The EMMIX Algorithm for the Fitting of Normal and t-Components

We consider the fitting of normal or t-component mixture models to multivariate data, using maximum likelikhood via the EM algorithm. This approach requires the initial specification of an initial estimate of the vector of unknown parameters, or equivalently of an initial classification of the data with respect to the components of the mixture model under fit. We describe an algorithm called EMMIX that automatically undertakes this fitting: including the provision of suitable initial values if not supplied by the user. The EMMIX algorithm has several options, including the option to carry out a resampling-based test for the number of components in the mixture model.

Microarray Based Diagnosis Profits from Better Documentation of Gene Expression Signatures

Microarray gene expression signatures hold great promise to improve diagnosis and prognosis of disease. However, current documentation standards of such signatures do not allow for an unambiguous application to study-external patients. This hinders independent evaluation, effectively delaying the use of signatures in clinical practice. Data from eight publicly available clinical microarray studies were analyzed and the consistency of study-internal with study-external diagnoses was evaluated. Study-external classifications were based on documented information only. Documenting a signature is conceptually different from reporting a list of genes. We show that even the exact quantitative specification of a classification rule alone does not define a signature unambiguously. We found that discrepancy between study-internal and study-external diagnoses can be as frequent as 30% (worst case) and 18% (median). By using the proposed documentation by value strategy, which documents quantitative preprocessing information, the median discrepancy was reduced to 1%. The process of evaluating microarray gene expression diagnostic signatures and bringing them to clinical practice can be substantially improved and made more reliable by better documentation of the signatures

An Algorithm for Fitting Mixtures of Gompertz Distributions to Censored Survival Data

We consider the fitting of a mixture of two Gompertz distributions to censored survival data. This model is therefore applicable where there are two distinct causes for failure that act in a mutually exclusive manner, and the baseline failure time for each cause follows a Gompertz distribution. For example, in a study of a disease such as breast cancer, suppose that failure corresponds to death, whose cause is attributed either to breast cancer or some other cause. In this example, the mixing proportion for the component of the mixture representing time to death from a cause other than breast cancer may be interpreted to be the cure rate for breast cancer (Gordon,'90a and'90b). This Gompertz mixture model whose components are adjusted multiplicatively to reflect the age of the patient at the origin of the survival time, is fitted by maximum likelihood via the EM algorithm (Dempster, Laird and Rubin,'77). There is the provision to handle the case where the mixing proportions are formulated in terms of a logistic model to depend on a vector of covariates associated with each survival time. The algorithm can also handle the case where there is only one cause of failure, but which may happen at infinity for some patients with a nonzero probability (Farewell,'82).

An evidence-base for the implementation of hospital-based palliative care programs in routine cancer practice:A systematic review

Background: Despite global support, there remain gaps in the integration of early palliative care into cancer care. The methods of implementation whereby evidence of benefits of palliative care is translated into practice deserve attention. Aim: To identify implementation frameworks utilised in integrated palliative care in hospital-based oncology services and to describe the associated enablers and barriers to service integration. Design: Systematic review with a narrative synthesis including qualitative, mixed methods, pre-post and quasi experimental designs following the guidance by the Centre for Reviews and Dissemination (PROSPERO registration CRD42021252092). Data sources: Six databases searched in 2021: EMBASE, EMCARE, APA PsycINFO, CINAHL, Cochrane Library and Ovid MEDLINE searched in 2023. Included were qualitative or quantitative studies, in English language, involving adults >18 years, and implementing hospital-based palliative care into cancer care. Critical appraisal tools were used to assess the quality and rigour. Results: Seven of the 16 studies explicitly cited the use of frameworks including those based on RE-AIM, Medical Research Council evaluation of complex interventions and WHO constructs of health service evaluation. Enablers included an existing supportive culture, clear introduction to the programme across services, adequate funding, human resources and identification of advocates. Barriers included a lack of communication with the patients, caregivers, physicians and palliative care team about programme goals, stigma around the term ‘palliative’, a lack of robust training, or awareness of guidelines and undefined staff roles. Conclusions: Implementation science frameworks provide a method to underpin programme development and evaluation as palliative care is integrated within the oncology setting.Farwa Rizvi, Helen Elizabeth Wilding, Nicole M Rankin, Roslyn Le Gautier, Lorna Gurren, Vijaya Sundararajan, Kylee Bellingham, Joyce Chua, Gregory B Crawford, Anna K Nowak, Brian Le, Geoff Mitchell, Sue-Anne McLachlan, Tanara Vieira Sousa, Robyn Hudson, Maarten IJzerman, Anna Collins, and Jennifer Phili

Blockade of ROS production inhibits oncogenic signaling in acute myeloid leukemia and amplifies response to precision therapies

Mutations in the type III receptor tyrosine kinase FLT3 are frequent in patients with acute myeloid leukemia (AML) and are associated with a poor prognosis. AML is characterized by the overproduction of reactive oxygen species (ROS), which can induce cysteine oxidation in redox-sensitive signaling proteins. Here, we sought to characterize the specific pathways affected by ROS in AML by assessing oncogenic signaling in primary AML samples. The oxidation or phosphorylation of signaling proteins that mediate growth and proliferation was increased in samples from patient subtypes with FLT3 mutations. These samples also showed increases in the oxidation of proteins in the ROS-producing Rac/NADPH oxidase-2 (NOX2) complex. Inhibition of NOX2 increased the apoptosis of FLT3-mutant AML cells in response to FLT3 inhibitors. NOX2 inhibition also reduced the phosphorylation and cysteine oxidation of FLT3 in patient-derived xenograft mouse models, suggesting that decreased oxidative stress reduces the oncogenic signaling of FLT3. In mice grafted with FLT3 mutant AML cells, treatment with a NOX2 inhibitor reduced the number of circulating cancer cells, and combining FLT3 and NOX2 inhibitors increased survival to a greater extent than either treatment alone. Together, these data raise the possibility that combining NOX2 and FLT3 inhibitors could improve the treatment of FLT3 mutant AML

Bayesian analysis of Jolly-Seber type models; Incorporating heterogeneity in arrival and departure

We propose the use of finite mixtures of continuous distributions in modelling the process by which new individuals, that arrive in groups, become part of a wildlife population. We demonstrate this approach using a data set of migrating semipalmated sandpipers (Calidris pussila) for which we extend existing stopover models to allow for individuals to have different behaviour in terms of their stopover duration at the site. We demonstrate the use of reversible jump MCMC methods to derive posterior distributions for the model parameters and the models, simultaneously. The algorithm moves between models with different numbers of arrival groups as well as between models with different numbers of behavioural groups. The approach is shown to provide new ecological insights about the stopover behaviour of semipalmated sandpipers but is generally applicable to any population in which animals arrive in groups and potentially exhibit heterogeneity in terms of one or more other processes

Homologous recombination DNA repair defects in PALB2- associated breast cancers

Abstract: Mono-allelic germline pathogenic variants in the Partner And Localizer of BRCA2 (PALB2) gene predispose to a high-risk of breast cancer development, consistent with the role of PALB2 in homologous recombination (HR) DNA repair. Here, we sought to define the repertoire of somatic genetic alterations in PALB2-associated breast cancers (BCs), and whether PALB2-associated BCs display bi-allelic inactivation of PALB2 and/or genomic features of HR-deficiency (HRD). Twenty-four breast cancer patients with pathogenic PALB2 germline mutations were analyzed by whole-exome sequencing (WES, n = 16) or targeted capture massively parallel sequencing (410 cancer genes, n = 8). Somatic genetic alterations, loss of heterozygosity (LOH) of the PALB2 wild-type allele, large-scale state transitions (LSTs) and mutational signatures were defined. PALB2-associated BCs were found to be heterogeneous at the genetic level, with PIK3CA (29%), PALB2 (21%), TP53 (21%), and NOTCH3 (17%) being the genes most frequently affected by somatic mutations. Bi-allelic PALB2 inactivation was found in 16 of the 24 cases (67%), either through LOH (n = 11) or second somatic mutations (n = 5) of the wild-type allele. High LST scores were found in all 12 PALB2-associated BCs with bi-allelic PALB2 inactivation sequenced by WES, of which eight displayed the HRD-related mutational signature 3. In addition, bi-allelic inactivation of PALB2 was significantly associated with high LST scores. Our findings suggest that the identification of bi-allelic PALB2 inactivation in PALB2-associated BCs is required for the personalization of HR-directed therapies, such as platinum salts and/or PARP inhibitors, as the vast majority of PALB2-associated BCs without PALB2 bi-allelic inactivation lack genomic features of HRD

The FANCM:p.Arg658* truncating variant is associated with risk of triple-negative breast cancer

Abstract: Breast cancer is a common disease partially caused by genetic risk factors. Germline pathogenic variants in DNA repair genes BRCA1, BRCA2, PALB2, ATM, and CHEK2 are associated with breast cancer risk. FANCM, which encodes for a DNA translocase, has been proposed as a breast cancer predisposition gene, with greater effects for the ER-negative and triple-negative breast cancer (TNBC) subtypes. We tested the three recurrent protein-truncating variants FANCM:p.Arg658*, p.Gln1701*, and p.Arg1931* for association with breast cancer risk in 67,112 cases, 53,766 controls, and 26,662 carriers of pathogenic variants of BRCA1 or BRCA2. These three variants were also studied functionally by measuring survival and chromosome fragility in FANCM−/− patient-derived immortalized fibroblasts treated with diepoxybutane or olaparib. We observed that FANCM:p.Arg658* was associated with increased risk of ER-negative disease and TNBC (OR = 2.44, P = 0.034 and OR = 3.79; P = 0.009, respectively). In a country-restricted analysis, we confirmed the associations detected for FANCM:p.Arg658* and found that also FANCM:p.Arg1931* was associated with ER-negative breast cancer risk (OR = 1.96; P = 0.006). The functional results indicated that all three variants were deleterious affecting cell survival and chromosome stability with FANCM:p.Arg658* causing more severe phenotypes. In conclusion, we confirmed that the two rare FANCM deleterious variants p.Arg658* and p.Arg1931* are risk factors for ER-negative and TNBC subtypes. Overall our data suggest that the effect of truncating variants on breast cancer risk may depend on their position in the gene. Cell sensitivity to olaparib exposure, identifies a possible therapeutic option to treat FANCM-associated tumors

CYP3A7*1C allele: linking premenopausal oestrone and progesterone levels with risk of hormone receptor-positive breast cancers

Funder: Breast Cancer Now (BCN); doi: https://doi.org/10.13039/100009794Funder: Cancer Research UK (CRUK); doi: https://doi.org/10.13039/501100000289Funder: RCUK | Medical Research Council (MRC); doi: https://doi.org/10.13039/501100000265Funder: U.S. Department of Health & Human Services | National Institutes of Health (NIH)Funder: Wellcome Trust (Wellcome); doi: https://doi.org/10.13039/100004440Funder: EC | EC Seventh Framework Programm | FP7 Ideas: European Research Council (FP7-IDEAS-ERC - Specific Programme: "Ideas" Implementing the Seventh Framework Programme of the European Community for Research, Technological Development and Demonstration Activities (2007 to 2013)); doi: https://doi.org/10.13039/100011199; Grant(s): HEALTH-F2-2009-223175, HEALTH-F2-2009-223175Funder: Genome Canada (Génome Canada); doi: https://doi.org/10.13039/100008762Funder: Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada); doi: https://doi.org/10.13039/501100000024Funder: Quebec Breast cancer Foundation Genome QuebecFunder: U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM); doi: https://doi.org/10.13039/100000092Funder: EC | EC Seventh Framework Programm | FP7 Ideas: European Research Council (FP7-IDEAS-ERC - Specific Programme: "Ideas" Implementing the Seventh Framework Programme of the European Community for Research, Technological Development and Demonstration Activities (2007 to 2013))Funder: European Union’s Horizon 2020Funder: Deutsche Krebshilfe (German Cancer Aid); doi: https://doi.org/10.13039/501100005972Funder: BCAST - European Union’s Horizon 2020Funder: Breast Cancer Now; doi: https://doi.org/10.13039/501100007913Abstract: Background: Epidemiological studies provide strong evidence for a role of endogenous sex hormones in the aetiology of breast cancer. The aim of this analysis was to identify genetic variants that are associated with urinary sex-hormone levels and breast cancer risk. Methods: We carried out a genome-wide association study of urinary oestrone-3-glucuronide and pregnanediol-3-glucuronide levels in 560 premenopausal women, with additional analysis of progesterone levels in 298 premenopausal women. To test for the association with breast cancer risk, we carried out follow-up genotyping in 90,916 cases and 89,893 controls from the Breast Cancer Association Consortium. All women were of European ancestry. Results: For pregnanediol-3-glucuronide, there were no genome-wide significant associations; for oestrone-3-glucuronide, we identified a single peak mapping to the CYP3A locus, annotated by rs45446698. The minor rs45446698-C allele was associated with lower oestrone-3-glucuronide (−49.2%, 95% CI −56.1% to −41.1%, P = 3.1 × 10–18); in follow-up analyses, rs45446698-C was also associated with lower progesterone (−26.7%, 95% CI −39.4% to −11.6%, P = 0.001) and reduced risk of oestrogen and progesterone receptor-positive breast cancer (OR = 0.86, 95% CI 0.82–0.91, P = 6.9 × 10–8). Conclusions: The CYP3A7*1C allele is associated with reduced risk of hormone receptor-positive breast cancer possibly mediated via an effect on the metabolism of endogenous sex hormones in premenopausal women

Making sense of a random world through statistics

With the growth in data in recent times, it is argued in this talk that there is a need for even more statistical methods in data mining. In so doing, we present some examples in which there is a need to adopt some fairly sophisticated statistical procedures (at least not off-the-shelf methods) to avoid misleading inferences being made about patterns in the data due to randomness. One example concerns the search for clusters in data. Having found an apparent clustering in a dataset, as evidenced in a visualisation of the dataset in some reduced form, the question arises of whether this clustering is representative of an underlying group structure or is merely due to random fluctuations. Another example concerns the supervised classification in the case of many variables measured on only a small number of objects. In this situation, it is possible to construct a classifier based on a relatively small subset of the variables that provides a perfect classification of the data (that is, its apparent error rate is zero). We discuss how statistics is needed to correct for the optimism in these results due to randomness and to provide a realistic interpretation