Comprehensive analysis of fibroblast activation protein expression across 23 tumor indications: insights for biomarker development in cancer immunotherapies

Introduction

Fibroblast activation protein (FAP) is predominantly upregulated in various tumor microenvironments and scarcely expressed in normal tissues.

Methods

We analyzed FAP across 1216 tissue samples covering 23 tumor types and 70 subtypes.

Results

Elevated FAP levels were notable in breast, pancreatic, esophageal, and lung cancers. Using immunohistochemistry and RNAseq, a correlation between FAP gene and protein expression was found. Evaluating FAP's clinical significance, we assessed 29 cohorts from 12 clinical trials, including both mono and combination therapies with the PD-L1 inhibitor atezolizumab and chemotherapy. A trend links higher FAP expression to poorer prognosis, particularly in RCC, across both treatment arms. However, four cohorts showed improved survival with high FAP, while in four others, FAP had no apparent survival impact.

Conclusions

Our results emphasize FAP's multifaceted role in therapy response, suggesting its potential as a cancer immunotherapy biomarker.

A community effort to create standards for evaluating tumor subclonal reconstruction

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.

Lack of UGT polymorphism association with idasanutlin pharmacokinetics in solid tumor patients

Purpose

Idasanutlin is a selective small-molecule MDM2 antagonist. It activates the tumor suppressor TP53 and is in phase 3 clinical trial for acute myeloid leukemia. Nonclinical studies have shown that glucuronidation is the major metabolizing mechanism for idasanutlin and UGT1A3 is the major metabolizing enzyme. There are reported examples of UGT polymorphisms associated with drug metabolism or response. Thus, the aim of this analysis is to investigate if UGT polymorphism is associated with idasanutlin pharmacokinetics.

Method

Idasanutlin clearance was derived and normalized from two phase I studies. Its clearance level was compared between patients with different genotypes at 44 non-monomorphic UGT SNPs. Several single-locus and multi-locus association analysis, including haplotype association analysis and pairwise SNP interaction (epistasis) analyses were performed to investigate if there is any association between UGT genotypes and idasanutlin clearance.

Results and conclusion

A total of 69 patients who have both idasanutlin pharmacokinetic data and UGT genotyping data were analyzed for association. The major clearance enzyme for idasanutlin, UGT1A3, has no association with idasanutlin clearance. Further single-locus and multi-locus association analyses also suggest that no significant UGT polymorphism association with idasanutlin clearance can be detected with the current datasets. However, the possibility of association with rare allele(s) of UGT family genes cannot be excluded due to the limited sample size of the current phase I studies.

The Immune Landscape of Cancer

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

Abstract 2878: BCAM (breast cancer attractor metagenes): A new tool for assessing breast cancer prognosis

The winning model of the Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge (Sci Transl Med, 17 April 2013: Vol. 5, Issue 181, p. 181ra50) made use of several novel molecular features, called attractor metagenes, as well as another metagene involving the expression levels of two genes, FGD3 and SUSD3, which are genomically adjacent to each other. Here we present the results of our follow-up work developing a breast cancer prognostic test, called BCAM (Breast Cancer Attractor Metagenes). BCAM was derived from the Challenge winning models by excluding unusable features and optimizing performance in predicting breast cancer specific survival. BCAM incorporates underlying tumor biology by including five molecular features: the FGD3-SUSD3 metagene and four attractor metagenes (CIN, MES, LYM, and HER2, which are associated with mitotic chromosomal instability, mesenchymal transition, lymphocyte infiltration, and expression of the HER2 amplicon, respectively); as well as incorporating the extent of disease: tumor size and the number of positive lymph nodes. Based on analysis of three breast cancer data sets with appropriate whole transcriptome and clinical outcomes data (allowing for time to recurrence as a phenotype), our results suggest that the combination of features used in BCAM outperforms the combination of features used in existing breast cancer biomarker products: Oncotype DX, Mammaprint and PAM50. The molecular features in BCAM were also shown to have improved performance against the Oncotype DX and PAM50 features in the subset of ER positive tumors treated with hormonal therapy, and against MammaPrint in the subset of lymph node negative tumors with size less than 50 mm. In addition, performance was significantly improved when the “ER group” of the Oncotype DX panel was replaced by the FGD3-SUSD3 metagene. All evaluations of prognostic performance were shown to be statistically significant by multiple rounds of random splitting. We also present a web-based version of BCAM (http://128.59.65.24:8080/brcabiomarker), in which uploaded gene expression data from a patient's tumor are analyzed and integrated with tumor size and number of positive nodes, and a report is generated containing a percentile prognostic score against the 2,000 patient METABRIC data set (Nature, 21 June 2012: Vol. 486, Issue 7403, p. 346-52), the corresponding ten-year breast cancer specific survival rate, and additional scores representing individual molecular features. We currently envision this facility evolving into an open crowd-sourced biomarker tool providing prognosis and therapy response prediction. This facility will also allow these scores to be directly obtained and used in any breast cancer cohort study.

Citation Format: Wei-Yi Cheng, Tai-Hsien Ou Yang, Matthew Maurer, Dimitris Anastassiou. BCAM (breast cancer attractor metagenes): A new tool for assessing breast cancer prognosis. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2878. doi:10.1158/1538-7445.AM2014-2878

Breast cancer prognostic biomarker using attractor metagenes and the FGD3-SUSD3 metagene

BACKGROUND

The winning model of the Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge made use of several molecular features, called attractor metagenes, as well as another metagene defined by the average expression level of the two genes FGD3 and SUSD3. This is a follow-up study toward developing a breast cancer prognostic test derived from and improving upon that model.

METHODS

We designed a feature selector facility calculating the prognostic scores of combinations of features, including those that we had used earlier, as well as those used in existing breast cancer biomarker assays, identifying the optimal selection of features for the test.

RESULTS

The resulting test, called BCAM (Breast Cancer Attractor Metagenes), is universally applicable to all clinical subtypes and stages of breast cancer and does not make any use of breast cancer molecular subtype or hormonal status information, none of which provided additional prognostic value. BCAM is composed of several molecular features: the breast cancer-specific FGD3-SUSD3 metagene, four attractor metagenes present in multiple cancer types (CIN, MES, LYM, and END), three additional individual genes (CD68, DNAJB9, and CXCL12), tumor size, and the number of positive lymph nodes.

CONCLUSIONS

Our analysis leads to the unexpected and remarkable suggestion that ER, PR, and HER2 status, or molecular subtype classification, do not provide additional prognostic value when the values of the FGD3-SUSD3 and attractor metagenes are taken into consideration.

IMPACT

Our results suggest that BCAM's prognostic predictions show potential to outperform those resulting from existing breast cancer biomarker assays.

JAK2 expression is associated with tumor-infiltrating lymphocytes and improved breast cancer outcomes: implications for evaluating JAK2 inhibitors

Janus kinase-2 (JAK2) supports breast cancer growth, and clinical trials testing JAK2 inhibitors are under way. In addition to the tumor epithelium, JAK2 is also expressed in other tissues including immune cells; whether the JAK2 mRNA levels in breast tumors correlate with outcomes has not been evaluated. Using a case-control design, JAK2 mRNA was measured in 223 archived breast tumors and associations with distant recurrence were evaluated by logistic regression. The frequency of correct pairwise comparisons of patient rankings based on JAK2 levels versus survival outcomes, the concordance index (CI), was evaluated using data from 2,460 patients in three cohorts. In the case-control study, increased JAK2 was associated with a decreasing risk of recurrence (multivariate P = 0.003, n = 223). Similarly, JAK2 was associated with a protective CI (<0.5) in the public cohorts: NETHERLANDS CI = 0.376, n = 295; METABRIC CI = 0.462, n = 1,981; OSLOVAL CI = 0.452, n = 184. Furthermore, JAK2 was strongly correlated with the favorable prognosis LYM metagene signature for infiltrating T cells (r = 0.5; P < 2 × 10(-16); n = 1,981) and with severe lymphocyte infiltration (P = 0.00003, n = 156). Moreover, the JAK1/2 inhibitor ruxolitinib potently inhibited the anti-CD3-dependent production of IFN-γ, a marker of the differentiation of Th cells along the tumor-inhibitory Th1 pathway. The potential for JAK2 inhibitors to interfere with the antitumor capacities of T cells should be evaluated.

Development of a prognostic model for breast cancer survival in an open challenge environment

The accuracy with which cancer phenotypes can be predicted by selecting and combining molecular features is compromised by the large number of potential features available. In an effort to design a robust prognostic model to predict breast cancer survival, we hypothesized that signatures consisting of genes that are coexpressed in multiple cancer types should correspond to molecular events that are prognostic in all cancers, including breast cancer. We previously identified several such signatures--called attractor metagenes--in an analysis of multiple tumor types. We then tested our attractor metagene hypothesis as participants in the Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge. Using a rich training data set that included gene expression and clinical features for breast cancer patients, we developed a prognostic model that was independently validated in a newly generated patient data set. We describe our model, which was based on three attractor metagenes associated with mitotic chromosomal instability, mesenchymal transition, or lymphocyte-based immune recruitment.