Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.

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Gene expression profiling predicts clinical outcome of breast cancer

Since the publication of the American Association for the Study of Liver Diseases (AASLD) practice guidelines on the management of hepatocellular carcinoma (HCC) in 2005, new information has emerged that requires that the guidelines be updated. The full version of the new guidelines is available on the AASLD Web site at http://www.aasld.org/practiceguidelines/ Documents/Bookmarked%20Practice%20Guidelines/ HCCUpdate2010.pdf. Here, we briefly describe only new or changed recommendations.

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AASLD PRACTICE GUIDELINE Management of Hepatocellular Carcinoma: An Update

Background A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy. Methods Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node–negative disease, and 144 had lymph-node–positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses. Results Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (±SE) overall 10-year survival rates were 54.6±4.4 percent and 94.5±2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6±4.5 percent in the group with a...

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A Gene-Expression Signature as a Predictor of Survival in Breast Cancer

Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

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Survey of clustering algorithms

https://aasldpubs.onlinelibrary.wiley.com/doi/pdf/10.1002/hep.20933

Management of hepatocellular carcinoma.

We study effects on the W mass measurements at LEP2 from non-perturbative interference effects in the fully hadronic decay channel. Based on a model for Bose-Einstein interference, which is in agreement with LEP1 data, we argue that there are no Bose-Einstein correlations between bosons coming from the different W's. For small reconnection probabilities we rule out the possible experimental signal of colour interference at LEP2, suggested in [1]. The conclusions from this paper are that the theoretical uncertainties in the W mass determination should be smaller than the experimental statistical error.

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Bose-Einstein and Colour Interference in W-pair Decays

Since first being described in 2000 (1), gene expression–based molecular subtypes have become an integral part of both basic and translational breast cancer research. Moreover, incremental research has found the subtypes to provide sufficient information on prognosis and systemic treatment selection to merit inclusion in international guidelines for breast cancer treatment (2). Although various subtype classifiers have been developed (3–6), the different classifiers generally agree on a taxonomy of breast cancer typically encompassing four subtypes (luminal A, luminal B, HER2-enriched, and basal-like).

However, there has been considerable controversy regarding the ability of different molecular subtyping methods to robustly assign the same molecular subtype to a specific sample (7). First, it is not clear that molecular subtypes can be robustly defined in general (8), and the definition of the subtypes has changed over time from the initial classifier by Sorlie et al. (3) to the PAM50 classifier (5) often used today. Although each version could be considered an improvement (9), development is still ongoing, exemplified by the recent revision of the PAM50 classifier in the commercial ProSigna assay (NanoString Technologies). Second, current classifiers cannot robustly assign subtype to individual samples in a truly independent manner. Briefly, to put gene expression measurements of a sample on a scale that is required by the classifier methods, gene expression data for each individual test tumor have to be centered against a large and heterogeneous reference tumor set (10). This gene-centering step makes the assignment of subtype to a tumor highly dependent on the composition of other tumors in a reference set (11).

The lack of robustness in assigning subtypes is not limited to gene expression assays like PAM50 (5) and SCMGENE (6), but is also observed in surrogate classifications based on clinico-pathological factors. For instance, there is considerable variation between laboratories in Ki-67 immunohistochemical measurement, which is used as a proliferation surrogate (2). Nevertheless, it has been argued, pending improved standardization, that local laboratory experience of Ki-67 levels could be used to stratify patients into Ki-67 “high” and “low” groups, classifications that may influence therapy decision (2). This argument parallels the de facto situation for gene expression–based molecular subtyping, where slightly different implementations are used in different studies. Although these differences may have limited impact on broad characterizations of the subtypes in terms of, for example, patient outcome, it is not satisfactory that classifications of individual patients are not robust. Thus, there is an imminent need for robust, standardized methods for the assignment of breast cancer molecular subtype to individual tumors independent of data from other tumors.

The article published in this issue of the Journal by Paquet and Hallett describes an approach to making gene expression–based tumor subtyping of individual tumors robust and truly independent (12). At the heart of designing a true single sample predictor lies eliminating the gene-centering step. To this aim, the investigators developed a subtyping approach, AIMS, that relates raw expression measurements of subtype-specific genes to the levels of other genes within each tumor sample, instead of using a gene-centering step. To prove the concept, the investigators applied AIMS to mimic the PAM50 subtyping scheme and conducted a number of rigorous evaluations demonstrating that AIMS is a robust subtype classifier for new samples. AIMS is based on a collection of binary rules, for example, samples with higher raw expression levels for FOXC1 than ESR1 are more likely to be classified as basal-like. The stability of subtyping was evaluated on tumor sets with different proportions of specific subtypes. In these analyses, conventional subtyping tools were found to be highly unstable when single subtypes were excluded from the test set. For instance, 20% to 30% of samples changed subtype when PAM50 was applied to test sets with Luminal A tumors excluded, whereas AIMS was fully independent of the composition of the test set. The absolute nature of AIMS is of profound value when subtyping is applied to studies focusing on restricted cohorts of patients, for example, when characterizing responders to different regimes of therapy in a cohort of only ER-positive/HER2-negative cases or in neoadjuvant trials. In the first scenario, the investigators showed that conventional subtyping assigns the ER-positive/HER2-negative cases to all subtypes in proportions inherent to the method (for example, approximately 20% were incorrectly classified as basal-like by PAM50). In neoadjuvant trials, robust subtyping is even more critical, as such trials typically are small and often underpowered for clinically relevant endpoints, making them sensitive to inflated chance findings based on unstable conventional subtyping. The investigators’ analyses also demonstrate that AIMS is consistently more stable than PAM50 when subtyping is applied to samples with missing measurements for some predictor genes, or to samples with a large proportion of non-neoplastic cells.

Subtyping approaches based on raw expression measurements also have limitations. One concern is that such methods may be highly sensitive to changes in the technology used to quantify expression levels. Since AIMS depends on the relative relations between expression levels of different genes in a sample, differences in measurement bias for a gene could affect its expression level relative to other genes in the sample, potentially causing instability in the subtyping. To explore this concern, the investigators analyzed samples profiled by both microarrays and RNA-sequencing and found that AIMS consistently assigns subtypes to samples across multiple platforms. Presumably, this stability is because of the construction of AIMS using relationships between genes having large differences in expression levels between subtypes, thereby making the relationships less sensitive to measurement bias.

The development of methods that can robustly assign subtype to individual tumors, based on absolute measurements, opens the door to address the issues with different subtype classification schemes in breast cancer. It may serve as a first step to standardize methodologies. The separation of luminal tumors into A and B is based on expression of proliferation-related genes, but it is likely that proliferation in luminal tumors is a continuum (11). An absolute classifier is critical to optimize cutpoints in proliferation that reproducibly separates luminal tumors with respect to response to systemic treatments, and to standardize risk of recurrence scores that are based on subtype assignments. Eventually, such studies may return more stringent subtyping tools even better suited for incorporation into clinical trials and routine clinical practice.

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Making Breast Cancer Molecular Subtypes Robust

Over the last decade, our knowledge in somatic genetic events related to breast cancer has increased -enormously. Through usage of various genome-wide molecular approaches, it has become increasingly clear that breast cancer is a vastly heterogeneous disease. Microarray-based gene expression profiling has divided breast cancer into five distinct intrinsic subtypes termed basal-like, HER2-enriched, normal-like, luminal A, and luminal B. Importantly, these subtypes are closely correlated to clinical variables as well as different outcomes, with luminal A tumors as the good prognostic group. Initial studies using genome-wide DNA copy number data broadly partitioned breast cancers into three types, complex, amplifier, and simple, and moreover associated distinct copy number changes with the intrinsic subtypes defined by gene expression profiles. More recently, this genomic classification was refined into six genomic subtypes demonstrating strong resemblance to the intrinsic gene expression classification. Additionally, inherited BRCA1- and BRCA2-mutated tumors were significantly correlated to specific subtypes. In this chapter, we will review the current status regarding genomic subtypes of nonfamilial breast cancer.

Nonfamilial breast cancer subtypes.

https://doi.org/10.1016/j.isci.2022.104303

Environmentally induced DNA methylation is inherited across generations in an aquatic keystone species

Supervised learning methods are used when one wants to construct a classifier. To use such a method, one has to know the correct classification of at least some samples, which are used to train the classifier. Once a classifier has been trained it can be used to predict the class of unknown samples. Supervised learning methods have been used numerous times in genomic applications and we will only provide some examples here. Different subtypes of cancers such as leukemia (Golub et al., 1999) and small round blue cell tumors (Khan et al., 2001) have been predicted based on their gene expression profiles obtained with microarrays. Microarray data has also been used in the construction of classifiers for the prediction of outcome of patients, such as whether a breast tumor is likely to give rise to a distant metastasis (van’t Veer et al., 2002) or whether a medulloblastoma patient is likely to have a favorable clinical outcome (Pomeroy et al., 2002). Proteomic patterns in serum have been used to identify ovarian cancer (Petricoin et al., 2002a) and prostate cancer (Adam et al., 2002); (Petricoin et al., 2002b).

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Markus Ringnér

Papers

Bose-Einstein and Colour Interference in W-pair Decays

Making Breast Cancer Molecular Subtypes Robust

Nonfamilial breast cancer subtypes.

Environmentally induced DNA methylation is inherited across generations in an aquatic keystone species

Classification of Genomic and Proteomic Data Using Support Vector Machines