RESULTS Mean baseline HbA1c in the study cohort was 8.55% (69.9 mmol/mol). At 24 weeks, the least squares mean (LSM) difference from placebo in HbA1c was −1.32% (95% CI −1.81 to −0.83; P < 0.001). Participants receiving mifepristone experienced reductions in body weight and waist circumference (placebo-adjusted LSM differences of −5.12 kg [95% CI −8.20 to −2.03] and −5.1 cm [−8.23 to −1.99], respectively). Of participants on mifepristone, 46% discontinued therapy, compared with 18% on placebo. Adverse events with mifepristone (>10% of participants) included hypokalemia, fatigue, nausea, vomiting, headache, peripheral edema, diarrhea, and dizziness, consistent with mifepristone’s known tolerability profile. Increases in blood pressure also occurred. CONCLUSIONS In individuals with inadequately controlled T2D and hypercortisolism, cortisol-directed medical therapy with mifepristone reduced HbA1c, with a manageable tolerability profile.

OBJECTIVE Despite the use of multiple glucose-lowering medications, glycemic targets are not met in a significant fraction of people with type 2 diabetes. In this prospective, observational study we assessed the prevalence of hypercortisolism, a potential contributing factor to inadequate glucose control. RESEARCH DESIGN AND METHODS Individuals with type 2 diabetes and HbA1c 7.5%–11.5% (58–102 mmol/mol) on two or more glucose-lowering medications with or without micro-/macrovascular complications or taking multiple blood pressure–lowering medications were screened with a 1-mg dexamethasone suppression test. Common causes of false-positive DSTs were excluded. The primary end point was the prevalence of hypercortisolism, defined as post-DST cortisol >1.8 μg/dL (50 nmol/L). Characteristics associated with hypercortisolism were assessed with multiple logistic regression. The percentage and characteristics of participants with hypercortisolism and adrenal imaging abnormalities were also assessed. RESULTS Post-DST cortisol was unsuppressed in 252 of 1,057 participants (prevalence 23.8%; 95% CI 21.3, 26.5). Hypercortisolism prevalence was 33.3% among participants with cardiac disorders and 36.6% among those taking three or more blood pressure–lowering medications. Adrenal imaging abnormalities were reported in 34.7% of participants with hypercortisolism. Use of sodium–glucose cotransporter 2 inhibitors (odds ratio 1.558), maximum-dose glucagon-like peptide 1 receptor agonists (1.544), tirzepatide (1.981), or a higher number of blood pressure–lowering medications (1.390); older age (1.316); BMI <30 kg/m2 (1.639); non-Latino/Hispanic ethnicity (3.718); and use of fibrates (2.676) or analgesics (1.457) were associated with higher prevalence (all P < 0.03). CONCLUSIONS Hypercortisolism was associated with hyperglycemia in approximately one-quarter of individuals with inadequately controlled type 2 diabetes despite multiple medications.

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<0.0001; irAE model: R2= 0.92; p-value <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<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.

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.

Abstract Aims In this protocol-predefined substudy of the RHAPSODY trial, the primary aim was to assess whether pericardial late gadolinium enhancement (LGE) was associated with time to pericarditis recurrence. Methods and results RHAPSODY was a Phase 3 double-blind, placebo-controlled, randomized-withdrawal trial that demonstrated the efficacy of rilonacept in recurrent pericarditis (RP). Patients with a history of multiple RP and an active recurrence were enrolled and had the option to participate in a cardiac magnetic resonance (CMR) imaging substudy. CMRs were interpreted by a blinded independent core laboratory with prespecified criteria to define pericardial LGE. Compared to patients with trace or mild pericardial LGE (n = 9), patients with moderate or severe pericardial LGE (n = 16) generally had a higher number of recurrent episodes per year (5.3 vs. 3.9) and a higher mean CRP level (3.6 vs. 1.1 mg/dL). Overall, 10/14 (71.4%) who received a placebo had a recurrence compared to 0/11 (0%) who received rilonacept. In patients randomized to placebo who had moderate or severe pericardial LGE, the median time to recurrence was 4.2 weeks compared to 10.7 weeks in patients who had trace or mild pericardial LGE. At the conclusion of the event-driven randomized-withdrawal period, among patients receiving a placebo, 5/7 (71.4%) with trace or mild pericardial LGE and 5/7 (71.4%) with moderate or severe pericardial LGE had a recurrence. Conclusions Among patients with multiple RP, these preliminary findings support the concept of pericardial LGE as an imaging biomarker that may inform the duration of treatment and risk of recurrence with cessation of therapy and larger studies should be considered. ClinicalTrials.gov Identifier NCT03737110

Abstract Introduction: Treatment modality selection for metastatic NSCLC patients (immunotherapy alone vs. combination of immunotherapy with chemotherapy) relies mainly on determining the programmed cell death 1 (PD-L1) expression levels in the tumor. However, available assays are only moderately predictive. Here we set to develop PROphet®, a plasma proteomics-based predictive model for informing treatment decisions for NSCLC patients receiving immune checkpoint inhibitor (ICI)-based therapy. Methods: Pre-ICI plasma samples were collected in 12 centers in a clinical trial (NCT04056247) from 367 advanced-stage NSCLC patients. Clinical benefit (CB) was assessed at 12 months based on the occurrence of progression-free survival (PFS) until this time point. Deep proteomic profiling of the plasma samples was performed using aptamer-based technology. A novel machine learning model was developed to determine the CB probability for each patient, and the performance was successfully evaluated on an independent validation set, followed by training and prediction over the entire cohort using cross-validation. The resulting PROphet® score (positive or negative) was determined by setting the median CB rate probability as a threshold. The patients were divided into four subgroups based on their PD-L1 expression level combined with their PROphet® score prediction, and the overall survival (OS) was examined for each subgroup. Results: The PROphet® computational model was evaluated in a blinded manner on a subset of 85 patients and displayed strong predictive capability with area under the curve (AUC) of the receiver operating characteristics (ROC) plot of 0.78 (p-value = 5.00e-05), outperforming a PD-L1-based predictive model (AUC = 0.62; p-value 2.76e-01). When combining PROphet® score with PD-L1 expression levels, four different outcome patterns were identified: (i) Patients with PD-L1 ≥50% and PROphet® negative score, who displayed significantly longer OS when treated with ICI-chemotherapy combination therapy compared to ICI monotherapy and may consider combination therapy. (ii) Patients with PD-L1 ≥50% and PROphet® positive score, who benefit similarly from either treatment modalities, and may consider monotherapy to avoid the potential toxicity of the combination therapy. (iii) Patients with PD-L1&lt;50% and PROphet® negative score, who do not benefit from either treatment modalities and may consider chemotherapy alone or treatment beyond standard of care. (iv) Patients with PD-L1&lt;50% and PROphet® positive score who benefit from combination therapy. Conclusions: Altogether, the PROphet® model, when combined with PD-L1 test, stratifies the patients into four subgroups, providing additional resolution to the PD-L1 biomarker currently used to guide treatment selection. Furthermore, the model accurately predicts CB at 12 months based on proteomic analysis of a pre-treatment plasma sample. Citation Format: Michal Harel, Petros Christopoulos, Coren Lahav, Itamar Sela, Nili Dahan, Niels Reinmuth, Ina Koch, Alona Zer, Mor Moskovitz, Adva Levy-Barda, Michal Lotem, Hovav Nechushtan, Rivka Katzenelson, Abed Agbarya, Mahmoud Abu-Amna, Maya Gottfried, Ido Wolf, Ella Tepper, Yanyan Lou, Raya Leibowitz, Adam P. Dicker, David Gandara, David P. Carbone. A pretreatment blood-based proteomic biomarker for enhanced decision-making in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2159.

Abstract We used a murine model of multiple sclerosis (MS) induced by Theiler's murine encephalomyelitis virus (TMEV) to test the effect of IL-6 on central nervous system (CNS) demyelination. Administration of human rIL-6 (2.5 micrograms/dose), beginning one day before infection and then twice daily for 28 days, dramatically reduced demyelination and inflammation in the spinal cord of susceptible SJL/J mice. Benefit also was observed when rIL-6 was used as a therapeutic agent and begun on day 15 after infection, a time in which there is the first evidence of inflammation and demyelination in the spinal cord. Suppression of myelin damage by treatment with rIL-6 was associated with fewer virus Ag-positive cells in the spinal cord. Infectious CNS virus titers, as measured by plaque assay, were reduced in rIL-6-treated animals on day 15 after infection, but not on day 7, 22, or 29 after infection. Total serum Igs and virus-specific Igs, as detected by indirect ELISA, were increased markedly in rIL-6-treated mice, whereas no effect was observed on TMEV-neutralizing Ab titers. In vivo administration of rIL-6 inhibited a murine CNS-demyelinating disease induced by a virus, suggesting that this IL may have application for the treatment of human MS.

Abstract Intracerebral infection of susceptible strains of mice with Theiler's virus, a picornavirus, results in central nervous system demyelination, which is similar to multiple sclerosis. Immunogenetic experiments indicate that the MHC (H-2) and, in particular, the D region that controls class I-restricted immune responses, is an important determinant to development of demyelination. We tested whether disruption of beta 2-microglobulin (beta 2-m) would abrogate resistance to demyelinating disease normally observed in H-2b mice. All (C57BI/6 x 129)F3 mice transgenic for homozygous beta 2-m gene disruption (-/-) developed chronic demyelination after Theiler's murine encephalomyelitis virus infection, whereas none of the infected littermates with normal expression of class I MHC (beta 2-m, +/+) developed demyelination. Demyelinated lesions showed class II MHC expression, macrophages, and TNF but no class I MHC expression or CD8+ T cells. No correlation was observed between development of demyelination and delayed-type hypersensitivity responses to virus Ag. Despite the presence of demyelinating lesions, none of the infected beta 2-m (-/-) mice developed neurologic deficits. Infectious virus and virus Ag persisted in the central nervous systems of infected beta 2-m (-/-) mice but not in beta 2-m (+/+) mice. These experiments support the hypothesis that a class I immune response mediated by CD8+ T cells is important in resistance to Theiler's murine encephalomyelitis virus-induced demyelination. Development of chronic neurologic deficits as observed in immunocompetent susceptible strains of mice may be dependent on the presence of class I MHC and CD8+ T cells.