Medication effects on the gut microbiome in allo-HCT.

Decreased gut microbiome diversity has been associated with negative outcome in allogeneic hematopoietic stem cell transfer (HCT). A study published in this issue of Cell identifies associations between non-antibiotic drug administration, microbiome state transitions, and response to HCT, highlighting the potential impact of such drugs on microbiome and HCT outcome.

Synthetic lethality-mediated precision oncology via the tumor transcriptome.

Precision oncology has made significant advances, mainly by targeting actionable mutations in cancer driver genes. Aiming to expand treatment opportunities, recent studies have begun to explore the utility of tumor transcriptome to guide patient treatment. Here, we introduce SELECT (synthetic lethality and rescue-mediated precision oncology via the transcriptome), a precision oncology framework harnessing genetic interactions to predict patient response to cancer therapy from the tumor transcriptome. SELECT is tested on a broad collection of 35 published targeted and immunotherapy clinical trials from 10 different cancer types. It is predictive of patients' response in 80% of these clinical trials and in the recent multi-arm WINTHER trial. The predictive signatures and the code are made publicly available for academic use, laying a basis for future prospective clinical studies.

The GPCR-Gαs-PKA signaling axis promotes T cell dysfunction and cancer immunotherapy failure.

Immune checkpoint blockade (ICB) targeting PD-1 and CTLA-4 has revolutionized cancer treatment. However, many cancers do not respond to ICB, prompting the search for additional strategies to achieve durable responses. G-protein-coupled receptors (GPCRs) are the most intensively studied drug targets but are underexplored in immuno-oncology. Here, we cross-integrated large singe-cell RNA-sequencing datasets from CD8+ T cells covering 19 distinct cancer types and identified an enrichment of Gαs-coupled GPCRs on exhausted CD8+ T cells. These include EP2, EP4, A2AR, ß1AR and ß2AR, all of which promote T cell dysfunction. We also developed transgenic mice expressing a chemogenetic CD8-restricted Gαs-DREADD to activate CD8-restricted Gαs signaling and show that a Gαs-PKA signaling axis promotes CD8+ T cell dysfunction and immunotherapy failure. These data indicate that Gαs-GPCRs are druggable immune checkpoints that might be targeted to enhance the response to ICB immunotherapies.

UVB-Induced Tumor Heterogeneity Diminishes Immune Response in Melanoma.

Although clonal neo-antigen burden is associated with improved response to immune therapy, the functional basis for this remains unclear. Here we study this question in a novel controlled mouse melanoma model that enables us to explore the effects of intra-tumor heterogeneity (ITH) on tumor aggressiveness and immunity independent of tumor mutational burden. Induction of UVB-derived mutations yields highly aggressive tumors with decreased anti-tumor activity. However, single-cell-derived tumors with reduced ITH are swiftly rejected. Their rejection is accompanied by increased T cell reactivity and a less suppressive microenvironment. Using phylogenetic analyses and mixing experiments of single-cell clones, we dissect two characteristics of ITH: the number of clones forming the tumor and their clonal diversity. Our analysis of melanoma patient tumor data recapitulates our results in terms of overall survival and response to immune checkpoint therapy. These findings highlight the importance of clonal mutations in robust immune surveillance and the need to quantify patient ITH to determine the response to checkpoint blockade.

Urea Cycle Dysregulation Generates Clinically Relevant Genomic and Biochemical Signatures.

The urea cycle (UC) is the main pathway by which mammals dispose of waste nitrogen. We find that specific alterations in the expression of most UC enzymes occur in many tumors, leading to a general metabolic hallmark termed "UC dysregulation" (UCD). UCD elicits nitrogen diversion toward carbamoyl-phosphate synthetase2, aspartate transcarbamylase, and dihydrooratase (CAD) activation and enhances pyrimidine synthesis, resulting in detectable changes in nitrogen metabolites in both patient tumors and their bio-fluids. The accompanying excess of pyrimidine versus purine nucleotides results in a genomic signature consisting of transversion mutations at the DNA, RNA, and protein levels. This mutational bias is associated with increased numbers of hydrophobic tumor antigens and a better response to immune checkpoint inhibitors independent of mutational load. Taken together, our findings demonstrate that UCD is a common feature of tumors that profoundly affects carcinogenesis, mutagenesis, and immunotherapy response.

Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality.

Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data. First, we show that the approach successfully captures known SL partners of tumor suppressors and oncogenes. We then validate SL predictions obtained for the tumor suppressor VHL. Next, we construct a genome-wide network of SL interactions in cancer and demonstrate its value in predicting gene essentiality and clinical prognosis. Finally, we identify synthetic lethality arising from gene overactivation and use it to predict drug efficacy. These results form a computational basis for exploiting synthetic lethality to uncover cancer-specific susceptibilities.

Mutation-selection balance and compensatory mechanisms in tumour evolution.

Intratumour heterogeneity and phenotypic plasticity, sustained by a range of somatic aberrations, as well as epigenetic and metabolic adaptations, are the principal mechanisms that enable cancers to resist treatment and survive under environmental stress. A comprehensive picture of the interplay between different somatic aberrations, from point mutations to whole-genome duplications, in tumour initiation and progression is lacking. We posit that different genomic aberrations generally exhibit a temporal order, shaped by a balance between the levels of mutations and selective pressures. Repeat instability emerges first, followed by larger aberrations, with compensatory effects leading to robust tumour fitness maintained throughout the tumour progression. A better understanding of the interplay between genetic aberrations, the microenvironment, and epigenetic and metabolic cellular states is essential for early detection and prevention of cancer as well as development of efficient therapeutic strategies.

Identification of bacteria-derived HLA-bound peptides in melanoma.

A variety of species of bacteria are known to colonize human tumours1-11, proliferate within them and modulate immune function, which ultimately affects the survival of patients with cancer and their responses to treatment12-14. However, it is not known whether antigens derived from intracellular bacteria are presented by the human leukocyte antigen class I and II (HLA-I and HLA-II, respectively) molecules of tumour cells, or whether such antigens elicit a tumour-infiltrating T cell immune response. Here we used 16S rRNA gene sequencing and HLA peptidomics to identify a peptide repertoire derived from intracellular bacteria that was presented on HLA-I and HLA-II molecules in melanoma tumours. Our analysis of 17 melanoma metastases (derived from 9 patients) revealed 248 and 35 unique HLA-I and HLA-II peptides, respectively, that were derived from 41 species of bacteria. We identified recurrent bacterial peptides in tumours from different patients, as well as in different tumours from the same patient. Our study reveals that peptides derived from intracellular bacteria can be presented by tumour cells and elicit immune reactivity, and thus provides insight into a mechanism by which bacteria influence activation of the immune system and responses to therapy.

Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models.

Recent studies exploring the impact of methylation in tumor evolution suggest that although the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Because changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumor's single-cell methylation lineage tree and for jointly identifying lineage-informative CpG sites that harbor changes in methylation status that are retained along the lineage. We apply Sgootr on single-cell bisulfite-treated whole-genome sequencing data of multiregionally sampled tumor cells from nine metastatic colorectal cancer patients, as well as multiregionally sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient. We show that the tumor lineages constructed reveal a simple model underlying tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and with more in concordance with the sequential-progression model of tumor evolution, with a running time a fraction of that used in prior studies. Lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to intra-CGIs, which have been the main regions of interest in genomic methylation-related analyses.

Transcriptional control of leukemogenesis by the chromatin reader SGF29.

ABSTRACT: Aberrant expression of stem cell-associated genes is a common feature in acute myeloid leukemia (AML) and is linked to leukemic self-renewal and therapy resistance. Using AF10-rearranged leukemia as a prototypical example of the recurrently activated "stemness" network in AML, we screened for chromatin regulators that sustain its expression. We deployed a CRISPR-Cas9 screen with a bespoke domain-focused library and identified several novel chromatin-modifying complexes as regulators of the TALE domain transcription factor MEIS1, a key leukemia stem cell (LSC)-associated gene. CRISPR droplet sequencing revealed that many of these MEIS1 regulators coordinately controlled the transcription of several AML oncogenes. In particular, we identified a novel role for the Tudor-domain-containing chromatin reader protein SGF29 in the transcription of AML oncogenes. Furthermore, SGF29 deletion impaired leukemogenesis in models representative of multiple AML subtypes in multiple AML subtype models. Our studies reveal a novel role for SGF29 as a nononcogenic dependency in AML and identify the SGF29 Tudor domain as an attractive target for drug discovery.

Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 has triggered an ongoing global pandemic of the severe pneumonia-like disease coronavirus disease 2019 (COVID-19)1. The development of a vaccine is likely to take at least 12-18 months, and the typical timeline for approval of a new antiviral therapeutic agent can exceed 10 years. Thus, repurposing of known drugs could substantially accelerate the deployment of new therapies for COVID-19. Here we profiled a library of drugs encompassing approximately 12,000 clinical-stage or Food and Drug Administration (FDA)-approved small molecules to identify candidate therapeutic drugs for COVID-19. We report the identification of 100 molecules that inhibit viral replication of SARS-CoV-2, including 21 drugs that exhibit dose-response relationships. Of these, thirteen were found to harbour effective concentrations commensurate with probable achievable therapeutic doses in patients, including the PIKfyve kinase inhibitor apilimod2-4 and the cysteine protease inhibitors MDL-28170, Z LVG CHN2, VBY-825 and ONO 5334. Notably, MDL-28170, ONO 5334 and apilimod were found to antagonize viral replication in human pneumocyte-like cells derived from induced pluripotent stem cells, and apilimod also demonstrated antiviral efficacy in a primary human lung explant model. Since most of the molecules identified in this study have already advanced into the clinic, their known pharmacological and human safety profiles will enable accelerated preclinical and clinical evaluation of these drugs for the treatment of COVID-19.

Systematic investigation of cytokine signaling activity at the tissue and single-cell levels.

Cytokines are critical for intercellular communication in human health and disease, but the investigation of cytokine signaling activity has remained challenging due to the short half-lives of cytokines and the complexity/redundancy of cytokine functions. To address these challenges, we developed the Cytokine Signaling Analyzer (CytoSig; https://cytosig.ccr.cancer.gov/ ), providing both a database of target genes modulated by cytokines and a predictive model of cytokine signaling cascades from transcriptomic profiles. We collected 20,591 transcriptome profiles for human cytokine, chemokine and growth factor responses. This atlas of transcriptional patterns induced by cytokines enabled the reliable prediction of signaling activities in distinct cell populations in infectious diseases, chronic inflammation and cancer using bulk and single-cell transcriptomic data. CytoSig revealed previously unidentified roles of many cytokines, such as BMP6 as an anti-inflammatory factor, and identified candidate therapeutic targets in human inflammatory diseases, such as CXCL8 for severe coronavirus disease 2019.

Glucose-6-Phosphate Dehydrogenase Deficiency and Coronavirus Disease 2019.

In this cohort study conducted in a national healthcare organization in Israel, we found that individuals with glucose-6-phosphate dehydrogenase deficiency had an increased risk of coronavirus disease 2019 (COVID-19) infection and severity, with higher rates of hospitalization and diagnosed long COVID.

Characterization of HLH-like manifestations as a CRS variant in patients receiving CD22 CAR T cells.

Chimeric antigen receptor (CAR) T-cell toxicities resembling hemophagocytic lymphohistiocytosis (HLH) occur in a subset of patients with cytokine release syndrome (CRS). As a variant of conventional CRS, a comprehensive characterization of CAR T-cell-associated HLH (carHLH) and investigations into associated risk factors are lacking. In the context of 59 patients infused with CD22 CAR T cells where a substantial proportion developed carHLH, we comprehensively describe the manifestations and timing of carHLH as a CRS variant and explore factors associated with this clinical profile. Among 52 subjects with CRS, 21 (40.4%) developed carHLH. Clinical features of carHLH included hyperferritinemia, hypertriglyceridemia, hypofibrinogenemia, coagulopathy, hepatic transaminitis, hyperbilirubinemia, severe neutropenia, elevated lactate dehydrogenase, and occasionally hemophagocytosis. Development of carHLH was associated with preinfusion natural killer(NK) cell lymphopenia and higher bone marrow T-cell:NK cell ratio, which was further amplified with CAR T-cell expansion. Following CRS, more robust CAR T-cell and CD8 T-cell expansion in concert with pronounced NK cell lymphopenia amplified preinfusion differences in those with carHLH without evidence for defects in NK cell mediated cytotoxicity. CarHLH was further characterized by persistent elevation of HLH-associated inflammatory cytokines, which contrasted with declining levels in those without carHLH. In the setting of CAR T-cell mediated expansion, clinical manifestations and immunophenotypic profiling in those with carHLH overlap with features of secondary HLH, prompting consideration of an alternative framework for identification and management of this toxicity profile to optimize outcomes following CAR T-cell infusion.

Astrocyte immunometabolic regulation of the tumour microenvironment drives glioblastoma pathogenicity.

Malignant brain tumours are the cause of a disproportionate level of morbidity and mortality among cancer patients, an unfortunate statistic that has remained constant for decades. Despite considerable advances in the molecular characterization of these tumours, targeting the cancer cells has yet to produce significant advances in treatment. An alternative strategy is to target cells in the glioblastoma microenvironment, such as tumour-associated astrocytes. Astrocytes control multiple processes in health and disease, ranging from maintaining the brain's metabolic homeostasis, to modulating neuroinflammation. However, their role in glioblastoma pathogenicity is not well understood. Here we report that depletion of reactive astrocytes regresses glioblastoma and prolongs mouse survival. Analysis of the tumour-associated astrocyte translatome revealed astrocytes initiate transcriptional programmes that shape the immune and metabolic compartments in the glioma microenvironment. Specifically, their expression of CCL2 and CSF1 governs the recruitment of tumour-associated macrophages and promotes a pro-tumourigenic macrophage phenotype. Concomitantly, we demonstrate that astrocyte-derived cholesterol is key to glioma cell survival, and that targeting astrocytic cholesterol efflux, via ABCA1, halts tumour progression. In summary, astrocytes control glioblastoma pathogenicity by reprogramming the immunological properties of the tumour microenvironment and supporting the non-oncogenic metabolic dependency of glioblastoma on cholesterol. These findings suggest that targeting astrocyte immunometabolic signalling may be useful in treating this uniformly lethal brain tumour.

Gene-expression profiles of pretreatment biopsies predict complete response of rectal cancer patients to preoperative chemoradiotherapy.

PURPOSE: Preoperative (neoadjuvant) chemoradiotherapy (CRT) and total mesorectal excision is the standard treatment for rectal cancer patients (UICC stage II/III). Up to one-third of patients treated with CRT achieve a pathological complete response (pCR). These patients could be spared from surgery and its associated morbidity and mortality, and assigned to a "watch and wait" strategy. However, reliably identifying pCR based on clinical or imaging parameters remains challenging. EXPERIMENTAL DESIGN: We generated gene-expression profiles of 175 patients with locally advanced rectal cancer enrolled in the CAO/ARO/AIO-94 and -04 trials. One hundred and sixty-one samples were used for building, training and validating a predictor of pCR using a machine learning algorithm. The performance of the classifier was validated in three independent cohorts, comprising 76 patients from (i) the CAO/ARO/AIO-94 and -04 trials (n = 14), (ii) a publicly available dataset (n = 38) and (iii) in 24 prospectively collected samples from the TransValid A trial. RESULTS: A 21-transcript signature yielded the best classification of pCR in 161 patients (Sensitivity: 0.31; AUC: 0.81), when not allowing misclassification of non-complete-responders (False-positive rate = 0). The classifier remained robust when applied to three independent datasets (n = 76). CONCLUSION: The classifier can identify >1/3 of rectal cancer patients with a pCR while never classifying patients with an incomplete response as having pCR. Importantly, we could validate this finding in three independent datasets, including a prospectively collected cohort. Therefore, this classifier could help select rectal cancer patients for a "watch and wait" strategy. TRANSLATIONAL RELEVANCE: Forgoing surgery with its associated side effects could be an option for rectal cancer patients if the prediction of a pathological complete response (pCR) after preoperative chemoradiotherapy would be possible. Based on gene-expression profiles of 161 patients a classifier was developed and validated in three independent datasets (n = 76), identifying over 1/3 of patients with pCR, while never misclassifying a non-complete-responder. Therefore, the classifier can identify patients suited for "watch and wait".

Targeting the Warburg effect via LDHA inhibition engages ATF4 signaling for cancer cell survival.

Nutrient restriction reprograms cellular signaling and metabolic network to shape cancer phenotype. Lactate dehydrogenase A (LDHA) has a key role in aerobic glycolysis (the Warburg effect) through regeneration of the electron acceptor NAD+ and is widely regarded as a desirable target for cancer therapeutics. However, the mechanisms of cellular response and adaptation to LDHA inhibition remain largely unknown. Here, we show that LDHA activity supports serine and aspartate biosynthesis. Surprisingly, however, LDHA inhibition fails to impact human melanoma cell proliferation, survival, or tumor growth. Reduced intracellular serine and aspartate following LDHA inhibition engage GCN2-ATF4 signaling to initiate an expansive pro-survival response. This includes the upregulation of glutamine transporter SLC1A5 and glutamine uptake, with concomitant build-up of essential amino acids, and mTORC1 activation, to ameliorate the effects of LDHA inhibition. Tumors with low LDHA expression and melanoma patients acquiring resistance to MAPK signaling inhibitors, which target the Warburg effect, exhibit altered metabolic gene expression reminiscent of the ATF4-mediated survival signaling. ATF4-controlled survival mechanisms conferring synthetic vulnerability to the approaches targeting the Warburg effect offer efficacious therapeutic strategies.

Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets.

Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy.