Refining the optimal CAF cluster marker for predicting TME-dependent survival expectancy and treatment benefits in NSCLC patients

Abstract

The tumor microenvironment (TME) plays a pivotal role in the onset, progression, and treatment response of cancer. Among the various components of the TME, cancer-associated fibroblasts (CAFs) are key regulators of both immune and non-immune cellular functions. Leveraging single-cell RNA sequencing (scRNA) data, we have uncovered previously hidden and promising roles within this specific CAF subgroup, paving the way for its clinical application. However, several critical questions persist, primarily stemming from the heterogeneous nature of CAFs and the use of different fibroblast markers in various sample analyses, causing confusion and hindrance in their clinical implementation. In this groundbreaking study, we have systematically screened multiple databases to identify the most robust marker for distinguishing CAFs in lung cancer, with a particular focus on their potential use in early diagnosis, staging, and treatment response evaluation. Our investigation revealed that COL1A1, COL1A2, FAP, and PDGFRA are effective markers for characterizing CAF subgroups in most lung adenocarcinoma datasets. Through comprehensive analysis of treatment responses, we determined that COL1A1 stands out as the most effective indicator among all CAF markers. COL1A1 not only deciphers the TME signatures related to CAFs but also demonstrates a highly sensitive and specific correlation with treatment responses and multiple survival outcomes. For the first time, we have unveiled the distinct roles played by clusters of CAF markers in differentiating various TME groups. Our findings confirm the sensitive and unique contributions of CAFs to the responses of multiple lung cancer therapies. These insights significantly enhance our understanding of TME functions and drive the translational application of extensive scRNA sequence results. COL1A1 emerges as the most sensitive and specific marker for defining CAF subgroups in scRNA analysis. The CAF ratios represented by COL1A1 can potentially serve as a reliable predictor of treatment responses in clinical practice, thus providing valuable insights into the influential roles of TME components. This research marks a crucial step forward in revolutionizing our approach to cancer diagnosis and treatment.

Keywords: Cancer associated fibroblasts; Marker selection; Therapy response; Tumor microenvironment; scRNA sequence data.

Figure 1

The markers mostly used to define the subgroup of TME fibroblast. Through screening and test on website of “Surface markers”, the most accepted and universally used fibroblasts in human (A) and in mouse (B) were displayed. (C) The markers of COL1A1 and PDGFRA showed the best specificity. (D) Plotting results indicated that COL1A1, COL1A2, and FAP showed best representation. (E) Fibroblasts showed close connections with other kinds of TME subgroups in GSE lung cancer samples, and each number indicated one special subgroup as was labeled (raw data could be achieved at http://117.50.127.228/CellMarker/CellMarker_communication.jsp).

Figure 2

The expression patterns of CAF markers in pan-cancer and lung cancer. (A,B) Stromal cells from all organs were enrolled for identify the fibroblasts markers, and the distribution of fibroblasts were labeled with dark purple plots. (C) Different CAF markers showed diverse expression motif and distribution patterns. Lung tissues were divided into different subtypes (D), and in cluster of c-9 of fibroblasts, COL1A1, COL1A2, FAP, and PDGFRA showed stronger expressions and better cluster enrichment.

Figure 3

CAF correlated with tumor group and indicated malignancy status. The markers of COL1A1 (A), COL1A2 (B), and FAP (C) are significantly highly expressed in lung cancer. COL1A1 (D), COL1A2 (E), and FAP (F) indicated a higher chance of metastasis. (G–I) The CAF markers of PDGFRA, PDGFRB, and VCAM1 did not successfully differentiate tumor group and normal tissues, or did not distinguish metastatic sites. (J,K) COL1A1 and COL1A2 were differently enriched in different cluster, and their enrichment were much stronger in c-6 group that can most prominently represent CAF function.

Figure 4

Survival predicating roles of the representative COL1A1, COL1A2, and FAP. As the specific CAF marker could distinguish the malignant state of the tumor and identify the presence or absence of metastasis, we further analyzed its role in predicting survival. Higher COL1A1 expression pointed to shorter overall survival (A), post-progression survival (B), and progression free survival (C) in whole cancer groups. In subgroup analysis, COL1A1 greatly helped to defined high-risk groups at early stage (D,E). Also, COL1A2 (F,G) and FAP (H,I) indicated a significant role in differentiating survival expectances. Other factors failed to distinguish differences in survival or differences in disease-free survival.

Figure 5

CAF ratios differences indicated different immune therapy response. COL1A1 positively correlated with inhibitory immune cells [(A,B), T-regulator ratios, raw data could be acquired at http://cis.hku.hk/TISIDB/data_temp/COL1A1_exp_LUAD_TIL_Treg.txt, and MDSC, raw data could be acquired at http://cis.hku.hk/TISIDB/data_temp/COL1A1_exp_LUAD_TIL_MDSC.txt], secretive CAF functional factors [(C), TGFβ expression and secretion, raw data could be acquired at http://cis.hku.hk/TISIDB/data_temp/COL1A1_exp_LUAD_Immunoinhibitor_TGFB1.txt], and negative immune regulator [(D), CD274 (PD-L1), raw data could be acquired at http://cis.hku.hk/TISIDB/data_temp/COL1A1_exp_LUAD_Immunoinhibitor_CD274.txt]. Increased CAF markers of COL1A1 (E), COL1A2 (F), PDGFRB (G), ACTA2 (H) indicated better immune therapy response.

Figure 6

The applicable clinical and translational usage for CAF detections and functions. The IHC results from protein-atlas were screened for checking the expressing patterns of enrolled CAF candidates of PDGFRB (A), FAP (B), and COL1A1 (C). These genes are not universally expressed in lung cancer tissues. (D) We applied three cell lines of cancer associated fibroblasts, in addition with control group of fibroblasts, to check the protein levels, and almost every CAF marker could be detected in different groups. (E) In vivo study indicated the higher tumor formation ability and highly proliferative ability of tumors triggered by co-embedded CAF groups when performing subcutaneous cancer cells injection.