https://cancerbiologyprogram.med.wayne.edu/pdfs/hallmarks_cancer_article.pdf

Hallmarks of cancer: the next generation.

There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters The analysis module and visualization module were combined into a reusable workflow Currently, clusterProfiler supports three species, including humans, mice, and yeast Methods provided in this package can be easily extended to other species and ontologies The clusterProfiler package is released under Artistic-20 License within Bioconductor project The source code and vignette are freely available at http://bioconductororg/packages/release/bioc/html/clusterProfilerhtml

/pdf/clusterprofiler-an-r-package-for-comparing-biological-themes-xbzzr3fbnj.pdf

clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters

http://aiocm.org/member/Immunity,%20Inflammation,%20and%20Cancer.pdf

Immunity, Inflammation, and Cancer

<h3>Background</h3> Immune checkpoint blockade (ICB) therapy has significantly improved clinical outcomes; however, a significant proportion of patients fail to develop durable responses.<sup>1</sup> Therefore, novel targetable biomarkers are needed. Our group has previously shown that the composition of the gut microbiome and presence of intratumoral tertiary lymphoid structures (TLS) can influence response to ICB.<sup>2-4</sup> Here, we study three randomized phase II neoadjuvant ICB trials [melanoma (MEL; NCT02519322; n=23), non-small-cell lung cancer (NSCLC; NCT03158129; n=31)<sup>5</sup> and sarcoma (SARC; NCT02301039; n=17)] to investigate the relationship and interplay between these two established determinants of response. <h3>Methods</h3> Patients were defined as responders (R) or non-responders (NR) based on major pathologic response (MPR; MEL and NSCLC-viable ≤ 10%; SARC-hyalinization ≥ 30%). Transcriptional profiles of tumor specimen collected pre-ICB (MEL and SARC) and post-ICB (MEL, SARC and NSCLC) were used to score and dichotomize patients (by median) based on a TLS gene signature (<i>LTA, LTB, TNFSF14, CXCL13, CCL18, CCL19, CCL21, CD79B, CR2, PAX54A1, MZB1</i>). Paired baseline stool samples were profiled via metagenomic sequencing to characterize the composition and the molecular and metabolic function using MetaPhlAn 3.0 and HUMAnN 3.0, respectively. <h3>Results</h3> There were 21 R overall (NSCLC n=9; MEL n=9; SARC n=3). TLS signature was significantly higher in R vs NR on-treatment with ICB (p=0.032; unpaired t-test). Longitudinal evaluation of transcriptional profiles showed increased expression of the TLS signature with treatment in R (p=0.032; paired t-test) but not in NR (p&gt;0.99; paired t-test). Following patient dichotomization by TLS score, analysis of baseline microbiome profiles revealed the presence of unique TLS-associated gut microbiome signatures, including enrichment of specific taxa such as <i>Holdemanella</i> and <i>Lactococcus</i> (ANCOM-BC; p&lt;0.0001). Further analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in baseline stool metagenomes suggested enhanced function of membrane transport proteins (q=0.006) in TLS<sup>high</sup>patients and increased carbohydrate metabolism (q=0.04) in TLS<sup>low</sup>patients. <h3>Conclusions</h3> Our data suggest that unique gut microbiome features are associated with TLS in the context of treatment with ICB in solid cancers. Future mechanistic and translational studies will seek to validate and parlay these novel findings into microbiome-based treatments to induce TLS and/or augment the efficacy of ICB in solid tumors. <h3>Trial Registration</h3> NCT02519322, NCT03158129, NCT02301039 <h3>References</h3> Korman AJ, SC Garrett-Thomson, and N Lonberg. The foundations of immune checkpoint blockade and the ipilimumab approval decennial. <i>Nat Rev Drug Discov</i>, 2022. <b>21</b>(7): 509–528. Gopalakrishnan V, <i>et al</i>. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. <i>Science</i>, 2018. <b>359</b>(6371): 97–103. Helmink BA, <i>et al</i>, B cells and tertiary lymphoid structures promote immunotherapy response. <i>Nature</i>, 2020;<b>577</b>(7791): 549–555. Baruch EN, <i>et al</i>. Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients. <i>Science</i>, 2021. <b>371</b>(6529): 602–609. Cascone T, <i>et al</i>. Neoadjuvant nivolumab or nivolumab plus ipilimumab in operable non-small cell lung cancer: the phase 2 randomized NEOSTAR trial. <i>Nat Med</i>, 2021;<b>27</b>(3): 504–514. <h3>Ethics Approval</h3> Patients were treated at the University of Texas MD Anderson Cancer Center and had tumor samples collected and analyzed under Institutional Review Board (IRB)-approved protocols (Melanoma: 2015-0041, 2012-0846, LAB00-063 and PA17-0261; NSCLC: 2016-0982; Sarcoma: 2017-0143) 

/pdf/590-metagenomic-sequencing-reveals-unique-gut-microbial-1buyxwgu.pdf

590 Metagenomic sequencing reveals unique gut microbial features associated with tertiary lymphoid structures in response to immune checkpoint blockade in solid cancers

Checkpoint inhibitors represent an effective treatment approach for a variety of cancers through their inhibition of immune regulatory pathways within the tumor microenvironment (TME). Unfortunately only a minority of patients with cancer achieve clinical benefit from immunotherapy, with the TME emerging as an important predictor of outcomes and sensitivity to therapy. The extent and pattern of T-cell infiltration can vary prominently within/across tumors and represents a biological continuum. Three immune profiles have been identified along this continuum: ‘immune-desert’ or ‘T-cell cold’ phenotype, ‘immune-active’, ‘inflamed’, or ‘T-cell hot’ phenotype, and ‘immune excluded’ phenotype. Of the three profiles, immune excluded remains the most ill-defined with no clear, universally accepted definition even though it is commonly associated with lack of response to immune checkpoint inhibitors and poor clinical outcomes. To address this, 16 multidisciplinary cancer experts from around the world were invited to participate in a symposium using a three-round modified Delphi approach. The first round was an open-ended questionnaire distributed via email and the second was an in-person discussion of the first round results that allowed for statements to be revised as necessary to achieve a maximum consensus (75% agreement) among the rating committee (RC). The final round questionnaire was distributed to the RC via email and had a 100% completion rate. The Delphi process resulted in moving us closer to a consensus definition for immune exclusion that is practical, clinically pertinent, and applicable across a wide range of cancer histologies. A general consensus of the role of immune exclusion in resistance to checkpoint therapy and five research priorities emerged from this process. Together, these tools could help efforts designed to address the underlying mechanisms of immune exclusion that span cancer types and, ultimately, aid in the development of treatments to target these mechanisms to improve patient outcomes.

/pdf/developing-a-definition-of-immune-exclusion-in-cancer-grwh379k.pdf

Developing a definition of immune exclusion in cancer: results of a modified Delphi workshop

Use of pdcd1lg1 and cxcl9 as markers for the prognosis of cancer

The present invention relates to methods for quantifying the population of cells of monocytic origin in a tissue sample. In particular, the present invention relates to a method for quantifying the population of cells of monocytic origin in a tissue sample obtained from a subject comprising determining the expression level of at least one gene selected from the group consisting of CSF1R, ADAP2, RASSF4, FPR3, TFEC, PLA2G7 and KYNU.

Methods for quantifying the population of cells of monocytic origin in a tissue sample

The present invention relates to methods for quantifying the population of myeloid dendritic cells in a tissue sample. The tumor microenvironment is a complex tissue which contains many distinct cell populations, and whose composition may predict prognosis and response to therapies. In the present disclosure, using samples of purified cell populations, the inventors defined specific and robust transcriptomic markers of the immune and stromal cell populations of the tumor microenvironment, and they quantitatively validated them in an in-vitro RNA mixture model. More, they developed the 'Microenvironment Cell Populations-counter' (MCP-counter) method, which, using transcriptomic data, allows a robust quantification of the abundance of immune and stromal populations in cellularly heterogeneous healthy and cancerous tissues. Thus, the present invention relates to a method for quantifying the population of myeloid dendritic cells in a tissue sample obtained from a subject comprising determining the expression level of at least one gene selected from the group consisting of CD1E, CLEC10A, CLIC2, CD1A, WFDC21P and CD1B.

Wolf H. Fridman

Papers

590 Metagenomic sequencing reveals unique gut microbial features associated with tertiary lymphoid structures in response to immune checkpoint blockade in solid cancers

Developing a definition of immune exclusion in cancer: results of a modified Delphi workshop

Use of pdcd1lg1 and cxcl9 as markers for the prognosis of cancer

Methods for quantifying the population of cells of monocytic origin in a tissue sample

Methods for quantifying the population of myeloid dendritic cells in a tissue sample