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Disease recurrence and drug resistance are major challenges in the clinical management of patients with colorectal cancer liver metastases (CLM), and because tumors are generally microsatellite stable (MSS), responses to immune therapies are poor. The mesenchymal phenotype is overrepresented in treatment-resistant cancers and is associated with an immunosuppressed microenvironment. The aim of this work was to molecularly identify and characterize a mesenchymal subgroup of MSS CLM to identify novel therapeutic approaches. We here generated a mesenchymal gene expression signature by analysis of resection specimens from 38 patients with CLM using ranked expression level of the epithelial-to-mesenchymal transition–related transcription factor PRRX1. Downstream pathway analysis based on the resulting gene signature was performed and independent, publicly available datasets were used to validate the findings. A subgroup comprising 16% of the analyzed CLM samples were classified as mesenchymal, or belonging to the PRRX1high group. Analysis of the PRRX1 signature genes revealed a distinct immunosuppressive phenotype with high expression of immune checkpoints HAVCR2/TIM-3 and VISTA, in addition to the M2 macrophage marker CD163. The findings were convincingly validated in datasets from three external CLM cohorts. Upregulation of immune checkpoints HAVCR2/TIM-3 and VISTA in the PRRX1high subgroup is a novel finding, and suggests immune evasion beyond the PD-1/PD-L1 axis, which may contribute to poor response to PD-1/PD-L1–directed immune therapy in MSS colorectal cancer. Importantly, these checkpoints represent potential novel opportunities for immune-based therapy approaches in a subset of MSS CLM.Significance:CLM is an important cause of colorectal cancer mortality where the majority of patients have yet to benefit from immunotherapies. In this study of gene expression profiling analyses, we uncovered novel immune checkpoint targets in a subgroup of patients with MSS CLMs harboring a mesenchymal phenotype.

Supplementary Table S2

Figure S1. Gene expression heatmaps. Identification of an EMT/MES subgroup.Figure S2. Validation of the PRRX1 signature in independent CLM data sets.Figure S3. CRP measure in patient blood samples analyzed across PRRX1 subgroups.

Suppl Data incl Figure S1 and Table S3

Supplementary File S1 Data S1-S9

Disease recurrence and drug resistance are major challenges in the clinical management of patients with colorectal cancer liver metastases (CLM), and because tumors are generally microsatellite stable (MSS), responses to immune therapies are poor. The mesenchymal phenotype is overrepresented in treatment-resistant cancers and is associated with an immunosuppressed microenvironment. The aim of this work was to molecularly identify and characterize a mesenchymal subgroup of MSS CLM to identify novel therapeutic approaches. We here generated a mesenchymal gene expression signature by analysis of resection specimens from 38 patients with CLM using ranked expression level of the epithelial-to-mesenchymal transition–related transcription factor PRRX1. Downstream pathway analysis based on the resulting gene signature was performed and independent, publicly available datasets were used to validate the findings. A subgroup comprising 16% of the analyzed CLM samples were classified as mesenchymal, or belonging to the PRRX1high group. Analysis of the PRRX1 signature genes revealed a distinct immunosuppressive phenotype with high expression of immune checkpoints HAVCR2/TIM-3 and VISTA, in addition to the M2 macrophage marker CD163. The findings were convincingly validated in datasets from three external CLM cohorts. Upregulation of immune checkpoints HAVCR2/TIM-3 and VISTA in the PRRX1high subgroup is a novel finding, and suggests immune evasion beyond the PD-1/PD-L1 axis, which may contribute to poor response to PD-1/PD-L1–directed immune therapy in MSS colorectal cancer. Importantly, these checkpoints represent potential novel opportunities for immune-based therapy approaches in a subset of MSS CLM.Significance:CLM is an important cause of colorectal cancer mortality where the majority of patients have yet to benefit from immunotherapies. In this study of gene expression profiling analyses, we uncovered novel immune checkpoint targets in a subgroup of patients with MSS CLMs harboring a mesenchymal phenotype.

Most patients whose large bowel cancer has spread to other organs do not respond to immune therapy. We detected a rare gene mutation, termed 9p24.1 copy-number gain (CNG), in an otherwise incurable colorectal cancer that provoked an immune therapy response. We identified this gene mutation by gene-panel sequencing of DNA from a liver metastasis biopsy from a patient who had disease refractory to standard therapies. Following immune checkpoint blockade (ICB) with pembrolizumab (anti–PD-1), the patient experienced conversion of the tumor phenotype from one with epithelial features to that of an inflamed microenvironment, detected by high-resolution RNA sequencing. Circulating tumor DNA disappeared over the first weeks of therapy. As assessed by standard radiographic measurement, the patient had a partial response that was durable. This patient's response may support the use of histology-agnostic ICB in solid tumors that carry the rare 9p24.1 CNG.

Supplementary Figures S1-S5, Tables 1-3. S1: Distributions of P values from interaction analysis of Discovery (a) and Validation (c) cohorts. Comparison of Permutation P values and parametric P values for interaction analysis in Discovery (b) and Validation (d) cohorts. S2: Plots of estimated fold-change distributions comparing TP53-WT to TP53-mutant tumors separated by ER status, PAM50 subtype, and Integrative Cluster (as in Figure 3). Gene lists were derived from (Gu-Trantien JCI 2013, reference 30), with probes listed in Supplementary Table 3. S3: Effect plots of CTL expression score grouped by Integrative Cluster and TP53 mutation status (as in Figure 1b). TP53-WT plotted in black, TP53-mutant plotted in blue. Points indicate mean, error bars indicate 95% confidence intervals calculated from twice the standard error. S4: Box plots of TP53 expression levels in tumors with no mutation or LOH (AB), LOH but no mutation (BB), or both mutation and LOH (B). S5: Plots of CTL pathway expression gene expression divided by TP53 mutation and LOH status in (left) HER2-enriched, ER-negative tumors and (right) HER2-enriched, ER-positive tumors. Supplemental Table 1: This table lists probes with significant interactions in both the Discovery and Validation cohort. Uncorrected P values are listed first, next to P values correct with Holm's multiple testing correction. Fold-change for each intrinsic subtype is listed. Supplemental Table 2: Gene symbols for TH1, TH2, and CTL pathways identified by Nanodissect analysis, along with microarray probe IDs assigned by manual review. NA for probe ID indicates no appropriate probe was found or was expressed above background on that platform. Supplementary Table 3: Gene symbols for TH1, TH2, TREG, and TH17 and CTL pathways identified by FACS sorting of individual cell populations followed by microarray analysis, along with Illumina microarray probe IDs assigned by manual review. NA for probe ID indicates no appropriate probe was found or was expressed above background on that platform.

Table shows the pathway deregulated in OUH and TCGA cohort.

Supplementary Figure legends. supplementary data

Purpose: To explore alterations in gene promoter methylation as a potential cause of acquired drug resistance to doxorubicin or combined treatment with 5-fluorouracil and mitomycin C in human breast cancers.Experimental Design: Paired tumor samples from locally advanced breast cancer patients treated with doxorubicin and 5-fluorouracil-mitomycin C were used in the genome-wide DNA methylation analysis as discovery cohort. An enlarged cohort from the same two prospective studies as those in the discovery cohort was used as a validation set in pyrosequencing analysis.Results: A total of 469 genes were differentially methylated after treatment with doxorubicin and revealed a significant association with canonical pathways enriched for immune cell response and cell-cycle regulating genes including CDKN2A, CCND2, CCNA1, which were also associated to treatment response. Treatment with FUMI resulted in 343 differentially methylated genes representing canonical pathways such as retinoate biosynthesis, gαi signaling, and LXR/RXR activation. Despite the clearly different genes and pathways involved in the metabolism and therapeutic effect of both drugs, 46 genes were differentially methylated before and after treatment with both doxorubicin and FUMI. DNA methylation profiles in genes such as BRCA1, FOXC1, and IGFBP3, and most notably repetitive elements like ALU and LINE1, were associated with TP53 mutations status.Conclusion: We identified and validated key cell-cycle regulators differentially methylated before and after neoadjuvant chemotherapy such as CDKN2A and CCNA1 and reported that methylation patterns of these genes may be potential predictive markers to anthracycline/mitomycine sensitivity. Clin Cancer Res; 20(24); 6357–66. ©2014 AACR.

PDF file - 29KB, Figure S6. Kaplan-Meier survival curves. A. OS of TP53 mutation status in whole cohort B. BCSS of TP53 mutation status in patients not treated with adjuvant chemotherapy. Numbers at risk are listed below each chart e = no. of all deaths (OS) or Breast Cancer specific deaths (BCSS).

Supplementary Figures Legends. Supplementary Figures S1-S3 Legends

Despite advances in diagnostics, less than 5% of patients with periampullary tumors experience an overall survival of five years or more. Periampullary tumors are neoplasms that arise in the vicinity of the ampulla of Vater, an enlargement of liver and pancreas ducts where they join and enter the small intestine. In this study, we analyzed copy number aberrations using Affymetrix SNP 6.0 arrays in 60 periampullary adenocarcinomas from Oslo University Hospital to identify genome-wide copy number aberrations, putative driver genes, deregulated pathways, and potential prognostic markers. Results were validated in a separate cohort derived from The Cancer Genome Atlas Consortium (n = 127). In contrast to many other solid tumors, periampullary adenocarcinomas exhibited more frequent genomic deletions than gains. Genes in the frequently codeleted region 17p13 and 18q21/22 were associated with cell cycle, apoptosis, and p53 and Wnt signaling. By integrating genomics and transcriptomics data from the same patients, we identified CCNE1 and ERBB2 as candidate driver genes. Morphologic subtypes of periampullary adenocarcinomas (i.e., pancreatobiliary or intestinal) harbor many common genomic aberrations. However, gain of 13q and 3q, and deletions of 5q were found specific to the intestinal subtype. Our study also implicated the use of the PAM50 classifier in identifying a subgroup of patients with a high proliferation rate, which had impaired survival. Furthermore, gain of 18p11 (18p11.21-23, 18p11.31-32) and 19q13 (19q13.2, 19q13.31-32) and subsequent overexpression of the genes in these loci were associated with impaired survival. Our work identifies potential prognostic markers for periampullary tumors, the genetic characterization of which has lagged. Cancer Res; 76(17); 5092–102. ©2016 AACR.

XLSX file - 94KB, Table S1 TP53 mutation status, TP53 IHC staining and lymphocyte infiltration. Table S2 Univariate Cox regression analysis (BCSS). Table S3 Multivariate Cox regression analysis (BCSS). Table S4 Multivariate Cox regression analysis (OS).

1 Figure and 4 Tables

PDF file, 120KB, Supplemental Table 1. Study Descriptions, Design and Sample Size for 19 DENSNP Studies. Caucasian Women Only. Supplemental Table 2. Source and Timing of Reproductive and Anthropometric Variable Collection. Supplemental Table 3. Description of Mammogram View, Side, Film Digitizer and Density Estimation Software for DENSNP Studies. Supplemental Table 4. Genotyping Platform and SNP Call Rates Performed by Each Study.

PDF file - 40KB, Figure S8. A. Frequency of TP53 mutations and LOH in PAM50 and IC subtypes. B. Kaplan-Meier survival curves showing BCSS of TP53 mutation status in combination with LOH and MDM2 amplification. C. BCSS of TP53 mutation status combined with lymphocyte infiltration in ER negative patients. Numbers at risk are listed below each chart e = no. of all deaths (OS) or Breast Cancer specific deaths (BCSS).

Supplementary Table 1: Distribution of clinico-pathological parameters among 112 patients included in the validation set. Supplementary Table 2: Assays and primers used for testing gene profile. Supplementary Table 3: Distribution of clinico-pathological parameters among 146 patients of the training set. Supplementary Table 4: Characteristics of patients included in and excluded from the technical transfer of the DBCG-RT profile to RT-qPCR and formalin fixed, paraffin embedded tissue (FFPE). Supplementary Table 5: Distribution of patients with "High LRR risk" and "Low LRR risk" within subgroups of clinical relevance for administration of PMRT.

Supplementary Figure 1. Canonical pathway:Communication between Innate and Adaptive Immune Cells

Supplementary Figure S2. Hierarchical clustering of microRNA expression

Table S1: Genes significantly differentially expressed in samples with pathological complete response compared to those without in the Combination arm. Table S2: DAVID analysis of 720 differentially expressed genes in tumors that achieved pathological complete reponse compared to those that did not, in the combination arm. Table S3 : Genes significantly differentially expressed in samples with pathological complete response compared to those without in the Chemotherapy arm. Table S4: DAVID analysis of 1243 differentially expressed genes in tumors that achieved pathological complete reponse compared to those that did not, in the chemotherapy arm. Table S5 : List of genes with significant lower expression in week 12 samples treated with combination therapy compared to those treated with chemotherapy Table S6: DAVID analysis of 42 differentially expressed genes in week-12 tumors in the combination arm compared to chemotherapy arm. Table S7 : Differentially expressed genes between week-0 and week-12 in Basal-like tumors treated with combination therapy Table S8 : Differentially expressed genes between week-0 and week-12 Luminal B tumors treated with combination therapy Table S9 : Differentially expressed genes between week-0 and week-12 Luminal A tumors treated with combination therapy Table S10: Differentially expressed genes between week-0 and week-12 Basal-like tumors treated with chemotherapy Table S11 : Differentially expressed genes between week-0 and week-12 Luminal B tumors treated with chemotherapy Table S12 : Differentially expressed genes between week-0 and week-12 Luminal A tumors treated with chemotherapy Table S13: Pathways significantly altered between Chemotherapy and Combination therapy arms from week-0 to week-12 LuminalB samples Table S14: Pathways significantly altered between anthracycline and taxane treatment in the Luminal A samples in chemotherapy arm

Substitution signature exposures estimated in 640 breast cancer WGS and the ovarian sample (AOCS-166).

The table shows the genes, chromosomal location and frequency of deletion and amplification of genes in OUH (PAs (n=54), cell lines (n=3) and IPMN (n=3) and TCGA data sets (n=127).

PDF file - 22KB, Figure S4. Distribution of TP53 mutations in IC subtypes. A. METABRIC cohort. B. TCGA cohort.

PDF file - 162KB, Figure S5. Boxplot diagram showing APOBEC3B mRNA expression levels as log intensity values in TP53 mutated and wildtype samples stratified by PAM50 subtypes. The bold black line represents the mean APOBEC3B expression value and the box represents 25th and 75th percentiles.

The usefulness of mechanistic models to disentangle complex multiscale cancer processes, such as treatment response, has been widely acknowledged. However, a major barrier for multiscale models to predict treatment outcomes in individual patients lies in their initialization and parametrization, which needs to reflect individual cancer characteristics accurately. In this study, we use multitype measurements acquired routinely on a single breast tumor, including histopathology, MRI, and molecular profiling, to personalize parts of a complex multiscale model of breast cancer treated with chemotherapeutic and antiangiogenic agents. The model accounts for drug pharmacokinetics and pharmacodynamics. We developed an open-source computer program that simulates cross-sections of tumors under 12-week therapy regimens and used it to individually reproduce and elucidate treatment outcomes of 4 patients. Two of the tumors did not respond to therapy, and model simulations were used to suggest alternative regimens with improved outcomes dependent on the tumor's individual characteristics. It was determined that more frequent and lower doses of chemotherapy reduce tumor burden in a low proliferative tumor while lower doses of antiangiogenic agents improve drug penetration in a poorly perfused tumor. Furthermore, using this model, we were able to correctly predict the outcome in another patient after 12 weeks of treatment. In summary, our model bridges multitype clinical data to shed light on individual treatment outcomes.Significance:Mathematical modeling is used to validate possible mechanisms of tumor growth, resistance, and treatment outcome.

PDF file, 20KB, Supplemental Table 5a. Associations of Common Breast Cancer Susceptibility Variants with Adjusted Percent Mammographic Density. Supplemental Table 5b. Associations of Common Breast Cancer Susceptibility Variants with Adjusted Mammographic Dense Area. Supplemental Table 5c. Associations of Common Breast Cancer Susceptibility Variants with Adjusted Mammographic Non Dense Area.

This file contains the legends of supplementary figures and tables.

Purpose: Triple-negative breast cancers (TNBC) are associated with high risk of early tumor recurrence and poor outcome. Common prognostic biomarkers give very restricted predictive information of tumor recurrences in TNBC. Human serum contains stably expressed microRNAs (miRNAs), which have been discovered to predict prognosis in patients with cancer. The purpose of this study was to identify circulating biomarkers able to predict clinical outcome in TNBC.Experimental Design: We performed genome-wide serum miRNA expression and real-time PCR analyses to investigate the ability of miRNAs in predicting tumor relapse in serum samples from 60 primary TNBC. Patients were divided into training and testing cohorts.Results: By Cox regression analysis, we identified a four-miRNA signature (miR-18b, miR-103, miR-107, and miR-652) that predicted tumor relapse and overall survival. This miRNA signature was further validated in an independent cohort of 70 TNBC. A high-risk signature score was developed and significantly associated with tumor recurrence and reduced survival. Multivariate Cox regression models indicated that the risk score based on the four-miRNA signature was an independent prognostic classifier of patients with TNBC.Conclusions: This signature may serve as a minimally invasive predictor of tumor relapse and overall survival for patients with TNBC. This prediction model may ultimately lead to better treatment options for patients with TNBC. Clin Cancer Res; 21(5); 1207–14. ©2014 AACR.

Mismatch repair (MMR)–deficient cancers have been discovered to be highly responsive to immune therapies such as PD-1 checkpoint blockade, making their definition in patients, where they may be relatively rare, paramount for treatment decisions. In this study, we utilized patterns of mutagenesis known as mutational signatures, which are imprints of the mutagenic processes associated with MMR deficiency, to identify MMR-deficient breast tumors from a whole-genome sequencing dataset comprising a cohort of 640 patients. We identified 11 of 640 tumors as MMR deficient, but only 2 of 11 exhibited germline mutations in MMR genes or Lynch Syndrome. Two additional tumors had a substantially reduced proportion of mutations attributed to MMR deficiency, where the predominant mutational signatures were related to APOBEC enzymatic activity. Overall, 6 of 11 of the MMR-deficient cases in this cohort were confirmed genetically or epigenetically as having abrogation of MMR genes. However, IHC analysis of MMR-related proteins revealed all but one of 10 samples available for testing as MMR deficient. Thus, the mutational signatures more faithfully reported MMR deficiency than sequencing of MMR genes, because they represent a direct pathophysiologic readout of repair pathway abnormalities. As whole-genome sequencing continues to become more affordable, it could be used to expose individually abnormal tumors in tissue types where MMR deficiency has been rarely detected, but also rarely sought. Cancer Res; 77(18); 4755–62. ©2017 AACR.

Mutations in MMR genes

Detection of MMR deficient breast cancers in whole exome sequencing data (WES)

1a) Amplification and deletion of cytobands in OUH cohort. 1b) Amplification and deletion of cytobands in TCGA cohort. The rows represent the cytobands while the columns represent the samples. The color bars on y-axis represent the chromosomes. The red color in the heatmap represents the amplification, green color represent the deletion while black represent the normal state.

PDF file - 26KB, Figure S2. Distribution of TP53 mutation class in breast cancer subtypes. A. PAM50 and B. IC subtypes.

The minor allele frequency is in blue and purple is the total copy number.

PDF file, 133KB, Supplemental Table 6a. Per-Allele Association (SE) of Breast Cancer Susceptibility Variants with Mammographic Percent Density Stratifying by Case Status, BMI, Menopausal Status and PMH Use. Supplemental Table 6b. Per-Allele Associations (SE) of Breast Cancer Susceptibility Variants with Adjusted Mammographic Dense Area Stratifying by Case Status, BMI Menopausal Status and PMH Use. Supplemental Table 6c. Per-Allele Associations (SE) of Breast Cancer Susceptibility Variants with Adjusted Mammographic Non Dense Area Stratifying by Case Status, BMI Menopausal Status and PMH Use.

Supplementary Table S2. The 71 microRNAs expressed in at least 75% of the samples in the training cohort

Purpose: Chemotherapy-induced alterations to gene expression are due to transcriptional reprogramming of tumor cells or subclonal adaptations to treatment. The effect on whole-transcriptome mRNA expression was investigated in a randomized phase II clinical trial to assess the effect of neoadjuvant chemotherapy with the addition of bevacizumab.Experimental Design: Tumor biopsies and whole-transcriptome mRNA profiles were obtained at three fixed time points with 66 patients in each arm. Altogether, 358 specimens from 132 patients were available, representing the transcriptional state before treatment start, at 12 weeks and after treatment (25 weeks). Pathologic complete response (pCR) in breast and axillary nodes was the primary endpoint.Results: pCR was observed in 15 patients (23%) receiving bevacizumab and chemotherapy and 8 patients (12%) receiving only chemotherapy. In the estrogen receptor–positive patients, 11 of 54 (20%) treated with bevacizumab and chemotherapy achieved pCR, while only 3 of 57 (5%) treated with chemotherapy reached pCR. In patients with estrogen receptor–positive tumors treated with combination therapy, an elevated immune activity was associated with good response. Proliferation was reduced after treatment in both treatment arms and most pronounced in the combination therapy arm, where the reduction in proliferation accelerated during treatment. Transcriptional alterations during therapy were subtype specific, and the effect of adding bevacizumab was most evident for luminal-B tumors.Conclusions: Clinical response and gene expression response differed between patients receiving combination therapy and chemotherapy alone. The results may guide identification of patients likely to benefit from antiangiogenic therapy. Clin Cancer Res; 23(16); 4662–70. ©2017 AACR.

Supplementary figures as referred to in the manuscript

Details of 11 MMR deficient breast cancers.

Table shows the correlation score (r) between allele frequency and gene expression in OUH and TCGA cohort

Purpose: In breast cancer, the TP53 gene is frequently mutated and the mutations have been associated with poor prognosis. The prognostic impact of the different types of TP53 mutations across the different molecular subtypes is still poorly understood. Here, we characterize the spectrum and prognostic significance of TP53 mutations with respect to the PAM50 subtypes and integrative clusters (IC).Experimental Design:TP53 mutation status was obtained for 1,420 tumor samples from the METABRIC cohort by sequencing all coding exons using the Sanger method.Results:TP53 mutations were found in 28.3% of the tumors, conferring a worse overall and breast cancer-specific survival [HR = 2.03; 95% confidence interval (CI), 1.65–2.48, P < 0.001], and were also found to be an independent marker of poor prognosis in estrogen receptor-positive cases (HR = 1.86; 95% CI, 1.39–2.49, P < 0.001). The mutation spectrum of TP53 varied between the breast cancer subtypes, and individual alterations showed subtype-specific association. TP53 mutations were associated with increased mortality in patients with luminal B, HER2-enriched, and normal-like tumors, but not in patients with luminal A and basal-like tumors. Similar observations were made in ICs, where mutation associated with poorer outcome in IC1, IC4, and IC5. The combined effect of TP53 mutation, TP53 LOH, and MDM2 amplification on mortality was additive.Conclusion: This study reveals that TP53 mutations have different clinical relevance in molecular subtypes of breast cancer, and suggests diverse roles for TP53 in the biology underlying breast cancer development. Clin Cancer Res; 20(13); 3569–80. ©2014 AACR.