Charlotte K.Y. Ng

Capturing intra-tumor genetic heterogeneity by de novo mutation profiling of circulating cell-free tumor DNA: a proof-of-principle

BACKGROUND Plasma-derived cell-free tumor DNA (ctDNA) constitutes a potential surrogate for tumor DNA obtained from tissue biopsies. We posit that massively parallel sequencing (MPS) analysis of ctDNA may help define the repertoire of mutations in breast cancer and monitor tumor somatic alterations during the course of targeted therapy. PATIENT AND METHODS A 66-year-old patient presented with synchronous estrogen receptor-positive/HER2-negative, highly proliferative, grade 2, mixed invasive ductal-lobular carcinoma with bone and liver metastases at diagnosis. DNA extracted from archival tumor material, plasma and peripheral blood leukocytes was subjected to targeted MPS using a platform comprising 300 cancer genes known to harbor actionable mutations. Multiple plasma samples were collected during the fourth line of treatment with an AKT inhibitor. RESULTS Average read depths of 287x were obtained from the archival primary tumor, 139x from the liver metastasis and between 200x and 900x from ctDNA samples. Sixteen somatic non-synonymous mutations were detected in the liver metastasis, of which 9 (CDKN2A, AKT1, TP53, JAK3, TSC1, NF1, CDH1, MML3 and CTNNB1) were also detected in >5% of the alleles found in the primary tumor sample. Not all mutations identified in the metastasis were reliably identified in the primary tumor (e.g. FLT4). Analysis of ctDNA, nevertheless, captured all mutations present in the primary tumor and/or liver metastasis. In the longitudinal monitoring of the patient, the mutant allele fractions identified in ctDNA samples varied over time and mirrored the pharmacodynamic response to the targeted therapy as assessed by positron emission tomography-computed tomography. CONCLUSIONS This proof-of-principle study is one of the first to demonstrate that high-depth targeted MPS of plasma-derived ctDNA constitutes a potential tool for de novo mutation identification and monitoring of somatic genetic alterations during the course of targeted therapy, and may be employed to overcome the challenges posed by intra-tumor genetic heterogeneity. REGISTERED CLINICAL TRIAL: www.clinicaltrials.gov, NCT01090960.BACKGROUND Plasma-derived cell-free tumor DNA (ctDNA) constitutes a potential surrogate for tumor DNA obtained from tissue biopsies. We posit that massively parallel sequencing (MPS) analysis of ctDNA may help define the repertoire of mutations in breast cancer and monitor tumor somatic alterations during the course of targeted therapy. PATIENT AND METHODS A 66-year-old patient presented with synchronous estrogen receptor-positive/HER2-negative, highly proliferative, grade 2, mixed invasive ductal-lobular carcinoma with bone and liver metastases at diagnosis. DNA extracted from archival tumor material, plasma and peripheral blood leukocytes was subjected to targeted MPS using a platform comprising 300 cancer genes known to harbor actionable mutations. Multiple plasma samples were collected during the fourth line of treatment with an AKT inhibitor. RESULTS Average read depths of 287x were obtained from the archival primary tumor, 139x from the liver metastasis and between 200x and 900x from ctDNA samples. Sixteen somatic non-synonymous mutations were detected in the liver metastasis, of which 9 (CDKN2A, AKT1, TP53, JAK3, TSC1, NF1, CDH1, MML3 and CTNNB1) were also detected in >5% of the alleles found in the primary tumor sample. Not all mutations identified in the metastasis were reliably identified in the primary tumor (e.g. FLT4). Analysis of ctDNA, nevertheless, captured all mutations present in the primary tumor and/or liver metastasis. In the longitudinal monitoring of the patient, the mutant allele fractions identified in ctDNA samples varied over time and mirrored the pharmacodynamic response to the targeted therapy as assessed by positron emission tomography-computed tomography. CONCLUSIONS This proof-of-principle study is one of the first to demonstrate that high-depth targeted MPS of plasma-derived ctDNA constitutes a potential tool for de novo mutation identification and monitoring of somatic genetic alterations during the course of targeted therapy, and may be employed to overcome the challenges posed by intra-tumor genetic heterogeneity. REGISTERED CLINICAL TRIAL www.clinicaltrials.gov, NCT01090960.

Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer.

Noninvasive mutation tracking in plasma can detect circulating tumor DNA arising from residual micrometastatic disease and thus identify patients at high risk of recurrence. Risk of recurrence Predicting whether a cancer patient will relapse remains a formidable challenge in modern medicine. Fortunately, circulating tumor DNA (ctDNA) present in the blood may give clues on residual disease—cancer cells left behind to seed new tumors even after treatment. Garcia-Murillas et al. developed a personalized ctDNA assay based on digital polymerase chain reaction to track mutations over time in patients with early-stage breast cancer who had received apparently curative treatments, surgery, and chemotherapy. Mutation tracking in serial samples accurately predicted metastatic relapse—in several instances, months before clinical relapse (median of ~8 months). Such unprecedented early prediction could allow for intervention before the reappearance of cancer in high-risk patients. In addition, the authors were able to shed light on the genetic events driving such metastases, by massively parallel sequencing of the ctDNA, which could inform new drug-based therapies on the basis of the patients’ individual mutations. The identification of early-stage breast cancer patients at high risk of relapse would allow tailoring of adjuvant therapy approaches. We assessed whether analysis of circulating tumor DNA (ctDNA) in plasma can be used to monitor for minimal residual disease (MRD) in breast cancer. In a prospective cohort of 55 early breast cancer patients receiving neoadjuvant chemotherapy, detection of ctDNA in plasma after completion of apparently curative treatment—either at a single postsurgical time point or with serial follow-up plasma samples—predicted metastatic relapse with high accuracy [hazard ratio, 25.1 (confidence interval, 4.08 to 130.5; log-rank P < 0.0001) or 12.0 (confidence interval, 3.36 to 43.07; log-rank P < 0.0001), respectively]. Mutation tracking in serial samples increased sensitivity for the prediction of relapse, with a median lead time of 7.9 months over clinical relapse. We further demonstrated that targeted capture sequencing analysis of ctDNA could define the genetic events of MRD, and that MRD sequencing predicted the genetic events of the subsequent metastatic relapse more accurately than sequencing of the primary cancer. Mutation tracking can therefore identify early breast cancer patients at high risk of relapse. Subsequent adjuvant therapeutic interventions could be tailored to the genetic events present in the MRD, a therapeutic approach that could in part combat the challenge posed by intratumor genetic heterogeneity.

Chromosomal instability determines taxane response

Microtubule-stabilizing (MTS) agents, such as taxanes, are important chemotherapeutics with a poorly understood mechanism of action. We identified a set of genes repressed in multiple cell lines in response to MTS agents and observed that these genes are overexpressed in tumors exhibiting chromosomal instability (CIN). Silencing 22/50 of these genes, many of which are involved in DNA repair, caused cancer cell death, suggesting that these genes are involved in the survival of aneuploid cells. Overexpression of these “CIN-survival” genes is associated with poor outcome in estrogen receptor–positive breast cancer and occurs frequently in basal-like and Her2-positive cases. In diploid cells, but not in chromosomally unstable cells, paclitaxel causes repression of CIN-survival genes, followed by cell death. In the OV01 ovarian cancer clinical trial, a high level of CIN was associated with taxane resistance but carboplatin sensitivity, indicating that CIN may determine MTS response in vivo. Thus, pretherapeutic assessment of CIN may optimize treatment stratification and clinical trial design using these agents.

Spatial and Temporal Heterogeneity in High-Grade Serous Ovarian Cancer: A Phylogenetic Analysis

Background The major clinical challenge in the treatment of high-grade serous ovarian cancer (HGSOC) is the development of progressive resistance to platinum-based chemotherapy. The objective of this study was to determine whether intra-tumour genetic heterogeneity resulting from clonal evolution and the emergence of subclonal tumour populations in HGSOC was associated with the development of resistant disease. Methods and Findings Evolutionary inference and phylogenetic quantification of heterogeneity was performed using the MEDICC algorithm on high-resolution whole genome copy number profiles and selected genome-wide sequencing of 135 spatially and temporally separated samples from 14 patients with HGSOC who received platinum-based chemotherapy. Samples were obtained from the clinical CTCR-OV03/04 studies, and patients were enrolled between 20 July 2007 and 22 October 2009. Median follow-up of the cohort was 31 mo (interquartile range 22–46 mo), censored after 26 October 2013. Outcome measures were overall survival (OS) and progression-free survival (PFS). There were marked differences in the degree of clonal expansion (CE) between patients (median 0.74, interquartile range 0.66–1.15), and dichotimization by median CE showed worse survival in CE-high cases (PFS 12.7 versus 10.1 mo, p = 0.009; OS 42.6 versus 23.5 mo, p = 0.003). Bootstrap analysis with resampling showed that the 95% confidence intervals for the hazard ratios for PFS and OS in the CE-high group were greater than 1.0. These data support a relationship between heterogeneity and survival but do not precisely determine its effect size. Relapsed tissue was available for two patients in the CE-high group, and phylogenetic analysis showed that the prevalent clonal population at clinical recurrence arose from early divergence events. A subclonal population marked by a NF1 deletion showed a progressive increase in tumour allele fraction during chemotherapy. Conclusions This study demonstrates that quantitative measures of intra-tumour heterogeneity may have predictive value for survival after chemotherapy treatment in HGSOC. Subclonal tumour populations are present in pre-treatment biopsies in HGSOC and can undergo expansion during chemotherapy, causing clinical relapse.

Cerebrospinal fluid-derived circulating tumour DNA better represents the genomic alterations of brain tumours than plasma

Cell-free circulating tumour DNA (ctDNA) in plasma has been shown to be informative of the genomic alterations present in tumours and has been used to monitor tumour progression and response to treatments. However, patients with brain tumours do not present with or present with low amounts of ctDNA in plasma precluding the genomic characterization of brain cancer through plasma ctDNA. Here we show that ctDNA derived from central nervous system tumours is more abundantly present in the cerebrospinal fluid (CSF) than in plasma. Massively parallel sequencing of CSF ctDNA more comprehensively characterizes the genomic alterations of brain tumours than plasma, allowing the identification of actionable brain tumour somatic mutations. We show that CSF ctDNA levels longitudinally fluctuate in time and follow the changes in brain tumour burden providing biomarkers to monitor brain malignancies. Moreover, CSF ctDNA is shown to facilitate and complement the diagnosis of leptomeningeal carcinomatosis.

Genomic analysis of genetic heterogeneity and evolution in high-grade serous ovarian carcinoma

Resistance to chemotherapy in ovarian cancer is poorly understood. Evolutionary models of cancer predict that, following treatment, resistance emerges either because of outgrowth of an intrinsically resistant sub-clone or evolves in residual disease under the selective pressure of treatment. To investigate genetic evolution in high-grade serous (HGS) ovarian cancers, we first analysed cell line series derived from three cases of HGS carcinoma before and after platinum resistance had developed (PEO1, PEO4 and PEO6; PEA1 and PEA2; and PEO14 and PEO23). Analysis with 24-colour fluorescence in situ hybridisation and single nucleotide polymorphism (SNP) array comparative genomic hybridisation (CGH) showed mutually exclusive endoreduplication and loss of heterozygosity events in clones present at different time points in the same individual. This implies that platinum-sensitive and -resistant disease was not linearly related, but shared a common ancestor at an early stage of tumour development. Array CGH analysis of six paired pre- and post-neoadjuvant treatment HGS samples from the CTCR-OV01 clinical study did not show extensive copy number differences, suggesting that one clone was strongly dominant at presentation. These data show that cisplatin resistance in HGS carcinoma develops from pre-existing minor clones but that enrichment for these clones is not apparent during short-term chemotherapy treatment.

Breast cancer intra-tumor heterogeneity

In recent years it has become clear that cancer cells within a single tumor can display striking morphological, genetic and behavioral variability. Burgeoning genetic, epigenetic and phenomenological data support the existence of intra-tumor genetic heterogeneity in breast cancers; however, its basis is yet to be fully defined. Two of the most widely evoked concepts to explain the origin of heterogeneity within tumors are the cancer stem cell hypothesis and the clonal evolution model. Although the cancer stem cell model appeared to provide an explanation for the variability among the neoplastic cells within a given cancer, advances in massively parallel sequencing have provided several lines of evidence to suggest that intra-tumor genetic heterogeneity likely plays a fundamental role in the phenotypic heterogeneity observed in cancers. Many challenges remain, however, in the interpretation of the next generation sequencing results obtained so far. Here we review the models that explain tumor heterogeneity, the causes of intra-tumor genetic diversity and their impact on our understanding and management of breast cancer, methods to study intra-tumor heterogeneity and the assessment of intra-tumor genetic heterogeneity in the clinic.

Hotspot activating PRKD1 somatic mutations in polymorphous low-grade adenocarcinomas of the salivary glands

Polymorphous low-grade adenocarcinoma (PLGA) is the second most frequent type of malignant tumor of the minor salivary glands. We identified PRKD1 hotspot mutations encoding p.Glu710Asp in 72.9% of PLGAs but not in other salivary gland tumors. Functional studies demonstrated that this kinase-activating alteration likely constitutes a driver of PLGA.

Progression from ductal carcinoma in situ to invasive breast cancer: Revisited

Ductal carcinoma in situ (DCIS) is an intraductal neoplastic proliferation of epithelial cells that is separated from the breast stroma by an intact layer of basement membrane and myoepithelial cells. DCIS is a non‐obligate precursor of invasive breast cancer, and up to 40% of these lesions progress to invasive disease if untreated. Currently, it is not possible to predict accurately which DCIS would be more likely to progress to invasive breast cancer as neither the significant drivers of the invasive transition have been identified, nor has the clinical utility of tests predicting the likelihood of progression been demonstrated. Although molecular studies have shown that qualitatively, synchronous DCIS and invasive breast cancers are remarkably similar, there is burgeoning evidence to demonstrate that intra‐tumor genetic heterogeneity is observed in a subset of DCIS, and that the process of progression to invasive disease may constitute an ‘evolutionary bottleneck’, resulting in the selection of subsets of tumor cells with specific genetic and/or epigenetic aberrations. Here we review the clinical challenge posed by DCIS, the contribution of the microenvironment and genetic aberrations to the progression from in situ to invasive breast cancer, the emerging evidence of the impact of intra‐tumor genetic heterogeneity on this process, and strategies to combat this heterogeneity.

Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations

BackgroundMassively parallel sequencing studies have led to the identification of a large number of mutations present in a minority of cancers of a given site. Hence, methods to identify the likely pathogenic mutations that are worth exploring experimentally and clinically are required. We sought to compare the performance of 15 mutation effect prediction algorithms and their agreement. As a hypothesis-generating aim, we sought to define whether combinations of prediction algorithms would improve the functional effect predictions of specific mutations.ResultsLiterature and database mining of single nucleotide variants (SNVs) affecting 15 cancer genes was performed to identify mutations supported by functional evidence or hereditary disease association to be classified either as non-neutral (n = 849) or neutral (n = 140) with respect to their impact on protein function. These SNVs were employed to test the performance of 15 mutation effect prediction algorithms. The accuracy of the prediction algorithms varies considerably. Although all algorithms perform consistently well in terms of positive predictive value, their negative predictive value varies substantially. Cancer-specific mutation effect predictors display no-to-almost perfect agreement in their predictions of these SNVs, whereas the non-cancer-specific predictors showed no-to-moderate agreement. Combinations of predictors modestly improve accuracy and significantly improve negative predictive values.ConclusionsThe information provided by mutation effect predictors is not equivalent. No algorithm is able to predict sufficiently accurately SNVs that should be taken forward for experimental or clinical testing. Combining algorithms aggregates orthogonal information and may result in improvements in the negative predictive value of mutation effect predictions.