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. 2021 Jul 20;17(7):e1009228. doi: 10.1371/journal.pcbi.1009228

Single-cell analysis reveals the pan-cancer invasiveness-associated transition of adipose-derived stromal cells into COL11A1-expressing cancer-associated fibroblasts

Kaiyi Zhu 1,2,3,¤,#, Lingyi Cai 1,2,3,#, Chenqian Cui 2, Juan R de los Toyos 4, Dimitris Anastassiou 1,2,3,5,*
Editor: Andrey Rzhetsky6
PMCID: PMC8323949  PMID: 34283835

Abstract

During the last ten years, many research results have been referring to a particular type of cancer-associated fibroblasts associated with poor prognosis, invasiveness, metastasis and resistance to therapy in multiple cancer types, characterized by a gene expression signature with prominent presence of genes COL11A1, THBS2 and INHBA. Identifying the underlying biological mechanisms responsible for their creation may facilitate the discovery of targets for potential pan-cancer therapeutics. Using a novel computational approach for single-cell gene expression data analysis identifying the dominant cell populations in a sequence of samples from patients at various stages, we conclude that these fibroblasts are produced by a pan-cancer cellular transition originating from a particular type of adipose-derived stromal cells naturally present in the stromal vascular fraction of normal adipose tissue, having a characteristic gene expression signature. Focusing on a rich pancreatic cancer dataset, we provide a detailed description of the continuous modification of the gene expression profiles of cells as they transition from APOD-expressing adipose-derived stromal cells to COL11A1-expressing cancer-associated fibroblasts, identifying the key genes that participate in this transition. These results also provide an explanation to the well-known fact that the adipose microenvironment contributes to cancer progression.

Author summary

Computational analysis of rich gene expression data at the single-cell level from cancer biopsies can lead to biological discoveries about the nature of the disease. Using a computational methodology that identifies the gene expression profile of the dominant cell population for a particular cell type in the microenvironment of tumors, we observed that there is a remarkably continuous modification of this profile among patients, corresponding to a cellular transition. Specifically, we found that the starting point of this transition has a unique characteristic signature corresponding to cells that are naturally residing in normal adipose tissue. We also found that the endpoint of the transition has another characteristic signature corresponding to a particular type of cancer-associated fibroblasts with prominent expression of gene COL11A1, which has been found strongly associated with invasiveness, metastasis and resistance to therapy in multiple cancer types. Our results provide an explanation to the well-known fact that the adipose tissue contributes to cancer progression, shedding light on the biological mechanism by which tumor cells interact with the adipose microenvironment. We provide a detailed description of the changing profile during the transition, identifying associated genes as potential targets for pan-cancer therapeutics inhibiting the underlying mechanism.

Introduction

This work investigates, using computational analysis of rich single-cell datasets from many patients, the nature and origin of a particular type of cancer-associated fibroblasts (CAFs) that has been found to be strongly associated with invasiveness, metastasis, resistance to therapy, and poor prognosis, in multiple cancer types. These fibroblasts can be identified by their characteristic signature with prominent presence of collagen COL11A1 and several other co-expressed genes such as THBS2 and INHBA. There are indications that the generation of those CAFs is part of a universal biological process in cancer that plays essential roles in cancer progression. Therefore, the driving vision for this research has been that it may provide testable hypotheses for the development of pan-cancer therapeutics targeting the biological mechanisms responsible for the creation of those CAFs. As described below, to achieve this task we used both established techniques for studying the dynamic changes in gene expression of cells associated with lineages, such as trajectory inference, as well as complementary computational approaches with novel application in single-cell data analysis. These techniques allowed the precise identification of the expression profile of the origin of the underlying cellular transition as a particular cell type of adipose derived stromal/stem cells (ASCs). We also independently validated the presence of those ASCs as naturally occurring, by applying the same computational methods in other available datasets of normal adipose tissue. In the remaining part of this section we provide introductory information about the COL11A1-expressing CAFs, explain the motivation for our choice of computational methods, and provide evidence for their advantages and unique capabilities analyzing the particular data sets that we used.

These CAFs were first identified in 2010 [1] by their cancer stage-associated signature. Specifically, a COL11A1/INHBA/THBS2-expressing gene signature was found to be present only after a particular staging threshold, different in each cancer type, was reached. For example, it only appeared in ovarian cancer of at least stage III; in colon cancer of at least stage II; and in breast cancer of at least invasive stage I (but not in carcinoma in situ). We had observed the striking consistency of that signature across cancer types, which was obvious at that time from bulk microarray data. For example, Table 1 shows the top 15 genes ranked in terms of fold change for three different cancer types (breast [2], ovarian [3], pancreatic [4]) using data provided in papers published independently. The breast cancer data compare invasive ductal carcinoma with ductal carcinoma in situ (supplementary data 3, “up in IDC” of the paper [2]); the ovarian cancer data compare metastatic tissue in the omentum with primary tumor (Table 2 of the paper [3]); and the pancreatic data compare whole tumor tissue with normal pancreatic tissue (Table 1 of the paper [4]). The four genes COL11A1, INHBA, THBS2, COL5A2 appear among the top 15 in all three sets (P = 6×10−23 by multi-set intersection test [5]). The actual P value is much lower than that, because, in addition to the above overlap, ten additional genes (COL10A1, COL1A1, COL5A1, FAP, FBN1, FN1, LOX, MFAP5, POSTN, SULF1) appear among the top 15 in at least two of the three sets (and are highly ranked in all three sets anyway). This similarity demonstrates that the signature is well-defined and associated with a universal biological mechanism in cancer.

Table 1. Top 15 ranked genes in terms of fold change (FC) for three different cancer types revealing the signature of the COL11A1-expressing cancer-associated fibroblasts.

Shown are the rankings, reported by the authors, for breast, ovarian and pancreatic cancers. We eliminated multiple entries of the same gene (keeping the one that appears first) and dashes. Genes shared in all three cancer types are highlighted in green, while genes appearing twice are highlighted in yellow.

Breast Ovarian Pancreatic
Rank Gene FC Gene FC Gene Log2FC
1 COL11A1 6.5 COL11A1 8.23 INHBA 5.15
2 COL10A1 4.07 COL1A1 5.67 COL10A1 5
3 MFAP5 3.73 TIMP3 5.52 POSTN 4.92
4 LRRC15 3.61 FN1 5.4 SULF1 4.63
5 INHBA 3.44 INHBA 4.94 COL8A1 4.6
6 FBN1 3.43 EFEMP1 4.86 COL11A1 4.4
7 SULF1 3.35 DSPG3 4.36 CTHRC1 4.38
8 GREM1 3.35 COL5A2 4.07 COL1A1 4.12
9 COL5A2 3.22 LOX 4.03 THBS2 3.97
10 LOX 3.22 MFAP5 4.01 HNT 3.9
11 COL5A1 3.08 POSTN 3.97 CSPG2 3.87
12 THBS2 2.99 COL5A1 3.95 WISP1 3.8
13 LAMB1 2.97 THBS2 3.91 FN1 3.69
14 FAP 2.96 FBN1 3.9 COMP 3.53
15 SPOCK 2.91 FAP 3.84 COL5A2 3.38

We had also found that gene COL11A1 serves as a proxy of the full signature, in the sense that it is the only gene from which all other genes of the signature are consistently top-ranked in terms of the correlation of their expression with that of COL11A1. Accordingly, we had identified a COL11A1-correlated pan-cancer gene signature, listed in table 4 of [1], which we deposited in the Molecular Signatures Database (MSigDB). We had referred to those CAFs as MAFs (“metastasis-associated fibroblasts”), because their presence suggests that metastasis is imminent. To avoid any inaccurate interpretation of the term as implying that such fibroblasts are markers of metastasis that has occurred already, here we refer to them as “COL11A1-expressing CAFs.”

Table 4. Ranked COL11A1-associated genes in five PDAC samples.

MI = Mutual Information.

Rank T23 MI T11 MI T6 MI T15 MI T18 MI
1 COL11A1 1 COL11A1 1 COL11A1 1 COL11A1 1 COL11A1 1
2 COL10A1 0.3603 CTHRC1 0.2434 MFAP5 0.2353 MFAP5 0.3198 MFAP5 0.3408
3 COL12A1 0.3383 MFAP5 0.2357 FNDC1 0.1997 GJB2 0.2583 SUGCT 0.3379
4 COL1A1 0.3187 COL12A1 0.2345 NTM 0.1912 COL10A1 0.2580 COL10A1 0.2899
5 THBS2 0.3167 COL10A1 0.2238 COL8A1 0.1877 INHBA 0.2561 C5orf46 0.2753
6 COL1A2 0.3099 C1QTNF3 0.2155 TWIST1 0.1714 C1QTNF3 0.2514 PPAPDC1A 0.2668
7 COL5A2 0.3003 THBS2 0.2123 COL10A1 0.1619 MATN3 0.2505 NTM 0.2649
8 CTHRC1 0.2854 COL1A2 0.2045 THBS2 0.1559 FNDC1 0.2503 COL8A1 0.2534
9 FN1 0.2781 COL8A1 0.2018 ITGA11 0.1556 COL8A2 0.2411 INHBA 0.2430
10 COL3A1 0.2770 AEBP1 0.2000 PPAPDC1A 0.1305 COL1A1 0.2399 FNDC1 0.2264
11 INHBA 0.2746 LUM 0.1989 DIO2 0.1298 COL12A1 0.2351 COL12A1 0.2194
12 AEBP1 0.2688 COL1A1 0.1985 IGFL2 0.1178 COL8A1 0.2325 IGFL2 0.2153
13 COL5A1 0.2626 FNDC1 0.1963 SUGCT 0.1170 THBS2 0.2292 THBS2 0.2094
14 VCAN 0.2457 SFRP2 0.1955 ADAM12 0.1165 NTM 0.2257 CTHRC1 0.2026
15 MFAP5 0.2449 GJB2 0.1879 C1QTNF3 0.1165 COL1A2 0.2220 SULF1 0.2015
16 MMP11 0.2360 MATN3 0.1817 ITGBL1 0.1109 GREM1 0.2156 COMP 0.1926
17 COL8A1 0.2357 COL3A1 0.1740 GREM1 0.1018 FN1 0.2146 STMN2 0.1926
18 COL6A3 0.2339 INHBA 0.1696 P4HA3 0.1008 IGFL2 0.2141 WNT2 0.1925
19 POSTN 0.2316 DCN 0.1692 INHBA 0.1002 CXCL14 0.2112 MMP11 0.1919
20 MFAP2 0.2275 CTGF 0.1691 COL5A1 0.0983 ITGBL1 0.2048 SPOCK1 0.1878

Since then, many research results were published connecting one of the genes COL11A1, INHBA, THBS2 with poor prognosis, invasiveness, metastasis, or resistance to therapy, in various cancer types [615].

Furthermore, several designated tumor subtypes were identified in individual cancer types as a result of the presence of those pan-cancer CAFs. For example, the top 15 genes distinguishing the ovarian "mesenchymal subtype" according to [16] are POSTN, COL11A1, THBS2, COL5A2, ASPN, FAP, MMP13, VCAN, LUM, COL10A1, CTSK, COMP, CXCL14, FABP4, INHBA. Similarly, the 24 characterizing genes of the "activated stroma subtype" of pancreatic cancer in Fig 2 of [17] are SPARC, COL1A2, COL3A1, POSTN, COL5A2, COL1A1, THBS2, FN1, COL10A1, COL5A1, SFRP2, CDH11, CTHRC1, PNDC1, SULF1, FAP, LUM, COL11A1, ITGA11, MMP11, INHBA, VCAN, GREM1, COMP. In both of these examples, these gene lists are clearly due to the presence of the COL11A1/INHBA/THBS2-expressing CAFs and therefore these are not cancer-type specific subtype signatures.

To computationally investigate the origin of those CAFs, we reasoned that analysis of rich datasets from single-cell RNA sequencing (scRNA-seq) provides unique opportunities for tracking the trajectories of cell differentiation lineages. There are several single-cell trajectory inference methods [18] performing “trajectory inference analysis,” ordering cells along a trajectory based on similarities in expression patterns.

In particular, we identified one exceptionally rich dataset [19] from pancreatic ductal adenocarcinoma, containing gene expression profiles from 24 tumor samples and 11 normal control samples. We found that several among the 24 tumor samples contained populations of cells strongly co-expressing COL11A1, THBS2 and INHBA, while none of the normal samples contained such cells. We also observed that the prominence of this co-expression signature varied significantly among the tumor samples, having only hints of their presence in some of them, suggesting that the corresponding patients were at various stages of the generation of COL11A1-expressing CAFs. This provides an opportunity to perform additional complementary computational analysis by comparing the prevalent fibroblastic cell populations across the tumor samples, and comparing them with those in the normal samples.

Therefore, in this paper we also used attractor analysis (Materials and Methods) in a novel manner for the analysis of rich scRNA-seq data. The unsupervised attractor algorithm [20] iteratively finds co-expression signatures converging to “attractor metagenes” pointing to the core (“heart”) of co-expression. Each attractor metagene is defined by a ranked set of genes along with scores determining their corresponding strengths within the signature, so the top-ranked genes are the most representative of the signature. The attractor algorithm has previously been used successfully for identifying features useful for breast cancer prognosis [21,22].When applied on single cell data from a sample, it identifies the gene expression profiles of the dominant cell populations in the sample, and the algorithm is designed to ensure that all the top-ranked genes are co-expressed in the same cells. The purpose of the attractor algorithm is not to classify cells into mutually exclusive subsets. Instead, it identifies the genes at the core of co-expression signatures representing cellular populations from single-cell data, and it provides information that cannot be deduced with traditional clustering methods (see Discussion).

When we applied the attractor algorithm separately in each of the normal samples, we identified a set of nearly identical attractor signatures, corresponding to a type of adipose-derived stromal/stem cells (ASCs) naturally present in the stromal vascular fraction (SVF) of normal adipose tissue, expressing a unique characteristic signature containing fibroblastic markers such as LUM and DCN as well as adipose-related genes, such as APOD, CFD and MGP.

When we applied the algorithm in each of the tumor samples, we found a set of signatures that were changing in a remarkably continuous manner across the samples, some of them being very similar to those of the normal samples, while others are similar to the COL11A1-based signature. This suggests that the signatures undergo a gradual change as the transition proceeds, starting from the state of the normal ASCs and passing through a continuum of intermediate states. These results were consistent with those found by applying trajectory inference analysis, but they provided additional significant information based on their unique capabilities. Accordingly, this method demonstrated that there is a continuous “ASC to COL11A1-expressing CAF transition.”

This finding explains the stage association of the COL11A1-expressing signature as resulting from the interaction of tumor cells with the adipose microenvironment: Indeed, adipose tissue is encountered when ovarian cancer cells reach the omentum (stage III); after colon cancer has grown outside the colon (stage II); and in breast cancer from the beginning of the spread (stage I, but not in situ stage 0).

Finally, we validated our results in other cancer types (head and neck, ovarian, lung, breast), suggesting the pan-cancer nature of the ASC to COL11A1-expressing CAF transition.

Results

ASC to COL11A1-expressing CAF transition identified in pancreatic ductal adenocarcinoma (PDAC)

The PDAC dataset [19] consists of 57,530 scRNA-seq profiles from 24 PDAC tumor samples (T1-T24) and 11 normal samples (N1-N11). To find the expression profile of the dominant fibroblastic population in each sample, we applied the attractor algorithm on the set of identified mesenchymal cells (Materials and Methods). All samples (11 normal and 23 tumor samples, excluding sample T20 as it did not contain identified fibroblasts) yielded strong co-expression signatures involving many genes with big overlap among them. Genes LUM, DCN, FBLN1, MMP2, SFRP2 and COL1A2 appear in the top 100 genes in at least 33 out of the 34 samples (S1 Table), revealing a strong similarity shared by all those fibroblastic expression profiles. This strong overlap is consistent with the continuous transition process, as described below.

Dominant fibroblastic population in the normal pancreatic samples is adipose-derived

There is a striking similarity among the attractor profiles (Materials and Methods) of the eleven normal pancreatic samples, indicating that they represent a stable and normally occurring cell population. Specifically, there are 12 genes commonly shared among the top 30 genes in the attractors of at least ten of the eleven normal samples (Table 2), of which four genes are shared among all the samples (P = 3×10−113 by multi-set intersection test [5]). In addition to fibroblastic markers, there are several strongly expressed adipose-related or stemness-related genes in the list, such as APOD, CXCL12, and DPT, revealing that they are ASCs. Consistently, Gene Set Enrichment Analysis (GSEA) of these 12 commonly shared genes identified the most significant enrichment (FDR q value = 2.16 ×10−19) in the “BOQUEST_STEM_CELL_UP” dataset of genes upregulated in stromal stem cells from adipose tissue versus the non-stem counterparts [23].

Table 2.  Top 30 genes of the identified attractors for each pancreatic normal sample (N1-N11).

12 commonly shared genes in at least ten of the eleven normal samples are highlighted.

Rank N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11
1 DCN LUM LUM C7 APOD LUM PTGDS C7 DCN MMP2 LUM
2 LUM DCN FBLN1 FBLN5 DPT DCN APOD LUM LUM APOD DCN
3 C7 C7 C7 LUM FBLN5 FBLN1 LUM DCN C7 LUM FBLN1
4 FBLN1 FBLN1 PTGDS DCN PDGFRA ADH1B FBLN1 APOD FBLN1 EFEMP1 SFRP2
5 MGP APOD C1S APOD CXCL12 DPT C7 FBLN1 APOD CTSK CFD
6 C1S MGP DPT PTGDS LUM ABCA8 ADH1B SFRP2 SFRP2 SFRP2 APOD
7 CCDC80 C1S PDGFRA FBLN1 COL6A3 C3 DPT PTGDS SERPINF1 PLTP MGP
8 PTGDS DPT APOD C1R PTGDS APOD COL6A3 CCDC80 PTGDS MGST1 SERPINF1
9 DPT CCDC80 SFRP2 DPT C7 MMP2 EFEMP1 FBLN5 GSN LSP1 CCDC80
10 C1R PTGDS DCN SRPX CCDC80 C1S PDGFRA C1S C1S FBLN1 C3
11 APOD FBLN5 CXCL12 FMO2 CFD C7 CXCL12 CXCL12 SEPP1 SPON2 ADH1B
12 SEPP1 SEPP1 C1R SEPP1 MRC2 PTGDS SCN7A C3 CCDC80 PTGDS PTGDS
13 FBLN5 COL1A2 COL6A3 CXCL12 FGF7 SFRP2 MMP2 CFD DPT SVEP1 C7
14 CXCL12 SFRP2 ADH1B CYR61 SFRP2 FBLN5 MEG3 C1R OLFML3 CXCL12 C1S
15 EFEMP1 SRPX SPON2 SFRP2 MARCKS C1R C1S MGP FBLN5 SCN7A CST3
16 COL1A2 SERPINF1 CFD CLEC11A LRP1 CXCL12 OLFML3 CFH C1R COL6A3 C1R
17 SFRP2 OLFML3 LAMA2 PDGFRA FMO2 CST3 SVEP1 COL6A3 PTN CCDC80 CXCL14
18 ALDH1A1 CST3 C3 NR2F1 NR2F1 MGP DCN SRPX MGP COLEC11 MMP2
19 CFD MEG3 FBLN5 C1S TNXB CCDC80 SFRP2 EFEMP1 ALDH1A1 PDGFRA GPNMB
20 COL6A3 C1R ABCA8 ABCA8 DCN MRC2 MRC2 SEPP1 PDGFRA HBP1 S100A4
21 EMP1 MFAP4 LRP1 CCDC80 LOX COL1A2 FBLN5 PDGFRA COL6A2 CYGB DPT
22 PCOLCE RARRES2 SLIT2 PTN C1R CFD C3 DPT CST3 ARSK MFAP4
23 C3 PCOLCE CFH SERPINF1 IGFBP3 SPRY1 COL1A2 CXCL14 COL6A3 SH3GL1 COL6A2
24 SRPX CFH SRPX SVEP1 HEG1 SMOC2 ABCA8 ADH1B CXCL14 OAF FBLN5
25 SERPINF1 CXCL12 COL1A2 CFD RP11-572C15.6 GSN SRPX NEGR1 C3 BMP1 SMOC2
26 ANXA1 FGF7 BOC LAMB1 F3 COL6A2 ACVRL1 COL6A2 CXCL12 LAMA2 ABCA8
27 CYR61 PDGFRA FSTL1 FTL ADAMTSL3 CFH TIMP2 BOC MMP2 GPC3 FMO2
28 CST3 COL6A3 SVEP1 ANTXR2 STK17B OLFML3 LAMA2 OLFML3 PCOLCE TMEM67 RP11-572C15.6
29 RARRES2 ALDH1A1 ABCA9 COL6A3 EMP1 PDGFRA DAB2 EMP1 IGF1 C1R PCOLCE
30 PDGFRA SPRY1 CYR61 MGP MPZL1 PCOLCE NR2F1 LAMA2 ABCA8 PLXDC1 SEPP1

To investigate the nature of this ASC population, we referred to recent results from single-cell analysis of general human adipose tissue [24]. We applied the attractor algorithm on the dataset with the single-cell expression profiles of all 26,350 cells taken from the SVF of normal adipose tissue from 25 samples, and compared the identified attractor with the “consensus attractor” (Materials and Methods) of the 11 normal pancreatic samples, which represented the main state of the normal fibroblastic population (Table 3). There are 14 overlapping genes between the top 30 gene lists (P = 10−33 by hypergeometric test), and most of the non-highlighted genes in each column are still ranked highly in the other column. This extreme similarity of the two gene expression profiles indicates that they correspond to the same naturally occurring cell population. Furthermore, excluding the general fibroblastic markers LUM and DCN, we found that gene APOD (Apolipoprotein D) has the highest average ranking in Table 3, and is top-ranked in the independently found SVF fibroblastic population of cluster VP4 (supplementary file 20) of [24]. Therefore, we selected APOD as the representative marker for the ASC population.

Table 3. Comparison of the attractors (top 30 genes) identified in the SVF of normal adipose tissue (Dataset 1) and in the normal pancreatic samples (Dataset 2).

Common genes are highlighted in yellow.

Rank Dataset 1 Dataset 2 Rank (cont’d) Dataset 1 Dataset 2
1 DCN LUM 16 FOS PDGFRA
2 LUM DCN 17 MGST1 SRPX
3 APOD FBLN1 18 COL1A2 COL6A3
4 CFD C7 19 COL6A3 ADH1B
5 CXCL14 APOD 20 LAPTM4A CFD
6 MGP PTGDS 21 CXCL12 OLFLM3
7 SERPINF1 SFRP2 22 WISP2 SERPINF1
8 GSN C1S 23 SRPX MMP2
9 GPX3 CCDC80 24 JUN CST3
10 MFAP4 MGP 25 MMP2 SEPP1
11 PLAC9 DPT 26 COL6A2 ABCA8
12 S100A13 CXCL12 27 C1S COL1A2
13 IGFBP6 C1R 28 CCDC80 LAMB1
14 DPT FBLN5 29 EGR1 SVEP1
15 MFAP5 C3 30 PCOLCE MEG3

Establishing the presence of COL11A1-expressing CAFs in PDAC tumor samples

Because COL11A1 serves as proxy of the full signature [1], a reliable test for determining if a sample contains the COL11A1-expressing CAFs is to rank all genes in terms of their association, measured by mutual information (Materials and Methods), with COL11A1 and see if INHBA and THBS2 are top ranked. Indeed, this happens in several tumor samples, as shown in Table 4 for some of them (T23, T11, T6, T15, T18). For each sample, the shown genes are co-expressed in the same cells, because of the high correlations in a single-cell dataset.

Dominant fibroblastic populations in the tumor PDAC samples exhibits a continuous transition from ASCs to COL11A1-expressing CAFs

Based on the selection of APOD as a representative marker for the ASC population as described previously, we rearranged the attractors of the PDAC tumor samples in terms of descending order of the rank of APOD (Table 5) from left to right. There is a remarkable continuity in the shown expression profiles. The samples at the right side of the table include COL11A1 at increasingly high ranks. The intermediate tumor samples shown in the middle have cells expressing genes that are top-ranked in both the lists on the left as well as on the right. In other words, these cells are in a genuine intermediate state, rather than being a mixture of distinct subtypes (see detailed discussion in Materials and Methods).

Table 5. Rearranged PDAC tumor samples showing the continuously changing pattern of the signature profile.

Columns are sorted based on APOD rankings. Genes APOD and COL11A1 are highlighted in green and red, respectively.

Rank T2 T13 T14 T19 T3 T10 T15 T18 T7 T6 T12 T4 T24 T1 T5 T22 T11 T21 T23 T9 T16 T17 T8
1 LUM LUM DCN SFRP2 MMP2 PDGFRA SFRP2 DCN CYP1B1 COL10A1 MMP2 COL10A1 DCN SFRP2 PDGFRA COL1A2 LUM COL10A1 COL1A1 LUM LUM COL10A1 COL11A1
2 APOD APOD APOD APOD LUM HTRA3 LUM SFRP2 SFRP2 PDGFRA LUM SFRP2 LUM VCAN CYP1B1 PDGFRA DCN CTHRC1 COL1A2 DCN DCN CTHRC1 COL10A1
3 VCAN DCN LUM LUM APOD DPT DCN LUM COL8A1 SFRP2 PDGFRA COL1A1 FBLN1 LUM SFRP2 THBS2 CTHRC1 THBS2 COL3A1 RARRES2 COL1A1 COL11A1 CREB3L1
4 SFRP4 FBLN1 SFRP4 IGF1 DCN APOD VCAN C3 PDGFRA CYP1B1 CTHRC1 MMP2 VCAN PDGFRA SFRP4 MMP2 SFRP2 GJB2 COL6A3 CTHRC1 COL1A2 ISLR RP11-400N13.3
5 SFRP2 MMP2 TSHZ2 EFEMP1 FBLN1 MEG3 APOD MMP2 COL10A1 MMP2 ITGBL1 LUM SFRP4 COL1A2 DPT COL1A1 COL10A1 SFRP2 LUM SFRP2 COL6A3 MMP2 SFRP2
6 MMP2 SFRP4 HTRA3 PDGFRA VCAN OMD FBLN1 APOD SFRP4 VCAN EFEMP1 COL1A2 COL1A2 EFEMP1 LUM COL3A1 RARRES2 COL11A1 FN1 AEBP1 COL3A1 COL1A1 BASP1
7 RARRES1 SFRP2 FBLN1 OGN FBLN5 ITGBL1 MMP2 EFEMP1 CTHRC1 CTHRC1 SFRP2 CTHRC1 MMP2 DCN MEG3 ITGBL1 AEBP1 CCDC80 COL5A2 COL10A1 VCAN COL1A2 PDPN
8 C3 RARRES1 MMP2 SFRP4 PDGFRA PAPPA COL6A3 MFAP4 APOD LUM FBLN5 DCN SFRP2 CCDC80 EFEMP1 COL10A1 NBL1 NBL1 VCAN NBL1 SFRP2 COL3A1 BNC2
9 MEG3 VCAN COL6A3 VCAN SFRP2 MRC2 COL1A1 FBLN1 MMP2 SFRP4 VCAN CTSK C1R ISLR VCAN CTHRC1 CTSK DCN COL5A1 MMP2 MEG3 AEBP1 C5orf46
10 HTRA3 HTRA3 VCAN CTSK C3 LSAMP COL1A2 SFRP4 PLXDC2 APOD APOD MFAP2 C1S SFRP4 IGF1 LUM VCAN AEBP1 THBS2 CTSK CTHRC1 MMP11 PLXDC2
11 FBLN1 ISLR GPC3 COL1A2 MGP CYP1B1 ISLR CCDC80 VCAN PLXDC2 COL8A1 APOD APOD COL6A3 FBLN5 SFRP2 CTGF INHBA SFRP2 THBS2 EFEMP1 THBS2 SPOCK1
12 MGP COL6A3 CTGF STEAP1 EFEMP1 COL10A1 COL10A1 RARRES1 BNC2 COL8A1 STEAP1 MATN3 CTSK CYP1B1 SERPINE2 EFEMP1 COL8A1 LUM CTHRC1 FBLN1 PDGFRA COL12A1 ADM
13 DCN SPON2 SFRP2 MMP2 OMD COL8A1 CTHRC1 C1S MRC2 FBLN1 COL1A2 MEG3 CCDC80 COL1A1 FBLN1 MRC2 THBS2 FBLN1 MMP2 VCAN FBLN2 HTRA1 MMP2
14 CYP1B1 CYP1B1 C1S CYP1B1 RP11-572C15.6 PDPN CCDC80 VCAN DPYSL3 OMD PTGDS ISLR ISLR APOD SCN7A COL6A3 MMP2 MMP2 COL10A1 CCDC80 LOX MMP14 ARL4C
15 COL1A2 LXN OMD PTGDS CCDC80 CXCL14 SFRP4 PTGDS COL1A2 THBS2 ISLR FBLN1 C3 CLDN11 LTBP2 PDPN COL11A1 COL6A3 COL11A1 COL1A1 COL5A1 SFRP2 MEG3
16 MOXD1 SERPINF1 SPON2 FBLN1 IGF1 MMP23B CTSK C1R FBLN5 FAP LXN COL11A1 EFEMP1 CTSK APOD VCAN INHBA OMD AEBP1 S100A6 COL8A1 SULF1 GJA1
17 PTGDS CTHRC1 C3 RARRES1 TSHZ2 ABCA9 COL3A1 CTSK CREB3L1 MFAP2 OLFML3 COL3A1 MGP FBLN1 PTGDS DPYSL3 C1QTNF3 MEG3 COL12A1 COL8A1 LXN LUM VCAN
18 FBLN5 CTSK F2R DCN ITM2A LUM S100A10 PDGFRA COL3A1 COL6A3 THBS2 SFRP4 TSHZ2 MMP2 ISLR LOX SFRP4 COL8A1 DCN TMSB10 MMP2 SDC1 FIBIN
19 PDGFRA F2R ANKH COL3A1 SFRP4 PDGFRL THBS2 SERPINF1 OMD RARRES1 FBLN1 CXCL14 PDGFRA COL3A1 PODN GJA1 MATN3 ISLR SPARC C1S S100A10 DCN COL1A2
20 COL6A3 FBLN5 CTSK MFAP5 RARRES1 STXBP6 HTRA1 MOXD1 RARRES1 EFEMP1 MGST1 ITGBL1 OMD PTGDS DCN APOD HTRA1 MMP11 TMSB10 COL1A2 FBLN1 MFAP5 ZFHX4
21 CTHRC1 COL1A2 C1R FBLN5 COL8A1 SVEP1 SEMA3C GPNMB PODN COL1A2 MEG3 COL6A3 FBLN5 FBLN2 MMP2 FAP ITGBL1 MFAP5 MMP14 COL3A1 ISLR VCAN MFAP2
22 FAP C7 IGFBP3 COL1A1 OGN BICC1 FBLN2 RARRES2 LSAMP ANKH PDPN VCAN COL1A1 CTHRC1 MGP MXRA5 MFAP5 PPAPDC1A SDC1 HTRA1 FAP COL6A3 MME
23 F2R TMEM119 MOXD1 ISLR C7 ABCA6 LRP1 FBLN5 THBS2 FNDC1 MFAP2 IGFL2 OGN C3 C7 PODN CXCL14 CTSK POSTN CD99 PPIC GJB2 MFAP5
24 ISLR EFEMP1 CTHRC1 C3 DPT MFAP2 MRC2 ISLR SVEP1 SPON2 DPT RARRES2 SERPINF1 PLXDC2 MGST1 CXCL14 GJB2 MXRA5 FBLN1 ISLR CYP1B1 GREM1 RAB3B
25 TIMP1 MOXD1 MEG3 MEG3 BICC1 BNC2 PDPN ITGBL1 ITGBL1 PDPN C3 FNDC1 CTGF RARRES1 SPOCK1 COL8A1 IGFBP3 FNDC1 INHBA SERPINF1 CREB3L1 TIMP2 ITGBL1
26 C7 CCDC80 PDGFRA CTHRC1 CTHRC1 WNT5A MXRA5 COL10A1 PTGDS CTSK MRC2 COL5A1 FBLN2 RP11-572C15.6 CCDC80 SFRP4 CCDC80 COL1A1 SERPINH1 TSC22D3 CCDC80 COL5A2 GJB2
27 PHLDA3 COL1A1 FBLN5 MOXD1 PODN CST3 OMD CYP1B1 FAP MFAP5 PTGIS OMD COL6A3 THBS2 SLC19A2 SEMA3C CD99 SDC1 MXRA5 FTL MRC2 FAP COL3A1
28 OMD PLXDC2 ITM2A COL6A3 COL6A3 SFRP2 ITGBL1 C7 LUM HTRA3 MOXD1 CST4 PODN C7 HTRA3 LRP1 C1S VCAN HTRA1 MFAP2 MFAP5 MFAP2 NTM
29 FBLN2 PDGFRA COL1A2 MGP CXCL14 MOXD1 RARRES2 PLXDC2 FBLN1 LRP1 HSD11B1 INHBA LTBP2 LAMA2 MOXD1 NTM LOXL1 GREM1 MMP11 ANXA2 THBS2 CTSK PDLIM4
30 SCN7A C3 PTGDS C7 COL1A2 ZFHX4 FBN1 CTHRC1 SULF1 COL1A1 SFRP4 GJB2 ITGBL1 OLFML3 ITGBL1 C3 FIBIN PDPN ISLR LAPTM4A CTSK COL5A1 CMTM8
31 EFEMP1 COL10A1 RARRES1 CILP ISLR RARRES1 PLXDC2 DPT INHBA DIO2 LSAMP IGFBP3 C7 MFAP4 CTSK COL11A1 FBLN1 FBLN2 MEG3 NNMT MXRA5 PPAPDC1A TANC2
32 COL10A1 C1S OLFML3 COL8A1 MEG3 BOC CXCL14 CLU SEMA3C DCN PLXDC2 HTRA3 PLXDC2 SLIT2 FBLN2 UNC5B PALLD MFAP2 TIMP2 C1R OGN FN1 NT5E
33 SERPINF1 PODN OGN THBS2 CTSK PODN PDGFRA NPC2 LOX ALDH1A3 MFAP4 CST1 MOXD1 IGF1 CTHRC1 LOXL1 SDC1 C1QTNF3 FSTL1 RPL27A RARRES2 TMEM158 TENM3
34 BNC2 LTBP2 ITGBL1 MRC2 HSD11B1 TMEM119 MATN3 MEG3 PDPN LOX OGN FBLN2 TMEM119 LRP1 SVEP1 COL5A2 MFAP2 COL5A2 COL6A2 INHBA FSTL1 POSTN EPDR1
35 CTSK NPC2 PTCH1 NR2F1 CTGF PTGIS COL8A1 RP11-572C15.6 IGF1 FBLN2 COL6A3 MFAP5 COL8A1 PDLIM3 MXRA5 SCARA3 ANXA2 CDH11 CTSK NUPR1 COL5A2 ANTXR1 MYH10
36 MRC2 HSD11B1 COL8A1 ITGBL1 C1S OGN NBL1 MGP LAMP5 OLFML3 SPON2 MXRA5 LRP1 LTBP2 STEAP1 COL8A2 APOD LOX MFAP2 LGALS1 LRP1 PLAU LOX
37 TSHZ2 MGP BOC TSHZ2 COL10A1 FAP TMEM119 COL6A3 FBLN2 TMEM119 COL1A1 THBS2 RARRES1 FBLN5 NEGR1 PTGDS OMD COL3A1 MFAP5 COL6A3 RARRES1 INHBA COL8A1
38 LRP1 DPT SERPINF1 STEAP2 SERPINE2 EFEMP1 HTRA3 LTBP2 OGN MEG3 LTBP2 CTGF BOC MGP C3 CCDC80 ISLR PDGFRA GAS1 COL11A1 FBLN5 GJA1 EVA1A
39 SLIT2 COL3A1 MGP MFAP2 SERPINF1 LAMA2 FAP AEBP1 PTGFRN FGF7 PODN MRC2 CYBRD1 MEG3 RARRES1 ALDH1A3 SLC6A6 F13A1 LRRC15 OMD FBN1 LGALS1 MXRA5
40 COL1A1 OMD IGFBP6 PDGFRL LTBP2 GSTM5 MEG3 PDPN GAS7 TMSB10 DCN GJA1 LXN BICC1 RP11-572C15.6 FGFR1 CYR61 FRMD6 COL8A2 CD55 GAS1 PTK7 BICC1
41 SVEP1 MFAP4 TIMP1 PDPN NEGR1 IGF1 FNDC1 COL1A1 CXCL14 CXCL14 CTSK FAP LOX MRC2 PLXDC2 BOC COL1A2 APOD FBLN2 PDPN BNC2 CD99 C1orf198
42 THBS2 TSHZ2 MFAP4 PLXDC2 MOXD1 F2R NTM COL8A1 COL11A1 ITGBL1 SPOCK1 SPON2 HTRA3 FGFR1 LRP1 PLXDC2 MMP11 COL1A2 GREM1 LOXL1 PLOD2 NBL1 INHBA
43 TMEM119 COL8A1 CLEC11A OMD C1R F3 DPYSL3 S100A13 FNDC1 IGFBP3 COL3A1 BICC1 RP11-572C15.6 ABL2 ABI3BP MFAP2 MEG3 DIO2 APOD FIBIN PDPN NTM PDGFC
44 IGFBP3 STEAP1 INHBA MMP23B FGF7 MMP2 SLC6A6 OGN HTRA3 CDH11 RARRES1 EMP1 CYP1B1 MXRA5 FAP MFAP5 PLXDC2 CD55 COL8A1 NTM MMP23B RARRES2 FBLN2
45 C1S PTGDS MXRA8 LXN COL1A1 SFRP4 CYP1B1 MMP23B MOXD1 IGF1 SVEP1 FGF7 CTHRC1 RGS2 COL6A3 TMSB10 FBLN2 HTRA1 CD99 S100A10 LTBP2 FBLN1 B4GALT1

Further demonstration of the continuity of the transition

As an additional confirmation of the continuity of the transition (as opposed to the presence of a mixture of distinct fibroblastic subtypes), Fig 1 shows scatter plots for genes APOD and COL11A1, color-coded for the expression of fibroblastic marker LUM, of the mesenchymal cells in two fibroblast-rich samples T11 and T23. The presence of cells covering the full range from the upper-left to the bottom-right sides of the plots, including the intermediate stages in which cells co-express both markers, demonstrates the presence in each sample of cells representing the continuously varying transition from ASCs to COL11A1-expressing CAFs.

Fig 1.

Fig 1

Scatter plots for fibroblast-rich samples for patients (A) T11 and (B) T23. Each dot represents a mesenchymal cell identified in the sample. The x- and y-axis denote the expression levels of COL11A1 and APOD, respectively. Dots are colored for the expression of fibroblastic marker LUM. The expression unit is the normalized log-transformed value from the count matrix (Materials and methods).

To further investigate the continuous transition, we partitioned the 34 pancreatic samples into three groups. Group 1 includes the eleven normal samples (N1 to N11). For tumor samples, we divided the rearranged samples in Table 5 into two groups (Group 2 and Group 3). Group 2 contains all samples to the left of and including T22, so that APOD is ranked before COL11A1 in the attractors of that Group, representing a relatively earlier stage of this transition. We then applied the consensus version of the attractor finding algorithm (Materials and Methods) and identified the signatures representing the main state of the fibroblasts for each of the above three sample groups (Table 6). Although there are many shared genes, the groups have distinct gene rankings. Group 1 (normal samples) contains many adipose-related genes, consistent with Table 2. Group 3 contains, in addition to COL11A1, many among the other CAF genes, such as THBS2, INHBA, AEBP1, MFAP5 and COL10A1. Group 2 displays an intermediate state, including markers of both ASCs as well as CAFs.

Table 6. Top 30 genes of the consensus attractors for three different PDAC sample groups.

Group1: normal samples; Group 3: T11, T21, T23, T9, T16, T17, T8; Group 2: other tumor samples.

Rank Group1 Group2 Group3 Rank (cont’d) Group1 Group2 Group3
1 LUM LUM COL1A1 16 PDGFRA CYP1B1 MMP2
2 DCN SFRP2 COL1A2 17 SRPX FBLN5 DCN
3 FBLN1 APOD COL3A1 18 COL6A3 MEG3 SFRP2
4 C7 SFRP4 FN1 19 ADH1B COL1A1 TMSB10
5 APOD MMP2 COL5A2 20 CFD C3 POSTN
6 PTGDS VCAN COL5A1 21 OLFML3 RARRES1 MXRA5
7 SFRP2 PDGFRA COL6A3 22 SERPINF1 CCDC80 COL6A2
8 C1S FBLN1 COL11A1 23 MMP2 MOXD1 ISLR
9 CCDC80 DCN CTHRC1 24 CST3 PLXDC2 AEBP1
10 MGP EFEMP1 THBS2 25 SEPP1 HTRA3 MEG3
11 DPT CTHRC1 VCAN 26 ABCA8 COL10A1 MFAP5
12 CXCL12 ISLR COL10A1 27 COL1A2 COL8A1 SERPINH1
13 C1R COL6A3 LUM 28 LAMB1 ITGBL1 MMP14
14 FBLN5 COL1A2 SPARC 29 SVEP1 OMD MFAP2
15 C3 CTSK COL12A1 30 MEG3 PTGDS INHBA

To find potential critical genes at the initiation phase of the cellular transition, we focused on the first tumor samples (with highest APOD ranking) in Table 5, so we can compare them with those of the normal ASCs.

We observe that gene SFRP4 stands out, as it appears for the first time remarkably among the top genes in all the first samples T2, T13, T14, T19, ranked 4th, 6th, 4th 8th, respectively. This suggests that the Wnt pathway is involved in the initiation of the cellular transition, because SFRP4 is a Wnt pathway regulator whose expression has been found associated with various cancer types [25,26]. Interestingly, SFRP4 disappears from the list of the attractors, indicating that it is downregulated in the final stage of the transition.

It is also known that gene RARRES1 (aka TIG1) plays an important role in regulating the proliferation and differentiation of ASCs [27]. Consistently, Table 6 reveals that RARRES1 appears for the first time in the attractors of the initial tumor samples. Just like SFRP4, RARRES1 is downregulated in the final stage, related to the fact that it has been suggested as a tumor suppressor [28,29].

We also performed differential expression (DE) analysis comparing the normal samples with the first samples (T2, T13, T14, T19) of Table 6 (Materials and Methods; S2 Table). The results of such DE analysis represent the full population of fibroblasts and not necessarily reflect the expression changes in the particular cells undergoing the ASC to COL11A1 expressing CAF transition. Gene CFD was found to be most downregulated, consistent with the expected downregulation of adipose-related genes as they differentiate into fibroblasts. Genes SFRP4 and RARRES1 are upregulated consistent with their appearance in the attractors.

On the other hand, the top upregulated gene is phospholipase A2 group IIA (PLA2G2A), which is not among the top genes of any attractors we identified, indicating that it is not expressed by cells undergoing the ASC to COL11A1-expressing CAF transition. It probably still plays, however, an important related parallel role and many previous studies referred to its effects on prognosis of multiple cancer types [3032]. The PLA2G2A protein is a member of a family of enzymes catalyzing the hydrolysis of phospholipids into free fatty acid. We hypothesize that this process leads to fatty acid oxidation, which may facilitate metastatic progression. Indeed, it has been recognized that fatty acid oxidation is associated with the final COL11A1-expressing stage of the transition [33]. These results suggest that lipid metabolic reprogramming plays an important role in the metastasis-associated biological mechanism [34], by potentially providing energy for the metastasizing tumor cells.

Validation with trajectory inference

We independently applied trajectory inference (TI) analysis on the PDAC fibroblasts by using the Slingshot [35] method in an unsupervised manner. We first performed unsupervised clustering on the identified fibroblasts (Materials and Methods), resulting in four subgroups X1, X2, X3, X4 (S1A Fig) with the top differentially expressed genes shown in S1B Fig. One of these clusters (X4) was discarded from further TI analysis, because it mainly expressed the IL1 CAF marker HAS1 (Hyaluronan Synthase 1), which is not expressed by either ASCs or COL11A1-expressing CAFs (and does not appear at all in S1 Table), and contained only 3% of fibroblasts resulting almost exclusively from patient T11 (S1C Fig).

As seen from the list of top differentially expressed genes of each cluster, X1 contains CAF genes top ranked (including MMP11, COL11A1, THBS2, INHBA), X2 has RARRES1 at the top, and X3 has ASC genes top ranked, including DPT, C7, CXCL12 and CFD. Consistently, S2A and S2B Fig show the single trajectory path resulting from TI analysis, where X3 is the starting point and X1 is the end point of the trajectory, while X2 (highly expressing RARRES1), is an intermediate point, thus validating the continuous ASC to COL11A1-expressing CAF transition. The orderings of patient groups and sample identity (S2C and S2D Fig) are also consistent with our previous findings based on attractor analysis. S3 Table shows the top 100 genes with zero P value, ranked by their variances, resulting from pseudotime-based differential gene expression analysis (Materials and Methods). We can clearly identify as top-ranked several ASC genes, as well as CAF genes, while some general fibroblastic markers, such as DCN, are missing, consistent with the continuity of the ASC to COL11A1-expressing CAF transition. We then used a generalized additive model (GAM) fit to pseudotime-ordered expression data to visualize the trend of gene expressions (Fig 2A).

Fig 2. Trajectory analysis of PDAC.

Fig 2

A. GAM fit to pseudotime ordered expression data to visualize the trend of gene expressions. B. Expression of adipose-related genes along the transition lineage. The x axis shows the cell orders and the y axis shows the normalized read count. C. Expression of COL11A1-associated genes along the transition lineage. D. Expression of RARRES1 and SFRP4 genes along the transition lineage.

There was a prominent difference between adipose-related genes and COL11A1-associated genes. The expression of the adipose-related genes steadily fell across the process (Fig 2B), while the expression of COL11A1-associated genes gradually increased (Fig 2C). There is a significant negative correlation between these two groups of genes, e.g., COL11A1 (the last among those genes to increase its expression) was exclusively overexpressed in the mature CAFs, which did not express C7. Of particular interest, genes SFRP4 and RARRES1 (Fig 2D) increased consistently at the beginning and then decreased after reaching a peak, suggesting that they may play important roles in the differentiation path.

Validation in other cancer types

Next, we validated the ASC to COL11A1-expressing CAF transition in other solid cancer types. Although we could not find currently available datasets as rich as the PDAC dataset, we selected those containing a large (at least 100) number of fibroblasts and separately analyzed each of them, obtaining consistent results. Specifically, we used four scRNA-seq datasets from head and neck cancer (HNSCC) [36], ovarian cancer[37], lung cancer [38] and breast cancer [39].

The COL11A1-expressing CAF signature has been confirmed to be a pan-cancer signature [4042]. Therefore, the most important validation task would be to confirm the existence of the APOD/CFD/CXCL12/MGP/PTGDS-expressing ASCs as the starting point of the transition, and to also confirm that some samples are at an intermediate stage, expressing genes such as SFRP4, RARRES1 and THBS2, in addition to the core ASC genes, demonstrating that they are at an intermediate stage of the transition.

Head and neck squamous cell carcinoma

For the HNSCC dataset, the authors of the paper presenting the data [36] reported that the cancer-associated fibroblasts in the dataset can be partitioned into two subsets, which they name CAF1 and CAF2. In S5 Table of that paper, the top three differentially expressed genes of the CAF2 group are CFD, APOD and CXCL12, while the full gene list for CAF2 presented in the same S5 Table also includes genes MGP, C3, C7, DPT, PTGDS. This strongly suggests that the partitioning used in the paper was influenced by the presence of an ASC cell subpopulation, identical, or at least very similar to, those discovered in the PDAC. Similarly, the list of differentially expressed genes for CAF1 in S5 Table includes genes INHBA, THBS2, CTHRC1, POSTN, MMP11, COL5A2, COL12A1, suggesting that the identified CAF1 subpopulation was influenced by the presence of differentiated CAFs, which would eventually express COL11A1. Finally, gene RARRES1 also appears among the list of CAF2 genes, suggesting that it was captured among cells which had started the process of ASC to COL11A1-expressing CAF transition.

In our independent analysis, we performed clustering identifying 1,026 fibroblasts from all available cells (S3A Fig; Materials and Methods). There were two fibroblastic clusters (X7 and X9) expressing CAF associated genes (COL11A1, COL12A1, MMP11, INHBA, THBS2, COL10A1, COL8A1, FN1) and ASC associated genes (APOD, C7, PTGDS), respectively (S4 Table), which confirmed the presence of these two populations in HNSCC.

Among the individual patients, we found that the most prominent case is sample HNSCC28, which contains a rich set of cells undergoing differentiation. Applying the attractor finding algorithm on the fibroblasts of that sample (S5 Table) resulted in genes LUM, APOD, COL6A3, PDGFRA, DCN, and CFD being among the top-ranked, revealing that it represents an ASC population. Furthermore, the presence of genes THBS2, MFAP5 and VCAN in the same attractor reveals that these cells have already started undergoing the transition.

Ovarian cancer

For the ovarian dataset, the clustering results showed two clusters (X6 and X9) expressing COL11A1-associated genes and ASC-associated genes, respectively (S3B Fig, S6 Table; Materials and Methods). Among the individual patients, we found that the ones validating our hypotheses most are HG2F and LG2, both of whose datasets, consistently, contain cells from the fatty omental tissue. S5 Table includes the corresponding two attractors identified in the cells of each patient. Among the top ranked genes for HG2F are DCN, LUM, C1S, C7, and C3, but also RARRES1, suggesting that they represent fibroblasts undergoing the transition, while the LG2-based attractor contains highly ranked all three genes COL11A1, INHBA, THBS2.

Lung cancer

The dataset contains a large number (> 50,000) of cells, but we only classified ~2% (= 1,346) among them as mesenchymal cells, including fibroblasts and pericytes (Materials and Methods). Among those cells, there were two fibroblastic clusters (X1 and X2) expressing related genes (COL11A1, COL12A1, MMP11, INHBA, THBS2, COL10A1, COL8A1, FN1) and ASC related genes (APOD, C7, PTGDS), respectively (S3C Fig, S7 Table). The presence of the transition is evident by the attractors identified in the mesenchymal cells for patients 4 and 3 (S5 Table). The former prominently contains genes CFD, PTGDS and C7, while the latter contains THBS2, COL10A1 and INHBA.

Breast cancer

The size of the breast cancer dataset is small (~1,500 cells in total), and 169 cells among them were classified as mesenchymal (Materials and Methods). By further clustering these cells, we identified ASCs (X1) and COL11A1-expressing CAFs (X3) (S3D Fig, S8 Table). ASC related genes (APOD, MFAP4, CFD) were identified in X1, while CAF-related genes (COL10A1, COL11A1, MMP11, INHBA, FN1, THBS2, AEBP1, COL12A1) are among the top 15 of X3. Patients PT089 and PT039 contain the highest proportions (>50%) of the ASC and COL11A1-expressing CAF subpopulations, respectively, and we found consistent results in their attractors (S5 Table), as the former contains C1S, C1R, CXCL12, PTGDS, C3, while the latter contains THBS2, COL11A1, COL10A1, at top-ranked positions.

Potential therapeutic targets inhibiting the invasiveness-associated transition

This work provides opportunities for identifying therapeutic targets inhibiting the cellular transition. For example, targeting of gene MFAP5 was recently found to enhance chemosensitivity in ovarian and pancreatic cancers [43]. Specifically, the author states that “MFAP5 blockade suppresses fibrosis through downregulating of fibrosis-related genes such as COL11A1.” Consistently, we found MFAP5 as one of the most highly associated genes with COL11A1 (Table 4).

As mentioned earlier, genes SFRP4 and RARRES1 are transiently expressed in Group 2 of Table 6, suggesting that they can be investigated for inhibiting the cellular transition.

Of particular interest as potential drivers are noncoding RNAs due to their typical regulatory role. Because the expression of these genes is not accurately captured by scRNA-seq technology, we did a thorough analysis of the full set of The Cancer Genome Atlas (TCGA) pan-cancer data. For the RNA sequencing and miRNA sequencing dataset of each cancer type, we removed the genes in which more than 50% of the samples have zero counts. Then we performed quantile normalization using the limma package [44] (v3.40.6) on log2 transformed counts. In each of the 33 cancer types, we ranked all protein-coding genes in terms of the association (using the metric of mutual information) of their expression with that of gene COL11A1. We excluded the 11 cancer types (LGG, SKCM, SARC, LAML, PCPG, GBM, TGCT, THYM, ACC, UVM, UCS) in which neither THBS2 nor INHBA was among the 50 top-ranked genes, because of the absence of significant amounts of COL11A1-expressing CAFs in those samples (1st sheet in S9 Table). In each of the remaining 22 cancer types, we then ranked all long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) in terms of their association with COL11A1 (2nd and 3rd sheets in S9 Table). Finally, we did pan-cancer sorting of all lncRNAs and miRNAs in terms of the median rank of all lncRNAs and miRNAs (4th sheet in S9 Table).

We found that LINC01614 represents a particularly promising therapeutic target. It had a perfect score of 1 in the pan-cancer sorting list, being strikingly the top-ranked gene in 14 (BRCA, UCEC, KIRC, HNSC, LUAD, LUAD, LUSC, OV, STAD, ESCA, PAAD, MESO, DLBC, CHOL) out of the 22 cancer types (2nd sheet in S9 Table). In fact, the association of LINC01614 was even higher than that of marker protein-coding gene INHBA. The pan-cancer consensus ranking of protein-coding genes in terms of LINC01614 (5th sheet in S9 Table) corresponds precisely to the COL11A1-expressing CAF signature. These rankings, in which marker genes unique to the original and intermediate stages are missing, indicate that LINC01614 is involved in the very final stage of the creation of the COL11A1-expressing CAFs. Therefore, we hypothesize that therapeutics targeting LINC01614 specifically in patients’ CAFs may inhibit the final metastasis-facilitating stage of the transition.

We also found that the three top-ranked miRNAs were miR-199a-1, miR-199b, miR-199a-2. The associated miR-214 is also very highly ranked (3rd sheet in S9 Table).

Discussion

Our results indicate that the cancer invasiveness-associated COL11A1-expressing CAFs are produced as a result of the interaction of tumor cells with the adipose microenvironment. Therefore, one contribution of our work is that it provides a potential explanation to the well-known fact that adipose tissue contributes to the development and progression of cancer [4547].

Another contribution is that it precisely identifies the ASC population, as evidenced by the consistent presence of its marker genes among the top-ranked attractor genes in each of the eleven columns of Table 2. The identification of those particular marker genes (APOD prominent among them) cannot be due to chance, because these were eleven totally independent unbiased experiments, and also because the attractor algorithm applied on the SVF of normal adipose tissue in another independent dataset identified precisely the same genes. This finding could not have been achieved with traditional methods.

There is consensus agreement that CAFs are a promising potential target for optimizing therapeutic strategies against cancer, but such developments are restricted by our current limitations in our understanding of the origin of CAFs and heterogeneity in CAF function [48]. Therefore, there is an urgent need to enhance our understanding of those matters. Our results provide clarity on one important particular component (out of several) of the heterogeneous fibroblast tumor microenvironment. To avoid potential erroneous conclusions after applying bioinformatics algorithms, single-cell data analysis provides an unprecedented capability to validate results, including those resulting from the attractor algorithm, by “seeing” individual cells in color-coded scatter plots, such as the one shown in Fig 1, observing and confirming the presence or absence of distinct populations characterized by the combined presence of particular marker genes.

In particular, there are several published papers relying on the application of clustering algorithms following dimensionality reduction on the particular datasets they use, and concluding that there exist a number of distinct and mutually exclusive CAF subpopulations. These reported fibroblastic subpopulations occasionally have gene expression profiles that are conflicting with each other in significant ways among these publications. Examples include the hC1 and hC0 clusters in [49], the C9 and C10 clusters in [42], the CAF2 and CAF1 clusters in [36], the iCAF and myCAF clusters in [50,51] and the iCAF an mCAF clusters in [52]. A review of such results in pancreatic cancer appears in [53].

As an example of conflicting results, the “iCAFs” identified in [52] have significant differences from those identified in other papers and are, in fact, identical to the normal ASCs (Fig 3B of [52]) identified in this paper, as evidenced by the list of its differentially expressed genes (PTGDS, LUM, CFD, FBLN1, APOD, DCN, CXCL14, SFRP2, MMP2, all of which appear in Table 3, further validating the ASC signature. Therefore, this identified cluster contains mainly normal cells at the origin of the transition, which should not even be called CAFs.

Similarly, a recent single-cell data analysis [54] identified two clusters “touching” each other in a UMAP plot (Fig 2A of [54]), C0 and C3, which are precisely the two endpoints of the ASC to COL11A1-expressing CAF transition. Indeed, as identified in Table S6-1 of [54], C0 cluster has the marker genes APOD, PTGDS, C7, C3, MGP… which the attractor algorithm had identified and validated in this paper. On the other hand, the marker genes of cluster C3 are precisely those of the COL11A1-expressing CAFs, in which all three genes COL11A1, INHBA and THBS2 are top-ranked (because the metastatic process was already underway). Importantly, as shown in Fig 2B of [54], the ASC marker genes APOD and PTGDS (top ranked in C0 and unrelated to CAFs) are significantly expressed even in the COL11A1-expressing cluster C3 of the paper, providing further evidence of the presence of intermediate states consistent with the transition–and the separating line between C0 and C3 in the diagram is not generated by any biologically reliable manner, consistent with the continuity.

On the other hand, our work is consistent with, and complementary to the results of [49] focusing on the immunotherapy response, in which the presence of the “TGF-beta CAFs” was inferred by an 11-gene signature consisting of MMP11, COL11A1, C1QTNF3, CTHRC1, COL12A1, COL10A1, COL5A2, THBS2, AEBP1, LRRC15, ITGA11. This population apparently represents the COL11A1-expressing CAF endpoint of the transition, and gene LRRC15 was selected as the representative gene based on the fact that it was found to be the most differentially expressed gene between CAFs and normal tissue fibroblasts in mouse models. Indeed, LRRC15 is a key member of the COL11A1-expressing CAF signature (Table 4 of [1]) and we also found that COL11A1 is the highest associated gene to LRRC15 in the Group 3 PDAC patients.

In our work we used a detailed gene association-based scrutiny of all our results, including numerous color-coded scatter plots, rather than blindly accepting clustering results. We believe that this nontraditional computational methodology, when used on rich single-cell data, represents a paradigm shift in which systems biology alone can be trusted, by itself, for producing reliable results. We hope that our results will give rise to testable hypotheses that could eventually lead to the development of pan-cancer therapeutics inhibiting the ASC to COL11A1-expressing CAF transition.

Materials and methods

Datasets availability

The pancreatic dataset [19] was downloaded from the Genome Sequence Archive with accession number CRA001160. The four validation datasets of other cancer types are also publicly available: HNSCC [36] (GSE103322), ovarian [37] (GSE118828), lung cancer [38] (E-MTAB-6149 and E-MTAB-6653), breast cancer [39] (GSE118389). We excluded samples from lymph nodes. The numbers of patients included in these datasets are 35 (PDAC), 18 (HNSCC), 9 (ovarian), 5 (lung), and 6 (breast).

Data processing and cell identification

We selected the Seurat R toolkit [55] for data processing and cell identification. Seurat implements the entire clustering workflow and has an advantage in speed and scalability to analyze large datasets [56]. We applied the Seurat (v3.1.4) to process the gene expression matrix and characterize the cell type identity for each scRNA-seq dataset. The count matrix was normalized and log transformed by using the NormalizeData function. We selected the 2,000 most variable features and then performed principal component analysis (PCA) followed by applying an unsupervised graph-based clustering approach. We used default parameter settings in all the above steps except that the resolution parameter in the FindCluster function is set to 1.0 to increase the granularity of downstream clustering. To identify differentially expressed genes for each cluster, we used the FindMarkers function. To characterize the identity of mesenchymal cells in each dataset, we made use of the expression of known markers: LUM, DCN, COL1A1 for fibroblasts, and RGS5, ACTA2, PDGFRB and ADIRF for pericytes.

For the smaller-size datasets (ovarian, breast), we performed clustering once on all cells for mesenchymal cell identification. For datasets of larger size (PDAC, HNSCC, lung), we applied ‘two-step clustering’ to ensure accuracy: The first step was initial clustering within individual samples. Then we excluded samples with very few (< 20) detected fibroblasts and pooled the mesenchymal cells of the remaining samples together for a second clustering, which resulted in the final set of mesenchymal cells for the dataset. For the PDAC dataset, we included an additional step to remove low-quality cells, by retaining cells for which at least one of the corresponding markers had expression levels ≥ 3.

Mutual information

Mutual information (MI) is a general measure of the association between two random variables [57]. We used a spline based estimator [58] to estimate MI values and normalized so the maximum possible value is 1. The MI value is clipped to zero if the Pearson correlation between the two variables is negative. The details of the estimation method are described in the paper introducing the attractor algorithm [20]. We used the getMI or getAllMIWz function implemented in the cafr R package with parameter negateMI = TRUE.

Attractor-based analysis

The attractor algorithm was first proposed for identifying co-expression signatures from bulk expression values in samples [20]. In this study, we use the attractor algorithm for the first time for the purpose of scrutinizing cell populations in single-cell data. Compared to conventional single-cell methods, the attractor algorithm features the unique capability of discovering precise profiles of cell populations, which other methods cannot achieve (see Discussion).

Briefly, the algorithm iteratively finds mutually associated genes from an expression matrix, converging to the core of the co-expression mechanism. The association measure used is the normalized mutual information (as described above), which captures the general relationships (including nonlinear effects) between variables. Using the expression vector corresponding to a seed gene as input, the algorithm converges to an “attractor” in the form of a list of ranked genes, together with scores (ranging from 0 to 1) for each of these genes measuring the strength of the membership of that gene in the signature. It has a characteristic property that using different “attractee” genes belonging to a co-expression signature as seeds leads to the identical attractor.

The attractor algorithm had previously been used to find co-expression signatures in bulk gene expression data, in which case a converged attractor could represent a mixture of contributions from distinct cell subpopulations. When using single-cell data, however, the characteristic genes of particular distinct subpopulations will have high expression values only in the cells from those subpopulations and low values in other cells. These genes will have pairwise positive and large correlations, and therefore they will be highly ranked in attractor signatures representing such individual subpopulations. On the other hand, two characteristic marker genes belonging to two different distinct subpopulations will have reverse-associated expression values across those cells, which will contribute negatively to the overall correlation between these two genes. Only if two genes are co-expressed across individual cells will they appear highly ranked in the same attractor.

For single dataset, we applied the attractor finding algorithm using the findAttractor function implemented in the cafr (v0.312) R package [20] with the general fibroblastic marker gene LUM as seed. Identical results in all samples will be found, with very rare exceptions, if other fibroblastic markers, such as DCN, are used. The exponent (a) was set to different values for scRNA-seq datasets profiled from different protocols. For the analysis of UMI based (e.g. 10x) and full-length-based (e.g. Smart-seq2) datasets, we used a = 3 and a = 5, respectively. To find the consensus attractor for multiple datasets, we applied the consensus version of the attractor finding algorithm as described in [59]. In the consensus version, the association measures between genes are evaluated as the weighted median of the corresponding measures taken from the individual datasets. The weights are proportional to the number of samples included in each individual dataset in log scale.

Trajectory inference (TI) analysis

We selected the Slingshot [35] method for TI analysis, based on its robustness and suggestions made by the benchmarking pipeline dynverse [18]. We used the raw counts as input and followed the Slingshot lineage analysis workflow (v1.4.0). To begin this process, Slingshot chose robustly expressed genes if it has at least 10 cells that have at least 1 read for each. After gene filtering, we proceeded to full quantile normalization. Following diffusion map dimensionality reduction, Gaussian mixture modelling was performed to classify cells, where the number of clusters in the Mclust function was set to 3 based on the fact that there were three clusters in our previous Seurat clustering results. The final step of lineage inference analysis used the slingshot wrapper function in an unsupervised manner. A cluster based minimum spanning tree was subjected to describe the lineage. After analyzing the global lineage structure, we fitted a generalized additive model (GAM) for pseudotime and computed P values. Genes were ranked by P values and variances. After running Slingshot, we identified genes whose expression values significantly vary over the derived pseudotime by using a GAM, allowing us to detect non-linear patterns in gene expression.

Statistical analysis

P value evaluation for overlapping genes from different sets

We applied the hypergeometric test for evaluating the significance of genes shared by different sets. If there are two sets to compare, we used the phyper R function. If there are more than two sets to compare, we employed the multi-set intersection test [5] by applying the cpsets function implemented in the SuperExactTest R package. Regarding the background universe size of genes, we used the total number of genes analyzed in the specific expression matrix. In the case of comparing sets coming from different studies, we used 20,000 as the universe size.

Differential expression analysis

We used a Wilcoxon Rank Sum test by applying the FindMarkers function in Seurat to identify the differentially expressed (DE) genes between fibroblasts of different groups. DE genes with |log fold change| > 0.25 and Bonferroni adjusted P value < 0.1 are considered as significant. The positive and negative DE genes are ranked separately in terms of the absolute values of their log fold-change.

Supporting information

S1 Fig. Overview of the PDAC fibroblasts.

A. 6,267 fibroblasts originated from 11 control pancreases and 23 tumor samples were petitioned into four groups X1-X4. Fractions of the fibroblasts were: 45%, 38%, 14%, and 3%. B. Table showing the top 20 DE genes for each cluster. C. Bar plots presenting the numbers of cells captured for each cluster.

(TIF)

S2 Fig. Trajectory analysis of 6,075 fibroblasts in PDAC dataset.

A. Colors coded for pseudotime changing, red presenting the beginning of differentiation and blue presenting the end. B. Color-coded trajectory analysis of fibroblasts for annotated three clusters. C. Color-coded trajectory analysis of fibroblasts for group information. D. Color-coded trajectory analysis of fibroblasts for sample identity.

(TIF)

S3 Fig. Unsupervised clustering of four datasets from HNSCC, ovarian cancer, lung cancer and breast cancer.

A. t-SNE embedding of the whole HNSCC dataset. B. t-SNE embedding of the whole ovarian cancer dataset. C. t-SNE embedding of the mesenchymal cells from lung cancer dataset. D. t-SNE embedding of the mesenchymal cells from breast cancer dataset.

(TIF)

S1 Table. LUM-seeded attractors (top 100 genes) identified in each PDAC sample.

(XLSX)

S2 Table. Differentially expressed genes comparing normal pancreatic samples against four PDAC samples at the initial phase of the transition.

(XLSX)

S3 Table. Top 100 genes of temporally expressed genes on the pseudotime variable.

(XLSX)

S4 Table. Differentially expressed genes among different clusters of HNSCC dataset.

(XLSX)

S5 Table. LUM-seeded attractors (top 100 genes) from validating samples of other cancer types.

(XLSX)

S6 Table. Differentially expressed genes among different clusters of ovarian cancer dataset.

(XLSX)

S7 Table. Differentially expressed genes among different clusters of mesenchymal cells from lung cancer dataset.

(XLSX)

S8 Table. Differentially expressed genes among different clusters of stromal cells from breast cancer dataset.

(XLSX)

S9 Table. Ranked genes lists in terms of their association (mutual information) with gene COL11A1 by using the full set of pan-cancer TCGA bulk RNA-seq data.

(XLSX)

Acknowledgments

The noncoding RNA-related results presented here are in whole based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This work was funded by Columbia University’s unrestricted-purpose allocation of inventor’s (D.A.) research of royalties resulting from intellectual property totally unrelated to the work described in this paper. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Overview of the PDAC fibroblasts.

A. 6,267 fibroblasts originated from 11 control pancreases and 23 tumor samples were petitioned into four groups X1-X4. Fractions of the fibroblasts were: 45%, 38%, 14%, and 3%. B. Table showing the top 20 DE genes for each cluster. C. Bar plots presenting the numbers of cells captured for each cluster.

(TIF)

S2 Fig. Trajectory analysis of 6,075 fibroblasts in PDAC dataset.

A. Colors coded for pseudotime changing, red presenting the beginning of differentiation and blue presenting the end. B. Color-coded trajectory analysis of fibroblasts for annotated three clusters. C. Color-coded trajectory analysis of fibroblasts for group information. D. Color-coded trajectory analysis of fibroblasts for sample identity.

(TIF)

S3 Fig. Unsupervised clustering of four datasets from HNSCC, ovarian cancer, lung cancer and breast cancer.

A. t-SNE embedding of the whole HNSCC dataset. B. t-SNE embedding of the whole ovarian cancer dataset. C. t-SNE embedding of the mesenchymal cells from lung cancer dataset. D. t-SNE embedding of the mesenchymal cells from breast cancer dataset.

(TIF)

S1 Table. LUM-seeded attractors (top 100 genes) identified in each PDAC sample.

(XLSX)

S2 Table. Differentially expressed genes comparing normal pancreatic samples against four PDAC samples at the initial phase of the transition.

(XLSX)

S3 Table. Top 100 genes of temporally expressed genes on the pseudotime variable.

(XLSX)

S4 Table. Differentially expressed genes among different clusters of HNSCC dataset.

(XLSX)

S5 Table. LUM-seeded attractors (top 100 genes) from validating samples of other cancer types.

(XLSX)

S6 Table. Differentially expressed genes among different clusters of ovarian cancer dataset.

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S7 Table. Differentially expressed genes among different clusters of mesenchymal cells from lung cancer dataset.

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S8 Table. Differentially expressed genes among different clusters of stromal cells from breast cancer dataset.

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S9 Table. Ranked genes lists in terms of their association (mutual information) with gene COL11A1 by using the full set of pan-cancer TCGA bulk RNA-seq data.

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Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.


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