Search

INTRODUCTION: Small cell carcinoma of the bladder (SCCB) is a rare variant of bladder cancer with poor outcomes. We evaluated long-term outcomes of nonmetastatic (M0) and metastatic (M1) SCCB and correlated pathologic response with genomic alterations of patients treated with neoadjuvant chemotherapy (NAC). PATIENTS AND METHODS: Clinical history and pathology samples from SCCB patients diagnosed at our institution were reviewed. RESULTS: One hundred and ninety-nine SCCB patients were identified. (M0: 147 [74%]; M1: 52 [26%]). Among M0 patients, 108 underwent radical cystectomy (RC) (NAC: 71; RC only: 23; adjuvant chemotherapy: 14); 14 received chemoradiotherapy; the rest received chemotherapy alone or no cancer-directed therapy. RC-only patients had a median follow-up of 9.1 years, and median disease-free survival (DFS) and overall survival (OS) were 1.1 and 1.2 years, respectively. NAC patients had pathologic response (

Supplementary Figure 1. Copy number pattern in paired lung cancer primaries and brain metastases from patients PP1, PP2, and PP4. Red, blue, and white indicate gain, loss, neutral states, respectively. The total number of shared cytobands (percent shared) between the primary and brain metastases are indicated at the bottom. Supplementary Figure 2A. Genome-wide copy number profile of the PP1 brain metastasis (A) and lung primary (B). Total log-copy ratio (logR) in the tumor versus normal and minor allele frequency (MAF) in the tumor are plotted. Yellow line represents joint segmentation of logR and MAF. Supplementary Figure 2B. Genome-wide copy number profile of the PP4 brain metastasis (A) and lung primary (B). Supplementary Figure 2C. Genome-wide copy number profile of the PP2 brain metastasis (A) and lung primary (B). Pooled normal was used to generate the logR plot. Supplementary Figure 2D. Genome-wide copy number profile of the PP3 brain metastasis (A) and PP5 brain metastasis (B).

ZMEL RNAseq table S2

A375 Lipidomics

ZMEL in vitro lipidomics

ZMEL in vivo lipidomics

Large-scale genomic characterization of squamous cell lung cancers (SQCLC) has revealed several putative oncogenic drivers. There are, however, little data to suggest that these alterations have clinical relevance. We performed comprehensive genomic profiling (including next-generation sequencing) of 79 stage IV SQCLCs and analyzed differences in the clinical characteristics of two major SQCLC subtypes: FGFR1 amplified and PI3K aberrant. Patients with PI3K-aberrant tumors had aggressive disease marked by worse survival (median overall survival, 8.6 vs. 19.1 months, P < 0.001), higher metastatic burden (>3 organs, 18% vs. 3%, P = 0.025), and greater incidence of brain metastases (27% vs. 0% in others, P < 0.001). We performed whole-exome and RNA sequencing on paired brain metastases and primary lung cancers to elucidate the metastatic process to brain. SQCLC primaries that gave rise to brain metastases exhibited truncal PTEN loss. SQCLC brain metastases exhibited a high degree of genetic heterogeneity and evidence of clonal differences between their primary sites.Significance: We performed next-generation sequencing of metastatic SQCLCs and primary lung–brain metastasis pairs, identifying PI3K-aberrant tumors as an aggressive subset associated with brain metastases. We identified genetic heterogeneity between lung primaries–brain metastases as well as clonal populations that may highlight alterations important in the metastatic process. Cancer Discov; 5(6); 610–21. ©2015 AACR.This article is highlighted in the In This Issue feature, p. 565

Supplementary Table 1. Complete list of somatic mutations and copy number alterations detected by IMPACT in 79 stage IV SQCLC patients with clinical annotation.

Supplementary Table 2. Frequency of selected genes in SQ-MAP vs. TCGA. Supplementary Table 3. Clinical characteristics of patients with resected brain metastases. Supplementary Table 4. Genes exhibiting clonal evolution from primary lung tumors and matched brain metastases. Data from PP1 and PP4 are shown. Supplementary Table 5. List of 279 oncogenes and tumor suppressors covered by the Integrated Mutation Profiling of Actionable Cancer Targets (IMPACT) assay. Supplementary Table 6. Computational estimates of tumor purity (fractional) from whole-exome sequencing data for the paired lung primary and brain metastasis tumor samples compared to pathological evaluation of tumor purity in the same samples.

Supplementary Figure S1: RNA-seq analysis of human A375 melanoma cells in co-culture with adipocytes compared to monoculture controls Supplementary Figure S2: Lipid transfer and melanoma cell uptake of long chain fatty acids Supplementary Figure S3: Adipocytes do not increase glycolysis in melanoma cells Supplementary Figure S4: Endogenous FATP expression in control tissues and patient-derived xenografts Supplementary Figure S5: Absence of lipid-laden tumor cells in in vivo setting devoid of adipocytes Supplementary Figure S6: Lipidomics reveals specific lipid species transferred from adipocytes to melanoma cells Supplementary Figure S7: FATP1 expression and survival Supplementary Figure S8: Validation of FATP1 knockdown Supplementary Figure S9: Lipofermata targets FATP1 Supplementary Figure S10: Compounds that are structurally similar to Lipofermata do not cause decreases in lipid uptake or increased cell death Supplementary Figure S11: Measurement of apoptosis in Lipofermata-treated cells

A375 RNA-seq

Supplementary Figure 3. Photomicrographs of chromosome 5p FISH from PP1-PP4 brain metastases. Red signals correspond to 5p13.

Supplementary Figure 4. Heatmap of mRNA expression for clustered protocadherin gene families. A. gamma cluster and B. beta cluster in 178 TCGA lung squamous cell carcinomas and 5 MSKCC brain metastasis samples. Yellow ticks under the dendrogram indicate the brain metastasis samples.

Primary prostate cancer (PCa) can show marked molecular heterogeneity. However, systematic analyses comparing primary PCa and matched metastases in individual patients are lacking. We aimed to address the molecular aspects of metastatic progression while accounting for the heterogeneity of primary PCa. In this pilot study, we collected 12 radical prostatectomy (RP) specimens from men who subsequently developed metastatic castration-resistant prostate cancer (mCRPC). We used histomorphology (Gleason grade, focus size, stage) and immunohistochemistry (IHC) (ERG and p53) to identify independent tumors and/or distinct subclones of primary PCa. We then compared molecular profiles of these primary PCa areas to matched metastatic samples using whole-exome sequencing (WES) and amplicon-based DNA and RNA sequencing. Based on combined pathology and molecular analysis, seven (58%) RP specimens harbored monoclonal and topographically continuous disease, albeit with some degree of intratumor heterogeneity; four (33%) specimens showed true multifocal disease; and one displayed monoclonal disease with discontinuous topography. Early (truncal) events in primary PCa included SPOP p.F133V (one patient), BRAF p.K601E (one patient), and TMPRSS2:ETS rearrangements (eight patients). Activating AR alterations were seen in nine (75%) mCRPC patients, but not in matched primary PCa. Hotspot TP53 mutations, found in metastases from three patients, were readily present in matched primary disease. Alterations in genes encoding epigenetic modifiers were observed in several patients (either shared between primary foci and metastases or in metastatic samples only). WES-based phylogenetic reconstruction and/or clonality scores were consistent with the index focus designated by pathology review in six out of nine (67%) cases. The three instances of discordance pertained to monoclonal, topographically continuous tumors, which would have been considered as unique disease in routine practice. Overall, our results emphasize pathologic and molecular heterogeneity of primary PCa, and suggest that comprehensive IHC-assisted pathology review and genomic analysis are highly concordant in nominating the 'index' primary PCa area. © 2022 The Authors. The Journal of Pathology published by John WileySons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

(A) Oncoprint showing similar rates of driver alterations between cutaneous melanomas and melanomas of unknown primary. This suggests the vast majority of melanomas of unknown primary arose from regressed cutaneous sites. (B) Sequenced melanomas arising in females (N=244) have a lower median TMB than those arising in males (N=475; 15.7 vs 17.6 mut/Mb, p= 0.024). (C) The median fraction of alterations associated with an ultraviolet signature varies by MAPK Driver (p=9.6e-9). (D) The median fraction of alterations associated with an ultraviolet signature varies by primary site (p=3.9e-10).

Supplementary Table 3: Clinicopathologic characteristics

Supplementary Table 5: Detailed summary of NOTCH gene family mutations

MAF file

A. Overall survival in derivation (MSKCC) and validation (European) cohorts. B. Recurrence free survival in derivation (MSKCC) and validation (European) cohorts.

Kaplan-Meier curves for overall survival. (A) PD-1 monotherapy by driver class. (B) nivolumab plus ipilimumab by driver class. (C) PD-1 monotherapy by primary site of melanoma. (D) nivolumab plus ipilimumab by primary site of melanoma.

Supplementary Table 6: Complete list of potentially-actionable alterations

Supplementary Table 4: Complete list of mutations and copy number alterations detected by MSK-IMPACT in 45 LCNECs

mRNA expression and hierarchical clustering of molecular risk score (MRS) genes in colorectal liver metastases in derivation cohort. Genes are indicated on the x-axis, patients on the y-axis.

Clinical data for patients receiving genomically-matched therapy following PD-1 progression.

Supplementary Table 7: Detailed chemosensitivity data

Purpose: Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a highly aggressive neoplasm, whose biologic relationship to small cell lung carcinoma (SCLC) versus non-SCLC (NSCLC) remains unclear, contributing to uncertainty regarding optimal clinical management. To clarify these relationships, we analyzed genomic alterations in LCNEC compared with other major lung carcinoma types.Experimental Design: LCNEC (n = 45) tumor/normal pairs underwent targeted next-generation sequencing of 241 cancer genes by Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) platform and comprehensive histologic, immunohistochemical, and clinical analysis. Genomic data were compared with MSK-IMPACT analysis of other lung carcinoma histologies (n = 242).Results: Commonly altered genes in LCNEC included TP53 (78%), RB1 (38%), STK11 (33%), KEAP1 (31%), and KRAS (22%). Genomic profiles segregated LCNEC into 2 major and 1 minor subsets: SCLC-like (n = 18), characterized by TP53+RB1 co-mutation/loss and other SCLC-type alterations, including MYCL amplification; NSCLC-like (n = 25), characterized by the lack of coaltered TP53+RB1 and nearly universal occurrence of NSCLC-type mutations (STK11, KRAS, and KEAP1); and carcinoid-like (n = 2), characterized by MEN1 mutations and low mutation burden. SCLC-like and NSCLC-like subsets revealed several clinicopathologic differences, including higher proliferative activity in SCLC-like tumors (P < 0.0001) and exclusive adenocarcinoma-type differentiation marker expression in NSCLC-like tumors (P = 0.005). While exhibiting predominant similarity with lung adenocarcinoma, NSCLC-like LCNEC harbored several distinctive genomic alterations, including more frequent mutations in NOTCH family genes (28%), implicated as key regulators of neuroendocrine differentiation.Conclusions: LCNEC is a biologically heterogeneous group of tumors, comprising distinct subsets with genomic signatures of SCLC, NSCLC (predominantly adenocarcinoma), and rarely, highly proliferative carcinoids. Recognition of these subsets may inform the classification and management of LCNEC patients. Clin Cancer Res; 22(14); 3618–29. ©2016 AACR.

Supplementary Data Legends

CONSORT diagram of samples that underwent MSK-IMPACT sequencing and analyzed for clinical outcomes with frontline PD-1 based therapy.

Purpose: Risk stratification after surgery for colorectal cancer liver metastases (CRLM) is achieved using clinicopathologic variables, however, is of limited accuracy. We sought to derive and externally validate a multigene expression assay prognostic of overall survival (OS) that is superior to clinicopathologic variables in patients with surgically resected CRLM.Experimental Design: We measured mRNA expression in prospectively collected frozen tumor from 96 patients with surgically resected CRLM at Memorial Sloan Kettering Cancer Center (MSKCC, New York, NY). We retrospectively generated a 20-gene molecular risk score (MRS) and compared its prognostic utility for OS and recurrence-free survival (RFS) with three common clinical risk scores (CRS). We then tested the prognostic ability of the MRS in an external validation cohort (European) of 119 patients with surgically resected CRLM at the University Medical Center Utrecht (Utrecht, the Netherlands) and Paul Brousse Hospital (Villejuif, France).Results: For OS in the MSKCC cohort, MRS was the strongest independent prognosticator (HR, 3.7–4.9; P < 0.001) followed by adjuvant chemotherapy (HR, 0.3; P ≤ 0.001). For OS in the European cohort, MRS was the only independent prognosticator (HR, 3.5; P = 0.007). For RFS, MRS was also independently prognostic in the MSKCC cohort (HR, 2.4–2.6; P ≤ 0.001) and the European cohort (HR, 1.6–2.5; P ≤ 0.05).Conclusions: Compared with CRSs, the MRS is more accurate, broadly applicable, and an independent prognostic biomarker of OS in resected CRLM. This MRS is the first externally validated prognostic multigene expression assay after metastasectomy for CRLM and warrants prospective validation. Clin Cancer Res; 22(10); 2575–82. ©2016 AACR.

Clinical data for each sequenced sample.

Supplementary Table 2: Immunohistochemical antibodies and protocols

Univariate and multivariate analyses of recurrence free survival in derivation (Table 1) and validation (Table 2) cohorts.

Genes selected for molecular risk score (MRS).

Purpose:Cutaneous and unknown primary melanomas frequently harbor alterations that activate the MAPK pathway. Whether MAPK driver detection beyond BRAF V600 is clinically relevant in the checkpoint inhibitor era is unknown.Experimental Design:Patients with melanoma were prospectively offered tumor sequencing of 341–468 genes. Oncogenic alterations in 28 RTK-RAS-MAPK pathway genes were used to construct MAPK driver groups. Time to treatment failure (TTF) was determined for patients who received first-line programmed cell death protein 1 (PD-1) monotherapy, nivolumab plus ipilimumab, or subsequent genomically matched targeted therapies. A Cox proportional hazards model was constructed for TTF using driver group and clinical variables.Results:A total of 670 of 696 sequenced melanomas (96%) harbored an oncogenic RTK-RAS-MAPK pathway alteration; 33% had ≥1 driver. Nine driver groups varied by clinical presentation and mutational burden. TTF of PD-1 monotherapy (N = 181) varied by driver, with worse outcomes for NRAS Q61 and BRAF V600 versus NF1 or other alterations (median 4.2, 7.5, 22, and not reached; P < 0.0001). Driver group remained significant, independent of tumor mutational burden and clinical features. TTF did not vary by driver for nivolumab plus ipilimumab (N = 141). Among 172 patients with BRAF V600 wild-type melanoma who progressed on checkpoint blockade, 27 were treated with genomically matched therapy, and eight (30%) derived clinical benefit lasting ≥6 months.Conclusions:Targeted capture multigene sequencing can detect oncogenic RTK-RAS-MAPK pathway alterations in almost all cutaneous and unknown primary melanomas. TTF of PD-1 monotherapy varies by mechanism of ERK activation. Oncogenic kinase fusions can be successfully targeted in immune checkpoint inhibitor–refractory melanoma.

Supplementary Figure 1: Example of KRAS-mutated LCNEC: Illustration of NGS and histopathologic features; Supplementary Figure 2: Dot plots for total number of genomic alterations in carcinoid-like vs other LCNECs; Supplementary Figure 3: OncoPrints for chromatin modifier, neurogenesis/neural function, DNA replication/repair and PI3K-AKT-mTOR pathway genes; Supplementary Figure 4: Map of NOTCH mutations; Supplementary Figure 5: Expression of NE genes in transcriptional subgroups of lung adenocarcinoma in TCGA series; Supplementary Figure 6: Kaplan-Meier survival curves

Univariate analysis of TTF outcomes to PD-1 monotherapy.

Supplementary Table 1: Next-generation sequencing (MSK-IMPACT) gene panel

It is unclear why some melanomas aggressively metastasize while others remain indolent. Available studies employing multi-omic profiling of melanomas are based on large primary or metastatic tumors. We examine the genomic landscape of early-stage melanomas diagnosed prior to the modern era of immunological treatments. Untreated cases with Stage II/III cutaneous melanoma were identified from institutions throughout the United States, Australia and Spain. FFPE tumor sections were profiled for mutation, methylation and microRNAs. Preliminary results from mutation profiling and clinical pathologic correlates show the distribution of four driver mutation sub-types: 31% BRAF; 18% NRAS; 21% NF1; 26% Triple Wild Type. BRAF mutant tumors had younger age at diagnosis, more associated nevi, more tumor infiltrating lymphocytes, and fewer thick tumors although at generally more advanced stage. NF1 mutant tumors were frequent on the head/neck in older patients with severe solar elastosis, thicker tumors but in earlier stages. Triple Wild Type tumors were predominantly male, frequently on the leg, with more perineural invasion. Mutations in TERT, TP53, CDKN2A and ARID2 were observed often, with TP53 mutations occurring particularly frequently in the NF1 sub-type. The InterMEL study will provide the most extensive multi-omic profiling of early-stage melanoma to date. Initial results demonstrate a nuanced understanding of the mutational and clinicopathological landscape of these early-stage tumors.

Clinical sequencing studies routinely involve molecular profiling of patients for mutations and copy number alterations. However, detection of "actionable" aberrations to guide treatment decisions require accurate, tumor purity-, ploidy-, and clonal heterogeneity-adjusted integer copy number calls. In this chapter, we describe the FACETS algorithm, an Allele-Specific Copy Number (ASCN) analysis tool with a broad application to whole-genome, whole-exome, as well as targeted panel sequencing platforms to annotate the genome for the detection of copy number alterations including homozygous/heterozygous deletions, copy-neutral loss-of-heterozygosity (LOH) events, allele-specific gains/amplifications, and cellular fraction profiles.We will describe some methodological details on joint segmentation of total and allele-specific copy number, on the estimation of integer copy number calls adjusting for tumor purity, ploidy, and intratumor heterogeneity, along with comprehensive output and integrated visualization. We also provide a tutorial on the installation, application, and tips to run and interpret FACETS.

Primary prostate cancer (PCa) can show marked molecular heterogeneity. However, systematic analyses comparing primary PCa and matched metastases in individual patients are lacking. We aimed to address the molecular aspects of metastatic progression while accounting for heterogeneity of primary PCa.In this pilot study, we collected 12 radical prostatectomy (RP) specimens from men who subsequently developed metastatic castration-resistant prostate cancer (mCRPC). We used histomorphology (Gleason grade, focus size, stage) and immunohistochemistry (IHC) (ERG and p53) to identify independent tumors and/or distinct subclones of primary PCa. We then compared molecular profiles of these primary PCa areas to matched metastatic samples using whole exome sequencing (WES) and amplicon-based DNA and RNA sequencing. Based on combined pathology and molecular analysis, seven (58%) RP specimens harbored monoclonal and topographically continuous disease, albeit with some degree of intra-tumor heterogeneity; four (33%) specimens showed true multifocal disease; and one displayed monoclonal disease with discontinuous topography. Early (truncal) events in primary PCa included SPOP p.F133V (one patient), BRAF p.K601E (one patient), and TMPRSS2:ETS rearrangements (nine patients). Activating AR alterations were seen in eight (67%) mCRPC patients, but not in matched primary PCa. Hotspot TP53 mutations, found in metastases from three patients, were readily present in matched primary disease. Alterations in genes encoding epigenetic modifiers were observed in several patients (either shared between primary foci and metastases or in metastatic samples only).WES-based phylogenetic reconstruction and/or clonality scores were consistent with the index focus designated by pathology review in six out of nine (67%) cases. The three instances of discordance pertained to monoclonal, topographically continuous tumors, which would have been considered as unique disease in routine practice.Overall, our results emphasize pathologic and molecular heterogeneity of primary PCa, and suggest that comprehensive IHC-assisted pathology review and genomic analysis are highly concordant in nominating the “index” primary PCa area.

PURPOSE Broad-panel sequencing of tumors facilitates routine care of people with cancer as well as clinical trial matching for novel genome-directed therapies. We sought to extend the use of broad-panel sequencing results to survival stratification and clinical outcome prediction. METHODS By using sequencing results from a cohort of 1,054 patients with advanced lung adenocarcinomas, we developed OncoCast, a machine learning tool for survival risk stratification and biomarker identification. RESULTS With OncoCast, we stratified this patient cohort into four risk groups on the basis of tumor genomic profile. Patients whose tumors harbored a high-risk profile had a median survival of 7.3 months (95% CI, 5.5 to 10.9 months) compared with a low-risk group with a median survival of 32.8 months (95% CI, 26.3 to 38.5 months) with a hazard ratio of 4.6 ( P < .001), far superior to any individual gene predictor or standard clinical characteristics. We found that comutations of both STK11 and KEAP1 are strong determinants of unfavorable prognosis with currently available therapies. In patients with targetable oncogenes (eg, EGFR, ALK, ROS1) who received targeted therapies, the tumor genetic background additionally differentiated survival with mutations in TP53 and ARID1A, which contributed to a higher risk score for shorter survival. CONCLUSION A mutational profile derived from broad-panel sequencing presents an effective genomic stratification for patient survival in advanced lung adenocarcinoma. OncoCast is available as a public resource that facilitates the incorporation of mutational data to predict individual patient prognosis and compare risk characteristics of patient populations.

To date, the vast preponderance of somatic variants observed in the cancer genome have been rare variants, and it is common in practice to encounter in a new tumor variants that have not been observed previously. Here we focus on probability estimation for encountering such hitherto unseen variants. We draw upon statistical methodology that has been developed in other fields of study, notably in species estimation in ecology, and word frequency estimation in computational linguistics. Analysis of whole-exome and targeted panel sequencing data sets reveal substantial variability in variant “richness” between genes that could be harnessed for clinically relevant problems. We quantify the variant-tissue association and show a strong gene-specific, lineage-dependent pattern of encountering new variants. This variability is largely determined by the proportion of observed variants that are rare. Our findings suggest that variants that occur at very low frequencies can harbor important signals that are clinically consequential., Sequencing cancer genomes reveals low frequency novel somatic variants without known function. Here, the authors leverage statistical methodology from the fields of computational linguistics and ecology to highlight the potentially important signals harboured by these novel variants that are often dismissed.

Objective To investigate associations between genetic mutations and qualitative as well as quantitative features on MRI in rectal adenocarcinoma at primary staging. Methods In this retrospective study, patients with rectal adenocarcinoma, genome sequencing, and pretreatment rectal MRI were included. Statistical analysis was performed to evaluate associations between qualitative features obtained from subjective evaluation of rectal MRI and gene mutations as well as between quantitative textural features and gene mutations. For the qualitative evaluation, Fisher’s Exact test was used to analyze categorical associations and Wilcoxon Rank Sum test was used for continuous clinical variables. For the quantitative evaluation, we performed manual segmentation of T2-weighted images for radiomics-based quantitative image analysis. Thirty-four texture features consisting of first order intensity histogram-based features (n = 4), second order Haralick textures (n = 5), and Gabor-edge based Haralick textures were computed at two different orientations. Consensus clustering was performed with 34 computed texture features using the K-means algorithm with Euclidean distance between the texture features. The clusters resulting from the algorithm were then used to enumerate the prevalence of gene mutations in those clusters. Results In 65 patients, 45 genes were mutated in more than 3/65 patients (5%) and were included in the statistical analysis. Regarding qualitative imaging features, on univariate analysis, tumor location was significantly associated with APC (p = 0.032) and RASA1 mutation (p = 0.032); CRM status was significantly associated with ATM mutation (p = 0.021); and lymph node metastasis was significantly associated with BRCA2 (p = 0.046) mutation. However, these associations were not significant after adjusting for multiple comparisons. Regarding quantitative imaging features, Cluster C1 had tumors with higher mean Gabor edge intensity compared with cluster C2 (θ = 0°, p = 0.018; θ = 45°, p = 0.047; θ = 90°, p = 0.037; cluster C3 (θ = 0°, p = 0.18; θ = 45°, p = 0.1; θ = 90°, p = 0.052), and cluster C4 (θ = 0°, p = 0.016; θ = 45°, p = 0.033; θ = 90°, p = 0.014) suggesting that the cluster C1 had tumors with more distinct edges or heterogeneous appearance compared with other clusters. Conclusions Although this preliminary study showed promising associations between quantitative features and genetic mutations, it did not show any correlation between qualitative features and genetic mutations. Further studies with larger sample size are warranted to validate our preliminary data.

Drug-based strategies to overcome tumour resistance to radiotherapy (R-RT) remain limited by the single-agent toxicity of traditional radiosensitizers (for example, platinums) and a lack of targeted alternatives. In a screen for compounds that restore radiosensitivity in p53 mutant zebrafish while tolerated in non-irradiated wild-type animals, we identified the benzimidazole anthelmintic oxfendazole. Surprisingly, oxfendazole acts via the inhibition of IRAK1, a kinase thus far implicated in interleukin-1 receptor (IL-1R) and Toll-like receptor (TLR) immune responses. IRAK1 drives R-RT in a pathway involving IRAK4 and TRAF6 but not the IL-1R/TLR-IRAK adaptor MyD88. Rather than stimulating nuclear factor-κB, radiation-activated IRAK1 prevented apoptosis mediated by the PIDDosome complex (comprising PIDD, RAIDD and caspase-2). Countering this pathway with IRAK1 inhibitors suppressed R-RT in tumour models derived from cancers in which TP53 mutations predict R-RT. Moreover, IRAK1 inhibitors synergized with inhibitors of PIN1, a prolyl isomerase essential for IRAK1 activation in response to pathogens and, as shown here, in response to ionizing radiation. These data identify an IRAK1 radiation-response pathway as a rational chemoradiation therapy target.

Background Comprehensive molecular profiling has revealed somatic variations in cancer at genomic, epigenomic, transcriptomic, and proteomic levels. The accumulating data has shown clearly that molecular phenotypes of cancer are complex and influenced by a multitude of factors. Conventional unsupervised clustering applied to a large patient population is inevitably driven by the dominant variation from major factors such as cell-of-origin or histology. Translation of these data into clinical relevance requires more effective extraction of information directly associated with patient outcome. Methods Drawing from ideas in supervised text classification, we developed survClust, an outcome-weighted clustering algorithm for integrative molecular stratification focusing on patient survival. survClust was performed on 18 cancer types across multiple data modalities including somatic mutation, DNA copy number, DNA methylation, and mRNA, miRNA, and protein expression from the Cancer Genome Atlas study to identify novel prognostic subtypes. Results Our analysis identified the prognostic role of high tumor mutation burden with concurrently high CD8 T cell immune marker expression and the aggressive clinical behavior associated with CDKN2A deletion across cancer types. Visualization of somatic alterations, at a genome-wide scale (total mutation burden, mutational signature, fraction genome altered) and at the individual gene level, using circomap further revealed indolent versus aggressive subgroups in a pan-cancer setting. Conclusions Our analysis has revealed prognostic molecular subtypes not previously identified by unsupervised clustering. The algorithm and tools we developed have direct utility toward patient stratification based on tumor genomics to inform clinical decision-making. The survClust software tool is available at https://github.com/arorarshi/survClust.

We wish to acknowledge the following funding bodies: National Health of Medical Research Council Career Development Fellowship AU, (KALC); Chan Zuckerberg initiative (AJA, VJC, ACC); Australian Research Council (AJA, KALC, MER); National Institute of Health, (EFDM, AD, SH); National Cancer Institute, (EFDM, VJC, CSG, GCY, EJF); Chair of bioinformatics in oncology of the CRCT (INSERM, Fondation Toulouse Cancer Santé, Pierre Fabre Research Institute, (AC, VP); Fondation Toulouse Cancer Santé, (AC, VP); Pierre Fabre Research Institute, (AC, VP); Michael Smith Foundation for Health Research, (AS); Mitacs, (AS); Kavli Neuroscience Discovery Institute, (GLSO); Johns Hopkins Provost Postdoctoral Fellowship, (GLSO); National Human Genome Research Institute, NIH, (JDW, VJC, CSG, WM, MIL); National Institute of Allergy and Infectious Diseases, NIH, (JDW, SH); National Institute of Mental Health, NIH, (JDW, MIL); EMBL PhD program, (RA); National Institutes of Health, National Institute on Aging, (GCY); DoD, (ACC); National Institute of Health, National Institute of Dental and Craniofacial Research, (EJF); Lustgarten Foundation, (EJF); Emerson Foundation, (EJF); Allegheny Health Network, (EJF). Peer Reviewed "Article signat per 31 autors/es: Kim-Anh Lê Cao, Al J. Abadi, Emily F. Davis-Marcisak, Lauren Hsu, Arshi Arora, Alexis Coullomb, Atul Deshpande, Yuzhou Feng, Pratheepa Jeganathan, Melanie Loth, Chen Meng, Wancen Mu, Vera Pancaldi, Kris Sankaran, Dario Righelli, Amrit Singh, Joshua S. Sodicoff, Genevieve L. Stein-O’Brien, Ayshwarya Subramanian, Joshua D. Welch, Yue You, Ricard Argelaguet, Vincent J. Carey, Ruben Dries, Casey S. Greene, Susan Holmes, Michael I. Love, Matthew E. Ritchie, Guo-Cheng Yuan, Aedin C. Culhane & Elana Fertig "