SARS-CoV-2-specific T cells generated for adoptive immunotherapy are capable of recognizing multiple SARS-CoV-2 variants.

Adoptive T-cell immunotherapy has provided promising results in the treatment of viral complications in humans, particularly in the context of immunocompromised patients who have exhausted all other clinical options. The capacity to expand T cells from healthy immune individuals is providing a new approach to anti-viral immunotherapy, offering rapid off-the-shelf treatment with tailor-made human leukocyte antigen (HLA)-matched T cells. While most of this research has focused on the treatment of latent viral infections, emerging evidence that SARS-CoV-2-specific T cells play an important role in protection against COVID-19 suggests that the transfer of HLA-matched allogeneic off-the-shelf virus-specific T cells could provide a treatment option for patients with active COVID-19 or at risk of developing COVID-19. We initially screened 60 convalescent individuals and based on HLA typing and T-cell response profile, 12 individuals were selected for the development of a SARS-CoV-2-specific T-cell bank. We demonstrate that these T cells are specific for up to four SARS-CoV-2 antigens presented by a broad range of both HLA class I and class II alleles. These T cells show consistent functional and phenotypic properties, display cytotoxic potential against HLA-matched targets and can recognize HLA-matched cells infected with different SARS-CoV-2 variants. These observations demonstrate a robust approach for the production of SARS-CoV-2-specific T cells and provide the impetus for the development of a T-cell repository for clinical assessment.

Identifying collateral and synthetic lethal vulnerabilities within the DNA-damage response.

BACKGROUND: A pair of genes is defined as synthetically lethal if defects on both cause the death of the cell but a defect in only one of the two is compatible with cell viability. Ideally, if A and B are two synthetic lethal genes, inhibiting B should kill cancer cells with a defect on A, and should have no effects on normal cells. Thus, synthetic lethality can be exploited for highly selective cancer therapies, which need to exploit differences between normal and cancer cells. RESULTS: In this paper, we present a new method for predicting synthetic lethal (SL) gene pairs. As neighbouring genes in the genome have highly correlated profiles of copy number variations (CNAs), our method clusters proximal genes with a similar CNA profile, then predicts mutually exclusive group pairs, and finally identifies the SL gene pairs within each group pairs. For mutual-exclusion testing we use a graph-based method which takes into account the mutation frequencies of different subjects and genes. We use two different methods for selecting the pair of SL genes; the first is based on the gene essentiality measured in various conditions by means of the "Gene Activity Ranking Profile" GARP score; the second leverages the annotations of gene to biological pathways. CONCLUSIONS: This method is unique among current SL prediction approaches, it reduces false-positive SL predictions compared to previous methods, and it allows establishing explicit collateral lethality relationship of gene pairs within mutually exclusive group pairs.

Performance evaluation measures for protein complex prediction.

Protein complexes play a dominant role in cellular organization and function. Prediction of protein complexes from the network of physical interactions between proteins (PPI networks) has thus become one of the important research areas. Recently, many computational approaches have been developed to identify these complexes. Various performance assessment measures have been proposed for evaluating the efficiency of these methods. However, there are many inconsistencies in the definitions and usage of the measures across the literature. To address this issue, we have gathered and presented the most important performance evaluation measures and developed a tool, named CompEvaluator, to critically assess the protein complex prediction methods. The tool and documentation are publicly available at https://sourceforge.net/projects/compevaluator/files/.

Transcriptional and metabolic rewiring of colorectal cancer cells expressing the oncogenic KRASG13D mutation.

BACKGROUND: Activating mutations in KRAS frequently occur in colorectal cancer (CRC) patients, leading to resistance to EGFR-targeted therapies. METHODS: To better understand the cellular reprogramming which occurs in mutant KRAS cells, we have undertaken a systems-level analysis of four CRC cell lines which express either wild type (wt) KRAS or the oncogenic KRASG13D allele (mtKRAS). RESULTS: RNAseq revealed that genes involved in ribosome biogenesis, mRNA translation and metabolism were significantly upregulated in mtKRAS cells. Consistent with the transcriptional data, protein synthesis and cell proliferation were significantly higher in the mtKRAS cells. Targeted metabolomics analysis also confirmed the metabolic reprogramming in mtKRAS cells. Interestingly, mtKRAS cells were highly transcriptionally responsive to EGFR activation by TGFα stimulation, which was associated with an unexpected downregulation of genes involved in a range of anabolic processes. While TGFα treatment strongly activated protein synthesis in wtKRAS cells, protein synthesis was not activated above basal levels in the TGFα-treated mtKRAS cells. This was likely due to the defective activation of the mTORC1 and other pathways by TGFα in mtKRAS cells, which was associated with impaired activation of PKB signalling and a transient induction of AMPK signalling. CONCLUSIONS: We have found that mtKRAS cells are substantially rewired at the transcriptional, translational and metabolic levels and that this rewiring may reveal new vulnerabilities in oncogenic KRAS CRC cells that could be exploited in future.

Breast cancer classification: linking molecular mechanisms to disease prognosis.

Breast cancer was traditionally perceived as a single disease; however, recent advances in gene expression and genomic profiling have revealed that breast cancer is in fact a collection of diseases exhibiting distinct anatomical features, responses to treatment and survival outcomes. Consequently, a number of schemes have been proposed for subtyping of breast cancer to bring out the biological and clinically relevant characteristics of the subtypes. Although some of these schemes capture underlying molecular differences, others predict variations in response to treatment and survival patterns. However, despite this diversity in the approaches, it is clear that molecular mechanisms drive clinical outcomes, and therefore an effective scheme should integrate molecular as well as clinical parameters to enable deeper understanding of cancer mechanisms and allow better decision making in the clinic. Here, using a large cohort of ∼550 breast tumours from The Cancer Genome Atlas, we systematically evaluate a number of expression-based schemes including at least eight molecular pathways implicated in breast cancer and three prognostic signatures, across a variety of classification scenarios covering molecular characteristics, biomarker status, tumour stages and survival patterns. We observe that a careful combination of these schemes yields better classification results compared with using them individually, thus confirming that molecular mechanisms and clinical outcomes are related and that an effective scheme should therefore integrate both these parameters to enable a deeper understanding of the cancer.

A fine-scale dissection of the DNA double-strand break repair machinery and its implications for breast cancer therapy.

DNA-damage response machinery is crucial to maintain the genomic integrity of cells, by enabling effective repair of even highly lethal lesions such as DNA double-strand breaks (DSBs). Defects in specific genes acquired through mutations, copy-number alterations or epigenetic changes can alter the balance of these pathways, triggering cancerous potential in cells. Selective killing of cancer cells by sensitizing them to further DNA damage, especially by induction of DSBs, therefore requires careful modulation of DSB-repair pathways. Here, we review the latest knowledge on the two DSB-repair pathways, homologous recombination and non-homologous end joining in human, describing in detail the functions of their components and the key mechanisms contributing to the repair. Such an in-depth characterization of these pathways enables a more mechanistic understanding of how cells respond to therapies, and suggests molecules and processes that can be explored as potential therapeutic targets. One such avenue that has shown immense promise is via the exploitation of synthetic lethal relationships, for which the BRCA1-PARP1 relationship is particularly notable. Here, we describe how this relationship functions and the manner in which cancer cells acquire therapy resistance by restoring their DSB repair potential.

Systematic tracking of dysregulated modules identifies novel genes in cancer.

MOTIVATION: Deciphering the modus operandi of dysregulated cellular mechanisms in cancer is critical to implicate novel cancer genes and develop effective anti-cancer therapies. Fundamental to this is meticulous tracking of the behavior of core modules, including complexes and pathways across specific conditions in cancer. RESULTS: Here, we performed a straightforward yet systematic identification and comparison of modules across pancreatic normal and cancer tissue conditions by integrating PPI, gene-expression and mutation data. Our analysis revealed interesting change-patterns in gene composition and expression correlation particularly affecting modules responsible for genome stability. Although in most cases these changes indicated impairment of essential functions (e.g., of DNA damage repair), in several other cases we noticed strengthening of modules possibly abetting cancer. Some of these compensatory modules showed switches in transcription regulation and recruitment of tumor inducers (e.g., SOX2 through overexpression). In-depth analysis revealed novel genes in pancreatic cancer, which showed susceptibility to copy-number alterations (e.g., for USP15 in 17 of 67 cases), supported by literature evidence for their involvement in other tumors (e.g., USP15 in glioblastoma). Two of the identified genes, YWHAE and DISC1, further supported the nexus between neural genes and pancreatic carcinogenesis. Extension of this assessment to BRCA1 and BRCA2 breast tumors showed specific differences even across the two sub-types and revealed novel genes involved therein (e.g., TRIM5 and NCOA6). AVAILABILITY: Our software CONTOURv1 is available at: http://bioinformatics.org.au/tools-data/.

Identifying conserved protein complexes between species by constructing interolog networks.

BACKGROUND: Protein complexes conserved across species indicate processes that are core to cellular machinery (e.g. cell-cycle or DNA damage-repair complexes conserved across human and yeast). While numerous computational methods have been devised to identify complexes from the protein interaction (PPI) networks of individual species, these are severely limited by noise and errors (false positives) in currently available datasets. Our analysis using human and yeast PPI networks revealed that these methods missed several important complexes including those conserved between the two species (e.g. the MLH1-MSH2-PMS2-PCNA mismatch-repair complex). Here, we note that much of the functionalities of yeast complexes have been conserved in human complexes not only through sequence conservation of proteins but also of critical functional domains. Therefore, integrating information of domain conservation might throw further light on conservation patterns between yeast and human complexes. RESULTS: We identify conserved complexes by constructing an interolog network (IN) leveraging on the functional conservation of proteins between species through domain conservation (from Ensembl) in addition to sequence similarity. We employ 'state-of-the-art' methods to cluster the interolog network, and map these clusters back to the original PPI networks to identify complexes conserved between the species. Evaluation of our IN-based approach (called COCIN) on human and yeast interaction data identifies several additional complexes (76% recall) compared to direct complex detection from the original PINs (54% recall). Our analysis revealed that the IN-construction removes several non-conserved interactions many of which are false positives, thereby improving complex prediction. In fact removing non-conserved interactions from the original PINs also resulted in higher number of conserved complexes, thereby validating our IN-based approach. These complexes included the mismatch repair complex, MLH1-MSH2-PMS2-PCNA, and other important ones namely, RNA polymerase-II, EIF3 and MCM complexes, all of which constitute core cellular processes known to be conserved across the two species. CONCLUSIONS: Our method based on integrating domain conservation and sequence similarity to construct interolog networks helps to identify considerably more conserved complexes between the PPI networks from two species compared to direct complex prediction from the PPI networks. We observe from our experiments that protein complexes are not conserved from yeast to human in a straightforward way, that is, it is not the case that a yeast complex is a (proper) sub-set of a human complex with a few additional proteins present in the human complex. Instead complexes have evolved multifold with considerable re-organization of proteins and re-distribution of their functions across complexes. This finding can have significant implications on attempts to extrapolate other kinds of relationships such as synthetic lethality from yeast to human, for example in the identification of novel cancer targets. AVAILABILITY: http://www.comp.nus.edu.sg/~leonghw/COCIN/.

Temporal dynamics of protein complexes in PPI networks: a case study using yeast cell cycle dynamics.

Complexes of physically interacting proteins are one of the fundamental functional units responsible for driving key biological mechanisms within the cell. With the advent of high-throughput techniques, significant amount of protein interaction (PPI) data has been catalogued for organisms such as yeast, which has in turn fueled computational methods for systematic identification and study of protein complexes. However, many complexes are dynamic entities - their subunits are known to assemble at a particular cellular space and time to perform a particular function and disassemble after that - and while current computational analyses have concentrated on studying the dynamics of individual or pairs of proteins in PPI networks, a crucial aspect overlooked is the dynamics of whole complex formations. In this work, using yeast as our model, we incorporate 'time' in the form of cell-cycle phases into the prediction of complexes from PPI networks and study the temporal phenomena of complex assembly and disassembly across phases. We hypothesize that 'staticness' (constitutive expression) of proteins might be related to their temporal "reusability" across complexes, and test this hypothesis using complexes predicted from large-scale PPI networks across the yeast cell cycle phases. Our results hint towards a biological design principle underlying cellular mechanisms - cells maintain generic proteins as 'static' to enable their "reusability" across multiple temporal complexes. We also demonstrate that these findings provide additional support and alternative explanations to findings from existing works on the dynamics in PPI networks.

Targeting BRF2 in Cancer Using Repurposed Drugs.

The overexpression of BRF2, a selective subunit of RNA polymerase III, has been shown to be crucial in the development of several types of cancers, including breast cancer and lung squamous cell carcinoma. Predominantly, BRF2 acts as a central redox-sensing transcription factor (TF) and is involved in rescuing oxidative stress (OS)-induced apoptosis. Here, we showed a novel link between BRF2 and the DNA damage response. Due to the lack of BRF2-specific inhibitors, through virtual screening and molecular dynamics simulation, we identified potential drug candidates that interfere with BRF2-TATA-binding Protein (TBP)-DNA complex interactions based on binding energy, intermolecular, and torsional energy parameters. We experimentally tested bexarotene as a potential BRF2 inhibitor. We found that bexarotene (Bex) treatment resulted in a dramatic decline in oxidative stress and Tert-butylhydroquinone (tBHQ)-induced levels of BRF2 and consequently led to a decrease in the cellular proliferation of cancer cells which may in part be due to the drug pretreatment-induced reduction of ROS generated by the oxidizing agent. Our data thus provide the first experimental evidence that BRF2 is a novel player in the DNA damage response pathway and that bexarotene can be used as a potential inhibitor to treat cancers with the specific elevation of oxidative stress.

Rapid whole-blood assay to detect SARS-CoV-2-specific memory T-cell immunity following a single dose of AstraZeneca ChAdOx1-S COVID-19 vaccine.

OBJECTIVES: With the ongoing emergence of SARS-CoV-2 variants and potential to evade vaccine-induced neutralisation, understanding the magnitude and breadth of vaccine-induced T-cell immunity will be critical for the ongoing optimisation of vaccine approaches. Strategies that provide a rapid and easily translatable means of assessing virus-specific T-cell responses provide an opportunity to monitor the impact of vaccine rollouts in the community. In this study, we assessed whether our recently developed SARS-CoV-2 whole-blood assay could be used effectively to analyse T-cell responses following vaccination. METHODS: Following a median of 15 days after the first dose of the ChAdOx1-S (AstraZeneca®) vaccine, peripheral blood was isolated from 58 participants. Blood was incubated overnight with an overlapping set of spike protein peptides and assessed for cytokine production using a cytometric bead array. RESULTS: The majority of vaccine recipients (51/58) generated a T helper 1 response (IFN-γ and/or IL-2) following a single dose of ChAdOx1-S. The magnitude of the IFN-γ and IL-2 response strongly correlated in vaccine recipients. While the production of other cytokines was evident in individuals who did not generate IFN-γ and IL-2, they showed no correlation in magnitude, nor did we see a correlation between sex or age and the magnitude of the response. CONCLUSIONS: The whole-blood cytokine assay provides a rapid approach to assessing T-cell immunity against SARS-CoV-2 in vaccine recipients. While the majority of participants generated a robust SARS-CoV-2-specific T-cell response following their first dose, some did not, demonstrating the likely importance of the booster dose in improving T-cell immunity.

'Off-the-shelf' allogeneic antigen-specific adoptive T-cell therapy for the treatment of multiple EBV-associated malignancies.

BACKGROUND: Epstein-Barr virus (EBV), an oncogenic human gammaherpesvirus, is associated with a wide range of human malignancies of epithelial and B-cell origin. Recent studies have demonstrated promising safety and clinical efficacy of allogeneic 'off-the-shelf' virus-specific T-cell therapies for post-transplant viral complications. METHODS: Taking a clue from these studies, we developed a highly efficient EBV-specific T-cell expansion process using a replication-deficient AdE1-LMPpoly vector that specifically targets EBV-encoded nuclear antigen 1 (EBNA1) and latent membrane proteins 1 and 2 (LMP1 and LMP2), expressed in latency II malignancies. RESULTS: These allogeneic EBV-specific T cells efficiently recognized human leukocyte antigen (HLA)-matched EBNA1-expressing and/or LMP1 and LMP2-expressing malignant cells and demonstrated therapeutic potential in a number of in vivo models, including EBV lymphomas that emerged spontaneously in humanized mice following EBV infection. Interestingly, we were able to override resistance to T-cell therapy in vivo using a 'restriction-switching' approach, through sequential infusion of two different allogeneic T-cell therapies restricted through different HLA alleles. Furthermore, we have shown that inhibition of the programmed cell death protein-1/programmed death-ligand 1 axis in combination with EBV-specific T-cell therapy significantly improved overall survival of tumor-bearing mice when compared with monotherapy. CONCLUSION: These findings suggest that restriction switching by sequential infusion of allogeneic T-cell therapies that target EBV through distinct HLA alleles may improve clinical response.

MCL-CAw: a refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure.

BACKGROUND: The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. RESULTS: Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. CONCLUSIONS: We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.

Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology.

BACKGROUND: In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN). RESULTS: The RNN topology is a weighted directed graph derived from PPI network, and it is a natural representation of the topological dependences between proteins within the PPI network. Similar to the original PPI network, we have observed that essential proteins tend to be hub proteins in RNN topology. Additionally, essential genes are enriched in clusters containing many hub proteins in RNN topology (RNN protein clusters). Based on these two properties of essential genes in RNN topology, we have proposed a new measure; the RNN cluster centrality. Results from a variety of PPI networks demonstrate that RNN cluster centrality outperforms other centrality measures with regard to the proportion of selected proteins that are essential proteins. We also investigated the biological importance of RNN clusters. CONCLUSIONS: This study reveals that RNN cluster centrality provides the best correlation of protein essentiality and placement of proteins in PPI network. Additionally, merged RNN clusters were found to be topologically important in that essential proteins are significantly enriched in RNN clusters, and biologically important because they play an important role in many Gene Ontology (GO) processes.

Proteome-wide analysis of T-cell response to BK polyomavirus in healthy virus carriers and kidney transplant recipients reveals a unique transcriptional and functional profile.

OBJECTIVES: Cellular immunity against BK polyomavirus (BKV)-encoded antigens plays a crucial role in long-term protection against virus-associated pathogenesis in transplant recipients. However, in-depth understanding on dynamics of these cellular immune responses is required to develop better immune monitoring and immunotherapeutic strategies. METHODS: Here, we have conducted a proteome-wide analysis of BKV-specific T-cell responses in a cohort of 53 healthy individuals and 26 kidney transplant recipients to delineate the functional and transcriptional profile of these effector cells and compared these characteristics to T cells directed against cytomegalovirus, which is also known to cause significant morbidity in transplant recipients. RESULTS: Profiling of BKV-specific CD4+ and CD8+ T cells revealed that kidney transplant recipients with high levels of circulating viraemia showed significantly reduced T-cell reactivity against large T and/or small T antigens when compared to healthy donors. Interestingly, T cells specific for these antigens showed strong cross-recognition to orthologous JC virus (JCV) peptides, including those exhibiting varying degrees of sequence identity. Ex vivo functional and phenotypic characterisation revealed that the majority of BKV-specific T cells from renal transplant recipients expressed low levels of the key transcriptional regulators T-bet and eomesodermin, which was coincident with undetectable expression of granzyme B and perforin. However, in vitro stimulation of T cells with BKV epitopes selectively enhanced the expression of T-bet, granzyme B and cellular trafficking molecules (CCR4, CD49d and CD103) with minimal change in eomesodermin and perforin. CONCLUSIONS: These observations provide an important platform for the future development of immune monitoring and adoptive T-cell therapy strategies for BKV-associated diseases in transplant recipients, which may also be exploited for similar therapeutic value in JCV-associated clinical complications.

Profiling HPV-16-specific T cell responses reveals broad antigen reactivities in oropharyngeal cancer patients.

Cellular immunotherapeutics targeting the human papillomavirus (HPV)-16 E6 and E7 proteins have achieved limited success in HPV-positive oropharyngeal cancer (OPC). Here we have conducted proteome-wide profiling of HPV-16-specific T cell responses in a cohort of 66 patients with HPV-associated OPC and 22 healthy individuals. Unexpectedly, HPV-specific T cell responses from OPC patients were not constrained to the E6 and E7 antigens; they also recognized E1, E2, E4, E5, and L1 proteins as dominant targets for virus-specific CD8+ and CD4+ T cells. Multivariate analysis incorporating tumor staging, treatment status, and smoking history revealed that treatment status had the most significant impact on HPV-specific CD8+ and CD4+ T cell immunity. Specifically, the breadth and overall strength of HPV-specific T cell responses were significantly higher before the commencement of curative therapy than after therapy. These data provide the first glimpse of the overall human T cell response to HPV in a clinical setting and offer groundbreaking insight into future development of cellular immunotherapies for HPV-associated OPC patients.

Rapid detection of SARS-CoV-2-specific memory T-cell immunity in recovered COVID-19 cases.

OBJECTIVES: There is emerging evidence that SARS-CoV-2-specific memory T-cell responses are likely to provide critical long-term protection against COVID-19. Strategies to rapidly assess T-cell responses are therefore likely to be important for assessing immunity in the global population. METHODS: Here, we have developed a rapid immune-monitoring strategy to assess virus-specific memory T-cell responses in the peripheral blood of COVID-19 convalescent individuals. We validated SARS-CoV-2-specific memory T-cell responses detected in whole blood using in vitro expansion with SARS-CoV-2 proteins. RESULTS: T-cell immunity characterised by the production of IFN-γ and IL-2 could be consistently detected in the whole blood of recovered participants. T cells predominantly recognised structural SARS-CoV-2 proteins. In vitro expansion demonstrated that while CD8+ T cells recognised nucleocapsid protein, spike protein and ORF3a, CD4+ T cells more broadly targeted multiple SARS-CoV-2 proteins. CONCLUSION: These observations provide a timely monitoring approach for identifying SARS-CoV-2 cellular immunity and may serve as a diagnostic for the stratification of risk in immunocompromised and other at-risk individuals.

Autologous CMV-specific T cells are a safe adjuvant immunotherapy for primary glioblastoma multiforme.

BACKGROUNDThe recent failure of checkpoint-blockade therapies for glioblastoma multiforme (GBM) in late-phase clinical trials has directed interest toward adoptive cellular therapies (ACTs). In this open-label, first-in-human trial, we have assessed the safety and therapeutic potential of cytomegalovirus-specific (CMV-specific) ACT in an adjuvant setting for patients with primary GBM, with an ultimate goal to prevent or delay recurrence and prolong overall survival.METHODSTwenty-eight patients with primary GBM were recruited to this prospective study, 25 of whom were treated with in vitro-expanded autologous CMV-specific T cells. Participants were monitored for safety, progression-free survival, overall survival (OS), and immune reconstitution.RESULTSNo participants showed evidence of ACT-related toxicities. Of 25 evaluable participants, 10 were alive at the completion of follow-up, while 5 were disease free. Reconstitution of CMV-specific T cell immunity was evident and CMV-specific ACT may trigger a bystander effect leading to additional T cell responses to nonviral tumor-associated antigens through epitope spreading. Long-term follow-up of participants treated before recurrence showed significantly improved OS when compared with those who progressed before ACT (median 23 months, range 7-65 vs. median 14 months, range 5-19; P = 0.018). Gene expression analysis of the ACT products indicated that a favorable T cell gene signature was associated with improved long-term survival.CONCLUSIONData presented in this study demonstrate that CMV-specific ACT can be safely used as an adjuvant therapy for primary GBM and, if offered before recurrence, this therapy may improve OS of GBM patients.TRIAL REGISTRATIONanzctr.org.au: ACTRN12615000656538.FUNDINGPhilanthropic funding and the National Health and Medical Research Council (Australia).

Extensive rewiring of the EGFR network in colorectal cancer cells expressing transforming levels of KRASG13D.

Protein-protein-interaction networks (PPINs) organize fundamental biological processes, but how oncogenic mutations impact these interactions and their functions at a network-level scale is poorly understood. Here, we analyze how a common oncogenic KRAS mutation (KRASG13D) affects PPIN structure and function of the Epidermal Growth Factor Receptor (EGFR) network in colorectal cancer (CRC) cells. Mapping >6000 PPIs shows that this network is extensively rewired in cells expressing transforming levels of KRASG13D (mtKRAS). The factors driving PPIN rewiring are multifactorial including changes in protein expression and phosphorylation. Mathematical modelling also suggests that the binding dynamics of low and high affinity KRAS interactors contribute to rewiring. PPIN rewiring substantially alters the composition of protein complexes, signal flow, transcriptional regulation, and cellular phenotype. These changes are validated by targeted and global experimental analysis. Importantly, genetic alterations in the most extensively rewired PPIN nodes occur frequently in CRC and are prognostic of poor patient outcomes.