Comparing modeling strategies combining changes in multiple serum tumor biomarkers for early prediction of immunotherapy non-response in non-small cell lung cancer

BACKGROUND: Patients treated with immune checkpoint inhibitors (ICI) are at risk of adverse events (AEs) even though not all patients will benefit. Serum tumor markers (STMs) are known to reflect tumor activity and might therefore be useful to predict response, guide treatment decisions and thereby prevent AEs.OBJECTIVE: This study aims to compare a range of prediction methods to predict non-response using multiple sequentially measured STMs.METHODS: Nine prediction models were compared to predict treatment non-response at 6-months (n = 412) using bi-weekly CYFRA, CEA, CA-125, NSE, and SCC measurements determined in the first 6-weeks of therapy. All methods were applied to six different biomarker combinations including two to five STMs. Model performance was assessed based on sensitivity, while model training aimed at 95% specificity to ensure a low false-positive rate.RESULTS: In the validation cohort, boosting provided the highest sensitivity at a fixed specificity across most STM combinations (12.9% -59.4%). Boosting applied to CYFRA and CEA achieved the highest sensitivity on the validation data while maintaining a specificity &gt;95%.CONCLUSIONS: Non-response in NSCLC patients treated with ICIs can be predicted with a specificity &gt;95% by combining multiple sequentially measured STMs in a prediction model. Clinical use is subject to further external validation.</p

PD-1T TILs as a predictive biomarker for clinical benefit to PD-1 blockade in patients with advanced NSCLC

PURPOSE Durable clinical benefit to PD-1 blockade in NSCLC is currently limited to a small fraction of patients, underlining the need for predictive biomarkers. We recently identified a tumor-reactive tumor-infiltrating T lymphocyte (TIL) pool, termed PD-1T TILs, with predictive potential in NSCLC. Here, we examined PD-1T TILs as biomarker in NSCLC. EXPERIMENTAL DESIGN PD-1T TILs were digitally quantified in120 baseline samples from advanced NSCLC patients treated with PD-1 blockade. Primary outcome was Disease Control (DC) at 6 months. Secondary outcomes were DC at 12 months and survival. Exploratory analyses addressed the impact of lesion-specific responses, tissue sample properties and combination with other biomarkers on the predictive value of PD-1T TILs. RESULTS PD-1T TILs as a biomarker reached 77% sensitivity and 67% specificity at 6 months, and 93% and 65% at 12 months, respectively. Particularly, a patient group without clinical benefit was reliably identified, indicated by a high negative predictive value (NPV) (88% at 6 months, 98% at 12 months). High PD-1T TILs related to significantly longer progression-free (HR 0.39, 95% CI: 0.24-0.63, p<0.0001) and overall survival (HR 0.46, 95% CI: 0.28-0.76, p<0.01). Predictive performance was increased when lesion-specific responses and samples obtained immediately before treatment were assessed. Notably, the predictive performance of PD-1TTILs was superior to PD-L1 and TLS in the same cohort. CONCLUSIONS This study established PD-1T TILs as predictive biomarker for clinical benefit to PD-1 blockade in advanced NSCLC patients. Most importantly, the high NPV demonstrates an accurate identification of a patient group without benefit

Detection and localization of early- and late-stage cancers using platelet RNA

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I–IV cancer patients and in half of 352 stage I–III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening

Modeling Diagnostic Strategies to Manage Toxic Adverse Events following Cancer Immunotherapy

Background: Although immunotherapy (IMT) provides significant survival benefits in selected patients, approximately 10% of patients experience (serious) immune-related adverse events (irAEs). The early detection of adverse events will prevent irAEs from progressing to severe stages, and routine testing for irAEs has become common practice. Because a positive test outcome might indicate a clinically manifesting irAE that requires treatment to (temporarily) discontinue, the occurrence of false-positive test outcomes is expected to negatively affect treatment outcomes. This study explores how the UPPAAL modeling environment can be used to assess the impact of test accuracy (i.e., test sensitivity and specificity), on the probability of patients entering palliative care within 11 IMT cycles. Methods: A timed automata-based model was constructed using real-world data and expert consultation. Model calibration was performed using data from 248 non–small-cell lung cancer patients treated with nivolumab. A scenario analysis was performed to evaluate the effect of changes in test accuracy on the probability of patients transitioning to palliative care. Results: The constructed model was used to estimate the cumulative probabilities for the patients’ transition to palliative care, which were found to match real-world clinical observations after model calibration. The scenario analysis showed that the specificity of laboratory tests for routine monitoring has a strong effect on the probability of patients transitioning to palliative care, whereas the effect of test sensitivity was limited. Conclusion: We have obtained interesting insights by simulating a care pathway and disease progression using UPPAAL. The scenario analysis indicates that an increase in test specificity results in decreased discontinuation of treatment due to suspicion of irAEs, through a reduction of false-positive test outcomes

Influenza vaccination in patients with lung cancer receiving anti-programmed death receptor 1 immunotherapy does not induce immune-related adverse events.

Influenza vaccination is recommended in patients with cancer to reduce influenza-related complications. Recently, more immune-related adverse events (irAEs) were demonstrated in patients with lung cancer who were vaccinated with the trivalent seasonal influenza vaccine during anti-programmed death receptor 1 (PD-1) immunotherapy. Confirmation of these findings is essential before recommendations on influenza vaccination may be revoked. In this cohort study in patients with lung cancer receiving nivolumab 3 mg/kg every 2 weeks during two influenza seasons (2015/16-2016/17), irAEs have been monitored. Incidence, timing and severity of irAEs were compared between vaccinated patients and non-vaccinated patients. In a compassionate use programme, 127 patients with lung cancer had been treated with at least one dose of nivolumab during two national influenza vaccination campaigns from September until December of 2015 and 2016. Forty-two patients had received the influenza vaccine, and 85 patients were not vaccinated. Median follow-up period was 118 days (interquartile range 106-119). Mean age was 64 years (range 46-83). In vaccinated and non-vaccinated patients, the incidence of irAEs was 26% and 22%, respectively, rate ratio 1.20 (95% confidence interval [CI] 0.51-2.65). The incidence of serious irAEs was 7% and 4%, respectively, rate ratio 2.07 (95% CI 0.28-15.43). Influenza vaccination while receiving nivolumab did not result in significant differences in the rates of discontinuation, death, clinical deterioration or tumour response between the groups. Influenza vaccination in patients with lung cancer receiving anti-PD-1 immunotherapy does not induce irAEs in our cohort. With this result, influenza vaccination should not be deterred from this group of patients

Nivolumab in pre-treated advanced non-small cell lung cancer: long term follow up data from the Dutch expanded access program and routine clinical care

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Modeling strategies to analyse longitudinal biomarker data: An illustration on predicting immunotherapy non-response in non-small cell lung cancer

Serum tumor markers acquired through a blood draw are known to reflect tumor activity. Their non-invasive nature allows for more frequent testing compared to traditional imaging methods used for response evaluations. Our study aims to compare nine prediction methods to accurately, and with a low false positive rate, predict progressive disease despite treatment (i.e. non-response) using longitudinal tumor biomarker data. Bi-weekly measurements of CYFRA, CA-125, CEA, NSE, and SCC were available from a cohort of 412 advanced stage non-small cell lung cancer (NSCLC) patients treated up to two years with immune checkpoint inhibitors. Serum tumor marker measurements from the first six weeks after treatment initiation were used to predict treatment response at 6 months. Nine models with varying complexity were evaluated in this study, showing how longitudinal biomarker data can be used to predict non-response to immunotherapy in NSCLC patients

A Serum Protein Classifier Identifying Patients with Advanced Non–Small Cell Lung Cancer Who Derive Clinical Benefit from Treatment with Immune Checkpoint Inhibitors

Purpose: Pretreatment selection of patients with non–small cell lung cancer (NSCLC) who would derive clinical benefit from treatment with immune checkpoint inhibitors (CPIs) would fulfill an unmet clinical need by reducing unnecessary toxicities from treatment and result in substantial health care savings. Experimental Design: In a retrospective study, mass spectrometry (MS)-based proteomic analysis was performed on pretreatment sera derived from patients with advanced NSCLC treated with nivolumab as part of routine clinical care (n ¼ 289). Machine learning combined spectral and clinical data to stratify patients into three groups with good (“sensitive”), intermediate, and poor (“resistant”) outcomes following treatment in the second-line setting. The test was applied to three independent patient cohorts and its biology was investigated using protein set enrichment analyses (PSEA). Results: A signature consisting of 274 MS features derived from a development set of 116 patients was associated wit

Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy

Background Checkpoint inhibitors provided sustained clinical benefit to metastatic lung cancer patients. Nonetheless, prognostic markers in metastatic settings are still under research. Imaging offers distinctive advantages, providing whole-body information non-invasively, while routinely available in most clinics. We hypothesized that more prognostic information can be extracted by employing artificial intelligence (AI) for treatment monitoring, superior to 2D tumor growth criteria. Methods A cohort of 152 stage-IV non-small-cell lung cancer patients (NSCLC) (73 discovery, 79 test, 903CTs), who received nivolumab were retrospectively collected. We trained a neural network to identify morphological changes on chest CT acquired during patients' follow-ups. A classifier was employed to link imaging features learned by the network with overall survival. Results Our results showed significant performance in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.69 (p < 0.01), up to AUC 0.75 (p < 0.01) in the first 3 to 5 months of treatment, and 0.67 AUC (p = 0.01) for durable clinical benefit (6 months progression-free survival). We found the AI-derived survival score to be independent of clinical, radiological, PDL1, and histopathological factors. Visual analysis of AI-generated prognostic heatmaps revealed relative prognostic importance of morphological nodal changes in the mediastinum, supraclavicular, and hilar regions, lung and bone metastases, as well as pleural effusions, atelectasis, and consolidations. Conclusions Our results demonstrate that deep learning can quantify tumor- and non-tumor-related morphological changes important for prognostication on serial imaging. Further investigation should focus on the implementation of this technique beyond thoracic imaging

eNose analysis for early immunotherapy response monitoring in non-small cell lung cancer

Objectives: Exhaled breath analysis by electronic nose (eNose) has shown to be a potential predictive biomarker before start of anti-PD-1 therapy in patients with non-small cell lung carcinoma (NSCLC). We hypothesized that the eNose could also be used as an early monitoring tool to identify responders more accurately at early stage of treatment when compared to baseline. In this proof-of-concept study we aimed to definitely discriminate responders from non-responders after six weeks of treatment. Materials and Methods: This was a prospective observational study in patients with advanced NSCLC eligible for anti-PD-1 treatment. The efficacy of treatment was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 at 3-month follow-up. We analyzed SpiroNose exhaled breath data of 94 patients (training cohort n = 62, validation cohort n = 32). Data analysis involved signal processing and statistics based on Independent Samples T-tests and Linear Discriminant Analysis (LDA) followed by Receiver Operating Characteristic (ROC) analysis. Results: In the training cohort, a specificity of 73% was obtained at a 100% sensitivity level to identify objective responders. The Area Under the Curve (AUC) was 0.95 (CI: 0.89–1.00). In the validation cohort, these results were confirmed with an AUC of 0.97 (CI: 0.91–1.00). Conclusion: Exhaled breath analysis by eNose early during treatment allows for a highly accurate, non-invasive and low-cost identification of advanced NSCLC patients who benefit from anti-PD-1 therapy