Jamie L. P. Lim’s research while affiliated with Memorial Sloan Kettering Cancer Center and other places

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Publications (16)


Ongoing genome doubling promotes evolvability and immune dysregulation in ovarian cancer
  • Preprint

July 2024

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39 Reads

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2 Citations

Andrew W. McPherson

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Matthew A. Myers

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Sohrab P. Shah

Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution in cancers with TP53 loss. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime, we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and the positive association between chromosomal instability and cGAS-STING pathway activation are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily 'active' mutational process in late stage ovarian cancer and link consequent genomic states with altered innate immune responses and immunosuppressive phenotypes.


TME of HGSOC at single-cell resolution
a, Overview of the MSK SPECTRUM cohort and specimen collection workflow. b, UMAP plot of cells profiled by scRNA-seq coloured by patient. Cell types are highlighted with grey outlines. c, Patient specificity for each cell type (Methods). Ov, ovarian. d, Number of cells identified per cell type next to a UMAP plot with cells coloured by cell type. e, Number of cells profiled per tumour site next to a UMAP plot with cells coloured by tumour site. UQ, upper quadrant. f, Site-specific enrichment of cell type composition in scRNA-seq, H&E and mpIF data fitted using a GLM. GLMs for H&E and mpIF data were separated by tumour (T) and stroma (S) regions. The colour gradient indicates the log2-transformed odds ratio (red, enrichment; blue, depletion), and sizes indicate the Bonferroni-corrected –log10(P value). g, Cell type composition based on scRNA-seq data for CD45⁻ and CD45⁺ samples. Upper panels, absolute and relative cell type numbers; lower panels, box plot distributions of sample ranks with respect to tumour site. h, Cell type composition based on H&E with lymphocyte ranks in tumour and stroma. Panels are analogous to those in g. i, Cell type composition based on mpIF with CD8⁺ T cell ranks in tumour and stroma. Panels are analogous to those in g. For c and g–i, violin plots and box plots are shown as the median, top and bottom quartiles; whiskers correspond to 1.5× interquartile range (IQR). *P < 0.05, **P < 0.01.
Site specificity of immunophenotypes
a, UMAP plot of T and NK cell clusters profiled by scRNA-seq. Clusters are coloured and numbered to reference cluster labels in c. b, Pairwise comparisons of kernel density estimates in UMAP space. c, Left, heatmap of average T cell state module scores (left) and signalling pathway activity scores (right) across CD4⁺ T, CD8⁺ T, innate lymphoid cell (ILC), NK and cycling cell clusters. Right, dot plot showing site-specific enrichment of T and NK cell clusters based on GLM. The colour gradient indicates the log2-transformed odds ratio (red, enrichment; blue, depletion), and sizes indicate the Bonferroni-corrected –log10(P value). d, Intra-sample diversity of T and NK cell clusters estimated by Shannon entropy with samples grouped by site (patient and sample counts shown) and intra- and inter-patient dissimilarity of T and NK cell cluster composition for pairs of samples, estimated using the Bray–Curtis distance (patient and sample pair counts shown). Pairwise dissimilarity is shown for all heterotypic pairs of sites (adnexa versus non-adnexa, adnexa versus ascites, non-adnexa versus ascites). Violin plots show the median, top and bottom quartiles; whiskers correspond to 1.5× IQR. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. e, Top, diffusion maps of the subset of CD8⁺ T cells profiled by scRNA-seq, with cells coloured by CD8⁺ T cell cluster and pseudotime. Bottom, relative expression of genes marking CD8⁺ T cell clusters in diffusion space. DC, diffusion component. f, Scaled module scores with respect to pseudotime.
Malignant cell phenotypes and association with mutational signatures
a, Left, UMAP plot of epithelial cells coloured by cluster. Clusters are numbered to reference cluster labels in the heatmap. Right, heatmap of scaled marker gene expression averaged per cluster, showing differentially expressed genes in rows and clusters in columns. The top two genes for each cluster are highlighted. b, Top, heatmap of average signalling pathway activity scores per site. Bottom, UMAP plots with cells coloured by signalling activity scores for pathways of interest. EGFR, epidermal growth factor receptor; MAPK, mitogen-activated protein kinase; PI3K, phosphoinositide 3-kinase; VEGF, vascular endothelial growth factor. c, Relative kernel densities showing enrichment (red) and depletion (blue) in UMAP space for pairwise comparisons of mutational signatures and sites. d, Left, estimated effects of anatomical site and mutational signature on epithelial cluster composition based on GLM. The colour gradient indicates the log2-transformed odds ratio (red, enrichment; blue, depletion), and sizes indicate the Bonferroni-corrected –log10(P value). Right, epithelial cluster compositions ranked by Cancer.cell.3 fraction. Box plot panels show distributions of scaled sample ranks by mutational signature. e,f, Distributions of signalling pathway activity scores (e) and HLA gene expression (f) in adnexal and non-adnexal samples as a function of mutational signature (patient counts shown). g, Left, intra-sample diversity of malignant cell clusters in adnexal and non-adnexal samples, with samples grouped by mutational signature and site (patient and sample counts shown). Right, intra- and inter-patient dissimilarity of malignant cluster composition for pairs of samples. Pairwise dissimilarity is shown for all pairs of sites (patient and sample pair counts shown) excluding ascites (top) and for adnexal versus non-adnexal pairs of sites (bottom). In d–g, box plots and violin plots show the median, top and bottom quartiles; whiskers correspond to 1.5× IQR. Colours in e–g are analogous to those in d. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; brackets indicate two-sided Wilcoxon pairwise comparisons in e–g.
Mutational signatures as determinants of immunophenotypes
a, Differences in kernel density estimates in UMAP space for pairwise comparisons of mutational signatures. b, Estimated effects of mutational signature and anatomical site on T and NK cell cluster composition based on a GLM, with models fitted excluding ascites samples. The colour gradient indicates the log2-transformed odds ratio (red, enrichment; blue, depletion), and sizes indicate the Bonferroni-corrected –log10(P value). c, Distributions of CD8⁺ T cell state module scores and JAK–STAT signalling pathway activity scores with respect to mutational signature (patient counts shown). d, Scaled module scores within the subset of CD8⁺ T cells with respect to pseudotime and mutational signature. e, Correlation of JAK–STAT signalling scores in CD8⁺ T cells in CD45⁺ samples with those in cancer cells in matched CD45⁻ samples. f, Left, intra-sample diversity of T and NK cell clusters in adnexal and non-adnexal samples estimated by Shannon entropy, with samples grouped by mutational signature (patient and sample counts shown). Right, intra- and inter-patient dissimilarity in T and NK cell cluster composition, with samples grouped by mutational signature, estimated using the Bray–Curtis distance. Pairwise dissimilarity is shown for all pairs of sites (patient and sample pair counts shown) excluding ascites (top) and for adnexal versus non-adnexal pairs of sites (bottom). g, Spatial density of CD8⁺ T cell phenotypes in adnexal and non-adnexal mpIF samples as a function of distance to the tumour–stroma interface, with samples grouped by mutational signature (Methods). In c and f, box plots and violin plots show the median, top and bottom quartiles; whiskers correspond to 1.5× IQR. Colours in f and g are analogous to those in c–e. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; brackets indicate two-sided Wilcoxon pairwise comparisons in c and f.
HLA loss as a mechanism of immune escape
a, Left, distribution over cells of chromosome arm 6p BAF in scRNA-seq data with ranking by median 6p BAF per cell type. Right, allelic imbalance in 6p BAF across cancer cell clusters. White vertical lines indicate the median. Chr., chromosome. b, Left, percentage of cancer cells with 6p LOH per patient. Right, site- and clone-specific percentage of cancer cells with 6p LOH. Het., heterozygous. c, Percentage of cancer cells with 6p LOH per sample as a function of mutational signature. Pie charts show the fraction of samples with heterozygous, subclonal LOH and clonal LOH 6p status. d, Percentage of patients with LOH of any HLA class I gene in the MSK-IMPACT HGSOC cohort (n = 1,298 patients) for BRCA1-, BRCA2- and CDK12-mutant and CCNE1-amplified tumours, mapping to HRD-Dup, HRD-Del, TD and FBI signatures, respectively. Error bars, 95% binomial confidence intervals. e, Percentage of cancer cells with 6p LOH per sample as a function of anatomical site. Pie charts show the fraction of samples by 6p status. f, UMAP plots of cancer cells from representative HRD-Dup and FBI cases. Density plots show site-specific 6p BAF. g, Fraction of naive and dysfunctional T cells in CD45⁺ samples as a function of the 6p LOH clonality of cancer cells in matched CD45⁻ samples. *P < 0.05; brackets indicate two-sided Wilcoxon pairwise comparisons. In b, c, e and g, 6p LOH status is defined as follows: heterozygous, percentage 6p LOH ≤ 20%; subclonal LOH, 20% < percentage 6p LOH ≤ 80%; clonal LOH, percentage 6p LOH > 80%. In c, e and g, box plots and violin plots show the median, top and bottom quartiles; whiskers correspond to 1.5× IQR. In a–e, only BAF estimates from cells with ≥10 reads aligning to 6p were considered and allelic imbalance states were assigned on the basis of the mean 6p BAF per cell as follows: balanced, BAF ≥ 0.35; imbalanced, 0.15 ≤ BAF < 0.35; LOH, BAF < 0.15 (Methods).

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Ovarian cancer mutational processes drive site-specific immune evasion
  • Article
  • Full-text available

December 2022

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463 Reads

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142 Citations

Nature

High-grade serous ovarian cancer (HGSOC) is an archetypal cancer of genomic instability1–4 patterned by distinct mutational processes5,6, tumour heterogeneity7–9 and intraperitoneal spread7,8,10. Immunotherapies have had limited efficacy in HGSOC11–13, highlighting an unmet need to assess how mutational processes and the anatomical sites of tumour foci determine the immunological states of the tumour microenvironment. Here we carried out an integrative analysis of whole-genome sequencing, single-cell RNA sequencing, digital histopathology and multiplexed immunofluorescence of 160 tumour sites from 42 treatment-naive patients with HGSOC. Homologous recombination-deficient HRD-Dup (BRCA1 mutant-like) and HRD-Del (BRCA2 mutant-like) tumours harboured inflammatory signalling and ongoing immunoediting, reflected in loss of HLA diversity and tumour infiltration with highly differentiated dysfunctional CD8+ T cells. By contrast, foldback-inversion-bearing tumours exhibited elevated immunosuppressive TGFβ signalling and immune exclusion, with predominantly naive/stem-like and memory T cells. Phenotypic state associations were specific to anatomical sites, highlighting compositional, topological and functional differences between adnexal tumours and distal peritoneal foci. Our findings implicate anatomical sites and mutational processes as determinants of evolutionary phenotypic divergence and immune resistance mechanisms in HGSOC. Our study provides a multi-omic cellular phenotype data substrate from which to develop and interpret future personalized immunotherapeutic approaches and early detection research. Multi-modal analysis of genomically unstable ovarian tumours characterizes the contribution of anatomical sites and mutational processes to evolutionary phenotypic divergence and immune resistance mechanisms.

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Single-cell genomic variation induced by mutational processes in cancer

October 2022

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284 Reads

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77 Citations

Nature

How cell-to-cell copy number alterations that underpin genomic instability1 in human cancers drive genomic and phenotypic variation, and consequently the evolution of cancer2, remains understudied. Here, by applying scaled single-cell whole-genome sequencing3 to wild-type, TP53-deficient and TP53-deficient;BRCA1-deficient or TP53-deficient;BRCA2-deficient mammary epithelial cells (13,818 genomes), and to primary triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGSC) cells (22,057 genomes), we identify three distinct ‘foreground’ mutational patterns that are defined by cell-to-cell structural variation. Cell- and clone-specific high-level amplifications, parallel haplotype-specific copy number alterations and copy number segment length variation (serrate structural variations) had measurable phenotypic and evolutionary consequences. In TNBC and HGSC, clone-specific high-level amplifications in known oncogenes were highly prevalent in tumours bearing fold-back inversions, relative to tumours with homologous recombination deficiency, and were associated with increased clone-to-clone phenotypic variation. Parallel haplotype-specific alterations were also commonly observed, leading to phylogenetic evolutionary diversity and clone-specific mono-allelic expression. Serrate variants were increased in tumours with fold-back inversions and were highly correlated with increased genomic diversity of cellular populations. Together, our findings show that cell-to-cell structural variation contributes to the origins of phenotypic and evolutionary diversity in TNBC and HGSC, and provide insight into the genomic and mutational states of individual cancer cells. Single-cell whole-genome sequencing shows that 'foreground' cell-to-cell structural variation and alterations in copy number are associated with genomic diversity and evolution in triple-negative breast and high-grade serous ovarian cancers.


Immune and malignant cell phenotypes of ovarian cancer are determined by distinct mutational processes

August 2021

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104 Reads

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4 Citations

High-grade serous ovarian cancer (HGSOC) is an archetypal cancer of genomic instability patterned by distinct mutational processes, intratumoral heterogeneity and intraperitoneal spread. We investigated determinants of immune recognition and evasion in HGSOC to elucidate co- evolutionary processes underlying malignant progression and tumor immunity. Mutational processes and anatomic sites of tumor foci were key determinants of tumor microenvironment cellular phenotypes, inferred from whole genome sequencing, single-cell RNA sequencing, digital histopathology and multiplexed immunofluorescence of 160 tumor sites from 42 treatment-naive HGSOC patients. Homologous recombination-deficient (HRD)-Dup ( BRCA1 mutant-like) and HRD- Del ( BRCA2 mutant-like) tumors harbored increased neoantigen burden, inflammatory signaling and ongoing immunoediting, reflected in loss of HLA diversity and tumor infiltration with highly- differentiated dysfunctional CD8 ⁺ T cells. Foldback inversion (FBI, non-HRD) tumors exhibited elevated TGFβ signaling and immune exclusion, with predominantly naive/stem-like and memory T cells. Our findings implicate distinct immune resistance mechanisms across HGSOC subtypes which can inform future immunotherapeutic strategies. HIGHLIGHTS Multi-region, multi-modal profiling of malignant and immune cell phenotypes in ovarian cancer Anatomic site specificity is a determinant of cancer cell and intratumoral immune phenotypes Tumor mutational processes impact mechanisms of immune control and immune evasion Spatial topology of HR-deficient tumors is defined by immune interactions absent from immune inert HR-proficient subtypes


The impact of mutational processes on structural genomic plasticity in cancer cells

June 2021

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80 Reads

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1 Citation

Structural genome alterations are determinants of cancer ontogeny and therapeutic response. While bulk genome sequencing has enabled delineation of structural variation (SV) mutational processes which generate patterns of DNA damage, we have little understanding of how these processes lead to cell-to-cell variations which underlie selection and rates of accrual of different genomic lesions. We analysed 309 high grade serous ovarian and triple negative breast cancer genomes to determine their mutational processes, selecting 22 from which we sequenced >22,000 single cell whole genomes across a spectrum of mutational processes. We show that distinct patterns of cell-to-cell variation in aneuploidy, copy number alteration (CNA) and segment length occur in homologous recombination deficiency (HRD) and fold-back inversion (FBI) phenotypes. Widespread aneuploidy through induction of HRD through BRCA1 and BRCA2 inactivation was mirrored by continuous whole genome duplication in HRD tumours, contrasted with early ploidy fixation in FBI. FBI tumours exhibited copy number distributions skewed towards gains, widespread clone-specific variation in amplitude of high-level amplifications, often impacting oncogenes, and break-point variability consistent with progressive genomic diversification, which we termed serriform structural variation (SSV). SSVs were consistent with a CNA-based molecular clock reflecting a continual and distributed process across clones within tumours. These observations reveal previously obscured genome plasticity and evolutionary properties with implications for cancer evolution, therapeutic targeting and response.


Abstract PR002: Global proteomic profiling of endometrial carcinomas identify prognostic markers

February 2021

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35 Reads

Clinical Cancer Research

p>While endometrial cancer (EC) has an overall good prognosis, some patients do poorly and there is room for refinement within current classification systems. Using the TCGA prognostic grouping of EC, our group developed the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE), which reliably and reproducibly stratifies ECs into four prognostic groups: POLE mut, p53wt/NSMP, MMRd (mismatch repair deficient), and p53abn, with the best prognosis for POLE mut to worst prognosis for p53abn. In the current study, global proteomic analysis was performed using the clinical SP3-CTP workflow on archival tissues from 151 patients including 40 MMRd, 34 POLE mut, 34 p53abn and 43 p53wt/NSMP, with clinical follow up data. Included in the cohort were 11 replicate samples (different parts of the same tumor) to examine spatial heterogeneity in the proteomic profiles. Replicate samples were highly correlated to each other, with the exception of three POLE mut cases with very poor correlation in the proteome in different parts of the tumor. As POLE mut tumors have an exceedingly high mutation burden, it is not surprising that this translates to heterogeneity at the proteomic level. Disease specific survival was examined to determine prognostic significance within the whole cohort and within individual molecular subgroups. High TOMM34, PLTP or TSFM expression was correlated to poor disease specific survival in the whole cohort and independently prognostic when molecular subtype, grade and histotype are considered. High MGST, NCL or XPNPEP3 were associated with poor outcomes within the p53wt/NSMP group. POLD2 and ENAH were prognostic within the MMRd group. Within the p53abn group, ACADVL and BABAM1 were found to be prognostic, and GRB7 was found to be enriched in the p53abn group compared to other molecular subtypes. As the group with the worst prognosis, p53abn group could benefit from novel therapeutic avenues. ACADVL, BABAM1 and GRB7 all lie within pathways that are potentially targetable. Our proteomic analysis has identified prognostic markers that may be useful in further refining current molecular classification to help guide treatment decisions. Furthermore, new therapeutic interventions could be developed to target proteins and pathways identified by this proteomics screen. Citation Format: Dawn R. Cochrane, Gian Negri, Jutta Huvila, David A. Farnell, Emily Thompson, Winnie Yang, Genny Trigo-Gonzales, Amy Lum, Sandra Spencer, Ryan Riley, Samuel Leung, Christine Chow, Jamie Lim, Martin Koebel, Stefan Kommoss, Friedrich Kommoss, Lien Hoang, David G. Huntsman, Gregg Morin, Jessica N. McAlpine. Global proteomic profiling of endometrial carcinomas identify prognostic markers [abstract]. In: Proceedings of the AACR Virtual Special Conference: Endometrial Cancer: New Biology Driving Research and Treatment; 2020 Nov 9-10. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(3_Suppl):Abstract nr PR002.</p


Single cell transcriptomes of normal endometrial derived organoids uncover novel cell type markers and cryptic differentiation of primary tumours

July 2020

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97 Reads

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50 Citations

The Journal of Pathology

Endometrial carcinoma, the most common gynaecological cancer, develops from endometrial epithelium which is composed of secretory and ciliated cells. Pathologic classification is unreliable and there is a need for prognostic tools. We used single cell sequencing to study organoid model systems derived from normal endometrial endometrium to discover novel markers specific for endometrial ciliated or secretory cells. A marker of secretory cells (MPST) and several markers of ciliated cells (FAM92B, WDR16 and DYDC2) were validated by immunohistochemistry on organoids and tissue sections. We performed single cell sequencing on endometrial and ovarian tumours and found both secretory‐like and ciliated‐like tumour cells. We found that ciliated cell markers (DYDC2, CTH, FOXJ1 and p73) and the secretory cell marker MPST were expressed in endometrial tumours and positively correlated with disease specific and overall survival of endometrial cancer patients. These findings suggest that expression of differentiation markers in tumours correlates with less aggressive disease, as would be expected for tumours that retain differentiation capacity, albeit cryptic in the case of ciliated cells. These markers could be used to improve the risk stratification of endometrial cancer patients, thereby improving their management. We further assessed whether consideration of MPST expression could refine the ProMiSE molecular classification system for endometrial tumours. We found that higher expression levels of MPST could be used to refine stratification of three of the four ProMiSE molecular subgroups, and that any level of MPST expression was able to significantly refine risk stratification of the copy number high subgroup which has the worst prognosis. Taken together, this shows that single cell sequencing of putative cells of origin has the potential to uncover novel biomarkers that could be used to guide management of cancers. This article is protected by copyright. All rights reserved.


Abstract B09: Single-cell RNA sequencing of normal endometrial organoids uncovers novel cell-type markers for prognostication of primary tumor samples

July 2020

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19 Reads

Clinical Cancer Research

Endometrial epithelium gives rise to both endometrial and ovarian cancers (of clear-cell and endometrioid subtypes), the latter arising from ectopic endometrium (endometriosis). Endometrial epithelium comprises mainly secretory cells, with a minor ciliated cell population. Due to their scarcity, little is known about the biology or function of endometrial ciliated cells. To understand the biology of endometrial epithelium, and by extension the cancers that arise from it, organoids derived from normal endometrial tissue were cultured. Notch signaling inhibitors were used to induce ciliated cell differentiation. Through single-cell RNA sequencing, distinct secretory and ciliated cell populations were observed, with the ciliated cell population increasing with Notch signaling inhibition. Many novel markers of ciliated cells were observed, but no highly specific markers of secretory cell differentiation. A marker of secretory cells (MST) and several markers of ciliated cells (FAM92B, WDR16 and DYDC2) were validated by immunohistochemistry on organoids and tissue sections. In endometrial tumors, both MST and FAM92B exhibited diffuse staining and were markers of better prognosis. This suggests that tumors expressing differentiation markers have better prognosis, whether it is a marker of secretory or ciliated cells. Interestingly, a small number of endometrial tumors stained positive for DYDC2; however, these tumors exhibited a variable staining pattern with 25-50% tumor cells staining intensely, and the remaining tumor cells not staining at all. A similar variable staining pattern had been observed previously with CTH, another ciliated cell marker. Endometrial and ovarian tumor tissue microarrays were stained with DYDC2, CTH and two ciliated cell markers, FOXJ1 and p73. For all these markers, a subset of tumors displayed a variable staining pattern and for endometrial cancers, the variable staining was a good prognostic indicator. Single-cell sequencing of endometrial tumors has been able to capture these two populations of tumor cells. In ovarian tumors, only variable CTH staining was a significant prognostic indicator. Normal endometrial secretory cells are able to differentiate into ciliated cells, and the variable staining pattern suggests that a subset of tumors retains this ability, and these are clinically less aggressive. Using single-cell sequencing technology on normal tissues to guide development of prognostic markers and provide insight into the biology of the tumors arising from these tissues may be useful for many other tumor types. Citation Format: Dawn R. Cochrane, Kieran R. Campbell, Kendall Greening, Germain C. Ho, James Hopkins, Minh Bui, Vassilena Sharlandjieva, Daniel Lai, Maya DeGrood, Evan W. Gibbard, Samuel Leung, Angela S. Cheng, Jamie L.P. Lim, Samantha Neilson, David Farnell, Friedrich Kommoss, Jessica N. McAlpine, Sohrab P. Shah, David G. Huntsman. Single-cell RNA sequencing of normal endometrial organoids uncovers novel cell-type markers for prognostication of primary tumor samples [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B09.



Fig. 4 Disentangling the effects of digestion time and digestion method on transcriptomic response. a Mean normalized expression of genes in the core gene set as a function of digestion time colored by digestion temperature. Digestion by collagenase causes upregulation of the gene set at all time points, with a subset showing further upregulation as digestion time increases. B Log fold changes of a 2-h vs. 30-min digestion for collagenase only as a function of log counts-per-million. c Log fold changes of a collagenase vs. cold protease digestion at 30-min digestion time as a function of log counts-per-million. d Log fold changes of a collagenase vs. cold protease digestion at 2-h digestion time as a function of log counts-per-million. e Log fold changes of a 2-h vs. 30-min digestion (collagenase only) compared to a collagenase vs. cold protease digestion at 2 h demonstrate a large overlap between genes affected (ρ = 0.8)
Overview of 48 single-cell experiments generated in this study. a Schematic showing the various substrates used to generate the 48 single-cell experiments in this dataset. b Descriptions of the cell status, substrate, cancer type, dissociation temperature, and tissue state of each sample in the dataset. c Substantial variability in three key QC metrics (number of genes detected, percentage of counts mapping to the mitochondrial genome, number of UMIs sequenced) across all experiments. d Embedding of all 48 single-cell experiments to a low-dimensional projection with uniform manifold approximation and projection [12]
Transcriptomic landscape of live, dead, and dying cells. a FACS analysis showing gating strategy for untreated, live cells (PI−/annexin V−) or TNFα-treated dying cells (PI/annexin V+) and dead cells (PI+/annexin V+). b PCA projection of the three cell conditions showing approximate segregation of cell status along the first principal component (PC1), with live and dying cells enriched at lower PC1 values and dead cells enriched at higher values. c PCA projection colored by the percentage mitochondrial genes (“% transcriptome mitochondrial”) shows significant increase along the PC1. d Dead cells exhibit significantly higher percentage of the transcriptome as mitochondrial compared to both live and dying cells. e Unsupervised clustering of the gene expression profiles clusters the cells into three groups, approximately tracking both PC1 of the data and the percentage of transcriptome mitochondrial. f The composition of each cluster demonstrates that cluster 1 is primarily composed of live cells and cluster 2 a mix of live, dying, and dead cells, while cluster 3 is composed mainly of dead cells. g The percentage of transcriptome mitochondrial is significantly different between the three clusters, with a step increase in proportion moving from cluster 1 to 2 and 2 to 3. h Cluster 2 significantly upregulates the MHC class I gene set, suggesting it represents stressed or pre-apoptotic cells. i Differential expression analysis of transcriptomically “healthy” cells within cluster 1 reveals residual differences between cells sorted as live and dead. j The distribution of absolute effect sizes (log fold change) of live vs. dead cells within cluster 1 (x-axis) compared to between clusters 1 and 2 (y-axis) demonstrates the residual effect on the transcriptome of being live/dead sorted is small compared to the inter-cluster expression variance
Dissociation with collagenase at 37 °C induces a distinct stress response in 23,731 cells from PDX samples that is minimized by dissociation at 6 °C. a The top 40 genes (by log fold change) from the 11,975 identified as significantly differentially expressed between cells digested at 6 °C and 37 °C. b UMAP plots of 23,731 cells colored by digestion temperature (top) then by normalized expression of three key stress response genes (FOS, JUNB, NR4A1) demonstrate a distinct concordance between temperature and induction of the stress gene signature. Expression values are log normalized counts winsorized to [0, 2) then scaled to [0, 1). c Pathway analysis of differentially expressed genes with the MSigDB hallmark gene sets highlights induction of genes involved in NF-κB signaling at 37 °C digestion with 46.5% of 200 genes annotated in the pathway being found in the 512 core gene set
Conserved stress response to the collagenase dissociation method in breast and ovarian patient tissues. a Histology of ovarian (top) and breast (bottom) cancer patient samples highlighting the architecture of the tumor microenvironment. b FACS analysis of ovarian tumor tissue dissociated at 37 °C with collagenase or 6 °C with cold active protease and stained with markers for tumor cells (EpCAM), endothelial cells (CD31), fibroblasts (FAP), lymphocytes (CD45), B cells (CD19), NK cells (CD56), and T cells (CD8, CD3). c UMAP of combined scRNA-seq experiments of ovarian cancer (n = 2) and breast cancer (n = 3) patient tissues with cell type assignments according to known gene markers for each cell type. d The top 40 genes from the gene set derived in Fig. 3 as expressed in each cell type in breast and ovarian patient samples. Black circles around points denote significance at 5% FDR. e Pathway analysis of the differential expression results with the MSigDB hallmark gene sets for each cell type
Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses

October 2019

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2,067 Reads

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183 Citations

Genome Biology

Background: Single-cell RNA sequencing (scRNA-seq) is a powerful tool for studying complex biological systems, such as tumor heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumor tissues and patient-derived mouse xenografts for scRNA-seq are not well understood. Results: We use low temperature (6 °C) protease and collagenase (37 °C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNA-seq dataset comprising 155,165 cells from patient cancer tissues, patient-derived breast cancer xenografts, and cancer cell lines. We observe substantial variation in standard quality control metrics of cell viability across conditions and tissues. From the contrast between tissue protease dissociation at 37 °C or 6 °C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37 °C), which are minimized by dissociation with a cold active protease (6 °C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues. Conclusions: The method and conditions of tumor dissociation influence cell yield and transcriptome state and are both tissue- and cell-type dependent. Interpretation of stress pathway expression differences in cancer single-cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with the identification of such effects in dissociated scRNA-seq experiments.


Citations (11)


... Most patients are diagnosed with metastatic disease, where tumors arise from divergent clonal trajectories and present heterogeneous immune cellularity and immune escape mechanisms [6][7][8][9][10] . ...

Reference:

A patient-derived ovarian cancer organoid platform to study susceptibility to natural killer cells
Ovarian cancer mutational processes drive site-specific immune evasion

Nature

... Mutational signature analysis has been a powerful tool in cancer genomics 1 , but its use has largely been limited to bulk sequencing data. Within single-cell data, signature analysis has mostly focused on copynumber and structural variant signatures 47 . The ability to reliably detect and analyze singlenucleotide variant signatures represents a significant methodological advance. ...

Single-cell genomic variation induced by mutational processes in cancer

Nature

... To expand the cohort, we also searched the institutional data warehouse for patients with MSK-IMPACT sequencing and available pre-treatment CT studies or H&E images. In addition to this retrospective curation, 36 patients were also included from the prospective MSK-SPECTRUM project 34 . Pathological stage was unavailable for 14 patients and we instead recorded the clinical stage as recorded in the institutional database for these patients. ...

Immune and malignant cell phenotypes of ovarian cancer are determined by distinct mutational processes

... Mutation signature analysis. We derived mutation signature probabilities using the MMCTM method, given signatures previously inferred from a set of high grade serous ovarian carcinomas and triple negative breast cancers 28,57 . The PDX sample datasets were clustered together with the other datasets to form clusters defined solely by signature activity using hierarchical clustering with Euclidean distance and Ward's linkage method. ...

The impact of mutational processes on structural genomic plasticity in cancer cells
  • Citing Preprint
  • June 2021

... Acquisition of 5 resistance may be related to specific mutational processes that drive genomic 6 heterogeneity [4,5] and clonal evolution [6,7]. HGSOC exhibits marked intra-site and 7 inter-site genetic heterogeneity across metastatic sites in the peritoneal cavity [5][6][7] with 8 altered immunological infiltrates and tumor microenvironments [8]. Detection of spatial or 9 temporal heterogeneity by multiple sampling in a single patient is expensive, invasive, and 10 often clinically impractical. ...

The interface of malignant and immunologic clonal dynamics in high-grade serous ovarian cancer

... In female mammals, the endometrium is the inner layer of the uterus, including the luminal epithelium and the glandular epithelium. Notably, the luminal epithelial cells within the layers are also composed of secretory and ciliated cells, and the distinctive markers for these two cell types have been demonstrated [148][149][150] (Figure 2C). In the endometrium of patients with recurrent miscarriages, ciliary abnormalities (such as shortening and fusion) have been observed in MCCs in addition to microvillar abnormalities in secretory epithelial cells. ...

Single cell transcriptomes of normal endometrial derived organoids uncover novel cell type markers and cryptic differentiation of primary tumours
  • Citing Article
  • July 2020

The Journal of Pathology

... First, most of the procedures were done under cold temperatures (on ice) to reduce artifactual changes in gene expression. Most importantly, we used Bacillus licheniformis protease (Adam, Potter, and Potter 2017;O'Flanagan et al. 2019;Brown et al. 2021) which possesses high proteolytic activity even at 6°C. Second, we adopted step-wise trituration with two types of fire-polished Pasteur pipettes with different tip diameters (first with 600-700 μm and second with 200-300 μm), and the resulting material was filtrated through a fine mesh (20 μm) cell strainer to obtain single-cell suspension. ...

Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses

Genome Biology

... Supervised classification-based methods are based on the classical machine learning approach where patterns from gene expression profiles are learned from labeled data (training set) and used to predict cell types in unlabeled data (test set) (Pasquini et al., 2021). Tools like CellAssign (Zhang et al., 2019a) incorporate prior knowledge of marker genes into probabilistic models to assign cell types, while scGPT (Cui et al., 2024) uses transformer-based architectures pre-trained on large annotated datasets to classify cells. These methods enhance accuracy and scalability but their limitations lie in their reliance on the quality and representativeness of labeled data, which can affect accuracy and generalizability, especially in cases of cellular heterogeneity from mixed or overlapping cell types. ...

Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling

Nature Methods

... Recently, whole-genome and whole-exome sequence analyses of fresh-frozen pathological specimens have identified recurring telomerase reverse transcriptase (TERT) promoter (−124C > T) and lysine methyltransferase 2D (KMT2D) truncating mutations in AGCTs. These molecular variants were associated with more aggressive disease [11][12][13]. Currently, however, there are no molecular markers to identify patients with increased risk of relapse or progression [12]. ...

Correction: TERT promoter mutation in adult granulosa cell tumor of the ovary
  • Citing Article
  • July 2018

Modern Pathology

... Subclonal resistance mechanisms underlie advanced stage disease with metastatic tumors exhibiting reduced immune cell infiltration and immunosuppressive microenvironments that are subtype-, patient-and even tumor site-specific [8][9][10][11][12][13][14] . T cell-based therapies and immune checkpoint inhibitors have so far been ineffective in treating ovarian cancer 15,16 . ...

Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer
  • Citing Article
  • May 2018

Cell