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The PROphet test provides a decision-making tool for first-line treatment of non-small cell lung cancer (NSCLC) patients without driver mutations. The test is based on plasma proteomics profiling using the SomaScan platform, followed by a machine learning-based analysis. During the PROphet predictor development, a set of predictive proteins was identified, termed resistance-associated proteins (RAPs). Here we set to examine the analytical validity of the SomaScan assay as the biomarker measurement method in the PROphet test, focusing either on all proteins measured using this platform, or specifically on the RAPs. Experimental precision analysis displayed a median coefficient of variation (CV) of 3.9% and 4.7% for intra-plate and inter-plate examination, respectively, when studying all proteins, with no difference between technicians. Notably, the RAPs displayed lower median CV values and lower CV range. The median accuracy rate of measurements obtained in sites was 88% and 94% for all proteins and for the RAPs, respectively, with no significant difference between technicians. Computational precision examination displayed high precision for 13 out of 14 examined samples, with median standard error of 0.1689. Cross-platform comparison between SomaScan platform and other proteomics platforms, including immunoassays, proximity extension assay (PEA) and mass spectrometry, displayed a median Spearman coefficient of 0.51 and 0.53 for all examined proteins and RAPs, with no significant difference between the two protein sets. Last, the effect of the cross-platform correlations on the prediction capabilities was examined, displaying only a minor effect of the correlation on the prediction capabilities of the PROphet predictor, with a median of 80% agreement between predictions obtained between the two examined proteomic platforms. Taken together, these results demonstrate strong analytical performance for the SomaScan technology, while the RAPs displayed improved capabilities in most of the analytical performance analyses.

Supplementary Figure and Table legends

Purpose:Treatment of metastatic melanoma has dramatically improved in recent years, thanks to the development of immunotherapy and BRAF-MEK–targeted therapies. However, these developments revealed marked heterogeneity in patient response, which is yet to be fully understood. In this work, we aimed to associate the proteomic profiles of metastatic melanoma with the patient clinical information, to identify protein correlates with metastatic location and prior treatments.Experimental Design:We performed mass spectrometry–based proteomic analysis of 185 metastatic melanoma samples and followed with bioinformatics analysis to examine the association of metastatic location, BRAF status, survival, and immunotherapy response with the tumor molecular profiles.Results:Bioinformatics analysis showed a high degree of functional heterogeneity associated with the site of metastasis. Lung metastases presented higher immune-related proteins, and higher mitochondrial-related processes, which were shown previously to be associated with better immunotherapy response. In agreement, epidemiological analysis of data from the National Cancer Database showed improved response to anti-programmed death 1, mainly in patients with lung metastasis. Focus on lung metastases revealed prognostic and molecular heterogeneity and highlighted potential tissue-specific biomarkers. Analysis of the BRAF mutation status and prior treatments with MAPK inhibitors proposed the molecular basis of the effect on immunotherapy response and suggested coordinated combination of immunotherapy and targeted therapy may increase treatment efficacy.Conclusions:Altogether, the proteomic data provided novel molecular determinants of critical clinical features, including the effects of sequential treatments and metastatic locations. These results can be the basis for development of site-specific treatments toward treatment personalization.

Supplementary Table S2

Supplementary Figure S2. Cell proliferation and glycolytic activity upon transient IDH2 overexpression A-B - Quantification of the integrated density of cellROX staining in control and IDH2-overexpressing cells (A) and in the control and IDH2 knockout cells (B) using ImageJ. C -Western blot analysis shows transient overexpression of IDH2 in HCC1500, HCC1954 and in HCC1937 breast cancer cell lines, with GFP overexpression as control. Vinculin serves as loading control. D-F - Transient overexpression of IDH2 does not affect the proliferation rate of the cell lines HCC1954 (D), HCC1500 (E) and HCC1937 (F). G - Re-expression of IDH2 in knockout HCC1143 cells with mCherry (mCh) as control. H - Rescue of sensitivity to oxidative damage in IDH2 knockout cells with re-expression of IDH2. I-K - Increased glucose uptake and lactate secretion, accompanied with elevated extracellular acidification rates measured by Seahorse flux analyzer showed higher glycolytic activity upon transient IDH2 overexpression. (*p-value

Comparison of healthy samples to each cancer subtype

Supplementary Table S3: Eigenprotein modules and the metabolic pathway enrichments

Supplementary Figures 1-7

Supplementary Fig. S1: Sample preparation and processing workflows. Supplementary Fig. S2: Quantitative assessment of consensus clustering results. Supplementary Fig. S3: 2D enrichment analysis of LumB-related gene sets revealed common enrichment in CC1 when compared to both CC2 and CC3. Supplementary Fig. S4: Clustering of TCGA-RPPA compared to proteomic consensus clusters.

Cancer-specific metabolic phenotypes and their vulnerabilities represent a viable area of cancer research. In this study, we explored the association of breast cancer subtypes with different metabolic phenotypes and identified isocitrate dehydrogenase 2 (IDH2) as a key player in triple-negative breast cancer (TNBC) and HER2. Functional assays combined with mass spectrometry–based analyses revealed the oncogenic role of IDH2 in cell proliferation, anchorage-independent growth, glycolysis, mitochondrial respiration, and antioxidant defense. Genome-scale metabolic modeling identified phosphoglycerate dehydrogenase (PHGDH) and phosphoserine aminotransferase (PSAT1) as the synthetic dosage lethal (SDL) partners of IDH2. In agreement, CRISPR-Cas9 knockout of PHGDH and PSAT1 showed the essentiality of serine biosynthesis proteins in IDH2-high cells. The clinical significance of the SDL interaction was supported by patients with IDH2-high/PHGDH-low tumors, who exhibited longer survival than patients with IDH2-high/PHGDH-high tumors. Furthermore, PHGDH inhibitors were effective in treating IDH2-high cells in vitro and in vivo. Altogether, our study creates a new link between two known cancer regulators and emphasizes PHGDH as a promising target for TNBC with IDH2 overexpression.Significance:These findings highlight the metabolic dependence of IDH2 on the serine biosynthesis pathway, adding an important layer to the connection between TCA cycle and glycolysis, which can be translated into novel targeted therapies.

Breast cancer classification has been the focus of numerous worldwide efforts, analyzing the molecular basis of breast cancer subtypes and aiming to associate them with clinical outcome and to improve the current diagnostic routine. Genomic and transcriptomic profiles of breast cancer have been well established, however the proteomic contribution to these profiles has yet to be elucidated. In this work, we utilized mass spectrometry–based proteomic analysis on more than 130 clinical breast samples to demonstrate intertumor heterogeneity across three breast cancer subtypes and healthy tissue. Unsupervised analysis identified four proteomic clusters, among them, one that represents a novel luminal subtype characterized by increased PI3K signaling. This subtype was further validated using an independent protein-based dataset, but not in two independent transcriptome cohorts. These results demonstrate the importance of deep proteomic analysis, which may affect cancer treatment decision making.Significance: These findings utilize extensive proteomics to identify a novel luminal breast cancer subtype, highlighting the added value of clinical proteomics in breast cancer to identify unique features not observable by genomic approaches. Cancer Res; 78(20); 6001–10. ©2018 AACR.

ImportanceAdvanced stage non-small cell lung cancer (NSCLC) patients with no driver mutations are typically treated with immune checkpoint inhibitor (ICI)-based therapy, either in the form of monotherapy or concurrently with chemotherapy, while treatment modality selection is based mainly on programmed death ligand 1 (PD-L1) expression levels in the tumor. However, PD-L1 assays are only moderately predictive of therapeutic benefit.ObjectiveTo develop a novel decision-making tool for physicians treating NSCLC patients on whether to administer immune checkpoint inhibitor (ICI) therapy alone or in combination with chemotherapy.Design, setting, and participantsThis multicenter observational study includes patients from an ongoing clinical trial (PROPHETIC;NCT04056247). Patients were recruited from 13 different centers (total n=425; 58 patients were excluded) from June 2016 and June 2021. Plasma samples were obtained prior to treatment initiation, and deep proteomic profiling was conducted. PROphet® computational model for predicting clinical benefit (CB) probability at 12 months was developed based on the plasma proteomic profile. The model performance was validated in a blinded manner. Following validation, training and prediction was performed over the entire cohort using cross-validation methodology. The patients were divided into four groups based on their PD-L1 expression level combined with their CB probability, and the survival outcome was examined for each group. The data were analyzed from July to October 2022.Main outcome and measuresClinical benefit from ICI-based treatment, overall survival (OS) and progression-free survival (PFS).ResultsThe model displayed strong predictive capability with an AUC of 0.78 (p-value = 5.00e-05), outperforming a PD-L1-based predictive model (AUC = 0.62; p-value 2.76e-01), and exhibited a significant difference in OS and PFS between patients with low and high CB probabilities. When combining CB probability with PD-L1 expression levels, four patient subgroups were identified; (i) patients with PD-L1≥50% and a negative PROphet result who significantly benefit from ICI-chemotherapy combination therapy compared to ICI monotherapy; (ii) patients with PD-L1≥50% and a positive PROphet result who benefit similarly from either treatment modalities; (iii) patients with PD-L1Conclusions and relevanceThe PROphet® model displayed good performance for prediction of CB at 12 months based on a plasma sample obtained prior to treatment. Our findings further demonstrate a potential clinical utility for informing treatment decisions for NSCLC patients treated with ICIs by adding resolution to the PD-L1 biomarker currently used to guide treatment selection, thereby enabling to select the most suitable treatment modality for each patient.

Adoptive cell therapy (ACT) based on ex vivo expanded autologous tumor-infiltrating lymphocytes (TILs) can mediate durable antitumor responses even in heavily pretreated patients. However, only a subset of patients responds to ACT; efforts to identify correlates of response have focused on profiling the tumor or the TIL but rarely in an interactive environment. Interactive profiling can provide unique insights into the clinical performance of TILs since the fate, function, and metabolism of TILs are influenced by autologous tumor-derived factors. Here, we performed a suite of cell-sparing assays dubbed holistic analysis of the bioactivity of interacting T cells and autologous tumor cells (HABITAT). HABITAT profiling of TILs used for human ACT and their autologous tumor cells included function-based single-cell profiling by timelapse imaging microscopy in nanowell grids (TIMING); multi-omics using RNA-sequencing and proteomics; metabolite inference using genome-scale metabolic modeling, and pulse-chase assays based on confocal microscopy to profile the uptake and fate of fatty acids (FA). Phenotypically, the ACT TILs from both responders (Rs) and nonresponders (NRs) were comprised of predominantly effector memory T cells (TEM cells) and did not express a high frequency of programmed death ligand-1 (PD-L1) and showed no differences in TCR diversity. Our results demonstrate that while tumor cells from both Rs and NRs are efficient at uptaking FAs, R TILs are significantly more efficient at utilizing FA through fatty acid oxidation (FAO) than NR TILs under nutrient starvation conditions. While it is likely that lipid and FA uptake is an inherent adaptation of TIL populations to lipid-rich environments, performing FAO sustains the survival of TILs and allows them to sustain antitumor cytolytic activity. We propose that metabolic plasticity enabling FAO is a desirable attribute of human TILs for ACT leading to clinical responses.

The magnitude of neuronal activation is commonly considered a critical factor for conscious perception of visual content. However, this dogma contrasts with the phenomenon of rapid adaptation, in which the magnitude of neuronal activation drops dramatically in a rapid manner, while the visual stimulus and the conscious experience it elicits remain stable. Here we report that the profiles of multi-site activation patterns and their relational geometry –i.e. the similarity distances between activation patterns, as revealed using iEEG recordings, are sustained during extended stimulation despite the major magnitude decrease. These results are compatible with the hypothesis that conscious perceptual content is associated with the neuronal pattern profiles and their similarity distances, rather than by the overall activation magnitude, in human visual cortex.

Introduction: Treatment modality selection for metastatic NSCLC patients (immunotherapy alone vs. combination of immunotherapy with chemotherapy) relies mainly on determining the programmed cell death 1 (PD-L1) expression levels in the tumor. However, available assays are only moderately predictive. Here we set to develop PROphet®, a plasma proteomics-based predictive model for informing treatment decisions for NSCLC patients receiving immune checkpoint inhibitor (ICI)-based therapy. Methods: Pre-ICI plasma samples were collected in 12 centers in a clinical trial (NCT04056247) from 367 advanced-stage NSCLC patients. Clinical benefit (CB) was assessed at 12 months based on the occurrence of progression-free survival (PFS) until this time point. Deep proteomic profiling of the plasma samples was performed using aptamer-based technology. A novel machine learning model was developed to determine the CB probability for each patient, and the performance was successfully evaluated on an independent validation set, followed by training and prediction over the entire cohort using cross-validation. The resulting PROphet® score (positive or negative) was determined by setting the median CB rate probability as a threshold. The patients were divided into four subgroups based on their PD-L1 expression level combined with their PROphet® score prediction, and the overall survival (OS) was examined for each subgroup. Results: The PROphet® computational model was evaluated in a blinded manner on a subset of 85 patients and displayed strong predictive capability with area under the curve (AUC) of the receiver operating characteristics (ROC) plot of 0.78 (p-value = 5.00e-05), outperforming a PD-L1-based predictive model (AUC = 0.62; p-value 2.76e-01). When combining PROphet® score with PD-L1 expression levels, four different outcome patterns were identified: (i) Patients with PD-L1 ≥50% and PROphet® negative score, who displayed significantly longer OS when treated with ICI-chemotherapy combination therapy compared to ICI monotherapy and may consider combination therapy. (ii) Patients with PD-L1 ≥50% and PROphet® positive score, who benefit similarly from either treatment modalities, and may consider monotherapy to avoid the potential toxicity of the combination therapy. (iii) Patients with PD-L1 Conclusions: Altogether, the PROphet® model, when combined with PD-L1 test, stratifies the patients into four subgroups, providing additional resolution to the PD-L1 biomarker currently used to guide treatment selection. Furthermore, the model accurately predicts CB at 12 months based on proteomic analysis of a pre-treatment plasma sample. Citation Format: Michal Harel, Petros Christopoulos, Coren Lahav, Itamar Sela, Nili Dahan, Niels Reinmuth, Ina Koch, Alona Zer, Mor Moskovitz, Adva Levy-Barda, Michal Lotem, Hovav Nechushtan, Rivka Katzenelson, Abed Agbarya, Mahmoud Abu-Amna, Maya Gottfried, Ido Wolf, Ella Tepper, Yanyan Lou, Raya Leibowitz, Adam P. Dicker, David Gandara, David P. Carbone. A pretreatment blood-based proteomic biomarker for enhanced decision-making in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2159.

Endosomes are compositionally dynamic organelles that regulate signaling, nutrient status and organelle quality by specifying whether material entering the cells will be shuttled back to the cell surface or degraded by the lysosome. Recently, membrane contact sites (MCSs) between the endoplasmic reticulum (ER) and endosomes have emerged as important players in endosomal protein sorting, dynamics and motility. Here, we show that PDZD8, a Synaptotagmin-like Mitochondrial lipid-binding Proteins (SMP) domain-containing ER transmembrane protein, utilizes distinct domains to interact with Rab7-GTP and the ER transmembrane protein Protrudin and together these components localize to an ER-late endosome MCS. At these ER-late endosome MCSs, mitochondria are also recruited to form a three-way contact. Thus, our data indicate that PDZD8 is a shared component of two distinct MCSs and suggest a role for SMP-mediated lipid transport in the regulation of endosome function., Membrane contact sites between organelles have been shown to play important biological roles. Here, the authors show that at the ER, PDZD8 associates with Protrudin and also with Rab7 endosomes and recruits mitochondria to form three-way contacts.

Summary Dysregulated homeostasis of neural activity has been hypothesized to drive Alzheimer’s disease (AD) pathogenesis. AD begins with a decades-long presymptomatic phase, but whether homeostatic mechanisms already begin failing during this silent phase is unknown. We show that before the onset of memory decline and sleep disturbances, familial AD (fAD) model mice display no deficits in CA1 mean firing rate (MFR) during active wakefulness. However, homeostatic down-regulation of CA1 MFR is disrupted during non-rapid eye movement (NREM) sleep and general anesthesia in fAD mouse models. The resultant hyperexcitability is attenuated by the mitochondrial dihydroorotate dehydrogenase (DHODH) enzyme inhibitor, which tunes MFR toward lower set-point values. Ex vivo fAD mutations impair downward MFR homeostasis, resulting in pathological MFR set points in response to anesthetic drug and inhibition blockade. Thus, firing rate dyshomeostasis of hippocampal circuits is masked during active wakefulness but surfaces during low-arousal brain states, representing an early failure of the silent disease stage., Graphical abstract, Highlights • CA1 firing rates are similar between WT and fAD model mice in active wakefulness • Down-regulation of CA1 firing rates by NREM sleep and anesthesia fails in fAD mice • fAD mutations impair stabilization of lower firing rate set points by anesthetics • DHODH inhibition suppresses CA1 hyperexcitability under anesthesia in fAD mice, Zarhin et al. show that firing rate dyshomeostasis is masked during active wakefulness but surfaces during anesthesia and NREM sleep in hippocampal circuits, preceding global sleep and memory disturbances in a mouse model of familial Alzheimer’s disease.

BackgroundImmune checkpoint inhibitors (ICIs) have revolutionized cancer treatment by shifting the focus from the tumor to the immune system of the host. Despite durable response to ICIs, only a small proportion of non-small lung cancer (NSCLC) patients respond to this treatment. Thus, great effort is currently focused on uncovering mechanisms of resistance and identifying predictive biomarkers for outcome.MethodsBlood plasma was obtained from 143 NSCLC patients treated with ICI-based therapy at baseline and early on-treatment (following the first treatment), and the levels of approximately 800 proteins were determined using ELISA-based arrays. Bioinformatic analysis was performed in order to detect novel patterns of resistance to ICI-based therapy. To identify a signature that predicts clinical outcome, a machine learning algorithm was applied.ResultsUnsupervised bioinformatic analysis of the plasma proteomic profiles classified the patients into 3 clusters with distinct clinical and biological features. Patients in cluster #1 exhibited resistance to therapy, bone metastasis and high TNM (tumors, nodes and metastasis) staging; this cluster displayed high levels of proteins related to glycan and pyrimidine metabolism and cell-adhesion. Cluster #2 was enriched with responders, males, and patients with low TNM staging; this cluster displayed a strong representation of desmoglein proteins. Cluster #3 was enriched with female patients while the proteome of these patients displayed high levels of MAPK signaling related proteins. Patient clusters were largely unchanged when comparing baseline and on-treatment data, suggesting pre-existing rather than acquired resistance to therapy. A further comparison between responders and non-responders identified six significantly differentially expressed proteins comprised of both host- and tumor-related proteins, with non-responders displaying a significant enrichment of neutrophil proteins at baseline and early-on-treatment. Notably, there was no significant difference in the neutrophil count between responders and non-responders, suggesting a functional shift in neutrophils upon treatment in non-responders. Lastly, we identified a predictive signature for response comprised of two proteins and two clinical features. The performance of the predictive signature reached an area under the curve (AUC) of the receiver operating characteristics (ROC) plot of 0.8 in an independent validation subset of the cohort, indicating a high predictive power.ConclusionsHere we performed a deep bioinformatic analysis of plasma proteome profiles of 143 NSCLC patients undergoing ICI-based therapy. Our study sheds light on underlying mechanisms of resistance to ICI-based therapy and reveals a predictive signature for response in NSCLC patients.Ethics ApprovalData and study specimens were purchased from Indivumed and Sheba medical center, approval number 0226-13-SMC (institutional review board). Participants gave informed consent before taking part.

Altered metabolism is a hallmark of cancer, but little is still known about its regulation. In this study, we measure transcriptomic, proteomic, phospho-proteomic and fluxomics data in a breast cancer cell-line (MCF7) across three different growth conditions. Integrating these multiomics data within a genome scale human metabolic model in combination with machine learning, we systematically chart the different layers of metabolic regulation in breast cancer cells, predicting which enzymes and pathways are regulated at which level. We distinguish between two types of reactions, directly and indirectly regulated. Directly-regulated reactions include those whose flux is regulated by transcriptomic alterations (~890) or via proteomic or phospho-proteomics alterations (~140) in the enzymes catalyzing them. We term the reactions that currently lack evidence for direct regulation as (putative) indirectly regulated (~930). Many metabolic pathways are predicted to be regulated at different levels, and those may change at different media conditions. Remarkably, we find that the flux of predicted indirectly regulated reactions is strongly coupled to the flux of the predicted directly regulated ones, uncovering a tiered hierarchical organization of breast cancer cell metabolism. Furthermore, the predicted indirectly regulated reactions are predominantly reversible. Taken together, this architecture may facilitate rapid and efficient metabolic reprogramming in response to the varying environmental conditions incurred by the tumor cells. The approach presented lays a conceptual and computational basis for mapping metabolic regulation in additional cancers.

The discovery that deep convolutional neural networks (DCNNs) achieve human performance in realistic tasks offers fresh opportunities for linking neuronal tuning properties to such tasks. Here we show that the face-space geometry, revealed through pair-wise activation similarities of face-selective neuronal groups recorded intracranially in 33 patients, significantly matches that of a DCNN having human-level face recognition capabilities. This convergent evolution of pattern similarities across biological and artificial networks highlights the significance of face-space geometry in face perception. Furthermore, the nature of the neuronal to DCNN match suggests a role of human face areas in pictorial aspects of face perception. First, the match was confined to intermediate DCNN layers. Second, presenting identity-preserving image manipulations to the DCNN abolished its correlation to neuronal responses. Finally, DCNN units matching human neuronal group tuning displayed view-point selective receptive fields. Our results demonstrate the importance of face-space geometry in the pictorial aspects of human face perception., Deep convolutional neural networks (DCNNs) are able to identify faces on par with humans. Here, the authors record neuronal activity from higher visual areas in humans and show that face-selective responses in the brain show similarity to those in the intermediate layers of the DCNN.

High-grade ovarian cancer (HGOC) is the leading cause of mortality from gynecological malignancies, because of diagnosis at a metastatic stage. Current screening options fail to improve mortality because of the absence of early-stage-specific biomarkers. We postulated that a liquid biopsy, such as utero-tubal lavage (UtL), may identify localized lesions better than systemic approaches of serum/plasma analysis. Further, while mutation-based assays are challenged by the rarity of tumor DNA within nonmutated DNA, analyzing the proteomic profile, is expected to enable earlier detection, as it reveals perturbations in both the tumor as well as in its microenvironment. To attain deep proteomic coverage and overcome the high dynamic range of this body fluid, we applied our method for microvesicle proteomics to the UtL samples. Liquid biopsies from HGOC patients (n = 49) and controls (n = 127) were divided into a discovery and validation sets. Data-dependent analysis of the samples on the Q-Exactive mass spectrometer provided depth of 8578 UtL proteins in total, and on average ∼3000 proteins per sample. We used support vector machine algorithms for sample classification, and crossed three feature-selection algorithms, to construct and validate a 9-protein classifier with 70% sensitivity and 76.2% specificity. The signature correctly identified all Stage I lesions. These results demonstrate the potential power of microvesicle-based proteomic biomarkers for early cancer diagnosis.

e21132 Background: To date, predicting response to immune checkpoint blockade (ICB) therapy in non-small cell lung cancer (NSCLC) patients is based on assessing PD-L1 levels in tumor biopsies. However, such assays are only moderately predictive. In addition, the assays require a tumor biopsy and do not aid in identifying patient-specific resistance mechanisms beyond PD-L1. To overcome these issues, we developed a novel computational approach for predicting response to ICB based on pre-treatment proteomic measurements in liquid biopsies. Methods: Plasma samples were collected from 184 NSCLC patients prior to treatment, along with comprehensive clinical data, as part of an ongoing multi-center clinical trial (PROPHETIC; NCT04056247). Overall response rate (ORR) was assessed 3- and 6-months following treatment initiation. A deep proteomic profiling of each plasma sample was performed, measuring the expression levels of approximately 7000 proteins. A novel proprietary machine learning approach was developed on a subset of samples (training set; n = 110) and then was tested on a blind independent validation set (n = 74). Results: A computational model was developed on the proteomic data by identifying patient-specific Resistance Associated Proteins (RAPs). Focusing on differentially expressed proteins between responders and non-responders, a protein was defined as a RAP in a given patient based on its expression level in the patient relative to the expression distribution of the RAP in responders and non-responders. The probability of response to ICB treatment was determined based on the patient’s RAP profile together with 4 clinical parameters. The RAP-based machine learning model successfully stratified between patients with prolonged and limited benefit with a hazard ratio (HR) of 4.5 (confidence interval 2.07-9.77; p-value < 0.0001) and 2.27 (confidence interval 1.7-4.03; p-value = 0.004) for overall survival and progression free survival, respectively. Each patient displayed a resistance map comprised of a unique combination of RAPs, suggesting a new approach for personalized medicine based on patient-specific pathway blockade. For example, a patient with KDR and IL-6 defined as RAPs may benefit from a clinical trial that targets any of these RAPS in combination with ICB. Last, an exploration into the biological functions of the identified RAPs revealed specific biological processes in each response group, including splicing, complement system, coagulation and signaling. Conclusions: We have developed a novel computational approach based on proteomic profiling of liquid biopsies for predicting response to ICB treatment in NSCLC patients. Our approach also sheds light on patient-specific resistance mechanisms, potentially enabling personalized treatment options and patient monitoring over time.

BackgroundImmune checkpoint inhibitors (ICIs) have revolutionized the cancer therapy landscape due to long-term benefits in patients with advanced metastatic disease. However, robust predictive biomarkers for response are still lacking and treatment resistance is not fully understood.MethodsWe profiled approximately 800 pre-treatment and on-treatment plasma proteins from 143 ICI-treated patients with non-small cell lung cancer (NSCLC) using ELISA-based arrays. Different clinical parameters were collected from the patients including specific mutations, smoking habits, and body mass index, among others. Machine learning algorithms were used to identify a predictive signature for response. Bioinformatics tools were used for the identification of patient subtypes and analysis of differentially expressed proteins and pathways in each response group.ResultsWe identified a predictive signature for response to treatment comprizing two proteins (CXCL8 and CXCL10) and two clinical parameters (age and sex). Bioinformatic analysis of the proteomic profiles identified three distinct patient clusters that correlated with multiple parameters such as response, sex and TNM (tumors, nodes, and metastasis) staging. Patients who did not benefit from ICI therapy exhibited significantly higher plasma levels of several proteins on-treatment, and enrichment in neutrophil-related proteins.ConclusionsOur study reveals potential biomarkers in blood plasma for predicting response to ICI therapy in patients with NSCLC and sheds light on mechanisms underlying therapy resistance.

Cancer-specific metabolic phenotypes and their vulnerabilities represent a viable area of cancer research. In this study, we explored the association of breast cancer subtypes with different metabolic phenotypes and identified isocitrate dehydrogenase 2 (IDH2) as a key player in triple-negative breast cancer (TNBC) and HER2. Functional assays combined with mass spectrometry–based analyses revealed the oncogenic role of IDH2 in cell proliferation, anchorage-independent growth, glycolysis, mitochondrial respiration, and antioxidant defense. Genome-scale metabolic modeling identified phosphoglycerate dehydrogenase (PHGDH) and phosphoserine aminotransferase (PSAT1) as the synthetic dosage lethal (SDL) partners of IDH2. In agreement, CRISPR-Cas9 knockout of PHGDH and PSAT1 showed the essentiality of serine biosynthesis proteins in IDH2-high cells. The clinical significance of the SDL interaction was supported by patients with IDH2-high/PHGDH-low tumors, who exhibited longer survival than patients with IDH2-high/PHGDH-high tumors. Furthermore, PHGDH inhibitors were effective in treating IDH2-high cells in vitro and in vivo. Altogether, our study creates a new link between two known cancer regulators and emphasizes PHGDH as a promising target for TNBC with IDH2 overexpression. Significance: These findings highlight the metabolic dependence of IDH2 on the serine biosynthesis pathway, adding an important layer to the connection between TCA cycle and glycolysis, which can be translated into novel targeted therapies.

The DNA damage response (DDR) is a complex signaling network that relies on cascades of protein phosphorylation, which are initiated by three protein kinases of the family of PI3-kinase-related protein kinases (PIKKs): ATM, ATR, and DNA-PK. ATM is missing or inactivated in the genome instability syndrome, ataxia-telangiectasia (A-T). The relative shares of these PIKKs in the response to genotoxic stress and the functional relationships among them are central questions in the genome stability field. We conducted a comprehensive phosphoproteomic analysis in human wild-type and A-T cells treated with the double-strand break-inducing chemical, neocarzinostatin, and validated the results with the targeted proteomic technique, selected reaction monitoring. We also matched our results with 34 published screens for DDR factors, creating a valuable resource for identifying strong candidates for novel DDR players. We uncovered fine-tuned dynamics between the PIKKs following genotoxic stress, such as DNA-PK-dependent attenuation of ATM. In A-T cells, partial compensation for ATM absence was provided by ATR and DNA-PK, with distinct roles and kinetics. The results highlight intricate relationships between these PIKKs in the DDR. ISSN:0261-4189 ISSN:1460-2075

Purpose: Treatment of metastatic melanoma has dramatically improved in recent years, thanks to the development of immunotherapy and BRAF-MEK–targeted therapies. However, these developments revealed marked heterogeneity in patient response, which is yet to be fully understood. In this work, we aimed to associate the proteomic profiles of metastatic melanoma with the patient clinical information, to identify protein correlates with metastatic location and prior treatments. Experimental Design: We performed mass spectrometry–based proteomic analysis of 185 metastatic melanoma samples and followed with bioinformatics analysis to examine the association of metastatic location, BRAF status, survival, and immunotherapy response with the tumor molecular profiles. Results: Bioinformatics analysis showed a high degree of functional heterogeneity associated with the site of metastasis. Lung metastases presented higher immune-related proteins, and higher mitochondrial-related processes, which were shown previously to be associated with better immunotherapy response. In agreement, epidemiological analysis of data from the National Cancer Database showed improved response to anti-programmed death 1, mainly in patients with lung metastasis. Focus on lung metastases revealed prognostic and molecular heterogeneity and highlighted potential tissue-specific biomarkers. Analysis of the BRAF mutation status and prior treatments with MAPK inhibitors proposed the molecular basis of the effect on immunotherapy response and suggested coordinated combination of immunotherapy and targeted therapy may increase treatment efficacy. Conclusions: Altogether, the proteomic data provided novel molecular determinants of critical clinical features, including the effects of sequential treatments and metastatic locations. These results can be the basis for development of site-specific treatments toward treatment personalization.

Maladaptive Daydreaming (MD) is a newly proposed mental disorder characterized by excessive, vivid fantasy activity impairing functioning. There is a high comorbidity of MD with Obsessive-Compulsive Spectrum Symptoms (OCSS), yet the mechanisms responsible for this relationship are still unclear. The present study set out to explore the relationship between MD and OCSS by: (1) examining dissociation, trauma, sense of control, and mind-wandering as potential mediators; (2) exploring whether MD is more strongly related to obsessions or compulsions; and (3) identifying patterns of specific obsessions and/or compulsions common in an MD sample. A group of 510 participants with self-identified MD completed a battery of online questionnaires. Dissociation and sense of control significantly mediated the MD-OCSS association. MD was moderately related to both obsessions and compulsions but was significantly more strongly related to the former. Frequently endorsed obsessions and compulsions among MD participants included checking and repetition compulsions, intrusive obsessions, and body-related obsessions and compulsions. We conclude that dissociative mechanisms, including dissociative absorption, play a major role in the relationship between MD and OCSS and may lead to consequent checking when transitioning back to reality, altered embodiment, intrusive images, and thoughts, and an impaired sense of mental control. Clinicians working with OCSS should be aware of the possible role of MD in the development or maintenance of symptoms. Future work should develop useful interventions for this type of shared psychopathology.

Tumor cell heterogeneity is primarily dictated by mutational changes, sometimes leading to clones that undergo a metastatic switch. However, little is known about tumor heterogeneity following chemotherapy perturbation. Here we studied the possible involvement of tumor-derived extracellular vesicles, often referred to as tumor-derived microparticles (TMPs), as mediators of the metastatic switch in the tumor microenvironment by hindering cell adhesion properties. Specifically, we show that highly metastatic or chemotherapy-treated breast cancer cells shed an increased number of TMPs compared to their respective controls. We found that these TMPs substantially reduce cell adhesion and disrupt actin filament structure, therefore increasing their biomechanical force pace, further implicating tumor cell dissemination as part of the metastatic cascade. Our results demonstrate that these pro-metastatic effects are mediated in part by CD44 which is highly expressed in TMPs obtained from highly metastatic cells or cells exposed to chemotherapy when compared to cells with low metastatic potential. Consequently, when we inhibited CD44 expression on TMPs by a pharmacological or a genetic approach, increased tumor cell adhesion and re-organized actin filament structure were observed. We also demonstrated that breast cancer patients treated with paclitaxel chemotherapy exhibited increased CD44-expressing TMPs. Overall, our study provides further insights into the role of TMPs in promoting metastasis, an effect which is augmented when tumor cells are exposed to chemotherapy.

BackgroundAdoptive transfer of ex vivo expanded tumor-infiltrating lymphocytes (TIL) have shown durable responses in metastatic melanoma, yet these responses are unpredictable. Bioenergetics dictates the function and fate of adoptively transferred human T cells within the tumor microenvironment but the nature of metabolic competition leading to T-cell function and dysfunction are incompletely understood.MethodsWe integrated the profiling of TIL co-cultured with their autologous primary tumor cells with the aid of a suite of high-throughput single-cell functional assays, transcriptional, and proteomic assays. We validated the results of the model using flow cytometry and confocal microscopy. Association of functional features with clinical outcome was assessed.ResultsTimelapse Imaging Microscopy In Nanowell Grids (TIMING) demonstrated that while TIL frequencies in killing autologous tumor cells are equivalent across the donors, R-TIL had a significantly higher survival rate than NR-TILs. Tumor cells from NR patients had higher motility and showed increased elongation compared to R-tumors. RNA-sequencing (RNA-seq) and proteomics showed that NR-tumors were enriched in pathways associated with utilization of fatty acids (FAs) and adipogenesis, as well as cancer cell metastasis, cellular motility, adhesion, and migration. Candidate genes associated with ameboidal migration (MYH9, MYH2; WNT5B and SERPINE1) and FA utilization (CD36 and PPARG) were enriched in the NR-tumors. Flow cytometry and confocal microscopy confirmed that NR-tumors showed increased CD36 expression and FA uptake compared to R-tumors. To simulate metabolic competition, we co-cultured the TIL with autologous tumors and sorted TIL for RNAseq. The R-TIL were enriched in pathways related to mitochondrial and carbohydrate metabolism; fatty acid oxidation (FAO), and long-chain FAs with a direct enrichment in fatty acyl CoA biosynthesis and both peroxisomal and mitochondrial FAO. Since patient-derived TILs were limiting for metabolomics type assays, we utilized genome-scale metabolic models to infer relevant metabolic pathways by comparison to the human metabolic Atlas (HMR2). At the level of individual metabolites, the significantly enriched metabolites within R TILs were dominated by peroxisome and mitochondria derived fatty acyl-CoA: e.g. palmitoyl-CoA, linoleoyl-CoA, and oleoyl-CoA. We utilized flow cytometry and confocal microscopy to perform pulse-chase assays with FAs for validation. R-TILs showed an increased accumulation of FA into the mitochondria confirming a direct role for TIL FAO.ConclusionsEfficient competition for FAs is a key attribute of T-cell efficacy in ACT. R-TILs are able to utilize FAs via FAO when in competition with autologous tumor cells whereas NR tumors effectively uptake and store FAs preventing T-cell function.AcknowledgementsThis abstract was supported by the NIH (R01CA174385); CPRIT (RP180466); MRA Established Investigator Award to NV (509800), Welch Foundation (E1774); NSF (1705464); CDMRP (CA160591); Owens Foundation (NV). We would like to acknowledge the MDACC Flow Cytometry and Cellular Imaging Core facility for the FACS sorting (NCI P30CA16672), UH Seq-n-edit core for RNA-seq, Intel for the loan of computing cluster, and the UH Center for Advanced Computing and Data Systems (CACDS) for high-performance computing facilities.Trial Registration protocol (2004–0069)Ethics ApprovalApproved by the Institutional Review Board (IRB) of the MD Anderson Cancer Center (Houston, TX) and an FDA- approved Investigational New Drug (IND) application (NCT00338377)