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We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI's Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials.

Futibatinib, a highly selective, irreversible FGFR1-4 inhibitor, was evaluated in a large multihistology phase I dose-expansion trial that enrolled 197 patients with advanced solid tumors. Futibatinib demonstrated an objective response rate (ORR) of 13.7%, with responses in a broad spectrum of tumors (cholangiocarcinoma and gastric, urothelial, central nervous system, head and neck, and breast cancer) bearing both known and previously uncharacterized FGFR1-3 aberrations. The greatest activity was observed in FGFR2 fusion/rearrangement-positive intrahepatic cholangiocarcinoma (ORR, 25.4%). Some patients with acquired resistance to a prior FGFR inhibitor also experienced responses with futibatinib. Futibatinib demonstrated a manageable safety profile. The most common treatment-emergent adverse events were hyperphosphatemia (81.2%), diarrhea (33.5%), and nausea (30.4%). These results formed the basis for ongoing futibatinib phase II/III trials and demonstrate the potential of genomically selected early-phase trials to help identify molecular subsets likely to benefit from targeted therapy. SIGNIFICANCE: This phase I dose-expansion trial demonstrated clinical activity and tolerability of the irreversible FGFR1-4 inhibitor futibatinib across a broad spectrum of FGFR-aberrant tumors. These results formed the rationale for ongoing phase II/III futibatinib trials in cholangiocarcinoma, breast cancer, gastroesophageal cancer, and a genomically selected disease-agnostic population.This article is highlighted in the In This Issue feature, p. 275.

Additional file 1: Supplementary Figure 1. ALRN-6924 combined with eribulin synergistically inhibit clonogenic capacity of MCF-7 breast cancer cell line. MCF-7 colony formation assay treated with either vehicle, ALRN-6924, paclitaxel, or combination of both. Total colony area was measured by using NIH ImageJ software. Mean +/- SD is demonstrated. Supplementary Figure 2. In vitro validation of in vivo functional proteomic changes after treatment with ALRN-6924 and paclitaxel. A. MCF-7 cells were treated with vehicle, ALRN-6924 or paclitaxel in a dose-dependent fashion for 24 hours followed by immunoblotting with indicated antibodies. B. Quantification of relative p53 levels by standardizing to β-actin. C. Quantification of relative p21 levels by standardizing to β-actin. D. Quantification of relative c-Myc levels by standardizing to β-actin. E. Quantification of relative p-Rb (Ser807/811) levels by standardizing to β-actin. F. Quantification of relative LC3-I/LC3-II ratio levels by standardizing to β-actin. G. Quantification of relative LC3 levels by standardizing to β-actin. H. Quantification of relative p-S6 (Ser235/236) levels by standardizing to β-actin. Supplementary Figure 3. Immunoblotting of signaling pathways affected by ALRN-6924 combined with paclitaxel. A. MCF-7 cells were treated with vehicle, ALRN-6924, paclitaxel or combination of both in a dose-dependent fashion for 24 hours followed by immunoblotting with indicated antibodies. B. Quantification of relative p53 levels by standardizing to β-actin. C. Quantification of relative p21 levels by standardizing to β-actin. D. Quantification of relative p-Rb (Ser807/811) levels by standardizing to β-actin. E. Quantification of relative LC3-I/LC3-II ratio levels by standardizing to β-actin. Supplementary Figure 4. MDMX knockout cell line models. A. Immunoblotting of MDMX. Two MDMX knockout clones (M1 and M4, equally KO-1 and KO-2) were established in Myeloid leukemia cell line OCI-AML3 cells. MDMX was blotted with anti-MDMX antibody. B. Cell proliferation assay. Cell proliferation of two MDMX knockout cell line clones and control cell line was examined using the same methods as described in Fig. 1. C-D. Drug sensitivity assay. Two MDMX knockout cell line clones and control cell line were treated ALRN-6924 (C) and RG7388 (D) to assess the efficacy of these drugs using the same method as described in Fig. 1. Supplementary Table 1. Cell line panels for drug screening. Supplementary Table 2. IPA analysis of MCF-7 ALRN-6924 + Paclitaxel vs Control.

Additional file 1: Table S1. Summary of treatment-emergent adverse events in the phase I safety population. Table S2. Summary of treatment-emergent adverse events (TEAE) in all grades of severity. Figure S1. Waterfall plot of maximum change in tumor measurements (per RECIST v1.1) for evaluable patients. Figure S2. Kaplan-Meier plot showing progression-free survival (PFS) and overall survival (OS) for all treated patients.

BackgroundNo therapies for targeting KRAS mutations in cancer have been approved. The KRAS p.G12C mutation occurs in 13% of non-small-cell lung cancers (NSCLCs) and in 1 to 3% of colorectal cancers and other cancers. Sotorasib is a small molecule that selectively and irreversibly targets KRASG12C.MethodsWe conducted a phase 1 trial of sotorasib in patients with advanced solid tumors harboring the KRAS p.G12C mutation. Patients received sotorasib orally once daily. The primary end point was safety. Key secondary end points were pharmacokinetics and objective response, as assessed according to Response Evaluation Criteria in Solid Tumors (RECIST), version 1.1.ResultsA total of 129 patients (59 with NSCLC, 42 with colorectal cancer, and 28 with other tumors) were included in dose escalation and expansion cohorts. Patients had received a median of 3 (range, 0 to 11) previous lines of anticancer therapies for metastatic disease. No dose-limiting toxic effects or treatment-related deaths were observed. A total of 73 patients (56.6%) had treatment-related adverse events; 15 patients (11.6%) had grade 3 or 4 events. In the subgroup with NSCLC, 32.2% (19 patients) had a confirmed objective response (complete or partial response) and 88.1% (52 patients) had disease control (objective response or stable disease); the median progression-free survival was 6.3 months (range, 0.0+ to 14.9 [with + indicating that the value includes patient data that were censored at data cutoff]). In the subgroup with colorectal cancer, 7.1% (3 patients) had a confirmed response, and 73.8% (31 patients) had disease control; the median progression-free survival was 4.0 months (range, 0.0+ to 11.1+). Responses were also observed in patients with pancreatic, endometrial, and appendiceal cancers and melanoma.ConclusionsSotorasib showed encouraging anticancer activity in patients with heavily pretreated advanced solid tumors harboring the KRAS p.G12C mutation. Grade 3 or 4 treatment-related toxic effects occurred in 11.6% of the patients. (Funded by Amgen and others; CodeBreaK100 ClinicalTrials.gov number, NCT03600883.).

Additional file 1. Complete Statement of Competing Interest

We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer.

The significance of overlap (y-axis, calculated using Fisher exact test) between hotspot-mutation-containing-genes and previously known cancer genes at various adjusted p value cutoffs (x-axis). (PDF 37 kb)

Number of samples available in different TCGA cancer types. (PDF 66 kb)

Number of hotspot mutations identified in individual tumor types using COSMIC data. (PDF 183 kb)

The percentage of different mutational subtypes across all defined hotspot mutations. On each hotspot locus, only the mutational subtype that occupies the highest number of mutations was counted. (PDF 85 kb)

Twenty mutation subtypes that were included in the statistical modeling of hotspot mutation definition. (PDF 34 kb)

Number of samples in 17 tumor types in COSMIC v71. (PDF 57 kb)

The significance of overlap (y-axis, calculated using Fisher exact test) between hotspot-mutation-containing-genes and previously known cancer genes at various adjusted p value cutoffs (x-axis). (PDF 37 kb)

Number of samples in 17 tumor types in COSMIC v71. (PDF 57 kb)

Relationship between the number of hotspot mutations and the total number of mutations (mutation burden) in each tumor type. (PDF 79 kb)

The percentage of different mutational subtypes across all defined hotspot mutations. On each hotspot locus, only the mutational subtype that occupies the highest number of mutations was counted. (PDF 85 kb)

Hotspot mutations exclusively detected in only one tumor type in TCGA pan-cancer data. (PDF 70 kb)

2â Ă â 2 table of calculating the prevalence of target mutation B in samples A. (PDF 46 kb)

Prevalence of hotspot mutations in different TCGA cancer types. 82 hotspot mutations were highly prevalent in one or more cancer types. Most are highly prevalent in only one tumor type, while a few were in two or more tumor types. (PDF 44 kb)

Twenty mutation subtypes that were included in the statistical modeling of hotspot mutation definition. (PDF 34 kb)

Number of hotspot mutations identified in individual tumor types using COSMIC data. (PDF 183 kb)

Number of samples available in different TCGA cancer types. (PDF 66 kb)

2â Ă â 2 table of calculating the prevalence of target mutation B in samples A. (PDF 46 kb)

Although histopathological diagnosis is essential in decision of therapeutic strategy for gliomas, sometimes the tumors diagnosed in one histological entity show thoroughly different clinical courses. This phenomenon is believed to be due primarily to the presence of the genetic subgroup. In fact, relationship between treatment response and certain genetic characteristics is indicated (e.g. better chemosensitivity in glioma with losses of 1p/19q (−1p/19q)). It is highly likely that genetic classification of glioma is useful to select the adjuvant treatment. Additionally, gain of 7q (+7q) and −1p/19q are early events in 2 distinct tumor lineages, astrocytic tumors and oligodendroglial tumors, respectively, and these tumors obtain additional genetic aberration (−9p, 10q) with tumor progression. On the other hand, concerning the tumors without +7q or −1p/19q, little is known about clinically important genetic aberration. Therefore the study on such tumors could provide useful information for the prognosis prediction and the determination of treatment strategy. METHODS: We selected 39 cases of gliomas without +7q or −1p/19q from 200 adult supratentorial glioma cases surgically treated and analyzed chromosomal DNA copy number aberrations (CNAs) by comparative genomic hybridization (CGH) from 2005 to 2012. We correlated clinical features of these tumors with histological characteristics, CNAs and IDH1 status. RESULTS: The clinical course of gliomas without +7q or −1p/19q was not correlated with additional genetic aberration of -9p or 10q, which have been known as genetic markers for poor prognosis, and absence of +7q or −1p/19q was maintained at the time of recurrence. The tumors without +7q or −1p/19q showed relatively favorable prognosis although mutation of IDH1 was infrequent in these tumors (35.8 %). CONCLUSION: The gliomas without +7q or −1p/19q have clinical features distinct from the +7q and −1p/19q gliomas. Prognostic markers for each subgroups could help establish therapeutic strategy against the tumor. DNA methylation is a mechanism altering the normal state of cells implicated in many cancers. Currently the methylation status of MGMT is one of the most widely utilized clinical genetic tests performed on glioblastoma multiforme (GBM). While several global gene expression signatures have been developed, it is unclear if genome-wide DNA methylation signatures can predict prognosis in cancer. We used a computational algorithm (MethylMix) to analyze genome-wide DNA methylation in GBM data obtained from The Cancer Genome Atlas (TCGA). MethylMix identified a set of driver genes that met criteria for being both differential and functional. Differential refers to a difference in cancer methylation compared to normal tissue; functional refers to having a significant correlation with matched gene expression changes. We then used these MethylMix driver genes to build multivariate models of overall survival using linear regression and validated these models in independent data sets. Applying MethylMix and linear regression we identified a novel methylation signature predictive of overall survival, which we here define as the GLIOMETH signature. Interestingly, GLIOMETH did not include MGMT, suggesting that MGMT methylation is not essential to predict prognosis in GBM. GLIOMETH was prognostically significant even in a multivariate analysis with known prognostic covariates, including MGMT methylation. We validated GLIOMETH in two external DNA methylation data sets and two gene expression data sets, using a leveraging technique predicting methylation in terms of gene expression, showing also a significant survival correlation. Differential and functional DNA methylation is predictive of overall survival in GBM independent of known prognostic factors. We identified GLIOMETH as a DNA methylation signature that is predictive of overall survival in GBM, outperforming MGMT methylation. The GLIOMETH model validated across multiple independent DNA methylation and gene expression validation data sets demonstrating its robustness as an independent predictor of prognosis in GBM. Recent next-generation genomic studies of medulloblastoma have revealed an unexpected and overwhelming convergence of somatic alterations affecting chromatin-modifying genes. Estimates informed by next-generation sequencing implicate that at least one third of all medulloblastomas have somatic mutations in a chromatin modifier, including those targeting histone methyltransfereses, histone demethylases, and related chromatin modulators that collectively function to influence chromatin conformation associated gene expression states. These mutations occur across all four medulloblastoma subgroups although different sets of genes appear to be selectively altered in a subgroup-specific manner. Despite the abundance of evidence implicating deregulation of chromatin modifiers as a key event in medulloblastoma pathogenesis, the medulloblastoma epigenome remains largely unexplored, and studies cataloguing histone modification states on a genome-wide scale have yet to be reported. To comprehensively investigate the histone code in medulloblastoma and the consequences associated with mutations affecting histone-modifying genes, we have performed ChIP-sequencing on a set of well-characterized primary medulloblastoma specimens. Histone marks examined in this study include the six modifications mandated by the International Human Epigenome Consortium (IHEC), including H3K4me3, H3K9me3, H3K27me3, H3K27ac, H3K4me1 and H3K36me3. Chromatin isolates from primary fresh-frozen tissues representative of each medulloblastoma subgroup were immunoprecipitated with the indicated antibodies and sequenced with a HiSeq Illumina sequencer to obtain at least 10 million unique reads per ChIP experiment. Peak calling was performed using multiple publicly available tools and data integrated with existing ENCODE data for the same histone marks. Inter-subgroup comparisons of histone modification states revealed a wealth of distinguishing genomic regions that were highly correlated with alternative patterns of gene expression existing between the subgroups. Moreover, integration with existing mutational profiles demonstrated aberrant chromatin states that could be linked to underlying mutations in select chromatin modifiers. Ongoing work will focus on expanding the cohort and integration with all levels of ‘omic data.

BackgroundIncreased pathologic complete response (pCR) rates observed with neoadjuvant chemotherapy (NCT) for some subsets of patients with invasive breast cancer have prompted interest in whether patients who achieved a pCR can be identified preoperatively and potentially spared the morbidity of surgery. The objective of this multicenter, retrospective study was to estimate the accuracy of preoperative magnetic resonance imaging (MRI) in predicting a pCR in the breast.MethodsMRI studies at baseline and after the completion of NCT plus data regarding pathologic response were collected retrospectively from 746 women who received treatment at 8 institutions between 2002 and 2011. Tumors were characterized by immunohistochemical phenotype into 4 categories based on receptor expression: hormone (estrogen and progesterone) receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative (n = 327), HR-positive/HER2-positive, (n = 148), HR-negative/HER2-positive, (n = 101), and triple-negative (HR-negative/HER2 negative; n = 155). In all, 194 of 249 patients (78%) with HER2-positive tumors received trastuzumab. Univariate and multivariate analyses of factors associated with radiographic complete response (rCR) and pCR were performed.ResultFor the total group, the rCR and pCR rates were 182 of 746 patients (24%) and 179 of 746 patients (24%), respectively, and the highest pCR rate was observed for the triple-negative subtype (57 of 155 patients; 37%) and the HER2-positive subtype (38 of 101 patients; 38%). The overall accuracy of MRI for predicting pCR was 74%. The variables sensitivity, negative predictive value, positive predictive value, and accuracy differed significantly among tumor subtypes, and the greatest negative predictive value was observed in the triple-negative (60%) and HER2-positive (62%) subtypes.ConclusionsThe overall accuracy of MRI for predicting pCR in invasive breast cancer patients who were receiving NCT was 74%. The performance of MRI differed between subtypes, possibly influenced by differences in pCR rates between groups. Future studies will determine whether MRI in combination with directed core biopsy improves the predictive value of MRI for pathologic response.

Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumours. Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects. Here we use reverse-phase protein arrays to analyse 3,467 patient samples from 11 TCGA 'Pan-Cancer' diseases, using 181 high-quality antibodies that target 128 total proteins and 53 post-translationally modified proteins. The resultant proteomic data are integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumour lineages. In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumour lineages. This integrative analysis, with an emphasis on pathways and potentially actionable proteins, provides a framework for determining the prognostic, predictive and therapeutic relevance of the functional proteome.

Effects of selinexor on the cell cycle. SUM-159PT and MDA-MB-468 cells were treated with 1 nM paclitaxel alone (2 nM for MDA-MB-468), 400 nM selinexor alone, and the combination of both. After 72Â hours, cells were stained with propidium iodide and analyzed via flow cytometry. (DOCX 514 kb)

PIK3CA, PTEN, TP53 and Ras/Raf status of cell lines in panel. A panel of breast cancer cell lines was tested with selinexor, and cell growth was measured after 72Â hours of treatment using the SRB assay. IC50 was then calculated using isobologram curves. The mutation statuses for PIK3CA, PTEN, TP53, and Ras/Raf status are reported. (DOCX 23 kb)

Effects of selinexor in breast cancer models. A Eight breast cancer cell lines were treated with vehicle or 5 ΟM selinexor. Cells were lysed and blotted with the indicated antibodies. B The bands were quantified and normalized to their respective β-actin controls. Data are shown as relative protein levels vs vehicle control of cell lines treated with selinexor. (DOCX 397 kb)

Tolerance of selinexor in vivo. Body weight–time curve is shown. Data are presented as mean ± SEM. (DOCX 126 kb)

Figure S1. MK-2206 inhibits tumor growth in ZR75-1 xenografts. A. Scatter plots show the change in tumor volume (TV) calculated for each tumor using the formula (Vf-V0)/V0, where V0 is initial volume (at the beginning of treatment) and Vf is final volume (at the end of treatment). Lines are at mean with SD. Treatment groups (MK-2206 dosed at 240 or 480â mg/kg) were compared with the vehicle (30% Captisol (CYDEX Pharmaceuticals)). B. Heatmap of the 13 significant dose-dependent proteins at the FDR of 0.1 in ZR-75-1 xenografts. (PPTX 130 kb)

Functional implications of hotspot mutations in RNA and protein expression. In OV, tumor samples with missense hotspot mutations (I195, Y220, R248 and R273) in TP53 show higher TP53 RNA and protein expression than those with non-hotspot mutations and without TP53 mutations. * indicates p

Functional implications of hotspot mutations in RNA and protein expression. In OV, tumor samples with missense hotspot mutations (I195, Y220, R248 and R273) in TP53 show higher TP53 RNA and protein expression than those with non-hotspot mutations and without TP53 mutations. * indicates p

PIK3CA, PTEN, TP53 and Ras/Raf status of cell lines in panel. A panel of breast cancer cell lines was tested with selinexor, and cell growth was measured after 72Â hours of treatment using the SRB assay. IC50 was then calculated using isobologram curves. The mutation statuses for PIK3CA, PTEN, TP53, and Ras/Raf status are reported. (DOCX 23 kb)

Supplementary methods, tables, and figures. (DOCX 646 kb)

Actionable DNA alterations in PDXs. BCX 6, BCX 10, BCX 11, and BCX 22 were analyzed by targeted exome sequencing of 202 cancer-relevant targets and whole-exome sequencing. BCX 51 was analyzed using targeted exome sequencing of 265 genes that included XPO1. HDEL: copy number 4. (DOCX 15 kb)

Effects of selinexor in breast cancer models. A Eight breast cancer cell lines were treated with vehicle or 5 ΟM selinexor. Cells were lysed and blotted with the indicated antibodies. B The bands were quantified and normalized to their respective β-actin controls. Data are shown as relative protein levels vs vehicle control of cell lines treated with selinexor. (DOCX 397 kb)

Actionable DNA alterations in PDXs. BCX 6, BCX 10, BCX 11, and BCX 22 were analyzed by targeted exome sequencing of 202 cancer-relevant targets and whole-exome sequencing. BCX 51 was analyzed using targeted exome sequencing of 265 genes that included XPO1. HDEL: copy number 4. (DOCX 15 kb)

Supplementary methods, tables, and figures. (DOCX 646 kb)

Figure S1. MK-2206 inhibits tumor growth in ZR75-1 xenografts. A. Scatter plots show the change in tumor volume (TV) calculated for each tumor using the formula (Vf-V0)/V0, where V0 is initial volume (at the beginning of treatment) and Vf is final volume (at the end of treatment). Lines are at mean with SD. Treatment groups (MK-2206 dosed at 240 or 480â mg/kg) were compared with the vehicle (30% Captisol (CYDEX Pharmaceuticals)). B. Heatmap of the 13 significant dose-dependent proteins at the FDR of 0.1 in ZR-75-1 xenografts. (PPTX 130 kb)

Tolerance of selinexor in vivo. Body weight–time curve is shown. Data are presented as mean ± SEM. (DOCX 126 kb)

Effects of selinexor on the cell cycle. SUM-159PT and MDA-MB-468 cells were treated with 1 nM paclitaxel alone (2 nM for MDA-MB-468), 400 nM selinexor alone, and the combination of both. After 72Â hours, cells were stained with propidium iodide and analyzed via flow cytometry. (DOCX 514 kb)

Table S1. Sequencing data from a 202 gene platform (T200) for two best responders in the study cohort. (DOCX 18 kb)

Table S1. Sequencing data from a 202 gene platform (T200) for two best responders in the study cohort. (DOCX 18 kb)

Several tumour types are responsive to immunotherapy, as shown by regulatory approvals for immune checkpoint inhibitors. However, many patients either do not respond or do not have durable clinical benefit. Thus, there is great interest in developing predictors of response to immunotherapy and rational combination therapies that can enhance efficacy by overcoming primary and acquired resistance. In this Review, we provide an assessment of immunotherapy response biomarkers that can identify patients who will benefit from monotherapy rather than from combinations. We review the rationale for combination therapy and different strategies, including combinations with chemotherapy, targeted therapy, radiation therapy, intratumoural therapies, other immunomodulators, and adaptive cell therapy, including chimeric antigen T-cell receptors and other novel T-cell receptor-based therapies. There are many combination partners in development; therefore, a programmatic approach is needed to develop a framework for biomarker-driven combination therapy selection.