Understanding Human Autoimmunity and Autoinflammation Through Transcriptomics.

Transcriptomics, the high-throughput characterization of RNAs, has been instrumental in defining pathogenic signatures in human autoimmunity and autoinflammation. It enabled the identification of new therapeutic targets in IFN-, IL-1- and IL-17-mediated diseases. Applied to immunomonitoring, transcriptomics is starting to unravel diagnostic and prognostic signatures that stratify patients, track molecular changes associated with disease activity, define personalized treatment strategies, and generally inform clinical practice. Herein, we review the use of transcriptomics to define mechanistic, diagnostic, and predictive signatures in human autoimmunity and autoinflammation. We discuss some of the analytical approaches applied to extract biological knowledge from high-dimensional data sets. Finally, we touch upon emerging applications of transcriptomics to study eQTLs, B and T cell repertoire diversity, and isoform usage.

A call from patient-researchers to advance research on long COVID.

Long COVID is a chronic and often disabling illness with long-term consequences. Although progress has been made in the clinical characterization of long COVID, no approved treatments exist and disconnects between patients and researchers threaten to hinder future progress. Incorporating patients as active collaborators in long COVID research can bridge the gap and accelerate progress toward treatments and cures.

Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Trellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses.

Patient-derived organoids (PDOs) can model personalized therapy responses; however, current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly multiplexed mass cytometry platform to measure post-translational modification (PTM) signaling, DNA damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer-associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell datasets, we developed "Trellis"-a highly scalable, tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rarer, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate PDO plasticity-shifting proliferative colonic stem cells (proCSCs) to slow-cycling revival colonic stem cells (revCSCs) to protect cancer cells from chemotherapy.

Discovery of highly neutralizing human antibodies targeting Pseudomonas aeruginosa.

Drug-resistant Pseudomonas aeruginosa (PA) poses an emerging threat to human health with urgent need for alternative therapeutic approaches. Here, we deciphered the B cell and antibody response to the virulence-associated type III secretion system (T3SS) in a cohort of patients chronically infected with PA. Single-cell analytics revealed a diverse B cell receptor repertoire directed against the T3SS needle-tip protein PcrV, enabling the production of monoclonal antibodies (mAbs) abrogating T3SS-mediated cytotoxicity. Mechanistic studies involving cryoelectron microscopy identified a surface-exposed C-terminal PcrV epitope as the target of highly neutralizing mAbs with broad activity against drug-resistant PA isolates. These anti-PcrV mAbs were as effective as treatment with conventional antibiotics in vivo. Our study reveals that chronically infected patients represent a source of neutralizing antibodies, which can be exploited as therapeutics against PA.

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.

Spatially confined sub-tumor microenvironments in pancreatic cancer.

Intratumoral heterogeneity is a critical frontier in understanding how the tumor microenvironment (TME) propels malignant progression. Here, we deconvolute the human pancreatic TME through large-scale integration of histology-guided regional multiOMICs with clinical data and patient-derived preclinical models. We discover "subTMEs," histologically definable tissue states anchored in fibroblast plasticity, with regional relationships to tumor immunity, subtypes, differentiation, and treatment response. "Reactive" subTMEs rich in complex but functionally coordinated fibroblast communities were immune hot and inhabited by aggressive tumor cell phenotypes. The matrix-rich "deserted" subTMEs harbored fewer activated fibroblasts and tumor-suppressive features yet were markedly chemoprotective and enriched upon chemotherapy. SubTMEs originated in fibroblast differentiation trajectories, and transitory states were notable both in single-cell transcriptomics and in situ. The intratumoral co-occurrence of subTMEs produced patient-specific phenotypic and computationally predictable heterogeneity tightly linked to malignant biology. Therefore, heterogeneity within the plentiful, notorious pancreatic TME is not random but marks fundamental tissue organizational units.

Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer.

Prognostically relevant RNA expression states exist in pancreatic ductal adenocarcinoma (PDAC), but our understanding of their drivers, stability, and relationship to therapeutic response is limited. To examine these attributes systematically, we profiled metastatic biopsies and matched organoid models at single-cell resolution. In vivo, we identify a new intermediate PDAC transcriptional cell state and uncover distinct site- and state-specific tumor microenvironments (TMEs). Benchmarking models against this reference map, we reveal strong culture-specific biases in cancer cell transcriptional state representation driven by altered TME signals. We restore expression state heterogeneity by adding back in vivo-relevant factors and show plasticity in culture models. Further, we prove that non-genetic modulation of cell state can strongly influence drug responses, uncovering state-specific vulnerabilities. This work provides a broadly applicable framework for aligning cell states across in vivo and ex vivo settings, identifying drivers of transcriptional plasticity and manipulating cell state to target associated vulnerabilities.

Potent Neutralizing Antibodies against SARS-CoV-2 Identified by High-Throughput Single-Cell Sequencing of Convalescent Patients' B Cells.

The COVID-19 pandemic urgently needs therapeutic and prophylactic interventions. Here, we report the rapid identification of SARS-CoV-2-neutralizing antibodies by high-throughput single-cell RNA and VDJ sequencing of antigen-enriched B cells from 60 convalescent patients. From 8,558 antigen-binding IgG1+ clonotypes, 14 potent neutralizing antibodies were identified, with the most potent one, BD-368-2, exhibiting an IC50 of 1.2 and 15 ng/mL against pseudotyped and authentic SARS-CoV-2, respectively. BD-368-2 also displayed strong therapeutic and prophylactic efficacy in SARS-CoV-2-infected hACE2-transgenic mice. Additionally, the 3.8 Å cryo-EM structure of a neutralizing antibody in complex with the spike-ectodomain trimer revealed the antibody's epitope overlaps with the ACE2 binding site. Moreover, we demonstrated that SARS-CoV-2-neutralizing antibodies could be directly selected based on similarities of their predicted CDR3H structures to those of SARS-CoV-neutralizing antibodies. Altogether, we showed that human neutralizing antibodies could be efficiently discovered by high-throughput single B cell sequencing in response to pandemic infectious diseases.

Tumor Evolution and Drug Response in Patient-Derived Organoid Models of Bladder Cancer.

Bladder cancer is the fifth most prevalent cancer in the U.S., yet is understudied, and few laboratory models exist that reflect the biology of the human disease. Here, we describe a biobank of patient-derived organoid lines that recapitulates the histopathological and molecular diversity of human bladder cancer. Organoid lines can be established efficiently from patient biopsies acquired before and after disease recurrence and are interconvertible with orthotopic xenografts. Notably, organoid lines often retain parental tumor heterogeneity and exhibit a spectrum of genomic changes that are consistent with tumor evolution in culture. Analyses of drug response using bladder tumor organoids show partial correlations with mutational profiles, as well as changes associated with treatment resistance, and specific responses can be validated using xenografts in vivo. Our studies indicate that patient-derived bladder tumor organoids represent a faithful model system for studying tumor evolution and treatment response in the context of precision cancer medicine.

Combination Cancer Therapy Can Confer Benefit via Patient-to-Patient Variability without Drug Additivity or Synergy.

Combination cancer therapies aim to improve the probability and magnitude of therapeutic responses and reduce the likelihood of acquired resistance in an individual patient. However, drugs are tested in clinical trials on genetically diverse patient populations. We show here that patient-to-patient variability and independent drug action are sufficient to explain the superiority of many FDA-approved drug combinations in the absence of drug synergy or additivity. This is also true for combinations tested in patient-derived tumor xenografts. In a combination exhibiting independent drug action, each patient benefits solely from the drug to which his or her tumor is most sensitive, with no added benefit from other drugs. Even when drug combinations exhibit additivity or synergy in pre-clinical models, patient-to-patient variability and low cross-resistance make independent action the dominant mechanism in clinical populations. This insight represents a different way to interpret trial data and a different way to design combination therapies.

Evolution of community outreach and engagement at National Cancer Institute-Designated Cancer Centers, an evolving journey.

Cancer mortality rates have declined during the last 28 years, but that process is not equitably shared. Disparities in cancer outcomes by race, ethnicity, socioeconomic status, sexual orientation and gender identity, and geographic location persist across the cancer care continuum. Consequently, community outreach and engagement (COE) efforts within National Cancer Institute-Designated Cancer Center (NCI-DCC) catchment areas have intensified during the last 10 years as has the emphasis on COE and catchment areas in NCI's Cancer Center Support Grant applications. This review article attempts to provide a historic perspective of COE within NCI-DCCs. Improving COE has long been an important initiative for the NCI, but it was not until 2012 and 2016 that NCI-DCCs were required to define their catchment areas rigorously and to provide specific descriptions of COE interventions, respectively. NCI-DCCs had previously lacked adequate focus on the inclusion of historically marginalized patients in cancer innovation efforts. Integrating COE efforts throughout the research and operational aspects of the cancer centers, at both the patient and community levels, will expand the footprint of COE efforts within NCI-DCCs. Achieving this change requires sustained commitment by the centers to adjust their activities and improve access and outcomes for historically marginalized communities.

Patient navigation across the cancer care continuum: An overview of systematic reviews and emerging literature.

Patient navigation is a strategy for overcoming barriers to reduce disparities and to improve access and outcomes. The aim of this umbrella review was to identify, critically appraise, synthesize, and present the best available evidence to inform policy and planning regarding patient navigation across the cancer continuum. Systematic reviews examining navigation in cancer care were identified in the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, Embase, Cumulative Index of Nursing and Allied Health (CINAHL), Epistemonikos, and Prospective Register of Systematic Reviews (PROSPERO) databases and in the gray literature from January 1, 2012, to April 19, 2022. Data were screened, extracted, and appraised independently by two authors. The JBI Critical Appraisal Checklist for Systematic Review and Research Syntheses was used for quality appraisal. Emerging literature up to May 25, 2022, was also explored to capture primary research published beyond the coverage of included systematic reviews. Of the 2062 unique records identified, 61 systematic reviews were included. Fifty-four reviews were quantitative or mixed-methods reviews, reporting on the effectiveness of cancer patient navigation, including 12 reviews reporting costs or cost-effectiveness outcomes. Seven qualitative reviews explored navigation needs, barriers, and experiences. In addition, 53 primary studies published since 2021 were included. Patient navigation is effective in improving participation in cancer screening and reducing the time from screening to diagnosis and from diagnosis to treatment initiation. Emerging evidence suggests that patient navigation improves quality of life and patient satisfaction with care in the survivorship phase and reduces hospital readmission in the active treatment and survivorship care phases. Palliative care data were extremely limited. Economic evaluations from the United States suggest the potential cost-effectiveness of navigation in screening programs.

Older adult participation in cancer clinical trials: A systematic review of barriers and interventions.

Cancer is a disease of aging and, as the world's population ages, the number of older persons with cancer is increasing and will make up a growing share of the oncology population in virtually every country. Despite this, older patients remain vastly underrepresented in research that sets the standards for cancer treatments. Consequently, most of what we know about cancer therapeutics is based on clinical trials conducted in younger, healthier patients, and effective strategies to improve clinical trial participation of older adults with cancer remain sparse. For this systematic review, the authors evaluated published studies regarding barriers to participation and interventions to improve participation of older adults in cancer trials. The quality of the available evidence was low and, despite a literature describing multifaceted barriers, only one intervention study aimed to increase enrollment of older adults in trials. The findings starkly amplify the paucity of evidence-based, effective strategies to improve participation of this underrepresented population in cancer trials. Within these limitations, the authors provide their opinion on how the current cancer research infrastructure must be modified to accommodate the needs of older patients. Several underused solutions are offered to expand clinical trials to include older adults with cancer. However, as currently constructed, these recommendations alone will not solve the evidence gap in geriatric oncology, and efforts are needed to meet older and frail adults where they are by expanding clinical trials designed specifically for this population and leveraging real-world data.

Innovations in research and clinical care using patient-generated health data.

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

IKKα Kinase Regulates the DNA Damage Response and Drives Chemo-resistance in Cancer.

Phosphorylated IKKα(p45) is a nuclear active form of the IKKα kinase that is induced by the MAP kinases BRAF and TAK1 and promotes tumor growth independent of canonical NF-κB signaling. Insights into the sources of IKKα(p45) activation and its downstream substrates in the nucleus remain to be defined. Here, we discover that IKKα(p45) is rapidly activated by DNA damage independent of ATM-ATR, but dependent on BRAF-TAK1-p38-MAPK, and is required for robust ATM activation and efficient DNA repair. Abolishing BRAF or IKKα activity attenuates ATM, Chk1, MDC1, Kap1, and 53BP1 phosphorylation, compromises 53BP1 and RIF1 co-recruitment to sites of DNA lesions, and inhibits 53BP1-dependent fusion of dysfunctional telomeres. Furthermore, IKKα or BRAF inhibition synergistically enhances the therapeutic potential of 5-FU and irinotecan to eradicate chemotherapy-resistant metastatic human tumors in vivo. Our results implicate BRAF and IKKα kinases in the DDR and reveal a combination strategy for cancer treatment.

Unconventional mechanism of action and resistance to rapalogs in renal cancer.

mTORC1 is aberrantly activated in renal cell carcinoma (RCC) and is targeted by rapalogs. As for other targeted therapies, rapalogs clinical utility is limited by the development of resistance. Resistance often results from target mutation, but mTOR mutations are rarely found in RCC. As in humans, prolonged rapalog treatment of RCC tumorgrafts (TGs) led to resistance. Unexpectedly, explants from resistant tumors became sensitive both in culture and in subsequent transplants in mice. Notably, resistance developed despite persistent mTORC1 inhibition in tumor cells. In contrast, mTORC1 became reactivated in the tumor microenvironment (TME). To test the role of the TME, we engineered immunocompromised recipient mice with a resistance mTOR mutation (S2035T). Interestingly, TGs became resistant to rapalogs in mTORS2035T mice. Resistance occurred despite mTORC1 inhibition in tumor cells and could be induced by coculturing tumor cells with mutant fibroblasts. Thus, enforced mTORC1 activation in the TME is sufficient to confer resistance to rapalogs. These studies highlight the importance of mTORC1 inhibition in nontumor cells for rapalog antitumor activity and provide an explanation for the lack of mTOR resistance mutations in RCC patients.

Investigation of the α9-nicotinic receptor single nucleotide polymorphisms induced oncogenic properties and molecular mechanisms in breast cancer.

α9-nAChR, a subtype of nicotinic acetylcholine receptor, is significantly overexpressed in female breast cancer tumor tissues compared to normal tissues. Previous studies have proposed that specific single nucleotide polymorphisms (SNPs) in the CHRNA9 (α9-nAChR) gene are associated with an increased risk of breast cancer in interaction with smoking. The study conducted a breast cancer risk assessment of the α9-nAChR SNP rs10009228 (NM_017581.4:c.1325A > G) in the Taiwanese female population, including 308 breast cancer patients and 198 healthy controls revealed that individuals with the heterozygous A/G or A/A wild genotype have an increased susceptibility to developing breast cancer in the presence of smoking compared to carriers of the G/G variant genotype. Our investigation confirmed the presence of this missense variation, resulting in an alteration of the amino acid sequence from asparagine (N442) to serine (S442) to facilitate phosphorylation within the α9-nAchR protein. Additionally, overexpression of N442 (A/A) in breast cancer cells significantly enhanced cell survival, migration, and cancer stemness compared to S442 (G/G). Four-line triple-negative breast cancer patient-derived xenograft (TNBC-PDX) models with distinct α9-nAChR rs10009228 SNP genotypes (A/A, A/G, G/G) further demonstrated that chronic nicotine exposure accelerated tumor growth through sustained activation of the α9-nAChR downstream oncogenic AKT/ERK/STAT3 pathway, particularly in individuals with the A/G or A/A genotype. Collectively, our study established the links between genetic variations in α9-nAChR and smoking exposure in promoting breast tumor development. This emphasizes the need to consider gene-environment interactions carefully while developing effective breast cancer prevention and treatment strategies.

GLA-modified RNA treatment lowers GB3 levels in iPSC-derived cardiomyocytes from Fabry-affected individuals.

Recent studies in non-human model systems have shown therapeutic potential of nucleoside-modified messenger RNA (modRNA) treatments for lysosomal storage diseases. Here, we assessed the efficacy of a modRNA treatment to restore the expression of the galactosidase alpha (GLA), which codes for α-Galactosidase A (α-GAL) enzyme, in a human cardiac model generated from induced pluripotent stem cells (iPSCs) derived from two individuals with Fabry disease. Consistent with the clinical phenotype, cardiomyocytes from iPSCs derived from Fabry-affected individuals showed accumulation of the glycosphingolipid Globotriaosylceramide (GB3), which is an α-galactosidase substrate. Furthermore, the Fabry cardiomyocytes displayed significant upregulation of lysosomal-associated proteins. Upon GLA modRNA treatment, a subset of lysosomal proteins were partially restored to wild-type levels, implying the rescue of the molecular phenotype associated with the Fabry genotype. Importantly, a significant reduction of GB3 levels was observed in GLA modRNA-treated cardiomyocytes, demonstrating that α-GAL enzymatic activity was restored. Together, our results validate the utility of iPSC-derived cardiomyocytes from affected individuals as a model to study disease processes in Fabry disease and the therapeutic potential of GLA modRNA treatment to reduce GB3 accumulation in the heart.

D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.

As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recommendations, a platform for clinical patient case retrieval and reliable drug sensitivity prediction is highly expected. Therefore, we built a database, D3EGFRdb, with the clinicopathologic characteristics and drug responses of 1339 patients with EGFR mutations via literature mining. On the basis of D3EGFRdb, we developed a deep learning-based prediction model, D3EGFRAI, for drug sensitivity prediction of new EGFR mutation-driven NSCLC. Model validations of D3EGFRAI showed a prediction accuracy of 0.81 and 0.85 for patients from D3EGFRdb and our hospitals, respectively. Furthermore, mutation scanning of the crucial residues inside drug-binding pockets, which may occur in the future, was performed to explore their drug sensitivity changes. D3EGFR is the first platform to achieve clinical-level drug response prediction of all approved small molecule drugs for EGFR mutation-driven lung cancer and is freely accessible at https://www.d3pharma.com/D3EGFR/index.php.