Molecularly targeted cancer drugs are often developed with companion diagnostics that attempt to identify which patients will have better outcome on the new drug than the control regimen. Such predictive biomarkers are playing an increasingly important role in precision oncology. For diagnostic tests, sensitivity, specificity, positive predictive value, and negative predictive are usually used as performance measures. This paper discusses these indices for predictive biomarkers, provides methods for their calculation with survival or response endpoints, and describes assumptions involved in their use.
Aberrant patterns of DNA methylation are abundant in cancer, and epigenetic pathways are increasingly being targeted in cancer drug treatment. Genetic components of the folate-mediated one-carbon metabolism pathway can affect DNA methylation and other vital cell functions, including DNA synthesis, amino acid biosynthesis, and cell growth.
We used a bioinformatics tool, the Transcriptional Pharmacology Workbench, to analyze temporal changes in gene expression among epigenetic regulators of DNA methylation and demethylation, and one-carbon metabolism genes in response to cancer drug treatment. We analyzed gene expression information from the NCI-60 cancer cell line panel after treatment with five antitumor agents, 5-azacytidine, doxorubicin, vorinostat, paclitaxel, and cisplatin. Each antitumor agent elicited concerted changes in gene expression of multiple pathway components across the cell lines. Expression changes of FOLR2, SMUG1, GART, GADD45A, MBD1, MTR, MTHFD1, and CTH were significantly correlated with chemosensitivity to some of the agents. Among many genes with concerted expression response to individual antitumor agents were genes encoding DNA methyltransferases DNMT1, DNMT3A, and DNMT3B, epigenetic and DNA repair factors MGMT, GADD45A, and MBD1, and one-carbon metabolism pathway members MTHFD1, TYMS, DHFR, MTR, MAT2A, SLC19A1, ATIC, and GART.
These transcriptional changes are likely to influence vital cellular functions of DNA methylation and demethylation, cellular growth, DNA biosynthesis, and DNA repair, and some of them may contribute to cytotoxic and apoptotic action of the drugs. This concerted molecular response was observed in a time-dependent manner, which may provide future guidelines for temporal selection of genetic drug targets for combination drug therapy treatment regimens.
Electronic supplementary material
The online version of this article (doi:10.1186/s13148-016-0240-3) contains supplementary material, which is available to authorized users.
Gene expression; DNA methylation; Folate metabolism; Cancer drug treatment; Epigenetic analysis; NCI-60 cell lines
The NCI-60 cell lines are the most frequently studied human tumor cell lines in cancer research. This panel has generated the most extensive cancer pharmacology database worldwide. In addition, these cell lines have been intensely investigated, providing a unique platform for hypothesis driven research focused on enhancing our understanding of tumor biology. Here, we report a comprehensive analysis of coding variants in the NCI-60 panel of cell lines identified by whole exome sequencing (WES), providing a list of possible cancer specific variants for the community. Furthermore, we identify pharmacogenomic correlations between specific variants in genes like TP53, BRAF, ERBBs and ATAD5 and anti-cancer agents such as nutlin, vemurafenib, erlotinib and bleomycin demonstrating one of many ways the data could be utilized to validate and generate novel hypotheses for further investigation. As new cancer genes are identified through large-scale sequencing studies, the data presented here for the NCI-60 will be an invaluable resource for identifying cell lines with mutations in such genes for hypothesis driven research. To enhance the utility of the data for the greater research community, the genomic variants are freely available in different formats and from multiple sources including the CellMiner and Ingenuity websites.
There is growing interest in the application of molecular profiling, including sequencing, genotyping, and/or mRNA expression profiling, to the analysis of patient tumors with the objective of applying these data to inform therapeutic choices for patients with advanced cancers. Multiple clinical trials that are attempting to validate this personalized or precision medicine approach are in various stages of development and execution. Although preliminary data from some of these efforts have fueled excitement about the value and utility of these studies, their execution has also provoked many questions about the best way to approach complicating factors such as tumor heterogeneity and the choice of which genetic mutations to target. This commentary highlights some of the challenges confronting the clinical application of molecular tumor profiling and the various trial designs being utilized to address these challenges. Randomized trials that rigorously test patient response to molecularly targeted agents assigned based on the presence of a defined set of mutations in putative cancer-driving pathways are required to address some of the current challenges and to identify patients likely to benefit from this approach.
Veliparib, a poly(ADP-ribose) polymerase (PARP) inhibitor, demonstrated clinical activity in combination with oral cyclophosphamide in patients with BRCA-mutant solid tumors in a phase 1 trial. To define the relative contribution of PARP inhibition to the observed clinical activity, we conducted a randomized phase 2 trial to determine the response rate of veliparib in combination with cyclophosphamide compared to cyclophosphamide alone in patients with pretreated BRCA-mutant ovarian cancer or in patients with pretreated primary peritoneal, fallopian tube, or high-grade serous ovarian cancers (HGSOC).
Adult patients were randomized to receive cyclophosphamide alone (50 mg orally once daily) or with veliparib (60 mg orally once daily) in 21-day cycles. Crossover to the combination was allowed at disease progression.
Seventy-five patients were enrolled and 72 were evaluable for response; 38 received cyclophosphamide alone and 37 the combination as their initial treatment regimen. Treatment was well tolerated. One complete response was observed in each arm, with three partial responses (PR) in the combination arm and six PRs in the cyclophosphamide alone arm. Genetic sequence and expression analyses were performed for 211 genes involved in DNA repair; none of the detected genetic alterations were significantly associated with treatment benefit.
This is the first trial that evaluated single agent, low dose cyclophosphamide in HGSOC, peritoneal, fallopian tube, and BRCA-mutant ovarian cancers. It was well tolerated and clinical activity was observed; the addition of veliparib at 60 mg daily did not improve either the response rate or the median progression free survival.
DNA damage repair; PARP; homologous recombination; chemopotentiation
To identify genetic alterations underlying rectal carcinogenesis, we used global gene expression profiling of a series of 17 locally advanced rectal adenocarcinomas and 20 normal rectal mucosa biopsies on oligonucleotide arrays. A total of 351 genes were differentially expressed (P < 1.0e–7) between normal rectal mucosa and rectal carcinomas, 77 genes had a >5-fold difference, and 85 genes always had at least a 2-fold change in all of the matched samples. Twelve genes satisfied all three of these criteria. Altered expression of genes such as PTGS2 (COX-2), WNT1, TGFB1, VEGF, and MYC was confirmed, whereas our data for other genes, like PPARD and LEF1, were inconsistent with previous reports. In addition, we found deregulated expression of many genes whose involvement in rectal carcinogenesis has not been reported. By mapping the genomic imbalances in the tumors using comparative genomic hybridization, we could show that DNA copy number gains of recurrently aneuploid chromosome arms 7p, 8q, 13q, 18q, 20p, and 20q correlated significantly with their average chromosome arm expression profile. Taken together, our results show that both the high-level, significant transcriptional deregulation of specific genes and general modification of the average transcriptional activity of genes residing on aneuploid chromosomes coexist in rectal adenocarcinomas.
To characterize patterns of global transcriptional deregulation in primary colon carcinomas, we did gene expression profiling of 73 tumors [Unio Internationale Contra Cancrum stage II (n = 33) and stage III (n = 40)] using oligonucleotide microarrays. For 30 of the tumors, expression profiles were compared with those from matched normal mucosa samples. We identified a set of 1,950 genes with highly significant deregulation between tumors and mucosa samples (P < 1e–7). A significant proportion of these genes mapped to chromosome 20 (P = 0.01). Seventeen genes had a >5-fold average expression difference between normal colon mucosa and carcinomas, including up-regulation of MYC and of HMGA1, a putative oncogene. Furthermore, we identified 68 genes that were significantly differentially expressed between lymph node–negative and lymph node–positive tumors (P < 0.001), the functional annotation of which revealed a preponderance of genes that play a role in cellular immune response and surveillance. The microarray-derived gene expression levels of 20 deregulated genes were validated using quantitative real-time reverse transcription-PCR in >40 tumor and normal mucosa samples with good concordance between the techniques. Finally, we established a relationship between specific genomic imbalances, which were mapped for 32 of the analyzed colon tumors by comparative genomic hybridization, and alterations of global transcriptional activity. Previously, we had conducted a similar analysis of primary rectal carcinomas. The systematic comparison of colon and rectal carcinomas revealed a significant overlap of genomic imbalances and transcriptional deregulation, including activation of the Wnt/β-catenin signaling cascade, suggesting similar pathogenic pathways.
There is a wide spectrum of tumor responsiveness of rectal adenocarcinomas to preoperative chemoradiotherapy ranging from complete response to complete resistance. This study aimed to investigate whether parallel gene expression profiling of the primary tumor can contribute to stratification of patients into groups of responders or nonresponders.
Patients and Methods
Pretherapeutic biopsies from 30 locally advanced rectal carcinomas were analyzed for gene expression signatures using microarrays. All patients were participants of a phase III clinical trial (CAO/ARO/AIO-94, German Rectal Cancer Trial) and were randomized to receive a preoperative combined-modality therapy including fluorouracil and radiation. Class comparison was used to identify a set of genes that were differentially expressed between responders and nonresponders as measured by T level downsizing and histopathologic tumor regression grading.
In an initial set of 23 patients, responders and nonresponders showed significantly different expression levels for 54 genes (P < .001). The ability to predict response to therapy using gene expression profiles was rigorously evaluated using leave-one-out cross-validation. Tumor behavior was correctly predicted in 83% of patients (P = .02). Sensitivity (correct prediction of response) was 78%, and specificity (correct prediction of nonresponse) was 86%, with a positive and negative predictive value of 78% and 86%, respectively.
Our results suggest that pretherapeutic gene expression profiling may assist in response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy. The implementation of gene expression profiles for treatment stratification and clinical management of cancer patients requires validation in large, independent studies, which are now warranted.
Prostate specific antigen sensitivity increases with lower threshold values but with a corresponding decrease in specificity. Magnetic resonance imaging/ultrasound targeted biopsy detects prostate cancer more efficiently and of higher grade than standard 12-core transrectal ultrasound biopsy but the optimal population for its use is not well defined. We evaluated the performance of magnetic resonance imaging/ultrasound targeted biopsy vs 12-core biopsy across a prostate specific antigen continuum.
Materials and Methods
We reviewed the records of all patients enrolled in a prospective trial who underwent 12-core transrectal ultrasound and magnetic resonance imaging/ultrasound targeted biopsies from August 2007 through February 2014. Patients were stratified by each of 4 prostate specific antigen cutoffs. The greatest Gleason score using either biopsy method was compared in and across groups as well as across the population prostate specific antigen range. Clinically significant prostate cancer was defined as Gleason 7 (4 + 3) or greater. Univariate and multivariate analyses were performed.
A total of 1,003 targeted and 12-core transrectal ultrasound biopsies were performed, of which 564 diagnosed prostate cancer for a 56.2% detection rate. Targeted biopsy led to significantly more upgrading to clinically significant disease compared to 12-core biopsy. This trend increased more with increasing prostate specific antigen, specifically in patients with prostate specific antigen 4 to 10 and greater than 10 ng/ml. Prostate specific antigen 5.2 ng/ml or greater captured 90% of upgrading by targeted biopsy, corresponding to 64% of patients who underwent multiparametric magnetic resonance imaging and subsequent fusion biopsy. Conversely a greater proportion of clinically insignificant disease was detected by 12-core vs targeted biopsy overall. These differences persisted when controlling for potential confounders on multivariate analysis.
Prostate cancer upgrading with targeted biopsy increases with an increasing prostate specific antigen cutoff. Above a prostate specific antigen threshold of 5.2 ng/ml most upgrading to clinically significant disease was achieved by targeted biopsy. In our population this corresponded to potentially sparing biopsy in 36% of patients who underwent multiparametric magnetic resonance imaging. Below this value 12-core biopsy detected more clinically insignificant cancer. Thus, the diagnostic usefulness of targeted biopsy is optimized in patients with prostate specific antigen 5.2 ng/ml or greater.
prostate; prostatic neoplasms; diagnostic imaging; prostate-specific antigen; biopsy
Cancer biomarkers are frequently evaluated using archived specimens collected from previously conducted therapeutic trials. Routine collection and banking of high quality specimens is an expensive and time-consuming process. Therefore, care should be taken to preserve these precious resources. Here we propose a novel two-stage adaptive cutoff (TACO) design that affords the possibility to stop the biomarker study early if an evaluation of the model performance is unsatisfactory at an early stage, thereby allowing one to preserve the remaining specimens for future research. In addition, our design integrates important elements necessary to meet statistical rigor and practical demands for developing and validating a prognostic biomarker signature, including maintaining strict separation between the datasets used to build and evaluate the model and producing a locked-down signature to facilitate future validation. We conduct simulation studies to evaluate the operating characteristics of the proposed design. We show that under the null hypothesis when the model performance is deemed undesirable, the proposed design maintains type I error at the nominal level, has high probabilities of terminating the study early, and results in substantial savings in specimens. Under the alternative hypothesis, power is generally high when the total sample size and the targeted degree of improvement in prediction accuracy are reasonably large. We illustrate the use of the procedure with a dataset in patients with diffuse large-B-cell lymphoma. The practical aspects of the proposed designs are discussed.
cancer biomarker; two-stage design; biomarker validation; cross-validation; early stopping
Targeted magnetic resonance (MR)/ultrasound fusion prostate biopsy has been shown to detect prostate cancer. The implications of targeted biopsy alone vs standard extended-sextant biopsy or the 2 modalities combined are not well understood.
To assess targeted vs standard biopsy and the 2 approaches combined for the diagnosis of intermediate- to high-risk prostate cancer.
Design, Setting, And Participants
Prospective cohort study of 1003 men undergoing both targeted and standard biopsy concurrently from 2007 through 2014 at the National Cancer Institute in the United States. Patients were referred for elevated level of prostate-specific antigen (PSA) or abnormal digital rectal examination results, often with prior negative biopsy results. Risk categorization was compared among targeted and standard biopsy and, when available, whole-gland pathology after prostatectomy as the “gold standard.”
Patients underwent multiparametric prostate magnetic resonance imaging to identify regions of prostate cancer suspicion followed by targeted MR/ultrasound fusion biopsy and concurrent standard biopsy.
Main Outcomes And Measures
The primary objective was to compare targeted and standard biopsy approaches for detection of high-risk prostate cancer (Gleason score ≥4 + 3); secondary end points focused on detection of low-risk prostate cancer (Gleason score 3 + 3 or low-volume 3 + 4) and the biopsy ability to predict whole-gland pathology at prostatectomy.
Targeted MR/ultrasound fusion biopsy diagnosed 461 prostate cancer cases, and standard biopsy diagnosed 469 cases. There was exact agreement between targeted and standard biopsy in 690 men (69%) undergoing biopsy. Targeted biopsy diagnosed 30% more high-risk cancers vs standard biopsy (173 vs 122 cases, P < .001) and 17% fewer low-risk cancers (213 vs 258 cases, P < .001). When standard biopsy cores were combined with the targeted approach, an additional 103 cases (22%) of mostly low-risk prostate cancer were diagnosed (83% low risk, 12% intermediate risk, and 5% high risk). The predictive ability of targeted biopsy for differentiating low-risk from intermediate- and high-risk disease in 170 men with whole-gland pathology after prostatectomy was greater than that of standard biopsy or the 2 approaches combined (area under the curve, 0.73, 0.59, and 0.67, respectively; P < .05 for all comparisons).
Conclusions and Relevance
Among men undergoing biopsy for suspected prostate cancer, targeted MR/ultrasound fusion biopsy, compared with standard extended-sextant ultrasound-guided biopsy, was associated with increased detection of high-risk prostate cancer and decreased detection of low-risk prostate cancer. Future studies will be needed to assess the ultimate clinical implications of targeted biopsy.
clinicaltrials.gov Identifier: NCT00102544
Phase 1 clinical trials are generally conducted to identify the maximum tolerated dose (MTD) or the biologically active dose (BAD) using a traditional dose escalation design. This design may not be applied to cancer vaccines, given their unique mechanism of action. The FDA recently published “Guidance for Industry: Clinical Considerations for Therapeutic Cancer Vaccines.” However, many questions about the design of cancer vaccine studies remain unanswered.
We analyzed the toxicity profile in 239 phase 1 therapeutic cancer vaccine trials. We addressed the ability of dose escalation to determine the MTD or the BAD in trials that used a dose escalation design.
The rate of grade 3/4 vaccine-related systemic toxicities was 1.25 adverse event per 100 patients and 2 per 1000 vaccines. Only 2 out of the 127 dose escalation trials reported vaccine-related dose limiting toxicities, both of which used bacterial vector vaccines. Out of the 116 trials analyzed for the dose-immune response relationship, we found a statistically significant dose-immune response correlation only when the immune response was measured by antibodies (p<0.001) or delayed type hypersensitivity (p<0.05). However, the increase in cellular immune response did not appear further sustainable with the continued increase in dose.
Our analysis suggests that the risks of serious toxicities with therapeutic cancer vaccines are extremely low and that toxicities do not correlate with dose levels. Accordingly, the conventional dose escalation design is not suitable for cancer vaccines with few exceptions. Here we propose an alternative design for therapeutic cancer vaccine development.
Cancer; Vaccine; Phase I; Trial; Design
Recent work indicates that the airways of persons with cystic fibrosis (CF) typically harbor complex bacterial communities. However, the day-to-day stability of these communities is unknown. Further, airway community dynamics during the days corresponding to the onset of symptoms of respiratory exacerbation have not been studied.
Using 16S rRNA amplicon sequencing of 95 daily sputum specimens collected from four adults with CF, we observed varying degrees of day-to-day stability in airway bacterial community structures during periods of clinical stability. Differences were observed between study subjects with respect to the degree of community changes at the onset of exacerbation. Decreases in the relative abundance of dominant taxa were observed in three subjects at exacerbation. We observed no relationship between total bacterial load and clinical status and detected no viruses by multiplex PCR.
CF airway microbial communities are relatively stable during periods of clinical stability. Changes in microbial community structure are associated with some, but not all, pulmonary exacerbations, supporting previous observations suggesting that distinct types of exacerbations occur in CF. Decreased abundance of species that are dominant at baseline suggests a role for less abundant taxa in some exacerbations. Daily sampling revealed patterns of change in microbial community structures that may prove useful in the prediction and management of CF pulmonary exacerbations.
Electronic supplementary material
The online version of this article (doi:10.1186/s40168-015-0074-9) contains supplementary material, which is available to authorized users.
Cystic fibrosis; Respiratory exacerbation; Lung microbiome; Airway microbiome
We have developed an informatics system, GeneMed, for the National Cancer Institute (NCI) molecular profiling-based assignment of cancer therapy (MPACT) clinical trial (NCT01827384) being conducted in the National Institutes of Health (NIH) Clinical Center. This trial is one of the first to use a randomized design to examine whether assigning treatment based on genomic tumor screening can improve the rate and duration of response in patients with advanced solid tumors. An analytically validated next-generation sequencing (NGS) assay is applied to DNA from patients’ tumors to identify mutations in a panel of genes that are thought likely to affect the utility of targeted therapies available for use in the clinical trial. The patients are randomized to a treatment selected to target a somatic mutation in the tumor or with a control treatment. The GeneMed system streamlines the workflow of the clinical trial and serves as a communications hub among the sequencing lab, the treatment selection team, and clinical personnel. It automates the annotation of the genomic variants identified by sequencing, predicts the functional impact of mutations, identifies the actionable mutations, and facilitates quality control by the molecular characterization lab in the review of variants. The GeneMed system collects baseline information about the patients from the clinic team to determine eligibility for the panel of drugs available. The system performs randomized treatment assignments under the oversight of a supervising treatment selection team and generates a patient report containing detected genomic alterations. NCI is planning to expand the MPACT trial to multiple cancer centers soon. In summary, the GeneMed system has been proven to be an efficient and successful informatics hub for coordinating the reliable application of NGS to precision medicine studies.
GeneMed; MPACT; next-generation sequencing; precision medicine; informatics system; clinical trial
We examine the role of stratification of treatment assignment with regard to biomarker value in clinical trials that accept biomarker positive and negative patients but have a primary objective of evaluating treatment effect separately for the marker positive subset. We also examine the issue of incomplete ascertainment of biomarker value and how this affects inference about treatment effect for the biomarker positive subset of patients. We find that stratifying the randomization for the biomarker ensures that all patients will have tissue collected but is not necessary for the validity of inference for the biomarker positive subset if there is complete ascertainment. If there is not complete ascertainment of biomarker values, it is important to establish that ascertainment is independent of treatment assignment. Having a large proportion of cases with biomarker ascertainment is not necessary for establishing internal validity of the treatment evaluation in biomarker positive patients; independence of ascertainment and treatment is the important factor. Having a large proportion of cases with biomarker ascertainment makes it more likely that biomarker positive patients with ascertainment are representative of the biomarker positive patients in the clinical trial (with and without ascertainment), but since the patients in the clinical trial are a convenience sample of the population of patients potentially eligible for the trial, requiring a large proportion of cases with ascertainment does not facilitate generalizability of conclusions.
Stratification; biomarker; clinical trails; ascertainment
The small and large intestine of the gastrointestinal tract (GIT) have evolved to have discrete functions with distinct anatomies and immune cell composition. The importance of these differences is underlined when considering that different pathogens have uniquely adapted to live in each region of the gut. Furthermore, different regions of the GIT are also associated with differences in susceptibility to diseases such as cancer and chronic inflammation. The large and small intestine, given their anatomical and functional differences, should be seen as two separate immunological sites. However, this distinction is often ignored with findings from one area of the GIT being inappropriately extrapolated to the other. Focussing largely on the murine small and large intestine, this review addresses the literature relating to the immunology and biology of the two sites, drawing comparisons between them and clarifying similarities and differences. We also highlight the gaps in our understanding and where further research is needed.
Large intestine; Small intestine; Epithelial; Immune; Microbial
Developments in biotechnology have stimulated the use of predictive biomarkers to identify patients who are likely to benefit from a targeted therapy. Several randomized phase III designs have been introduced for development of a targeted therapy using a diagnostic test. Most such designs require biomarkers measured before treatment. In many cases, it has been very difficult to identify such biomarkers. Promising candidate biomarkers can sometimes be effectively measured after a short run-in period on the new treatment.
We introduce a new design for phase III trials with a candidate predictive pharmacodynamic biomarker measured after a short run-in period. Depending on the therapy and the biomarker performance, the trial would either randomize all patients but perform a separate analysis on the biomarker-positive patients or only randomize marker-positive patients after the run-in period. We evaluate the proposed design compared with the conventional phase III design and discuss how to design a run-in trial based on phase II studies.
The proposed design achieves a major sample size reduction compared with the conventional randomized phase III design in many cases when the biomarker has good sensitivity (≥0.7) and specificity (≥0.7). This requires that the biomarker be measured accurately and be indicative of drug activity. However, the proposed design loses some of its advantage when the proportion of potential responders is large (>50%) or the effect on survival from run-in period is substantial.
Incorporating a pharmacodynamic biomarker requires careful consideration but can expand the capacity of clinical trials to personalize treatment decisions and enhance therapeutics development.
The US National Cancer Institute (NCI), in collaboration with scientists representing multiple areas of expertise relevant to ‘omics’-based test development, has developed a checklist of criteria that can be used to determine the readiness of omics-based tests forguiding patient care in clinical trials. The checklist criteria cover issues relating to specimens, assays, mathematical modelling, clinical trial design, and ethical, legal and regulatory aspects. Funding bodies and journals are encouraged to consider the checklist, which they may find useful for assessing study quality and evidence strength. The checklist will be used to evaluate proposals for NCI-sponsored clinical trials in which omics tests will be used to guide therapy.
Modern medicine has graduated from broad spectrum treatments to targeted therapeutics. New drugs recognize the recently discovered heterogeneity of many diseases previously considered to be fairly homogeneous. These treatments attack specific genetic pathways which are only dysregulated in some smaller subset of patients with the disease. Often this subset is only rudimentarily understood until well into large-scale clinical trials. As such, standard practice has been to enroll a broad range of patients and run post hoc subset analysis to determine those who may particularly benefit. This unnecessarily exposes many patients to hazardous side effects, and may vastly decrease the efficiency of the trial (especially if only a small subset of patients benefit). In this manuscript, we propose a class of adaptive enrichment designs that allow the eligibility criteria of a trial to be adaptively updated during the trial, restricting entry to patients likely to benefit from the new treatment. We show that our designs both preserve the type 1 error, and in a variety of cases provide a substantial increase in power.
Adaptive clinical trials; Biomarker; Cutpoint; Enrichment
The standard paradigm for the design of phase III clinical trials is not
suitable for evaluation of molecularly targeted treatments in biologically
heterogeneous groups of patients. Here we comment on alternative clinical trial
designs and propose a prospective discovery/evaluation framework for using tumor
genomics in the design phase III trials.
Alveolar soft part sarcoma (ASPS) is a rare, highly vascular tumor, for which no effective standard systemic treatment exists for patients with unresectable disease. Cediranib is a potent, oral small-molecule inhibitor of all three vascular endothelial growth factor receptors (VEGFRs).
Patients and Methods
We conducted a phase II trial of once-daily cediranib (30 mg) given in 28-day cycles for patients with metastatic, unresectable ASPS to determine the objective response rate (ORR). We also compared gene expression profiles in pre- and post-treatment tumor biopsies and evaluated the effect of cediranib on tumor proliferation and angiogenesis using positron emission tomography and dynamic contrast-enhanced magnetic resonance imaging.
Of 46 patients enrolled, 43 were evaluable for response at the time of analysis. The ORR was 35%, with 15 of 43 patients achieving a partial response. Twenty-six patients (60%) had stable disease as the best response, with a disease control rate (partial response + stable disease) at 24 weeks of 84%. Microarray analysis with validation by quantitative real-time polymerase chain reaction on paired tumor biopsies from eight patients demonstrated downregulation of genes related to vasculogenesis.
In this largest prospective trial to date of systemic therapy for metastatic ASPS, we observed that cediranib has substantial single-agent activity, producing an ORR of 35% and a disease control rate of 84% at 24 weeks. On the basis of these results, an open-label, multicenter, randomized phase II registration trial is currently being conducted for patients with metastatic ASPS comparing cediranib with another VEGFR inhibitor, sunitinib.
Developments in biotechnology and genomics have increased the focus of biostatisticians on prediction problems. This has led to many exciting developments for predictive modeling where the number of variables is larger than the number of cases. Heterogeneity of human diseases and new technology for characterizing them presents new opportunities and challenges for the design and analysis of clinical trials.
In oncology, treatment of broad populations with regimens that do not benefit most patients is less economically sustainable with expensive molecularly targeted therapeutics. The established molecular heterogeneity of human diseases requires the development of new paradigms for the design and analysis of randomized clinical trials as a reliable basis for predictive medicine[1, 2].
We have reviewed prospective designs for the development of new therapeutics with candidate predictive biomarkers. We have also outlined a prediction based approach to the analysis of randomized clinical trials that both preserves the type I error and provides a reliable internally validated basis for predicting which patients are most likely or unlikely to benefit from the new regimen.
Developing new treatments with predictive biomarkers for identifying the patients who are most likely or least likely to benefit makes drug development more complex. But for many new oncology drugs it is the only science based approach and should increase the chance of success. It may also lead to more consistency in results among trials and has obvious benefits for reducing the number of patients who ultimately receive expensive drugs which expose them risks of adverse events but no benefit. This approach also has great potential value for controlling societal expenditures on health care. Development of treatments with predictive biomarkers requires major changes in the standard paradigms for the design and analysis of clinical trials. Some of the key assumptions upon which current methods are based are no longer valid. In addition to reviewing a variety of new clinical trial designs for co-development of treatments and predictive biomarkers, we have outlined a prediction based approach to the analysis of randomized clinical trials. This is a very structured approach whose use requires careful prospective planning. It requires further development but may serve as a basis for a new generation of predictive clinical trials which provide the kinds of reliable individualized information which physicians and patients have long sought, but which have not been available from the past use of post-hoc subset analysis.
Plastic surgery training worldwide has seen a thorough restructuring over the past decade, with the introduction of formal training curricula and work-based assessment tools. Part of this process has been the introduction of revalidation and a greater use of simulation in training delivery. Simulation is an increasingly important tool for educators because it provides a way to reduce risks to both trainees and patients, whilst facilitating improved technical proficiency. Current microsurgery training interventions are often predicated on theories of skill acquisition and development that follow a 'practice makes perfect' model. Given the changing landscape of surgical training and advances in educational theories related to skill development, research is needed to assess the potential benefits of alternative models, particularly cross-training, a model now widely used in non-medical areas with significant benefits. Furthermore, with the proliferation of microsurgery training interventions and therefore diversity in length, cost, content and models used, appropriate standardisation will be an important factor to ensure that courses deliver consistent and effective training that achieves appropriate levels of competency. Key research requirements should be gathered and used in directing further research in these areas to achieve on-going improvement of microsurgery training.
Surgery, plastic; Microsurgery; Inservice training; Patient simulation; Education
The alveolar epithelium is characteristically abnormal in fibrotic lung disease, and we recently established a direct link between injury to the type II alveolar epithelial cell (AEC) and the accumulation of interstitial collagen. The mechanisms by which damage to the epithelium induces lung scarring remain poorly understood. It is particularly controversial whether an insult to the type II AEC initiates an inflammatory response that is required for the development of fibrosis. To explore whether local inflammation occurs following a targeted epithelial insult and contributes to lung fibrosis, we administered diphtheria toxin to transgenic mice with type II AEC-restricted expression of the diphtheria toxin receptor. We employed immunophenotyping techniques and diphtheria toxin receptor-expressing, chemokine-receptor-2 deficient (CCR2−/−) mice to determine the participation of lung leukocyte subsets in pulmonary fibrogenesis. Our results demonstrate that targeted type II AEC injury induces an inflammatory response that is enriched for CD11b+ non-resident exudate macrophages (ExM) and their precursors, Ly-6Chigh monocytes. CCR2-deficiency abrogates the accumulation of both cell populations and protects mice from fibrosis, weight loss, and death. Further analyses revealed that the ExM are alternatively-activated and that ExM and Ly-6Chigh monocytes express mRNA for IL-13, TGF-β, and the collagen genes, COL1A1 and COLIIIA1. Furthermore, the accumulated ExM and Ly-6Chigh monocytes contain intracellular collagen as detected by immunostaining. Together, these results implicate CCR2 and the accumulation of ExM and Ly-6Chigh monocytes as critical determinants of pulmonary fibrosis induced by selective type II AEC injury.
Pulmonary; Collagen; Inflammation; CCR2; Alveolar Epithelium
The introduction of next-generation sequencing (NGS) technology has made it possible to detect genomic alterations within tumor cells on a large scale. However, most applications of NGS show the genetic content of mixtures of cells. Recently developed single cell sequencing technology can identify variation within a single cell. Characterization of multiple samples from a tumor using single cell sequencing can potentially provide information on the evolutionary history of that tumor. This may facilitate understanding how key mutations accumulate and evolve in lineages to form a heterogeneous tumor.
We provide a computational method to infer an evolutionary mutation tree based on single cell sequencing data. Our approach differs from traditional phylogenetic tree approaches in that our mutation tree directly describes temporal order relationships among mutation sites. Our method also accommodates sequencing errors. Furthermore, we provide a method for estimating the proportion of time from the earliest mutation event of the sample to the most recent common ancestor of the sample of cells. Finally, we discuss current limitations on modeling with single cell sequencing data and possible improvements under those limitations.
Inferring the temporal ordering of mutational sites using current single cell sequencing data is a challenge. Our proposed method may help elucidate relationships among key mutations and their role in tumor progression.