Progress of Gastric Cancer Surgery in the era of Precision Medicine

With the development of genomics, the update of modern imaging technology and the advent of artificial intelligence and big data, the surgical treatment of gastric cancer has gradually stepped into precision medicine. Precision surgery treatment of gastric cancer is based on accurate molecular typing and staging using modern molecular diagnostic technology and imaging, and the formulation of precise and individualized surgical treatment plans, with the concept of minimally invasive and accelerated rehabilitation surgery running through it. For intermediate-stage gastric cancer, we have adopted a comprehensive treatment approach including traditional radiotherapy and chemotherapy, targeted therapy and immunotherapy. Utilize artificial intelligence and big data technology to improve the standardization and interconnectivity of specialty data and realize the transformation of evidence-based medicine. Promoting the standardization, standardization and individualization of gastric cancer surgical treatment, providing patients with precise diagnosis and treatment, and further improving patients'' prognosis are the opportunities and challenges in the development of gastric cancer surgery.     

肿瘤精准免疫诊断综合评估

近年来,肿瘤免疫治疗(cancer immunotherapies)已成为晚期恶性肿瘤治疗的重要手段之一。肿瘤免疫治疗并不直接攻击癌细胞,而是通过调节人体自身免疫系统来抗击肿瘤,有望像抗生素改变抗感染治疗一样改变肿瘤治疗范式。抗PD-1/L1和抗CTLA-4抗体药物作为肿瘤免疫治疗的代表药物,使晚期癌症患者五年生存率达成了数倍的提升,被认为是真正有希望治愈癌症的治疗方式。然而,肿瘤免疫治疗只对部分患者有效,并且存在耐药、超进展、不良反应等问题。如何准确筛选出最有可能从治疗中获益的人群成为肿瘤免疫治疗研究中的一个重大挑战。目前有多个与免疫治疗相关的生物标志物正在研究中,并且有望被用于临床筛选治疗获益人群;但这些生物标记物也存在很多缺陷。未来,围绕免疫治疗敏感性和副反应的多项指标综合评估可能成为一个趋势。     

Cancer stem cells as a therapeutic target in bladder cancer

Bladder cancer is one of the most prevalent genitourinary cancers responsible for about 150,000 deaths per year worldwide. Currently, several treatments, such as endoscopic and open surgery, appended by local or systemic immunotherapy, chemotherapy, and radiotherapy are used to treat this malignancy. However, the differences in treatment outcome among patients suffering from bladder cancer are considered as one of the important challenges. In recent years, cancer stem cells, representing a population of undifferentiated cells with stem-cell like properties, have been eyed as a major culprit for the high recurrence rate in superficial papillary bladder cancer. Cancer stem cells have been reported to be resistant to conventional treatments, such as chemotherapy, radiation, and immunotherapy, which induce selective pressure on tumoral populations resulting in selection and growth of the resistant cells. Therefore, targeting the therapeutic aspects of cancer stem cells in bladder cancer may be promising. In this study, we briefly discuss the biology of bladder cancer and then address the possible relationship between molecular biology of bladder cancer and cancer stem cells. Subsequently, the mechanisms of resistance applied by cancer stem cells against the conventional therapeutic tools, especially chemotherapy, are discussed. Moreover, by emphasizing the biomarkers described for cancer stem cells in bladder cancer, we have provided, described, and proposed targets on cancer stem cells for therapeutic interventions and, finally, reviewed some immunotargeting strategies against bladder cancer stem cells.     

Addressing the Challenges of High‐Throughput Cancer Tissue Proteomics for Clinical Application: ProCan

The cancer tissue proteome has enormous potential as a source of novel predictive biomarkers in oncology. Progress in the development of mass spectrometry (MS)‐based tissue proteomics now presents an opportunity to exploit this by applying the strategies of comprehensive molecular profiling and big‐data analytics that are refined in other fields of ‘omics research. ProCan (ProCan is a registered trademark) is a program aiming to generate high‐quality tissue proteomic data across a broad spectrum of cancer types. It is based on data‐independent acquisition–MS proteomic analysis of annotated tissue samples sourced through collaboration with expert clinical and cancer research groups. The practical requirements of a high‐throughput translational research program have shaped the approach that ProCan is taking to address challenges in study design, sample preparation, raw data acquisition, and data analysis. The ultimate goal is to establish a large proteomics knowledge‐base that, in combination with other cancer ‘omics data, will accelerate cancer research.     

Proceedings of the 2016 China Cancer Immunotherapy Workshop

A1 Proceedings of 2016 China Cancer Immunotherapy Workshop, Beijing, China Bin Xue, Jiaqi Xu, Wenru Song, Zhimin Yang, Ke Liu, Zihai Li A2 Set the stage: fundamental immunology in forty minutes Zihai Li A3 What have we learnt from the anti-PD-1/PD-L1 therapy of advanced human cancer? Lieping Chen A4 Immune checkpoint inhibitors in lung cancer Edward B. Garon A5 Mechanisms of response and resistance to checkpoint inhibitors in melanoma Siwen Hu-Lieskovan A6 Checkpoint inhibitor immunotherapy in lymphoid malignancies Wei Ding A7 Translational research to improve the efficacy of immunotherapy in genitourinary malignancies Chong-Xian Pan A8 Immune checkpoint inhibitors in gastrointestinal malignancies Weijing Sun A9 What’s next beyond PD-1/PDL1? Yong-Jun Liu A10 Cancer vaccines: new insights into the oldest immunotherapy strategy Lei Zheng A11 Bispecific antibodies for cancer immunotherapy Delong Liu A12 Updates on CAR-T immunotherapy Michel Sadelain A13 Adoptive T cell therapy: personalizing cancer treatment Cassian Yee A14 Immune targets and neoantigens for cancer immunotherapy Rongfu Wang A15 Phase I/IIa trial of chimeric antigen receptor modified T cells against CD133 in patients with advanced and metastatic solid tumors Meixia Chen, Yao Wang, Zhiqiang Wu, Hanren Dai, Can Luo, Yang Liu, Chuan Tong, Yelei Guo, Qingming Yang, Weidong Han A16 Cancer immunotherapy biomarkers: progress and issues Lisa H. Butterfield A17 Shaping of immunotherapy response by cancer genomes Timothy A. Chan A18 Unique development consideration for cancer immunotherapy Wenru Song A19 Immunotherapy combination Ruirong Yuan A20 Immunotherapy combination with radiotherapy Bo Lu A21 Cancer immunotherapy: past, present and future Ke Liu A22 Breakthrough therapy designation drug development and approval Max Ning A23 Current European regulation of innovative oncology medicines: opportunities for immunotherapy Harald Enzmann, Heinz Zwierzina     

Immunotherapy for head and neck cancer

Head and neck cancer represents a challenging disease. Despite recent treatment advances, which have improved functional outcomes, the long-term survival of head and neck cancer patients has remained unchanged for the past 25 years. One of the goals of adjuvant cancer therapy is to eradicate local regional microscopic and micrometastatic disease with minimal toxicity to surrounding normal cells. In this respect, antigen-specific immunotherapy is an attractive therapeutic approach. With the advances in molecular genetics and fundamental immunology, antigen-specific immunotherapy is being actively explored using DNA, bacterial vector, viral vector, peptide, protein, dendritic cell, and tumor-cell based vaccines. Early phase clinical trials have demonstrated the safety and feasibility of these novel therapies and the emphasis is now shifting towards the development of strategies, which can increase the potency of these vaccines. As the field of immunotherapy matures and as our understanding of the complex interaction between tumor and host develops, we get closer to realizing the potential of immunotherapy as an adjunctive method to control head and neck cancer and improve long-term survival in this patient population.     

免疫治疗背景下提高临床肿瘤教学水平的探讨

肿瘤发病率的日益攀升以及临床肿瘤学的迅速发展,对培养专业的临床肿瘤学人才提出了更高的要求。随着第一个细胞免疫治疗药物Sipuleucel-T被美国FDA批准用于晚期前列腺癌的治疗,肿瘤治疗进入免疫治疗的新时代,现有传统临床肿瘤学教学方法及观念已经不能适应目前的要求,因此必须提高临床教学水平。本文在肿瘤免疫治疗迅速发展背景下,对如何提高临床肿瘤教学水平进行思考与探讨,通过加强肿瘤分子生物学及细胞分子免疫学理论教学,以及在教学过程中贯穿循证医学与个体化治疗相结合的理念、引入多学科参与的肿瘤综合治疗模式,并强调心理干预和人文关怀的重要性,兼顾教学理论和临床实践最新进展及趋势,有效地帮助学生树立全面、科学的临床肿瘤治疗理念,掌握改善临床肿瘤治疗的有效方法,切实提升临床肿瘤学的教学质量和水平,培养出能胜任现代临床肿瘤学实践需求的专业人才。     

STRN-ALK rearranged pediatric malignant peritoneal mesothelioma – Functional testing of 527 cancer drugs in patient-derived cancer cells

Genetic rearrangements involving the anaplastic lymphoma kinase (ALK) gene create oncogenic drivers for several cancers, including malignant peritoneal mesothelioma (MPeM). Here, we report genomic and functional precision oncology profiling on a rare case of a 5-year old patient diagnosed with wide-spread and aggressive MPeM, driven by STRN-ALK rearrangement. We established genomically representative patient-derived cancer cells (PDCs) from the tumor sample and performed high-throughput drug sensitivity testing with 527 oncology compounds to identify potent inhibitors. As expected, the PDCs were overall sensitive to the ALK inhibitors, although the eight different inhibitors tested had variable efficacy. We also discovered other effective inhibitors, such as MEK/ERK inhibitors and those targeting pathways downstream of ALK as well as Bcl-xl inhibitors. In contrast, most cytotoxic drugs were not very effective. ALK inhibitors synergized with MEK and PI3K/mTOR inhibitors, highlighting potential combinatorial strategies to enhance drug efficacy and tackle drug resistance. Based on genomic data and associated functional validation, the patient was treated with the ALK inhibitor crizotinib in combination with conventional chemotherapy (cisplatin and gemcitabine). A complete disease remission was reached, lasting now for over 3 years. Our results illustrate a rare pediatric cancer case, and highlight the potential of functional precision oncology to discover pathogenetic drivers, validate dependency on driver signals, compare different inhibitors against each other and potentially enhance targeted treatments by drug combinations. Such real-time implementation of functional precision oncology could pave the way towards safer and more effective personalized cancer therapies for individual pediatric cancer patients with rare tumors.     

金属有机骨架在肿瘤治疗中的应用进展

金属有机骨架(metal-organic frameworks,MOFs)是一类由金属结点和有机配体配位组装而成的晶体材料.金属有机骨架具有孔隙度大、孔径和尺寸可调、生物相容性好、成分可调、表面可修饰等优越性能,在肿瘤治疗领域具有重要的应用潜力.本文首先介绍了金属有机骨架用于小分子药物、生物大分子药物等药物递送体系的构建方法.随后,我们总结了近年来MOFs药物递送体系在肿瘤的化学治疗、光动力学治疗、放射性治疗、免疫治疗、光热治疗等方面的应用进展.最后,本文总结了MOFs在肿瘤治疗方面的进展和特点,并展望了MOFs在肿瘤治疗领域的研究挑战和应用前景.     

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape.     

Bioinformatics for cancer immunology and immunotherapy

Recent mechanistic insights obtained from preclinical studies and the approval of the first immunotherapies has motivated increasing number of academic investigators and pharmaceutical/biotech companies to further elucidate the role of immunity in tumor pathogenesis and to reconsider the role of immunotherapy. Additionally, technological advances (e.g., next-generation sequencing) are providing unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and ultimately enable individualized treatment. However, the increasing complexity of the generated data and the plethora of bioinformatics methods and tools pose considerable challenges to both tumor immunologists and clinical oncologists. In this review, we describe current concepts and future challenges for the management and analysis of data for cancer immunology and immunotherapy. We first highlight publicly available databases with specific focus on cancer immunology including databases for somatic mutations and epitope databases. We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling. Mathematical models are powerful tools that can predict and explain important patterns in the genetic and clinical progression of cancer. Therefore, a survey of mathematical models for tumor evolution and tumor–immune cell interaction is included. Finally, we discuss future challenges for individualized immunotherapy and suggest how a combined computational/experimental approaches can lead to new insights into the molecular mechanisms of cancer, improved diagnosis, and prognosis of the disease and pinpoint novel therapeutic targets.     

前列腺干细胞抗原（PSCA）在肿瘤诊断及治疗中的应用*

前列腺干细胞抗原(PSCA)是最早发现于前列腺癌的GPI锚定的细胞膜蛋白,PSCA的在正常或肿瘤细胞中的具体的病理生理功能还不清楚。PSCA在前列腺癌、胰腺癌、膀胱癌等肿瘤中表达增加,相反在食管癌和胃癌中表达降低可能在胃上皮中发挥肿瘤抑制功能,PSCA发挥致瘤和抑瘤两种截然不同的作用与细胞所处的环境不同有关。除此之外,PSCA作为肿瘤的免疫治疗的靶点也显示出其良好的临床应用潜力。因此PSCA不仅成为肿瘤诊断和预后判断的生物学标记也是肿瘤免疫治疗重要的候选靶蛋白。本文对PSCA的功能和在前列腺癌以及其他肿瘤的临床诊断、预后判断以及治疗应用中的研究进展进行了综述,并讨论了未来PSCA的研究前景。     

Computational methods and challenges in analyzing intratumoral microbiome data

The human microbiome is intimately related to cancer biology and plays a vital role in the efficacy of cancer treatments, including immunotherapy. Extraordinary evidence has revealed that several microbes influence tumor development through interaction with the host immune system, that is, immuno–oncology–microbiome (IOM). This review focuses on the intratumoral microbiome in IOM and describes the available data and computational methods for discovering biological insights of microbial profiling from host bulk, single-cell, and spatial sequencing data. Critical challenges in data analysis and integration are discussed. Specifically, the microorganisms associated with cancer and cancer treatment in the context of IOM are collected and integrated from the literature. Lastly, we provide our perspectives for future directions in IOM research.     

Cancer biomarker development and two-dimensional difference gel electrophoresis (2D-DIGE)

Cancer results from the accumulation of genomic alterations. As the genome is functionally translated to the proteome and regulates tumor cell behavior, proteomics studies are expected to further the current understanding of the molecular mechanisms underlying carcinogenesis and cancer progression. Biomarkers are potential tools to classify cancers for therapy, predict responses to treatments, and support treatment-related decision-making. Biomarker development has been actively pursued in oncology by proteomic approaches. Two-dimensional difference gel electrophoresis (2D-DIGE) is a proteomics technique based on two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). In 2D-DIGE, protein samples are labeled with distinct fluorescent dyes before fractionation via 2D-PAGE. 2D-DIGE offers advantages to identify biomarker candidates, including reproducibility, high sensitivity, comprehensiveness, and high throughput. 2D-DIGE has contributed to the establishment of tissue biomarkers, which potentially facilitate precision medicine. 2D-DIGE is thus expected to yield major advancements in cancer biomarker identification and development.     

Performance Characteristics of the BluePrint® Breast Cancer Diagnostic Test

The analytical performance of a multi-gene diagnostic signature depends on many parameters, including precision, repeatability, reproducibility and intra-tumor heterogeneity. Here we study the analytical performance of the BluePrint 80-gene breast cancer molecular subtyping test through determination of these performance characteristics. BluePrint measures the expression of 80 genes that assess functional pathways which determine the intrinsic breast cancer molecular subtypes (i.e. Luminal-type, HER2-type, Basal-type). Knowing a tumor's dominant functional pathway can help allocate effective treatment to appropriate patients.Here we show that BluePrint is a highly precise and highly reproducible test with correlations above 98% based on the generated index and subtype concordance above 99%. Therefore, BluePrint can be used as a robust and reliable tool to identify breast cancer molecular subtypes.     

Single cell-derived spheroids capture the self-renewing subpopulations of metastatic ovarian cancer

High Grade Serous Ovarian cancer (HGSOC) is a major unmet need in oncology, due to its precocious dissemination and the lack of meaningful human models for the investigation of disease pathogenesis in a patient-specific manner. To overcome this roadblock, we present a new method to isolate and grow single cells directly from patients’ metastatic ascites, establishing the conditions for propagating them as 3D cultures that we refer to as single cell-derived metastatic ovarian cancer spheroids (sMOCS). By single cell RNA sequencing (scRNAseq) we define the cellular composition of metastatic ascites and trace its propagation in 2D and 3D culture paradigms, finding that sMOCS retain and amplify key subpopulations from the original patients’ samples and recapitulate features of the original metastasis that do not emerge from classical 2D culture, including retention of individual patients’ specificities. By enabling the enrichment of uniquely informative cell subpopulations from HGSOC metastasis and the clonal interrogation of their diversity at the functional and molecular level, this method provides a powerful instrument for precision oncology in ovarian cancer.Subject terms: Cancer models, Cancer stem cells, Experimental models of disease