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Translation of whole human genome sequencing to clinical practice: The Joint Initiative for Metrology in Biology (JIMB) is a collaboration between NIST and Stanford University

Posted in Big Data, Bio Instrumentation in Experimental Life Sciences Research, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Cell Biology, Cell Biology, Signaling & Cell Circuits, Chemical Genetics, Clinical Genomics, DNA repair, Gene Regulation, Gene Regulation and Evolution, Gene Therapy & Gene Editing Development, Genetics & Innovations in Treatment, Genome Biology, Genomic Testing: Methodology for Diagnosis, Variation in human protein-coding regions on December 18, 2016| Leave a Comment »

Translation of whole human genome sequencing to clinical practice: The Joint Initiative for Metrology in Biology (JIMB) is a collaboration between the National Institute of Standards & Technology (NIST) and Stanford University.

Reporter: Aviva Lev-Ari, PhD, RN

 

JIMB’s mission is to advance the science of measuring biology (biometrology). JIMB is pursuing fundamental research, standards development, and the translation of products that support confidence in biological measurements and reliable reuse of materials and results. JIMB is particularly focused on measurements and technologies that impact, are related to, or enabled by ongoing advances in and associated with the reading and writing of DNA.

Stanford innovators and industry entrepreneurs have joined forces with the measurement experts from NIST to create a new engine powering the bioeconomy. It’s called JIMB — “Jim Bee” — the Joint Initiative for Metrology in Biology. JIMB unites people, platforms, and projects to underpin standards-based research and innovation in biometrology.

Genome in a Bottle
Authoritative Characterization of
Benchmark Human Genomes

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The Genome in a Bottle Consortium is a public-private-academic consortium hosted by NIST to develop the technical infrastructure (reference standards, reference methods, and reference data) to enable translation of whole human genome sequencing to clinical practice. The priority of GIAB is authoritative characterization of human genomes for use in analytical validation and technology development, optimization, and demonstration. In 2015, NIST released the pilot genome Reference Material 8398, which is genomic DNA (NA12878) derived from a large batch of the Coriell cell line GM12878, characterized for high-confidence SNPs, indel, and homozygous reference regions (Zook, et al., Nature Biotechnology 2014).

There are four new GIAB reference materials available.  With the addition of these new reference materials (RMs) to a growing collection of “measuring sticks” for gene sequencing, we can now provide laboratories with even more capability to accurately “map” DNA for genetic testing, medical diagnoses and future customized drug therapies. The new tools feature sequenced genes from individuals in two genetically diverse groups, Asians and Ashkenazic Jews; a father-mother-child trio set from Ashkenazic Jews; and four microbes commonly used in research. For more information click here.  To purchase them, visit:

• www.tinyurl.com/giabajson
• www.tinyurl.com/giabajtrio
• www.tinyurl.com/giabchineseson
• www.tinyurl.com/nist4microbialgenomes

Data and analyses are publicly available (GIAB GitHub). A description of data generated by GIAB is published here. To standardize best practices for using GIAB genomes for benchmarking, we are working with the Global Alliance for Genomics and Health Benchmarking Team (benchmarking tools).

High-confidence small variant and homozygous reference calls are available for NA12878, the Ashkenazim trio, and the Chinese son with respect to GRCh37.  Preliminary high-confidence calls with respect to GRCh38 are also available for NA12878.   The latest version of these calls is under the latest directory for each genome on the GIAB FTP.

The consortium was initiated in a set of meetings in 2011 and 2012, and the consortium holds open, public workshops in January at Stanford University in Palo Alto, CA and in August/September at NIST in Gaithersburg, MD. Slides from workshops and conferences are available online. The consortium is open and welcomes new participants.

SOURCE

http://jimb.stanford.edu/giab

Stanford innovators and industry entrepreneurs have joined forces with the measurement experts from NIST to create a new engine powering the bioeconomy. It’s called JIMB — “Jim Bee” — the Joint Initiative for Metrology in Biology. JIMB unites people, platforms, and projects to underpin standards-based research and innovation in biometrology.

May 22, 2017

JIMB WORLD METROLOGY DAY SYMPOSIUM

Stanford University

JIMB’s mission is to motivate standards-based measurement innovation to facilitate translation of basic science and technology development breakthroughs in genomics and synthetic biology.

By advancing biometrology, JIMB will push the boundaries of discovery science, accelerate technology development and dissemination, and generate reusable resources.

 SOURCE

http://jimb.stanford.edu/

VIEW VIDEO

https://player.vimeo.com/video/184956195?wmode=opaque&api=1″,”url”:”https://vimeo.com/184956195″,”width”:640,”height”:360,”providerName”:”Vimeo”,”thumbnailUrl”:”https://i.vimeocdn.com/video/594555038_640.jpg”,”resolvedBy”:”vimeo”}” data-block-type=”32″>

Other related articles published in this Open Access Online Scientific Journal include the following:

“Genome in a Bottle”: NIST’s new metrics for Clinical Human Genome Sequencing

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2012/09/06/genome-in-a-bottle-nists-new-metrics-for-clinical-human-genome-sequencing/

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mRNA Data Survival Analysis

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Big Data, Bio Instrumentation in Experimental Life Sciences Research, BioIT: BioInformatics, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Cancer and Current Therapeutics, Clinical Genomics, Computational Biology/Systems and Bioinformatics, Disease Biology, Exosomes, FDA Regulatory Affairs, Gene Regulation, Genetics & Pharmaceutical, Molecular Genetics & Pharmaceutical, Pharmacogenomics, Population Health Management, Genetics & Pharmaceutical, RNA Biology, Cancer and Therapeutics, tagged Exosomes, multivariate statistical analysis, survival analysis on June 18, 2016| Leave a Comment »

mRNA Data Survival Analysis

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

 

 

SURVIV for survival analysis of mRNA isoform variation

Shihao Shen, Yuanyuan Wang, Chengyang Wang, Ying Nian Wu & Yi Xing
Nature Communications7,Article number:11548 Feb 2016      doi:10.1038/ncomms11548

The rapid accumulation of clinical RNA-seq data sets has provided the opportunity to associate mRNA isoform variations to clinical outcomes. Here we report a statistical method SURVIV (Survival analysis of mRNA Isoform Variation), designed for identifying mRNA isoform variation associated with patient survival time. A unique feature and major strength of SURVIV is that it models the measurement uncertainty of mRNA isoform ratio in RNA-seq data. Simulation studies suggest that SURVIV outperforms the conventional Cox regression survival analysis, especially for data sets with modest sequencing depth. We applied SURVIV to TCGA RNA-seq data of invasive ductal carcinoma as well as five additional cancer types. Alternative splicing-based survival predictors consistently outperform gene expression-based survival predictors, and the integration of clinical, gene expression and alternative splicing profiles leads to the best survival prediction. We anticipate that SURVIV will have broad utilities for analysing diverse types of mRNA isoform variation in large-scale clinical RNA-seq projects.

Eukaryotic cells generate remarkable regulatory and functional complexity from a finite set of genes. Production of mRNA isoforms through alternative processing and modification of RNA is essential for generating this complexity. A prevalent mechanism for producing mRNA isoforms is the alternative splicing of precursor mRNA¹. Over 95% of the multi-exon human genes undergo alternative splicing^{2, 3}, resulting in an enormous level of plasticity in the regulation of gene function and protein diversity. In the last decade, extensive genomic and functional studies have firmly established the critical role of alternative splicing in cancer^{4, 5, 6}. Alternative splicing is involved in a full spectrum of oncogenic processes including cell proliferation, apoptosis, hypoxia, angiogenesis, immune escape and metastasis^{7, 8}. These cancer-associated alternative splicing patterns are not merely the consequences of disrupted gene regulation in cancer but in numerous instances actively contribute to cancer development and progression. For example, alternative splicing of genes encoding the Bcl-2 family of apoptosis regulators generates both anti-apoptotic and pro-apoptotic protein isoforms⁹. Alternative splicing of the pyruvate kinase M (PKM) gene has a significant impact on cancer cell metabolism and tumour growth¹⁰. A transcriptome-wide switch of the alternative splicing programme during the epithelial–mesenchymal transition plays an important role in cancer cell invasion and metastasis^{11, 12}.

RNA sequencing (RNA-seq) has become a popular and cost-effective technology to study transcriptome regulation and mRNA isoform variation^{13, 14}. As the cost of RNA-seq continues to decline, it has been widely adopted in large-scale clinical transcriptome projects, especially for profiling transcriptome changes in cancer. For example, as of April 2015 The Cancer Genome Atlas (TCGA) consortium had generated RNA-seq data on over 11,000 cancer patient specimens from 34 different cancer types. Within the TCGA data, breast invasive carcinoma (BRCA) has the largest sample size of RNA-seq data covering over 1,000 patients, and clinical information such as survival times, tumour stages and histological subtypes is available for the majority of the BRCA patients¹⁵. Moreover, the median follow-up time of BRCA patients is ~400 days, and 25% of the patients have more than 1,200 days of follow-up. Collectively, the large sample size and long follow-up time of the TCGA BRCA data set allow us to correlate genomic and transcriptomic profiles to clinical outcomes and patient survival times.

To date, systematic analyses have been performed to reveal the association between copy number variation, DNA methylation, gene expression and microRNA expression profiles with cancer patient survival^{16, 17}. By contrast, despite the importance of mRNA isoform variation and alternative splicing, there have been limited efforts in transcriptome-wide survival analysis of alternative splicing in cancer patients. Most RNA-seq studies of alternative splicing in cancer transcriptomes focus on identifying ‘cancer-specific’ alternative splicing events by comparing cancer tissues with normal controls (see refs 18, 19, 20, 21, 22, 23 for examples). A recent analysis of TCGA RNA-seq data identified 163 recurrent differential alternative splicing events between cancer and normal tissues of three cancer types, among which five were found to have suggestive survival signals for breast cancer at a nominal P-value cutoff of 0.05 (ref. 21). Some other studies reported a significant survival difference between cancer patient subgroups after stratifying patients with overall mRNA isoform expression profiles^{24, 25}. However, systematic cancer survival analyses of alternative splicing at the individual exon resolution have been lacking. Two main challenges exist for survival analyses of mRNA isoform variation and alternative splicing using RNA-seq data. The first challenge is to account for the estimation uncertainty of mRNA isoform ratios inferred from RNA-seq read counts. The statistical confidence of mRNA isoform ratio estimation depends on the RNA-seq read coverage for the events of interest, with larger read coverage leading to a more reliable estimation¹⁴. Modelling the estimation uncertainty of mRNA isoform ratio is an essential component of RNA-seq analyses of alternative splicing, as shown by various statistical algorithms developed for detecting differential alternative splicing from multi-group RNA-seq data^{14, 26, 27, 28,29}. The second challenge, which is a general issue in survival analysis, is to properly model the association of mRNA isoform ratio with survival time, while accounting for missing data in survival time because of censoring, that is, patients still alive at the end of the survival study, whose precise survival time would be uncertain. To date, no algorithm has been developed for survival analyses of mRNA isoform variation that accounts for these sources of uncertainty simultaneously.

Here we introduce SURVIV (Survival analysis of mRNA Isoform Variation), a statistical model for identifying mRNA isoform ratios associated with patient survival times in large-scale cancer RNA-seq data sets. SURVIV models the estimation uncertainty of mRNA isoform ratios in RNA-seq data and tests the survival effects of isoform variation in both censored and uncensored survival data. In simulation studies, SURVIV consistently outperforms the conventional Cox regression survival analysis that ignores the measurement uncertainty of mRNA isoform ratio. We used SURVIV to identify alternatively spliced exons whose exon-inclusion levels significantly correlated with the survival times of invasive ductal carcinoma (IDC) patients from the TCGA breast cancer cohort. Survival-associated alternative splicing events are identified in gene pathways associated with apoptosis, oxidative stress and DNA damage repair. Importantly, we show that alternative splicing-based survival predictors outperform gene expression-based survival predictors in the TCGA IDC RNA-seq data set, as well as in TCGA data of five additional cancer types. Moreover, the integration of clinical information, gene expression and alternative splicing profiles leads to the best prediction of survival time.

SURVIV statistical model

The statistical model of SURVIV assesses the association between mRNA isoform ratio and patient survival time. While the model is generic for many types of alternative isoform variation, here we use the exon-skipping type of alternative splicing to illustrate the model (Fig. 1a). For each alternative exon involved in exon-skipping, we can use the RNA-seq reads mapping to its exon-inclusion or -skipping isoform to estimate its exon-inclusion level (denoted as ψ, or PSI that is Per cent Spliced In¹⁴). A key feature of SURVIV is that it models the RNA-seq estimation uncertainty of exon-inclusion level as influenced by the sequencing coverage for the alternative splicing event of interest. This is a critical issue in accurate quantitative analyses of mRNA isoform ratio in large-scale RNA-seq data sets^{14, 26, 27, 28, 29}. Therefore, SURVIV contains two major components: the first to model the association of mRNA isoform ratio with patient survival time across all patients, and the second to model the estimation uncertainty of mRNA isoform ratio in each individual patient (Fig. 1a).

Figure 1: The statistical framework of the SURVIV model.

(a) For each patient k, the patient’s hazard rate λ_k(t) is associated with the baseline hazard rate λ₀(t) and this patient’s exon-inclusion level ψ_k. The association of exon-inclusion level with patient survival is estimated by the survival coefficient β. The exon-inclusion level ψ_k is estimated from the read counts for the exon-inclusion isoform IC_k and the exon-skipping isoform SC_k. The proportion of the inclusion and skipping reads is adjusted by a normalization function f that considers the lengths of the exon-inclusion and -skipping isoforms (see details in Results and Supplementary Methods). (b) A hypothetical example to illustrate the association of exon-inclusion level with patient survival probability over time S_k(t), with the survival coefficient β=−1 and a constant baseline hazard rate λ₀(t)=1. In this example, patients with higher exon-inclusion levels have lower hazard rates and higher survival probabilities. (c) The schematic diagram of an exon-skipping event. The exon-inclusion reads IC_k are the reads from the upstream splice junction, the alternative exon itself and the downstream splice junction. The exon-skipping reads SC_k are the reads from the skipping splice junction that directly connects the upstream exon to the downstream exon.

• Full size image (75 KB)

Briefly, for any individual exon-skipping event, the first component of SURVIV uses a proportional hazards model to establish the relationship between patient k’s exon-inclusion level ψ_k and hazard rate λ_k(t).

For each exon, the association between the exon-inclusion level and patient survival time is reflected by the survival coefficient β. A positive β means increased exon inclusion is associated with higher hazard rate and poorer survival, while a negative β means increased exon inclusion is associated with lower hazard rate and better survival. λ₀(t) is the baseline hazard rate estimated from the survival data of all patients (see Supplementary Methods for the detailed estimation procedure). A particular patient’s survival probability over time S_k(t) can be calculated from the patient-specific hazard rate λ_k(t) as . Figure 1b illustrates a simple example with a negative β=−1 and a constant baseline hazard rate λ₀(t)=1, where higher exon-inclusion levels are associated with lower hazard rates and higher survival probabilities.

The second component of SURVIV models the exon-inclusion level and its estimation uncertainty in individual patient samples. As illustrated in Fig. 1c, the exon-inclusion level ψ_k of a given exon in a particular sample can be estimated by the RNA-seq read count specific to the exon inclusion isoform (IC_k) and the exon-skipping isoform (SC_k). Other types of alternative splicing and mRNA isoform variation can be similarly modelled by this framework²⁹. Given the effective lengths (that is, the number of unique isoform-specific read positions) of the exon-inclusion isoform (l_I) and the exon-skipping isoform (l_S), the exon-inclusion level ψ_k can be estimated as . Assuming that the exon-inclusion read count IC_k follows a binomial distribution with the total read count n_k=IC_k+SC_k, we have:

The binomial distribution models the estimation uncertainty of ψ_k as influenced by the total read count n_k, in which the parameter p_k represents the proportion of reads from the exon-inclusion isoform, given the exon-inclusion level ψ_k adjusted by a length normalization function f(ψ_k) based on the effective lengths of the isoforms. The definitions of effective lengths for all basic types of alternative splicing patterns are described in ref. 29.

Distinct from conventional survival analyses in which predictors do not have estimation uncertainty, the predictors in SURVIV are exon-inclusion levels ψ_k estimated from RNA-seq count data, and the confidence of ψ_k estimate for a given exon in a particular sample depends on the RNA-seq read coverage. We use the statistical framework of survival measurement error model³⁰ to incorporate the estimation uncertainty of isoform ratio in the proportional hazards model. Using a likelihood ratio test, we test whether the exon-inclusion levels have a significant association with patient survival over the null hypothesis H₀:β=0. The false discovery rate (FDR) is estimated using the Benjamini and Hochberg approach³¹. Details of the parameter estimation and likelihood ratio test in SURVIV are described in Supplementary Methods.

 

Figure 2: Simulation studies to assess the performance of SURVIV and the importance of modelling the estimation uncertainty of mRNA isoform ratio.

We compared our SURVIV model with Cox regression using point estimates of exon-inclusion levels, which does not consider the estimation uncertainty of the mRNA isoform ratio. (a) To study the effect of RNA-seq depth, we simulated the mean total splice junction read counts equal to 5, 10, 20, 50, 80 and 100 reads. We generated two sets of simulations with and without data-censoring. For each simulation, the true-positive rate (TPR) at 5% false-positive rate is plotted. The inset figure shows the empirical distribution of the mean total splice junction read counts in the TCGA IDC RNA-seq data (x axis in the log10 scale). (b) To faithfully represent the read count distribution in a real data set, we performed another simulation with read counts directly sampled from the TCGA IDC data. Sampled read counts were then multiplied by different factors ranging from 10 to 300% to simulate data sets with different RNA-seq read depth. Continuous and dashed lines represent the performance of SURVIV and Cox regression, respectively. Red lines represent the area under curve (AUC) of the ROC curve (TPR versus false-positive rate plot). Black lines represent the TPR at 5% false-positive rate.

• Full size image (78 KB)

 

Using these simulated data, we compared SURVIV with Cox regression in two settings, without or with censoring of the survival time. In the setting without censoring, the death and survival time of each individual is known. In the setting with censoring, certain individuals are still alive at the end of the survival study. Consequently, these patients have unknown death and survival time. Here, in the simulation with censoring, we assumed that 85% of the patients were still alive at the end of the study, similar to the censoring rate of the TCGA IDC data set. In both settings and with different depths of RNA-seq coverage, SURVIV consistently outperformed Cox regression in the true-positive rate at the same false-positive rate of 5% (Fig. 2a). As expected, we observed a more significant improvement in SURVIV over Cox regression when the RNA-seq read coverage was low (Fig. 2a).

To more faithfully recapitulate the read count distribution in a real cancer RNA-seq data set, we performed another simulation study with read counts directly sampled from the TCGA IDC data. To assess the influence of RNA-seq read depth on the performance of SURVIV and Cox regression, sampled read counts were then multiplied by different factors ranging from 10 to 300% to simulate data sets with different RNA-seq read depths (Fig. 2b). The TCGA IDC data set has an average RNA-seq depth of ~60 million paired-end reads per patient. Thus, the read depth of these simulated RNA-seq data sets ranged from ~6 million reads to 180 million reads per patient, representing low-coverage RNA-seq studies designed primarily for gene expression analysis³² up to high-coverage RNA-seq studies designed primarily for alternative isoform analysis²⁹. At all levels of RNA-seq depth, SURVIV consistently outperformed Cox regression, as reflected by the area under curve of the receiver operating characteristic (ROC) curve as well as the true-positive rate at 5% false-positive rate (Fig. 2b). The improvement of SURVIV over Cox regression was particularly prominent when the read depth was low. For example, at 10% read depth, SURVIV had 7% improvement in area under curve (68% versus 61%) and 8% improvement in the true-positive rate at 5% false-positive rate (46% versus 38%). Collectively, these simulation results suggest that SURVIV achieves a higher accuracy by accounting for the estimation uncertainty of mRNA isoform ratio in RNA-seq data.

SURVIV analysis of TCGA IDC breast cancer data

To illustrate the practical utility of SURVIV, we used it to analyse the overall survival time of 682 IDC patients from the TCGA breast cancer (BRCA) RNA-seq data set (see Methods for details of the data source and processing pipeline). We chose to analyse IDC because it is the most frequent type of breast cancer³³, comprising ~70% of patients in the TCGA breast cancer data set. To control for the effects of significant clinical parameters such as tumour stage and subtype and identify alternative splicing events associated with patient outcomes across multiple molecular and clinical subtypes, we followed the procedure of Croce and colleagues in analysing mRNA and microRNA prognostic signature of IDC³³ and stratified the patients according to their clinical parameters. We then conducted SURVIV analysis in 26 clinical subgroups with at least 50 patients in each subgroup. We identified 229 exon-skipping events associated with patient survival in multiple clinical subgroups that met the criteria of SURVIV P-value≤0.01 in at least two subgroups of the same clinical parameter (cancer subtype, stage, lymph node, metastasis, tumour size, oestrogen receptor status, progesterone receptor status, HER2 status and age as shown in Fig. 3). DAVID (Database for Annotation, Visualization and Integrated Discovery) Gene Ontology analyses³⁴ of the 229 alternative splicing events suggest an enrichment of genes in cancer-related functional categories such as intracellular signalling, apoptosis, oxidative stress and response to DNA damage (Supplementary Fig. 1). Table 1 shows a few selected examples of survival-associated alternative splicing events in cancer-related genes. Using two-means clustering of each individual exon’s inclusion levels, the 682 IDC patients can be segregated into two subgroups with significantly different survival times as illustrated by the Kaplan–Meier survival plot (Fig. 4). We also carried out hierarchical clustering of IDC patients using 176 survival-associated alternative exons (P≤0.01; SURVIV analysis of all IDC patients). Using the exon-inclusion levels of these 176 exons, we clustered IDC patients into three major subgroups, with 95, 194 and 389 patients, respectively. As illustrated by the Kaplan–Meier survival plots, the three subgroups had significantly different survival times (Supplementary Fig. 2).

Figure 3: SURVIV analysis of exon-skipping events in the TCGA IDC RNA-seq data set.

IDC patients are stratified into multiple clinical subgroups based on clinical parameters including cancer subtype, stage, lymph node status, metastasis, tumour size, oestrogen receptor status, progesterone receptor status, HER2 status and age. Only clinical subgroups with at least 50 patients are included in further analyses. Numbers of patients in the subgroups are indicated next to the names of the subgroups. Shown in the heatmap are the log10 SURVIV P-values of the 229 exons associated with patient survival (P≤0.01) in at least two subgroups of the same class of clinical parameters. Turquoise colour indicates positive correlation that higher exon-inclusion levels are associated with higher survival probabilities. Magenta colour indicates negative correlation that lower exon-inclusion levels are associated with higher survival probabilities.

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TABLE 1 (not shown)

Figure 4: Kaplan–Meier survival plots of IDC patients stratified by two-means clustering of the exon-inclusion levels of four survival-associated alternative splicing events.

Clustering was generated for each of the four exons separately. Black lines represent patients with high exon-inclusion levels. Red lines represent patients with low exon-inclusion levels. The P-values are from SURVIV analysis of the TCGA IDC RNA-seq data. (a) ATRIP. (b) BCL2L11. (c) CD74. (d) PCBP4.

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Figure 5: Alternative splicing of STAT5A exon 5 is significantly associated with IDC patient survival.

(a) The gene structure of the STAT5A full-length isoform compared to the ΔEx5 isoform skipping the 5th exon. (b) Kaplan–Meier survival plot of IDC patients stratified by two-means clustering using exon-inclusion levels of STAT5A exon 5. The 420 patients in Group 1 (average exon 5 inclusion level=95%) have significantly higher survival probabilities than the 262 patients in Group 2 (average exon 5 inclusion level=85%) (SURVIV P=6.8e−4). (c) Exon 5 inclusion levels of IDC patients stratified by two-means clustering using exon 5 inclusion levels. Group 1 has 420 patients with average exon-inclusion level at 95%. Group 2 has 262 patients with average exon-inclusion level at 85%. (d) STAT5A exon 5 inclusion levels in normal breast tissues versus breast cancer tumour samples. Exon-inclusion levels are extracted from 86 TCGA breast cancer patients with matched normal and tumour samples. Normal breast tissues have average exon 5 inclusion level at 95%, compared to 91% average exon-inclusion level in tumour samples. Error bars represent 95% confidence interval of the mean.

• Full size image (64 KB)

Network of survival-associated alternative splicing events

…see http://www.nature.com/ncomms/2016/160609/ncomms11548/full/ncomms11548.html

Figure 6: Splicing factor regulatory network of survival-associated alternative splicing events in IDC.

(a–c) Kaplan–Meier survival plots of IDC patients stratified by the gene expression levels of three splicing factors: TRA2B (a, Cox regression P=1.8e−4), HNRNPH1 (b, P=3.4e−4) and SFRS3 (c, P=2.8e−3). Black lines represent patients with high gene expression levels. Red lines represent patients with low gene expression levels. (d) The exon-inclusion levels of a DHX30 alternative exon are negatively correlated with TRA2B gene expression levels (robust correlation coefficient r=−0.26, correlation P=1.2e−17). (e) The exon-inclusion levels of a MAP3K4 alternative exon are positively correlated withHNRNPH1 gene expression levels (robust correlation coefficient r=0.16, correlation P=2.6e−06). (f) A splicing co-expression network of the three splicing factors and their correlated survival-associated alternative exons. In total, 84 survival-associated alternative exons are significantly correlated with the three splicing factors. The positive/negative correlation between splicing factors and alternative exons is represented by blue/red lines, respectively. Exons whose inclusion levels are positively/negatively correlated with survival times are represented by blue/red dots, respectively. The size of the splicing factor circles is proportional to the number of correlated exons within the network.

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…..

Alternative splicing predictors of cancer patient survival

see http://www.nature.com/ncomms/2016/160609/ncomms11548/full/ncomms11548.html

Figure 7: Cross-validation of different classes of IDC survival predictors measured by the C-index

A C-index of 1 indicates perfect prediction accuracy and a C-index of 0.5 indicates random guess. The plots indicate the distribution of C-indexes from 100 rounds of cross-validation. The centre value of the box plot is the median C-index from 100 rounds of cross-validation. The notch represents the 95%confidence interval of the median. The box represents the 25 and 75% quantiles. The whiskers extended out from the box represent the 5 and 95% quantiles. Two-sided Wilcoxon test was used to compare different survival predictors. The different classes of predictors are: (a) clinical information (median C-index 0.67). (b) Gene expression (median C-index 0.68). (c) Alternative splicing (median C-index 0.71). (d) Clinical information+gene expression (median C-index 0.69). (e) Clinical information+alternative splicing (median C-index 0.73). (f) Clinical information+gene expression+alternative splicing (median C-index 0.74). Note that ‘Gene’ refers to ‘Gene-level expression’ in these plots.

• Full size image (73 KB)

Next, we carried out the SURVIV analysis in five additional cancer types in TCGA, including GBM (glioblastoma multiforme), KIRC (kidney renal clear cell carcinoma), LGG (lower grade glioma), LUSC (lung squamous cell carcinoma) and OV (ovarian serous cystadenocarcinoma). As expected, the number of significant events at different FDR or P-value significance cutoffs varied across cancer types, with LGG having the strongest survival-associated alternative splicing signals with 660 significant exon-skipping events at FDR≤5% (Supplementary Data 3 and 4). Strikingly, regardless of the number of significant events, alternative splicing-based survival predictors outperformed gene expression-based survival predictors across all cancer types (Supplementary Fig. 3), consistent with our initial observation on the IDC data set.

 

Alternative processing and modification of mRNA, such as alternative splicing, allow cells to generate a large number of mRNA and protein isoforms with diverse regulatory and functional properties. The plasticity of alternative splicing is often exploited by cancer cells to produce isoform switches that promote cancer cell survival, proliferation and metastasis^{7, 8}. The widespread use of RNA-seq in cancer transcriptome studies^{15, 47, 48} has provided the opportunity to comprehensively elucidate the landscape of alternative splicing in cancer tissues. While existing studies of alternative splicing in large-scale cancer transcriptome data largely focused on the comparison of splicing patterns between cancer and normal tissues or between different subtypes of cancer^{18, 21, 49}, additional computational tools are needed to characterize the clinical relevance of alternative splicing using massive RNA-seq data sets, including the association of alternative splicing with phenotypes and patient outcomes.

We have developed SURVIV, a novel statistical model for survival analysis of alternative isoform variation using cancer RNA-seq data. SURVIV uses a survival measurement error model to simultaneously model the estimation uncertainty of mRNA isoform ratio in individual patients and the association of mRNA isoform ratio with survival time across patients. Compared with the conventional Cox regression model that uses each patient’s mRNA isoform ratio as a point estimate, SURVIV achieves a higher accuracy as indicated by simulation studies under a variety of settings. Of note, we observed a particularly marked improvement of SURVIV over Cox regression for low- and moderate-depth RNA-seq data (Fig. 2b). This has important practical value because many clinical RNA-seq data sets have large sample size but relatively modest sequencing depth.

Using the TCGA IDC breast cancer RNA-seq data of 682 patients, SURVIV identified 229 alternative splicing events associated with patient survival time, which met the criteria of SURVIVP-values≤0.01 in multiple clinical subgroups. While the statistical threshold seemed loose, several lines of evidence suggest the functional and clinical relevance of these survival-associated alternative splicing events. These alternative splicing events were frequently identified and enriched in the gene functional groups important for cancer development and progression, including apoptosis, DNA damage response and oxidative stress. While some of these events may simply reflect correlation but not causal effect on cancer patient survival, other events may play an active role in regulating cancer cell phenotypes. For example, a survival-associated alternative splicing event involving exon 5 of STAT5A is known to regulate the activity of this transcription factor with important roles in epithelial cell growth and apoptosis³⁷. Using a co-expression network analysis of splicing factor to exon correlation across all patients, we identified three splicing factors (TRA2B, HNRNPH1 and SFRS3) as potential hubs of the survival-associated alternative splicing network of IDC. The expression levels of all three splicing factors were negatively associated with patient survival times (Fig. 6a–c), and both TRA2B and HNRNPH1 were previously reported to have an impact on cancer-related molecular pathways^{40, 41, 42, 43, 44, 45}. Finally, despite the limited power in detecting individual events, we show that the survival-associated alternative splicing events can be used to construct a predictor for patient survival, with an accuracy higher than predictors based on clinical parameters or gene expression profiles (Fig. 7). This further demonstrates the potential biological relevance and clinical utility of the identified alternative splicing events.

We performed cross-validation analyses to evaluate and compare the prognostic value of alternative splicing, gene expression and clinical information for predicting patient survival, either independently or in combination. As expected, the combined use of all three types of information led to the best prediction accuracy. Because we used penalized regression to build the prediction model, combining information from multiple layers of data did not necessarily increase the number of predictors in the model. The perhaps more surprising and intriguing result is that alternative splicing-based predictors appear to outperform gene expression-based predictors when used alone and when either type of data was combined with clinical information (Fig. 7). We observed the same trend in five additional cancer types (Supplementary Fig. 3). We note that this finding was consistent with a previous report that cancer subtype classification based on splicing isoform expression performed better than gene expression-based classification²⁵. While this trend seems counterintuitive because accurate estimation of gene expression requires much lower RNA-seq depth than accurate estimation of alternative splicing²⁹, one possible explanation may be the inherent characteristic of isoform ratio data. By definition, mRNA isoform ratio is estimated as the ratio of multiple mRNA isoforms from a single gene. Therefore, mRNA isoform ratio data have a ‘built-in’ internal control that could be more robust against certain artefacts and confounding issues that influence gene expression estimates across large clinical RNA-seq data sets, such as poor sample quality and RNA degradation¹². Regardless of the reasons, our data call for further studies to fully explore the utility of mRNA isoform ratio data for various clinical research applications.

The SURVIV source code is available for download at https://github.com/Xinglab/SURVIV. SURVIV is a general statistical model for survival analysis of mRNA isoform ratio using RNA-seq data. The current statistical framework of SURVIV is applicable to RNA-seq based count data for all basic types of alternative splicing patterns involving two isoform choices from an alternatively spliced region, such as exon-skipping, alternative 5′ splice sites, alternative 3′ splice sites, mutually exclusive exons and retained introns, as well as other forms of alternative isoform variation such as RNA editing. With the rapid accumulation of clinical RNA-seq data sets, SURVIV will be a useful tool for elucidating the clinical relevance and potential functional significance of alternative isoform variation in cancer and other diseases.

 

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What about PDL-1 in oncotherapy diagnostics for NSCLC?

Posted in Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Cancer and Current Therapeutics, Cell Biology, Signaling & Cell Circuits, Clinical & Translational, Clinical Diagnostics, Conference Coverage with Social Media, Diagnostics and Lab Tests, Disease Biology, Disease Biology, Small Molecules in Development of Therapeutic Drugs, FDA Regulatory Affairs, Gene Regulation, Genetics & Pharmaceutical, Human aging, Human Immune System in Health and in Disease, Immunodiagnostics, Lung and pulmonology, tagged diagnostic PDL1 tests, harmonization, Lung cancer, NSCLC, PD-1/PDL1, PDL-1 biomarker, tumor biopsy on May 2, 2016| Leave a Comment »

What about PDL-1 in oncotherapy diagnostics for NSCLC?

Larry H. Bernstein, MD, FCAP, Curator

LPBI

UPDATED 5/15/2019

Questions on PD-L1 Diagnostics for Immunotherapy in NSCLC
Alexander M. Castellino, PhD
http://www.medscape.com/viewarticle/862275

Coverage from the

• American Association for Cancer Research (AACR) 2016 Annual Meeting

Medscape Medical News > Conference News     

Two immunotherapies that target the cell programmed death (PD) pathway are now available, and both nivolumab (Opdivo, Bristol-Myers Squibb Company) and pembrolizumab (Keytruda, Merck Sharp & Dohme Corp) are approved for treating advanced, refractory, non–small cell lung cancer (NSCLC). Across several studies in patients with NSCLC, response to these agents has been correlated with PD-L1 staining, which determines PD-L1 levels in the tumor tissue. How do the available assays for PD-L1 compare?

The linear correlation between three commercially available assays is good across a range of cutoff points, concluded a presentation at the 2016 American Association for Clinical Research Annual Meeting.

Cutoffs are defined as the percentage of cells expressing PD-L1 when analyzed histochemically. “The dataset builds confidence that the assays may be used according to the cutoff clinically validated for the drug in question,” Marianne J. Radcliffe, MD, diagnostic associate director at AstraZeneca, toldMedscape Medical News.

“The correlation is good between the assays across the range examined,” she added.

However, a recently published study showed a high rate of discordance between another set of PD-L1 assays that were tested.

Dr Marianne Radcliffe

“Different diagnostic tests yield different results, depending on the cutoff for each assay. We need to harmonize the assays so clinicians are talking about the same thing,” Brendon Stiles, MD, associate professor of cardiothoracic surgery at Weill Cornell Medicine and New York-Presbyterian Hospital, New York City, told Medscape Medical News.

For Dr Stiles, these studies raise the issue that it is difficult to compare results of diagnostic testing across the different drugs and even with the same drug that are derived from different assays. “More importantly, it raises confusion in clinical practice when a patient’s sample stains positive for PD-L1 with one assay and negative with another,” he said.

“The commercial strategy for developing companion diagnostics for each drug is not in the best interests of the patients. It generates confusion among both clinicians and patients,” Dr Stiles commented. “We need to know if these assays can be used interchangeably,” he said.

As new agents come into the clinic, Dr Stiles believes there should be a universal yes-or-no answer, so that clinicians can use the assay to help decide on the use of immunotherapy.

Three Assays Tested

The study presented by Dr Radcliffe and colleagues investigated three commercially available assays, Ventana SP263, Dako 22C3, and Dako 28-8, with regard to how they compare at different cutoffs. Different studies use different cutoffs to express positivity.

Ventana SP263 was developed as a companion diagnostic for durvalumab (under development by AstraZeneca) using a rabbit monoclonal antibody. Positivity is defined as ≥25% staining of tumor cells.

Dako 22C3 was developed, and is approved, as a companion diagnostic for pembrolizumab. It uses a mouse monoclonal antibody. Positivity is defined as ≥1% and ≥50% staining of tumor cells.

Dako 28-8 was developed as a companion diagnostic for nivolumab and uses a rabbit monoclonal antibody (different from the one used in the Ventana SP263). In clinical practice, this assay is used as a complementary diagnostic for nivolumab, but the drug is approved for use regardless of PD-L1 expression. Positivity is defined as ≥1%, ≥5%, or ≥10% staining of tumor cells.

Ventana SP142 was not included in the study because it is not commercially available, Dr Ratcliffe indicated.

The three assays were used on consecutive sections of 500 archival NSCLC tumor samples obtained from commercial vendors. A single pathologist trained by the manufacturer read all samples in batches on an assay-by-assay basis. Samples were assessed per package inserts provided by Ventana and Dako in a Clinical Laboratory Improvement Amendments program-certified laboratory.

Dr Ratcliffe indicated that between reads of samples from the same patient, there was a washout period for the pathologist to remove bias.

The NSCLC samples included patients with stage I (38%), II (39%), III (20%), and IV (<1%) disease. Histologies included nonsquamous (54%) and squamous (43%) cancers.

All three PD-L1 assays showed similar patterns of staining in the range of 0% to 100%, Dr Ratcliffe indicated.

 

The correlation between any two of the assays was determined from tumor cell membrane staining. The correlation was linear with Spearman correlation of 0.911 for Ventana SP263 vs Dako 22C3; 0.935 for Ventana SP263 vs Dako 28-8; and 0.954 for Dako 28-8 vs Dako 22C3.

“With an overall predictive value of >90%, the assays have closely aligned dynamic ranges, but more work is needed,” Dr Ratcliffe said. “In general, scoring of immunohistochemical assays can be more variable between 1% and 10%, and we plan to look at this in more detail,” she said. These samples need to be reviewed by an independent pathologist, she added.

Dr Radcliffe said that currently, “Direct clinical efficacy data supporting a specific diagnostic test should still be considered as the highest standard of proof for diagnostic clinical utility.”

Why Correlations Are Needed

Pembrolizumab is approved for use only in patients with PD-L1-positive, previously treated NSCLC. A similar patient profile is being considered for nivolumab, for which testing for PD-L1 expression is not required.

For new PD-immunotherapy agents in clinical development, it is not clear whether PD-L1 testing will be mandated.

However, in clinical practice, it is clear that some patients respond to therapy, even if they are PD-L1 negative, as defined from the study. “Is it a failure of the assay, tumor heterogeneity, or is there another time point when PD-L1 expression is turned on?” Dr Stiles asked.

Dr Stiles also pointed out that a recent publication from Yale researchers showed a high a rate of discordance. In this study, PD-L1 expression was determined using two rabbit monoclonal antibodies. Both of these were different from the ones used in the Ventana SP263 and Dako 28-8 assays.

In this study, whole-tissue sections from 49 NSCLC samples were used, and a corresponding tissue microarray was also used with the same 49 samples. Researchers showed that in 49 NSCLC tissue samples, there was intra-assay variability, with results showing fair to poor concordance with the two antibodies. “Assessment of 588 serial section fields of view from whole tissue showed discordant expression at a frequency of 25%.

“Objective determination of PD-L1 protein levels in NSCLC reveals heterogeneity within tumors and prominent interassay variability or discordance. This could be due to different antibody affinities, limited specificity, or distinct target epitopes. Efforts to determine the clinical value of these observations are under way,” the study authors conclude.

The Blueprint Proposal

Coincidentally, a blueprint proposal was announced here at the AACR meeting at a workshop entitled FDA-AACR-ASCO Complexities in Personalized Medicine: Harmonizing Companion Diagnostics across a Class of Targeted Therapies.

The blueprint proposal was developed by four pharmaceutical giants (Bristol-Myers Squibb Company, Merck & Co, Inc, AstraZeneca PLC, and Genentech, Inc) and two diagnostic companies (Agilent Technologies, Inc/Dako Corp and Roche/Ventana Medical Systems, Inc).

In this proposal, the development of an evidence base for PD-1/PD-L1 companion diagnostic characterization for NSCLC would be built into studies conducted in the preapproval stage. Once the tests are approved, the information will lay the foundation for postapproval studies to inform stakeholders (eg, patients, physicians, pathologists) on how the test results can best be used to make treatment decisions.

The blueprint proposal is available online.

Dr Ratcliffe is an employee and shareholder of AstraZeneca. Dr Stiles has disclosed no relevant financial relationships.

 American Association for Cancer Research (AACR) 2016 Annual Meeting: Abstract LB-094, presented April 18, 2016.

Quantitative Assessment of the Heterogeneity of PD-L1 Expression in Non–Small-Cell Lung Cancer

Joseph McLaughlin, ^1,2; Gang Han, ³; Kurt A. Schalper, ²; ….,  Roy Herbst, ¹; Patricia LoRusso, ¹; David L. Rimm, ²

JAMA Oncol. 2016;2(1):46-54.       http://dx.doi.org:/10.1001/jamaoncol.2015.3638.

Importance  Early-phase trials with monoclonal antibodies targeting PD-1 (programmed cell death protein 1) and PD-L1 (programmed cell death 1 ligand 1) have demonstrated durable clinical responses in patients with non–small-cell lung cancer (NSCLC). However, current assays for the prognostic and/or predictive role of tumor PD-L1 expression are not standardized with respect to either quantity or distribution of expression.

Objective  To demonstrate PD-L1 protein distribution in NSCLC tumors using both conventional immunohistochemistry (IHC) and quantitative immunofluorescence (QIF) and compare results obtained using 2 different PD-L1 antibodies.

Design, Setting, and Participants  PD-L1 was measured using E1L3N and SP142, 2 rabbit monoclonal antibodies, in 49 NSCLC whole-tissue sections and a corresponding tissue microarray with the same 49 cases. Non–small-cell lung cancer biopsy specimens from 2011 to 2012 were collected retrospectively from the Yale Thoracic Oncology Program Tissue Bank. Human melanoma Mel 624 cells stably transfected with PD-L1 as well as Mel 624 parental cells, and human term placenta whole tissue sections were used as controls and for antibody validation. PD-L1 protein expression in tumor and stroma was assessed using chromogenic IHC and the AQUA (Automated Quantitative Analysis) method of QIF. Tumor-infiltrating lymphocytes (TILs) were scored in hematoxylin-eosin slides using current consensus guidelines. The association between PD-L1 protein expression, TILs, and clinicopathological features were determined.

Main Outcomes and Measures  PD-L1 expression discordance or heterogeneity using the diaminobenzidine chromogen and QIF was the main outcome measure selected prior to performing the study.

Results  Using chromogenic IHC, both antibodies showed fair to poor concordance. The PD-L1 antibodies showed poor concordance (Cohen κ range, 0.124-0.340) using conventional chromogenic IHC and showed intra-assay heterogeneity (E1L3N coefficient of variation [CV], 6.75%-75.24%; SP142 CV, 12.17%-109.61%) and significant interassay discordance using QIF (26.6%). Quantitative immunofluorescence showed that PD-L1 expression using both PD-L1 antibodies was heterogeneous. Using QIF, the scores obtained with E1L3N and SP142 for each tumor were significantly different according to nonparametric paired test (P < .001). Assessment of 588 serial section fields of view from whole tissue showed discordant expression at a frequency of 25%. Expression of PD-L1 was correlated with high TILs using both E1L3N (P = .007) and SP142 (P = .02).

Conclusions and Relevance  Objective determination of PD-L1 protein levels in NSCLC reveals heterogeneity within tumors and prominent interassay variability or discordance. This could be due to different antibody affinities, limited specificity, or distinct target epitopes. Efforts to determine the clinical value of these observations are under way.

 

Invited Commentary
PD-L1 Expression as a Predictive Biomarker: Is Absence of Proof the Same as Proof of Absence?
JAMA Oncol. 2016;2(1):54-55.  http://dx.doi.org:/10.1001/jamaoncol.2015.3782.

Viewpoint
PD-L1 Testing in Cancer: Challenges in Companion Diagnostic Development
JAMA Oncol. 2016;2(1):15-16.   http://dx.doi.org:/10.1001/jamaoncol.2015.4685.

Prognostic and Predictive Value in KRASin Non–Small-Cell Lung Cancer: A Review

JAMA Oncol. Published online April 21, 2016.;():.    http://dx.doi.org:/10.1001/jamaoncol.2016.0405.

Association Between Younger Age and Targetable Genomic Alterations and Prognosis in Non–Small-Cell Lung Cancer

JAMA Oncol. 2016;2(3):313-320.    http://dx.doi.org:/10.1001/jamaoncol.2015.4482.

Clinical Application of Liquid Biopsies

JAMA Oncol. Published online April 07, 2016.;():.    http://dx.doi.org:/10.1001/jamaoncol.2016.0240.

A Blueprint Proposal for Companion Diagnostic Comparability

A highlight of the FDA-AACR-ASCO Complexities in Personalized Medicine: Harmonizing Companion Diagnostics across a Class of Targeted Therapies workshop was the unveiling of a blueprint proposal developed by four pharmaceutical companies (Bristol-Myers Squibb Company, Merck & Co. Inc., AstraZeneca PLC, and Genentech, Inc.) and two diagnostic companies (Agilent Technologies, Inc./Dako Corp and Roche/Ventana Medical Systems, Inc.). The proposed study would help build an evidence base for PD-1/PD-L1 companion diagnostic characterization for non-small cell lung cancer in the pre-approval stage, such that once the tests are approved, the information generated can lay the groundwork for post-approval studies that will help inform patients, physicians, pathologists, and others on how best to use the test results to determine treatment decisions.

Read the Blueprint Proposal.

During the workshop the blueprint development team solicited input and feedback from interested parties. Read the feedback on the blueprint from

• Dr. David Rimm and colleagues; and
• Fight Colorectal Cancer and colleagues.

The blueprint team has considered the feedback received. Read the team’s combined response to both groups.Back to the Complexities in Personalized Medicine Workshop page.

 

A Blueprint Proposal for Companion Diagnostic Comparability
http://www.aacr.org/AdvocacyPolicy/GovernmentAffairs/Documents/FDA-AACR-ASCO-Complexities-in-Personalized-Medicine-Blueprint-Proposal.pdf

Introduction We are in an era of rapid incorporation of basic scientific discoveries into the drug development pipeline. Currently, numerous sponsors are developing therapeutic products that may use similar or identical biomarkers for therapeutic selection, measured or detected by an in vitro companion diagnostic device. The current practice is to independently develop a companion diagnostic for each therapeutic. Thus, the matrix of therapeutics and companion diagnostics, if each therapeutic were approved in conjunction with a companion diagnostic, may present a complex challenge for testing and decision making in the clinic, potentially putting patients at risk if inappropriate diagnostic tests were used to make treatment decisions. To address this challenge, there is a desire to understand assay comparability and/or standardize analytical and clinical performance characteristics supporting claims that are shared across companion diagnostic devices. Pathologists and oncologists also need clarity on how to interpret test results to inform downstream treatment options for their patients.

Clearly using each of the companion diagnostics to select one of the several available targeted therapies in the same class is not practical and may be impossible. Likewise, having a single test or assay as a sole companion test for all of the multiple therapeutic options within a class is also impractical since the individual therapies have differing modes of action, intended use populations, specificities, safety and efficacy outcomes. Thus, a single assay or test may not adequately capture the appropriate patient population that may benefit (or not) from each individual therapeutic option within a class of therapies. Furthermore, aligning multiple sponsors’ study designs and timelines in order that they all adopt a single companion test may inadvertently slow down development of critical therapeutic products and delay patient access to these life-saving products.

Any solution to this challenge will be multifaceted and will, by necessity, involve multiple stakeholders. Thus, the US Food and Drug Administration (FDA), the American Association for Cancer Research (AACR) and American Society of Clinical Oncology (ASCO) convened a workshop titled “Complexities in Personalized Medicine: Harmonizing Companion Diagnostics Across a Class of Targeted Therapies” to draw out and assess possible solutions. Recognizing that the complex scientific, regulatory and market forces at play here require a collaborative effort, an industry workgroup volunteered to develop a blueprint proposal of potential solutions using nonsmall cell lung cancer (NSCLC) as the use case indication.

Goal and Scope of Blueprint The imminent arrival to the market of multiple PD1 / PD-L1 compounds and the possibility of one or more associated companion diagnostics is unprecedented in the field of oncology. Some may assume that since these products target the same biological pathway, they are interchangeable; however, each PD1/PD-L1 compound is unique with respect to its clinical pharmacology and each compound is being developed in the context of a unique biological scientific hypothesis and registration strategy. Similarly, each companion diagnostic has been optimized within the individual therapeutic development programs to meet specific development goals, e.g., 1) validation for patient selection, 2) subgroup analysis as a prognostic variable, or 3) enrichment.

Further, each companion diagnostic test is optimized for its specific therapy and with its own unique performance characteristics and scoring/interpretation guidelines.

The blueprint development group recognizes that to assume that any one of the available tests could be used for guiding the treatment decision with any one or all of the drugs available in this class presents a potential risk to patients that must be addressed.

The goal of this proposal is to agree and deliver, via cross industry collaboration, a package of information /data upon which analytic comparison of the various diagnostic assays may be conducted, potentially paving the way for post-market standardization and/or practice guideline development as appropriate.

A comparative study of PD-L1 diagnostic assays and the classification of patients as PD-L1 positive and PD-L1 negative

Presentation Time:	Monday, Apr 18, 2016, 8:00 AM -12:00 PM
Location:	Section 10
Poster Board Number:	18
Author Block:	Marianne J. Ratcliffe1, Alan Sharpe2, Anita Midha1, Craig Barker2, Paul Scorer2, Jill Walker2. 1AstraZeneca, Alderley Park, United Kingdom; 2AstraZeneca, Cambridge, United Kingdom
Abstract Body:	Background: PD-1/PD-L1 directed antibodies are emerging as effective therapeutics in multiple oncology settings. Keynote 001 and Checkmate 057 have shown more frequent response to PD-1 targeted therapies in NSCLC patients with high tumour PD-L1 expression than patients with low or no PD-L1 expression. Multiple diagnostic PD-L1 tests are available using different antibody clones, different staining protocols and diverse scoring algorithms. It is vital to compare these assays to allow appropriate interpretation of clinical outcomes. Such understanding will promote harmonization of PD-L1 testing in clinical practice. Methods: Approximately 500 tumour biopsy samples from NSCLC patients, including squamous and non-squamous histologies, will be assessed using three leading PD-L1 diagnostics assays. PD-L1 assessment by the Ventana SP263 assay that is currently being used in Durvalumab clinical trials (positivity cut off: ≥25% tumour cells with membrane staining) will be compared with the Dako 28-8 assay (used in the Nivolumab Checkmate 057 trial at the 1%, 5% and 10% tumour membrane positivity cut offs), and the Dako 22C3 assay (used in the Pembrolizumab Keynote 001 trial) at the 1% and 50% cut offs). Results: Preliminary data from 81 non-squamous patients indicated good concordance between the Ventana SP263 and Dako 28-8 assays. Optimal overall percent agreement (OPA) was observed between Dako 28-8 at the 10% cut off and the Ventana SP263 assay (OPA; 96%, Positive percent agreement (PPA); 91%, Negative percent agreement (NPA); 98%), where the Ventana SP263 assay was set as the reference. Data on the full cohort will be presented for all three assays, and a lower 95% confidence interval calculated using the Clopper-Pearson method. Conclusions: This study indicates that the patient population defined by Ventana SP263 at the 25% cut off is similar to that identified by the Dako-28-8 assay at the 10% tumour membrane cut off. This, together with data on the 22C3 assay, will enable cross comparison of studies using different PD-L1 tests, and widen options for harmonization of PD-L1 diagnostic testing.

http://www.abstractsonline.com/Plan/ViewAbstract.aspx

Table 1

	Reference: Ventana SP-263 (≥25% tumour membrane staining)
Dako 28-8 assay cut off	PPA (%)	NPA (%)	OPA (%)
>1%	58	100	81
>5%	72	100	90
>10%	91	98	96

UPDATED 5/19/2019

Incidence of Adverse Events for PD-1/PD-L1 Inhibitors Underscores Toxicity Risk

https://www.cancernetwork.com/immuno-oncology/incidence-adverse-events-pd-1pd-l1-inhibitors-underscores-toxicity-risk

• Christina Bennett, MS

May 7, 2019

• Immuno Oncology, News

Approximately two-thirds of cancer patients who received a programmed death 1 (PD-1) or programmed death ligand 1 (PD-L1) inhibitor in clinical trials experienced treatment-related adverse events, according to a systematic review and meta-analysis recently published in JAMA Oncology. The study findings may facilitate discussions with cancer patients who are considering PD-1 or PD-L1 therapy.

“The vast majority of patients with advanced cancer want to be on the [PD-1 or PD-L1] therapy,” Eric H. Bernicker, MD, a thoracic medical oncologist with Houston Methodist Cancer Center, told Cancer Network. Not involved in the current study, Bernicker explained that patients perceive these therapies to have “very different” side effects and risks from chemotherapy.

While they do, Bernicker explained, it’s important to underscore, which this study does, that these are not “completely innocuous” therapies. The study findings allow physicians to give numbers to patients and families when counseling them about the risks involved, he said.

The systematic review and meta-analysis is based on data from 125 clinical trials and 20,128 participants. Clinical trials were identified by systematically searching for published clinical trials that evaluated single-agent PD-1 and PD-L1 inhibitors and reported treatment-related adverse events in PubMed, Web of Science, Embase, and Scopus. The majority of trials evaluated nivolumab (n = 46) or pembrolizumab (n = 49), and the most common cancer types were lung cancer (n = 26), genitourinary cancer (n = 22), melanoma (n = 16), and gastrointestinal cancer (n = 14).

In all, 66.0% of clinical trial participants reported at least 1 adverse event of any grade, and 14.0% reported at least 1 grade 3 or higher adverse event. The most frequently reported adverse events of any grade were fatigue (18.26%), pruritus (10.61%), and diarrhea (9.47%). As for grade 3 or higher events, the most commonly reported were fatigue (0.89%), anemia (0.78%), and aspartate aminotransferase (AST) increase (0.75%).

Frequently reported immune-related adverse events of any grade included diarrhea (9.47%), AST increase (3.39%), vitiligo (3.26%), alanine aminotransferase (ALT) increase (3.14%), pneumonitis (2.79%), and colitis (1.24%). Grade 3 or higher immune-related adverse events included AST increase (0.75%), ALT increase (0.70%), pneumonitis (0.67%), diarrhea (0.59%), and colitis (0.47%).

If present, certain adverse events had increased likelihood of being grade 3 or higher, including hepatitis (risk ratio [RR], 50.59%), pneumonitis (RR, 24.01%), type 1 diabetes (RR, 41.86%), and colitis (RR, 37.90%).

“In terms of the rough percentage of side effects and the breadth of the side effects, this is pretty much what most of us see in the clinic,” Bernicker said, noting that none of the findings were particularly surprising.

Although no differences in adverse event incidence were found across different cancer types, differences were found between PD-1 and PD-L1 inhibitors in a subgroup analysis. Overall, compared with PD-L1 inhibitors, PD-1 inhibitors had a higher mean incidence of grade 3 or higher events (odds ratio [OR], 1.58; 95% CI, 1.00–2.54). Specifically, nivolumab had a higher mean incidence of grade 3 or higher events (OR, 1.81; 95% CI, 1.04–3.01) compared with PD-L1 inhibitors.

Bernicker commented that these incidence differences on the basis of drug type were “intriguing” but not clinically useful, given that PD-1 and PD-L1 inhibitors are not interchangeable. He said the finding “needs to be further looked at.”

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