Development and validation of a reliable DNA copy-number-based machine learning algorithm (CopyClust) for breast cancer integrative cluster classification.

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.

Predictability of B cell clonal persistence and immunosurveillance in breast cancer.

B cells and T cells are important components of the adaptive immune system and mediate anticancer immunity. The T cell landscape in cancer is well characterized, but the contribution of B cells to anticancer immunosurveillance is less well explored. Here we show an integrative analysis of the B cell and T cell receptor repertoire from individuals with metastatic breast cancer and individuals with early breast cancer during neoadjuvant therapy. Using immune receptor, RNA and whole-exome sequencing, we show that both B cell and T cell responses seem to coevolve with the metastatic cancer genomes and mirror tumor mutational and neoantigen architecture. B cell clones associated with metastatic immunosurveillance and temporal persistence were more expanded and distinct from site-specific clones. B cell clonal immunosurveillance and temporal persistence are predictable from the clonal structure, with higher-centrality B cell antigen receptors more likely to be detected across multiple metastases or across time. This predictability was generalizable across other immune-mediated disorders. This work lays a foundation for prioritizing antibody sequences for therapeutic targeting in cancer.

Clustering of HR + /HER2- breast cancer in an Asian cohort is driven by immune phenotypes.

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.

Criteria and indicators to evaluate quality of care in genitourinary tumour boards.

PURPOSE: Genitourinary (GU) multidisciplinary tumour boards (GUMTBs) are key components of patient care, as they might lead to changes in treatment plan, improved survival, and increased adherence to guidelines. However, there are no guidelines on how GUMTBs should operate or how to assess their quality of performance. METHODS: A systematic literature review was conducted to identify criteria and indicators to evaluate quality in GUMTBs. A scientific committee-comprising 12 GU cancer specialists from seven disciplines-proposed a list of criteria and developed indicators, evaluated in two rounds of Delphi method. Appropriateness and utility of indicators were scored using a 9-point Likert scale. Consensus was defined as at least two-thirds of Delphi respondents selecting a score sub-category that encompassed the median score of the group. RESULTS: Forty-five criteria were selected to evaluate the quality of GUMTBs covering five dimensions: organisation, personnel, protocol and documentation, resources, and interaction with patients. Then, 33 indicators were developed and evaluated in the first round of Delphi, leading to a selection of 26 indicators in two dimensions: function, governance and resources, and GUMTB sessions. In the second round, consensus was reached on the appropriateness of all 26 indicators and on the utility of 24 of them. Index cards for criteria and indicators were developed to be used in clinical practice. CONCLUSIONS: Criteria and indicators were developed to evaluate the quality of GUMTBs, aiming to serve as a guide to improve quality of care and health outcomes in patients with GU cancer.

Vitamin B5 supports MYC oncogenic metabolism and tumor progression in breast cancer.

Tumors are intrinsically heterogeneous and it is well established that this directs their evolution, hinders their classification and frustrates therapy1-3. Consequently, spatially resolved omics-level analyses are gaining traction4-9. Despite considerable therapeutic interest, tumor metabolism has been lagging behind this development and there is a paucity of data regarding its spatial organization. To address this shortcoming, we set out to study the local metabolic effects of the oncogene c-MYC, a pleiotropic transcription factor that accumulates with tumor progression and influences metabolism10,11. Through correlative mass spectrometry imaging, we show that pantothenic acid (vitamin B5) associates with MYC-high areas within both human and murine mammary tumors, where its conversion to coenzyme A fuels Krebs cycle activity. Mechanistically, we show that this is accomplished by MYC-mediated upregulation of its multivitamin transporter SLC5A6. Notably, we show that SLC5A6 over-expression alone can induce increased cell growth and a shift toward biosynthesis, whereas conversely, dietary restriction of pantothenic acid leads to a reversal of many MYC-mediated metabolic changes and results in hampered tumor growth. Our work thus establishes the availability of vitamins and cofactors as a potential bottleneck in tumor progression, which can be exploited therapeutically. Overall, we show that a spatial understanding of local metabolism facilitates the identification of clinically relevant, tractable metabolic targets.

Comparison of tumor-informed and tumor-naïve sequencing assays for ctDNA detection in breast cancer.

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.

Ileal Conduit Versus Orthotopic Neobladder Urinary Diversion in Robot-assisted Radical Cystectomy: Results from a Multi-institutional Series.

Background: Head-to-head comparisons between ileal conduit (IC) and orthotopic neobladder (ONB) in terms of peri- and postoperative outcomes and complications, in the specific setting of robot-assisted radical cystectomy (RARC), are not available. Objective: To address the impact of the type of urinary diversion (UD, IC vs ONB) on RARC morbidity, as well as operative time (OT), length of stay (LOS), and readmissions. Design setting and participants: Urothelial bladder cancer patients treated with RARC at nine high-volume European institutions between 2008 and 2020 were identified. Intervention: RARC with either IC or ONB. Outcome measurements and statistical analysis: Intra- and postoperative complications were collected and reported according to the Intraoperative Complications Assessment and Reporting with Universal Standards recommendations and European Association of Urology guidelines, respectively. Multivariable logistic regression models tested the impact of UD on outcomes, after adjustment for clustering at single hospital level. Results and limitations: Overall, 555 nonmetastatic RARC patients were identified. In 280 (51%) and 275 (49%) patients, an IC and an ONB were performed, respectively. Eighteen intraoperative complications were recorded. The rates of intraoperative complications were 4% in IC patients and 3% in ONB patients (p = 0.4). The median LOS and readmission rates were 10 versus 12 d (p < 0.001) and 20% versus 21% (p = 0.8) in IC versus ONB patients, respectively. At a multivariable logistic regression analyses, the type of UD (IC vs ONB) reached the independent predictor status for prolonged OT (odds ratio [OR]: 0.61, p = 0.03) and prolonged LOS (OR: 0.34, p < 0.001), but not for readmission (OR: 0.92, p = 0.7). Overall, 513 postoperative complications were experienced by 324 patients (58%). At least one postoperative complication was experienced by 160 (57%) IC patients versus 164 (60%) ONB patients (p = 0.6). The type of UD reached the status of an independent predictor of UD-related complications (OR: 0.64, p = 0.03). Conclusions: Compared with RARC with ONB, RARC with IC is less prone to UD-related postoperative complications, prolonged OT, and prolonged LOS. Patient summary: To date, the impact of the type of urinary diversion, namely, ileal conduit versus orthotopic neobladder, on peri- and postoperative outcomes of robot-assisted radical cystectomy is unknown. Based on a rigorous data accrual, which relied on established complication reporting systems (Intraoperative Complications Assessment and Reporting with Universal Standards and European Association of Urology recommended systems), we reported intra- and postoperative complications according to urinary diversion type. Moreover, we found that ileal conduit was associated with lower operative time and length of stay, and yielded a protective effect in terms of urinary diversion-related complications.

Evaluation and comparison of different breast cancer prognosis scores based on gene expression data.

BACKGROUND: Breast cancer is one of the three most common cancers worldwide and is the most common malignancy in women. Treatment approaches for breast cancer are diverse and varied. Clinicians must balance risks and benefits when deciding treatments, and models have been developed to support this decision-making. Genomic risk scores (GRSs) may offer greater clinical value than standard clinicopathological models, but there is limited evidence as to whether these models perform better than the current clinical standard of care. METHODS: PREDICT and GRSs were adapted using data from the original papers. Univariable Cox proportional hazards models were produced with breast cancer-specific survival (BCSS) as the outcome. Independent predictors of BCSS were used to build multivariable models with PREDICT. Signatures which provided independent prognostic information in multivariable models were incorporated into the PREDICT algorithm and assessed for calibration, discrimination and reclassification. RESULTS: EndoPredict, MammaPrint and Prosigna demonstrated prognostic power independent of PREDICT in multivariable models for ER-positive patients; no score predicted BCSS in ER-negative patients. Incorporating these models into PREDICT had only a modest impact upon calibration (with absolute improvements of 0.2-0.8%), discrimination (with no statistically significant c-index improvements) and reclassification (with 4-10% of patients being reclassified). CONCLUSION: Addition of GRSs to PREDICT had limited impact on model fit or treatment received. This analysis does not support widespread adoption of current GRSs based on our implementations of commercial products.

Copy number heterogeneity identifies ER+ breast cancer patients that do not benefit from adjuvant endocrine therapy.

BACKGROUND: Endocrine therapy forms the backbone of adjuvant treatment for oestrogen-receptor-positive (ER+) breast cancer. However, it remains unclear whether adjuvant treatment improves survival rates in low-risk patients. Low intra-tumour heterogeneity (ITH) has been shown to confer low risk for recurrent disease. Here, it is studied if chromosomal copy-number ITH (CNH) can identify low-risk ER+, lymph-node-negative breast cancer patients who do not benefit from adjuvant endocrine therapy. METHODS: Lymph-node-negative ER+ patients from the observational METABRIC dataset were retrospectively analysed (n = 708). CNH was determined from a single bulk copy-number measurement for each patient. Survival rates were compared between patients that did or did not receive adjuvant endocrine therapy for CNH-low, middle and high groups with Cox proportional-hazards models, using propensity-score weights to correct for confounders. RESULTS: Adjuvant endocrine therapy improved the relapse-free survival (RFS) for CNH-high patients treatment (HR = 0.55), but not for CNH-low patients treatment (HR = 0.88). For CNH-low patients adjuvant endocrine therapy was associated with impaired OS (HR = 1.62). CONCLUSIONS: This retrospective study of lymph-node-negative, ER+ breast cancer finds that patients identified as low risk using CNH do not benefit from adjuvant endocrine therapy.

Breast tumor microenvironment structures are associated with genomic features and clinical outcome.

The functions of the tumor microenvironment (TME) are orchestrated by precise spatial organization of specialized cells, yet little is known about the multicellular structures that form within the TME. Here we systematically mapped TME structures in situ using imaging mass cytometry and multitiered spatial analysis of 693 breast tumors linked to genomic and clinical data. We identified ten recurrent TME structures that varied by vascular content, stromal quiescence versus activation, and leukocyte composition. These TME structures had distinct enrichment patterns among breast cancer subtypes, and some were associated with genomic profiles indicative of immune escape. Regulatory and dysfunctional T cells co-occurred in large 'suppressed expansion' structures. These structures were characterized by high cellular diversity, proliferating cells and enrichment for BRCA1 and CASP8 mutations and predicted poor outcome in estrogen-receptor-positive disease. The multicellular structures revealed here link conserved spatial organization to local TME function and could improve patient stratification.

Nucleoporin-93 reveals a common feature of aggressive breast cancers: robust nucleocytoplasmic transport of transcription factors.

By establishing multi-omics pipelines, we uncover overexpression and gene copy-number alterations of nucleoporin-93 (NUP93), a nuclear pore component, in aggressive human mammary tumors. NUP93 overexpression enhances transendothelial migration and matrix invasion in vitro, along with tumor growth and metastasis in animal models. These findings are supported by analyses of two sets of naturally occurring mutations: rare oncogenic mutations and inactivating familial nephrotic syndrome mutations. Mechanistically, NUP93 binds with importins, boosts nuclear transport of importins' cargoes, such as ß-catenin, and activates MYC. Likewise, NUP93 overexpression enhances the ultimate nuclear transport step shared by additional signaling pathways, including TGF-ß/SMAD and EGF/ERK. The emerging addiction to nuclear transport exposes vulnerabilities of NUP93-overexpressing tumors. Congruently, myristoylated peptides corresponding to the nuclear translocation signals of SMAD and ERK can inhibit tumor growth and metastasis. Our study sheds light on an emerging hallmark of advanced tumors, which derive benefit from robust nucleocytoplasmic transport.

The Cancer of the Bladder Risk Assessment (COBRA) score accurately predicts cancer-specific survival after radical cystectomy: external validation and lymphovascular invasion assessment value to improve its performance.

The Cancer of the Bladder Risk Assessment (COBRA) score is a predictive tool for estimating Cancer Specific Survival (CSS) after Radical Cystectomy (RC) for urothelial carcinoma. COBRA score variables are: age at RC, Tumor stage and Lymph Node Density (LND). We sought to externally validate the COBRA score and to improve its performance in estimating CSS adding Lymphovascular Invasion (LVI) as a further variable (Modified COBRA score). Clinicopathological and survival data from 789 patients who underwent RC and Pelvic Lymph Node Dissection (PLND) between January 2007 and December 2020 in two European referral centers (Paris, France and Badalona, Spain) were analyzed. COBRA score was applied to our cohort and CSS Kaplan-Meier curves were performed. Univariable and Multivariable analysis was performed in order to identify risk factors for Cancer Specific Mortality (CSM) and a score was assigned for any statistically significant risk factor; afterward, c-index calculation was performed and CCS curves have been plotted for the model after having integrated LVI variable to the COBRA score. Finally, we compared both COBRA score and Modified COBRA score models with the established AJCC model. A total of 789 patients underwent RC during the observation period. Complete data were available for 731 patients with a median follow-up of 32 months (8-47). CSM was 27.6% (no. 218 patients) at follow-up. When COBRA score was applied to our cohort, c-index was 0.76. Regression COX analysis has shown HR 0.36, CI 95% (0.16-0.83), P = .016 for patients with COBRA score 1; HR 0, CI 95% (0-1.77), P =.94 for score 2; HR 0.51, CI 95% (0.39 -0.67), P =.001 for score 3; HR 1.67, CI 95% (1.23-2.27), P =.001 for score 4; HR 2.45, CI 95% (1.51-3.99), P =.001 for score 5; HR 2.01, CI 95% (1.42-2.85), P =.001 for score 6 and HR 0.66, CI 95% (0.09-4.73), P =.682 for score 7. When the LVI variable was added to the CSS predictive model the discriminatory power increased to a c-index of 0.78. COBRA score adequately identifies those patients with a higher risk of CSM, with a c-index of 0.76. Moreover, LVI variable further improves its predictive accuracy from c-index of 0.76 to c-index of 0.78. LVI variable could be integrated in the COBRA score to optimizing prognosis stratification for patients who undergo RC.

Multi-omic machine learning predictor of breast cancer therapy response.

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment1. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy2. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.

Positive correlation between transcriptomic stemness and PI3K/AKT/mTOR signaling scores in breast cancer, and a counterintuitive relationship with PIK3CA genotype.

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.

Hyperpolarized Carbon-13 MRI for Early Response Assessment of Neoadjuvant Chemotherapy in Breast Cancer Patients.

Hyperpolarized 13C-MRI is an emerging tool for probing tissue metabolism by measuring 13C-label exchange between intravenously injected hyperpolarized [1-13C]pyruvate and endogenous tissue lactate. Here, we demonstrate that hyperpolarized 13C-MRI can be used to detect early response to neoadjuvant therapy in breast cancer. Seven patients underwent multiparametric 1H-MRI and hyperpolarized 13C-MRI before and 7-11 days after commencing treatment. An increase in the lactate-to-pyruvate ratio of approximately 20% identified three patients who, following 5-6 cycles of treatment, showed pathological complete response. This ratio correlated with gene expression of the pyruvate transporter MCT1 and lactate dehydrogenase A (LDHA), the enzyme catalyzing label exchange between pyruvate and lactate. Analysis of approximately 2,000 breast tumors showed that overexpression of LDHA and the hypoxia marker CAIX was associated with reduced relapse-free and overall survival. Hyperpolarized 13C-MRI represents a promising method for monitoring very early treatment response in breast cancer and has demonstrated prognostic potential. SIGNIFICANCE: Hyperpolarized carbon-13 MRI allows response assessment in patients with breast cancer after 7-11 days of neoadjuvant chemotherapy and outperformed state-of-the-art and research quantitative proton MRI techniques.

Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain).

This paper analyses COVID-19 patients' dynamics during the first wave in the region of Castilla y León (Spain) with around 2.4 million inhabitants using multi-state competing risk survival models. From the date registered as the start of the clinical process, it is assumed that a patient can progress through three intermediate states until reaching an absorbing state of recovery or death. Demographic characteristics, epidemiological factors such as the time of infection and previous vaccinations, clinical history, complications during the course of the disease and drug therapy for hospitalised patients are considered as candidate predictors. Regarding risk factors associated with mortality and severity, consistent results with many other studies have been found, such as older age, being male, and chronic diseases. Specifically, the hospitalisation (death) rate for those over 69 is 27.2% (19.8%) versus 5.3% (0.7%) for those under 70, and for males is 14.5%(7%) versus 8.3%(4.6%)for females. Among patients with chronic diseases the highest rates of hospitalisation are 26.1% for diabetes and 26.3% for kidney disease, while the highest death rate is 21.9% for cerebrovascular disease. Moreover, specific predictors for different transitions are given, and estimates of the probability of recovery and death for each patient are provided by the model. Some interesting results obtained are that for patients infected at the end of the period the hazard of transition from hospitalisation to ICU is significatively lower (p < 0.001) and the hazard of transition from hospitalisation to recovery is higher (p < 0.001). For patients previously vaccinated against pneumococcus the hazard of transition to recovery is higher (p < 0.001). Finally, internal validation and calibration of the model are also performed.

DNA methylation landscapes of 1538 breast cancers reveal a replication-linked clock, epigenomic instability and cis-regulation.

DNA methylation is aberrant in cancer, but the dynamics, regulatory role and clinical implications of such epigenetic changes are still poorly understood. Here, reduced representation bisulfite sequencing (RRBS) profiles of 1538 breast tumors and 244 normal breast tissues from the METABRIC cohort are reported, facilitating detailed analysis of DNA methylation within a rich context of genomic, transcriptional, and clinical data. Tumor methylation from immune and stromal signatures are deconvoluted leading to the discovery of a tumor replication-linked clock with genome-wide methylation loss in non-CpG island sites. Unexpectedly, methylation in most tumor CpG islands follows two replication-independent processes of gain (MG) or loss (ML) that we term epigenomic instability. Epigenomic instability is correlated with tumor grade and stage, TP53 mutations and poorer prognosis. After controlling for these global trans-acting trends, as well as for X-linked dosage compensation effects, cis-specific methylation and expression correlations are uncovered at hundreds of promoters and over a thousand distal elements. Some of these targeted known tumor suppressors and oncogenes. In conclusion, this study demonstrates that global epigenetic instability can erode cancer methylomes and expose them to localized methylation aberrations in-cis resulting in transcriptional changes seen in tumors.

TSHZ2 is an EGF-regulated tumor suppressor that binds to the cytokinesis regulator PRC1 and inhibits metastasis.

Unlike early transcriptional responses to mitogens, later events are less well-characterized. Here, we identified delayed down-regulated genes (DDGs) in mammary cells after prolonged treatment with epidermal growth factor (EGF). The expression of these DDGs was low in mammary tumors and correlated with prognosis. The proteins encoded by several DDGs directly bind to and inactivate oncoproteins and might therefore act as tumor suppressors. The transcription factor teashirt zinc finger homeobox 2 (TSHZ2) is encoded by a DDG, and we found that overexpression of TSHZ2 inhibited tumor growth and metastasis and accelerated mammary gland development in mice. Although the gene TSHZ2 localizes to a locus (20q13.2) that is frequently amplified in breast cancer, we found that hypermethylation of its promoter correlated with down-regulation of TSHZ2 expression in patients. Yeast two-hybrid screens and protein-fragment complementation assays in mammalian cells indicated that TSHZ2 nucleated a multiprotein complex containing PRC1/Ase1, cyclin B1, and additional proteins that regulate cytokinesis. TSHZ2 increased the inhibitory phosphorylation of PRC1, a key driver of mitosis, mediated by cyclin-dependent kinases. Furthermore, similar to the tumor suppressive transcription factor p53, TSHZ2 inhibited transcription from the PRC1 promoter. By recognizing DDGs as a distinct group in the transcriptional response to EGF, our findings uncover a group of tumor suppressors and reveal a role for TSHZ2 in cell cycle regulation.

Deciphering the signaling network of breast cancer improves drug sensitivity prediction.

One goal of precision medicine is to tailor effective treatments to patients' specific molecular markers of disease. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data-on more than 80 million single cells from 4,000 conditions-were used to fit mechanistic signaling network models that provide insight into how cancer cells process information. Our dynamic single-cell-based models accurately predicted drug sensitivity and identified genomic features associated with drug sensitivity, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. We observed similar trends in genotype-drug sensitivity associations in patient-derived xenograft mouse models. This work provides proof of principle that patient-specific single-cell measurements and modeling could inform effective precision medicine strategies.

Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response.

The heterogeneity of breast cancer plays a major role in drug response and resistance and has been extensively characterized at the genomic level. Here, a single-cell breast cancer mass cytometry (BCMC) panel is optimized to identify cell phenotypes and their oncogenic signalling states in a biobank of patient-derived tumour xenograft (PDTX) models representing the diversity of human breast cancer. The BCMC panel identifies 13 cellular phenotypes (11 human and 2 murine), associated with both breast cancer subtypes and specific genomic features. Pre-treatment cellular phenotypic composition is a determinant of response to anticancer therapies. Single-cell profiling also reveals drug-induced cellular phenotypic dynamics, unravelling previously unnoticed intra-tumour response diversity. The comprehensive view of the landscapes of cellular phenotypic heterogeneity in PDTXs uncovered by the BCMC panel, which is mirrored in primary human tumours, has profound implications for understanding and predicting therapy response and resistance.