Biography
Interests
Lin Tian1* & Sicong Zhang2
1Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, 1275
York Avenue, New York, NY 10065, USA
2Laboratory of Biochemistry and Molecular Biology, The Rockefeller University, 1230 York Avenue, New York, NY
10065, USA
*Correspondence to: Dr. Lin Tian, Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
Copyright © 2018 Dr. Lin Tian, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
More than 1.5 million new cancer cases are diagnosed in US every year [1]. Unlike the normal cells that
cooperate with each other to form an ecosystem within the tissue, the cancer cells break the rules by
bypassing the normal proliferation, breaching the basement membrane, disseminating into circulation and
finally establishing distant colonization [2]. To initiate this malignant cellular program, cancer cells hijack
a variety of existing cellular mechanisms that are critical to normal tissue development and regeneration
[3]. Despite the increasing knowledge on these molecular regulators leveraged by cancer cells to fulfill the
aberrancy, most advanced cancers are still incurable, indicating the therapies to treat cancer have not kept
up. In fact, the majority of current therapeutic regimens are designed to target cancer cells. However, the
neighbors of cancer cells - especially the connective tissue or stroma – are far from passive bystanders [4].
Tumor-associated stromal cells including endothelial cells, fibroblasts and immune cells can also behave
aberrantly in tumor microenvironment and contribute to disease progression and therapeutic resistance [2].
An important hallmark of cancer is the dysregulation of angiogenesis, which leads to abnormal vascular
network within the tumor that is filled by dilated, tortuous, and hyperpermeable vessels [5]. It has been
reported that normalization of tumor vasculature by inhibiting the pro-angiogenic activity of vascular endothelial growth factor (VEGF) can improve the efficacy of chemotherapy and immune checkpoint
blockade therapy [6-8]. In this short communication, we extend the concept of “vascular normalization” to
“microenvironment normalization”. The underlying hypothesis is that alteration of cancer-associated stroma
abates the drug delivery and therefore limits the efficacy of therapeutic regimen. Thus, normalizing tumorassociated
stromal components through molecular or pharmacologic perturbation might provide a novel
opportunity for cancer treatment by enhancing the efficacy of conventional therapy and immune infiltration,
in addition to antiangiogenesis therapy (Fig. 1).
Results
We first ask which cancer type can be the best model to test microenvironment normalization hypothesis.
We think the ideal model should have high abundance of stroma components. To estimate the percent of
stromal cells within the tumor, we used ESTIMATOR [9], a computational tool that uses gene expression
data as input and predicts the tumor purity, to estimate the abundance of stromal cells. Across 22 different
tumor types in TCGA database, we found that the pancreatic tumors are of the highest stromal scores (Fig.
2a), which is consistent with previous studies [10]. Importantly, the high stroma scores correlate with poor
prognosis in pancreatic cancer patients (Fig. 2b). It is also notable that the progression of pancreatic cancer
is accompanied by the increase of stroma, indicating that the stromal cells within malignant tissues may
have evolved to cope with tumor progression (Fig. 2c). Based on these observations, “microenvironment
normalization” may bring the great therapeutic benefit for pancreatic cancer patients.
We next investigate which stroma-related pathways are enriched in pancreatic adenocarcinoma tissues versus normal tissues. We found the hydrolase activity is substantially increased in pancreatic adenocarcinoma (Fig. 3a, b). Indeed, in pancreatic cancers, the serine hydrolase RBBP9 has been found to promote anchorageindependent growth in vitro and tumor-initiation in vivo [11]. In addition, hyaluronidase inhibitor PEGPH20 has been developed to reduce the “desmoplastic reaction” in the stage IV metastatic pancreatic cancer, and is currently in the Phase II clinical trial. However, since hydrolase activity is also essential to diverse cellular processes such as myofibroblast and fetal development, systemic inhibition of hydrolase activity can cause side effect on other healthy tissues. Therefore, we queried the L1000CDS2 database to search for other compounds that can adverse the hydrolase activity (Fig. 4a). L1000CDS2 is a dataset that integrates the LINCS L1000 small molecule expression profiles and is under development by Ma’ayan Lab at the Icahn School of Medicine at Mount Sinai [12]. The L1000CDS2 dataset will search for the small molecules that can potentially reverse the custom defined gene signature. We found that the top candidate compounds target the Ras signaling (Fig. 4b). Indeed, more than 50% of the pancreatic tumors carry Ras mutation (Fig. 4c). And inhibiting Ras signaling in cancer cells alone may be an effective strategy to treat pancreatic cancers. Based on our bioinformatics prediction, suppressing Ras signaling may also normalize microenvironment and increase therapeutic efficacies, which may present promising strategies for potential synergy with chemotherapy and immune checkpoint therapy, respectively (Fig. 4d).
a). Heat map showing the top 15 gene signatures enriched in adenocarcinoma versus normal pancreatic tissues. Column-side annotation shows the types of the samples. b). Volcano plot showing the differential expression of the genes (n = 1,327) in Regulation of Hydrolase Activity (GO:0051336). The expression difference between adenocarcinoma and normal tissues is plotted on the x axis (log scale), and the false discovery rate (adjusted significance) is plotted on the y axis (-log scale). Upregulated and downregulated genes in adenocarcinoma are labeled with red and blue, respectively.
a). Schematic diagram showing the principle of in silico chemical screening using L1000CDS2 database.
b). A table showing the top candidate compounds that can adverse the pancreatic adenocarcinoma-specific
hydrolase gene signature. The effect of compound is calculated based on the α degree range from 0°
(stimulation) and 180° (inhibition).
c). Bar plot showing the mutation frequency of Ras (K-, H-, N-Ras) mutations across 24 cancer types in
TCGA dataset.
d). Expected working model: as more than 50% of the pancreatic tumors carry Ras mutation, the inhibitors
targeting RAS signaling mediators including MEK, JAK and Aurora will be effective on malignant cells.
In addition, the suppression of Ras signaling in tumor stroma may inhibit the hydrolase activity, thereby
normalizing microenvironment and providing promising strategies for potential synergy with chemotherapy.
Tipifarnib [13], a potent and specific farnesyltransferase inhibitor, ranks among the top compounds that can reverse the hydrolase activity in pancreatic cancer (Fig. 4b). Upon treatment with Tipifarnib, the prenylation of the CxxX tail motif in Ras kinase will be blocked, which prevents Ras from binding to the membrane upon activation. As a result, Tipifarnib prominently abates the proliferation of Ras mutant cells. However, the activity of membrane-free cytoplasmic Ras is not suppressed by Tipifarnib, indicating Tipifarnib monotherapy may not be effective. This might also partially explain why Tipifarnib was suspended in Phase III clinical trials [14].
Discussion
Despite the ineffectiveness of Tipifarnib monotherapy, Tipifarnib now provides the opportunity to address
the questions associated with the “microenvironment normalization” hypothesis. How does the hydrolase
activity change in pancreatic cancer stroma after Tipifarnib treatment? And, how does the treatment of
Tipifarnib alter pancreatic cancer microenvironment, such as hypoxia, and drug delivery efficiency? It is also
notable that immune checkpoint blockade therapy works poorly for pancreatic cancer, presumably due to
the low immune infiltration efficiency. It will be interesting to investigate whether the combination of anti-
PD1/anti-CTLA with Tipifarnib can enhance immune cytotoxic and forge the innovative combinatory
intervention to pancreatic cancer.
Methods
TCGA RNA-seq data were download from UCSC Cancer Genome Browser (https://genome-cancer.
ucsc.edu/) and used as input for the R package of ESTIMATOR to estimate the stroma percent. Gene
signature were calculated using single sample Gene Set Enrichment Analysis (ssgGSEA) on the server of
GenePattern (https://genepattern.broadinstitute.org/gp). In silico drug screening was performing using the
online graphical user inference of L1000CDS2 (http://amp.pharm.mssm.edu/L1000CDS2)
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