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Genomic medicine has transformed the lives of patients with cancer by enabling individualised and evidence‐based clinical decision‐making. Despite this progress, the implementation of precision cancer medicine is limited by its dependence on isolated biomarkers. The development of bulk and single‐cell multiomic technologies has revealed the enormous complexity of the cancer ecosystem. Beyond the cancer cell, the tumour microenvironment, macroenvironment and host factors, including the microbiome, profoundly influence the cancer phenotype, and accounting for these enhances the resolution of precision medicine. The advent of robust multiomic profiling and interpretable machine learning algorithms mark the dawn of a new postgenomic era of personalised cancer medicine. In Precision Cancer Medicine 2.0, high‐resolution personalised clinical decision‐making is informed by the comprehensive multiomic profiling of tumour and host, integrated using artificial intelligence.

Abstract Whole genome sequencing (WGS) provides comprehensive, individualised cancer genomic information. However, routine tumour biopsies are formalin-fixed and paraffin-embedded (FFPE), damaging DNA, historically limiting their use in WGS. Here we analyse FFPE cancer WGS datasets from England’s 100,000 Genomes Project, comparing 578 FFPE samples with 11,014 fresh frozen (FF) samples across multiple tumour types. We use an approach that characterises rather than discards artefacts. We identify three artefactual signatures, including one known (SBS57) and two previously uncharacterised (SBS FFPE, ID FFPE), and develop an “FFPEImpact” score that quantifies sample artefacts. Despite inferior sequencing quality, FFPE-derived data identifies clinically-actionable variants, mutational signatures and permits algorithmic stratification. Matched FF/FFPE validation cohorts shows good concordance while acknowledging SBS, ID and copy-number artefacts. While FF-derived WGS data remains the gold standard, FFPE-samples can be used for WGS if required, using analytical advancements developed here, potentially democratising whole cancer genomics to many.

Abstract Triple-negative breast cancers (TNBCs) are a subset of breast cancers that have remained difficult to treat. A proportion of TNBCs arising in non-carriers of BRCA pathogenic variants have genomic features that are similar to BRCA carriers and may also benefit from PARP inhibitor treatment. Using genomic data from 129 TNBC samples from the Malaysian Breast Cancer (MyBrCa) cohort, we developed a gene expression-based machine learning classifier for homologous recombination deficiency (HRD) in TNBCs. The classifier identified samples with HRD mutational signature at an AUROC of 0.93 in MyBrCa validation datasets and 0.84 in TCGA TNBCs. Additionally, the classifier strongly segregated HRD-associated genomic features in TNBCs from TCGA, METABRIC, and ICGC. Thus, our gene expression classifier may identify triple-negative breast cancer patients with homologous recombination deficiency, suggesting an alternative method to identify individuals who may benefit from treatment with PARP inhibitors or platinum chemotherapy.

Abstract The Integrative Cluster subtypes (IntClusts) provide a framework for the classification of breast cancer tumors into 10 distinct groups based on copy number and gene expression, each with unique biological drivers of disease and clinical prognoses. Gene expression data is often lacking, and accurate classification of samples into IntClusts with copy number data alone is essential. Current classification methods achieve low accuracy when gene expression data are absent, warranting the development of new approaches to IntClust classification. Copy number data from 1980 breast cancer samples from METABRIC was used to train multiclass XGBoost machine learning algorithms (CopyClust). A piecewise constant fit was applied to the average copy number profile of each IntClust and unique breakpoints across the 10 profiles were identified and converted into ~ 500 genomic regions used as features for CopyClust. These models consisted of two approaches: a 10-class model with the final IntClust label predicted by a single multiclass model and a 6-class model with binary reclassification in which four pairs of IntClusts were combined for initial multiclass classification. Performance was validated on the TCGA dataset, with copy number data generated from both SNP arrays and WES platforms. CopyClust achieved 81% and 79% overall accuracy with the TCGA SNP and WES datasets, respectively, a nine-percentage point or greater improvement in overall IntClust subtype classification accuracy. CopyClust achieves a significant improvement over current methods in classification accuracy of IntClust subtypes for samples without available gene expression data and is an easily implementable algorithm for IntClust classification of breast cancer samples with copy number data.

Abstract Breast cancer exhibits significant heterogeneity, manifesting in various subtypes that are critical in guiding treatment decisions. This study aimed to investigate the existence of distinct subtypes of breast cancer within the Asian population, by analysing the transcriptomic profiles of 934 breast cancer patients from a Malaysian cohort. Our findings reveal that the HR + /HER2− breast cancer samples display a distinct clustering pattern based on immune phenotypes, rather than conforming to the conventional luminal A-luminal B paradigm previously reported in breast cancers from women of European descent. This suggests that the activation of the immune system may play a more important role in Asian HR + /HER2− breast cancer than has been previously recognized. Analysis of somatic mutations by whole exome sequencing showed that counter-intuitively, the cluster of HR + /HER2− samples exhibiting higher immune scores was associated with lower tumour mutational burden, lower homologous recombination deficiency scores, and fewer copy number aberrations, implicating the involvement of non-canonical tumour immune pathways. Further investigations are warranted to determine the underlying mechanisms of these pathways, with the potential to develop innovative immunotherapeutic approaches tailored to this specific patient population.

Abstract Obesity is associated with an increased risk of developing breast cancer (BC) and worse prognosis in BC patients, yet its impact on BC biology remains understudied in humans. This study investigates how the biology of untreated primary BC differs according to patients’ body mass index (BMI) using data from >2,000 patients. We identify several genomic alterations that are differentially prevalent in overweight or obese patients compared to lean patients. We report evidence supporting an ageing accelerating effect of obesity at the genetic level. We show that BMI-associated differences in bulk transcriptomic profile are subtle, while single cell profiling allows detection of more pronounced changes in different cell compartments. These analyses further reveal an elevated and unresolved inflammation of the BC tumor microenvironment associated with obesity, with distinct characteristics contingent on the estrogen receptor status. Collectively, our analyses imply that obesity is associated with an inflammaging-like phenotype. We conclude that patient adiposity may play a significant role in the heterogeneity of BC and should be considered for BC treatment tailoring.

Abstract Analysis of circulating tumor DNA (ctDNA) to monitor cancer dynamics and detect minimal residual disease has been an area of increasing interest. Multiple methods have been proposed but few studies have compared the performance of different approaches. Here, we compare detection of ctDNA in serial plasma samples from patients with breast cancer using different tumor‐informed and tumor‐naïve assays designed to detect structural variants (SVs), single nucleotide variants (SNVs), and/or somatic copy‐number aberrations, by multiplex PCR, hybrid capture, and different depths of whole‐genome sequencing. Our results demonstrate that the ctDNA dynamics and allele fractions (AFs) were highly concordant when analyzing the same patient samples using different assays. Tumor‐informed assays showed the highest sensitivity for detection of ctDNA at low concentrations. Hybrid capture sequencing targeting between 1,347 and 7,491 tumor‐identified mutations at high depth was the most sensitive assay, detecting ctDNA down to an AF of 0.00024% (2.4 parts per million, ppm). Multiplex PCR targeting 21–47 tumor‐identified SVs per patient detected ctDNA down to 0.00047% AF (4.7 ppm) and has potential as a clinical assay.

In early project phases, cross-functional collaboration is required to lead a capital project from an investment option to a full definition before its design and construction commenced. Given that many capital projects are large, complex, and take years to develop, for stakeholders to realize effective project definition and development it is important that they identify—in the early phases—cross-functional collaboration barriers and their contributing factors. The current study conducted a total of 20 interviews with experts from both the business units and project teams in owner companies and Engineering, Procurement, and Construction (EPC) contractors. It identified 14 barriers to effective cross-functional collaboration and 43 associated contributing factors in the early phases of capital projects. To rank the barriers, researchers then applied the Delphi method with a panel of 12 subject matter experts (SMEs). The outcomes of this study can guide practitioners to improve the effectiveness of cross-functional collaboration in early project phases.

Abstract Despite significant progress in breast cancer (BC) therapy, it is globally the most commonly diagnosed cancer and leads to the death of over 650,000 women annually. Androgen receptor (AR) is emerging as a potential new therapeutic target in BC. While the role of AR is well established in prostate cancer (PCa), its function in BC remains incompletely understood. Emerging data show that AR’s role in BC is dependent on several factors including, but not limited to, disease subtype, tumour microenvironment, and levels of circulating oestrogens and androgens. While targeting AR in PCa is becoming increasingly effective, these advances have yet to make any significant impact on the care of BC patients. However, this approach is increasingly being evaluated in BC and it is clear that improvements in our understanding of AR’s role in BC will increase the likelihood of success for AR-targeted therapies. This review summarizes our current understanding of the function of AR across BC subtypes. We highlight limitations in our current knowledge and demonstrate the importance of categorizing BC subtypes effectively, in relation to determining AR activity. Further, we describe the current state of the art regarding AR-targeted approaches for BC as monotherapy or in combination with radiotherapy.

Chromosomal instability is a major challenge to patient stratification and targeted drug development for high-grade serous ovarian carcinoma. Here we show that identification of clonal somatic copy number alterations in frequently amplified cancer genes could inform therapeutics for precision medicine.

Summary: Vasculogenic mimicry (VM) describes the formation of pseudo blood vessels constructed of tumor cells that have acquired endothelial-like properties. VM channels endow the tumor with a tumor-derived vascular system that directly connects to host blood vessels, and their presence is generally associated with poor patient prognosis. Here we show that the transcription factor, Foxc2, promotes VM in diverse solid tumor types by driving ectopic expression of endothelial genes in tumor cells, a process that is stimulated by hypoxia. VM-proficient tumors are resistant to anti-angiogenic therapy, and suppression of Foxc2 augments response. This work establishes co-option of an embryonic endothelial transcription factor by tumor cells as a key mechanism driving VM proclivity and motivates the search for VM-inhibitory agents that could form the basis of combination therapies with anti-angiogenics.

CDK4/6 inhibitor resistance is common in breast cancer. Here, the authors show that p16 overexpression may be linked to reduced efficacy of CDK4/6 inhibition, and show that the combination with PI3K inhibitors may increase anti-tumour effects.

Abstract PIK3CA mutations are the most common in breast cancer, particularly in the estrogen receptor-positive cohort, but the benefit of PI3K inhibitors has had limited success compared with approaches targeting other less common mutations. We found a frequent allelic expression imbalance between the missense mutant and wild-type PIK3CA alleles in breast tumors from the METABRIC (70.2%) and the TCGA (60.1%) projects. When considering the mechanisms controlling allelic expression, 27.7% and 11.8% of tumors showed imbalance due to regulatory variants in cis, in the two studies respectively. Furthermore, preferential expression of the mutant allele due to cis-regulatory variation is associated with poor prognosis in the METABRIC tumors (P = 0.031). Interestingly, ER−, PR−, and HER2+ tumors showed significant preferential expression of the mutated allele in both datasets. Our work provides compelling evidence to support the clinical utility of PIK3CA allelic expression in breast cancer in identifying patients of poorer prognosis, and those with low expression of the mutated allele, who will unlikely benefit from PI3K inhibitors. Furthermore, our work proposes a model of differential regulation of a critical cancer-promoting gene in breast cancer.

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.

Abstract Circulating tumor DNA (ctDNA) sequencing studies could provide novel insights into the molecular pathology of cancer in sub-Saharan Africa. In 15 patient plasma samples collected at the time of diagnosis as part of the Ghana Breast Health Study and unselected for tumor grade and subtype, ctDNA was detected in a majority of patients based on whole- genome sequencing at high (30×) and low (0.1×) depths. Breast cancer driver copy number alterations were observed in the majority of patients.

Understanding the molecular mechanisms underlying DNA methylation in cancer and its clinical relevance remains crucial. Here, the authors study RRBS-based profiles of 1538 breast tumours and 244 normal breast tissues from the METABRIC cohort and report epigenomic instability and cis-regulatory effects.

Abstract Background Normal human breast tissues are a heterogeneous mix of epithelial and stromal subtypes in different cell states. Delineating the spectrum of cellular heterogeneity will provide new insights into normal cellular properties within the breast tissue that might become dysregulated in the initial stages of cancer. Investigation of surface marker expression provides a valuable approach to resolve complex cell populations. However, the majority of cell surface maker expression of primary breast cells have not been investigated. Methods To determine the differences in expression of a range of uninvestigated cell surface markers between the normal breast cell subpopulations, primary human breast cells were analysed using high-throughput flow cytometry for the expression of 242 cell surface proteins in conjunction with EpCAM/CD49f staining. Results We identified 35 surface marker proteins expressed on normal breast epithelial and/or stromal subpopulations that were previously unreported. We also show multiple markers were equally expressed in all cell populations (e.g. CD9, CD59, CD164) while other surface markers were confirmed to be enriched in different cell lineages: CD24, CD227 and CD340 in the luminal compartment, CD10 and CD90 in the basal population, and CD34 and CD140b on stromal cells. Conclusions Our dataset of CD marker expression in the normal breast provides better definition for breast cellular heterogeneity.

Abstract The biology of breast cancer response to neoadjuvant therapy is underrepresented in the literature and provides a window-of-opportunity to explore the genomic and microenvironment modulation of tumours exposed to therapy. Here, we characterised the mutational, gene expression, pathway enrichment and tumour-infiltrating lymphocytes (TILs) dynamics across different timepoints of 35 HER2-negative primary breast cancer patients receiving neoadjuvant eribulin therapy (SOLTI-1007 NEOERIBULIN-NCT01669252). Whole-exome data (N = 88 samples) generated mutational profiles and candidate neoantigens and were analysed along with RNA-Nanostring 545-gene expression (N = 96 samples) and stromal TILs (N = 105 samples). Tumour mutation burden varied across patients at baseline but not across the sampling timepoints for each patient. Mutational signatures were not always conserved across tumours. There was a trend towards higher odds of response and less hazard to relapse when the percentage of subclonal mutations was low, suggesting that more homogenous tumours might have better responses to neoadjuvant therapy. Few driver mutations (5.1%) generated putative neoantigens. Mutation and neoantigen load were positively correlated (R 2 = 0.94, p =

Abstract Rare protein-truncating variants (PTVs) in PALB2 confer increased risk to breast cancer, but relatively few studies have reported the characteristics of tumours with PALB2 PTVs. In this study, we describe molecular characteristics of tumours with either germline or somatic alterations in PALB2. DNA from fresh frozen tumour tissues and matched peripheral blood lymphocytes for 560 breast cancer patients was subjected for whole-exome sequencing (WES), and RNA from tumour tissues was subjected to RNA sequencing (RNA-seq). We found six cases with germline and three with somatic protein-truncating variants in PALB2. The characteristics of tumours in patients with PALB2 PTVs were similar to those with BRCA1 and BRCA2 PTVs, having significantly more somatic alterations, and a high proportion of the mutational signature and genomic scar scores characteristic of deficiencies in homologous recombination (HR), compared to tumours arising in non-carriers. Unlike tumours arising in patients with BRCA1 and BRCA2 PTVs, PALB2 tumours did not have high prevalence of TP53 somatic alterations or an enriched immune microenvironment. In summary, PALB2 tumours show the homologous recombination deficiencies characteristic of BRCA1 and BRCA2 tumours, and highlight the potential clinical relevance of PALB2 mutational status in guiding therapeutic choices.

The heterogeneity of breast cancer has a major role in drug response and resistance. In this study, the authors use patient-derived tumour xenografts as a platform for drug testing and correlation with single-cell proteomic phenotypes characterized by mass cytometry.

BRCA1 driven breast cancer arises from luminal progenitor cells but how BRCA1 loss-of-function affects the luminal progenitor cell state during premalignant stages of the disease is still unclear. Here, the authors demonstrate an aberrant differentiation of luminal progenitors towards a partial secretory luminal cell phenotype that occurs in a Brca1 deficient mouse model of breast cancer at early stages of tumour initiation and in breast cells from BRCA1 carriers.

Abstract Background FGFR1 amplification, but not overexpression, has been related to adverse prognosis in hormone-positive breast cancer (HRPBC). Whether FGFR1 overexpression and amplification are correlated, what is their distribution among luminal A or B HRPBC, and if there is a potential different prognostic role for amplification and overexpression are currently unknown features. The role of FGFR1 inhibitors in HRPBC is also unclear. Methods FGFR1 amplification (FISH) and overexpression (RNAscope) were investigated in a N = 251 HRPBC patients cohort and the METABRIC cohort; effects on survival and FISH-RNAscope concordance were determined. We generated hormonal deprivation resistant (LTED-R) and FGFR1-overexpressing cell line variants of the ER+ MCF7 and T47-D and the ER+, FGFR1-amplified HCC1428 cell lines. The role of ER, CDK4/6, and/or FGFR1 blockade alone or in combinations in Rb phosphorylation, cell cycle, and survival were studied. Results FGFR1 overexpression and amplification was non-concordant in > 20% of the patients, but both were associated to a similar relapse risk (~ 2.5-fold; P

Abstract Background NRG1 gene fusions may be clinically actionable, since cancers carrying the fusion transcripts can be sensitive to tyrosine kinase inhibitors. The NRG1 gene encodes ligands for the HER2(ERBB2)-ERBB3 heterodimeric receptor tyrosine kinase, and the gene fusions are thought to lead to autocrine stimulation of the receptor. The NRG1 fusion expressed in the breast cancer cell line MDA-MB-175 serves as a model example of such fusions, showing the proposed autocrine loop and exceptional drug sensitivity. However, its structure has not been properly characterised, its oncogenic activity has not been fully explained, and there is limited data on such fusions in breast cancer. Methods We analysed genomic rearrangements and transcripts of NRG1 in MDA-MB-175 and a panel of 571 breast cancers. Results We found that the MDA-MB-175 fusion—originally reported as a DOC4(TENM4)-NRG1 fusion, lacking the cytoplasmic tail of NRG1—is in reality a double fusion, PPP6R3-TENM4-NRG1, producing multiple transcripts, some of which include the cytoplasmic tail. We hypothesise that many NRG1 fusions may be oncogenic not for lacking the cytoplasmic domain but because they do not encode NRG1’s nuclear-localised form. The fusion in MDA-MB-175 is the result of a very complex genomic rearrangement, which we partially characterised, that creates additional expressed gene fusions, RSF1-TENM4, TPCN2-RSF1, and MRPL48-GAB2. We searched for NRG1 rearrangements in 571 breast cancers subjected to genome sequencing and transcriptome sequencing and found four cases (0.7%) with fusions, WRN-NRG1, FAM91A1-NRG1, ARHGEF39-NRG1, and ZNF704-NRG1, all splicing into NRG1 at the same exon as in MDA-MB-175. However, the WRN-NRG1 and ARHGEF39-NRG1 fusions were out of frame. We identified rearrangements of NRG1 in many more (8% of) cases that seemed more likely to inactivate than to create activating fusions, or whose outcome could not be predicted because they were complex, or both. This is not surprising because NRG1 can be pro-apoptotic and is inactivated in some breast cancers. Conclusions Our results highlight the complexity of rearrangements of NRG1 in breast cancers and confirm that some do not activate but inactivate. Careful interpretation of NRG1 rearrangements will therefore be necessary for appropriate patient management.

Molecular profiling of breast cancer in non-Caucasian populations remains underexplored. Here the authors report a high prevalence of HER2-subtypes and enriched immune score with improved survival and higher rates of TP53 somatic mutations with poorer survival in ER+ tumours in a Malaysian cohort.

TP53 gene mutations are very common in human cancer. While such mutations abrogate the tumor suppressive activities of the wild‐type (wt) p53 protein, some of them also endow the mutant (mut) protein with oncogenic gain of function (GOF), facilitating cancer progression. Yet, p53 may acquire altered functionality even without being mutated; in particular, experiments with cultured cells revealed that wtp53 can be rewired to adopt mut‐like features in response to growth factors or cancer‐mimicking genetic manipulations. To assess whether such rewiring also occurs in human tumors, we interrogated gene expression profiles and pathway deregulation patterns in the METABRIC breast cancer (BC) dataset as a function of TP53 gene mutation status. Harnessing the power of machine learning, we optimized a gene expression classifier for ER+Her2‐ patients that distinguishes tumors carrying TP53 mutations from those retaining wt TP53. Interestingly, a small subset of wt TP53 tumors displayed gene expression and pathway deregulation patterns markedly similar to those of TP53‐mutated tumors. Moreover, similar to TP53‐mutated tumors, these ‘pseudomutant’ cases displayed a signature for enhanced proliferation and had worse prognosis than typical wtp53 tumors. Notably, these tumors revealed upregulation of genes which, in BC cell lines, were reported to be positively regulated by p53 GOF mutants. Thus, such tumors may benefit from mut p53‐associated activities without having to accrue TP53 mutations.

A comprehensive translational cancer research approach focused on personalized and precision medicine, and covering the entire cancer research–care–prevention continuum has the potential to achieve in 2030 a 10‐year cancer‐specific survival for 75% of patients diagnosed in European Union (EU) member states with a well‐developed healthcare system. Concerted actions across this continuum that spans from basic and preclinical research through clinical and prevention research to outcomes research, along with the establishment of interconnected high‐quality infrastructures for translational research, clinical and prevention trials and outcomes research, will ensure that science‐driven and social innovations benefit patients and individuals at risk across the EU. European infrastructures involving comprehensive cancer centres (CCCs) and CCC‐like entities will provide researchers with access to the required critical mass of patients, biological materials and technological resources and can bridge research with healthcare systems. Here, we prioritize research areas to ensure a balanced research portfolio and provide recommendations for achieving key targets. Meeting these targets will require harmonization of EU and national priorities and policies, improved research coordination at the national, regional and EU level and increasingly efficient and flexible funding mechanisms. Long‐term support by the EU and commitment of Member States to specialized schemes are also needed for the establishment and sustainability of trans‐border infrastructures and networks. In addition to effectively engaging policymakers, all relevant stakeholders within the entire continuum should consensually inform policy through evidence‐based advice.

In breast cancer the contribution of different genetic variants to disease heritability is complex and not fully understood. Here, the authors present a network-based analysis in 84,567 patients studying ~7.3 million variants, identifying gene modules associated with breast cancer survival.

Hospitals provide diverse tasks essential for the delivery of patient care and are comprised of many functional units. This makes healthcare construction projects highly complex among other types of building projects due to the specific regulations, multiple functions it must provide, complicated mechanical and electrical systems, and so on. This complexity embodies potential risk events during its construction, which not only influences the completion of the project but can impact the patients’ safety and health conditions even after the project is finished. To prevent such outcomes, risk management is a crucial process that can identify, evaluate, and properly mitigate risks along the project lifecycle. A key aspect of risk management is that it requires the interaction and contribution from multiple stakeholders of the project. Various frameworks and tools that enable collaborative management of risks among multiple stakeholders have been developed in the past. However, the developed frameworks are not suitable in the sense that it does not protect the confidentiality of individual inputs from the stakeholders. Moreover, these frameworks are centralized systems, which can bring issues related to the security and transparency of the information that is being stored. Blockchain technology is an emergent distributed ledger technology (DLT) that can provide solutions to the listed problems found in centralized systems. It is a novel system that records information on a decentralized, distributed ledger, where transactions are constantly duplicated and updated. This study explores the applicability of blockchain technology for healthcare risk management. The key functional elements of blockchain that can resolve the challenges faced by prior risk management frameworks have been identified and discussed. Based on the discussions, a conceptual information management model for managing healthcare project risks on a blockchain has been conceived. The development of the initial prototype has been explained. The research illustrates the process, benefits, and limitations of adopting blockchain technology for collaborative risk management in healthcare projects.

A PI3Kα-selective inhibitor has recently been approved for use in breast tumors harboring mutations in PIK3CA, the gene encoding p110α. Preclinical studies have suggested that the PI3K/AKT/mTOR signaling pathway influences stemness, a dedifferentiation-related cellular phenotype associated with aggressive cancer. However, to date, no direct evidence for such a correlation has been demonstrated in human tumors. In two independent human breast cancer cohorts, encompassing nearly 3,000 tumor samples, transcriptional footprint-based analysis uncovered a positive linear association between transcriptionally-inferred PI3K/AKT/mTOR signaling scores and stemness scores. Unexpectedly, stratification of tumors according to PIK3CA genotype revealed a “biphasic” relationship of mutant PIK3CA allele dosage with these scores. Relative to tumor samples without PIK3CA mutations, the presence of a single copy of a hotspot PIK3CA variant was associated with lower PI3K/AKT/mTOR signaling and stemness scores, whereas the presence of multiple copies of PIK3CA hotspot mutations correlated with higher PI3K/AKT/mTOR signaling and stemness scores. This observation was recapitulated in a human cell model of heterozygous and homozygous PIK3CAH1047R expression. Collectively, our analysis (1) provides evidence for a signaling strength-dependent PI3K-stemness relationship in human breast cancer; (2) supports evaluation of the potential benefit of patient stratification based on a combination of conventional PI3K pathway genetic information with transcriptomic indices of PI3K signaling activation. Author summary Breast cancers often have increased activity of the so-called PI3Kα enzyme and the pathway it activates, usually attributed to genetic alterations in the PIK3CA gene, encoding a critical PI3Kα component. Recent cell studies have shown that effects of a PIK3CA mutation depend on how many copies are present. For example, two copies of a strong mutation, but not one, fix cells in a state of “stemness”, a property associated with tumor aggressiveness and therapy failure. To determine relationships among PI3K genetic variation, PI3K activity and stemness in breast cancers we used data from independent patient cohorts encompassing nearly 3,000 tumors. Using PI3K signaling or stemness scores derived from gene expression data, we found a strong, positive association between the scores: aggressive tumors show the highest scores. In contrast, the relationship of these scores with PIK3CA mutation status was unexpected–cancers with one PIK3CA mutant copy showed a decrease in both scores, while they increased in cancers with additional copies. This was confirmed in cellular models. This suggests that using binary information about a PIK3CA mutation to define patient groups for trials may miss important effects of allele dosage. We suggest that grouping may be improved by combining PIK3CA mutational information with functional indices of PI3K pathway activation.

Abstract Advances in sequencing technology have enabled the genomic and transcriptomic characterization of human malignancies with unprecedented detail. However, this wealth of information has been slow to translate into clinically meaningful outcomes. Different models to study human cancers have been established and extensively characterized. Using these models, functional genomic screens and pre‐clinical drug screening platforms have identified genetic dependencies that can be exploited with drug therapy. These genetic dependencies can also be used as biomarkers to predict response to treatment. For many cancers, the identification of such biomarkers remains elusive. In this review, we discuss the development and characterization of models used to study human cancers, RNA interference and CRISPR screens to identify genetic dependencies, large‐scale pharmacogenomics studies and drug screening approaches to improve pre‐clinical drug screening and biomarker discovery.

Summary: 13C nuclear spin hyperpolarization can increase the sensitivity of detection in an MRI experiment by more than 10,000-fold. 13C magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized 13C label exchange between injected [1-13C]pyruvate and the endogenous tumor lactate pool can be used clinically to assess tumor grade and response to treatment. We describe here an experimental protocol for using this technique in patient-derived and established cell line xenograft models of breast cancer in the mouse.For complete details on the use and execution of this protocol, please refer to Ros et al. (2020).

The evolutionary trade-offs faced by tumors are poorly understood, yet could contribute to the molecular diversity of cancer. Here, the authors analyze the diversity of genetic alterations, genes expressed and drug sensitivities among solid tumors to explore what evolutionary trade-offs may explain the molecular diversity of tumors.

Abstract Due to compromised homologous recombination (HR) repair, BRCA1‐ and BRCA2‐mutated tumours accumulate DNA damage and genomic rearrangements conducive of tumour progression. To identify drugs that target specifically BRCA2‐deficient cells, we screened a chemical library containing compounds in clinical use. The top hit was chlorambucil, a bifunctional alkylating agent used for the treatment of chronic lymphocytic leukaemia (CLL). We establish that chlorambucil is specifically toxic to BRCA1/2‐deficient cells, including olaparib‐resistant and cisplatin‐resistant ones, suggesting the potential clinical use of chlorambucil against disease which has become resistant to these drugs. Additionally, chlorambucil eradicates BRCA2‐deficient xenografts and inhibits growth of olaparib‐resistant patient‐derived tumour xenografts (PDTXs). We demonstrate that chlorambucil inflicts replication‐associated DNA double‐strand breaks (DSBs), similarly to cisplatin, and we identify ATR, FANCD2 and the SNM1A nuclease as determinants of sensitivity to both drugs. Importantly, chlorambucil is substantially less toxic to normal cells and tissues in vitro and in vivo relative to cisplatin. Because chlorambucil and cisplatin are equally effective inhibitors of BRCA2‐compromised tumours, our results indicate that chlorambucil has a higher therapeutic index than cisplatin in targeting BRCA‐deficient tumours.

Cancer Core Europe is a European legal alliance consisting of seven leading cancer centres – most of them Comprehensive Cancer Centres (CCCs) – with a single portal system to engage in various research projects with partners. Cancer Core Europe was established to create a sustainable, high‐level, shared research infrastructure platform hosting research collaborations and task forces (data sharing, clinical trials, genomics, immunotherapy, imaging, education and training, and legal and ethical issues), with a controlled expansion agenda. Translational cancer research covers the cancer research continuum from basic to preclinical to early clinical, late clinical, and outcomes research. Basic–preclinical research serves as the ‘engine’ for early clinical research by bridging the early translational research gap and is the primary and current focus of the consortium as exemplified by the launching of the Basket of Baskets trial, Europe's largest precision cancer medicine trial. Inspired by the creation of Cancer Core Europe, the prevention community established Cancer Prevention Europe, a consortium of ten cancer prevention centres aimed at supporting the complete prevention research continuum. Presently, Cancer Core Europe and Cancer Prevention Europe are integrating therapeutics and prevention strategies to address in partnership the widening cancer problem. By providing innovative approaches for cancer research, links to healthcare systems, development of quality‐assured multidisciplinary cancer care, and assessment of long‐term outcomes, the virtual infrastructure will serve as a hub to connect and interact with other centres across Europe and beyond. Together, Cancer Core Europe and Cancer Prevention Europe are prepared to function as a central engine to tackle, in collaboration with various partners, a potential ‘mission on cancer’ addressing the cancer burden.

When examining the evolution of treatment resistance in breast cancer, perceived genomic changes may be due to clonal evolution or heterogeneous tumors. Here, the authors show that apparent clonal change can in fact be due to pre-treatment heterogeneity, and samples from at least two regions are necessary to detect treatment-induced clonal shifts

Abstract Circulating tumour DNA (ctDNA) detection and monitoring have enormous potential clinical utility in oncology. We describe here a fast, flexible and cost-effective method to profile multiple genes simultaneously in low input cell-free DNA (cfDNA): Next Generation-Targeted Amplicon Sequencing (NG-TAS). We designed a panel of 377 amplicons spanning 20 cancer genes and tested the NG-TAS pipeline using cell-free DNA from two HapMap lymphoblastoid cell lines. NG-TAS consistently detected mutations in cfDNA when mutation allele fraction was > 1%. We applied NG-TAS to a clinical cohort of metastatic breast cancer patients, demonstrating its potential in monitoring the disease. The computational pipeline is available at https://github.com/cclab-brca/NGTAS_pipeline.

“O sobrinho do mago” faz parte da série de sete livros que compõem ‘As Crônicas de Nárnia’, do escritor irlandês C. S. Lewis, professor de Literatura Medieval e Renascentista nas universidades de Oxford e, posteriormente, Cambridge. Este artigo tem como meta demonstrar que o livro “O sobrinho do mago” pode ser interpretado como um recontar imaginativo da narrativa da criação registrada na Bíblia, especificamente no livro de Gênesis. Como base teórica busca-se compreender a interpretação e recepção da obra de arte, assim como a aplicabilidade do gênero fantasia.

Deep sequencing of B cell receptor (BCR) heavy chains from a cohort of 31 COVID-19 patients from the UK reveals a stereotypical naive immune response to SARS-CoV-2 which is consistent across patients. Clonal expansion of the B cell population is also observed and may be the result of memory bystander effects. There was a strong convergent sequence signature across patients, and we identified 1,254 clonotypes convergent between at least four of the COVID-19 patients, but not present in healthy controls or individuals following seasonal influenza vaccination. A subset of the convergent clonotypes were homologous to known SARS and SARS-CoV-2 spike protein neutralizing antibodies. Convergence was also demonstrated across wide geographies by comparison of data sets between patients from UK, USA, and China, further validating the disease association and consistency of the stereotypical immune response even at the sequence level. These convergent clonotypes provide a resource to identify potential therapeutic and prophylactic antibodies and demonstrate the potential of BCR profiling as a tool to help understand patient responses.

The Northern Irish author C. S. Lewis (1898-1963) was one of the outstanding Christian thinkers of the last century. A prolific author, he moved through different areas, such as literary criticism, youth literature, science fiction, and texts of theological exposition and of apologetics. In science fiction there is his remarkable “Cosmic Trilogy”: Beyond the Silent Planet, Perelandra, and That Hid­eous Strength. In these three books, Lewis presents a vast array of themes. Among these is angelology,the systematic study of heavenly beings known as angels. The aim of this article is to present the major influences that Lewis used to build his angelology: old Jewish literature, exemplified in texts such as the Ethiopian Enoch (or the Book of Enoch or First Enoch), and the biblical tradition itself. The article will seek also to defend the hypothesis that, using fiction, Lewis builds an imaginative and suggestive theology that is a critique of the rationalism of continental theol­ogy of his day.

Background: The addition of adjuvant trastuzumab to chemotherapy has significantly improved outcomes for people with human epidermal growth factor receptor 2 (HER2)-positive, early, potentially curable breast cancer. Twelve months’ trastuzumab, tested in registration trials, was adopted as standard adjuvant treatment in 2006. Subsequently, similar outcomes were demonstrated using 9 weeks of trastuzumab. Shorter durations were therefore tested for non-inferiority. Objectives: To establish whether or not 6 months’ adjuvant trastuzumab is non-inferior to 12 months’ in the treatment of HER2-positive early breast cancer using a primary end point of 4-year disease-free survival. Design: This was a Phase III randomised controlled non-inferiority trial. Setting: The setting was 152 NHS hospitals. Participants: A total of 4088 patients with HER2-positive early breast cancer who it was planned would receive both chemotherapy and trastuzumab took part. Intervention: Randomisation (1 : 1) to 6 months’ or 12 months’ trastuzumab treatment. Main outcomes: The primary end point was disease-free survival. The secondary end points were overall survival, cost-effectiveness and cardiac function during treatment with trastuzumab. Assuming a 4-year disease-free survival rate of 80% with 12 months’ trastuzumab, 4000 patients were required to demonstrate non-inferiority of 6 months’ trastuzumab (5% one-sided significance, 85% power), defining the non-inferiority limit as no worse than 3% below the standard arm. Costs and quality-adjusted life-years were estimated using a within-trial analysis and a lifetime decision-analytic model. Results: Between 4 October 2007 and 31 July 2015, 2045 patients were randomised to 12 months’ trastuzumab and 2043 were randomised to 6 months’ trastuzumab. Sixty-nine per cent of patients had ER-positive disease; 90% received anthracyclines (49% with taxanes; 41% without taxanes); 10% received taxanes without anthracyclines; 54% received trastuzumab sequentially after chemotherapy; and 85% received adjuvant chemotherapy (58% were node negative). At 6.1 years’ median follow-up, with 389 (10%) deaths and 566 (14%) disease-free survival events, the 4-year disease-free survival rates for the 4088 patients were 89.5% (95% confidence interval 88.1% to 90.8%) in the 6-month group and 90.3% (95% confidence interval 88.9% to 91.5%) in the 12-month group (hazard ratio 1.10, 90% confidence interval 0.96 to 1.26; non-inferiority p = 0.01), demonstrating non-inferiority of 6 months’ trastuzumab. Congruent results were found for overall survival (non-inferiority p = 0.0003) and landmark analyses 6 months from starting trastuzumab [non-inferiority p = 0.03 (disease-free-survival) and p = 0.006 (overall survival)]. Six months’ trastuzumab resulted in fewer patients reporting adverse events of severe grade [365/1929 (19%) vs. 460/1935 (24%) for 12-month patients; p = 0.0003] or stopping early because of cardiotoxicity [61/1977 (3%) vs. 146/1941 (8%) for 12-month patients; p

Summary: Although thousands of solid tumors have been sequenced to date, a fundamental under-sampling bias is inherent in current methodologies. This is caused by a tissue sample input of fixed dimensions (e.g., 6 mm biopsy), which becomes grossly under-powered as tumor volume scales. Here, we demonstrate representative sequencing (Rep-Seq) as a new method to achieve unbiased tumor tissue sampling. Rep-Seq uses fixed residual tumor material, which is homogenized and subjected to next-generation sequencing. Analysis of intratumor tumor mutation burden (TMB) variability shows a high level of misclassification using current single-biopsy methods, with 20% of lung and 52% of bladder tumors having at least one biopsy with high TMB but low clonal TMB overall. Misclassification rates by contrast are reduced to 2% (lung) and 4% (bladder) when a more representative sampling methodology is used. Rep-Seq offers an improved sampling protocol for tumor profiling, with significant potential for improved clinical utility and more accurate deconvolution of clonal structure.

Accumulating evidence demonstrates the decisive role of the gut microbiota in determining the effectiveness of anticancer therapeutics such as immunogenic chemotherapy or immune checkpoint blockade in preclinical tumor models, as well as in cancer patients. In synthesis, it appears that a normal intestinal microbiota supports therapeutic anticancer responses, while a dysbiotic microbiota that lacks immunostimulatory bacteria or contains overabundant immunosuppressive species causes treatment failure. These findings have led to the design of clinical trials that evaluate the capacity of modulation of the gut microbiota to synergize with treatment and hence limit tumor progression. Along the lines of this Trial Watch, we discuss the rationale for harnessing the gut microbiome in support of cancer therapy and the progress of recent clinical trials testing this new therapeutic paradigm in cancer patients.

Abstract Poly(ADP‐ribose) polymerase (PARP) inhibitors (PARPi) are effective in cancers with defective homologous recombination DNA repair (HRR), including BRCA1/2‐related cancers. A test to identify additional HRR‐deficient tumors will help to extend their use in new indications. We evaluated the activity of the PARPi olaparib in patient‐derived tumor xenografts (PDXs) from breast cancer (BC) patients and investigated mechanisms of sensitivity through exome sequencing, BRCA1 promoter methylation analysis, and immunostaining of HRR proteins, including RAD51 nuclear foci. In an independent BC PDX panel, the predictive capacity of the RAD51 score and the homologous recombination deficiency (HRD) score were compared. To examine the clinical feasibility of the RAD51 assay, we scored archival breast tumor samples, including PALB2‐related hereditary cancers. The RAD51 score was highly discriminative of PARPi sensitivity versus PARPi resistance in BC PDXs and outperformed the genomic test. In clinical samples, all PALB2‐related tumors were classified as HRR‐deficient by the RAD51 score. The functional biomarker RAD51 enables the identification of PARPi‐sensitive BC and broadens the population who may benefit from this therapy beyond BRCA1/2‐related cancers.

Abstract Tumour heterogeneity leads to the development of multiple resistance mechanisms during targeted therapies. Identifying the dominant driver(s) is critical for treatment decision. We studied the relative dynamics of multiple oncogenic drivers in longitudinal plasma of 50 EGFR‐mutant non‐small‐cell lung cancer patients receiving gefitinib and hydroxychloroquine. We performed digital PCR and targeted sequencing on samples from all patients and shallow whole‐genome sequencing on samples from three patients who underwent histological transformation to small‐cell lung cancer. In 43 patients with known EGFR mutations from tumour, we identified them accurately in plasma of 41 patients (95%, 41/43). We also found additional mutations, including EGFR T790M (31/50, 62%), TP53 (23/50, 46%), PIK3CA (7/50, 14%) and PTEN (4/50, 8%). Patients with both TP53 and EGFR mutations before treatment had worse overall survival than those with only EGFR. Patients who progressed without T790M had worse PFS during TKI continuation and developed alternative alterations, including small‐cell lung cancer‐associated copy number changes and TP53 mutations, that tracked subsequent treatment responses. Longitudinal plasma analysis can help identify dominant resistance mechanisms, including non‐druggable genetic information that may guide clinical management.

Abstract Background Patient-Derived Tumour Xenografts (PDTXs) have emerged as the pre-clinical models that best represent clinical tumour diversity and intra-tumour heterogeneity. The molecular characterization of PDTXs using High-Throughput Sequencing (HTS) is essential; however, the presence of mouse stroma is challenging for HTS data analysis. Indeed, the high homology between the two genomes results in a proportion of mouse reads being mapped as human. Results In this study we generated Whole Exome Sequencing (WES), Reduced Representation Bisulfite Sequencing (RRBS) and RNA sequencing (RNA-seq) data from samples with known mixtures of mouse and human DNA or RNA and from a cohort of human breast cancers and their derived PDTXs. We show that using an In silico Combined human-mouse Reference Genome (ICRG) for alignment discriminates between human and mouse reads with up to 99.9% accuracy and decreases the number of false positive somatic mutations caused by misalignment by >99.9%. We also derived a model to estimate the human DNA content in independent PDTX samples. For RNA-seq and RRBS data analysis, the use of the ICRG allows dissecting computationally the transcriptome and methylome of human tumour cells and mouse stroma. In a direct comparison with previously reported approaches, our method showed similar or higher accuracy while requiring significantly less computing time. Conclusions The computational pipeline we describe here is a valuable tool for the molecular analysis of PDTXs as well as any other mixture of DNA or RNA species.