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Supplementary figures and tables

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Table_S1

Table S1. Genes included in the targeted next-generation sequencing HNSCC gene panel. Table S2. Compounds (n=220) and their drug classes/targets used in the drug screen. Table S3. qPCR primers and qPCR vs GE_microarray correlation. Table S4. Drug sensitivity scores (DSS) of the 220 compounds across 45 UT-SCC HNSCC cell lines. Table S5. All coding mutations found in 45 UT-SCC HNSCC cell lines by targeted sequencing of 694 genes. Table S6. Copy number alterations (CNA) found in 45 UT-SCC HNSCC cell lines. Table S7. Association between mutations and DSS. Table S8. Significant CNA-Drug associations. Table S9. Genes whose amplification is associated with concomitant overexpression. Table S10. Genes whose deletion is associated with concomitant underexpression. Table S11. Significant CNA_gene expression_DSS integration results. Table S12. Gene signatures associated with response to mTOR, MEK and EGFR inhibitors.

XLSX file 452K, DSRT Oncology drug collection

Immune checkpoint inhibitors activate T cells to reject tumors. Unique tumor mutations are key T-cell targets, but a comprehensive understanding of the nature of a successful antitumor T-cell response is lacking. To investigate the T-cell receptor (TCR) repertoire associated with treatment success versus failure, we used a well-characterized mouse carcinoma that is rejected by CD8 T cells in mice treated with radiotherapy (RT) and anti–CTLA-4 in combination, but not as monotherapy, and comprehensively analyzed tumor-infiltrating lymphocytes (TILs) by high-throughput sequencing of the TCRΒ CDR3 region. The combined treatment increased TIL density and CD8/CD4 ratio. Assessment of the frequency of T-cell clones indicated that anti–CTLA-4 resulted in fewer clones and a more oligoclonal repertoire compared with untreated tumors. In contrast, RT increased the CD8/CD4 ratio and broadened the TCR repertoire, and when used in combination with anti–CTLA-4, these selected T-cell clones proliferated. Hierarchical clustering of CDR3 sequences showed a treatment-specific clustering of TCRs that were shared by different mice. Abundant clonotypes were commonly shared between animals and yet treatment-specific. Analysis of amino-acid sequence similarities revealed a significant increase in the number and richness of dominant CDR3 motifs in tumors treated with RT + anti–CTLA-4 compared with control. The repertoire of TCRs reactive with a single tumor antigen recognized by CD8+ T cells was heterogeneous but highly clonal, irrespective of treatment. Overall, data support a model whereby a diverse TCR repertoire is required to achieve tumor rejection and may underlie the synergy between RT and CTLA-4 blockade. Cancer Immunol Res; 6(2); 139–50. ©2017 AACR.

We generated ex vivo drug-response and multiomics profiling data for a prospective series of 252 samples from 186 patients with acute myeloid leukemia (AML). A functional precision medicine tumor board (FPMTB) integrated clinical, molecular, and functional data for application in clinical treatment decisions. Actionable drugs were found for 97% of patients with AML, and the recommendations were clinically implemented in 37 relapsed or refractory patients. We report a 59% objective response rate for the individually tailored therapies, including 13 complete responses, as well as bridging five patients with AML to allogeneic hematopoietic stem cell transplantation. Data integration across all cases enabled the identification of drug response biomarkers, such as the association of IL15 overexpression with resistance to FLT3 inhibitors. Integration of molecular profiling and large-scale drug response data across many patients will enable continuous improvement of the FPMTB recommendations, providing a paradigm for individualized implementation of functional precision cancer medicine.Significance:Oncogenomics data can guide clinical treatment decisions, but often such data are neither actionable nor predictive. Functional ex vivo drug testing contributes significant additional, clinically actionable therapeutic insights for individual patients with AML. Such data can be generated in four days, enabling rapid translation through FPMTB.See related commentary by Letai, p. 290.This article is highlighted in the In This Issue feature, p. 275

Structure factors and crystal information

Figure S1. Heat map of drug responses of 220 compounds across 45 HPV negative cell lines. Figure S2. DSS scores and grouping of the used drugs based on their functional categories. Figure S3. An effect of transient knock-down of RAB25 and FAM83H to the cellular response of EGFR inhibitors afatinib and erlotinib.

There is an unmet need for effective targeted therapies for patients with advanced head and neck squamous cell carcinoma (HNSCC). We correlated gene expression, gene copy numbers, and point mutations in 45 human papillomavirus–negative HNSCC cell lines with the sensitivity to 220 anticancer drugs to discover predictive associations to genetic alterations. The drug response profiles revealed diverse efficacy of the tested drugs across the cell lines. Several genomic abnormalities and gene expression differences were associated with response to mTOR, MEK, and EGFR inhibitors. NOTCH1 and FAT1 were the most commonly mutated genes after TP53 and also showed some association with response to MEK and/or EGFR inhibitors. MYC amplification and FAM83H overexpression associated with sensitivity to EGFR inhibitors, and PTPRD deletion with poor sensitivity to MEK inhibitors. The connection between high FAM83H expression and responsiveness to the EGFR inhibitor erlotinib was validated by gene silencing and from the data set at the Cancer Cell Line Encyclopedia. The data provide several novel genomic alterations that associated to the efficacy of targeted drugs in HNSCC. These findings require further validation in experimental models and clinical series. Mol Cancer Ther; 17(9); 2060–71. ©2018 AACR.

Figure S1. Relative composition of TCRs in unsorted tumors within each treatment group. Figure S2. TCRβ CDR3 sequence distance among AH1-pentamer+/tetramer+CD8+ T cells. Table S1. Summary of TCR sequencing of whole 4T1 tumors. Table S2. Summary of TCR sequencing of AH1-pentamer+CD8+ T cells. Table S3. Abundance of AH1 clone signature (amino acid sequences) in CD4+ or CD8+ sorted T cells. Table S4. Three AH1 reactive T cell clones were shared between 4T1 tumors and AH1 peptide-vaccinated animals.

11 supplement tables

Supplement text file including supplement figures, supplementary table legends and supplement methods

Supplementary text and figures

Most non–small cell lung cancers (NSCLC) contain nontargetable mutations, including KRAS, TP53, or STK11/LKB1 alterations. By coupling ex vivo drug sensitivity profiling with in vivo drug response studies, we aimed to identify drug vulnerabilities for these NSCLC subtypes. Primary adenosquamous carcinoma (ASC) or adenocarcinoma (AC) cultures were established from KrasG12D/+;Lkb1fl/fl (KL) tumors or AC cultures from KrasG12D/+;p53fl/fl (KP) tumors. Although p53-null cells readily propagated as conventional cultures, Lkb1-null cells required conditional reprograming for establishment. Drug response profiling revealed short-term response to MEK inhibition, yet long-term clonogenic assays demonstrated resistance, associated with sustained or adaptive activation of receptor tyrosine kinases (RTK): activation of ERBBs in KL cultures, or FGFR in AC cultures. Furthermore, pan-ERBB inhibition reduced the clonogenicity of KL cultures, which was exacerbated by combinatorial MEK inhibition, whereas combinatorial MEK and FGFR inhibition suppressed clonogenicity of AC cultures. Importantly, in vivo studies confirmed KL-selective sensitivity to pan-ERBB inhibition, which correlated with high ERBB ligand expression and activation of ERBB receptors, implying that ERBB network activity may serve as a predictive biomarker of drug response. Interestingly, in human NSCLCs, phosphorylation of EGFR or ERBB3 was frequently detected in ASCs and squamous cell carcinomas. We conclude that analysis of in situ ERBB signaling networks in conjunction with ex vivo drug response profiling and biochemical dissection of adaptive RTK activities may serve as a valid diagnostic approach to identify tumors sensitive to ERBB network inhibition.

PDF file 400K, Supplementary Figures 1-9; Supplementary Tables 4-9 and Supplementary Methods

Basal-like breast cancers (BLBC) are characterized by defects in homologous recombination (HR), deficient mitotic checkpoint, and high-proliferation activity. Here, we discover CIP2A as a candidate driver of BLBC. CIP2A was essential for DNA damage–induced initiation of mouse BLBC-like mammary tumors and for survival of HR–defective BLBC cells. CIP2A was dispensable for normal mammary gland development and for unperturbed mitosis, but selectively essential for mitotic progression of DNA damaged cells. A direct interaction between CIP2A and a DNA repair scaffold protein TopBP1 was identified, and CIP2A inhibition resulted in enhanced DNA damage–induced TopBP1 and RAD51 recruitment to chromatin in mammary epithelial cells. In addition to its role in tumor initiation, and survival of BRCA-deficient cells, CIP2A also drove proliferative MYC and E2F1 signaling in basal-like triple-negative breast cancer (BL-TNBC) cells. Clinically, high CIP2A expression was associated with poor patient prognosis in BL-TNBCs but not in other breast cancer subtypes. Small-molecule reactivators of PP2A (SMAP) inhibited CIP2A transcription, phenocopied the CIP2A-deficient DNA damage response (DDR), and inhibited growth of patient-derived BLBC xenograft. In summary, these results demonstrate that CIP2A directly interacts with TopBP1 and coordinates DNA damage–induced mitotic checkpoint and proliferation, thereby driving BLBC initiation and progression. SMAPs could serve as a surrogate therapeutic strategy to inhibit the oncogenic activity of CIP2A in BLBCs. Significance: These results identify CIP2A as a nongenetic driver and therapeutic target in basal-like breast cancer that regulates DNA damage–induced G2–M checkpoint and proliferative signaling.

Immune-checkpoint inhibitors (ICI), although revolutionary in improving long-term survival outcomes, are mostly effective in patients with immune-responsive tumors. Most patients with cancer either do not respond to ICIs at all or experience disease progression after an initial period of response. Treatment resistance to ICIs remains a major challenge and defines the biggest unmet medical need in oncology worldwide. In a collaborative workshop, thought leaders from academic, biopharma, and nonprofit sectors convened to outline a resistance framework to support and guide future immune-resistance research. Here, we explore the initial part of our effort by collating seminal discoveries through the lens of known biological processes. We highlight eight biological processes and refer to them as immune resistance nodes. We examine the seminal discoveries that define each immune resistance node and pose critical questions, which, if answered, would greatly expand our notion of immune resistance. Ultimately, the expansion and application of this work calls for the integration of multiomic high-dimensional analyses from patient-level data to produce a map of resistance phenotypes that can be utilized to guide effective drug development and improved patient outcomes.

Today many university teachers attend competency development courses as individuals. In this paper we consider collegial and collective competency development. We examine what role collegial peer review could play in developing the Scholarship of Teaching and Learning for university teachers. The paper is based on a case study that explores how a group of university teachers experienced collegial development and peer review. Based on the teachers’ account, we identify how the activity helped them break the isolation they experienced at their department, enabled them to navigate the landscape of different courses and strengthened their roles as teacher, e.g., through collaboration with colleagues. We also present some frictions and tensions that were reported. Based on the case study, we propose that professional development in academia could benefit from acknowledging collegial peer review as an output in its own right.

Supplementary Table S3. Comprehensive drug sensitivity and resistance testing with 200 cancer drugs. Cell viability in GIST882 and GIST48 cell lines was measured in five different concentrations over a 10,000-fold concentration range.

Supplementary Figure S1. A box-whisker plot showing the relative KIT, ANO1, and PRKCQ (encodes for protein kinase C theta) mRNA expression in soft-tissue sarcoma samples in the MediSapiens database. Supplementary Figure S2. PDE3A (A) and KIT (B) expression in human intestinal smooth muscle tissue (magnification x400, scale bar 100 μm). Supplementary Figure S3 A, Effects of the combinations of PDE3 siRNAs and DMSO on the GIST882 and GIST48 cell lines. siPDE3B and siPDE3A/B reduced GIST882 cell viability, and none of the treatments influenced markedly GIST48 viability. SEM < 0.05 in all data points. B, Effects of PDE3 inhibitors cilostazol, milrinone, and amrinone given with DMSO on GIST882 and GIST48 cell line viability. SE < 0.05 in all data points. Supplementary Figure S4. A Sankey diagram of top SPIA pathways. A Sankey diagram showing differentially expressed miRNAs, their predicted target genes, and the biological pathways estimated to be significantly impacted by the expression change (SPIA pGFDR

Supplemental legend

Supplementary Figure from Concurrent Inhibition of IGF1R and ERK Increases Pancreatic Cancer Sensitivity to Autophagy Inhibitors

The aggressive nature of pancreatic ductal adenocarcinoma (PDAC) mandates the development of improved therapies. As KRAS mutations are found in 95% of PDAC and are critical for tumor maintenance, one promising strategy involves exploiting KRAS-dependent metabolic perturbations. The macrometabolic process of autophagy is upregulated in KRAS-mutant PDAC, and PDAC growth is reliant on autophagy. However, inhibition of autophagy as monotherapy using the lysosomal inhibitor hydroxychloroquine (HCQ) has shown limited clinical efficacy. To identify strategies that can improve PDAC sensitivity to HCQ, we applied a CRISPR-Cas9 loss-of-function screen and found that a top sensitizer was the receptor tyrosine kinase (RTK) insulin-like growth factor 1 receptor (IGF1R). Additionally, reverse phase protein array pathway activation mapping profiled the signaling pathways altered by chloroquine (CQ) treatment. Activating phosphorylation of RTKs, including IGF1R, was a common compensatory increase in response to CQ. Inhibition of IGF1R increased autophagic flux and sensitivity to CQ-mediated growth suppression both in vitro and in vivo. Cotargeting both IGF1R and pathways that antagonize autophagy, such as ERK–MAPK axis, was strongly synergistic. IGF1R and ERK inhibition converged on suppression of glycolysis, leading to enhanced dependence on autophagy. Accordingly, concurrent inhibition of IGF1R, ERK, and autophagy induced cytotoxicity in PDAC cell lines and decreased viability in human PDAC organoids. In conclusion, targeting IGF1R together with ERK enhances the effectiveness of autophagy inhibitors in PDAC.Significance:Compensatory upregulation of IGF1R and ERK–MAPK signaling limits the efficacy of autophagy inhibitors chloroquine and hydroxychloroquine, and their concurrent inhibition synergistically increases autophagy dependence and chloroquine sensitivity in pancreatic ductal adenocarcinoma.

Supplementary Materials and Methods

The molecular pathways that drive cancer progression and treatment resistance are highly redundant and variable between individual patients with the same cancer type. To tackle this complex rewiring of pathway cross-talk, personalized combination treatments targeting multiple cancer growth and survival pathways are required. Here we implemented a computational–experimental drug combination prediction and testing (DCPT) platform for efficient in silico prioritization and ex vivo testing in patient-derived samples to identify customized synergistic combinations for individual cancer patients. DCPT used drug–target interaction networks to traverse the massive combinatorial search spaces among 218 compounds (a total of 23,653 pairwise combinations) and identified cancer-selective synergies by using differential single-compound sensitivity profiles between patient cells and healthy controls, hence reducing the likelihood of toxic combination effects. A polypharmacology-based machine learning modeling and network visualization made use of baseline genomic and molecular profiles to guide patient-specific combination testing and clinical translation phases. Using T-cell prolymphocytic leukemia (T-PLL) as a first case study, we show how the DCPT platform successfully predicted distinct synergistic combinations for each of the three T-PLL patients, each presenting with different resistance patterns and synergy mechanisms. In total, 10 of 24 (42%) of selective combination predictions were experimentally confirmed to show synergy in patient-derived samples ex vivo. The identified selective synergies among approved drugs, including tacrolimus and temsirolimus combined with BCL-2 inhibitor venetoclax, may offer novel drug repurposing opportunities for treating T-PLL.Significance: An integrated use of functional drug screening combined with genomic and molecular profiling enables patient-customized prediction and testing of drug combination synergies for T-PLL patients. Cancer Res; 78(9); 2407–18. ©2018 AACR.