bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2022‒11‒20
thirteen papers selected by
Sergio Marchini
Humanitas Research
  1. Region-specific denoising identifies spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics data.
  2. How is spatial transcriptomics influencing cancer research and diagnostics?
  3. MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution.
  4. Spatial multi-omics analyses of the tumor immune microenvironment.
  5. The current landscape of predictive and prognostic biomarkers for immune checkpoint blockade in ovarian cancer.
  6. Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer.
  7. TIMEDB: tumor immune micro-environment cell composition database with automatic analysis and interactive visualization.
  8. Evaluation of cell-free DNA approaches for multi-cancer early detection.
  9. TGFβ control of immune responses in cancer: a holistic immuno-oncology perspective.
  10. To kill or not to kill - The role of the tumor microenvironment in shaping group 1 ILC functions.
  11. A review of spatial profiling technologies for characterizing the tumor microenvironment in immuno-oncology.
  12. Genomic and transcriptomic analysis of a diffuse pleural mesothelioma patient-derived xenograft library.
  13. Development of a novel BRCAness score that predicts response to PARP inhibitors.
  1. Nat Commun. 2022 Nov 14. 13(1): 6912
    Region-specific denoising identifies spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics data.
    Linhua Wang, Mirjana Maletic-Savatic, Zhandong Liu.
      Spatially resolved transcriptomics is a relatively new technique that maps transcriptional information within a tissue. Analysis of these datasets is challenging because gene expression values are highly sparse due to dropout events, and there is a lack of tools to facilitate in silico detection and annotation of regions based on their molecular content. Therefore, we develop a computational tool for detecting molecular regions and region-based Missing value Imputation for Spatially Transcriptomics (MIST). We validate MIST-identified regions across multiple datasets produced by 10x Visium Spatial Transcriptomics, using manually annotated histological images as references. We benchmark MIST against a spatial k-nearest neighboring baseline and other imputation methods designed for single-cell RNA sequencing. We use holdout experiments to demonstrate that MIST accurately recovers spatial transcriptomics missing values. MIST facilitates identifying intra-tissue heterogeneity and recovering spatial gene-gene co-expression signals. Using MIST before downstream analysis thus provides unbiased region detections to facilitate annotations with the associated functional analyses and produces accurately denoised spatial gene expression profiles.
    DOI:  https://doi.org/10.1038/s41467-022-34567-0
  2. Biotechniques. 2022 Nov 18.
    How is spatial transcriptomics influencing cancer research and diagnostics?
    Annie Coulson.
      [Formula: see text] Spatial transcriptomics has continued to rise in popularity since it was developed in 2016; but how is it being utilized in tumor biology and diagnostics research?
    Keywords:  Cancer; Diagnostics; Spatial biology; Spatial transcriptomics; Transcriptomics; Tumor
    DOI:  https://doi.org/10.2144/btn-2022-0111
  3. Genome Biol. 2022 Nov 14. 23(1): 241
    MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution.
    Tom L Kaufmann, Marina Petkovic, Thomas B K Watkins, Emma C Colliver, Sofya Laskina, Nisha Thapa, Darlan C Minussi, Nicholas Navin, Charles Swanton, Peter Van Loo, Kerstin Haase, Maxime Tarabichi, Roland F Schwarz.
      Aneuploidy, chromosomal instability, somatic copy-number alterations, and whole-genome doubling (WGD) play key roles in cancer evolution and provide information for the complex task of phylogenetic inference. We present MEDICC2, a method for inferring evolutionary trees and WGD using haplotype-specific somatic copy-number alterations from single-cell or bulk data. MEDICC2 eschews simplifications such as the infinite sites assumption, allowing multiple mutations and parallel evolution, and does not treat adjacent loci as independent, allowing overlapping copy-number events. Using simulations and multiple data types from 2780 tumors, we use MEDICC2 to demonstrate accurate inference of phylogenies, clonal and subclonal WGD, and ancestral copy-number states.
    Keywords:  Aneuploidy; Cancer evolution; Chromosomal instability; Intratumor heterogeneity; Phylogenetic reconstruction; Single-cell sequencing; Somatic copy-number alterations; Whole-genome doubling
    DOI:  https://doi.org/10.1186/s13059-022-02794-9
  4. J Biomed Sci. 2022 Nov 14. 29(1): 96
    Spatial multi-omics analyses of the tumor immune microenvironment.
    Wan-Chen Hsieh, Bugi Ratno Budiarto, Yi-Fu Wang, Chih-Yu Lin, Mao-Chun Gwo, Dorothy Kazuno So, Yi-Shiuan Tzeng, Shih-Yu Chen.
      In the past decade, single-cell technologies have revealed the heterogeneity of the tumor-immune microenvironment at the genomic, transcriptomic, and proteomic levels and have furthered our understanding of the mechanisms of tumor development. Single-cell technologies have also been used to identify potential biomarkers. However, spatial information about the tumor-immune microenvironment such as cell locations and cell-cell interactomes is lost in these approaches. Recently, spatial multi-omics technologies have been used to study transcriptomes, proteomes, and metabolomes of tumor-immune microenvironments in several types of cancer, and the data obtained from these methods has been combined with immunohistochemistry and multiparameter analysis to yield markers of cancer progression. Here, we review numerous cutting-edge spatial 'omics techniques, their application to study of the tumor-immune microenvironment, and remaining technical challenges.
    Keywords:  Heterogeneity; Multi-omics; Spatial; Tumor-immune microenvironment (TIME)
    DOI:  https://doi.org/10.1186/s12929-022-00879-y
  5. Front Immunol. 2022 ;13 1045957
    The current landscape of predictive and prognostic biomarkers for immune checkpoint blockade in ovarian cancer.
    Yufei Xu, Fengli Zuo, Huiling Wang, Jing Jing, Xiujing He.
      Immune checkpoint blockade (ICB) therapy has evoked a prominent shift in anticancer therapy. Durable clinical antitumor activity to ICB has been observed in patients with ovarian cancer (OC). However, only a subset of patients derive clinical benefit, and immune-related adverse events (irAEs) caused by ICB therapy can lead to permanent tissue damage and even fatal consequences. It is thus urgent to develop predictive biomarkers to optimize patient outcomes and minimize toxicity risk. Herein, we review current predictive and prognostic biomarkers for checkpoint immunotherapy in OC and highlight emerging biomarkers to guide treatment with ICB. The prevalent biomarkers, such as PD-L1 expression status, tumor-infiltrating lymphocytes, mutational burden, and immune gene signatures, are further discussed. We provide a state-of-the-art survey on prognostic and predictive biomarkers for checkpoint immunotherapy and offer valuable information for guiding precision immunotherapy.
    Keywords:  biomarker; immune checkpoint blockade; immunotherapy response; ovarian cancer; prognosis
    DOI:  https://doi.org/10.3389/fimmu.2022.1045957
  6. Nature. 2022 Nov 16.
    Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer.
    Jorge Luis Galeano Niño, Hanrui Wu, Kaitlyn D LaCourse, Andrew G Kempchinsky, Alexander Baryiames, Brittany Barber, Neal Futran, Jeffrey Houlton, Cassie Sather, Ewa Sicinska, Alison Taylor, Samuel S Minot, Christopher D Johnston, Susan Bullman.
      The tumour-associated microbiota is an intrinsic component of the tumour microenvironment across human cancer types1,2. Intratumoral host-microbiota studies have so far largely relied on bulk tissue analysis1-3, which obscures the spatial distribution and localized effect of the microbiota within tumours. Here, by applying in situ spatial-profiling technologies4 and single-cell RNA sequencing5 to oral squamous cell carcinoma and colorectal cancer, we reveal spatial, cellular and molecular host-microbe interactions. We adapted 10x Visium spatial transcriptomics to determine the identity and in situ location of intratumoral microbial communities within patient tissues. Using GeoMx digital spatial profiling6, we show that bacterial communities populate microniches that are less vascularized, highly immuno‑suppressive and associated with malignant cells with lower levels of Ki-67 as compared to bacteria-negative tumour regions. We developed a single-cell RNA-sequencing method that we name INVADEseq (invasion-adhesion-directed expression sequencing) and, by applying this to patient tumours, identify cell-associated bacteria and the host cells with which they interact, as well as uncovering alterations in transcriptional pathways that are involved in inflammation, metastasis, cell dormancy and DNA repair. Through functional studies, we show that cancer cells that are infected with bacteria invade their surrounding environment as single cells and recruit myeloid cells to bacterial regions. Collectively, our data reveal that the distribution of the microbiota within a tumour is not random; instead, it is highly organized in microniches with immune and epithelial cell functions that promote cancer progression.
    DOI:  https://doi.org/10.1038/s41586-022-05435-0
  7. Nucleic Acids Res. 2022 Nov 18. pii: gkac1006. [Epub ahead of print]
    TIMEDB: tumor immune micro-environment cell composition database with automatic analysis and interactive visualization.
    Xueying Wang, Lingxi Chen, Wei Liu, Yuanzheng Zhang, Dawei Liu, Chenxin Zhou, Shuai Shi, Jiajie Dong, Zhengtao Lai, Bingran Zhao, Wenjingyu Zhang, Haoyue Cheng, Shuaicheng Li.
      Deciphering the cell-type composition in the tumor immune microenvironment (TIME) can significantly increase the efficacy of cancer treatment and improve the prognosis of cancer. Such a task has benefited from microarrays and RNA sequencing technologies, which have been widely adopted in cancer studies, resulting in extensive expression profiles with clinical phenotypes across multiple cancers. Current state-of-the-art tools can infer cell-type composition from bulk expression profiles, providing the possibility of investigating the inter-heterogeneity and intra-heterogeneity of TIME across cancer types. Much can be gained from these tools in conjunction with a well-curated database of TIME cell-type composition data, accompanied by the corresponding clinical information. However, currently available databases fall short in data volume, multi-platform dataset integration, and tool integration. In this work, we introduce TIMEDB (https://timedb.deepomics.org), an online database for human tumor immune microenvironment cell-type composition estimated from bulk expression profiles. TIMEDB stores manually curated expression profiles, cell-type composition profiles, and the corresponding clinical information of a total of 39,706 samples from 546 datasets across 43 cancer types. TIMEDB comes readily equipped with online tools for automatic analysis and interactive visualization, and aims to serve the community as a convenient tool for investigating the human tumor microenvironment.
    DOI:  https://doi.org/10.1093/nar/gkac1006
  8. Cancer Cell. 2022 Nov 15. pii: S1535-6108(22)00513-X. [Epub ahead of print]
    Evaluation of cell-free DNA approaches for multi-cancer early detection.
    Arash Jamshidi, Minetta C Liu, Eric A Klein, Oliver Venn, Earl Hubbell, John F Beausang, Samuel Gross, Collin Melton, Alexander P Fields, Qinwen Liu, Nan Zhang, Eric T Fung, Kathryn N Kurtzman, Hamed Amini, Craig Betts, Daniel Civello, Peter Freese, Robert Calef, Konstantin Davydov, Saniya Fayzullina, Chenlu Hou, Roger Jiang, Byoungsok Jung, Susan Tang, Vasiliki Demas, Joshua Newman, Onur Sakarya, Eric Scott, Archana Shenoy, Seyedmehdi Shojaee, Kristan K Steffen, Virgil Nicula, Tom C Chien, Siddhartha Bagaria, Nathan Hunkapiller, Mohini Desai, Zhao Dong, Donald A Richards, Timothy J Yeatman, Allen L Cohn, David D Thiel, Donald A Berry, Mohan K Tummala, Kristi McIntyre, Mikkael A Sekeres, Alan Bryce, Alexander M Aravanis, Michael V Seiden, Charles Swanton.
      In the Circulating Cell-free Genome Atlas (NCT02889978) substudy 1, we evaluate several approaches for a circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test by defining clinical limit of detection (LOD) based on circulating tumor allele fraction (cTAF), enabling performance comparisons. Among 10 machine-learning classifiers trained on the same samples and independently validated, when evaluated at 98% specificity, those using whole-genome (WG) methylation, single nucleotide variants with paired white blood cell background removal, and combined scores from classifiers evaluated in this study show the highest cancer signal detection sensitivities. Compared with clinical stage and tumor type, cTAF is a more significant predictor of classifier performance and may more closely reflect tumor biology. Clinical LODs mirror relative sensitivities for all approaches. The WG methylation feature best predicts cancer signal origin. WG methylation is the most promising technology for MCED and informs development of a targeted methylation MCED test.
    Keywords:  CCGA; MCED; cancer screening; cfDNA; multi-cancer early detection; single nucleotide variants; somatic copy number alterations; whole-genome bisulfite sequencing; whole-genome methylation
    DOI:  https://doi.org/10.1016/j.ccell.2022.10.022
  9. Nat Rev Immunol. 2022 Nov 15.
    TGFβ control of immune responses in cancer: a holistic immuno-oncology perspective.
    Briana G Nixon, Shengyu Gao, Xinxin Wang, Ming O Li.
      The immune system responds to cancer in two main ways. First, there are prewired responses involving myeloid cells, innate lymphocytes and innate-like adaptive lymphocytes that either reside in premalignant tissues or migrate directly to tumours, and second, there are antigen priming-dependent responses, in which adaptive lymphocytes are primed in secondary lymphoid organs before homing to tumours. Transforming growth factor-β (TGFβ) - one of the most potent and pleiotropic regulatory cytokines - controls almost every stage of the tumour-elicited immune response, from leukocyte development in primary lymphoid organs to their priming in secondary lymphoid organs and their effector functions in the tumour itself. The complexity of TGFβ-regulated immune cell circuitries, as well as the contextual roles of TGFβ signalling in cancer cells and tumour stromal cells, necessitates the use of rigorous experimental systems that closely recapitulate human cancer, such as autochthonous tumour models, to uncover the underlying immunobiology. The diverse functions of TGFβ in healthy tissues further complicate the search for effective and safe cancer therapeutics targeting the TGFβ pathway. Here we discuss the contextual complexity of TGFβ signalling in tumour-elicited immune responses and explain how understanding this may guide the development of mechanism-based cancer immunotherapy.
    DOI:  https://doi.org/10.1038/s41577-022-00796-z
  10. Semin Immunol. 2022 Nov 10. pii: S1044-5323(22)00088-4. [Epub ahead of print]61-64 101670
    To kill or not to kill - The role of the tumor microenvironment in shaping group 1 ILC functions.
    Nils Christian Müller, Chiara Romagnani.
      Group 1 innate lymphoid cells (ILC) comprise two major IFN-γ producing populations, namely Natural Killer (NK) cells, and ILC1s. Recent studies have revealed a complex and diverse composition of group 1 ILC subsets infiltrating different tumors. In this review, we will outline the commonalities and differences between group 1 ILC subsets in both mice and humans, discuss how the tissue and tumor microenvironment shapes their phenotype and functions, as well as describe their contrasting roles in the response to different cancers.
    Keywords:  Cancer; IL-15; ILC1; NK cells; TGF-β; Tumor microenvironment
    DOI:  https://doi.org/10.1016/j.smim.2022.101670
  11. Front Immunol. 2022 ;13 996721
    A review of spatial profiling technologies for characterizing the tumor microenvironment in immuno-oncology.
    Bian Hu, Muhammad Sajid, Rong Lv, Lianxin Liu, Cheng Sun.
      Interpreting the mechanisms and principles that govern gene activity and how these genes work according to -their cellular distribution in organisms has profound implications for cancer research. The latest technological advancements, such as imaging-based approaches and next-generation single-cell sequencing technologies, have established a platform for spatial transcriptomics to systematically quantify the expression of all or most genes in the entire tumor microenvironment and explore an array of disease milieus, particularly in tumors. Spatial profiling technologies permit the study of transcriptional activity at the spatial or single-cell level. This multidimensional classification of the transcriptomic and proteomic signatures of tumors, especially the associated immune and stromal cells, facilitates evaluation of tumor heterogeneity, details of the evolutionary trajectory of each tumor, and multifaceted interactions between each tumor cell and its microenvironment. Therefore, spatial profiling technologies may provide abundant and high-resolution information required for the description of clinical-related features in immuno-oncology. From this perspective, the present review will highlight the importance of spatial transcriptomic and spatial proteomics analysis along with the joint use of other sequencing technologies and their implications in cancers and immune-oncology. In the near future, advances in spatial profiling technologies will undoubtedly expand our understanding of tumor biology and highlight possible precision therapeutic targets for cancer patients.
    Keywords:  immuno-oncology; proteome; spatial profiling technologies; transcriptome; tumor heterogeneity
    DOI:  https://doi.org/10.3389/fimmu.2022.996721
  12. Genome Med. 2022 Nov 15. 14(1): 127
    Genomic and transcriptomic analysis of a diffuse pleural mesothelioma patient-derived xenograft library.
    Michael Offin, Jennifer L Sauter, Sam E Tischfield, Jacklynn V Egger, Shweta Chavan, Nisargbhai S Shah, Parvathy Manoj, Katia Ventura, Viola Allaj, Elisa de Stanchina, William Travis, Marc Ladanyi, Andreas Rimner, Valerie W Rusch, Prasad S Adusumilli, John T Poirier, Marjorie G Zauderer, Charles M Rudin, Triparna Sen.
      BACKGROUND: Diffuse pleural mesothelioma (DPM) is an aggressive malignancy that, despite recent treatment advances, has unacceptably poor outcomes. Therapeutic research in DPM is inhibited by a paucity of preclinical models that faithfully recapitulate the human disease.METHODS: We established 22 patient-derived xenografts (PDX) from 22 patients with DPM and performed multi-omic analyses to deconvolute the mutational landscapes, global expression profiles, and molecular subtypes of these PDX models and compared features to those of the matched primary patient tumors. Targeted next-generation sequencing (NGS; MSK-IMPACT), immunohistochemistry, and histologic subtyping were performed on all available samples. RNA sequencing was performed on all available PDX samples. Clinical outcomes and treatment history were annotated for all patients. Platinum-doublet progression-free survival (PFS) was determined from the start of chemotherapy until radiographic/clinical progression and grouped into < or ≥ 6 months.
    RESULTS: PDX models were established from both treatment naïve and previously treated samples and were noted to closely resemble the histology, genomic landscape, and proteomic profiles of the parent tumor. After establishing the validity of the models, transcriptomic analyses demonstrated overexpression in WNT/β-catenin, hedgehog, and TGF-β signaling and a consistent suppression of immune-related signaling in PDXs derived from patients with worse clinical outcomes.
    CONCLUSIONS: These data demonstrate that DPM PDX models closely resemble the genotype and phenotype of parental tumors, and identify pathways altered in DPM for future exploration in preclinical studies.
    DOI:  https://doi.org/10.1186/s13073-022-01129-4
  13. Biomark Res. 2022 Nov 12. 10(1): 80
    Development of a novel BRCAness score that predicts response to PARP inhibitors.
    Masanori Oshi, Shipra Gandhi, Rongrong Wu, Mariko Asaoka, Li Yan, Akimitsu Yamada, Shinya Yamamoto, Kazutaka Narui, Takashi Chishima, Takashi Ishikawa, Itaru Endo, Kazuaki Takabe.
      BACKGROUND: BRCAness is a characteristic feature of homologous recombination deficiency (HRD) mimicking BRCA gene mutation in breast cancer. We hypothesized that a measure to quantify BRCAness that causes synthetic lethality in BRCA mutated tumors will identify responders to PARP inhibitors.METHODS: A total of 6753 breast cancer patients from 3 large independent cohorts were analyzed. A score was generated by transcriptomic profiling using gene set variation analysis algorithm on 34 BRCA1-mutation related genes selected by high AUC levels in ROC curve between BRCA1 mutation and wildtype breast cancer.
    RESULTS: The score was significantly associated with BRCA1 mutation, high mutation load and intratumoral heterogeneity as expected, as well as with high HRD, DNA repair and MKi67 expression regardless of BRCA mutations. High BRCAness tumors enriched not only DNA repair, but also all five Hallmark cell proliferation-related gene sets. High BRCAness tumors were significantly associated with higher cytolytic activity and with higher anti-cancerous immune cell infiltration. Not only did the breast cancer cell lines with BRCA-mutation show high score, but even the other cells in human breast cancer tumor microenvironment were contributing to the score. The BRCAness score was the highest in triple-negative breast cancer consistently in all 3 cohorts. BRCAness was associated with response to chemotherapy and correlated strongly with response to PARP inhibitor in both triple-negative and ER-positive/HER2-negative breast cancer.
    CONCLUSIONS: We established a novel BRCAness score using BRCA-mutation-related gene expressions and found that it associates with DNA repair and predicts response to PARP inhibitors regardless of BRCA mutation.
    Keywords:  BRCAness; Biomarker; Breast cancer; Gene expression; Signaling
    DOI:  https://doi.org/10.1186/s40364-022-00427-8
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