In many cancers, cells undergo re-programming of metabolism, cell survival and anti-apoptotic defense strategies, with the proteins mediating this reprogramming representing potential biomarkers. Here, we searched for novel biomarker proteins in chronic lymphocytic leukemia (CLL) that can impact diagnosis, treatment and prognosis by comparing the protein expression profiles of peripheral blood mononuclear cells from CLL patients and healthy donors using specific antibodies, mass spectrometry and binary logistic regression analyses and other bioinformatics tools. Mass spectrometry (LC-HR-MS/MS) analysis identified 1,360 proteins whose expression levels were modified in CLL-derived lymphocytes. Some of these proteins were previously connected to different cancer types, including CLL, while four other highly expressed proteins were not previously reported to be associated with cancer, and here, for the first time, DDX46 and AK3 are linked to CLL. Down-regulation expression of two of these proteins resulted in cell growth inhibition. High DDX46 expression levels were associated with shorter survival of CLL patients and thus can serve as a prognosis marker. The proteins with modified expression include proteins involved in RNA splicing and translation and particularly mitochondrial proteins involved in apoptosis and metabolism. Thus, we focused on several metabolism- and apoptosis-modulating proteins, particularly on the voltage-dependent anion channel 1 (VDAC1), regulating both metabolism and apoptosis. Expression levels of Bcl-2, VDAC1, MAVS, AIF and SMAC/Diablo were markedly increased in CLL-derived lymphocytes. VDAC1 levels were highly correlated with the amount of CLL-cancerous CD19+/CD5+ cells and with the levels of all other apoptosis-modulating proteins tested. Binary logistic regression analysis demonstrated the ability to predict probability of disease with over 90% accuracy. Finally, based on the changes in the levels of several proteins in CLL patients, as revealed from LC-HR-MS/MS, we could distinguish between patients in a stable disease state and those who would be later transferred to anti-cancer treatments. The over-expressed proteins can thus serve as potential biomarkers for early diagnosis, prognosis, new targets for CLL therapy, and treatment guidance of CLL, forming the basis for personalized therapy.
Cell-free DNA (cfDNA) next-generation sequencing has the potential to capture tumor heterogeneity and genomic evolution under treatment pressure in a non-invasive manner. Here, we report the detection of EGFR L792 mutations, a non-covalent mechanism of osimertinib resistance, using Guardant360 cfDNA testing in a patient with metastatic EGFR-mutant non-small cell lung cancer (NSCLC) whose disease progressed on osimertinib. We subsequently analyzed a large cohort of over 1800 additional patient samples harboring an EGFR T790M mutation and identified a concomitant L792 mutation in a total of 22 (1.2%) cases. In vitro functional assays demonstrated that the EGFR L858R/T790M/L792F/H mutations conferred intermediate-level resistance to osimertinib. Further understanding of potential acquired resistance mechanisms to targeted therapy may help inform treatment strategy in EGFR-mutant NSCLC.
Purpose: Mutations in KRAS/NRAS (RAS) predict lack of anti-EGFR efficacy in metastatic colorectal cancer (mCRC). However, it is unclear if all RAS mutations have similar impact, and atypical mutations beyond those in standard guidelines exist. Experimental Design: We reviewed 7 tissue and 1 cell-free DNA cohorts of 9,485 patients to characterize atypical RAS variants. Using an in vitro cell-based assay (functional annotation for cancer treatment), Ba/F3 transformation, and in vivo xenograft models of transduced isogenic clones, we assessed signaling changes across mutations. Results: KRAS exon 2, extended RAS, and atypical RAS mutations were noted in 37.8%, 9.5%, and 1.2% of patients, respectively. Among atypical variants, KRAS L19F, Q22K, and D33E occurred at prevalence ≥0.1%, whereas no NRAS codon 117/146 and only one NRAS codon 59 mutation was noted. Atypical RAS mutations had worse overall survival than RAS/BRAF wild-type mCRC (HR, 2.90; 95% confidence interval, 1.24–6.80; P = 0.014). We functionally characterized 114 variants with the FACT assay. All KRAS exon 2 and extended RAS mutations appeared activating. Of 57 atypical RAS variants characterized, 18 (31.6%) had signaling below wild-type, 23 (40.4%) had signaling between wild-type and activating control, and 16 (28.1%) were hyperactive beyond the activating control. Ba/F3 transformation (17/18 variants) and xenograft model (7/8 variants) validation was highly concordant with FACT results, and activating atypical variants were those that occurred at highest prevalence in clinical cohorts. Conclusions: We provide best available evidence to guide treatment when atypical RAS variants are identified. KRAS L19F, Q22K, D33E, and T50I are more prevalent than many guideline-included RAS variants and functionally relevant.
9045 Background: Immune checkpoint inhibitors (ICI) have become the standard treatment for metastatic NSCLC, although only a small proportion of patients derive durable benefit. PDL1 expression is the only approved biomarker to select NSCLC patients for treatment with single-agent pembrolizumab, however its predictive value is limited and better predictive biomarkers are needed. The spatial arrangement of immune cells in the tumor microenvironment (TME), namely tumor infiltrating lymphocytes (TILs), emerges as a potential biomarker for ICI efficacy. In this work, we utilized deep-learning (DL) models to extract TME features from digitized H&E slides and evaluated their predictive and prognostic role in patients with mNSCLC treated with Pembrolizumab. Methods: NSCLC patients (n=90) treated with single-agent 1st line pembrolizumab in two centers were identified. 47 patients from one center were used to train the model, and 43 patients from another center were used for validating the model. Pre-treatment H&E whole slide images (WSI) were analyzed using a deep-learning model to identify and classify tumor cells, TILs, tumor and stromal areas, and spatial features were calculated. Spatial features were correlated with clinical outcome data to train a binary classifier that identifies patients with a favorable clinical outcome. The resulting classifier combined three spatial features and three clinical features. The classifier was then applied to the validation set and differences in duration of treatment (DOT), and overall survival (OS) between patients with positive and negative scores were assessed. Results: The classifier identified patients in the validation set to have either positive (n=18) or negative (n=25) scores. Baseline patient characteristics and PDL1 score were similar between the positive and negative groups. In a Kaplan-Meier (KM) analysis, OS was significantly higher in patients with a positive score compared to patients with a negative score (HR=0.35, 95% CI 0.13-0.98; p<0.05). Positive patients had a significantly higher median OS (NR vs.17.8m, p<0.05) and 2-year OS (70.8% vs. 33%, p=0.02) than negative patients. Median DOT was also higher in positive patients compared to negative patients (10.1m vs. 6.5m). Conclusions: Deep-learning models that analyze the TME from H&E whole-slide images can identify NSCLC patients with durable benefit on Pembrolizumab. Identifying NSCLC patients who are exceptionally sensitive to anti-PD-1 therapy as monotherapy may improve clinical decision making and spare patients the unnecessary adverse effects associated with the addition chemotherapy or another IO agent.
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