Combining Genomic Biomarkers to Guide Immunotherapy in Non-Small Cell Lung Cancer

PURPOSE:The clinical value of STK11, KEAP1, and EGFR alterations for guiding immune checkpoint blockade (ICB) therapy in non-small cell lung cancer (NSCLC) remains controversial, as some patients with these proposed resistance biomarkers show durable ICB responses. More specific combinatorial biomarker approaches are urgently needed for this disease. EXPERIMENTAL DESIGN: To develop a combinatorial biomarker strategy with increased specificity for ICB unresponsiveness in NSCLC, we performed a comprehensive analysis of 254 patients with NSCLC treated with ligand programmed death-ligand 1 (PD-L1) blockade monotherapy, including a discovery cohort of 75 patients subjected to whole-genome sequencing (WGS), and an independent validation cohort of 169 patients subjected to tumor-normal large panel sequencing. The specificity of STK11/KEAP1/EGFR alterations for ICB unresponsiveness was assessed in the contexts of a low (&lt;10 muts/Mb) or high (≥10 muts/Mb) tumor mutational burden (TMB).RESULTS:In low TMB cases, STK11/KEAP1/EGFR alterations were highly specific biomarkers for ICB resistance, with 0/15 (0.0%) and 1/34 (2.9%) biomarker-positive patients showing treatment benefit in the discovery and validation cohorts, respectively. This contrasted with high TMB cases, where 11/13 (85%) and 15/34 (44%) patients with at least one STK11/KEAP1/EGFR alteration showed durable treatment benefit in the discovery and validation cohorts, respectively. These findings were supported by analyses of progression-free survival and overall survival. CONCLUSIONS: The unexpected ICB responses in patients carrying resistance biomarkers in STK11, KEAP1, and EGFR were almost exclusively observed in patients with a high TMB. Considering these alterations in context, the TMB offered a highly specific combinatorial biomarker strategy for limiting overtreatment in NSCLC.</p

Combining Genomic Biomarkers to Guide Immunotherapy in Non-Small Cell Lung Cancer

PURPOSE:The clinical value of STK11, KEAP1, and EGFR alterations for guiding immune checkpoint blockade (ICB) therapy in non-small cell lung cancer (NSCLC) remains controversial, as some patients with these proposed resistance biomarkers show durable ICB responses. More specific combinatorial biomarker approaches are urgently needed for this disease. EXPERIMENTAL DESIGN: To develop a combinatorial biomarker strategy with increased specificity for ICB unresponsiveness in NSCLC, we performed a comprehensive analysis of 254 patients with NSCLC treated with ligand programmed death-ligand 1 (PD-L1) blockade monotherapy, including a discovery cohort of 75 patients subjected to whole-genome sequencing (WGS), and an independent validation cohort of 169 patients subjected to tumor-normal large panel sequencing. The specificity of STK11/KEAP1/EGFR alterations for ICB unresponsiveness was assessed in the contexts of a low (&lt;10 muts/Mb) or high (≥10 muts/Mb) tumor mutational burden (TMB).RESULTS:In low TMB cases, STK11/KEAP1/EGFR alterations were highly specific biomarkers for ICB resistance, with 0/15 (0.0%) and 1/34 (2.9%) biomarker-positive patients showing treatment benefit in the discovery and validation cohorts, respectively. This contrasted with high TMB cases, where 11/13 (85%) and 15/34 (44%) patients with at least one STK11/KEAP1/EGFR alteration showed durable treatment benefit in the discovery and validation cohorts, respectively. These findings were supported by analyses of progression-free survival and overall survival. CONCLUSIONS: The unexpected ICB responses in patients carrying resistance biomarkers in STK11, KEAP1, and EGFR were almost exclusively observed in patients with a high TMB. Considering these alterations in context, the TMB offered a highly specific combinatorial biomarker strategy for limiting overtreatment in NSCLC.</p

Combining Genomic Biomarkers to Guide Immunotherapy in Non-Small Cell Lung Cancer

Purpose: The clinical value of STK11, KEAP1, and EGFR alterations for guiding immune checkpoint blockade (ICB) therapy in non-small cell lung cancer (NSCLC) remains controversial, as some patients with these proposed resistance biomarkers show durable ICB responses. More specific combinatorial biomarker approaches are urgently needed for this disease. Experimental Design: To develop a combinatorial biomarker strategy with increased specificity for ICB unresponsiveness in NSCLC, we performed a comprehensive analysis of 254 patients with NSCLC treated with ligand programmed death-ligand 1 (PD-L1) blockade monotherapy, including a discovery cohort of 75 patients subjected to whole-genome sequencing (WGS), and an independent validation cohort of 169 patients subjected to tumor-normal large panel sequencing. The specificity of STK11/KEAP1/EGFR alterations for ICB unresponsiveness was assessed in the contexts of a low (<10 muts/Mb) or high (≥10 muts/Mb) tumor mutational burden (TMB). Results: In low TMB cases, STK11/KEAP1/EGFR alterations were highly specific biomarkers for ICB resistance, with 0/15 (0.0%) and 1/34 (2.9%) biomarker-positive patients showing treatment benefit in the discovery and validation cohorts, respectively. This contrasted with high TMB cases, where 11/13 (85%) and 15/34 (44%) patients with at least one STK11/ KEAP1/EGFR alteration showed durable treatment benefit in the discovery and validation cohorts, respectively. These findings were supported by analyses of progression-free survival and overall survival. Conclusions: The unexpected ICB responses in patients carrying resistance biomarkers in STK11, KEAP1, and EGFR were almost exclusively observed in patients with a high TMB. Considering these alterations in context, the TMB offered a highly specific combinatorial biomarker strategy for limiting overtreatment in NSCLC

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

Biomarkers for clinical benefit to immune checkpoint blockade treatment in NSCLC

Pharmacological blockade of the PD-1/PD-L1 pathway has transformed the treatment landscape of advanced stage NSCLC. These immune checkpoint blocking (ICB) agents have demonstrated the capacity to induce durable responses. However, 60%-70% of patients experience disease progression within six months of treatment. This raises concerns about unnecessary exposure of patients to side effects, financial costs, and delayed access to alternative therapies. Hence, the identification of biomarkers capable of selecting patients that will not derive benefit from PD-(L)1 blockade therapy has become an urgent necessity. In this thesis, the presence of a distinct population of tumor-reactive T cells, known as PD-1T TILs, was established as a novel biomarker for long-term benefit to PD-1 blockade in NSCLC with high negative predictive value. Consequently, a tumor’s PD-1T TIL status was translated into an mRNA signature using the Nanostring nCounter platform to facilitate its implementation as biomarker in routine diagnostics. In the second part of this thesis, alternative bio-sources for biomarker assessment were explored to avoid the need for invasive and complicated biopsy procedures. A serum-based protein signature was developed to stratify patient outcomes without the need for invasive tissue biopsies. Additionally, this research demonstrated that cell-free DNA extracted from the supernatant of pleural effusion can accurately detect targetable oncogenes and resistance mechanisms. Collectively, these findings enhance the ability to tailor treatment strategies and diagnostic tools for advanced-stage NSCLC patients treated with PD-1 blockade therapy

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

Cell-free DNA in the supernatant of pleural effusion can be used to detect driver and resistance mutations, and can guide tyrosine kinase inhibitor treatment decisions

Objectives Molecular profiling of tumours has become the mainstay of diagnostics for metastasised solid malignancies and guides personalised treatment, especially in nonsmall cell lung cancer (NSCLC). In current practice, it is often challenging to obtain sufficient tumour material for reliable molecular analysis. Cell-free DNA (cfDNA) in blood or other bio-sources could present an alternative approach to obtain genetic information from the tumour. In a retrospective cohort we analysed the added value of cfDNA analysis in pleural effusions for molecular profiling. Methods We retrospectively analysed both the supernatant and the cell pellet of 44 pleural effusions sampled from 39 stage IV patients with KRAS (n=23) or EGFR (n=16) mutated tumours to detect the original driver mutation as well as for EGFR T790M resistance mutations. Patients were diagnosed with either NSCLC (n=32), colon carcinoma (n=4), appendiceal carcinoma (n=2) or adenocarcinoma of unknown primary (n=1). Samples collected in the context of routine clinical care were stored at the Netherlands Cancer Institute biobank. We used droplet digital PCR for analysis. Results The driver mutation could be detected in 36 of the 44 pleural effusions by analysis of both the supernatant (35 out of 44 positive) and the cell pellet (31 out of 44 positive). In seven out of 20 pleural effusions from patients with EGFR mutation-positive tumours, a T790M mutation was detected. All seven supernatants and cell pellets were positive. Conclusions cfDNA in pleural effusion can be used to detect driver mutations as well as resistance mechanisms like EGFR T790M in pleural effusion with high accuracy and is therefore a valuable bio-source

Mesenchymal Differentiation Mediated by NF-kappa B Promotes Radiation Resistance in Glioblastoma

<p>Despite extensive study, few therapeutic targets have been identified for glioblastoma (GBM). Here we show that patient-derived glioma sphere cultures (GSCs) that resemble either the proneural (PN) or nnesenchymal (MES) transcriptomal subtypes differ significantly in their biological characteristics. Moreover, we found that a subset of the PN GSCs undergoes differentiation to a MES state in a TNF-alpha/NF-kappa B-dependent manner with an associated enrichment of CD44 subpopulations and radioresistant phenotypes. We present data to suggest that the tumor microenvironment cell types such as macrophages/microglia may play an integral role in this process. We further show that the MES signature, CD44 expression, and NF-kappa B activation correlate with poor radiation response and shorter survival in patients with GBM.</p>