Multimodal evaluation of drug antibacterial activity reveals cinnamaldehyde analog anti-biofilm effects against Haemophilus influenzae.

Biofilm formation by the pathobiont Haemophilus influenzae is associated with human nasopharynx colonization, otitis media in children, and chronic respiratory infections in adults suffering from chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD). ß-lactam and quinolone antibiotics are commonly used to treat these infections. However, considering the resistance of biofilm-resident bacteria to antibiotic-mediated killing, the use of antibiotics may be insufficient and require being replaced or complemented with novel strategies. Moreover, unlike the standard minimal inhibitory concentration assay used to assess antibacterial activity against planktonic cells, standardization of methods to evaluate anti-biofilm drug activity is limited. In this work, we detail a panel of protocols for systematic analysis of drug antimicrobial effect on bacterial biofilms, customized to evaluate drug effects against H. influenzae biofilms. Testing of two cinnamaldehyde analogs, (E)-trans-2-nonenal and (E)-3-decen-2-one, demonstrated their effectiveness in both H. influenzae inhibition of biofilm formation and eradication or preformed biofilms. Assay complementarity allowed quantifying the dynamics and extent of the inhibitory effects, also observed for ampicillin resistant clinical strains forming biofilms refractory to this antibiotic. Moreover, cinnamaldehyde analog encapsulation into poly(lactic-co-glycolic acid) (PLGA) polymeric nanoparticles allowed drug vehiculization while maintaining efficacy. Overall, we demonstrate the usefulness of cinnamaldehyde analogs against H. influenzae biofilms, present a test panel that can be easily adapted to a wide range of pathogens and drugs, and highlight the benefits of drug nanoencapsulation towards safe controlled release.

An Interpretable and Accurate Deep-Learning Diagnosis Framework Modeled With Fully and Semi-Supervised Reciprocal Learning.

The deployment of automated deep-learning classifiers in clinical practice has the potential to streamline the diagnosis process and improve the diagnosis accuracy, but the acceptance of those classifiers relies on both their accuracy and interpretability. In general, accurate deep-learning classifiers provide little model interpretability, while interpretable models do not have competitive classification accuracy. In this paper, we introduce a new deep-learning diagnosis framework, called InterNRL, that is designed to be highly accurate and interpretable. InterNRL consists of a student-teacher framework, where the student model is an interpretable prototype-based classifier (ProtoPNet) and the teacher is an accurate global image classifier (GlobalNet). The two classifiers are mutually optimised with a novel reciprocal learning paradigm in which the student ProtoPNet learns from optimal pseudo labels produced by the teacher GlobalNet, while GlobalNet learns from ProtoPNet's classification performance and pseudo labels. This reciprocal learning paradigm enables InterNRL to be flexibly optimised under both fully- and semi-supervised learning scenarios, reaching state-of-the-art classification performance in both scenarios for the tasks of breast cancer and retinal disease diagnosis. Moreover, relying on weakly-labelled training images, InterNRL also achieves superior breast cancer localisation and brain tumour segmentation results than other competing methods.

Automatic Segmentation and Quantification of Nigrosome-1 Neuromelanin and Iron in MRI: A Candidate Biomarker for Parkinson's Disease.

BACKGROUND: There is a lack of automated tools for the segmentation and quantification of neuromelanin (NM) and iron in the nigrosome-1 (N1). Existing tools evaluate the N1 sign, i.e., the presence or absence of the "swallow-tail" in iron-sensitive MRI, or globally analyze the MRI signal in an area containing the N1, without providing a volumetric delineation. PURPOSE: Present an automated method to segment the N1 and quantify differences in N1's NM and iron content between Parkinson's disease (PD) patients and healthy controls (HCs). Study whether N1 degeneration is clinically related to PD and could be used as a biomarker of the disease. STUDY TYPE: Prospective. SUBJECTS: Seventy-one PD (65.3 ± 10.3 years old, 34 female/37 male); 30 HC (62.7 ± 7.8 years old, 17 female/13 male). FIELD STRENGTH/SEQUENCE: 3 T Anatomical T1-weighted MPRAGE, NM-MRI T1-weighted gradient with magnetization transfer, susceptibility-weighted imaging (SWI). ASSESSMENT: N1 was automatically segmented in SWI images using a multi-image atlas, populated with healthy N1 structures manually annotated by a neurologist. Relative NM and iron content were quantified and their diagnostic performance assessed and compared with the substantia nigra pars compacta (SNc). The association between image parameters and clinically relevant variables was studied. STATISTICAL TESTS: Nonparametric tests were used (Mann-Whitney's U, chi-square, and Friedman tests) at P = 0.05. RESULTS: N1's relative NM content decreased and relative iron content increased in PD patients compared with HCs (NM-CRHC = 22.55 ± 1.49; NM-CRPD = 19.79 ± 1.92; NM-nVolHC = 2.69 × 10-5 ± 1.02 × 10-5 ; NM-nVolPD = 1.18 × 10-5 ± 0.96 × 10-5 ; Iron-CRHC = 10.51 ± 2.64; Iron-CRPD = 19.35 ± 7.88; Iron-nVolHC = 0.72 × 10-5 ± 0.81 × 10-5 ; Iron-nVolPD = 2.82 × 10-5 ± 2.04 × 10-5 ). Binary logistic regression analyses combining N1 and SNc image parameters yielded a top AUC = 0.955. Significant correlation was found between most N1 parameters and both disease duration (ρNM-CR = -0.31; ρiron-CR = 0.43; ρiron-nVol = 0.46) and the motor status (ρNM-nVol = -0.27; ρiron-CR = 0.33; ρiron-nVol = 0.28), suggesting NM reduction along with iron accumulation in N1 as the disease progresses. DATA CONCLUSION: This method provides a fully automatic N1 segmentation, and the analyses performed reveal that N1 relative NM and iron quantification improves diagnostic performance and suggest a relative NM reduction along with a relative iron accumulation in N1 as the disease progresses. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

In vitro modeling of polyclonal infection dynamics within the human airways by Haemophilus influenzae differential fluorescent labeling.

IMPORTANCE: Genomic diversity of nontypeable H. influenzae strains confers phenotypic heterogeneity. Multiple strains of H. influenzae can be simultaneously isolated from clinical specimens, but we lack detailed information about polyclonal infection dynamics by this pathogen. A long-term barrier to our understanding of this host-pathogen interplay is the lack of genetic tools for strain engineering and differential labeling. Here, we present a novel plasmid toolkit named pTBH (toolbox for Haemophilus), with standardized modules for fluorescent or bioluminescent labeling, adapted to H. influenzae requirements but designed to be versatile so it can be utilized in other bacterial species. We present detailed experimental and quantitative image analysis methods, together with proof-of-principle examples, and show the ample possibilities of 3D microscopy, combined with quantitative image analysis, to model H. influenzae polyclonal infection lifestyles and unravel the co-habitation and co-infection dynamics of this respiratory pathogen.

A hybrid physics-based and data-driven framework for cellular biological systems: Application to the morphogenesis of organoids.

How cells orchestrate their cellular functions remains a crucial question to unravel how they organize in different patterns. We present a framework based on artificial intelligence to advance the understanding of how cell functions are coordinated spatially and temporally in biological systems. It consists of a hybrid physics-based model that integrates both mechanical interactions and cell functions with a data-driven model that regulates the cellular decision-making process through a deep learning algorithm trained on image data metrics. To illustrate our approach, we used data from 3D cultures of murine pancreatic ductal adenocarcinoma cells (PDAC) grown in Matrigel as tumor organoids. Our approach allowed us to find the underlying principles through which cells activate different cell processes to self-organize in different patterns according to the specific microenvironmental conditions. The framework proposed here expands the tools for simulating biological systems at the cellular level, providing a novel perspective to unravel morphogenetic patterns.

Synplex: In Silico Modeling of the Tumor Microenvironment From Multiplex Images.

Multiplex immunofluorescence is a novel, high-content imaging technique that allows simultaneous in situ labeling of multiple tissue antigens. This technique is of growing relevance in the study of the tumor microenvironment, and the discovery of biomarkers of disease progression or response to immune-based therapies. Given the number of markers and the potential complexity of the spatial interactions involved, the analysis of these images requires the use of machine learning tools that rely for their training on the availability of large image datasets, extremely laborious to annotate. We present Synplex, a computer simulator of multiplexed immunofluorescence images from user-defined parameters: i. cell phenotypes, defined by the level of expression of markers and morphological parameters; ii. cellular neighborhoods based on the spatial association of cell phenotypes; and iii. interactions between cellular neighborhoods. We validate Synplex by generating synthetic tissues that accurately simulate real cancer cohorts with underlying differences in the composition of their tumor microenvironment and show proof-of-principle examples of how Synplex could be used for data augmentation when training machine learning models, and for the in silico selection of clinically relevant biomarkers. Synplex is publicly available at https://github.com/djimenezsanchez/Synplex.

The Cell Tracking Challenge: 10 years of objective benchmarking.

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

Prussian Blue nanoparticles: An FDA-approved substance that may quickly degrade at physiological pH.

Prussian blue (PB) is a coordination polymer based on the Fe2+…CN…Fe3+ sequence. It is an FDA-approved drug, intended for oral use at the acidic pH of the stomach and of most of the intestine track. However, based on FDA approval, a huge number of papers proposed the use of PB nanoparticles (PBnp) under "physiological conditions", meaning pH buffered at 7.4 and high saline concentration. While most of these papers report that PBnp are stable at this pH, a small number of papers describes instead PBnp degradation at the same or similar pH values, i.e. in the 7-8 range. Here we give a definitively clear picture: PBnp are intrinsically unstable at pH ≥ 7, degrading with the fast disappearance of their 700 nm absorption band, due to the formation of OH- complexes from the labile Fe3+ centers. However, we show also that the presence of a polymeric coating (PVP) can protect PBnp at pH 7.4 for over 24 h. Moreover, we demonstrate that when "physiological conditions" include serum, a protein corona is rapidly formed on PBnp, efficiently avoiding degradation. We also show that the viability of PBnp-treated EA.hy926, NCI-H1299, and A549 cells is not affected in a wide range of conditions that either prevent or promote PBnp degradation.

ADMANI: Annotated Digital Mammograms and Associated Non-Image Datasets.

Supplemental material is available for this article. Keywords: Mammography, Screening, Convolutional Neural Network (CNN) Published under a CC BY 4.0 license. See also the commentary by Cadrin-Chênevert in this issue.

Brainstem neuromelanin and iron MRI reveals a precise signature for idiopathic and LRRK2 Parkinson's disease.

Neuromelanin (NM) loss in substantia nigra pars compacta (SNc) and locus coeruleus (LC) reflects neuronal death in Parkinson's disease (PD). Since genetically-determined PD shows varied clinical expressivity, we wanted to accurately quantify and locate brainstem NM and iron, to discover whether specific MRI patterns are linked to Leucine-rich repeat kinase 2 G2019S PD (LRRK2-PD) or idiopathic Parkinson's disease (iPD). A 3D automated MRI atlas-based segmentation pipeline (3D-ABSP) for NM/iron-sensitive MRI images topographically characterized the SNc, LC, and red nucleus (RN) neuronal loss and calculated NM/iron contrast ratio (CR) and normalized volume (nVol). Left-side NM nVol was larger in all groups. PD had lower NM CR and nVol in ventral-caudal SNc, whereas iron increased in lateral, medial-rostral, and caudal SNc. The SNc NM CR reduction was associated with psychiatric symptoms. LC CR and nVol discriminated better among subgroups: LRRK2-PD had similar LC NM CR and nVol as that of controls, and larger LC NM nVol and RN iron CR than iPD. PD showed higher iron SNc nVol than controls, especially among LRRK2-PD. ROC analyses showed an AUC > 0.92 for most pairwise subgroup comparisons, with SNc NM being the best discriminator between HC and PD. NM measures maintained their discriminator power considering the subgroup of PD patients with less than 5 years of disease duration. The SNc iron CR and nVol increase was associated with longer disease duration in PD patients. The 3D-ABSP sensitively identified NM and iron MRI patterns strongly correlated with phenotypic PD features.

Weakly supervised deep learning to predict recurrence in low-grade endometrial cancer from multiplexed immunofluorescence images.

Predicting recurrence in low-grade, early-stage endometrial cancer (EC) is both challenging and clinically relevant. We present a weakly-supervised deep learning framework, NaroNet, that can learn, without manual expert annotation, the complex tumor-immune interrelations at three levels: local phenotypes, cellular neighborhoods, and tissue areas. It uses multiplexed immunofluorescence for the simultaneous visualization and quantification of CD68 + macrophages, CD8 + T cells, FOXP3 + regulatory T cells, PD-L1/PD-1 protein expression, and tumor cells. We used 489 tumor cores from 250 patients to train a multilevel deep-learning model to predict tumor recurrence. Using a tenfold cross-validation strategy, our model achieved an area under the curve of 0.90 with a 95% confidence interval of 0.83-0.95. Our model predictions resulted in concordance for 96,8% of cases (κ = 0.88). This method could accurately assess the risk of recurrence in EC, outperforming current prognostic factors, including molecular subtyping.

The potential of bioprinting for preparation of nanoparticle-based calibration standards for LA-ICP-ToF-MS quantitative imaging.

This paper discusses the feasibility of a novel strategy based on the combination of bioprinting nano-doping technology and laser ablation-inductively coupled plasma time-of-flight mass spectrometry analysis for the preparation and characterization of gelatin-based multi-element calibration standards suitable for quantitative imaging. To achieve this, lanthanide up-conversion nanoparticles were added to a gelatin matrix to produce the bioprinted calibration standards. The features of this bioprinting approach were compared with manual cryosectioning standard preparation, in terms of throughput, between batch repeatability and elemental signal homogeneity at 5 µm spatial resolution. By using bioprinting, the between batch variability for three independent standards of the same concentration of 89Y (range 0-600 mg/kg) was reduced to 5% compared to up to 27% for cryosectioning. On this basis, the relative standard deviation (RSD) obtained between three independent calibration slopes measured within 1 day also reduced from 16% (using cryosectioning) to 5% (using bioprinting), supporting the use of a single standard preparation replicate for each of the concentrations to achieve good calibration performance using bioprinting. This helped reduce the analysis time by approximately 3-fold. With cryosectioning each standard was prepared and sectioned individually, whereas using bio-printing it was possible to have up to six different standards printed simultaneously, reducing the preparation time from approximately 2 h to under 20 min (by approximately 6-fold). The bio-printed calibration standards were found stable for a period of 2 months when stored at ambient temperature and in the dark.

Design and validation of a tunable inertial microfluidic system for the efficient enrichment of circulating tumor cells in blood.

The analysis of circulating tumor cells (CTCs) in blood is a powerful noninvasive alternative to conventional tumor biopsy. Inertial-based separation is a promising high-throughput, marker-free sorting strategy for the enrichment and isolation of CTCs. Here, we present and validate a double spiral microfluidic device that efficiently isolates CTCs with a fine-tunable cut-off value of 9 µm and a separation range of 2 µm. We designed the device based on computer simulations that introduce a novel, customized inertial force term, and provide practical fabrication guidelines. We validated the device using calibration beads, which allowed us to refine the simulations and redesign the device. Then we validated the redesigned device using blood samples and a murine model of metastatic breast cancer. Finally, as a proof of principle, we tested the device using peripheral blood from a patient with hepatocellular carcinoma, isolating more than 17 CTCs/ml, with purity/removal values of 96.03% and 99.99% of white blood cell and red blood cells, respectively. These results confirm highly efficient CTC isolation with a stringent cut-off value and better separation results than the state of the art.

NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images.

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.

Tumor ENPP1 (CD203a)/Haptoglobin Axis Exploits Myeloid-Derived Suppressor Cells to Promote Post-Radiotherapy Local Recurrence in Breast Cancer.

ABSTRACT: Locoregional failure (LRF) in patients with breast cancer post-surgery and post-irradiation is linked to a dismal prognosis. In a refined new model, we identified ectonucleotide pyrophosphatase/phosphodiesterase 1/CD203a (ENPP1) to be closely associated with LRF. ENPP1hi circulating tumor cells (CTC) contribute to relapse by a self-seeding mechanism. This process requires the infiltration of polymorphonuclear myeloid-derived suppressor cells and neutrophil extracellular trap (NET) formation. Genetic and pharmacologic ENPP1 inhibition or NET blockade extends relapse-free survival. Furthermore, in combination with fractionated irradiation, ENPP1 abrogation obliterates LRF. Mechanistically, ENPP1-generated adenosinergic metabolites enhance haptoglobin (HP) expression. This inflammatory mediator elicits myeloid invasiveness and promotes NET formation. Accordingly, a significant increase in ENPP1 and NET formation is detected in relapsed human breast cancer tumors. Moreover, high ENPP1 or HP levels are associated with poor prognosis. These findings unveil the ENPP1/HP axis as an unanticipated mechanism exploited by tumor cells linking inflammation to immune remodeling favoring local relapse. SIGNIFICANCE: CTC exploit the ENPP1/HP axis to promote local recurrence post-surgery and post-irradiation by subduing myeloid suppressor cells in breast tumors. Blocking this axis impairs tumor engraftment, impedes immunosuppression, and obliterates NET formation, unveiling new opportunities for therapeutic intervention to eradicate local relapse and ameliorate patient survival. This article is highlighted in the In This Issue feature, p. 1171.

Disparities in ACL Reconstruction: the Influence of Gender and Race on Incidence, Treatment, and Outcomes.

PURPOSE OF REVIEW: Anterior cruciate ligament (ACL) rupture is a common injury that has important clinical and economic implications. We aimed to review the literature to identify gender, racial and ethnic disparities in incidence, treatment, and outcomes of ACL injury. RECENT FINDINGS: Females are at increased risk for ACL injury compared to males. Intrinsic differences such as increased quadriceps angle and increased posterior tibial slope may be contributing factors. Despite lower rates of injury, males undergo ACL reconstruction (ACLR) more frequently. There is conflicting evidence regarding gender differences in graft failure and ACL revision rates, but males demonstrate higher return to sport (RTS) rates. Females report worse functional outcome scores and have worse biomechanical metrics following ACLR. Direct evidence of racial and ethnic disparities is limited, but present. White athletes have greater risk of ACL injury compared to Black athletes. Non-White and Spanish-speaking patients are less likely to undergo ACLR after ACL tear. Black and Hispanic youth have greater surgical delay to ACLR, increased risk for loss to clinical follow-up, and less physical therapy sessions, thereby leading to greater deficits in knee extensor strength during rehabilitation. Hispanic and Black patients also have greater risk for hospital admission after ACLR, though this disparity is improving. Females have higher rates of ACL injury with inconclusive evidence on anatomic predisposition and ACL failure rate differences between genders. Recent literature has suggested inferior RTS and functional outcomes following ACLR in females. Though there is limited and mixed data on incidence and outcome differences between races and ethnic groups, recent studies suggest there may be disparities in those who undergo ACLR and time to treatment.

Complement C5a induces the formation of neutrophil extracellular traps by myeloid-derived suppressor cells to promote metastasis.

Myeloid-derived suppressor cells (MDSCs) play a major role in cancer progression. In this study, we investigated the mechanisms by which complement C5a increases the capacity of polymorphonuclear MDSCs (PMN-MDSCs) to promote tumor growth and metastatic spread. Stimulation of PMN-MDSCs with C5a favored the invasion of cancer cells via a process dependent on the formation of neutrophil extracellular traps (NETs). NETosis was dependent on the production of high mobility group box 1 (HMGB1) by cancer cells. Moreover, C5a induced the surface expression of the HMGB1 receptors TLR4 and RAGE in PMN-MDSCs. In a mouse lung metastasis model, inhibition of C5a, C5a receptor-1 (C5aR1) or NETosis reduced the number of circulating-tumor cells (CTCs) and the metastatic burden. In support of the translational relevance of these findings, C5a was able to stimulate migration and NETosis in PMN-MDSCs obtained from lung cancer patients. Furthermore, myeloperoxidase (MPO)-DNA complexes, as markers of NETosis, were elevated in lung cancer patients and significantly correlated with C5a levels. In conclusion, C5a induces the formation of NETs from PMN-MDSCs in the presence of cancer cells, which may facilitate cancer cell dissemination and metastasis.

CD137 (4-1BB) costimulation of CD8+ T cells is more potent when provided in cis than in trans with respect to CD3-TCR stimulation.

CD137 (4-1BB; TNFSR9) is an activation-induced surface receptor that through costimulation effects provide antigen-primed T cells with augmented survival, proliferation and effector functions as well as metabolic advantages. These immunobiological mechanisms are being utilised for cancer immunotherapy with agonist CD137-binding and crosslinking-inducing agents that elicit CD137 intracellular signaling. In this study, side-by-side comparisons show that provision of CD137 costimulation in-cis with regard to the TCR-CD3-ligating cell is superior to that provided in-trans in terms of T cell activation, proliferation, survival, cytokine secretion and mitochondrial fitness in mouse and human. Cis ligation of CD137 relative to the TCR-CD3 complex results in more intense canonical and non-canonical NF-κB signaling and provides a more robust induction of cell cycle and DNA damage repair gene expression programs. Here we report that the superiority of cis versus trans CD137-costimulation is readily observed in vivo and is relevant for understanding the immunotherapeutic effects of CAR T cells and CD137 agonistic therapies currently undergoing clinical trials, which may provide costimulation either in cis or in trans.

CRMP2 as a Candidate Target to Interfere with Lung Cancer Cell Migration.

Collapsin response mediator protein 2 (CRMP2) is an adaptor protein that adds tubulin dimers to the growing tip of a microtubule. First described in neurons, it is now considered a ubiquitous protein that intervenes in processes such as cytoskeletal remodeling, synaptic connection and trafficking of voltage channels. Mounting evidence supports that CRMP2 plays an essential role in neuropathology and, more recently, in cancer. We have previously described a positive correlation between nuclear phosphorylation of CRMP2 and poor prognosis in lung adenocarcinoma patients. In this work, we studied whether this cytoskeleton molding protein is involved in cancer cell migration. To this aim, we evaluated CRMP2 phosphorylation and localization in the extending lamella of lung adenocarcinoma migrating cells using in vitro assays and in vivo confocal microscopy. We demonstrated that constitutive phosphorylation of CRMP2 impaired lamella formation, cell adhesion and oriented migration. In search of a mechanistic explanation of this phenomenon, we discovered that CRMP2 Ser522 phospho-mimetic mutants display unstable tubulin polymers, unable to bind EB1 plus-Tip protein and the cortical actin adaptor IQGAP1. In addition, integrin recycling is defective and invasive structures are less evident in these mutants. Significantly, mouse xenograft tumors of NSCLC expressing CRMP2 phosphorylation mimetic mutants grew significantly less than wild-type tumors. Given the recent development of small molecule inhibitors of CRMP2 phosphorylation to treat neurodegenerative diseases, our results open the door for their use in cancer treatment.