Circumventing drug resistance in gastric cancer: A spatial multi-omics exploration of chemo and immuno-therapeutic response dynamics.

BACKGROUND: Gastric Cancer (GC) characteristically exhibits heterogeneous responses to treatment, particularly in relation to immuno plus chemo therapy, necessitating a precision medicine approach. This study is centered around delineating the cellular and molecular underpinnings of drug resistance in this context. METHODS: We undertook a comprehensive multi-omics exploration of postoperative tissues from GC patients undergoing the chemo and immuno-treatment regimen. Concurrently, an image deep learning model was developed to predict treatment responsiveness. RESULTS: Our initial findings associate apical membrane cells with resistance to fluorouracil and oxaliplatin, critical constituents of the therapy. Further investigation into this cell population shed light on substantial interactions with resident macrophages, underscoring the role of intercellular communication in shaping treatment resistance. Subsequent ligand-receptor analysis unveiled specific molecular dialogues, most notably TGFB1-HSPB1 and LTF-S100A14, offering insights into potential signaling pathways implicated in resistance. Our SVM model, incorporating these multi-omics and spatial data, demonstrated significant predictive power, with AUC values of 0.93 and 0.84 in the exploration and validation cohorts respectively. Hence, our results underscore the utility of multi-omics and spatial data in modeling treatment response. CONCLUSION: Our integrative approach, amalgamating mIHC assays, feature extraction, and machine learning, successfully unraveled the complex cellular interplay underlying drug resistance. This robust predictive model may serve as a valuable tool for personalizing therapeutic strategies and enhancing treatment outcomes in gastric cancer.

A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer.

Colorectal cancer (CRC) is a common malignancy involving multiple cellular components. The CRC tumor microenvironment (TME) has been characterized well at single-cell resolution. However, a spatial interaction map of the CRC TME is still elusive. Here, we integrate multiomics analyses and establish a spatial interaction map to improve the prognosis, prediction, and therapeutic development for CRC. We construct a CRC immune module (CCIM) that comprises FOLR2+ macrophages, exhausted CD8+ T cells, tolerant CD8+ T cells, exhausted CD4+ T cells, and regulatory T cells. Multiplex immunohistochemistry is performed to depict the CCIM. Based on this, we utilize advanced deep learning technology to establish a spatial interaction map and predict chemotherapy response. CCIM-Net is constructed, which demonstrates good predictive performance for chemotherapy response in both the training and testing cohorts. Lastly, targeting FOLR2+ macrophage therapeutics is used to disrupt the immunosuppressive CCIM and enhance the chemotherapy response in vivo.

Multi-instance Multi-task Learning for Joint Clinical Outcome and Genomic Profile Predictions from the Histopathological Images.

With the remarkable success of digital histopathology and the deep learning technology, many whole-slide pathological images (WSIs) based deep learning models are designed to help pathologists diagnose human cancers. Recently, rather than predicting categorical variables as in cancer diagnosis, several deep learning studies are also proposed to estimate the continuous variables such as the patients' survival or their transcriptional profile. However, most of the existing studies focus on conducting these predicting tasks separately, which overlooks the useful intrinsic correlation among them that can boost the prediction performance of each individual task. In addition, it is sill challenge to design the WSI-based deep learning models, since a WSI is with huge size but annotated with coarse label. In this study, we propose a general multi-instance multi-task learning framework (HistMIMT) for multi-purpose prediction from WSIs. Specifically, we firstly propose a novel multi-instance learning module (TMICS) considering both common and specific task information across different tasks to generate bag representation for each individual task. Then, a soft-mask based fusion module with channel attention (SFCA) is developed to leverage useful information from the related tasks to help improve the prediction performance on target task. We evaluate our method on three cancer cohorts derived from the Cancer Genome Atlas (TCGA). For each cohort, our multi-purpose prediction tasks range from cancer diagnosis, survival prediction and estimating the transcriptional profile of gene TP53. The experimental results demonstrated that HistMIMT can yield better outcome on all clinical prediction tasks than its competitors.

Harnessing Nanotechnology: Emerging Strategies for Multiple Myeloma Therapy.

Advances in nanotechnology have provided novel avenues for the diagnosis and treatment of multiple myeloma (MM), a hematological malignancy characterized by the clonal proliferation of plasma cells in the bone marrow. This review elucidates the potential of nanotechnology to revolutionize myeloma therapy, focusing on nanoparticle-based drug delivery systems, nanoscale imaging techniques, and nano-immunotherapy. Nanoparticle-based drug delivery systems offer enhanced drug targeting, reduced systemic toxicity, and improved therapeutic efficacy. We discuss the latest developments in nanocarriers, such as liposomes, polymeric nanoparticles, and inorganic nanoparticles, used for the delivery of chemotherapeutic agents, siRNA, and miRNA in MM treatment. We delve into nanoscale imaging techniques which provide spatial multi-omic data, offering a holistic view of the tumor microenvironment. This spatial resolution can help decipher the complex interplay between cancer cells and their surrounding environment, facilitating the development of highly targeted therapies. Lastly, we explore the burgeoning field of nano-immunotherapy, which employs nanoparticles to modulate the immune system for myeloma treatment. Specifically, we consider how nanoparticles can be used to deliver tumor antigens to antigen-presenting cells, thus enhancing the body's immune response against myeloma cells. In conclusion, nanotechnology holds great promise for improving the prognosis and quality of life of MM patients. However, several challenges remain, including the need for further preclinical and clinical trials to assess the safety and efficacy of these emerging strategies. Future research should also focus on developing personalized nanomedicine approaches, which could tailor treatments to individual patients based on their genetic and molecular profiles.

Rational Identification of Novel Antibody-Drug Conjugate with High Bystander Killing Effect against Heterogeneous Tumors.

Bystander-killing payloads can significantly overcome the tumor heterogeneity issue and enhance the clinical potential of antibody-drug conjugates (ADC), but the rational design and identification of effective bystander warheads constrain the broader implementation of this strategy. Here, graph attention networks (GAT) are constructed for a rational bystander killing scoring model and ADC construction workflow for the first time. To generate efficient bystander-killing payloads, this model is utilized for score-directed exatecan derivatives design. Among them, Ed9, the most potent payload with satisfactory permeability and bioactivity, is further used to construct ADC. Through linker optimization and conjugation, novel ADCs are constructed that perform excellent anti-tumor efficacy and bystander-killing effect in vivo and in vitro. The optimal conjugate T-VEd9 exhibited therapeutic efficacy superior to DS-8201 against heterogeneous tumors. These results demonstrate that the effective scoring approach can pave the way for the discovery of novel ADC with promising bystander payloads to combat tumor heterogeneity.

Protocol for isolating single cells from human pancreatic cancer tissues and analyzing major immune cell populations using flow cytometry.

Here, we present a protocol for collecting, dissociating, isolating, staining, and analyzing immune cells from pancreatic cancer tissues for flow cytometry. The isolated cells can also be used for single-cell RNA sequencing and other related procedures. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2023).1.

Multi-omics analysis reveals the chemoresistance mechanism of proliferating tissue-resident macrophages in PDAC via metabolic adaptation.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer that typically demonstrates resistance to chemotherapy. Tumor-associated macrophages (TAMs) are essential in tumor microenvironment (TME) regulation, including promoting chemoresistance. However, the specific TAM subset and mechanisms behind this promotion remain unclear. We employ multi-omics strategies, including single-cell RNA sequencing (scRNA-seq), transcriptomics, multicolor immunohistochemistry (mIHC), flow cytometry, and metabolomics, to analyze chemotherapy-treated samples from both humans and mice. We identify four major TAM subsets within PDAC, among which proliferating resident macrophages (proliferating rMφs) are strongly associated with poor clinical outcomes. These macrophages are able to survive chemotherapy by producing more deoxycytidine (dC) and fewer dC kinases (dCKs) to decrease the absorption of gemcitabine. Moreover, proliferating rMφs promote fibrosis and immunosuppression in PDAC. Eliminating them in the transgenic mouse model alleviates fibrosis and immunosuppression, thereby re-sensitizing PDAC to chemotherapy. Consequently, targeting proliferating rMφs may become a potential treatment strategy for PDAC to enhance chemotherapy.

Selective oxidative protection leads to tissue topological changes orchestrated by macrophage during ulcerative colitis.

Ulcerative colitis is a chronic inflammatory bowel disorder with cellular heterogeneity. To understand the composition and spatial changes of the ulcerative colitis ecosystem, here we use imaging mass cytometry and single-cell RNA sequencing to depict the single-cell landscape of the human colon ecosystem. We find tissue topological changes featured with macrophage disappearance reaction in the ulcerative colitis region, occurring only for tissue-resident macrophages. Reactive oxygen species levels are higher in the ulcerative colitis region, but reactive oxygen species scavenging enzyme SOD2 is barely detected in resident macrophages, resulting in distinct reactive oxygen species vulnerability for inflammatory macrophages and resident macrophages. Inflammatory macrophages replace resident macrophages and cause a spatial shift of TNF production during ulcerative colitis via a cytokine production network formed with T and B cells. Our study suggests components of a mechanism for the observed macrophage disappearance reaction of resident macrophages, providing mechanistic hints for macrophage disappearance reaction in other inflammation or infection situations.

Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors From Pathological Images and Multi-Omics Data.

The tumor-infiltrating lymphocytes (TILs) and its correlation with tumors have shown significant values in the development of cancers. Many observations indicated that the combination of the whole-slide pathological images (WSIs) and genomic data can better characterize the immunological mechanisms of TILs. However, the existing image-genomic studies evaluated the TILs by the combination of pathological image and single-type of omics data (e.g., mRNA), which is difficulty in assessing the underlying molecular processes of TILs holistically. Additionally, it is still very challenging to characterize the intersections between TILs and tumor regions in WSIs and the high dimensional genomic data also brings difficulty for the integrative analysis with WSIs. Based on the above considerations, we proposed an end-to-end deep learning framework i.e., IMO-TILs that can integrate pathological image with multi-omics data (i.e., mRNA and miRNA) to analyze TILs and explore the survival-associated interactions between TILs and tumors. Specifically, we firstly apply the graph attention network to describe the spatial interactions between TILs and tumor regions in WSIs. As to genomic data, the Concrete AutoEncoder (i.e., CAE) is adopted to select survival-associated Eigengenes from the high-dimensional multi-omics data. Finally, the deep generalized canonical correlation analysis (DGCCA) accompanied with the attention layer is implemented to fuse the image and multi-omics data for prognosis prediction of human cancers. The experimental results on three cancer cohorts derived from the Cancer Genome Atlas (TCGA) indicated that our method can both achieve higher prognosis results and identify consistent imaging and multi-omics bio-markers correlated strongly with the prognosis of human cancers.

Induction of Tumor Ferroptosis-Dependent Immunity via an Injectable Attractive Pickering Emulsion Gel.

The combination of ferroptosis inducers and immune checkpoint blockade can enhance antitumor effects. However, the efficacy in tumors with low immunogenicity requires further investigation. In this work, a water-in-oil Pickering emulsion gel is developed to deliver (1S, 3R)-RSL-3 (RSL-3), a ferroptosis inducer dissolved in iodized oil, and programmed death-1 (PD-1) antibody, the most commonly used immune checkpoint inhibitor dissolved in water, with optimal characteristics (RSL-3 + PD-1@gel). Tumor lipase degrades the continuous oil phase, which results in the slow release of RSL-3 and PD-1 antibody and a notable antitumor effect against low-immunogenic hepatocellular carcinoma and pancreatic cancer. Intriguingly, the RSL-3 + PD-1@gel induces ferroptosis of tumor cells, resulting in antitumor immune response via accumulation of helper T lymphocyte cells and cytotoxic T cells. Additionally, the single-cell sequence profiling analysis during tumor treatment reveals the induction of ferroptosis in tumor cells together with strong antitumor immune response in ascites.

FAM3L: Feature-Aware Multi-Modal Metric Learning for Integrative Survival Analysis of Human Cancers.

Survival analysis is to estimate the survival time for an individual or a group of patients, which is a valid solution for cancer treatments. Recent studies suggested that the integrative analysis of histopathological images and genomic data can better predict the survival of cancer patients than simply using single bio-marker, for different bio-markers may provide complementary information. However, for the given multi-modal data that may contain irrelevant or redundant features, it is still challenge to design a distance metric that can simultaneously discover significant features and measure the difference of survival time among different patients. To solve this issue, we propose a Feature-Aware Multi-modal Metric Learning method (FAM3L), which not only learns the metric for distance constraints on patients' survival time, but also identifies important images and genomic features for survival analysis. Specifically, for each modality of data, we firstly design one feature-aware metric that can be decoupled into a traditional distance metric and a diagonal weight for important feature identification. Then, in order to explore the complex correlation across multiple modality data, we apply Hilbert-Schmidt Independence Criterion (HSIC) to jointly learn multiple metrics. Finally, based on the learned distance metrics, we apply the Cox proportional hazards model for prognosis prediction. We evaluate the performance of our proposed FAM3L method on three cancer cohorts derived from The Cancer Genome Atlas (TCGA), the experimental results demonstrate that our method can not only achieve superior performance for cancer prognosis, but also identify meaningful image and genomic features correlating strongly with cancer survival.

Case Report: Partial response to single-agent pembrolizumab in a chemotherapy-resistant metastatic pancreatic cancer patient with a high tumor mutation burden.

Single-agent immune checkpoint blockade has shown no clinical benefits in pancreatic cancer. Recently, the programmed cell death protein 1 (PD-1) antibody pembrolizumab has been recommended as a treatment option for high tumor mutational burden (TMB) solid tumors based on the data from a basket trial. However, no pancreatic cancer patients were enrolled in that trial. Whether pancreatic cancer patients with high TMB respond to PD-1 blockade as well remains unclear. Here, we report a case with a partial response to single-agent immunotherapy with pembrolizumab in pancreatic cancer with high TMB after the failure of several lines of chemotherapy. This result indicates that single-agent immunotherapy may be effective in pancreatic cancer patients with high TMB. In addition, in order to understand the basic immune state of our patients, we also analyzed the changes in immune cells in peripheral blood with cytometry by time-of-flight mass spectrometry (CyTOF) before and after pembrolizumab treatment.

Preoperative sintilimab plus transarterial chemoembolization for hepatocellular carcinoma exceeding the Milan criteria: A phase II trial.

BACKGROUND AND AIMS: Many patients with HCC of Barcelona Clinic Liver Cancer (BCLC) stage A exceeding the Milan criteria, or of BCLC stage B, can undergo resection after successful preoperative therapy, but an optimal approach has not been identified. We investigated preoperative drug-eluting bead transarterial chemoembolization (DEB-TACE) plus sintilimab, in this setting. APPROACH AND RESULTS: In this prospective, phase II study (NCT04174781), adults with HCC of BCLC stage A exceeding the Milan criteria, or BCLC stage B, and ineligible for surgical resection, received sintilimab 200 mg and DEB-TACE. The primary endpoint was progression-free survival by modified RECIST. Secondary endpoints included objective response rate, pathologic response rate, and safety. At the data cutoff (July 2022), among 60 patients, the objective response rate was 62% (37/60) and 51 patients had undergone surgery. After a median follow-up of 26.0 months (range, 3.4-31.8), the median progression-free survival was 30.5 months (95% CI: 16.1-not reached). Among patients undergoing surgery, median progression-free survival was not reached and the 12-month progression-free survival rate was 76% (95% CI: 67-91). A pathologic complete response was achieved in 14% (7/51) of these patients. All patients experienced at least one adverse event, but these were generally manageable. Exploratory analyses showed an association between cytokeratin, V-domain Ig-containing Suppressor of T-cell Activation, CD68, CD169, and cluster 13 fibroblasts and recurrence after surgery. CONCLUSIONS: Sintilimab plus DEB-TACE before surgery showed good efficacy and safety in patients with HCC of BCLC stage A exceeding the Milan criteria or BCLC stage B.

Runx3-overexpression cooperates with ex vivo AKT inhibition to generate receptor-engineered T cells with better persistence, tumor-residency, and antitumor ability.

BACKGROUND: Solid tumors pose unique roadblocks to treatment with chimeric antigen receptor (CAR) T cells, including limited T-cell persistence, inefficient tumor infiltration, and an immunosuppressive tumor microenvironment. To date, attempts to overcome these roadblocks have been unsatisfactory. Herein, we reported a strategy of combining Runx3 (encoding RUNX family transcription factor 3)-overexpression with ex vivo protein kinase B (AKT) inhibition to generate CAR-T cells with both central memory and tissue-resident memory characteristics to overcome these roadblocks. METHODS: We generated second-generation murine CAR-T cells expressing a CAR against human carbonic anhydrase 9 together with Runx3-overexpression and expanded them in the presence of AKTi-1/2, a selective and reversible inhibitor of AKT1/AKT2. We explored the influence of AKT inhibition (AKTi), Runx3-overexpression, and their combination on CAR-T cell phenotypes using flow cytometry, transcriptome profiling, and mass cytometry. The persistence, tumor-infiltration, and antitumor efficacy of CAR-T cells were evaluated in subcutaneous pancreatic ductal adenocarcinoma (PDAC) tumor models. RESULTS: AKTi generated a CD62L+central memory-like CAR-T cell population with enhanced persistence, but promotable cytotoxic potential. Runx3-overexpression cooperated with AKTi to generate CAR-T cells with both central memory and tissue-resident memory characteristics. Runx3-overexpression enhanced the potential of CD4+CAR T cells and cooperated with AKTi to inhibit the terminal differentiation of CD8+CAR T cells induced by tonic signaling. While AKTi promoted CAR-T cell central memory phenotype with prominently enhanced expansion ability, Runx3-overexpression promoted the CAR-T cell tissue-resident memory phenotype and further enhanced persistence, effector function, and tumor-residency. These novel AKTi-generated Runx3-overexpressing CAR-T cells exhibited robust antitumor activity and responded well to programmed cell death 1 blockade in subcutaneous PDAC tumor models. CONCLUSIONS: Runx3-overexpression cooperated with ex vivo AKTi to generate CAR-T cells with both tissue-resident and central memory characteristics, which equipped CAR-T cells with better persistence, cytotoxic potential, and tumor-residency ability to overcome roadblocks in the treatment of solid tumors.

Oxygen microcapsules improve immune checkpoint blockade by ameliorating hypoxia condition in pancreatic ductal adenocarcinoma.

Rationale: Hypoxia in tumor microenvironment (TME) represents an obstacle to the efficacy of immunotherapy for pancreatic ductal adenocarcinoma (PDAC) through several aspects such as increasing the expression of immune checkpoints or promoting fibrosis. Reversing hypoxic TME is a potential strategy to improve the validity of immune checkpoint blockade (ICB). Methods: Here, we synthesized polydopamine-nanoparticle-stabilized oxygen microcapsules with excellent stabilization, bioavailability, and biocompatibility for direct oxygen delivery into tumor sites by interfacial polymerization. Results: We observed oxygen microcapsules enhanced the oxygen concentration in the hypoxia environment and maintained the oxygen concentration for a long period both in vitro and in vivo. We found that oxygen microcapsules could significantly improve the efficiency of ICB against PDAC in vivo. Mechanismly, combined treatments using oxygen microcapsules and ICB could reduce the infiltration of tumor-associated macrophages (TAMs) and polarized pro-tumor M2 macrophages into anti-tumor M1 macrophages. In addition, combined treatments could elevate the proportion of T helper subtype 1 cells (Th1 cells) and cytotoxic T lymphocytes cells (CTLs) to mediate anti-tumor immune response in TME. Conclusion: In summary, this pre-clinical study indicated that reversing hypoxia in TME by using oxygen microcapsules was an effective strategy to improve the performances of ICB on PDAC, which holds great potential for treating PDAC in the future.

VG161 activates systemic antitumor immunity in pancreatic cancer models as a novel oncolytic herpesvirus expressing multiple immunomodulatory transgenes.

The VG161 represents the first recombinant oncolytic herpes simplex virus type 1 carrying multiple synergistic antitumor immuno-modulating factors. Here, we report its antitumor mechanisms and thus provide firm theoretical foundation for the upcoming clinical application in pancreatic cancer. Generally, the VG161-mediated antitumor outcomes were analyzed by a collaboration of techniques, namely the single-cell sequencing, airflow-assisted desorption electrospray ionization-mass spectrometry imaging (AFADSI-MSI) and nanostring techniques. In vitro, the efficacy of VG161 together with immune checkpoint inhibitors (ICIs) has been successfully shown to grant a long-term antitumor effect by altering tumor immunity and remodeling tumor microenvironment (TME) metabolisms. Cellular functional pathways and cell subtypes detected from patient samples before and after the treatment had undergone distinctive changes including upregulated CD8+ T and natural killer cells. More importantly, significant antitumor signals have emerged since the administration of VG161 injection. In conclusion, VG161 can systematically activate acquired and innate immunity in pancreatic models, as well as improve the tumor immune microenvironment, indicative of strong antitumor potential. The more robusting antitumor outcome for VG161 monotherapy or in combination with other therapies on pancreatic cancer is worth of being explored in further clinical trials.

Genetic mutation and tumor microbiota determine heterogenicity of tumor immune signature: Evidence from gastric and colorectal synchronous cancers.

Both colorectal and gastric cancer are lethal solid-tumor malignancies, leading to the majority of cancer-associated deaths worldwide. Although colorectal cancer (CRC) and gastric cancer (GC) share many similarities, the prognosis and drug response of CRC and GC are different. However, determinants for such differences have not been elucidated. To avoid genetic background variance, we performed multi-omics analysis, including single-cell RNA sequencing, whole-exome sequencing, and microbiome sequencing, to dissect the tumor immune signature of synchronous primary tumors of GC and CRC. We found that cellular components of juxta-tumoral sites were quite similar, while tumoral cellular components were specific to the tumoral sites. In addition, the mutational landscape and microbiome contributed to the distinct TME cellular components. Overall, we found that different prognoses and drug responses of GC and CRC were mainly due to the distinct TME determined by mutational landscape and microbiome components.

Ultrasmall Ruthenium Nanoparticles with Boosted Antioxidant Activity Upregulate Regulatory T Cells for Highly Efficient Liver Injury Therapy.

Nanozymes exhibiting antioxidant activity are beneficial for the treatment of oxidative stress-associated diseases. Ruthenium nanoparticles (RuNPs) with multiple enzyme-like activities have attracted growing attention, but the relatively low antioxidant enzyme-like activities hinder their practical biomedical applications. Here, a size regulation strategy is presented to significantly boost the antioxidant enzyme-like activities of RuNPs. It is found that as the size of RuNPs decreases to ≈2.0 nm (sRuNP), the surface-oxidized Ru atoms become dominant, thus possessing an unprecedentedly boosted antioxidant activity as compared to medium-sized (≈3.9 nm) or large-sized counterparts (≈5.9 nm) that are mainly composed of surface metallic Ru atoms. Notably, based on their antioxidant enzyme-like activities and ultrasmall size, sRuNP can not only sustainably ameliorate oxidative stress but also upregulate regulatory T cells in late-stage acetaminophen (APAP)-induced liver injury (ALI). Consequently, sRuNPs perform highly efficient therapeutic efficiency on ALI mice even when treated at 6 h after APAP intoxication. This strategy is insightful for tuning the catalytic performances of nanozymes for their extensive biomedical applications.

Molecular Subgroups of Intrahepatic Cholangiocarcinoma Discovered by Single-Cell RNA Sequencing-Assisted Multiomics Analysis.

Intrahepatic cholangiocarcinoma (ICC) is a relatively rare but highly aggressive tumor type that responds poorly to chemotherapy and immunotherapy. Comprehensive molecular characterization of ICC is essential for the development of novel therapeutics. Here, we constructed two independent cohorts from two clinic centers. A comprehensive multiomics analysis of ICC via proteomic, whole-exome sequencing (WES), and single-cell RNA sequencing (scRNA-seq) was performed. Novel ICC tumor subtypes were derived in the training cohort (n = 110) using proteomic signatures and their associated activated pathways, which were further validated in a validation cohort (n = 41). Three molecular subtypes, chromatin remodeling, metabolism, and chronic inflammation, with distinct prognoses in ICC were identified. The chronic inflammation subtype was associated with a poor prognosis. Our random forest algorithm revealed that mutation of lysine methyltransferase 2D (KMT2D) frequently occurred in the metabolism subtype and was associated with lower inflammatory activity. scRNA-seq further identified an APOE+C1QB+ macrophage subtype, which showed the capacity to reshape the chronic inflammation subtype and contribute to a poor prognosis in ICC. Altogether, with single-cell transcriptome-assisted multiomics analysis, we identified novel molecular subtypes of ICC and validated APOE+C1QB+ tumor-associated macrophages as potential immunotherapy targets against ICC.

Venetoclax plus 3 + 7 daunorubicin and cytarabine chemotherapy as first-line treatment for adults with acute myeloid leukaemia: a multicentre, single-arm, phase 2 trial.

BACKGROUND: Adults with acute myeloid leukaemia have unsatisfactory clinical outcomes and rates of complete remission. Venetoclax combined with azacytidine or low-dose cytarabine has shown efficacy in adults aged 75 years or older (or 18-74 years with comorbidities precluding intensive chemotherapy) with acute myeloid leukaemia. We aimed to investigate the activity and safety of venetoclax plus 3+7 daunorubicin and cytarabine chemotherapy in adults with acute myeloid leukaemia. METHODS: We conducted a two-stage, single-arm, phase 2 trial at three public hospitals in China. We enrolled patients aged 18-60 years with previously untreated de novo acute myeloid leukaemia and an Eastern Cooperative Oncology Group performance status of 0-2. Patients received induction treatment with intravenous daunorubicin (60 mg/m2 on days 1-3), intravenous cytarabine (100 mg/m2 on days 1-7), and oral venetoclax (100 mg on day 4, 200 mg on day 5, and 400 mg on days 6-11; DAV regimen). For induction therapy, the length of the treatment was 28-35 days per cycle and the number of treatment cycles was one or two. The primary endpoint was the composite complete remission rate (complete remission plus complete remission with incomplete blood cell count recovery) after one cycle of induction treatment, assessed in the as-treated population. Secondary endpoints were bone marrow measurable residual disease by flow cytometry, event-free survival, overall survival, and adverse events. This trial is ongoing and is registered with Chinese Clinical Trial Registry, ChiCTR2000041509. FINDINGS: Between Dec 25, 2020, and July 7, 2021, 36 patients were assessed for eligibility and 33 were enrolled. 15 (45%) patients were men and 18 (55%) were women, and all were Asian. The composite complete remission rate after one cycle of DAV regimen was 91% (95% CI 76-98; 30 of 33 patients) in the entire cohort. 29 (97%) of 30 patients who reached complete remission had undetectable measurable residual disease (ie, <0·1%). Grade 3 or worse adverse events included neutropenia in 33 (100%) of 33 patients, thrombocytopenia in 33 (100%), anaemia in 33 (100%), febrile neutropenia in 18 (55%), pneumonia in seven (21%), and sepsis in four (12%). No treatment-related deaths occurred. With a median follow-up of 11 months (IQR 9-12), estimated 1-year overall survival was 97% (95% CI 91-100) and 1-year event-free survival was 72% (56-94). INTERPRETATION: The DAV regimen represents an effective induction therapy for newly diagnosed adults with acute myeloid leukaemia, which resulted in a high rate of complete remission. These findings are an important contribution to the field, showing a safe strategy to incorporate venetoclax into the most common induction regimen used to treat newly diagnosed acute myeloid leukaemia internationally. FUNDING: Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang, National Natural Science Foundation of China, Key Research and Development Program of Zhejiang. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.