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Metastasis is a leading cause of cancer-related deaths, yet understanding how metastatic tumors adapt from their origin to their target tissues remains a fundamental challenge. To address this, we assessed whether primary and metastatic tumors more closely resemble their tissues of origin or target tissues in terms of gene expression. We analyzed expression profiles from multiple cancer types and normal tissues, including single-cell and bulk RNA sequencing data from both paired and unpaired patient cohorts. Primary tumors were overall more transcriptomically similar to their tissues of origin, while metastases shifted toward their target tissues. However, pathway-level analysis highlighted critical metabolic and immune transcriptomic changes toward target tissues during metastasis in both primary and metastatic tumors. In addition, primary tumors exhibited higher activity in cancer hallmarks such as "Activating Invasion and Metastasis" when compared to metastases. This comprehensive analysis provides a transcriptome-wide view of the processes through which cancer tumors adapt to their metastatic environments before and after metastasis.

Aneuploidy results in a stoichiometric imbalance of protein complexes that jeopardizes cellular fitness. Aneuploid cells thus need to compensate for the imbalanced DNA levels by regulating their RNA and protein levels, but the underlying molecular mechanisms remain unknown. In this study, we dissected multiple diploid versus aneuploid cell models. We found that aneuploid cells cope with transcriptional burden by increasing several RNA degradation pathways, and are consequently more sensitive to the perturbation of RNA degradation. At the protein level, aneuploid cells mitigate proteotoxic stress by reducing protein translation and increasing protein degradation, rendering them more sensitive to proteasome inhibition. These findings were recapitulated across hundreds of human cancer cell lines and primary tumors, and aneuploidy levels were significantly associated with the response of patients with multiple myeloma to proteasome inhibitors. Aneuploid cells are therefore preferentially dependent on several key nodes along the gene expression process, creating clinically actionable vulnerabilities in aneuploid cells. Significance: Aneuploidy is a hallmark of cancer that is associated with poor prognosis and worse drug response. We reveal that cells with extra chromosomes compensate for their imbalanced DNA content by altering their RNA and protein metabolism, rendering them more sensitive to perturbation of RNA and protein degradation. See related commentary by Bakhoum, p. 2315., (©2024 American Association for Cancer Research.)

Systematic multi-omics analysis revealed ancestry-dependent molecular alterations, but their impact on the efficacy of anti-cancer treatment is yet largely unknown. Here, we analyzed clinical trials from ClinicalTrials.gov and found that only 8,779/102,721 (8.5%) oncology clinical trials posted information on enrollment by race/ethnicity. The underrepresentation of non-White populations suggests that it remains challenging to determine differences in the efficacy of anti-tumor treatments among different racial groups. Through a comprehensive analysis of clinically actionable genes, imputed drug responses, and immune features, we identified potential differences in treatment response to targeted, chemo and immunotherapies between different ancestral populations. Further analysis of multiple independent cohorts confirmed some of our key findings. Such potential ancestral effects are also identified in response to emerging new treatments like CAR-T therapy and PROTACs. These findings are made publicly available in a comprehensive web portal, Ancestral Differences of Efficacy in Cancers (ADEC; https://hanlaboratory.com/ADEC), to facilitate their further investigation.

Background: Blockade of the programmed cell death protein 1 (PD-1) immune checkpoint (ICB) is revolutionizing cancer therapy, but little is known about the mechanisms governing its expression on CD8 T cells. Because PD-1 is induced during activation of T cells, we set out to uncover regulators whose inhibition suppresses PD-1 abundance without adversely impacting on T cell activation., Methods: To identify PD-1 regulators in an unbiased fashion, we performed a whole-genome, fluorescence-activated cell sorting (FACS)-based CRISPR-Cas9 screen in primary murine CD8 T cells. A dual-readout design using the activation marker CD137 allowed us to uncouple genes involved in PD-1 regulation from those governing general T cell activation., Results: We found that the inactivation of one of several members of the TMED/EMP24/GP25L/p24 family of transport proteins, most prominently TMED10, reduced PD-1 cell surface abundance, thereby augmenting T cell activity. Another client protein was cytotoxic T lymphocyte-associated protein 4 (CTLA-4), which was also suppressed by TMED inactivation. Treatment with TMED inhibitor AGN192403 led to lysosomal degradation of the TMED-PD-1 complex and reduced PD-1 abundance in tumor-infiltrating CD8 T cells (TIL) in mice, thus reversing T cell dysfunction. Clinically corroborating these findings, single-cell RNA analyses revealed a positive correlation between TMED expression in CD8 TIL, and both a T cell dysfunction signature and lack of ICB response. Similarly, patients receiving a TIL product with high TMED expression had a shorter overall survival., Conclusion: Our results uncover a novel mechanism of PD-1 regulation, and identify a pharmacologically tractable target whose inhibition suppresses PD-1 abundance and T cell dysfunction., Competing Interests: Competing interests: DSP and MAL are co-founders, shareholders and advisors of Flindr Tx (previously Immagene), which is unrelated to this study. The other authors declare no competing interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Immuno-oncology has transformed the treatment of cancer, with several immunotherapies becoming the standard treatment across histologies. Despite these advancements, the majority of patients do not experience durable clinical benefits, highlighting the imperative for ongoing advancement in immuno-oncology. Computational immuno-oncology emerges as a forefront discipline that draws on biomedical data science and intersects with oncology, immunology, and clinical research, with the overarching goal to accelerate the development of effective and safe immuno-oncology treatments from the laboratory to the clinic. In this review, we outline 10 critical challenges and opportunities in computational immuno-oncology, emphasizing the importance of robust computational strategies and interdisciplinary collaborations amid the constantly evolving interplay between clinical needs and technological innovation., Competing Interests: Competing interests: RB declares PCT/US15/612657 (Cancer Immunotherapy), PCT/US18/36052 (Microbiome Biomarkers for Anti-PD-1/PD-L1 Responsiveness: Diagnostic, Prognostic and Therapeutic Uses Thereof), PCT/US63/055227 (Methods and Compositions for Treating Autoimmune and Allergic Disorders). AM is an equity holder in Picture Health, Elucid Bioimaging, and Inspirata. Currently, he serves on the advisory board of Picture Health, and SimBioSys. He currently consults for Takeda; He also has sponsored research agreements with AstraZeneca and Bristol Myers-Squibb; his technology has been licensed to Picture Health and Elucid Bioimaging; he is also involved in two different R01 grants with Inspirata; he also serves as a member for the Frederick National Laboratory Advisory Committee.VDJ has performed consulting for VincerX Pharmaceuticals, Providence St. John, Los Angeles; she is a founder of Bioinformatica. JSB-S, EK, DM are full time, paid employees of NCI/NIH. EJF is on the Scientific Advisory Board of Resistance Bio, a consultant for Mestag and Merck, and receives research funding from AbbVie. SG reports current and past research funding from Regeneron Pharmaceuticals, Boehringer Ingelheim, BMS (Celgene), Genentech, EMD Serono, Pfizer, and Takeda. ER is a co-founder of MedAware, Metabomed and Pangea Biomed (divested), and an unpaid member of Pangea Biomed’s and GSK Oncology scientific advisory boards. DVV is a co-founder and chief scientist of Barrier Biosciences and holds equity in the company. NV is on the Consulting/Advisory Boards of Sanofi/Genzyme (2022), Oncocyte (2021), Eli Lilly (2021), Regeneron (2022), Amgen (2023), Xencor (2023), AstraZeneca (2023), Tempus (2023), Pfizer (2024), Summit Therapeutics (2024), OncoHost (2024); she reports travel reimbursement from Regeneron; research grants from Oncocyte (2022–), Circulogene and Mirati, funding from Circulogene, and honoraria from Nebraska Oncology Society, Scienomics Group, Grace, OncLive and OMNI-Oncology. EMVA reports personal fees from Tango Therapeutics, Genome Medical, Genomic Life, Monte Rosa Therapeutics, Manifold Bio, Enara Bio, Riva Therapeutics, Foaley & Hoag, and Serinus Bio, grants and personal fees from Novartis Institute for Biomedical Research, and grants from BMS, Janssen, and Sanofi outside the submitted work; in addition, EMVA has a patent for institutional patents filed on chromatin mutations and immunotherapy response and methods for clinical interpretation pending., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The co-occurrence of chromosome 10 loss and chromosome 7 gain in gliomas is the most frequent loss-gain co-aneuploidy pair in human cancers. This phenomenon has been investigated since the late 1980s without resolution. Expanding beyond previous gene-centric studies, we investigated the co-occurrence in a genome-wide manner, taking an evolutionary perspective. Mining of large-scale tumor aneuploidy data confirmed the previous finding of a small-scale longitudinal study that the most likely order is chromosome 10 loss, followed by chromosome 7 gain. Extensive analysis of genomic and transcriptomic data from both patients and cell lines revealed that this co-occurrence can be explained by functional rescue interactions that are highly enriched on chromosome 7, which could potentially compensate for any detrimental consequences arising from the loss of chromosome 10. Transcriptomic data from various normal, noncancerous human brain tissues were analyzed to assess which tissues may be most predisposed to tolerate compensation of chromosome 10 loss by chromosome 7 gain. The analysis indicated that the preexisting transcriptomic states in the cortex and frontal cortex, where gliomas arise, are more favorable than other brain regions for compensation by rescuer genes that are active on chromosome 7. Collectively, these findings suggest that the phenomenon of chromosome 10 loss and chromosome 7 gain in gliomas is orchestrated by a complex interaction of many genes residing within these two chromosomes and provide a plausible reason why this co-occurrence happens preferentially in cancers originating in certain regions of the brain.  Significance: Increased expression of multiple rescuer genes on the gained chromosome 7 could compensate for the downregulation of several vulnerable genes on the lost chromosome 10, resolving the long-standing mystery of this frequent co-occurrence in gliomas., (©2024 American Association for Cancer Research.)

Immune checkpoint blockade (ICB) is a promising cancer therapy; however, resistance frequently develops. To explore ICB resistance mechanisms, we develop Immunotherapy Resistance cell-cell Interaction Scanner (IRIS), a machine learning model aimed at identifying cell-type-specific tumor microenvironment ligand-receptor interactions relevant to ICB resistance. Applying IRIS to deconvolved transcriptomics data of the five largest melanoma ICB cohorts, we identify specific downregulated interactions, termed resistance downregulated interactions (RDI), as tumors develop resistance. These RDIs often involve chemokine signaling and offer a stronger predictive signal for ICB response compared to upregulated interactions or the state-of-the-art published transcriptomics biomarkers. Validation across multiple independent melanoma patient cohorts and modalities confirms that RDI activity is associated with CD8 + T cell infiltration and highly manifested in hot/brisk tumors. This study presents a strongly predictive ICB response biomarker, highlighting the key role of downregulating chemotaxis-associated ligand-receptor interactions in inhibiting lymphocyte infiltration in resistant tumors., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Aneuploidy is a hallmark of human cancer, yet the molecular mechanisms to cope with aneuploidy-induced cellular stresses remain largely unknown. Here, we induce chromosome mis-segregation in non-transformed RPE1-hTERT cells and derive multiple stable clones with various degrees of aneuploidy. We perform a systematic genomic, transcriptomic and proteomic profiling of 6 isogenic clones, using whole-exome DNA, mRNA and miRNA sequencing, as well as proteomics. Concomitantly, we functionally interrogate their cellular vulnerabilities, using genome-wide CRISPR/Cas9 and large-scale drug screens. Aneuploid clones activate the DNA damage response and are more resistant to further DNA damage induction. Aneuploid cells also exhibit elevated RAF/MEK/ERK pathway activity and are more sensitive to clinically-relevant drugs targeting this pathway, and in particular to CRAF inhibition. Importantly, CRAF and MEK inhibition sensitize aneuploid cells to DNA damage-inducing chemotherapies and to PARP inhibitors. We validate these results in human cancer cell lines. Moreover, resistance of cancer patients to olaparib is associated with high levels of RAF/MEK/ERK signaling, specifically in highly-aneuploid tumors. Overall, our study provides a comprehensive resource for genetically-matched karyotypically-stable cells of various aneuploidy states, and reveals a therapeutically-relevant cellular dependency of aneuploid cells., (© 2024. The Author(s).)

In this study, we explore the possibility of inferring characteristics of the tumor immune microenvironment from the blood. Specifically, we investigate two datasets of patients with head and neck squamous cell carcinoma with matched single-cell RNA sequencing (scRNA-seq) from peripheral blood mononuclear cells (PBMCs) and tumor tissues. Our analysis shows that the immune cell fractions and gene expression profiles of various immune cells within the tumor microenvironment can be inferred from the matched PBMC scRNA-seq data. We find that the established exhausted T-cell signature can be predicted from the blood and serve as a valuable prognostic blood biomarker of immunotherapy response. Additionally, our study reveals that the inferred ratio between tumor memory B- and regulatory T-cell fractions is predictive of immunotherapy response and is superior to the well-established cytolytic and exhausted T-cell signatures. These results highlight the promising potential of PBMC scRNA-seq in cancer immunotherapy and warrant, and will hopefully facilitate, further investigations on a larger scale. The code for predicting tumor immune microenvironment from PBMC scRNA-seq, TIMEP, is provided, offering other researchers the opportunity to investigate its prospective applications in various other indications., Significance: Our work offers a new and promising paradigm in liquid biopsies to unlock the power of blood single-cell transcriptomics in cancer immunotherapy., (©2024 The Authors; Published by the American Association for Cancer Research.)

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets. ENLIGHT-DeepPT successfully predicts true responders in five independent patient cohorts involving four different treatments spanning six cancer types, with an overall odds ratio of 2.28 and a 39.5% increased response rate among predicted responders versus the baseline rate. Notably, its prediction accuracy, obtained without any training on the treatment data, is comparable to that achieved by directly predicting the response from the images, which requires specific training on the treatment evaluation cohorts., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Despite the revolutionary impact of immune checkpoint blockade (ICB) in cancer treatment, accurately predicting patient responses remains challenging. Here, we analyzed a large dataset of 2,881 ICB-treated and 841 non-ICB-treated patients across 18 solid tumor types, encompassing a wide range of clinical, pathologic and genomic features. We developed a clinical score called LORIS (logistic regression-based immunotherapy-response score) using a six-feature logistic regression model. LORIS outperforms previous signatures in predicting ICB response and identifying responsive patients even with low tumor mutational burden or programmed cell death 1 ligand 1 expression. LORIS consistently predicts patient objective response and short-term and long-term survival across most cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling precise patient stratification. As an accurate, interpretable method using a few readily measurable features, LORIS may help improve clinical decision-making in precision medicine to maximize patient benefit. LORIS is available as an online tool at https://loris.ccr.cancer.gov/ ., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Low response rate, treatment relapse, and resistance remain key challenges for cancer treatment with immune checkpoint blockade (ICB). Here we report that loss of specific tumor suppressors (TS) induces an inflammatory response and promotes an immune suppressive tumor microenvironment. Importantly, low expression of these TSs is associated with a higher expression of immune checkpoint inhibitory mediators. Here we identify, by using in vivo CRISPR/Cas9 based loss-of-function screening, that NF1, TSC1, and TGF-β RII as TSs regulating immune composition. Loss of each of these three TSs leads to alterations in chromatin accessibility and enhances IL6-JAK3-STAT3/6 inflammatory pathways. This results in an immune suppressive landscape, characterized by increased numbers of LAG3+ CD8 and CD4 T cells. ICB targeting LAG3 and PD-L1 simultaneously inhibits metastatic progression in preclinical triple negative breast cancer (TNBC) mouse models of NF1-, TSC1- or TGF-β RII- deficient tumors. Our study thus reveals a role of TSs in regulating metastasis via non-cell-autonomous modulation of the immune compartment and provides proof-of-principle for ICB targeting LAG3 for patients with NF1-, TSC1- or TGF-β RII-inactivated cancers., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

The study of the tumor microbiome has been garnering increased attention. We developed a computational pipeline (CSI-Microbes) for identifying microbial reads from single-cell RNA sequencing (scRNA-seq) data and for analyzing differential abundance of taxa. Using a series of controlled experiments and analyses, we performed the first systematic evaluation of the efficacy of recovering microbial unique molecular identifiers by multiple scRNA-seq technologies, which identified the newer 10x chemistries (3' v3 and 5') as the best suited approach. We analyzed patient esophageal and colorectal carcinomas and found that reads from distinct genera tend to co-occur in the same host cells, testifying to possible intracellular polymicrobial interactions. Microbial reads are disproportionately abundant within myeloid cells that up-regulate proinflammatory cytokines like IL1 Β and CXCL8 , while infected tumor cells up-regulate antigen processing and presentation pathways. These results show that myeloid cells with bacteria engulfed are a major source of bacterial RNA within the tumor microenvironment (TME) and may inflame the TME and influence immunotherapy response.

Downregulation of the urea cycle enzyme argininosuccinate synthase (ASS1) in multiple tumors is associated with a poor prognosis partly because of the metabolic diversion of cytosolic aspartate for pyrimidine synthesis, supporting proliferation and mutagenesis owing to nucleotide imbalance. Here, we find that prolonged loss of ASS1 promotes DNA damage in colon cancer cells and fibroblasts from subjects with citrullinemia type I. Following acute induction of DNA damage with doxorubicin, ASS1 expression is elevated in the cytosol and the nucleus with at least a partial dependency on p53; ASS1 metabolically restrains cell cycle progression in the cytosol by restricting nucleotide synthesis. In the nucleus, ASS1 and ASL generate fumarate for the succination of SMARCC1, destabilizing the chromatin-remodeling complex SMARCC1-SNF5 to decrease gene transcription, specifically in a subset of the p53-regulated cell cycle genes. Thus, following DNA damage, ASS1 is part of the p53 network that pauses cell cycle progression, enabling genome maintenance and survival. Loss of ASS1 contributes to DNA damage and promotes cell cycle progression, likely contributing to cancer mutagenesis and, hence, adaptability potential., (© 2024. The Author(s).)

Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are state-of-the-art data-driven means to enhance diagnostic accuracy but are also time consuming and not widely available. Here, to address these limitations, we developed Deep lEarning from histoPathoLOgy and methYlation (DEPLOY), a deep learning model that classifies CNS tumors to ten major categories from histopathology. DEPLOY integrates three distinct components: the first classifies CNS tumors directly from slide images ('direct model'), the second initially generates predictions for DNA methylation beta values, which are subsequently used for tumor classification ('indirect model'), and the third classifies tumor types directly from routinely available patient demographics. First, we find that DEPLOY accurately predicts beta values from histopathology images. Second, using a ten-class model trained on an internal dataset of 1,796 patients, we predict the tumor categories in three independent external test datasets including 2,156 patients, achieving an overall accuracy of 95% and balanced accuracy of 91% on samples that are predicted with high confidence. These results showcase the potential future use of DEPLOY to assist pathologists in diagnosing CNS tumors within a clinically relevant short time frame., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intratumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the cell-extrinsic drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the TME exhibit substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAFs) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLC's adaptable nature, opening possibilities for reprogramming the TME-tumor communications that shape SCLC tumor states., Competing Interests: Declaration of interests A.T. received grants to NCI from EMD Serono Research & Development, AstraZeneca, Gilead Sciences, and ProLynx during the conduct of the study., (Published by Elsevier Inc.)

The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While the TME's impact on immunotherapy has been extensively studied, its role in chemotherapy response remains less explored. To address this, we developed DECODEM (DEcoupling Cell-type-specific Outcomes using DEconvolution and Machine learning), a generic computational framework leveraging cellular deconvolution of bulk transcriptomics to associate the gene expression of individual cell types in the TME with clinical response. Employing DECODEM to analyze the gene expression of breast cancer (BC) patients treated with neoadjuvant chemotherapy, we find that the gene expression of specific immune cells ( myeloid , plasmablasts , B-cells ) and stromal cells ( endothelial , normal epithelial , CAFs ) are highly predictive of chemotherapy response, going beyond that of the malignant cells. These findings are further tested and validated in a single-cell cohort of triple negative breast cancer. To investigate the possible role of immune cell-cell interactions (CCIs) in mediating chemotherapy response, we extended DECODEM to DECODEMi to identify such CCIs, validated in single-cell data. Our findings highlight the importance of active pre-treatment immune infiltration for chemotherapy success. The tools developed here are made publicly available and are applicable for studying the role of the TME in mediating response from readily available bulk tumor expression in a wide range of cancer treatments and indications., Competing Interests: COMPETING INTERESTS E.R. is a co-founder of Medaware Ltd. (https://www.medaware.com/), Metabomed (https://www.metabomed.com/), and Pangea Biomed (https://pangeamedicine.com/). He has divested and serves as an unpaid scientific consultant to the latter company. The rest of the authors declare no conflicts of interest.

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Longitudinal monitoring of patients with advanced cancers is crucial to evaluate both disease burden and treatment response. Current liquid biopsy approaches mostly rely on the detection of DNA-based biomarkers. However, plasma RNA analysis can unleash tremendous opportunities for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of RNA within plasma extracellular vesicles (evRNA), we successfully predicted consensus molecular subtypes in patients with metastatic colorectal cancer. Analysis of plasma evRNA also enabled monitoring of changes in transcriptomic subtype under treatment selection pressure and identification of molecular pathways associated with recurrence. This approach also revealed expressed gene fusions and neoepitopes from evRNA. These results demonstrate the feasibility of using transcriptomic-based liquid biopsy platforms for precision oncology approaches, spanning from the longitudinal monitoring of tumor subtype changes to the identification of expressed fusions and neoantigens as cancer-specific therapeutic targets, sans the need for tissue-based sampling., Significance: The development of an approach to interrogate molecular subtypes, cancer-associated pathways, and differentially expressed genes through RNA sequencing of plasma extracellular vesicles lays the foundation for liquid biopsy-based longitudinal monitoring of patient tumor transcriptomes., (©2024 American Association for Cancer Research.)

Cytotoxic T lymphocyte (CTL) and terminal exhausted T lymphocyte (ETL) activities crucially influence immune checkpoint inhibitor (ICI) response. Despite this, the efficacy of ETL and CTL transcriptomic signatures for response prediction remains limited. Investigating this across the TCGA and publicly available single-cell cohorts, we find a strong positive correlation between ETL and CTL expression signatures in most cancers. We hence posited that their limited predictability arises due to their mutually canceling effects on ICI response. Thus, we developed DETACH, a computational method to identify a gene set whose expression pinpoints to a subset of melanoma patients where the CTL and ETL correlation is low. DETACH enhances CTL's prediction accuracy, outperforming existing signatures. DETACH signature genes activity also demonstrates a positive correlation with lymphocyte infiltration and the prevalence of reactive T cells in the tumor microenvironment (TME), advancing our understanding of the CTL cell state within the TME., Competing Interests: E.R. is a co-founder of Medaware Ltd. (https://www.medaware.com/), Metabomed (https://www.metabomed.com/), and Pangea Biomed (https://pangeamedicine.com/). He has divested and serves as an unpaid scientific consultant to the latter company.

Background: As artificial intelligence (AI) tools become widely accessible, more patients and medical professionals will turn to them for medical information. Large language models (LLMs), a subset of AI, excel in natural language processing tasks and hold considerable promise for clinical use. Fields such as oncology, in which clinical decisions are highly dependent on a continuous influx of new clinical trial data and evolving guidelines, stand to gain immensely from such advancements. It is therefore of critical importance to benchmark these models and describe their performance characteristics to guide their safe application to clinical oncology. Accordingly, the primary objectives of this work were to conduct comprehensive evaluations of LLMs in the field of oncology and to identify and characterize strategies that medical professionals can use to bolster their confidence in a model's response., Methods: This study tested five publicly available LLMs (LLaMA 1, PaLM 2, Claude-v1, generative pretrained transformer 3.5 [GPT-3.5], and GPT-4) on a comprehensive battery of 2044 oncology questions, including topics from medical oncology, surgical oncology, radiation oncology, medical statistics, medical physics, and cancer biology. Model prompts were presented independently of each other, and each prompt was repeated three times to assess output consistency. For each response, models were instructed to provide a self-appraised confidence score (from 1 to 4). Model performance was also evaluated against a novel validation set comprising 50 oncology questions curated to eliminate any risk of overlap with the data used to train the LLMs., Results: There was significant heterogeneity in performance between models (analysis of variance, P<0.001). Relative to a human benchmark (2013 and 2014 examination results), GPT-4 was the only model to perform above the 50th percentile. Overall, model performance varied as a function of subject area across all models, with worse performance observed in clinical oncology subcategories compared with foundational topics (medical statistics, medical physics, and cancer biology). Within the clinical oncology subdomain, worse performance was observed in female-predominant malignancies. A combination of model selection, prompt repetition, and confidence self-appraisal allowed for the identification of high-performing subgroups of questions with observed accuracies of 81.7 and 81.1% in the Claude-v1 and GPT-4 models, respectively. Evaluation of the novel validation question set produced similar trends in model performance while also highlighting improved performance in newer, centrally hosted models (GPT-4 Turbo and Gemini 1.0 Ultra) and local models (Mixtral 8×7B and LLaMA 2)., Conclusions: Of the models tested on a standardized set of oncology questions, GPT-4 was observed to have the highest performance. Although this performance is impressive, all LLMs continue to have clinically significant error rates, including examples of overconfidence and consistent inaccuracies. Given the enthusiasm to integrate these new implementations of AI into clinical practice, continued standardized evaluations of the strengths and limitations of these products will be critical to guide both patients and medical professionals. (Funded by the National Institutes of Health Clinical Center for Research and the Intramural Research Program of the National Institutes of Health; Z99 CA999999.).

Ferroptosis and apoptosis are key cell-death pathways implicated in several human diseases including cancer. Ferroptosis is driven by iron-dependent lipid peroxidation and currently has no characteristic biomarkers or gene signatures. Here a continuous phenotypic gradient between ferroptosis and apoptosis coupled to transcriptomic and metabolomic landscapes is established. The gradual ferroptosis-to-apoptosis transcriptomic landscape is used to generate a unique, unbiased transcriptomic predictor, the Gradient Gene Set (GGS), which classified ferroptosis and apoptosis with high accuracy. Further GGS optimization using multiple ferroptotic and apoptotic datasets revealed highly specific ferroptosis biomarkers, which are robustly validated in vitro and in vivo. A subset of the GGS is associated with poor prognosis in breast cancer patients and PDXs and contains different ferroptosis repressors. Depletion of one representative, PDGFA-assaociated protein 1(PDAP1), is found to suppress basal-like breast tumor growth in a mouse model. Omics and mechanistic studies revealed that ferroptosis is associated with enhanced lysosomal function, glutaminolysis, and the tricarboxylic acid (TCA) cycle, while its transition into apoptosis is attributed to enhanced endoplasmic reticulum(ER)-stress and phosphatidylethanolamine (PE)-to-phosphatidylcholine (PC) metabolic shift. Collectively, this study highlights molecular mechanisms underlying ferroptosis execution, identified a highly predictive ferroptosis gene signature with prognostic value, ferroptosis versus apoptosis biomarkers, and ferroptosis repressors for breast cancer therapy., (© 2024 The Authors. Advanced Science published by Wiley‐VCH GmbH.)

Identifying sex differences in outcomes and toxicity between males and females in oncology clinical trials is important and has also been mandated by National Institutes of Health policies. Here we analyze the Trialtrove database, finding that, strikingly, only 472/89,221 oncology clinical trials (0.5%) had curated post-treatment sex comparisons. Among 288 trials with comparisons of survival, outcome, or response, 16% report males having statistically significant better survival outcome or response, while 42% reported significantly better survival outcome or response for females. The strongest differences are in trials of EGFR inhibitors in lung cancer and rituximab in non-Hodgkin's lymphoma (both favoring females). Among 44 trials with side effect comparisons, more trials report significantly lesser side effects in males (N = 22) than in females (N = 13). Thus, while statistical comparisons between sexes in oncology trials are rarely reported, important differences in outcome and toxicity exist. These considerable outcome and toxicity differences highlight the need for reporting sex differences more thoroughly going forward., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

The majority of human genetic diseases are caused by single nucleotide variants (SNVs) in the genome sequence. Excitingly, new genomic techniques known as base editing have opened efficient pathways to correct erroneous nucleotides. Due to reliance on deaminases, which have the capability to convert A to I(G) and C to U, the direct applicability of base editing might seem constrained in terms of the range of mutations that can be reverted. In this evaluation, we assess the potential of DNA and RNA base editing methods for treating human genetic diseases. Our findings indicate that 62% of pathogenic SNVs found within genes can be amended by base editing; 30% are G>A and T>C SNVs that can be corrected by DNA base editing, and most of them by RNA base editing as well, and 29% are C>T and A>G SNVs that can be corrected by DNA base editing directed to the complementary strand. For each, we also present several factors that affect applicability such as bystander and off-target occurrences. For cases where editing the mismatched nucleotide is not feasible, we introduce an approach that calculates the optimal substitution of the deleterious amino acid with a new amino acid, further expanding the scope of applicability. As personalized therapy is rapidly advancing, our demonstration that most SNVs can be treated by base editing is of high importance. The data provided will serve as a comprehensive resource for those seeking to design therapeutic base editors and study their potential in curing genetic diseases., (© 2024. The Author(s).)

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., (© 2024 American Society of Hematology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)

Background: Synthetic lethality (SL) denotes a genetic interaction between two genes whose co-inactivation is detrimental to cells. Because more than 25 years have passed since SL was proposed as a promising way to selectively target cancer vulnerabilities, it is timely to comprehensively assess its impact so far and discuss its future., Methods: We systematically analyzed the literature and clinical trial data from the PubMed and Trialtrove databases to portray the preclinical and clinical landscape of SL oncology., Findings: We identified 235 preclinically validated SL pairs and found 1,207 pertinent clinical trials, and the number keeps increasing over time. About one-third of these SL clinical trials go beyond the typically studied DNA damage response (DDR) pathway, testifying to the recently broadening scope of SL applications in clinical oncology. We find that SL oncology trials have a greater success rate than non-SL-based trials. However, about 75% of the preclinically validated SL interactions have not yet been tested in clinical trials., Conclusions: Dissecting the recent efforts harnessing SL to identify predictive biomarkers, novel therapeutic targets, and effective combination therapy, our systematic analysis reinforces the hope that SL may serve as a key driver of precision oncology going forward., Funding: Funded by the Samsung Research Funding & Incubation Center of Samsung Electronics, the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Republic of Korea government (MSIT), the Kwanjeong Educational Foundation, the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute (NCI), and Center for Cancer Research (CCR)., Competing Interests: Declaration of interests E.R. is a co-founder of Medaware, Ltd.; Metabomed, Ltd.; and Pangea Biomed, Ltd. (divested from the latter). E.R. serves as a non-paid scientific consultant to Pangea Biomed, Ltd., a company developing a precision oncology SL-based multiomics approach. J.S.L. is a scientific consultant at Pangea Biomed, Ltd., and a founder of NGen Biointelligence, Inc., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Immune-checkpoint inhibitors (ICIs) are now widely used for the treatment of patients with advanced-stage hepatocellular carcinoma (HCC). Two different ICI-containing regimens, atezolizumab plus bevacizumab and tremelimumab plus durvalumab, are now approved standard-of-care first-line therapies in this setting. However, and despite substantial improvements in survival outcomes relative to sorafenib, most patients with advanced-stage HCC do not derive durable benefit from these regimens. Advances in genome sequencing including the use of single-cell RNA sequencing (both of tumour material and blood samples), as well as immune cell identification strategies and other techniques such as radiomics and analysis of the microbiota, have created considerable potential for the identification of novel predictive biomarkers enabling the accurate selection of patients who are most likely to derive benefit from ICIs. In this Review, we summarize data on the immunology of HCC and the outcomes in patients receiving ICIs for the treatment of this disease. We then provide an overview of current biomarker use and developments in the past 5 years, including gene signatures, circulating tumour cells, high-dimensional flow cytometry, single-cell RNA sequencing as well as approaches involving the microbiome, radiomics and clinical markers. Novel concepts for further biomarker development in HCC are then discussed including biomarker-driven trials, spatial transcriptomics and integrated 'big data' analysis approaches. These concepts all have the potential to better identify patients who are most likely to benefit from ICIs and to promote the development of new treatment approaches., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Given the current status of coronavirus disease 2019 (COVID-19) as a global pandemic, it is of high priority to gain a deeper understanding of the disease's development and how the virus impacts its host. Adenosine (A)-to-Inosine (I) RNA editing is a post-transcriptional modification, catalyzed by the ADAR family of enzymes, that can be considered part of the inherent cellular defense mechanism as it affects the innate immune response in a complex manner. It was previously reported that various viruses could interact with the host's ADAR enzymes, resulting in epigenetic changes both to the virus and the host. Here, we analyze RNA-seq of nasopharyngeal swab specimens as well as whole-blood samples of COVID-19 infected individuals and show a significant elevation in the global RNA editing activity in COVID-19 compared to healthy controls. We also detect specific coding sites that exhibit higher editing activity. We further show that the increment in editing activity during the disease is temporary and returns to baseline shortly after the symptomatic period. These significant epigenetic changes may contribute to the immune system response and affect adverse outcomes seen in post-viral cases., (© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Proteolysis-targeting chimera (PROTAC) and other targeted protein degradation (TPD) molecules that induce degradation by the ubiquitin-proteasome system (UPS) offer new opportunities to engage targets that remain challenging to be inhibited by conventional small molecules. One fundamental element in the degradation process is the E3 ligase. However, less than 2% amongst hundreds of E3 ligases in the human genome have been engaged in current studies in the TPD field, calling for the recruiting of additional ones to further enhance the therapeutic potential of TPD. To accelerate the development of PROTACs utilizing under-explored E3 ligases, we systematically characterize E3 ligases from seven different aspects, including chemical ligandability, expression patterns, protein-protein interactions (PPI), structure availability, functional essentiality, cellular location, and PPI interface by analyzing 30 large-scale data sets. Our analysis uncovers several E3 ligases as promising extant PROTACs. In total, combining confidence score, ligandability, expression pattern, and PPI, we identified 76 E3 ligases as PROTAC-interacting candidates. We develop a user-friendly and flexible web portal ( https://hanlaboratory.com/E3Atlas/ ) aimed at assisting researchers to rapidly identify E3 ligases with promising TPD activities against specifically desired targets, facilitating the development of these therapies in cancer and beyond., (© 2023. Springer Nature Limited.)

Chimeric antigen receptor (CAR) T cell therapies have yielded transformative clinical successes for patients with blood tumors, but their full potential remains to be unleashed against solid tumors. One challenge is finding selective targets, which we define intuitively to be cell surface proteins that are expressed widely by cancer cells but minimally by healthy cells in the tumor microenvironment and other normal tissues. Analyzing patient tumor single-cell transcriptomics data, we first defined and quantified selectivity and safety scores of existing CAR targets for indications in which they are in clinical trials or approved. We then sought new candidate cell surface CAR targets that have better selectivity and safety scores than those currently being tested. Remarkably, in almost all cancer types, we could not find such better targets, testifying to the near optimality of the current target space. However, in human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSC), for which there is currently a dearth of existing CAR targets, we identified a total of twenty candidate novel CAR targets, five of which have both superior selectivity and safety scores. These newly identified cell surface targets lay a basis for future investigations that may lead to better CAR treatments in HNSC.

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., Competing Interests: Potential conflicts of interest . E. R. reports financial interests as a cofounder of MedAware and Metabomed, Ltd, and is a divested cofounder and unpaid scientific consultant of Pangea Biomed. E. M. reports honoraria for lectures from Teva, Medisone, and Astra Zeneca; participation on advisory boards for Merck and SK-Pharma; roles as a board member of the Israeli National Diabetes Council and the Israeli Society of ADHD; and a role as a committee member of the Israeli National Dementia Prevention Program. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Systemic immune activation, hallmarked by C-reactive protein (CRP) and interleukin-6 (IL-6), can modulate antitumor immune responses. In this study, we evaluated the role of IL-6 and CRP in the stratification of patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs). We also interrogated the underlying immunosuppressive mechanisms driven by the IL-6/CRP axis., Methods: In cohort A (n=308), we estimated the association of baseline CRP with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) in patients with NSCLC treated with ICIs alone or with chemo-immunotherapy (Chemo-ICI). Baseline tumor bulk RNA sequencing (RNA-seq) of lung adenocarcinomas (LUADs) treated with pembrolizumab (cohort B, n=59) was used to evaluate differential expression of purine metabolism, as well as correlate IL-6 expression with PFS. CODEFACS approach was applied to deconvolve cohort B to characterize the tumor microenvironment by reconstructing the cell-type-specific transcriptome from bulk expression. Using the LUAD cohort from The Cancer Genome Atlas (TCGA) we explored the correlation between IL-6 expression and adenosine gene signatures. In a third cohort (cohort C, n=18), plasma concentrations of CRP, adenosine 2a receptor (A2aR), and IL-6 were measured using ELISA., Results: In cohort A, 67.2% of patients had a baseline CRP≥10 mg/L (CRP-H). Patients with CRP-H achieved shorter OS (8.6 vs 14.8 months; p=0.006), shorter PFS (3.3 vs 6.6 months; p=0.013), and lower ORR (24.7% vs 46.3%; p=0.015). After adjusting for relevant clinical variables, CRP-H was confirmed as an independent predictor of increased risk of death (HR 1.51, 95% CI: 1.09 to 2.11) and lower probability of achieving disease response (OR 0.34, 95% CI: 0.13 to 0.89). In cohort B, RNA-seq analysis demonstrated higher IL-6 expression on tumor cells of non-responders, along with a shorter PFS (p<0.05) and enrichment of the purinergic pathway. Within the TCGA LUAD cohort, tumor IL-6 expression strongly correlated with the adenosine signature (R=0.65; p<2.2e-16). Plasma analysis in cohort C demonstrated that CRP-H patients had a greater median baseline level of A2aR (6.0 ng/mL vs 1.3 ng/mL; p=0.01)., Conclusions: This study demonstrates CRP as a readily available blood-based prognostic biomarker in ICI-treated NSCLC. Additionally, we elucidate a potential link of the CRP/IL-6 axis with the immunosuppressive adenosine signature pathway that could drive inferior outcomes to ICIs in NSCLC and also offer novel therapeutic avenues., Competing Interests: Competing interests: The authors disclose no conflicts of interest in relation to the published work. AC received grants for consultancies/advisory boards: BMS, MSD, OncoC4, IQVIA, Roche, GSK, AstraZeneca, Access Infinity, Ardelis Health. He also received speaker fees from AstraZeneca, EISAI, MSD and Pierre-Fabre. DJP received lecture fees from ViiV Healthcare, Bayer Healthcare, AstraZeneca, Roche, IPSEN and travel expenses from BMS and Bayer Healthcare; consulting fees for Mina Therapeutics, EISAI, Roche, AstraZeneca, DaVolterra, Exact Sciences, MURSLA, Avamune, BMS; received research funding (to institution) from MSD, BMS, GSK., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

The tremendous success of chimeric antigen receptor (CAR) T cells in children and young adults (CAYAs) with relapsed/refractory B-cell acute lymphoblastic leukemia is tempered by toxicities such as cytokine release syndrome (CRS). Despite expansive information about CRS, profiling of specific end-organ toxicities secondary to CAR T-cell therapy in CAYAs is limited. This retrospective, single-center study sought to characterize end-organ specific adverse events (AEs) experienced by CAYAs during the first 30 days after CAR T-cell infusion. AEs graded using Common Terminology Criteria for Adverse Events were retrospectively analyzed for 134 patients enrolled in 1 of 3 phase 1 CAR T-cell trials (NCT01593696, NCT02315612, and NCT03448393), targeting CD19 and/or CD22. A total of 133 patients (99.3%) experienced at least 1 grade ≥3 (≥Gr3) AE across 17 organ systems, of which 75 (4.4%) were considered dose- or treatment-limiting toxicities. Excluding cytopenias, 109 patients (81.3%) experienced a median of 3 ≥Gr3 noncytopenia (NC) AEs. The incidence of ≥Gr3 NC AEs was associated with the development and severity of CRS as well as preinfusion disease burden (≥ 25% marrow blasts). Although those with complete remission trended toward experiencing more ≥Gr3 NC AEs than nonresponders (median, 4 vs 3), nonresponders experiencing CRS (n = 17; 37.8%) had the highest degree of NC AEs across all patients (median, 7 vs 4 in responders experiencing CRS). Greater understanding of these toxicities and the ability to predict which patients may experience more toxicities is critical as the array of CAR T-cell therapies expand. This retrospective study was registered at www.clinicaltrials.gov as NCT03827343., (© 2023 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)