Analysis of circulating tumor DNA during checkpoint inhibition in metastatic melanoma using a tumor-agnostic panel.

Immunotherapy has revolutionized treatment of patients diagnosed with metastatic melanoma, where nearly half of patients receive clinical benefit. However, immunotherapy is also associated with immune-related adverse events, which may be severe and persistent. It is therefore important to identify patients that do not benefit from therapy early. Currently, regularly scheduled CT scans are used to investigate size changes in target lesions to evaluate progression and therapy response. This study aims to explore if panel-based analysis of circulating tumor DNA (ctDNA) taken at 3-week intervals may provide a window into the growing cancer, can be used to identify nonresponding patients early, and determine genomic alterations associated with acquired resistance to checkpoint immunotherapy without analysis of tumor tissue biopsies. We designed a gene panel for ctDNA analysis and sequenced 4-6 serial plasma samples from 24 patients with unresectable stage III or IV melanoma and treated with first-line checkpoint inhibitors enrolled at the Department of Oncology at Aarhus University Hospital, Denmark. TERT was the most mutated gene found in ctDNA and associated with a poor prognosis. We detected more ctDNA in patients with high metastatic load, which indicates that more aggressive tumors release more ctDNA into the bloodstream. Although we did not find evidence of specific mutations associated with acquired resistance, we did demonstrate in this limited cohort of 24 patients that untargeted, panel-based ctDNA analysis has the potential to be used as a minimally invasive tool in clinical practice to identify patients where the benefits of immunotherapy outweigh the drawbacks.

Increased Soluble PD-1 Predicts Response to Nivolumab plus Ipilimumab in Melanoma.

BACKGROUND: Checkpoint inhibitors have revolutionized the treatment of metastatic melanoma, yielding long-term survival in a considerable proportion of the patients. Yet, 40-60% of patients do not achieve a long-term benefit from such therapy, emphasizing the urgent need to identify biomarkers that can predict response to immunotherapy and guide patients for the best possible treatment. Here, we exploited an unsupervised machine learning approach to identify potential inflammatory cytokine signatures from liquid biopsies, which could predict response to immunotherapy in melanoma. METHODS: We studied a cohort of 77 patients diagnosed with unresectable advanced-stage melanoma undergoing treatment with first-line nivolumab plus ipilimumab or pembrolizumab. Baseline and on-treatment plasma samples were tested for levels of PD-1, PD-L1, IFNγ, IFNß, CCL20, CXCL5, CXCL10, IL6, IL8, IL10, MCP1, and TNFα and analyzed by Uniform Manifold Approximation and Projection (UMAP) dimension reduction method and k-means clustering analysis. RESULTS: Interestingly, using UMAP analysis, we found that treatment-induced cytokine changes measured as a ratio between baseline and on-treatment samples correlated significantly to progression-free survival (PFS). For patients treated with nivolumab plus ipilimumab we identified a group of patients with superior PFS that were characterized by significantly higher baseline-to-on-treatment increments of PD-1, PD-L1, IFNγ, IL10, CXCL10, and TNFα compared to patients with worse PFS. Particularly, a high PD-1 increment was a strong individual predictor for superior PFS (HR = 0.13; 95% CI 0.034-0.49; p = 0.0026). In contrast, decreasing levels of IFNγ and IL6 and increasing levels of CXCL5 were associated with superior PFS in the pembrolizumab group, although none of the cytokines were individually predictors for PFS. CONCLUSIONS: In short, our study demonstrates that a high increment of PD-1 is associated with superior PFS in advanced-stage melanoma patients treated with nivolumab plus ipilimumab. In contrast, decreasing levels of IFNγ and IL6, and increasing levels of CXCL5 are associated with response to pembrolizumab. These results suggest that using serial samples to monitor changes in cytokine levels early during treatment is informative for treatment response.

Transcriptomic and proteomic intra-tumor heterogeneity of colorectal cancer varies depending on tumor location within the colorectum.

BACKGROUND: Intra-tumor heterogeneity (ITH) of colorectal cancer (CRC) complicates molecular tumor classification, such as transcriptional subtyping. Differences in cellular states, biopsy cell composition, and tumor microenvironment may all lead to ITH. Here we analyze ITH at the transcriptomic and proteomic levels to ascertain whether subtype discordance between multiregional biopsies reflects relevant biological ITH or lack of classifier robustness. Further, we study the impact of tumor location on ITH. METHODS: Multiregional biopsies from stage II and III CRC tumors were analyzed by RNA sequencing (41 biopsies, 14 tumors) and multiplex immune protein analysis (89 biopsies, 29 tumors). CRC subtyping was performed using consensus molecular subtypes (CMS), CRC intrinsic subtypes (CRIS), and TUMOR types. ITH-scores and network maps were defined to determine the origin of heterogeneity. A validation cohort was used with one biopsy per tumor (162 tumors). RESULTS: Overall, inter-tumor transcriptional variation exceeded ITH, and subtyping calls were frequently concordant between multiregional biopsies. Still, some tumors had high transcriptional ITH and were classified discordantly. Subtyping of proximal MSS tumors were discordant for 50% of the tumors, this ITH was related to differences in the microenvironment. Subtyping of distal MSS tumors were less discordant, here the ITH was more cancer-cell related. The subtype discordancy reflected actual molecular ITH within the tumors. The relevance of the subtypes was reflected at protein level where several inflammation markers were significantly increased in immune related transcriptional subtypes, which was verified in an independent cohort (Wilcoxon rank sum test; p<0.05). Unsupervised hierarchical clustering of the protein data identified large ITH at protein level; as the multiregional biopsies clustered together for only 9 out of 29 tumors. CONCLUSION: Our transcriptomic and proteomic analyses show that the tumor location along the colorectum influence the ITH of CRC, which again influence the concordance of subtyping.

BCL9L Dysfunction Impairs Caspase-2 Expression Permitting Aneuploidy Tolerance in Colorectal Cancer.

Chromosomal instability (CIN) contributes to cancer evolution, intratumor heterogeneity, and drug resistance. CIN is driven by chromosome segregation errors and a tolerance phenotype that permits the propagation of aneuploid genomes. Through genomic analysis of colorectal cancers and cell lines, we find frequent loss of heterozygosity and mutations in BCL9L in aneuploid tumors. BCL9L deficiency promoted tolerance of chromosome missegregation events, propagation of aneuploidy, and genetic heterogeneity in xenograft models likely through modulation of Wnt signaling. We find that BCL9L dysfunction contributes to aneuploidy tolerance in both TP53-WT and mutant cells by reducing basal caspase-2 levels and preventing cleavage of MDM2 and BID. Efforts to exploit aneuploidy tolerance mechanisms and the BCL9L/caspase-2/BID axis may limit cancer diversity and evolution.

TumorTracer: a method to identify the tissue of origin from the somatic mutations of a tumor specimen.

BACKGROUND: A substantial proportion of cancer cases present with a metastatic tumor and require further testing to determine the primary site; many of these are never fully diagnosed and remain cancer of unknown primary origin (CUP). It has been previously demonstrated that the somatic point mutations detected in a tumor can be used to identify its site of origin with limited accuracy. We hypothesized that higher accuracy could be achieved by a classification algorithm based on the following feature sets: 1) the number of nonsynonymous point mutations in a set of 232 specific cancer-associated genes, 2) frequencies of the 96 classes of single-nucleotide substitution determined by the flanking bases, and 3) copy number profiles, if available. METHODS: We used publicly available somatic mutation data from the COSMIC database to train random forest classifiers to distinguish among those tissues of origin for which sufficient data was available. We selected feature sets using cross-validation and then derived two final classifiers (with or without copy number profiles) using 80 % of the available tumors. We evaluated the accuracy using the remaining 20 %. For further validation, we assessed accuracy of the without-copy-number classifier on three independent data sets: 1669 newly available public tumors of various types, a cohort of 91 breast metastases, and a set of 24 specimens from 9 lung cancer patients subjected to multiregion sequencing. RESULTS: The cross-validation accuracy was highest when all three types of information were used. On the left-out COSMIC data not used for training, we achieved a classification accuracy of 85 % across 6 primary sites (with copy numbers), and 69 % across 10 primary sites (without copy numbers). Importantly, a derived confidence score could distinguish tumors that could be identified with 95 % accuracy (32 %/75 % of tumors with/without copy numbers) from those that were less certain. Accuracy in the independent data sets was 46 %, 53 % and 89 % respectively, similar to the accuracy expected from the training data. CONCLUSIONS: Identification of primary site from point mutation and/or copy number data may be accurate enough to aid clinical diagnosis of cancers of unknown primary origin.

Clonal status of actionable driver events and the timing of mutational processes in cancer evolution.

Deciphering whether actionable driver mutations are found in all or a subset of tumor cells will likely be required to improve drug development and precision medicine strategies. We analyzed nine cancer types to determine the subclonal frequencies of driver events, to time mutational processes during cancer evolution, and to identify drivers of subclonal expansions. Although mutations in known driver genes typically occurred early in cancer evolution, we also identified later subclonal "actionable" mutations, including BRAF (V600E), IDH1 (R132H), PIK3CA (E545K), EGFR (L858R), and KRAS (G12D), which may compromise the efficacy of targeted therapy approaches. More than 20% of IDH1 mutations in glioblastomas, and 15% of mutations in genes in the PI3K (phosphatidylinositol 3-kinase)-AKT-mTOR (mammalian target of rapamycin) signaling axis across all tumor types were subclonal. Mutations in the RAS-MEK (mitogen-activated protein kinase kinase) signaling axis were less likely to be subclonal than mutations in genes associated with PI3K-AKT-mTOR signaling. Analysis of late mutations revealed a link between APOBEC-mediated mutagenesis and the acquisition of subclonal driver mutations and uncovered putative cancer genes involved in subclonal expansions, including CTNNA2 and ATXN1. Our results provide a pan-cancer census of driver events within the context of intratumor heterogeneity and reveal patterns of tumor evolution across cancers. The frequent presence of subclonal driver mutations suggests the need to stratify targeted therapy response according to the proportion of tumor cells in which the driver is identified.

Expression of regulators of mitotic fidelity are associated with intercellular heterogeneity and chromosomal instability in primary breast cancer.

Regulators of transition through mitosis such as SURVIVIN and Aurora kinase A (AURKA) have been previously implicated in the initiation of chromosomal instability (CIN), a driver of intratumour heterogeneity. We investigate the relationship between protein expression of these genes and directly quantified CIN, and their prognostic utility in breast cancer. The expression of SURVIVIN and AURKA was determined by immunohistochemistry in a cohort of 426 patients with primary breast cancer. The association between protein expression and histopathological characteristics, clinical outcome and CIN status, as determined by centromeric FISH and defined by modal centromere deviation, was analysed. Significantly poorer clinical outcome was observed in patients with high AURKA expression levels. Expression of SURVIVIN was elevated in ER-negative relative to ER-positive breast cancer. Both AURKA and SURVIVIN increased expression were significantly associated with breast cancer grade. There was a significant association between increased CIN and both increased AURKA and SURVIVIN expression. AURKA gene amplification was also associated with increased CIN. To our knowledge this is the largest study assessing CIN status in parallel with the expression of the mitotic regulators AURKA and SURVIVIN. These data suggest that elevated expression of AURKA and SURVIVIN, together with AURKA gene amplification, are associated with increased CIN in breast cancer, and may be used as a proxy for CIN in breast cancer samples in the absence of more advanced molecular measurements.

Systematic evaluation of the prognostic impact and intratumour heterogeneity of clear cell renal cell carcinoma biomarkers.

BACKGROUND: Candidate biomarkers have been identified for clear cell renal cell carcinoma (ccRCC) patients, but most have not been validated. OBJECTIVE: To validate published ccRCC prognostic biomarkers in an independent patient cohort and to assess intratumour heterogeneity (ITH) of the most promising markers to guide biomarker optimisation. DESIGN, SETTING, AND PARTICIPANTS: Cancer-specific survival (CSS) for each of 28 identified genetic or transcriptomic biomarkers was assessed in 350 ccRCC patients. ITH was interrogated in a multiregion biopsy data set of 10 ccRCCs. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Biomarker association with CSS was analysed by univariate and multivariate analyses. RESULTS AND LIMITATIONS: A total of 17 of 28 biomarkers (TP53 mutations; amplifications of chromosomes 8q, 12, 20q11.21q13.32, and 20 and deletions of 4p, 9p, 9p21.3p24.1, and 22q; low EDNRB and TSPAN7 expression and six gene expression signatures) were validated as predictors of poor CSS in univariate analysis. Tumour stage and the ccB expression signature were the only independent predictors in multivariate analysis. ITH of the ccB signature was identified in 8 of 10 tumours. Several genetic alterations that were significant in univariate analysis were enriched, and chromosomal instability indices were increased in samples expressing the ccB signature. The study may be underpowered to validate low-prevalence biomarkers. CONCLUSIONS: The ccB signature was the only independent prognostic biomarker. Enrichment of multiple poor prognosis genetic alterations in ccB samples indicated that several events may be required to establish this aggressive phenotype, catalysed in some tumours by chromosomal instability. Multiregion assessment may improve the precision of this biomarker. PATIENT SUMMARY: We evaluated the ability of published biomarkers to predict the survival of patients with clear cell kidney cancer in an independent patient cohort. Only one molecular test adds prognostic information to routine clinical assessments. This marker showed good and poor prognosis results within most individual cancers. Future biomarkers need to consider variation within tumours to improve accuracy.

Tracking genomic cancer evolution for precision medicine: the lung TRACERx study.

The importance of intratumour genetic and functional heterogeneity is increasingly recognised as a driver of cancer progression and survival outcome. Understanding how tumour clonal heterogeneity impacts upon therapeutic outcome, however, is still an area of unmet clinical and scientific need. TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy [Rx]), a prospective study of patients with primary non-small cell lung cancer (NSCLC), aims to define the evolutionary trajectories of lung cancer in both space and time through multiregion and longitudinal tumour sampling and genetic analysis. By following cancers from diagnosis to relapse, tracking the evolutionary trajectories of tumours in relation to therapeutic interventions, and determining the impact of clonal heterogeneity on clinical outcomes, TRACERx may help to identify novel therapeutic targets for NSCLC and may also serve as a model applicable to other cancer types.

Computational optimisation of targeted DNA sequencing for cancer detection.

Despite recent progress thanks to next-generation sequencing technologies, personalised cancer medicine is still hampered by intra-tumour heterogeneity and drug resistance. As most patients with advanced metastatic disease face poor survival, there is need to improve early diagnosis. Analysing circulating tumour DNA (ctDNA) might represent a non-invasive method to detect mutations in patients, facilitating early detection. In this article, we define reduced gene panels from publicly available datasets as a first step to assess and optimise the potential of targeted ctDNA scans for early tumour detection. Dividing 4,467 samples into one discovery and two independent validation cohorts, we show that up to 76% of 10 cancer types harbour at least one mutation in a panel of only 25 genes, with high sensitivity across most tumour types. Our analyses demonstrate that targeting "hotspot" regions would introduce biases towards in-frame mutations and would compromise the reproducibility of tumour detection.

Tumor mutation burden forecasts outcome in ovarian cancer with BRCA1 or BRCA2 mutations.

BACKGROUND: Increased number of single nucleotide substitutions is seen in breast and ovarian cancer genomes carrying disease-associated mutations in BRCA1 or BRCA2. The significance of these genome-wide mutations is unknown. We hypothesize genome-wide mutation burden mirrors deficiencies in DNA repair and is associated with treatment outcome in ovarian cancer. METHODS AND RESULTS: The total number of synonymous and non-synonymous exome mutations (Nmut), and the presence of germline or somatic mutation in BRCA1 or BRCA2 (mBRCA) were extracted from whole-exome sequences of high-grade serous ovarian cancers from The Cancer Genome Atlas (TCGA). Cox regression and Kaplan-Meier methods were used to correlate Nmut with chemotherapy response and outcome. Higher Nmut correlated with a better response to chemotherapy after surgery. In patients with mBRCA-associated cancer, low Nmut was associated with shorter progression-free survival (PFS) and overall survival (OS), independent of other prognostic factors in multivariate analysis. Patients with mBRCA-associated cancers and a high Nmut had remarkably favorable PFS and OS. The association with survival was similar in cancers with either BRCA1 or BRCA2 mutations. In cancers with wild-type BRCA, tumor Nmut was associated with treatment response in patients with no residual disease after surgery. CONCLUSIONS: Tumor Nmut was associated with treatment response and with both PFS and OS in patients with high-grade serous ovarian cancer carrying BRCA1 or BRCA2 mutations. In the TCGA cohort, low Nmut predicted resistance to chemotherapy, and for shorter PFS and OS, while high Nmut forecasts a remarkably favorable outcome in mBRCA-associated ovarian cancer. Our observations suggest that the total mutation burden coupled with BRCA1 or BRCA2 mutations in ovarian cancer is a genomic marker of prognosis and predictor of treatment response. This marker may reflect the degree of deficiency in BRCA-mediated pathways, or the extent of compensation for the deficiency by alternative mechanisms.

Inconsistency in large pharmacogenomic studies.

Two large-scale pharmacogenomic studies were published recently in this journal. Genomic data are well correlated between studies; however, the measured drug response data are highly discordant. Although the source of inconsistencies remains uncertain, it has potential implications for using these outcome measures to assess gene-drug associations or select potential anticancer drugs on the basis of their reported results.

Parallel evolution of tumour subclones mimics diversity between tumours.

Intratumour heterogeneity (ITH) may foster tumour adaptation and compromise the efficacy of personalized medicine approaches. The scale of heterogeneity within a tumour (intratumour heterogeneity) relative to genetic differences between tumours (intertumour heterogeneity) is unknown. To address this, we obtained 48 biopsies from eight stage III and IV clear cell renal cell carcinomas (ccRCCs) and used DNA copy-number analyses to compare biopsies from the same tumour with 440 single tumour biopsies from the Cancer Genome Atlas (TCGA). Unsupervised hierarchical clustering of TCGA and multi-region ccRCC samples revealed segregation of samples from the same tumour into unrelated clusters; 25% of multi-region samples appeared more similar to unrelated samples than to any other sample originating from the same tumour. We found that the majority of recurrent DNA copy number driver aberrations in single biopsies were not present ubiquitously in late-stage ccRCCs and were likely to represent subclonal events acquired during tumour progression. Such heterogeneous subclonal genetic alterations within individual tumours may impair the identification of robust ccRCC molecular subtypes classified by distinct copy number alterations and clinical outcomes. The co-existence of distinct subclonal copy number events in different regions of individual tumours reflects the diversification of individual ccRCCs through multiple evolutionary routes and may contribute to tumour sampling bias and impact upon tumour progression and clinical outcome.

Profiles of genomic instability in high-grade serous ovarian cancer predict treatment outcome.

PURPOSE: High-grade serous cancer (HGSC) is the most common cancer of the ovary and is characterized by chromosomal instability. Defects in homologous recombination repair (HRR) are associated with genomic instability in HGSC, and are exploited by therapy targeting DNA repair. Defective HRR causes uniparental deletions and loss of heterozygosity (LOH). Our purpose is to profile LOH in HGSC and correlate our findings to clinical outcome, and compare HGSC and high-grade breast cancers. EXPERIMENTAL DESIGN: We examined LOH and copy number changes using single nucleotide polymorphism array data from three HGSC cohorts and compared results to a cohort of high-grade breast cancers. The LOH profiles in HGSC were matched to chemotherapy resistance and progression-free survival (PFS). RESULTS: LOH-based clustering divided HGSC into two clusters. The major group displayed extensive LOH and was further divided into two subgroups. The second group contained remarkably less LOH. BRCA1 promoter methylation was associated with the major group. LOH clusters were reproducible when validated in two independent HGSC datasets. LOH burden in the major cluster of HGSC was similar to triple-negative, and distinct from other high-grade breast cancers. Our analysis revealed an LOH cluster with lower treatment resistance and a significant correlation between LOH burden and PFS. CONCLUSIONS: Separating HGSC by LOH-based clustering produces remarkably stable subgroups in three different cohorts. Patients in the various LOH clusters differed with respect to chemotherapy resistance, and the extent of LOH correlated with PFS. LOH burden may indicate vulnerability to treatment targeting DNA repair, such as PARP1 inhibitors.

CERT depletion predicts chemotherapy benefit and mediates cytotoxic and polyploid-specific cancer cell death through autophagy induction.

Chromosomal instability (CIN) has been implicated in multidrug resistance and the silencing of the ceramide transporter, CERT, promotes sensitization to diverse cytotoxics. An improved understanding of mechanisms governing multidrug sensitization might provide insight into pathways contributing to the death of CIN cancer cells. Using an integrative functional genomics approach, we find that CERT-specific multidrug sensitization is associated with enhanced autophagosome-lysosome flux, resulting from the expression of LAMP2 following CERT silencing in colorectal and HER2(+) breast cancer cell lines. Live cell microscopy analysis revealed that CERT depletion induces LAMP2-dependent death of polyploid cells following exit from mitosis in the presence of paclitaxel. We find that CERT is relatively over-expressed in HER2(+) breast cancer and CERT protein expression acts as an independent prognostic variable and predictor of outcome in adjuvant chemotherapy-treated patients with primary breast cancer. These data suggest that the induction of LAMP2-dependent autophagic flux through CERT targeting may provide a rational approach to enhance multidrug sensitization and potentiate the death of polyploid cells following paclitaxel exposure to limit the acquisition of CIN and intra-tumour heterogeneity.