We introduce quanTIseq, a method to quantify the fractions of ten immune cell types from bulk RNA-sequencing data. quanTIseq was extensively validated in blood and tumor samples using simulated, flow cytometry, and immunohistochemistry data. quanTIseq analysis of 8000 tumor samples revealed that cytotoxic T cell infiltration is more strongly associated with the activation of the CXCR3/CXCL9 axis than with mutational load and that deconvolution-based cell scores have prognostic value in several solid cancers. Finally, we used quanTIseq to show how kinase inhibitors modulate the immune contexture and to reveal immune-cell types that underlie differential patients’ responses to checkpoint blockers. Availability: quanTIseq is available at http://icbi.at/quantiseq . Electronic supplementary material The online version of this article (10.1186/s13073-019-0638-6) contains supplementary material, which is available to authorized users.

We introduce quanTIseq, a method to quantify the tumor immune contexture, determined by the type and density of tumor-infiltrating immune cells. quanTIseq is based on a novel deconvolution algorithm for RNA sequencing data that was validated with independent data sets. Complementing the deconvolution output with image data from tissue slides enables in silico multiplexed immunodetection and provides an efficient method for the immunophenotyping of a large number of tumor samples.Cancer immunotherapy with antibodies targeting immune checkpoints has shown durable benefit or even curative potential in various cancers 1,2 . As only a fraction of patients are responsive to immune checkpoint blockers, efforts are underway to identify predictive markers as well as mechanistic rationale for combination therapies with synergistic potential. Thus, comprehensive and quantitative immunological characterization of tumors in a large number of clinical samples is of utmost importance, but it is currently hampered by the lack of simple and efficient methods. Therefore, we developed quanTIseq, a computational pipeline for the quantification of the Tumor Immune contexture using RNA-seq data and images of haematoxylin and eosin (H&E)-stained tissue slides (Fig. 1a). As part of quanTIseq, we first developed a deconvolution algorithm based on constrained least squares regression 12 . We then designed a signature matrix from a compendium of 51 RNA-seq data sets (Supplementary (Fig. 1c).To validate quanTIseq we first used both simulated data and published data. We simulated 1,700RNA-seq data sets from human breast tumors by mixing various numbers of reads from tumor and immune-cell RNA-seq data, considering different immune compositions and sequencing depths.quanTIseq obtained a high correlation between the true and the estimated fractions and accurately quantified tumor content (measured by the fraction of "other" cells) (Supplementary Figure 1). We then validated quanTIseq using experimental data from a previous study 13 , in which peripheral blood mononuclear cell (PBMC) mixtures were subjected to both, RNA-seq and flow cytometry. A high accuracy of quanTIseq estimates was also observed with this data set ( Fig. 1d and Supplementary Figure 2). Additionally, we successfully validated quanTIseq using two previous published data sets (Supplementary Figures 3 and 4).As most of the validation data sets available in the literature are based on microarray data or consider a limited number of phenotypes, we generated RNA-seq and flow cytometry data from mixtures of peripheral-blood immune cells collected from nine healthy donors. Flow cytometry was used to quantify all the immune sub-populations considered by quanTIseq signature matrix except macrophages, which are not present in blood. Comparison between quanTIseq cell estimates and flow cytometry fractions showed a high correlation at a single and multiple cell-type level ( Fig. 1e and Supplementary Figure 5).We then validated quanTIseq using two independent data sets. The first data...

Bristol-Myers Squibb, and Genentech. JAS is a compensated member of the advisory boards of Bristol-Myers Squibb, Pfizer, Array, Genentech, Incyte, and Curis and has received research support from Pfizer, Bristol-Myers Squibb, and Curis. PBF receives research funding from Incyte. JMB, MES, MVE, VS, and DBJ are coauthors on a patent pending for use of MHC-II to predict responses from immunotherapy (15/376,276). RSD, DMS, DBJ, and JMB are coauthors on a patent pending for use of FCRL6 antibodies for cancer therapy (62/584,458). JB and JYK are employees of Navigate BioPharma Services and receive compensation as such.

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Clinical trials have demonstrated the efficacy of combining phosphoinositide 3-kinase (PI3K) inhibitors with endocrine therapies in hormone therapy-refractory breast cancer. However, biomarkers of PI3K pathway dependence in ER+ breast cancer have not been fully established. Hotspot mutations in the alpha isoform of PI3K (PIK3CA) are frequent in ER+ disease and may identify tumors that respond to PI3K inhibitors. It is unclear whether PIK3CA mutations are the only biomarker to suggest pathway dependence and response to therapy. We performed correlative molecular characterization of primary and metastatic tissue from patients enrolled in a phase Ib study combining buparlisib (NVP-BKM-120), a pan-PI3K inhibitor, with letrozole in ER+, human epidermal growth factor-2 (HER2)-negative, metastatic breast cancer. Activating mutations in PIK3CA and inactivating MAP3K1 mutations marked tumors from patients with clinical benefit (≥6 months of stable disease). Patients harboring mutations in both genes exhibited the greatest likelihood of clinical benefit. In ER+ breast cancer cell lines, siRNA-mediated knockdown of MAP3K1 did not affect the response to buparlisib. In a subset of patients treated with buparlisib or the PI3Kα inhibitor alpelisib each with letrozole where PAM50 analysis was performed, nearly all tumors from patients with clinical benefit had a luminal A subtype. Mutations in MAP3K1 in ER+ breast cancer may be associated with clinical benefit from combined inhibition of PI3K and ER, but we could not ascribe direct biological function therein, suggesting they may be a surrogate for luminal A status. We posit that luminal A tumors may be a target population for this therapeutic combination.