New Directions in Compensation Research: Synergies, Risk, and Survival

We describe and use two theoretical frameworks, the resource-based view of the firm and institutional theory, as lenses for examining three promising areas of compensation research. First, we examine the nature of the relationship between pay and effectiveness. Does pay typically have a main effect or, instead, does the relationship depend on other human resource activities and organization characteristics? If the latter is true, then there are synergies between pay and these other factors and thus, conclusions drawn from main effects models may be misleading. Second, we discuss a relatively neglected issue in pay research, the concept of risk as it applies to investments in pay programs. Although firms and researchers tend to focus on expected returns from compensation interventions, analysis of the risk, or variability, associated with these returns may be essential for effective decision-making. Finally ,pay program survival, which has been virtually ignored in systematic pay research, is investigated. Survival appears to have important consequences for estimating pay plan risk and returns, and is also integral to the discussion of pay synergies. Based upon our two theoretical frameworks, we suggest specific research directions for pay program synergies, risk, and survival

Envisat - taking the measure of North Atlantic storms

Envisat carries a number of sensors able to provide quantitative information on raining clouds: AATSR delivers information on cloud microphysics (particle size, temperature etc.), MWR-2 gives columnar totals for liquid and vapour forms of water, and RA-2 yields rain rate and wind speed. This paper examines the complementarity of these sensors, with a focussed study on significant rain events in the N. Atlantic, covering both coherent large storms and fronts with smaller scale structure. The difference in liquid water estimates from the infra-red and passive systems appears to be related to the temperature and sizes of drops being detected

Measuring rainfall from above and below the sea surface

Satellites play a major role in the determination of the rainfall at sea. Researchers at Southampton Oceanography Centre (SOC) have been involved in two projects addressing this task. First they have been instrumental in developing techniques to retrieve rain rate information from the 10+ years of dual-frequency altimeter data. The TOPEX radar measures rainfall via the attenuation it causes, producing a climatology that is independent of those derived from passive microwave (PM) and infrared (IR) sensors. Because TOPEX is an active microwave sensor, it can have a much smaller footprint than PM sensors. Therefore it can be used to estimate the size of rain cells, showing that the ITCZ and mid-latitude storm tracks are characterized by larger rain systems than elsewhere. TOPEX’s simultaneous recording of wind and wave data reveal that, for mid-latitude systems, rain is most likely in association with developing seas.All satellite-based datasets require validation, and SOC's work on the development and testing of acoustic rain gauges is the second aspect of this paper. By listening at a range of frequencies, an underwater hydrophone may distinguish the spectra of wind, rain, shipping etc., and estimate the wind speed or rain rate according to the magnitude of the signals. All our campaigns have shown a good acoustic response to changes in wind speed. However the quantitative inversion for recent trials has given values that are too high, possibly because of significant acoustic reflection from the sea bottom. The changes in spectral slope often agree with other observations of rain, although validation experiments in coastal regions are hampered by the extraneous sources present. Acoustic rain gauges would eventually see service not only for routine satellite validation, but also for real-time monitoring of locations of interest

CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples

Phylogenetic trees based on copy number profiles from multiple samples of a patient are helpful to understand cancer evolution. Here, we develop a new maximum likelihood method, CNETML, to infer phylogenies from such data. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers of longitudinal samples. Our extensive simulations suggest CNETML performs well on copy numbers relative to ploidy and under slight violation of model assumptions. The application of CNETML to real data generates results consistent with previous discoveries and provides novel early copy number events for further investigation

The mutational signatures of formalin fixation on the human genome

Clinical archives of patient material near-exclusively consist of formalin-fixed and paraffin-embedded (FFPE) blocks. The ability to precisely characterise mutational signatures from FFPE-derived DNA has tremendous translational potential. However, sequencing of DNA derived from FFPE material is known to be riddled with artefacts. Here we derive genome-wide mutational signatures caused by formalin fixation. We show that the FFPE-signature is highly similar to signature 30 (the signature of Base Excision Repair deficiency due to NTHL1 mutations), and chemical repair of DNA lesions leads to a signature highly similar to signature 1 (clock-like signature due to spontaneous deamination of methylcytosine). We demonstrate that using uncorrected mutational catalogues of FFPE samples leads to major mis-assignment of signature activities. To correct for this, we introduce FFPEsig, a computational algorithm to rectify the formalin-induced artefacts in the mutational catalogue. We demonstrate that FFPEsig enables accurate mutational signature analysis both in simulated and whole-genome sequenced FFPE cancer samples. FFPEsig thus provides an opportunity to unlock additional clinical potential of archival patient tissues.Peer reviewe

A Breast Cancer Meta-Analysis of Two Expression Measures of Chromosomal Instability Reveals a Relationship with Younger Age at Diagnosis and High Risk Histopathological Variables

Breast cancer in younger patients often presents with adverse histopathological features, including increased frequency of estrogen receptor negative and lymph node positive disease status. Chromosomal instability (CIN) is increasingly recognised as an important prognostic variable in solid tumours. In a breast cancer meta-analysis of 2423 patients we examine the relationship between clinicopathological parameters and two distinct chromosomal instability gene expression signatures in order to address whether younger age at diagnosis is associated with increased tumour genome instability. We find that CIN, assessed by the two independently derived CIN expression signatures, is significantly associated with increased tumour size, ER negative or HER2 positive disease, higher tumour grade and younger age at diagnosis in ER negative breast cancer. These data support the hypothesis that chromosomal instability may be a defining feature of breast cancer biology and clinical outcome

A breast cancer meta-analysis of two expression measures of chromosomal instability reveals a relationship with younger age at diagnosis and high risk histopathological variables

Breast cancer in younger patients often presents with adverse histopathological features, including increased frequency of estrogen receptor negative and lymph node positive disease status. Chromosomal instability (CIN) is increasingly recognised as an important prognostic variable in solid tumours. In a breast cancer meta-analysis of 2423 patients we examine the relationship between clinicopathological parameters and two distinct chromosomal instability gene expression signatures in order to address whether younger age at diagnosis is associated with increased tumour genome instability. We find that CIN, assessed by the two independently derived CIN expression signatures, is significantly associated with increased tumour size, ER negative or HER2 positive disease, higher tumour grade and younger age at diagnosis in ER negative breast cancer. These data support the hypothesis that chromosomal instability may be a defining feature of breast cancer biology and clinical outcome

A computational modelling approach for deriving biomarkers to predict cancer risk in premalignant disease.

The lack of effective biomarkers for predicting cancer risk in premalignant disease is a major clinical problem. There is a near-limitless list of candidate biomarkers and it remains unclear how best to sample the tissue in space and time. Practical constraints mean that only a few of these candidate biomarker strategies can be evaluated empirically and there is no framework to determine which of the plethora of possibilities is the most promising. Here we have sought to solve this problem by developing a theoretical platform for in silico biomarker development. We construct a simple computational model of carcinogenesis in premalignant disease and use the model to evaluate an extensive list of tissue sampling strategies and different molecular measures of these samples. Our model predicts that: (i) taking more biopsies improves prog-nostication, but with diminishing returns for each additional biopsy; (ii) longitudinally-collected biopsies provide slightly more prognostic information than a single biopsy collected at the latest possible time-point; (iii) measurements of clonal diversity are more prognostic than measurements of the presence or absence of a particular abnormality and are particularly robust to confounding by tissue sampling; and (iv) the spatial pattern of clonal expansions is a particularly prognostic measure. This study demonstrates how the use of a mechanistic framework provided by computational modelling can diminish empirical constraints on biomarker development

Quantification of within-sample genetic heterogeneity from SNP-array data

Intra-tumour genetic heterogeneity (ITH) fosters drug resistance and is a critical hurdle to clinical treatment. ITH can be well-measured using multi-region sampling but this is costly and challenging to implement. There is therefore a need for tools to estimate ITH in individual samples, using standard genomic data such as SNP-arrays, that could be implemented routinely. We designed two novel scores S and R, respectively based on the Shannon diversity index and Ripley’s L statistic of spatial homogeneity, to quantify ITH in single SNP-array samples. We created in-silico and in-vitro mixtures of tumour clones, in which diversity was known for benchmarking purposes. We found significant but highly-variable associations of our scores with diversity in-silico (p < 0.001) and moderate associations in–vitro (p = 0.015 and p = 0.085). Our scores were also correlated to previous ITH estimates from sequencing data but heterogeneity in the fraction of tumour cells present across samples hampered accurate quantification. The prognostic potential of both scores was moderate but significantly predictive of survival in several tumour types (corrected p = 0.03). Our work thus shows how individual SNP-arrays reveal intra-sample clonal diversity with moderate accuracy