Oncogenic translation directs spliceosome dynamics revealing an integral role for SF3A3 in breast cancer.

Splicing is a central RNA-based process commonly altered in human cancers; however, how spliceosomal components are co-opted during tumorigenesis remains poorly defined. Here we unravel the core splice factor SF3A3 at the nexus of a translation-based program that rewires splicing during malignant transformation. Upon MYC hyperactivation, SF3A3 levels are modulated translationally through an RNA stem-loop in an eIF3D-dependent manner. This ensures accurate splicing of mRNAs enriched for mitochondrial regulators. Altered SF3A3 translation leads to metabolic reprogramming and stem-like properties that fuel MYC tumorigenic potential in vivo. Our analysis reveals that SF3A3 protein levels predict molecular and phenotypic features of aggressive human breast cancers. These findings unveil a post-transcriptional interplay between splicing and translation that governs critical facets of MYC-driven oncogenesis.

Disruption of the TP53 locus in osteosarcoma leads to TP53 promoter gene fusions and restoration of parts of the TP53 signalling pathway.

TP53 is the most frequently mutated gene in human cancer. This gene shows not only loss-of-function mutations but also recurrent missense mutations with gain-of-function activity. We have studied the primary bone malignancy osteosarcoma, which harbours one of the most rearranged genomes of all cancers. This is odd since it primarily affects children and adolescents who have not lived the long life thought necessary to accumulate massive numbers of mutations. In osteosarcoma, TP53 is often disrupted by structural variants. Here, we show through combined whole-genome and transcriptome analyses of 148 osteosarcomas that TP53 structural variants commonly result in loss of coding parts of the gene while simultaneously preserving and relocating the promoter region. The transferred TP53 promoter region is fused to genes previously implicated in cancer development. Paradoxically, these erroneously upregulated genes are significantly associated with the TP53 signalling pathway itself. This suggests that while the classical tumour suppressor activities of TP53 are lost, certain parts of the TP53 signalling pathway that are necessary for cancer cell survival and proliferation are retained. In line with this, our data suggest that transposition of the TP53 promoter is an early event that allows for a new normal state of genome-wide rearrangements in osteosarcoma. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Author Correction: Landscape of somatic mutations in 560 breast cancer whole-genome sequences.

In the Methods section of this Article, 'greater than' should have been 'less than' in the sentence 'Putative regions of clustered rearrangements were identified as having an average inter-rearrangement distance that was at least 10 times greater than the whole-genome average for the individual sample. '. The Article has not been corrected.

FHL2 expression by cancer-associated fibroblasts promotes metastasis and angiogenesis in lung adenocarcinoma.

Cancer-associated fibroblasts (CAFs) contribute to the progression of lung cancer. Four and a half LIM domain protein-2 (FHL2) is a component of focal adhesion structures. We analyzed the function of FHL2 expressed by CAFs in lung adenocarcinoma. Expression of FHL2 in fibroblast subtypes was investigated using database of single-cell RNA-sequencing of lung cancer tissue. The role of FHL2 in the proliferation and migration of CAFs was assessed. The effects of FHL2 knockout on the migration and invasion of human lung adenocarcinoma cells and tube formation of endothelial cells induced by CAF-conditioned medium (CM) were evaluated. The effect of FHL2 knockout in CAFs on metastasis was determined using a murine orthotopic lung cancer model. The prognostic significance of stromal FHL2 was assessed by immunohistochemistry in human adenocarcinoma specimens. FHL2 is highly expressed in myofibroblasts in cancer tissue. TGF-ß1 upregulated FHL2 expression in CAFs and FHL2 knockdown attenuated CAF proliferation. FHL2 knockout reduced CAF induced migration of A110L and H23 human lung adenocarcinoma cell lines, and the induction of tube formation of endothelial cells. FHL2 knockout reduced CAF-induced metastasis of lung adenocarcinomas in an orthotopic model in vivo. The concentration of Osteopontin (OPN) in CM from CAF was downregulated by FHL2 knockout. siRNA silencing and antibody blocking of OPN reduced the pro-migratory effect of CM from CAF on lung cancer cells. In resected lung adenocarcinoma specimens, positive stromal FHL2 expression was significantly associated with higher microvascular density and worse prognosis. In conclusion, FHL2 expression by CAFs enhances the progression of lung adenocarcinoma by promoting angiogenesis and metastasis.

Genomic alterations associated with resistance and circulating tumor DNA dynamics for early detection of progression on CDK4/6 inhibitor in advanced breast cancer.

Combined CDK4/6 inhibitor (CDK4/6i) and endocrine therapy significantly improves outcome for patients with estrogen receptor-positive (ER+) metastatic breast cancer, but drug resistance and thus disease progression inevitably occur. Herein, we aimed to identify genomic alterations associated with combined CDK4/6i and endocrine therapy resistance, and follow the levels of specific mutations in longitudinal circulating tumor DNA (ctDNA) for early detection of progression. From a cohort of 86 patients with ER+ metastatic breast cancer we performed whole exome sequencing or targeted sequencing of paired tumor (N = 8) or blood samples (N = 5) obtained before initiation of combined CDK4/6i and endocrine therapy and at disease progression. Mutations in oncogenic genes at progression were rare, while amplifications of growth-regulating genes were more frequent. The most frequently acquired alterations observed were PIK3CA and TP53 mutations and PDK1 amplification. Longitudinal ctDNA dynamics of mutant PIK3CA or private mutations revealed increased mutation levels at progression in 8 of 10 patients (80%). Impressively, rising levels of PIK3CA-mutated ctDNA were detected 4-17 months before imaging. Our data add to the growing evidence supporting longitudinal ctDNA analysis for real-time monitoring of CDK4/6i response and early detection of progression in advanced breast cancer. Further, our analysis suggests that amplification of growth-related genes may contribute to combined CDK4/6i and endocrine therapy resistance.

Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification.

BACKGROUND: Genomic rearrangements in cancer cells can create fusion genes that encode chimeric proteins or alter the expression of coding and non-coding RNAs. In some cancer types, fusions involving specific kinases are used as targets for therapy. Fusion genes can be detected by whole genome sequencing (WGS) and targeted fusion panels, but RNA sequencing (RNA-Seq) has the advantageous capability of broadly detecting expressed fusion transcripts. RESULTS: We developed a pipeline for validation of fusion transcripts identified in RNA-Seq data using matched WGS data from The Cancer Genome Atlas (TCGA) and applied it to 910 tumors from 11 different cancer types. This resulted in 4237 validated gene fusions, 3049 of them with at least one identified genomic breakpoint. Utilizing validated fusions as true positive events, we trained a machine learning classifier to predict true and false positive fusion transcripts from RNA-Seq data. The final precision and recall metrics of the classifier were 0.74 and 0.71, respectively, in an independent dataset of 249 breast tumors. Application of this classifier to all samples with RNA-Seq data from these cancer types vastly extended the number of likely true positive fusion transcripts and identified many potentially targetable kinase fusions. Further analysis of the validated gene fusions suggested that many are created by intrachromosomal amplification events with microhomology-mediated non-homologous end-joining. CONCLUSIONS: A classifier trained on validated fusion events increased the accuracy of fusion transcript identification in samples without WGS data. This allowed the analysis to be extended to all samples with RNA-Seq data, facilitating studies of tumor biology and increasing the number of detected kinase fusions. Machine learning could thus be used in identification of clinically relevant fusion events for targeted therapy. The large dataset of validated gene fusions generated here presents a useful resource for development and evaluation of fusion transcript detection algorithms.

Comparison of SP142 and 22C3 PD-L1 assays in a population-based cohort of triple-negative breast cancer patients in the context of their clinically established scoring algorithms.

BACKGROUND: Immunohistochemical (IHC) PD-L1 expression is commonly employed as predictive biomarker for checkpoint inhibitors in triple-negative breast cancer (TNBC). However, IHC evaluation methods are non-uniform and further studies are needed to optimize clinical utility. METHODS: We compared the concordance, prognostic value and gene expression between PD-L1 IHC expression by SP142 immune cell (IC) score and 22C3 combined positive score (CPS; companion IHC diagnostic assays for atezolizumab and pembrolizumab, respectively) in a population-based cohort of 232 early-stage TNBC patients. RESULTS: The expression rates of PD-L1 for SP142 IC ≥ 1%, 22C3 CPS ≥ 10, 22C3 CPS ≥ 1 and 22C3 IC ≥ 1% were 50.9%, 27.2%, 53.9% and 41.8%, respectively. The analytical concordance (kappa values) between SP142 IC+ and these three different 22C3 scorings were 73.7% (0.48, weak agreement), 81.5% (0.63) and 86.6% (0.73), respectively. The SP142 assay was better at identifying 22C3 positive tumors than the 22C3 assay was at detecting SP142 positive tumors. PD-L1 (CD274) gene expression (mRNA) showed a strong positive association with all two-categorical IHC scorings of the PD-L1 expression, irrespective of antibody and cut-off (Spearman Rho ranged from 0.59 to 0.62; all p-values < 0.001). PD-L1 IHC positivity and abundance of tumor infiltrating lymphocytes were of positive prognostic value in univariable regression analyses in patients treated with (neo)adjuvant chemotherapy, where it was strongest for 22C3 CPS ≥ 10 and distant relapse-free interval (HR = 0.18, p = 0.019). However, PD-L1 status was not independently prognostic when adjusting for abundance of tumor infiltrating lymphocytes in multivariable analyses. CONCLUSION: Our findings support that the SP142 and 22C3 IHC assays, with their respective clinically applied scoring algorithms, are not analytically equivalent where they identify partially non-overlapping subpopulations of TNBC patients and cannot be substituted with one another regarding PD-L1 detection. Trial registration The Swedish Cancerome Analysis Network - Breast (SCAN-B) study, retrospectively registered 2nd Dec 2014 at ClinicalTrials.gov; ID NCT02306096.

Molecular characteristics of lung adenocarcinoma with respect to patient age at diagnosis.

Lung cancer is primarily a disease of the elderly, with a median age at diagnosis around 70 years. In our study we sought to address the question of whether and how clinical characteristics, molecular alterations and molecular phenotypes differ between patient populations with early-stage lung adenocarcinoma (AC) with respect to age at diagnosis. Patients were stratified based on age at diagnosis into five systematic age bins (<50, 50-60, 60-70, 70-80 and ≥80 years). To assess clinicopathological variables on a population-based level, we accessed data from the national quality registry for lung cancer in Sweden. In parallel, we used compiled datasets from public cohorts to investigate focal and genome-wide DNA alterations, epigenetic alterations, immune composition and transcriptional patterns in relation to age at diagnosis. Gender, stage, WHO performance and likelihood of receiving chemotherapy as adjuvant treatment were linked to age at diagnosis. Associations between younger patient age and likelihood of harboring certain driver mutations (eg, in EGFR and ALK) were confirmed. We also found an association between age at diagnosis and certain mutational signatures. However, age did not seem to drive transcriptional, copy number, or epigenetic variation in the tumors. Based on our findings, age at diagnosis alone does not appear to provide an additional layer of biological complexity above that of proposed genetic and transcriptional phenotypes of AC.

Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor.

Gene-expression profiling can be used to classify human tumors into molecular subtypes or risk groups, representing potential future clinical tools for treatment prediction and prognostication. However, it is less well-known how prognostic gene signatures derived in one malignancy perform in a pan-cancer context. In this study, a gene-rule-based single sample predictor (SSP) called classifier for lung adenocarcinoma molecular subtypes (CLAMS) associated with proliferation was tested in almost 15 000 samples from 32 cancer types to classify samples into better or worse prognosis. Of the 14 malignancies that presented both CLAMS classes in sufficient numbers, survival outcomes were significantly different for breast, brain, kidney and liver cancer. Patients with samples classified as better prognosis by CLAMS were generally of lower tumor grade and disease stage, and had improved prognosis according to other type-specific classifications (e.g. PAM50 for breast cancer). In all, 99.1% of non-lung cancer cases classified as better outcome by CLAMS were comprised within the range of proliferation scores of lung adenocarcinoma cases with a predicted better prognosis by CLAMS. This finding demonstrates the potential of tuning SSPs to identify specific levels of for instance tumor proliferation or other transcriptional programs through predictor training. Together, pan-cancer studies such as this may take us one step closer to understanding how gene-expression-based SSPs act, which gene-expression programs might be important in different malignancies, and how to derive tools useful for prognostication that are efficient across organs.

The circular RNome of primary breast cancer.

Circular RNAs (circRNAs) are a class of RNAs that is under increasing scrutiny, although their functional roles are debated. We analyzed RNA-seq data of 348 primary breast cancers and developed a method to identify circRNAs that does not rely on unmapped reads or known splice junctions. We identified 95,843 circRNAs, of which 20,441 were found recurrently. Of the circRNAs that match exon boundaries of the same gene, 668 showed a poor or even negative (R < 0.2) correlation with the expression level of the linear gene. In silico analysis showed only a minority (8.5%) of circRNAs could be explained by known splicing events. Both these observations suggest that specific regulatory processes for circRNAs exist. We confirmed the presence of circRNAs of CNOT2, CREBBP, and RERE in an independent pool of primary breast cancers. We identified circRNA profiles associated with subgroups of breast cancers and with biological and clinical features, such as amount of tumor lymphocytic infiltrate and proliferation index. siRNA-mediated knockdown of circCNOT2 was shown to significantly reduce viability of the breast cancer cell lines MCF-7 and BT-474, further underlining the biological relevance of circRNAs. Furthermore, we found that circular, and not linear, CNOT2 levels are predictive for progression-free survival time to aromatase inhibitor (AI) therapy in advanced breast cancer patients, and found that circCNOT2 is detectable in cell-free RNA from plasma. We showed that circRNAs are abundantly present, show characteristics of being specifically regulated, are associated with clinical and biological properties, and thus are relevant in breast cancer.

Performance of gene expression-based single sample predictors for assessment of clinicopathological subgroups and molecular subtypes in cancers: a case comparison study in non-small cell lung cancer.

The development of multigene classifiers for cancer prognosis, treatment prediction, molecular subtypes or clinicopathological groups has been a cornerstone in transcriptomic analyses of human malignancies for nearly two decades. However, many reported classifiers are critically limited by different preprocessing needs like normalization and data centering. In response, a new breed of classifiers, single sample predictors (SSPs), has emerged. SSPs classify samples in an N-of-1 fashion, relying on, e.g. gene rules comparing expression values within a sample. To date, several methods have been reported, but there is a lack of head-to-head performance comparison for typical cancer classification problems, representing an unmet methodological need in cancer bioinformatics. To resolve this need, we performed an evaluation of two SSPs [k-top-scoring pair classifier (kTSP) and absolute intrinsic molecular subtyping (AIMS)] for two case examples of different magnitude of difficulty in non-small cell lung cancer: gene expression-based classification of (i) tumor histology and (ii) molecular subtype. Through the analysis of ~2000 lung cancer samples for each case example (n = 1918 and n = 2106, respectively), we compared the performance of the methods for different sample compositions, training data set sizes, gene expression platforms and gene rule selections. Three main conclusions are drawn from the comparisons: both methods are platform independent, they select largely overlapping gene rules associated with actual underlying tumor biology and, for large training data sets, they behave interchangeably performance-wise. While SSPs like AIMS and kTSP offer new possibilities to move gene expression signatures/predictors closer to a clinical context, they are still importantly limited by the difficultness of the classification problem at hand.

Landscape of somatic mutations in 560 breast cancer whole-genome sequences.

We analysed whole-genome sequences of 560 breast cancers to advance understanding of the driver mutations conferring clonal advantage and the mutational processes generating somatic mutations. We found that 93 protein-coding cancer genes carried probable driver mutations. Some non-coding regions exhibited high mutation frequencies, but most have distinctive structural features probably causing elevated mutation rates and do not contain driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed twelve base substitution and six rearrangement signatures. Three rearrangement signatures, characterized by tandem duplications or deletions, appear associated with defective homologous-recombination-based DNA repair: one with deficient BRCA1 function, another with deficient BRCA1 or BRCA2 function, the cause of the third is unknown. This analysis of all classes of somatic mutation across exons, introns and intergenic regions highlights the repertoire of cancer genes and mutational processes operating, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer.

Molecular analyses of triple-negative breast cancer in the young and elderly.

BACKGROUND: Breast cancer in young adults has been implicated with a worse outcome. Analyses of genomic traits associated with age have been heterogenous, likely because of an incomplete accounting for underlying molecular subtypes. We aimed to resolve whether triple-negative breast cancer (TNBC) in younger versus older patients represent similar or different molecular diseases in the context of genetic and transcriptional subtypes and immune cell infiltration. PATIENTS AND METHODS: In total, 237 patients from a reported population-based south Swedish TNBC cohort profiled by RNA sequencing and whole-genome sequencing (WGS) were included. Patients were binned in 10-year intervals. Complimentary PD-L1 and CD20 immunohistochemistry and estimation of tumor-infiltrating lymphocytes (TILs) were performed. Cases were analyzed for differences in patient outcome, genomic, transcriptional, and immune landscape features versus age at diagnosis. Additionally, 560 public WGS breast cancer profiles were used for validation. RESULTS: Median age at diagnosis was 62 years (range 26-91). Age was not associated with invasive disease-free survival or overall survival after adjuvant chemotherapy. Among the BRCA1-deficient cases (82/237), 90% were diagnosed before the age of 70 and were predominantly of the basal-like subtype. In the full TNBC cohort, reported associations of patient age with changes in Ki67 expression, PIK3CA mutations, and a luminal androgen receptor subtype were confirmed. Within DNA repair deficiency or gene expression defined molecular subgroups, age-related alterations in, e.g., overall gene expression, immune cell marker gene expression, genetic mutational and rearrangement signatures, amount of copy number alterations, and tumor mutational burden did, however, not appear distinct. Similar non-significant associations for genetic alterations with age were obtained for other breast cancer subgroups in public WGS data. Consistent with age-related immunosenescence, TIL counts decreased linearly with patient age across different genetic TNBC subtypes. CONCLUSIONS: Age-related alterations in TNBC, as well as breast cancer in general, need to be viewed in the context of underlying genomic phenotypes. Based on this notion, age at diagnosis alone does not appear to provide an additional layer of biological complexity above that of proposed genetic and transcriptional phenotypes of TNBC. Consequently, treatment decisions should be less influenced by age and more driven by tumor biology.

Distinct mechanisms of resistance to fulvestrant treatment dictate level of ER independence and selective response to CDK inhibitors in metastatic breast cancer.

BACKGROUND: Resistance to endocrine treatment in metastatic breast cancer is a major clinical challenge. Clinical tools to predict both drug resistance and possible treatment combination approaches to overcome it are lacking. This unmet need is mainly due to the heterogeneity underlying both the mechanisms involved in resistance development and breast cancer itself. METHODS: To study the complexity of the mechanisms involved in the resistance to the selective estrogen receptor degrader (SERD) fulvestrant, we performed comprehensive biomarker analyses using several in vitro models that recapitulate the heterogeneity of developed resistance. We further corroborated our findings in tissue samples from patients treated with fulvestrant. RESULTS: We found that different in vitro models of fulvestrant resistance show variable stability in their phenotypes, which corresponded with distinct genomic alterations. Notably, the studied models presented adaptation at different cell cycle nodes to facilitate progression through the cell cycle and responded differently to CDK inhibitors. Cyclin E2 overexpression was identified as a biomarker of a persistent fulvestrant-resistant phenotype. Comparison of pre- and post-treatment paired tumor biopsies from patients treated with fulvestrant revealed an upregulation of cyclin E2 upon development of resistance. Moreover, overexpression of this cyclin was found to be a prognostic factor determining resistance to fulvestrant and shorter progression-free survival. CONCLUSIONS: These data highlight the complexity of estrogen receptor positive breast cancer and suggest that the development of diverse resistance mechanisms dictate levels of ER independence and potentially cross-resistance to CDK inhibitors.

A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis.

Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expression-based AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminal-respiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample predictor (SSP) for molecular subtype assignment and risk stratification that could function in a clinical setting. Two-class (TRU/nonTRU=SSP2) and three-class (TRU/PP/PI=SSP3) SSPs using the AIMS algorithm were trained in 1655 ACs (n = 9659 genes) from public repositories vs TCGA centroid subtypes. Validation and survival analysis were performed in 977 patients using overall survival (OS) and distant metastasis-free survival (DMFS) as endpoints. In the validation cohort, SSP2 and SSP3 showed accuracies of 0.85 and 0.81, respectively. SSPs captured relevant biology previously associated with the TCGA subtypes and were associated with prognosis. In survival analysis, OS and DMFS for cases discordantly classified between TCGA and SSP2 favored the SSP2 classification. In resected Stage I patients, SSP2 identified TRU-cases with better OS (hazard ratio [HR] = 0.30; 95% confidence interval [CI] = 0.18-0.49) and DMFS (TRU HR = 0.52; 95% CI = 0.33-0.83) independent of age, Stage IA/IB and gender. SSP2 was transformed into a NanoString nCounter assay and tested in 44 Stage I patients using RNA from formalin-fixed tissue, providing prognostic stratification (relapse-free interval, HR = 3.2; 95% CI = 1.2-8.8). In conclusion, gene expression-based SSPs can provide molecular subtype and independent prognostic information in early-stage lung ACs. SSPs may overcome critical limitations in the applicability of gene signatures in lung cancer.

Hyperglucagonemia in youth is associated with high plasma free fatty acids, visceral adiposity, and impaired glucose tolerance.

OBJECTIVE: To delineate potential mechanisms for fasting hyperglucagonemia in childhood obesity by studying the associations between fasting plasma glucagon concentrations and plasma lipid parameters and fat compartments. METHODS: Cross-sectional study of children and adolescents with obesity (n = 147) and lean controls (n = 43). Differences in free fatty acids (FFAs), triglycerides, insulin, and fat compartments (quantified by magnetic resonance imaging) across quartiles of fasting plasma glucagon concentration were analyzed. Differences in oral glucose tolerance test (OGTT) glucagon response was tested in high vs low FFAs, triglycerides, and insulin. Human islets of Langerhans were cultured at 5.5 mmol/L glucose and in the absence or presence of a FFA mixture with total FFA concentration of 0.5 mmol/L and glucagon secretion quantified. RESULTS: In children with obesity, the quartile with the highest fasting glucagon had higher insulin (201 ± 174 vs 83 ± 39 pmol/L, P < .01), FFAs (383 ± 52 vs 338 ± 109 µmol/L, P = .02), triglycerides (1.5 ± 0.9 vs 1.0 ± 0.7 mmol/L, P < .01), visceral adipose tissue volume (1.9 ± 0.8 vs 1.2 ± 0.3 dm3 , P < .001), and a higher prevalence of impaired glucose tolerance (IGT; 41% vs 8%, P = .01) than the lowest quartile. During OGTT, children with obesity and high insulin had a worse suppression of glucagon during the first 10 minutes after glucose intake. Glucagon secretion was 2.6-fold higher in islets treated with FFAs than in those not treated with FFAs. CONCLUSIONS: Hyperglucagonemia in childhood obesity is associated with hyperinsulinemia, high plasma FFAs, high plasma triglycerides, visceral adiposity, and IGT. The glucagonotropic effect of FFAs on isolated human islets provides a potential mechanism linking high fasting plasma FFAs and glucagon levels.

Pre-operative plasma cell-free circulating tumor DNA and serum protein tumor markers as predictors of lung adenocarcinoma recurrence.

Background: Lung cancer patients have a risk of recurrence even after curatively intended surgery. Cell-free circulating tumor DNA (ctDNA) and circulating tumor marker measurements are easily accessible through peripheral blood and could potentially identify patients with worse prognosis. The aim of this study was to examine ctDNA in pre-operative plasma and the role of tumor markers in pre-operative serum for their predictive potential on risk of tumor recurrence. Methods: Mutation analysis by 26-gene targeted sequencing was performed on 157 lung adenocarcinomas (ACs) from patients surgically treated at the Lund University Hospital 2005-2014. Of these, 58 tumors from patients in stages I-IIIA (34 stage I, 14 stage II and 10 stage III) with mutation(s) in EGFR, BRAF or KRAS were included. ctDNA from corresponding plasma (median 1.5 ml, range 1-1.6) was analyzed for one tumor-specific mutation in either of these three oncogenes using ultrasensitive IBSAFE droplet digital PCR (ddPCR). The tumor markers cancer antigen 125 (CA 125) and carbohydrate antigen 19-9 (CA 19-9) were analyzed in corresponding serum with electrochemiluminiscence immunoassay. Results: 6/7 patients with ctDNA and 19/51 without detected ctDNA were diagnosed with recurrence (log-rank test p = .001). 8/10 patients with positive serum tumor markers and 17/47 without tumor markers were diagnosed with recurrence (log-rank test, p = .0002). Fifteen patients had positive ctDNA and/or tumor markers, 12 of these had recurrence (log-rank test, p < .0001). Conclusion: A combination of tumor markers and ctDNA single mutation detection in low-volume pre-operative blood samples is a promising prognostic test. Prediction of recurrent disease in surgically treated early stage lung cancer can likely be further improved by using larger volumes of blood.

Identification and validation of single-sample breast cancer radiosensitivity gene expression predictors.

BACKGROUND: Adjuvant radiotherapy is the standard of care after breast-conserving surgery for primary breast cancer, despite a majority of patients being over- or under-treated. In contrast to adjuvant endocrine therapy and chemotherapy, no diagnostic tests are in clinical use that can stratify patients for adjuvant radiotherapy. This study presents the development and validation of a targeted gene expression assay to predict the risk of ipsilateral breast tumor recurrence and response to adjuvant radiotherapy after breast-conserving surgery in primary breast cancer. METHODS: Fresh-frozen primary tumors from 336 patients radically (clear margins) operated on with breast-conserving surgery with or without radiotherapy were collected. Patients were split into a discovery cohort (N = 172) and a validation cohort (N = 164). Genes predicting ipsilateral breast tumor recurrence in an Illumina HT12 v4 whole transcriptome analysis were combined with genes identified in the literature (248 genes in total) to develop a targeted radiosensitivity assay on the Nanostring nCounter platform. Single-sample predictors for ipsilateral breast tumor recurrence based on a k-top scoring pairs algorithm were trained, stratified for estrogen receptor (ER) status and radiotherapy. Two previously published profiles, the radiosensitivity signature of Speers et al., and the 10-gene signature of Eschrich et al., were also included in the targeted panel. RESULTS: Derived single-sample predictors were prognostic for ipsilateral breast tumor recurrence in radiotherapy-treated ER+ patients (AUC 0.67, p = 0.01), ER+ patients without radiotherapy (AUC = 0.89, p = 0.02), and radiotherapy-treated ER- patients (AUC = 0.78, p < 0.001). Among ER+ patients, radiotherapy had an excellent effect on tumors classified as radiosensitive (p < 0.001), while radiotherapy had no effect on tumors classified as radioresistant (p = 0.36) and there was a high risk of ipsilateral breast tumor recurrence (55% at 10 years). Our single-sample predictors developed in ER+ tumors and the radiosensitivity signature correlated with proliferation, while single-sample predictors developed in ER- tumors correlated with immune response. The 10-gene signature negatively correlated with both proliferation and immune response. CONCLUSIONS: Our targeted single-sample predictors were prognostic for ipsilateral breast tumor recurrence and have the potential to stratify patients for adjuvant radiotherapy. The correlation of models with biology may explain the different performance in subgroups of breast cancer.

An integrated genomics analysis of epigenetic subtypes in human breast tumors links DNA methylation patterns to chromatin states in normal mammary cells.

BACKGROUND: Aberrant DNA methylation is frequently observed in breast cancer. However, the relationship between methylation patterns and the heterogeneity of breast cancer has not been comprehensively characterized. METHODS: Whole-genome DNA methylation analysis using Illumina Infinium HumanMethylation450 BeadChip arrays was performed on 188 human breast tumors. Unsupervised bootstrap consensus clustering was performed to identify DNA methylation epigenetic subgroups (epitypes). The Cancer Genome Atlas data, including methylation profiles of 669 human breast tumors, was used for validation. The identified epitypes were characterized by integration with publicly available genome-wide data, including gene expression levels, DNA copy numbers, whole-exome sequencing data, and chromatin states. RESULTS: We identified seven breast cancer epitypes. One epitype was distinctly associated with basal-like tumors and with BRCA1 mutations, one epitype contained a subset of ERBB2-amplified tumors characterized by multiple additional amplifications and the most complex genomes, and one epitype displayed a methylation profile similar to normal epithelial cells. Luminal tumors were stratified into the remaining four epitypes, with differences in promoter hypermethylation, global hypomethylation, proliferative rates, and genomic instability. Specific hyper- and hypomethylation across the basal-like epitype was rare. However, we observed that the candidate genomic instability drivers BRCA1 and HORMAD1 displayed aberrant methylation linked to gene expression levels in some basal-like tumors. Hypomethylation in luminal tumors was associated with DNA repeats and subtelomeric regions. We observed two dominant patterns of aberrant methylation in breast cancer. One pattern, constitutively methylated in both basal-like and luminal breast cancer, was linked to genes with promoters in a Polycomb-repressed state in normal epithelial cells and displayed no correlation with gene expression levels. The second pattern correlated with gene expression levels and was associated with methylation in luminal tumors and genes with active promoters in normal epithelial cells. CONCLUSIONS: Our results suggest that hypermethylation patterns across basal-like breast cancer may have limited influence on tumor progression and instead reflect the repressed chromatin state of the tissue of origin. On the contrary, hypermethylation patterns specific to luminal breast cancer influence gene expression, may contribute to tumor progression, and may present an actionable epigenetic alteration in a subset of luminal breast cancers.