Type of publication:

Conference abstract

Author(s):

Gandara D.R.; Carbone D.P.; Dicker A.P.; Christopoulos P.; Puzanov I.; Jain P.; Farrugia D.; Brown S.; Moskovitz M.; Bar J.; Hassani A.; *Chatterjee A.; Abu-Amna M.; Polychronis A.; Brewster A.; Lou Y.; VanderWalde N.A.; Gottfried M.; Lahav C.; Lowenthal G.; Sela I.; Harel M.; Elon Y.; Schneider M.A.

Citation:

Journal of Thoracic Oncology. Conference: The 2024 World Conference on Lung Cancer. San Diego United States. 19(10 Supplement) (pp S354-S355), 2024. Date of Publication: October 2024.

Abstract:

Introduction: Small cell lung cancer (SCLC) is an aggressive disease with limited treatment options. Immune checkpoint inhibitor (ICI) therapy with concurrent chemotherapy is the preferred first-line treatment for patients with extensive-stage SCLC. However, the addition of ICIs to chemotherapy only modestly improves clinical outcomes while posing a risk of ICI-related toxicities. Thus, identifying patients likely to benefit from ICIs is critical for optimizing treatment decisions. Here, we describe a test derived from a novel computational model that analyzes pretreatment plasma proteomic profiles to predict clinical outcomes in patients with SCLC receiving ICI-based therapies. Method(s): An observational study collected pretreatment plasma samples from 79 patients with extensive-stage SCLC treated with ICI-based therapy (NCT04056247). Proteomic profiling of plasma samples was performed using aptamer-based technology, measuring approximately 7000 proteins per sample. A machine learning model was developed to predict the clinical benefit (CB) from ICI-based therapy, where CB was defined as 6-month progression-free survival. Given the limited cohort size, CB prediction was achieved by integrating two computational models. Model 1, based on 146 plasma proteomic biomarkers, was developed from the SCLC dataset using cross-validation. Model 2, based on a 4-protein signature, was developed from a previously reported NSCLC dataset (Christopoulos et al. JCO prec. onc. 2024). The hybrid model stratified patients into two groups (i.e., 'positive' or 'negative') based on a pre-defined CB probability threshold. Bioinformatic analysis of the SCLC-specific proteomic biomarkers was performed to gain insight into the potential mechanisms driving ICI therapeutic benefit and resistance in SCLC. Result(s): The model displayed a robust predictive capability, as demonstrated by the area under the curve (AUC) of the receiver operating characteristic (ROC) plot of 0.63 (p-value = 0.02) and a high correlation between the predicted CB (i.e., model output) and the observed CB rate (R2 = 0.93). Furthermore, overall survival (OS) was significantly longer in patients stratified to the positive group compared to those in the negative group. Median OS was 14 months versus 8.8 months in positive versus negative patient groups (Hazard ratio = 0.47, 95% Confidence interval: 0.25-0.90, p-value = 0.02). Bioinformatic analysis of model proteins revealed significant enrichment of lung tumor-associated proteins, poor prognostic factors in lung cancer, extracellular matrix-related proteins, intermediate filaments, and replicative immortality (Fisher exact test; FDR<0.1). Multiple model proteins are also known to be involved in fibroblast growth factor signaling and glutathione metabolism. Given their association with different treatment resistance mechanisms, such proteins represent potential targets for intervention. Conclusion(s): We describe preliminary results from a novel pretreatment plasma proteomics-based predictive model that can potentially inform treatment decisions for patients with SCLC. Bioinformatic analysis demonstrates that the model is based on a composite of biologically and clinically relevant biomarkers. The potential clinical utility of this model is being investigated in a large prospective clinical trial.

Type of publication:
Conference abstract

Author(s):
Anderson V.; Dalrymple P.; Crowley G.; Shephard P.; Holmes C.; *McAdams J.; Morley J.; Sarah E.; Ivey S.; Bentley K.; Bate G.B.; English P.; Russell G.; Bostock L.

Citation:
Lung Cancer. Conference: 22nd Annual British Thoracic Oncology Group Conference 2024. Belfast United Kingdom. 190(Supplement 1) (no pagination), 2024. Article Number: 107683. Date of Publication: April 2024.

Abstract:
Aims "Bridging The Gap" report by UKLCC (2022) suggested addressing health inequalities in lung cancer has a significant impact on patient outcomes. LCNUK investigated a possible correlation between social isolation (lack of social contacts and having few people to interact with regularly) and time on pathway. Methods A Literature review conducted, highlighted a lack of UK research in this area. LCNUK members were surveyed for feedback and data was collected on 90 patients across 9 regions in the UK. Results 56 completed surveys were received from LCNUK members. 50% of responders felt that social isolation impacted patient progress on the pathway & 41% believed it influenced uptake of treatment. [Formula presented] Conclusion 50% of lung cancer nurses felt social isolation would negatively impact upon the length of the pathway, this project found that to be unproven. Data suggested a possible link between social isolation and uptake of MDT recommended treatment. This is a small sample size and may not be representative of the national picture and therefore more research is needed. Disclosure: No significant relationships.

Type of publication:
Conference abstract

Author(s):
Naidoo J.; Reinmuth N.; Puzanov I.; Bar J.; Kamer I.; Koch I.; Moskovitz M.; Levy-Barda A.; Agbarya A.; Zer A.; Abu-Amna M.; Farrugia D.; Lotem M.; Price G.; Harkovsky T.; Hassani A.; Katzenelson R.; *Chatterjee A.; Yelin B.; Sela I.; Dicker A.; Elon Y.; Harel M.; Leibowitz R.

Citation:
Journal for ImmunoTherapy of Cancer. Conference: 38th Annual Meeting of the Society for Immunotherapy of Cancer's, SITC 2023. San Diego, CA United States. 11(Supplement 1) (pp A1356), 2023. Date of Publication: November 2023.

Abstract:
Background Immune-related adverse events (irAEs) resulting from immune checkpoint inhibitors (ICIs) can substantially affect patient quality of life and treatment trajectory. Currently, there are no reliable pre-treatment biomarkers for predicting the development of irAEs; hence, there is a clinical need for irAE predictive biomarkers. Methods Plasma samples were obtained at baseline from 426 non-small cell lung cancer (NSCLC) patients treated with ICIs as part of an ongoing multi-center clinical trial (NCT04056247; approved by local IRB committees from each site) with irAE-related information. Proteomic profiling of plasma samples was performed using the SomaScan assay (SomaLogic Inc.), enabling deep coverage of approximately 7000 proteins in each sample. A machine learning-based model was developed to predict significant irAEs arising up to 3 months from treatment initiation; significant irAEs were defined as irAEs with CTCAE grade >=3 or irAEs that induced treatment discontinuation. Using the model, we identified a set of plasma proteins, termed Toxicity Associated Proteins (TAPs), that serve as indicators of irAEs depending on their plasma level in the individual patient. Bioinformatic analysis was performed to decipher the biology underlying immunerelated toxicity implied by the TAPs. Results Overall, 60 patients experienced significant irAEs at early onset; 197 patients had low grade irAEs, irAEs at late onset or AEs that are not immune-related; and 169 patients did not display any adverse event. A computational model was generated to predict significant irAEs, showing a strong correlation between the predicted probability of significant irAEs and the observed rate of such events (R2= 0.92; pvalue <0.0001), implying good prediction capabilities. The prediction was based on a set of 449 TAPs. Interestingly, nearly half of these TAPs were previously identified as proteins associated with clinical benefit from ICI therapy, suggesting a close relationship between irAEs and clinical benefit, in accordance with previous reports. A detailed examination of the TAPs revealed some key findings. Patients who experienced irAEs had a larger number of TAPs related to neutrophils, inflammation, and cell death resistance, while the number of lymphocyte-related TAPs was low in these patients. Patients who did not experience irAEs displayed higher levels of extracellular matrix-related proteins. Conclusions We describe a novel computational model for predicting significant irAEs in patients with NSCLC based on proteomic profiling of pre-treatment plasma samples. The TAPs provide insights into the biological processes underlying irAEs. Early prediction of irAEs could enable personalized management plans and mitigation strategies to reduce the risk of irAEs in NSCLC.

Type of publication:
Conference abstract

Author(s):
*Ekhelikar S.; Muthusami R.; *Orme R.; *Ahmad N.

Citation:
American Journal of Respiratory and Critical Care Medicine. Conference: International Conference of the American Thoracic Society, ATS 2022. San Francisco, CA United States. 205(1) (no pagination), 2022.

Abstract:
Introduction: Pulmonary actinomycosis is a rare bacterial infection that can mimic malignant and chronic suppurative lung conditions, and therefore is often misdiagnosed initially as one of the more common differential diagnoses. The challenge lies in diagnosing this condition prior to surgery as it is completely curable with antibiotics. Case description: A 48 year old man, cigarette smoker and previous intravenous drug user, presented with exertional breathlessness, persistent cough and night sweats. There was no fever or weight loss. A Chest Xray (CXR) and Computerised Tomography (CT) scan showed a left upper lobe cavitating lesion leading to differential diagnoses of bronchogenic malignancy and tuberculosis (TB). A Positron Emission Tomography (PET) scan confirmed a fluorodeoxyglucose (FDG) avid left upper lobe cavitating lesion with enlarged FDG avid thoracic lymphadenopathy. Bronchoscopy and Endobronchial Ultrasound (EBUS) were nondiagnostic. He underwent left upper lobectomy with histopathology confirming Pulmonary actinomycosis and was commenced on Amoxicillin treatment. <br/>Discussion(s): Pulmonary actinomycosis is the third most common type of actinomycosis, behind cervicofacial and abdominal, constituting 15% of total cases. It can occur at all ages, but most case series describe a peak incidence in the 4th and 5th decades. Symptoms are non-specific and often mimic those of it's more common differentials as above and so diagnosing this condition early presents a challenge. Basic laboratory tests reflect the non-specific inflammatory nature of the disease. Imaging modalities (CXR, CT, PET) are helpful, but not diagnostic. The gold standard for diagnosis remains histological examination & bacterial culture of lung biopsy specimen. Histopathologic evidence of granulomas containing neutrophils and sulfur granules with Actinomyces colonies are the hallmark of actinomycosis. Recent data suggests it is increasingly possible to avoid unwarranted surgical procedures, by performing bronchoscopic and percutaneous biopsy techniques. These represent the best chance at preventing unnecessary surgery and should be pursued as they can help exclude malignancy. Penicillin remains the drug of choice for Pulmonary actinomycosis and with correct treatment, the prognosis is excellent. However, those with complications may still require surgery. The chief challenge with Pulmonary actinomycosis is identifying it early, because it is rare, and it also mimics diseases like lung cancer and TB often. We were unable to exclude malignancy with pre-surgical diagnostics and so our patient had surgery. However, clinicians should be aware and consider Pulmonary actinomycosis as an important differential when investigating cavitating lung lesions as diagnosing it early could help prevent physical and psychological morbidity, including unwarranted surgery. (Figure Presented).

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