Aspects Of Tumor Heterogeneity Research Articles

Single-cell RNA sequencing unveils tumor heterogeneity and immune microenvironment between subungual and plantar melanoma

Acral melanoma (AM) is a subtype of melanoma with high prevalence in East Asians. AM is characterized by greater aggressiveness and lower survival rates. However, there are still fewer studies on immune mechanisms of AM especially subungual melanoma (SM) versus non-subungual melanoma (NSM). In order to explore tumor heterogeneity and immune microenvironment in different subtypes of AM, we applied single-cell RNA sequencing to 24,789 single cells isolated from the SM and plantar melanoma (PM) patients. Aspects of tumor heterogeneity, melanocytes from PM and SM had significant differences in gene expression, CNV and pathways in which tumor-associated such as NF-kb and Wnt were involved. Regarding the immune microenvironment, PM contained more fibroblasts and T/NK cells. The EPHA3-EFNA1 axis was expressed only in cancer-associated fibroblast (CAF) and melanocytes of PM, and the TIGIT-NECTIN2 axis was expressed in both AM subtypes of T/NK cells and melanocytes. Altogether, our study helps to elucidate the tumor heterogeneity in AM subpopulations and provides potential therapeutic targets for clinical research.

Abstract 1375: The intra-tumoral spatial heterogeneity of T-cell antigens in glioblastoma: An integrated multi-omics approach

Abstract Glioblastoma is the most common primary malignant neoplasm of the central nervous system in adults. Current treatment options comprise maximal surgical resection followed by radiation and/or chemotherapy with temozolomide. However, these procedures are unable to eliminate all tumor cells, which in turn lead to disease recurrence accounting for the poor prognosis. Glioblastoma is a highly infiltrative tumor in which recurrence originates from the unresectable peritumoral infiltration zone. Thus, novel treatment options specifically targeting the tumor cells in the infiltration zone are needed to prevent relapse and enable long-lasting remission. In this work, we performed multi-omics spatial analysis of the necrotic center (NEC), the gadolinium contrast-enhanced region (T1), and the infiltration zone (INF) to assess immunological relevant aspects of tumor heterogeneity. By integrating mass spectrometry-based immunopeptidome analysis with next generation sequencing methods (whole exome and RNA sequencing, DNA methylation), we aimed to identify the intra-tumoral regional heterogeneity of T cell antigens with a special focus on the peritumoral infiltration zone. For multi-omics analysis, HLA peptides and genetic material from 15 glioblastoma patients were extracted and analyzed from the three zones NEC, T1, INF and for four patients additionally from adjacent benign (BEN) brain tissue. A total of 24,699 unique HLA class I and 17,394 HLA class II peptides were identified. Comparative profiling of peptides from our study and a benign tissue database (in-house (n=429) combined with HLA ligand atlas (https://hla-ligand-atlas.org)) revealed that 6% (970/15,579), 5% (829/16,442) and 5% (725/13,891) of HLA class I peptides were exclusively presented in the INF, T1 and NEC zone, respectively. 6% (394/7,150), 7% (589/8,736) and 13% (1,725/12,908) of HLA class II peptides showed exclusive representation in the INF, T1 and NEC zone, respectively. Importantly, due to the spatial analysis of the immunopeptidome, we revealed 970 HLA class I and 394 HLA class II peptides exclusively presented in the INF zone. Interestingly, one INF-associated HLA class I peptide, which showed frequent presentation in 36% of glioblastoma immunopeptidomes, is derived from the brain and acute leukemia cytoplasmic protein (BAALC), a highly expressed cell cycle inducer (via MEK kinase-1) in several cancers, including glioblastoma. Integrated RNA/DNA sequencing enabled a greater understanding of spatial tumor antigen presentation and lead to the identification of INF-specific neoepitopes derived from tumor-specific mutations. In summary, we identified the intra-tumoral regional heterogeneity of tumor antigens, which could be used in the future for specific immunotherapy approaches targeting the infiltration zone of glioblastoma. Citation Format: Marcel Wacker, Gioele Medici, Marissa Dubbelaar, Jens Bauer, Annika Nelde, Luca Regli, Michael Weller, Helmut R. Salih, Hans-Georg Rammensee, Marian C. Neidert, Juliane S. Walz. The intra-tumoral spatial heterogeneity of T-cell antigens in glioblastoma: An integrated multi-omics approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1375.

P16.01 Clonal Landscape Predicts Outcome in Lung Squamous Cell Carcinoma Patients with a Low Tumor Mutational Burden

Tumor heterogeneity metrics have become widely used in research, however none have been clinically approved. Two such methods of interest are PyClone and Mutant Allele Tumor Heterogeneity (MATH). Although PyClone has shown predictive power in manycancers, no statistically significant relationship has been found in lung squamous cell carcinoma (LUSC) . While MATH has shown clinical implications in head and neck squamous cell carcinoma. To investigate this relationship we integrated tumor mutational burden (TMB), a clinically approved biomarker for immunotherapy, MATH, and PyClone. Patients were classified as high/low if their TMB, MATH, and PyClone values were above or below their respective medians. Together these metrics may yield stronger predictive value than each metric on it’s own. Data for 178 patients was obtained from the GDC data portal (SNV and CNV) and the cBioPortal (Clinical). Dividing the total number of somatic mutations by exom size (38Mb) yielded TMB. For PyClone, a minimum cluster size of 2 was used. MATH calculations were performed via maftools, a publicly available python library. TMB, MATH, and PyClone values were able to be calculated for 100 patients who had survival outcome data available. This cohort was composed of 57% Stage I, 24% Stage II, 16% Stage III, and 2% Stage IV patients at an average age of 69. All statistical analysis was performed in python via publicly available libraries. Individually, TMB, MATH, and PyClone did not have a statistically significant association with outcome. MATH score did not significantly contribute to statistical power and was not used after initial assessment. Low TMB/High PyClone patients had superior PFS (51 vs. 18 months, p = 0.002, HR 0.39 CI 0.21-0.72) and OS (51 vs. 22 months, p=0.0437, HR 0.56 CI 0.32 - 0.99) than their Low TMB/Low PyClone counterparts. Low TMB/Low PyClone group was 70% Stage I, 22% Stage II, and 8% Stage III with an average age of 73. Low TMB/High PyClone group was 52% Stage I, 30% Stage II, and 18% Stage III with an average age of 68. TMB, MATH, and PyClone are all derived in different ways and may capture different aspects of tumor heterogeneity. When patients were classified based on median TMB and PyClone, clonal distribution measured by PyClone predicted survival in lung squamous cell carcinoma patients with a low TMB. Our study suggests interaction between TMB and clonality in predicting survival, awaiting validation with further studies.

Glioma-Specific Diffusion Signature in Diffusion Kurtosis Imaging.

Purpose: This study aimed to assess the relationship between mean kurtosis (MK) and mean diffusivity (MD) values from whole-brain diffusion kurtosis imaging (DKI) parametric maps in preoperative magnetic resonance (MR) images from 2016 World Health Organization Classification of Tumors of the Central Nervous System integrated glioma groups. Methods: Seventy-seven patients with histopathologically confirmed treatment-naïve glioma were retrospectively assessed between 1 August 2013 and 30 October 2017. The area on scatter plots with a specific combination of MK and MD values, not occurring in the healthy brain, was labeled, and the corresponding voxels were visualized on the fluid-attenuated inversion recovery (FLAIR) images. Reversely, the labeled voxels were compared to those of the manually segmented tumor volume, and the Dice similarity coefficient was used to investigate their spatial overlap. Results: A specific combination of MK and MD values in whole-brain DKI maps, visualized on a two-dimensional scatter plot, exclusively occurs in glioma tissue including the perifocal infiltrative zone and is absent in tissue of the normal brain or from other intracranial compartments. Conclusions: A unique diffusion signature with a specific combination of MK and MD values from whole-brain DKI can identify diffuse glioma without any previous segmentation. This feature might influence artificial intelligence algorithms for automatic tumor segmentation and provide new aspects of tumor heterogeneity.

Abstract PR11: Tumor cell-intrinsic factors underlie the heterogeneity of immune infiltration and response to immunotherapy in pancreatic cancer

Abstract Intertumoral heterogeneity—the biologic and functional differences among different individual tumors—poses a challenge for immunotherapy. To understand the tumor cell-intrinsic factors underlying the heterogeneity of tumor immunity and sensitivity to immunotherapy, we established a new experimental system by generating a library of congenic pancreatic tumor cell clones from a genetic mouse model driven by mutant Kras and p53. These tumor cell clones robustly formed implanted tumors that recapitulated the T cell-inflamed and non-T cell-inflamed tumor microenvironments in human patients, associated with distinct patterns of infiltration by T cells and myeloid cells. We found that the non-T cell-inflamed phenotype was dominant over the T cell-inflamed phenotype in the local tumor microenvironment. Both quantitative and qualitative features, specifically expression of markers of prior TCR activation, of intratumoral CD8+ T cells predicted the response to immunotherapies. An integrated transcriptomic and epigenetic analysis revealed that tumor cell-intrinsic expression of the chemokine CXCL1 as a major determinant of the non-T cell-inflamed microenvironment, and ablation of tumor cell-intrinsic CXCL1, promoted T-cell infiltration and sensitivity to a combination of chemotherapies, CD40 agonist, and checkpoint blockades. These results demonstrated that heterogeneity of tumor immune phenotypes is driven by tumor cell-intrinsic factors that can be manipulated to influence the outcome of immunotherapies. The observation that non-T cell-inflamed phenotype is dominant emphasized the importance of targeting mechanisms driving T-cell low phenotype for improving immunotherapy response. This experimental system will provide opportunities for understanding other aspects of tumor heterogeneity as well. This abstract is also being presented as Poster A45. Citation Format: Jinyang Li, Katelyn T. Byrne, Fangxue Yan, Taiji Yamazoe, Zeyu Chen, Timour Baslan, Lee P. Richman, Jeffrey Lin, Yu H. Sun, Shannon M. Liudahl, John W. Tobias, Scott Lowe, Lisa M. Coussens, John E Wherry, Robert H. Vonderheide, Ben Z. Stanger. Tumor cell-intrinsic factors underlie the heterogeneity of immune infiltration and response to immunotherapy in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr PR11.

Abstract 5923: Developing a novel system for studying tumor microenvironment heterogeneity in pancreatic adenocarcinoma

Abstract Pancreatic cancer (PDA) has a dismal prognosis and responds poorly to all existing therapies. This is largely attributed to the existence of dense stroma and lack of T cell infiltration. PDA exhibits heterogeneous tumor microenvironments that are composed of various stromal cells. Human PDA has been shown to have various levels of T cell infiltration. The mechanisms underlying the inter-tumor heterogeneity of immune microenvironment in PDA and its effects on distinct responses of PDA to therapies, especially immune-modulating therapies, remain largely unknown. Our lab has demonstrated that there is no difference in somatic mutation burden or neo-epitope load between human PDA tumors with high or low amount of cytolytic T cells. We hypothesize that tumor-cell-intrinsic determinants, transcriptional and/or epigenetic factors, shape the heterogenous tumor immune microenvironment, and thereby result in different responses to therapies. For the reason that tumor cellularity of PDA is very low, the profiling of bulk tumor samples cannot provide enough information for understanding tumor-cell-intrinsic molecular events associated with differential immune microenvironment. Therefore, we developed a panel of ~60 cell lines (diversity-cell-panel) from PDA tumors derived from inbred C57BL/6 KPC mouse model, which recapitulates the major histopathological and molecular features of human PDA. Our preliminary results have demonstrated that the diversity-cell-panel recapitulates the immune microenvironments heterogeneity in PDA. Tumor cells mainly fell into two subgroups: T-cell-high and T-cell-low. Tumors resulted from T-cell-high tumor cells also possess less amount of immune-suppressive myeloid cells. To study whether this difference is caused by various amount of mutations, we performed exome-sequencing for 8 cell lines within this cell-panel, and showed that T cell-high and T-cell-low tumor cells have similar amount of mutations and neo-epitopes. To further study the distinct molecular features of T-cell-high and T-cell-low tumor cells, we will perform RNA-seq and ATAC-seq on tumor cells sorted from resulting tumors. To understand whether T cell infiltration affects the response of PDA tumors to immune-modulating therapies, I have been testing the response of T-cell-high and T-cell-low tumors to a group of therapies, including chemotherapy, CD40 therapy and immune checkpoint blockade therapy. Utilizing this novel system, the cell-panel, this study will delineate in unprecedented detail the novel molecular determinants of immune heterogeneity in PDA and the implication of the heterogeneity to therapies. This diversity-cell-panel will also be useful for understanding other aspects of tumor heterogeneity, including angiogenesis, drug resistance and metastatic disease. Citation Format: Jinyang Li. Developing a novel system for studying tumor microenvironment heterogeneity in pancreatic adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5923. doi:10.1158/1538-7445.AM2017-5923

Tumor heterogeneity and its implication for drug delivery

Evidence continues to accumulate that patient tumors contain heterogeneous cell populations, each of which may contribute differently in extent and mechanism to the progression of malignancy. However, the field of tumor drug delivery research, while continually presenting new and innovative approaches, in many ways continues to operate on the premise that essentially all tumor cells are identical. In some in vivo models, xenograft tumors using cell lines may actually be comparatively homogeneous, and thus result in overly encouraging results when a particular drug or delivery system is reported to successfully treat tumors in mice. It is well known, however, that many drugs that show success in preclinical studies will fail in clinical trials. Tumor heterogeneity is possibly one of the most significant factors that most treatment methods fail to address sufficiently. While a particular drug may exhibit initial success, the eventual relapse of tumor growth is due in many cases to subpopulations of cells that are either not affected by the drug mechanism, possess or acquire a greater drug resistance, or have a localized condition in their microenvironment that enables them to evade or withstand the drug. These various subpopulations may include cancer stem cells, mutated clonal variants, and tumor-associated stromal cells, as well as cells experiencing a spatially different condition such as hypoxia within a diffusion-limited tumor region. This review briefly discusses some of the many aspects of tumor heterogeneity and their potential implications for future drug design and delivery methods.