Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures

Germline polygenic risk scores are associated with immune gene expression signature and immune cell infiltration in breast cancer

The tumor immune microenvironment (TIME) plays key roles in tumor progression and response to immunotherapy. Previous studies have identified individual germline variants associated with differences in TIME. Here, we hypothesize that common variants associated with breast cancer risk or cancer-related traits, represented by polygenic risk scores (PRSs), may jointly influence immune features in TIME. We derived 154 immune traits from bulk gene expression profiles of 764 breast tumors and 598 adjacent normal tissue samples from 825 individuals with breast cancer in the Nurses' Health Study (NHS) and NHSII. Immunohistochemical staining of four immune cell markers were available for a subset of 205 individuals. Germline PRSs were calculated for 16 different traits including breast cancer, autoimmune diseases, type 2 diabetes, ages at menarche and menopause, body mass index (BMI), BMI-adjusted waist-to-hip ratio, alcohol intake, and tobacco smoking. Overall, we identified 44 associations between germline PRSs and immune traits at false discovery rate q < 0.25, including 3 associations with q < 0.05. We observed consistent inverse associations of inflammatory bowel disease (IBD) and Crohn disease (CD) PRSs with interferon signaling and STAT1 scores in breast tumor and adjacent normal tissue; these associations were replicated in a Norwegian cohort. Inverse associations were also consistently observed for IBD PRS and B cell abundance in normal tissue. We also observed positive associations between CD PRS and endothelial cell abundance in tumor. Our findings suggest that the genetic mechanisms that influence immune-related diseases are also associated with TIME in breast cancer.

The Immunogenomic Landscape of Peripheral High-Dose IL-2 Pharmacodynamics in Patients with Metastatic Renal Cell Carcinoma: A Benchmark for Next-Generation IL-2-Based Immunotherapies

High-dose (HD) IL-2 was the first immuno-oncology agent approved for treating advanced renal cell carcinoma and metastatic melanoma, but its use was limited because of substantial toxicities. Multiple next-generation IL-2 agents are being developed to improve tolerability. However, a knowledge gap still exists for the genomic markers that define the target pharmacology for HD IL-2 itself. In this retrospective observational study, we collected PBMC samples from 23 patients with metastatic renal cell carcinoma who were treated with HD IL-2 between 2009 and 2015. We previously reported the results of flow cytometry analyses. In this study, we report the results of our RNA-sequencing immunogenomic survey, which was performed on bulk PBMC samples from immediately before (day 1), during (day 3), and after treatment (day 5) in cycle 1 and/or cycle 2 of the first course of HD IL-2. As part of a detailed analysis of immunogenomic response to HD IL-2 treatment, we analyzed the changes in individual genes and immune gene signatures. By day 3, most lymphoid cell types had transiently decreased, whereas myeloid transcripts increased. Although most genes and/or signatures generally returned to pretreatment expression levels by day 5, certain ones representative of B cell, NK cell, and T cell proliferation and effector functions continued to increase, along with B cell (but not T cell) oligoclonal expansion. Regulatory T cells progressively expanded during and after treatment. They showed strong negative correlation with myeloid effector cells. This detailed RNA-sequencing immunogenomic survey of IL-2 pharmacology complements results of prior flow cytometry analyses. These data provide valuable pharmacological context for assessing PBMC gene expression data from patients dosed with IL-2-related compounds that are currently in development.

The VEGF-Hypoxia Signature Is Upregulated in Basal-like Breast Tumors from Women of African Ancestry and Associated with Poor Outcomes in Breast Cancer

PURPOSE

Black women experience the highest breast cancer mortality rate compared with women of other racial/ethnic groups. To gain a deeper understanding of breast cancer heterogeneity across diverse populations, we examined a VEGF-hypoxia gene expression signature in breast tumors from women of diverse ancestry.

EXPERIMENTAL DESIGN

We developed a NanoString nCounter gene expression panel and applied it to breast tumors from Nigeria (n = 182) and the University of Chicago (Chicago, IL; n = 161). We also analyzed RNA sequencing data from Nigeria (n = 84) and The Cancer Genome Atlas (TCGA) datasets (n = 863). Patient prognosis was analyzed using multiple datasets.

RESULTS

The VEGF-hypoxia signature was highest in the basal-like subtype compared with other subtypes, with greater expression in Black women compared with White women. In TCGA dataset, necrotic breast tumors had higher scores for the VEGF-hypoxia signature compared with non-necrosis tumors (P < 0.001), with the highest proportion in the basal-like subtype. Furthermore, necrotic breast tumors have higher scores for the proliferation signature, suggesting an interaction between the VEGF-hypoxia signature, proliferation, and necrosis. T-cell gene expression signatures also correlated with the VEGF-hypoxia signature when testing all tumors in TCGA dataset. Finally, we found a significant association of the VEGF-hypoxia profile with poor outcomes when using all patients in the METABRIC (P < 0.0001) and SCAN-B datasets (P = 0.002).

CONCLUSIONS

These data provide further evidence for breast cancer heterogeneity across diverse populations and molecular subtypes. Interventions selectively targeting VEGF-hypoxia and the immune microenvironment have the potential to improve overall survival in aggressive breast cancers that disproportionately impact Black women in the African Diaspora.

A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma

An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8+ T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8+ T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC.

A 14-gene B-cell immune signature in early-stage triple-negative breast cancer (TNBC): a pooled analysis of seven studies

BACKGROUND

Early-stage triple-negative breast cancer (TNBC) displays clinical and biological diversity. From a biological standpoint, immune infiltration plays a crucial role in TNBC prognosis. Currently, there is a lack of genomic tools aiding in treatment decisions for TNBC. This study aims to assess the effectiveness of a B-cell/immunoglobulin signature (IGG) alone, or in combination with tumor burden, in predicting prognosis and treatment response in patients with TNBC.

METHODS

Genomic and clinical data were retrieved from 7 cohorts: SCAN-B (N = 874), BrighTNess (n = 482), CALGB-40603 (n = 389), METABRIC (n = 267), TCGA (n = 118), GSE58812 (n = 107), GSE21653 (n = 67). IGG and a risk score integrating IGG with tumor/nodal staging (IGG-Clin) were assessed for event-free survival (EFS) and overall survival (OS) in each cohort. Random effects model was used to derive pooled effect sizes. Association of IGG with pathological complete response (pCR) was assessed in CALGB-40603 and BrighTNess. Immune significance of IGG was estimated through CIBERSORTx and EcoTyper.

FINDINGS

IGG was associated with improved EFS (pooled HR = 0.77, [95% CI = 0.70-0.85], I2 = 18%) and OS (pooled HR = 0.79, [0.73-0.85], I2 = 0%) across cohorts, and was predictive of pCR in CALGB-40603 (OR 1.25, [1.10-1.50]) and BrighTNess (OR 1.57 [1.25-1.98]). IGG-Clin was predictive of recurrence (pooled HR = 2.11, [1.75-2.55], I2 = 0%) and death (pooled HR = 1.99, 95% [0.84-4.73], I2 = 79%) across cohorts. IGG was associated with adaptive immune response at CIBERSORTx and EcoTyper analysis.

INTERPRETATION

IGG is linked to improved prognosis and pCR in early-stage TNBC. The integration of IGG alongside tumor and nodal staging holds promise as an approach to identify patients benefitting from intensified or de-intensified treatments.

FUNDING

This study received funding from: Associació Beca Marta Santamaria, European Union's Horizon 2020 research and innovation and Marie Skłodowska-Curie Actions programs, Fundación FERO, Fundación CRIS contra el cáncer, Agència de Gestó d'Ajuts Universitaris i de Recerca, Instituto de Salud Carlos III, Fundación Contigo, Asociación Cáncer de Mama Metastásico IV, Breast Cancer Research Foundation, RESCUER, Fundación científica AECC and FSEOM.

Generative Adversarial Networks Accurately Reconstruct Pan-Cancer Histology from Pathologic, Genomic, and Radiographic Latent Features

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of novel molecular features. These approaches distill cancer histologic images into high-level features which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network - HistoXGAN - capable of reconstructing representative histology using feature vectors produced by common feature extractors. We evaluate HistoXGAN across 29 cancer subtypes and demonstrate that reconstructed images retain information regarding tumor grade, histologic subtype, and gene expression patterns. We leverage HistoXGAN to illustrate the underlying histologic features for deep learning models for actionable mutations, identify model reliance on histologic batch effect in predictions, and demonstrate accurate reconstruction of tumor histology from radiographic imaging for a 'virtual biopsy'.

Cell-cycle inhibition and immune microenvironment in breast cancer treated with ribociclib and letrozole or chemotherapy

In this study, we performed genomic analyses of cell cycle and tumor microenvironment changes during and after ribociclib and letrozole or chemotherapy in the CORALLEEN trial. 106 women with untreated PAM50-defined Luminal B early breast cancers were randomly assigned to receive neoadjuvant ribociclib and letrozole or standard-of-care chemotherapy. Ki67 immunohistochemistry, tumor-infiltrating lymphocytes quantification, and RNA sequencing were obtained from tissue biopsies pre-treatment, on day 14 of treatment, and tumor specimens from surgical resection. Results showed that at surgery, Ki67 and the PAM50 proliferation scores were lower after ribociclib compared to chemotherapy. However, consistent reactivation of tumor cell proliferation from day 14 to surgery was only observed in the ribociclib arm. In tumors with complete cell cycle arrest (CCCA) at surgery, PAM50 proliferation scores were lower in the ribociclib arm compared to chemotherapy (p < 0.001), whereas the opposite was observed with tumor cellularity (p = 0.002). Gene expression signatures (GES) associated with antigen-presenting cells (APCs) and innate immune system activity showed increased expression post-chemotherapy but decreased expression post-ribociclib. Interferon-associated GES had decreased expression with CCCA and increased expression with non-CCCA. Our findings suggest that while both treatment strategies decreased proliferation, the depth and the patterns over time differed by treatment arm. Immunologically, ribociclib was associated with downregulated GES associated with APCs and the innate immune system in Luminal B tumors, contrary to existing preclinical data. Further studies are needed to understand the effect of CDK4/6 inhibition on the tumor cells and microenvironment, an effect which may vary according to tumor subtypes.

Gene-Expression Profiling to Decipher Breast Cancer Inter- and Intratumor Heterogeneity

Breast cancer is heterogeneous and differs substantially across different tumors (intertumor heterogeneity) and even within an individual tumor (intratumor heterogeneity). Gene-expression profiling has considerably impacted our understanding of breast cancer biology. Four main "intrinsic subtypes" of breast cancer (i.e., luminal A, luminal B, HER2-enriched, and basal-like) have been consistently identified by gene expression, showing significant prognostic and predictive value in multiple clinical scenarios. Thanks to the molecular profiling of breast tumors, breast cancer is a paradigm of treatment personalization. Several standardized prognostic gene-expression assays are presently being used in the clinic to guide treatment decisions. Moreover, the development of single-cell-level resolution molecular profiling has allowed us to appreciate that breast cancer is also heterogeneous within a single tumor. There is an evident functional heterogeneity within the neoplastic and tumor microenvironment cells. Finally, emerging insights from these studies suggest a substantial cellular organization of neoplastic and tumor microenvironment cells, thus defining breast cancer ecosystems and highlighting the importance of spatial localizations.

Tamoxifen Response at Single-Cell Resolution in Estrogen Receptor-Positive Primary Human Breast Tumors

PURPOSE

In estrogen receptor-positive (ER+)/HER2- breast cancer, multiple measures of intratumor heterogeneity are associated with a worse response to endocrine therapy. We sought to develop a novel experimental model to measure heterogeneity in response to tamoxifen treatment in primary breast tumors.

EXPERIMENTAL DESIGN

To investigate heterogeneity in response to treatment, we developed an operating room-to-laboratory pipeline for the collection of live normal breast specimens and human tumors immediately after surgical resection for processing into single-cell workflows for experimentation and genomic analyses. Live primary cell suspensions were treated ex vivo with tamoxifen (10 μmol/L) or control media for 12 hours, and single-cell RNA libraries were generated using the 10X Genomics droplet-based kit.

RESULTS

In total, we obtained and processed normal breast tissue from two women undergoing reduction mammoplasty and tumor tissue from 10 women with ER+/HER2- invasive breast carcinoma. We demonstrate differences in tamoxifen response by cell type and identify distinctly responsive and resistant subpopulations within the malignant cell compartment of human tumors. Tamoxifen resistance signatures from resistant subpopulations predict poor outcomes in two large cohorts of ER+ breast cancer patients and are enriched in endocrine therapy-resistant tumors.

CONCLUSIONS

This novel ex vivo model system now provides the foundation to define responsive and resistant subpopulations within heterogeneous human tumors, which can be used to develop precise single cell-based predictors of response to therapy and to identify genes and pathways driving therapeutic resistance.

Immunological and clinicopathological features predict HER2-positive breast cancer prognosis in the neoadjuvant NeoALTTO and CALGB 40601 randomized trials

The identification of prognostic markers in patients receiving neoadjuvant therapy is crucial for treatment optimization in HER2-positive breast cancer, with the immune microenvironment being a key factor. Here, we investigate the complexity of B and T cell receptor (BCR and TCR) repertoires in the context of two phase III trials, NeoALTTO and CALGB 40601, evaluating neoadjuvant paclitaxel with trastuzumab and/or lapatinib in women with HER2-positive breast cancer. BCR features, particularly the number of reads and clones, evenness and Gini index, are heterogeneous according to hormone receptor status and PAM50 subtypes. Moreover, BCR measures describing clonal expansion, namely evenness and Gini index, are independent prognostic factors. We present a model developed in NeoALTTO and validated in CALGB 40601 that can predict event-free survival (EFS) by integrating hormone receptor and clinical nodal status, breast pathological complete response (pCR), stromal tumor-infiltrating lymphocyte levels (%) and BCR repertoire evenness. A prognostic score derived from the model and including those variables, HER2-EveNT, allows the identification of patients with 5-year EFS > 90%, and, in those not achieving pCR, of a subgroup of immune-enriched tumors with an excellent outcome despite residual disease.

A comprehensive prognostic score for head and neck squamous cancer driver genes and phenotype traits

BACKGROUND

Head and neck squamous cancer (HNSCC) presents variable phenotype and progression features. Clinically applicable, high-accuracy multifactorial prognostic models for HNSCC survival outcomes are warranted and an active area of research. This study aimed to construct a comprehensive prognostic tool for HNSCC overall survival by integrating cancer driver genes with tumor clinical and phenotype information.

METHODS

Key overall survival-related cancer driver genes were screened from among main effector and reciprocal gene pairs using TCGA data using univariate Cox proportional hazard regression analysis. Independent validation was performed using the GSE41613 dataset. The main effector genes among these were selected using LASSO regression and transcriptome score modeling was performed using multivariate Cox regression followed by validation analysis of the prognostic score. Next, multivariate Cox regression analysis was performed using the transcriptome score combined with age, grade, gender, and stage. An 'Accurate Prediction Model of HNSCC Overall Survival Score' (APMHO) was computed and validated. Enriched functional pathways, gene mutational landscape, immune cell infiltration, and immunotherapy sensitivity markers associated with high and low APMHO scores were analyzed.

RESULTS

Screening 107 overall survival-related cancer genes and 402 interacting gene pairs, 6 genes: CRLF2, HSP90AA1, MAP2K1, PAFAH1B2, MYCL and SET genes, were identified and a transcriptional score was obtained. Age, stage and transcriptional score were found to be significant predictors in Cox regression analysis and used to construct a final APMHO model showing an AUC > 0.65 and validated. Transcriptional score, age, pathologic_N, pathologic_T, stage, and TCGA_subtype were significantly different in distribution between high and low APMHO groups. High APMHO samples showed significantly higher mutation rate, enriched tumor-related pathways including Hypoxia, unfold_protein_response, Glycolysis, and mTORC1 signaling, along with differences in immune cell infiltration and immune checkpoint, interferon-γ pathway and m6A regulator expression patterns.

CONCLUSION

The APMHO score combining transcriptional and clinical variables showed good prognostic ability for HNSCC overall survival outcomes and was associated with different patterns of phenotypical features, immune and mutational landscape, and immunotherapy sensitivity marker expression. Future studies should validate this score in independent clinical cohorts.

B-cells and regulatory T-cells in the microenvironment of HER2+ breast cancer are associated with decreased survival: a real-world analysis of women with HER2+ metastatic breast cancer

BACKGROUND

Despite major improvements in treatment of HER2-positive metastatic breast cancer (MBC), only few patients achieve complete remission and remain progression free for a prolonged time. The tumor immune microenvironment plays an important role in the response to treatment in HER2-positive breast cancer and could contain valuable prognostic information. Detailed information on the cancer-immune cell interactions in HER2-positive MBC is however still lacking. By characterizing the tumor immune microenvironment in patients with HER2-positive MBC, we aimed to get a better understanding why overall survival (OS) differs so widely and which alternative treatment approaches may improve outcome.

METHODS

We included all patients with HER2-positive MBC who were treated with trastuzumab-based palliative therapy in the Netherlands Cancer Institute between 2000 and 2014 and for whom pre-treatment tissue from the primary tumor or from metastases was available. Infiltrating immune cells and their spatial relationships to one another and to tumor cells were characterized by immunohistochemistry and multiplex immunofluorescence. We also evaluated immune signatures and other key pathways using next-generation RNA-sequencing data. With nine years median follow-up from initial diagnosis of MBC, we investigated the association between tumor and immune characteristics and outcome.

RESULTS

A total of 124 patients with 147 samples were included and evaluated. The different technologies showed high correlations between each other. T-cells were less prevalent in metastases compared to primary tumors, whereas B-cells and regulatory T-cells (Tregs) were comparable between primary tumors and metastases. Stromal tumor-infiltrating lymphocytes in general were not associated with OS. The infiltration of B-cells and Tregs in the primary tumor was associated with unfavorable OS. Four signatures classifying the extracellular matrix of primary tumors showed differential survival in the population as a whole.

CONCLUSIONS

In a real-world cohort of 124 patients with HER2-positive MBC, B-cells, and Tregs in primary tumors are associated with unfavorable survival. With this paper, we provide a comprehensive insight in the tumor immune microenvironment that could guide further research into development of novel immunomodulatory strategies.

HCK Promotes High-Grade Serous Ovarian Cancer Tumorigenesis through CD44 and NOTCH3 Signaling

High-grade serous ovarian cancer (HGSOC) is a highly aggressive and lethal subtype of ovarian cancer. While most patients initially respond to standard-of-care treatment, the majority will eventually relapse and succumb to their disease. Despite significant advances in our understanding of this disease, the mechanisms that govern the distinctions between HGSOC with good and poor prognosis remain unclear. In this study, we implemented a proteogenomic approach to analyze gene expression, proteomic and phosphoproteomic profiles of HGSOC tumor samples to identify molecular pathways that distinguish HGSOC tumors relative to clinical outcome. Our analyses identify significant upregulation of hematopoietic cell kinase (HCK) expression and signaling in poor prognostic HGSOC patient samples. Analyses of independent gene expression datasets and IHC of patient samples confirmed increased HCK signaling in tumors relative to normal fallopian or ovarian samples and demonstrated aberrant expression in tumor epithelial cells. Consistent with the association between HCK expression and tumor aggressiveness in patient samples, in vitro phenotypic studies showed that HCK can, in part, promote cell proliferation, colony formation, and invasive capacity of cell lines. Mechanistically, HCK mediates these phenotypes, partly through CD44 and NOTCH3-dependent signaling, and inhibiting CD44 or NOTCH3 activity, either genetically or through gamma-secretase inhibitors, can revert HCK-driven phenotypes.

IMPLICATIONS

Collectively, these studies establish that HCK acts as an oncogenic driver of HGSOC through aberrant activation of CD44 and NOTCH3 signaling and identifies this network as a potential therapeutic opportunity in a subset of patients with aggressive and recurrent HGSOC.

Integrated Multimodal Analyses of DNA Damage Response and Immune Markers as Predictors of Response in Metastatic Triple-Negative Breast Cancer in the TNT Trial (NCT00532727)

PURPOSE

The TNT trial (NCT00532727) showed no evidence of carboplatin superiority over docetaxel in metastatic triple-negative breast cancer (mTNBC), but carboplatin benefit was observed in the germline BRCA1/2 mutation subgroup. Broader response-predictive biomarkers are needed. We explored the predictive ability of DNA damage response (DDR) and immune markers.

EXPERIMENTAL DESIGN

Tumor-infiltrating lymphocytes were evaluated for 222 of 376 patients. Primary tumors (PT) from 186 TNT participants (13 matched recurrences) were profiled using total RNA sequencing. Four transcriptional DDR-related and 25 immune-related signatures were evaluated. We assessed their association with objective response rate (ORR) and progression-free survival (PFS). Conditional inference forest clustering was applied to integrate multimodal data. The biology of subgroups was characterized by 693 gene expression modules and other markers.

RESULTS

Transcriptional DDR-related biomarkers were not predictive of ORR to either treatment overall. Changes from PT to recurrence were demonstrated; in chemotherapy-naïve patients, transcriptional DDR markers separated carboplatin responders from nonresponders (P values = 0.017; 0.046). High immune infiltration was associated with docetaxel ORR (interaction P values < 0.05). Six subgroups were identified; the immune-enriched cluster had preferential docetaxel response [62.5% (D) vs. 29.4% (C); P = 0.016]. The immune-depleted cluster had preferential carboplatin response [8.0% (D) vs. 40.0% (C); P = 0.011]. DDR-related subgroups were too small to assess ORR.

CONCLUSIONS

High immune features predict docetaxel response, and high DDR signature scores predict carboplatin response in treatment-naïve mTNBC. Integrating multimodal DDR and immune-related markers identifies subgroups with differential treatment sensitivity. Treatment options for patients with immune-low and DDR-proficient tumors remains an outstanding need. Caution is needed using PT-derived transcriptional signatures to direct treatment in mTNBC, particularly DDR-related markers following prior chemotherapy.

SOX4-SMARCA4 complex promotes glycolysis-dependent TNBC cell growth through transcriptional regulation of Hexokinase 2

Tumor cells rely on increased glycolytic capacity to promote cell growth and progression. While glycolysis is known to be upregulated in the majority of triple negative (TNBC) or basal-like subtype breast cancers, the mechanism remains unclear. Here, we used integrative genomic analyses to identify a subset of basal-like tumors characterized by increased expression of the oncogenic transcription factor SOX4 and its co-factor the SWI/SNF ATPase SMARCA4. These tumors are defined by unique gene expression programs that correspond with increased tumor proliferation and activation of key metabolic pathways, including glycolysis. Mechanistically, we demonstrate that the SOX4-SMARCA4 complex mediates glycolysis through direct transcriptional regulation of Hexokinase 2 (HK2) and that aberrant HK2 expression and altered glycolytic capacity are required to mediate SOX4-SMARCA4-dependent cell growth. Collectively, we have defined the SOX4-SMARCA4-HK2 signaling axis in basal-like breast tumors and established that this axis promotes metabolic reprogramming which is required to maintain tumor cell growth.

Unexpected Durable Complete Response With Anti-PD-L1 Blockade in Metastatic Undifferentiated Pleomorphic Sarcoma: A Case Report With Host and Tumor Biomarker Analysis

We extensively characterized a metastatic UPS with a 71-month lasting complete response to immunotherapy.

Stratification of Tamoxifen Synergistic Combinations for the Treatment of ER+ Breast Cancer

Breast cancer alone accounts for the majority of cancer deaths among women, with the most commonly diagnosed subtype being estrogen receptor positive (ER+). Survival has greatly improved for patients with ER+ breast cancer, due in part to the development of antiestrogen compounds, such as tamoxifen. While treatment of the primary disease is often successful, as many as 30% of patients will experience recurrence and metastasis, mainly due to developed endocrine therapy resistance. In this study, we discovered two tamoxifen combination therapies, with simeprevir and VX-680, that reduce the tumor burden in animal models of ER+ breast cancer more than either compound or tamoxifen alone. Additionally, these tamoxifen combinations reduced the expression of HER2, a hallmark of tamoxifen treatment, which can facilitate acquisition of a treatment-resistant phenotype. These combinations could provide clinical benefit by potentiating tamoxifen treatment in ER+ breast cancer.

Individualizing Curative-Intent Therapy in HER2-Positive Early-Stage Breast Cancer

OPINION STATEMENT

Human epidermal growth factor receptor 2-positive (HER2+) breast cancers have been historically considered an aggressive entity with high rates of recurrence and poor survival. However, during the last 20 years, there has been a dramatic change in prognosis due to the incorporation of different anti-HER2 therapies into the neo/adjuvant chemotherapy backbone. Neoadjuvant dual blockade with trastuzumab and pertuzumab has become the standard of care for women with stage II and III HER2+ breast cancer. Trastuzumab emtansine (T-DM1) has been shown to improve outcomes if pathological complete response (pCR) is not achieved, and adjuvant extended therapy with neratinib has increased disease-free survival (DFS) and may have an impact in central nervous system (CNS) recurrences. However, these agents are both toxic for individual patients and costly for the overall healthcare system, and there are still patients that experience recurrence despite therapy improvements. At the same time, it has been shown that some patients with early-stage HER2+ breast cancer can be effectively treated with less intensive systemic therapy, using only taxane and trastuzumab, or that the chemotherapy backbone can be omitted completely. The current challenge is to properly identify which patients can receive a de-intensified regimen and which need new intensification strategies. Tumor size, nodal status, and pCR achievement after neoadjuvant treatment are well-known risk factors that can aid in making clinical decisions, but they do not accurately predict all patient outcomes. Various biomarkers have been proposed to better characterize the clinical and biological heterogeneity of HER2+ breast cancer. Immune infiltration, intrinsic subtype, intratumoral heterogeneity, and dynamic changes during treatment have been described as important prognostic and/or predictive features. The integration of all these factors will be key in the proper identification of the true risk, and individualized treatment strategy, for each patient.

Tamoxifen Response at Single Cell Resolution in Estrogen Receptor-Positive Primary Human Breast Tumors

In ER+/HER2- breast cancer, multiple measures of intra-tumor heterogeneity are associated with worse response to endocrine therapy. To investigate heterogeneity in response to treatment, we developed an operating room-to-laboratory pipeline for the collection of live human tumors and normal breast specimens immediately after surgical resection for processing into single-cell workflows for experimentation and genomic analyses. We demonstrate differences in tamoxifen response by cell type and identify distinctly responsive and resistant subpopulations within the malignant cell compartment of human tumors. Tamoxifen resistance signatures from 3 distinct resistant subpopulations are prognostic in large cohorts of ER+ breast cancer patients and enriched in endocrine therapy resistant tumors. This novel ex vivo model system now provides a foundation to define responsive and resistant sub-populations within heterogeneous tumors, to develop precise single cell-based predictors of response to therapy, and to identify genes and pathways driving resistance to therapy.

Prognostic and Predictive Value of Immune-Related Gene Expression Signatures vs Tumor-Infiltrating Lymphocytes in Early-Stage ERBB2/HER2-Positive Breast Cancer: A Correlative Analysis of the CALGB 40601 and PAMELA Trials

Importance

Both tumor-infiltrating lymphocytes (TILs) assessment and immune-related gene expression signatures by RNA profiling predict higher pathologic complete response (pCR) and improved event-free survival (EFS) in patients with early-stage ERBB2/HER2-positive breast cancer. However, whether these 2 measures of immune activation provide similar or additive prognostic value is not known.

Objective

To examine the prognostic ability of TILs and immune-related gene expression signatures, alone and in combination, to predict pCR and EFS in patients with early-stage ERBB2/HER2-positive breast cancer treated in 2 clinical trials.

Design, Setting, and Participants

In this prognostic study, a correlative analysis was performed on the Cancer and Leukemia Group B (CALGB) 40601 trial and the PAMELA trial. In the CALGB 40601 trial, 305 patients were randomly assigned to weekly paclitaxel with trastuzumab, lapatinib, or both for 16 weeks. The primary end point was pCR, with a secondary end point of EFS. In the PAMELA trial, 151 patients received neoadjuvant treatment with trastuzumab and lapatinib for 18 weeks. The primary end point was the ability of the HER2-enriched subtype to predict pCR. The studies were conducted from October 2013 to November 2015 (PAMELA) and from December 2008 to February 2012 (CALGB 40601). Data analyses were performed from June 1, 2020, to January 1, 2022.

Main Outcomes and Measures

Immune-related gene expression profiling by RNA sequencing and TILs were assessed on 230 CALGB 40601 trial pretreatment tumors and 138 PAMELA trial pretreatment tumors. The association of these biomarkers with pCR (CALGB 40601 and PAMELA) and EFS (CALGB 40601) was studied by logistic regression and Cox analyses.

Results

The median age of the patients was 50 years (IQR, 42-50 years), and 305 (100%) were women. Of 202 immune signatures tested, 166 (82.2%) were significantly correlated with TILs. In both trials combined, TILs were significantly associated with pCR (odds ratio, 1.01; 95% CI, 1.01-1.02; P = .02). In addition to TILs, 36 immune signatures were significantly associated with higher pCR rates. Seven of these signatures outperformed TILs for predicting pCR, 6 of which were B-cell related. In a multivariable Cox model adjusted for clinicopathologic factors, including PAM50 intrinsic tumor subtype, the immunoglobulin G signature, but not TILs, was independently associated with EFS (immunoglobulin G signature-adjusted hazard ratio, 0.63; 95% CI, 0.42-0.93; P = .02; TIL-adjusted hazard ratio, 1.00; 95% CI, 0.98-1.02; P = .99).

Conclusions and Relevance

Results of this study suggest that multiple B-cell-related signatures were more strongly associated with pCR and EFS than TILs, which largely represent T cells. When both TILs and gene expression are available, the prognostic value of immune-related signatures appears to be superior.

Tumor immune contexture predicts recurrence after prostatectomy and efficacy of androgen deprivation and immunotherapy in prostate cancer

BACKGROUND

Prostate cancer is one of the most common cancers in men with notable interpatient heterogeneity. Implications of the immune microenvironment in predicting the biochemical recurrence-free survival (BCRFS) after radical prostatectomy and the efficacy of systemic therapies in prostate cancer remain ambiguous.

METHODS

The tumor immune contexture score (TICS) involving eight immune contexture-related signatures was developed using seven cohorts of 1120 patients treated with radical prostatectomy (training: GSE46602, GSE54460, GSE70769, and GSE94767; validation: GSE70768, DKFZ2018, and TCGA). The association between the TICS and treatment efficacy was investigated in GSE111177 (androgen deprivation therapy [ADT]) and EGAS00001004050 (ipilimumab).

RESULTS

A high TICS was associated with prolonged BCRFS after radical prostatectomy in the training (HR = 0.32, 95% CI 0.24-0.45, P < 0.001) and the validation cohorts (HR = 0.45, 95% CI 0.32-0.62, P < 0.001). The TICS showed stable prognostic power independent of tumor stage, surgical margin, pre-treatment prostatic specific antigen (PSA), and Gleason score (multivariable HR = 0.50, 95% CI 0.39-0.63, P < 0.001). Adding the TICS into the prognostic model constructed using clinicopathological features significantly improved its 1/2/3/4/5-year area under curve (P < 0.05). A low TICS was associated with high homologous recombination deficiency scores, abnormally activated pathways concerning DNA replication, cell cycle, steroid hormone biosynthesis, and drug metabolism, and fewer tumor-infiltrating immune cells (P < 0.05). The patients with a high TICS had favorable BCRFS with ADT (HR = 0.25, 95% CI 0.06-0.99, P = 0.034) or ipilimumab monotherapy (HR = 0.23, 95% CI 0.06-0.81, P = 0.012).

CONCLUSIONS

Our study delineates the associations of tumor immune contexture with molecular features, recurrence after radical prostatectomy, and the efficacy of ADT and immunotherapy. The TICS may improve the existing risk stratification systems and serve as a patient-selection tool for ADT and immunotherapy in prostate cancer.

Circulating tumor DNA reveals complex biological features with clinical relevance in metastatic breast cancer

Liquid biopsy has proven valuable in identifying individual genetic alterations; however, the ability of plasma ctDNA to capture complex tumor phenotypes with clinical value is unknown. To address this question, we have performed 0.5X shallow whole-genome sequencing in plasma from 459 patients with metastatic breast cancer, including 245 patients treated with endocrine therapy and a CDK4/6 inhibitor (ET + CDK4/6i) from 2 independent cohorts. We demonstrate that machine learning multi-gene signatures, obtained from ctDNA, identify complex biological features, including measures of tumor proliferation and estrogen receptor signaling, similar to what is accomplished using direct tumor tissue DNA or RNA profiling. More importantly, 4 DNA-based subtypes, and a ctDNA-based genomic signature tracking retinoblastoma loss-of-heterozygosity, are significantly associated with poor response and survival outcome following ET + CDK4/6i, independently of plasma tumor fraction. Our approach opens opportunities for the discovery of additional multi-feature genomic predictors coming from ctDNA in breast cancer and other cancer-types.

Translating transcriptomic findings from cancer model systems to humans through joint dimension reduction

Model systems are an essential resource in cancer research. They simulate effects that we can infer into humans, but come at a risk of inaccurately representing human biology. This inaccuracy can lead to inconclusive experiments or misleading results, urging the need for an improved process for translating model system findings into human-relevant data. We present a process for applying joint dimension reduction (jDR) to horizontally integrate gene expression data across model systems and human tumor cohorts. We then use this approach to combine human TCGA gene expression data with data from human cancer cell lines and mouse model tumors. By identifying the aspects of genomic variation joint-acting across cohorts, we demonstrate how predictive modeling and clinical biomarkers from model systems can be improved.

Multiomics in primary and metastatic breast tumors from the AURORA US network finds microenvironment and epigenetic drivers of metastasis

The AURORA US Metastasis Project was established with the goal to identify molecular features associated with metastasis. We assayed 55 females with metastatic breast cancer (51 primary cancers and 102 metastases) by RNA sequencing, tumor/germline DNA exome and low-pass whole-genome sequencing and global DNA methylation microarrays. Expression subtype changes were observed in ~30% of samples and were coincident with DNA clonality shifts, especially involving HER2. Downregulation of estrogen receptor (ER)-mediated cell-cell adhesion genes through DNA methylation mechanisms was observed in metastases. Microenvironment differences varied according to tumor subtype; the ER+/luminal subtype had lower fibroblast and endothelial content, while triple-negative breast cancer/basal metastases showed a decrease in B and T cells. In 17% of metastases, DNA hypermethylation and/or focal deletions were identified near HLA-A and were associated with reduced expression and lower immune cell infiltrates, especially in brain and liver metastases. These findings could have implications for treating individuals with metastatic breast cancer with immune- and HER2-targeting therapies.

Penalized estimation of a class of single-index varying-coefficient models for integrative genomic analysis

Recent technological advances have made it possible to collect high-dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understanding of the disease mechanisms. Despite extensive studies on integrative analysis, it remains an ongoing challenge to model the interaction effects between clinical and genomic variables, due to high dimensionality of the data and heterogeneity across data types. In this paper, we propose an integrative approach that models interaction effects using a single-index varying-coefficient model, where the effects of genomic features can be modified by clinical variables. We propose a penalized approach for separate selection of main and interaction effects. Notably, the proposed methods can be applied to right-censored survival outcomes based on a Cox proportional hazards model. We demonstrate the advantages of the proposed methods through extensive simulation studies and provide applications to a motivating cancer genomic study.

Landscape of the intratumroal microenvironment in bladder cancer: Implications for prognosis and immunotherapy

Introduction

This study aims to present the landscape of the intratumoral microenvironment and by which establish a classification system that can be used to predict the prognosis of bladder cancer patients and their response to anti-PD-L1 immunotherapy.

Methods

The expression profiles of 1554 bladder cancer cases were downloaded from seven public datasets. Single-sample gene set enrichment analysis (ssGSEA), univariate Cox regression analysis, and meta-analysis were employed to establish the bladder cancer immune prognostic index (BCIPI). Extensive analyses were executed to investigate the association between BCIPI and overall survival, tumor-infiltrated immunocytes, immunotherapeutic response, mutation load, etc.

Results

The results obtained from seven independent cohorts and meta-analyses suggested that the BCIPI is an effective classification system for estimating bladder cancer patients' overall survival. Patients in the BCIPI-High subgroup revealed different immunophenotypic outcomes from those in the BCIPI-Low subgroup regarding tumor-infiltrated immunocytes and mutated genes. Subsequent analysis suggested that patients in the BCIPI-High subgroup were more sensitive to anti-PD-L1 immunotherapy than those in the BCIPI-Low subgroup.

Conclusions

The newly established BCIPI is a valuable tool for predicting overall survival outcomes and immunotherapeutic responses in patients with bladder cancer.

HER2DX genomic test in HER2-positive/hormone receptor-positive breast cancer treated with neoadjuvant trastuzumab and pertuzumab: A correlative analysis from the PerELISA trial

Background

HER2DX is a prognostic and predictive assay in early-stage HER2-positive breast cancer based on clinical features and the expression of 4 gene signatures (immune, proliferation, luminal differentiation and HER2 amplicon), including ERBB2 mRNA levels. Here, we evaluated the ability of HER2DX to predict efficacy of a de-escalated, chemotherapy-free neoadjuvant regimen in HER2-positive/hormone receptor-positive breast cancer.

Methods

HER2DX was evaluated on pre-treatment tumour samples from the PerELISA phase II study focused on postmenopausal patients with operable HER2-positive/hormone receptor-positive breast cancer. Patients received 2-weeks of letrozole, and then underwent a re-biopsy for Ki67 evaluation. Patients with endocrine therapy sensitive disease (ESD) (i.e., >20.0% Ki67 relative reduction at week 2) continued letrozole and 5 cycles of trastuzumab and pertuzumab. Primary aim was to test the ability of HER2DX risk-score, HER2DX pCR score and HER2DX ERBB2 mRNA score (as continuous variables and group categories) to predict pathological complete response (pCR) in patients with ESD. Logistic regression and receiver–operator curve (ROC) analysis assessed associations of HER2DX scores with pCR and ESD.

Findings

HER2DX was evaluated in 55 patients (86.0%) enrolled in PerELISA and 40 patients (73.0%) had ESD. The pCR rate in patients with ESD was 22.5% (9/40). In this group, HER2DX pCR score and HER2DX ERBB2 mRNA score were significantly associated with pCR (p = 0.008 and p = 0.003, univariate logistic regression model; area under ROC [AUC] = 0.803 and 0.896). The pCR rate in low, medium, and high HER2DX pCR score groups was 7.7% (2/26), 46.2% (6/13) and 100.0% (1/1), respectively. The pCR rate in low, medium, and high HER2DX ERBB2 score groups was 0.0% (0/12), 7.7% (1/13) and 53.3% (8/15), respectively. HER2DX pCR score was also significantly associated with Ki-67 response following 2-weeks of letrozole (p = 0.002, univariate logistic regression model; AUC = 0.775). The rate of ESD in low, medium, and high HER2DX pCR score groups was 89.7% (26/29), 65.0% (13/20) and 16.7% (1/6), respectively.

Interpretation

HER2DX predicts response following neoadjuvant letrozole in combination with dual HER2 blockade with trastuzumab and pertuzumab in early-stage HER2-positive/hormone receptor-positive breast cancer.

Funding

This study received funding from Reveal Genomics.

Heterogeneity and Functions of Tumor-Infiltrating Antibody Secreting Cells: Lessons from Breast, Ovarian, and Other Solid Cancers

Simple Summary

B cells are gaining increasing recognition as important contributors to the tumor microenvironment, influencing, positively or negatively, tumor growth, patient survival, and response to therapies. Antibody secreting cells (ASCs) constitute a variable fraction of tumor-infiltrating B cells in most solid tumors, and they produce tumor-specific antibodies that can drive distinct immune responses depending on their isotypes and specificities. In this review, we discuss the current knowledge of the heterogeneity of ASCs infiltrating solid tumors and how both their canonical and noncanonical functions shape antitumor immunity, with a special emphasis on breast and ovarian cancers.

Abstract

Neglected for a long time in cancer, B cells and ASCs have recently emerged as critical actors in the tumor microenvironment, with important roles in shaping the antitumor immune response. ASCs indeed exert a major influence on tumor growth, patient survival, and response to therapies. The mechanisms underlying their pro- vs. anti-tumor roles are beginning to be elucidated, revealing the contributions of their secreted antibodies as well as of their emerging noncanonical functions. Here, concentrating mostly on ovarian and breast cancers, we summarize the current knowledge on the heterogeneity of tumor-infiltrating ASCs, we discuss their possible local or systemic origin in relation to their immunoglobulin repertoire, and we review the different mechanisms by which antibody (Ab) subclasses and isoforms differentially impact tumor cells and anti-tumor immunity. We also discuss the emerging roles of cytokines and other immune modulators produced by ASCs in cancer. Finally, we propose strategies to manipulate the tumor ASC compartment to improve cancer therapies.

Updated Neoadjuvant Treatment Landscape for Early Triple Negative Breast Cancer: Immunotherapy, Potential Predictive Biomarkers, and Novel Agents

Simple Summary

In recent years, several agents have been tested in randomized clinical trials in addition to anthracycline and taxane-based neoadjuvant chemotherapy (NACT) in early-stage triple-negative breast cancer (TNBC) to improve pathological complete response rate and, ultimately, survival outcome. Platinum agents, immune checkpoint inhibitors (ICIs), and PARP-inhibitors are the most extensively studied, while established predictors of their efficacy are lacking. Based on the biological features of TNBC, the purpose of this review is to provide an overview of the role of platinum agents, immunotherapy, and novel target therapies in the neoadjuvant setting. Moreover, based on safety issues and financial costs, we provide an overview of potential biomarkers associated with increased likelihood of benefit from the addition of platinum, ICIs, and novel target therapies to NACT.

Abstract

Triple-negative breast cancer (TNBC) is characterized by the absence of hormone receptor and HER2 expression, and therefore a lack of therapeutic targets. Anthracyclines and taxane-based neoadjuvant chemotherapy have historically been the cornerstone of treatment of early TNBC. However, genomic and transcriptomic analyses have suggested that TNBCs include various subtypes, characterized by peculiar genomic drivers and potential therapeutic targets. Therefore, several efforts have been made to expand the therapeutic landscape of early TNBC, leading to the introduction of platinum and immunomodulatory agents into the neoadjuvant setting. This review provides a comprehensive overview of the currently available evidence regarding platinum agents and immune-checkpoint-inhibitors for the neoadjuvant treatment of TNBC, as well as the novel target therapies that are currently being evaluated in this setting. Taking into account the economic issues and the side effects of the expanding therapeutic options, we focus on the potential efficacy biomarkers of the emerging therapies, in order to select the best therapeutic strategy for each specific patient.

Integrated DNA and RNA Sequencing Reveals Drivers of Endocrine Resistance in Estrogen Receptor-Positive Breast Cancer

PURPOSE

Endocrine therapy resistance (ETR) remains the greatest challenge in treating patients with hormone receptor-positive breast cancer. We set out to identify molecular mechanisms underlying ETR through in-depth genomic analysis of breast tumors.

EXPERIMENTAL DESIGN

We collected pre-treatment and sequential on-treatment tumor samples from 35 patients with estrogen receptor-positive breast cancer treated with neoadjuvant then adjuvant endocrine therapy; 3 had intrinsic resistance, 19 acquired resistance, and 13 remained sensitive. Response was determined by changes in tumor volume neoadjuvantly and by monitoring for adjuvant recurrence. Twelve patients received two or more lines of endocrine therapy, with subsequent treatment lines being initiated at the time of development of resistance to the previous endocrine therapy. DNA whole-exome sequencing and RNA sequencing were performed on all samples, totalling 169 unique specimens. DNA mutations, copy-number alterations, and gene expression data were analyzed through unsupervised and supervised analyses to identify molecular features related to ETR.

RESULTS

Mutations enriched in ETR included ESR1 and GATA3. The known ESR1 D538G variant conferring ETR was identified, as was a rarer E380Q variant that confers endocrine hypersensitivity. Resistant tumors which acquired resistance had distinct gene expression profiles compared with paired sensitive tumors, showing elevated pathways including ER, HER2, GATA3, AKT, RAS, and p63 signaling. Integrated analysis in individual patients highlighted the diversity of ETR mechanisms.

CONCLUSIONS

The mechanisms underlying ETR are multiple and characterized by diverse changes in both somatic genetic and transcriptomic profiles; to overcome resistance will require an individualized approach utilizing genomic and genetic biomarkers and drugs tailored to each patient.

Molecular signatures of in situ to invasive progression for basal-like breast cancers: An integrated mouse model and human DCIS study

Ductal carcinoma in situ (DCIS) of the breast is a non-obligate precursor of Invasive Ductal Carcinoma (IDC) and thus the identification of features that may predict DCIS progression would be of potential clinical value. Experimental mouse models can be used to address this challenge by studying DCIS-to-IDC biology. Here we utilize single cell RNA sequencing (scRNAseq) on the C3Tag genetically engineered mouse model that forms DCIS-like precursor lesions and for which many lesions progress into end-stage basal-like molecular subtype IDC. We also perform bulk RNAseq analysis on 10 human synchronous DCIS-IDC pairs comprised of estrogen receptor (ER) positive and ER-negative subsets and utilize 2 additional public human DCIS data sets for comparison to our mouse model. By identifying malignant cells using inferred DNA copy number changes from the murine C3Tag scRNAseq data, we show the existence of cancer cells within the C3Tag pre-DCIS, DCIS, and IDC-like tumor specimens. These cancer cells were further classified into proliferative, hypoxic, and inflammatory subpopulations, which change in frequency in DCIS versus IDC. The C3Tag tumor progression model was also associated with increase in Cancer-Associated Fibroblasts and decrease in activated T cells in IDC. Importantly, we translate the C3Tag murine genomic findings into human DCIS where we find common features only with human basal-like DCIS, suggesting there are intrinsic subtype unique DCIS features. This study identifies several tumor and microenvironmental features associated with DCIS progression and may also provide genomic signatures that can identify progression-prone DCIS within the context of human basal-like breast cancers.

The Transcriptomic Portrait of Locally Advanced Breast Cancer and Its Prognostic Value in a Multi-Country Cohort of Latin American Patients

Purposes

Most molecular-based published studies on breast cancer do not adequately represent the unique and diverse genetic admixture of the Latin American population. Searching for similarities and differences in molecular pathways associated with these tumors and evaluating its impact on prognosis may help to select better therapeutic approaches.

Patients and Methods

We collected clinical, pathological, and transcriptomic data of a multi-country Latin American cohort of 1,071 stage II-III breast cancer patients of the Molecular Profile of Breast Cancer Study (MPBCS) cohort. The 5-year prognostic ability of intrinsic (transcriptomic-based) PAM50 and immunohistochemical classifications, both at the cancer-specific (OSC) and disease-free survival (DFS) stages, was compared. Pathway analyses (GSEA, GSVA and MetaCore) were performed to explore differences among intrinsic subtypes.

Results

PAM50 classification of the MPBCS cohort defined 42·6% of tumors as LumA, 21·3% as LumB, 13·3% as HER2E and 16·6% as Basal. Both OSC and DFS for LumA tumors were significantly better than for other subtypes, while Basal tumors had the worst prognosis. While the prognostic power of traditional subtypes calculated with hormone receptors (HR), HER2 and Ki67 determinations showed an acceptable performance, PAM50-derived risk of recurrence best discriminated low, intermediate and high-risk groups. Transcriptomic pathway analysis showed high proliferation (i.e. cell cycle control and DNA damage repair) associated with LumB, HER2E and Basal tumors, and a strong dependency on the estrogen pathway for LumA. Terms related to both innate and adaptive immune responses were seen predominantly upregulated in Basal tumors, and, to a lesser extent, in HER2E, with respect to LumA and B tumors.

Conclusions

This is the first study that assesses molecular features at the transcriptomic level in a multicountry Latin American breast cancer patient cohort. Hormone-related and proliferation pathways that predominate in PAM50 and other breast cancer molecular classifications are also the main tumor-driving mechanisms in this cohort and have prognostic power. The immune-related features seen in the most aggressive subtypes may pave the way for therapeutic approaches not yet disseminated in Latin America.

Clinical Trial Registration

ClinicalTrials.gov (Identifier: NCT02326857).

Impact of Duration of Neoadjuvant Aromatase Inhibitors on Molecular Expression Profiles in Estrogen Receptor-positive Breast Cancers

PURPOSE

Aromatase inhibitor (AI) treatment is the standard of care for postmenopausal women with primary estrogen receptor-positive breast cancer. The impact of duration of neoadjuvant endocrine therapy (NET) on molecular characteristics is still unknown. We evaluated and compared changes of gene expression profiles under short-term (2-week) versus longer-term neoadjuvant AIs.

EXPERIMENTAL DESIGN

Global gene expression profiles from the PeriOperative Endocrine Therapy for Individualised Care (POETIC) trial (137 received 2 weeks of AIs and 47 received no treatment) and targeted gene expression from 80 patients with breast cancer treated with NET for more than 1 month (NeoAI) were assessed. Intrinsic subtyping, module scores covering different cancer pathways and immune-related genes were calculated for pretreated and posttreated tumors.

RESULTS

The differences in intrinsic subtypes after NET were comparable between the two cohorts, with most Luminal B (90.0% in the POETIC trial and 76.3% in NeoAI) and 50.0% of HER2 enriched at baseline reclassified as Luminal A or normal-like after NET. Downregulation of proliferative-related pathways was observed after 2 weeks of AIs. However, more changes in genes from cancer-signaling pathways such as MAPK and PI3K/AKT/mTOR and immune response/immune-checkpoint components that were associated with AI-resistant tumors and differential outcome were observed in the NeoAI study.

CONCLUSIONS

Tumor transcriptional profiles undergo bigger changes in response to longer NET. Changes in HER2-enriched and Luminal B subtypes are similar between the two cohorts, thus AI-sensitive intrinsic subtype tumors associated with good survival might be identified after 2 weeks of AI. The changes of immune-checkpoint component expression in early AI resistance and its impact on survival outcome warrants careful investigation in clinical trials.

Evaluating the efficacy of a priming dose of cyclophosphamide prior to pembrolizumab to treat metastatic triple negative breast cancer

PURPOSE

Triple negative breast cancer (TNBC) is characterized by the presence of immune cells in the tumor microenvironment, however, the response to single-agent immune checkpoint inhibitor (ICI) therapy is modest. Preclinical models have demonstrated that intratumoral regulatory T cells (T_regs) dampen the antitumor response to ICI. We performed a single-arm phase II trial to evaluate the efficacy of a single low dose of cyclophosphamide (Cy) to deplete T_regs administered before initiating pembrolizumab.

PATIENTS AND METHODS

40 patients with pretreated metastatic TNBC were enrolled. The primary endpoints were progression-free survival (PFS) and change in peripheral blood T_regs after Cy. Secondary endpoints included overall response rate (ORR), duration of response, overall survival, treatment-related adverse events (AEs), and correlative evaluations.

RESULTS

Median PFS was 1.8 months, and the ORR was 21%. T_regs were not significantly decreased after Cy prior to ICI (-3.3%, p=0.19), and increased significantly after the first cycle of therapy (+21% between cycles 1 and 2, p=0.005). Immune-related AEs were similar to historical pembrolizumab monotherapy, and were associated with response to therapy (p=0.02). Patients with pretreatment tumors harboring increased expression of B cell metagene signatures and increased circulating B cell receptor repertoire diversity were associated with clinical response and immune-related toxicity (IRT).

CONCLUSIONS

Among patients with heavily pretreated TNBC, Cy prior to pembrolizumab did not significantly deplete T_regs, and in those with decreased numbers there was rapid recovery following therapy. Increased B cell gene expression in baseline samples was associated with clinical response and IRT.

Molecular analysis of TCGA breast cancer histologic types

Breast cancer is classified into multiple distinct histologic types, and many of the rarer types have limited characterization. Here, we extend The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset with additional histologic type annotations, in a total of 1063 breast cancers. We analyze this extended dataset to define transcriptomic and genomic profiles of six rare special histologic types: cribriform, micropapillary, mucinous, papillary, metaplastic, and invasive carcinoma with medullary pattern. We show the broader applicability of our constructed special histologic type gene signatures in the TCGA Pan-Cancer Atlas dataset with a predictive model that detects mucinous histologic type across cancers of other organ systems. Using a normal mammary cell differentiation score analysis, we order histologic types into a continuum from stem cell-like to luminal progenitor-like to mature luminal-like. Finally, we classify TCGA-BRCA into 12 consensus groups based on integrated genomic and histological features. We present a rich openly accessible resource of histologic and genomic characterization of TCGA-BRCA to enable studies of the range of breast cancers.

MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data, thus providing an objective means to estimating the number of possible groups or cell-type populations present.

Clinical and genomic assessment of PD-L1 SP142 expression in triple-negative breast cancer

PURPOSE

The SP142 PD-L1 assay is a companion diagnostic for atezolizumab in metastatic triple-negative breast cancer (TNBC). We strove to understand the biological, genomic, and clinical characteristics associated with SP142 PD-L1 positivity in TNBC patients.

METHODS

Using 149 TNBC formalin-fixed paraffin-embedded tumor samples, tissue microarray (TMA) and gene expression microarrays were performed in parallel. The VENTANA SP142 assay was used to identify PD-L1 expression from TMA slides. We next generated a gene signature reflective of SP142 status and evaluated signature distribution according to TNBCtype and PAM50 subtypes. A SP142 gene expression signature was identified and was biologically and clinically evaluated on the TNBCs of TCGA, other cohorts, and on other malignancies treated with immune checkpoint inhibitors (ICI).

RESULTS

Using SP142, 28.9% of samples were PD-L1 protein positive. The SP142 PD-L1-positive TNBC had higher CD8+ T cell percentage, stromal tumor-infiltrating lymphocyte levels, and higher rate of the immunomodulatory TNBCtype compared to PD-L1-negative samples. The recurrence-free survival was prolonged in PD-L1-positive TNBC. The SP142-guided gene expression signature consisted of 94 immune-related genes. The SP142 signature was associated with a higher pathologic complete response rate and better survival in multiple TNBC cohorts. In the TNBC of TCGA, this signature was correlated with lymphocyte-infiltrating signature scores, but not with tumor mutational burden or total neoantigen count. In other malignancies treated with ICIs, the SP142 genomic signature was associated with improved response and survival.

CONCLUSIONS

We provide multi-faceted evidence that SP142 PDL1-positive TNBC have immuno-genomic features characterized as highly lymphocyte-infiltrated and a relatively favorable survival.

Efficacy and correlative analyses of avelumab plus axitinib versus sunitinib in sarcomatoid renal cell carcinoma: post hoc analysis of a randomized clinical trial

BACKGROUND

Among patients with advanced renal cell carcinoma (RCC), those with sarcomatoid histology (sRCC) have the poorest prognosis. This analysis assessed the efficacy of avelumab plus axitinib versus sunitinib in patients with treatment-naive advanced sRCC.

METHODS

The randomized, open-label, multicenter, phase III JAVELIN Renal 101 trial (NCT02684006) enrolled patients with treatment-naive advanced RCC. Patients were randomized 1 : 1 to receive either avelumab plus axitinib or sunitinib following standard doses and schedules. Assessments in this post hoc analysis of patients with sRCC included efficacy (including progression-free survival) and biomarker analyses.

RESULTS

A total of 108 patients had sarcomatoid histology and were included in this post hoc analysis; 47 patients in the avelumab plus axitinib arm and 61 in the sunitinib arm. Patients in the avelumab plus axitinib arm had improved progression-free survival [stratified hazard ratio, 0.57 (95% confidence interval, 0.325-1.003)] and a higher objective response rate (46.8% versus 21.3%; complete response in 4.3% versus 0%) versus those in the sunitinib arm. Correlative gene expression analyses of patients with sRCC showed enrichment of gene pathway scores for cancer-associated fibroblasts and regulatory T cells, CD274 and CD8A expression, and tumors with The Cancer Genome Atlas m3 classification.

CONCLUSIONS

In this subgroup analysis of JAVELIN Renal 101, patients with sRCC in the avelumab plus axitinib arm had improved efficacy outcomes versus those in the sunitinib arm. Correlative analyses provide insight into this subtype of RCC and suggest that avelumab plus axitinib may increase the chance of overcoming the aggressive features of sRCC.

Harmine inhibits breast cancer cell migration and invasion by inducing the degradation of Twist1

Breast cancer is the leading cause of cancer-related deaths in the United States. The majority of deaths (90%) in breast cancer patients is caused by invasion and metastasis-two features related to the epithelial-to-mesenchymal transition (EMT). Twist1 is a key transcription factor that promotes the EMT, which leads to cell migration, invasion, cancer metastasis, and therapeutic resistance. Harmine is a beta-carboline alkaloid found in a variety of plants and was recently shown to be able to induce degradation of Twist Family BHLH Transcription Factor 1 (Twist1) in non-small cell lung cancer cells (NSCLC). In this study, we show that harmine can inhibit migration and invasion of both human and mouse breast cancer cells in a dose-dependent manner. Further study shows that this inhibition is most likely achieved by inducing a proteasome-dependent Twist1 degradation. At the concentrations tested, harmine did not affect the viability of cells significantly, suggesting that its inhibition of cancer cell migration and invasion is largely independent of its cytotoxicity, but due to its ability to affect regulators of EMT such as Twist1. This result may facilitate the development of strategies that target Twist1 to treat metastatic breast cancer, as Twist1 is expressed at a high level in metastatic breast cancer cells but not in normal cells.

FGFR4 regulates tumor subtype differentiation in luminal breast cancer and metastatic disease

Mechanisms driving tumor progression from less aggressive subtypes to more aggressive states represent key targets for therapy. We identified a subset of luminal A primary breast tumors that give rise to HER2-enriched (HER2E) subtype metastases, but remain clinically HER2 negative (cHER2-). By testing the unique genetic and transcriptomic features of these cases, we developed the hypothesis that FGFR4 likely participates in this subtype switching. To evaluate this, we developed 2 FGFR4 genomic signatures using a patient-derived xenograft (PDX) model treated with an FGFR4 inhibitor, which inhibited PDX growth in vivo. Bulk tumor gene expression analysis and single-cell RNA sequencing demonstrated that the inhibition of FGFR4 signaling caused molecular switching. In the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) breast cancer cohort, FGFR4-induced and FGFR4-repressed signatures each predicted overall survival. Additionally, the FGFR4-induced signature was an independent prognostic factor beyond subtype and stage. Supervised analysis of 77 primary tumors with paired metastases revealed that the FGFR4-induced signature was significantly higher in luminal/ER+ tumor metastases compared with their primaries. Finally, multivariate analysis demonstrated that the FGFR4-induced signature also predicted site-specific metastasis for lung, liver, and brain, but not for bone or lymph nodes. These data identify a link between FGFR4-regulated genes and metastasis, suggesting treatment options for FGFR4-positive patients, whose high expression is not caused by mutation or amplification.

In silico APC/C substrate discovery reveals cell cycle-dependent degradation of UHRF1 and other chromatin regulators

The anaphase-promoting complex/cyclosome (APC/C) is an E3 ubiquitin ligase and critical regulator of cell cycle progression. Despite its vital role, it has remained challenging to globally map APC/C substrates. By combining orthogonal features of known substrates, we predicted APC/C substrates in silico. This analysis identified many known substrates and suggested numerous candidates. Unexpectedly, chromatin regulatory proteins are enriched among putative substrates, and we show experimentally that several chromatin proteins bind APC/C, oscillate during the cell cycle, and are degraded following APC/C activation, consistent with being direct APC/C substrates. Additional analysis revealed detailed mechanisms of ubiquitylation for UHRF1, a key chromatin regulator involved in histone ubiquitylation and DNA methylation maintenance. Disrupting UHRF1 degradation at mitotic exit accelerates G1-phase cell cycle progression and perturbs global DNA methylation patterning in the genome. We conclude that APC/C coordinates crosstalk between cell cycle and chromatin regulatory proteins. This has potential consequences in normal cell physiology, where the chromatin environment changes depending on proliferative state, as well as in disease.

Neoadjuvant pazopanib and molecular analysis of tissue response in renal cell carcinoma

BACKGROUNDSurgery remains the frontline therapy for patients with localized clear cell renal cell carcinoma (ccRCC); however, 20%-40% recur. Angiogenesis inhibitors have improved survival in metastatic patients and may result in responses in the neoadjuvant setting. The impact of these agents on the tumor genetic heterogeneity or the immune milieu is largely unknown. This phase II study was designed to evaluate safety, response, and effect on tumor tissue of neoadjuvant pazopanib.METHODSccRCC patients with localized disease received pazopanib (800 mg daily; median 8 weeks), followed by nephrectomy. Five tumors were examined for mutations by whole exome sequencing from samples collected before therapy and at nephrectomy. These samples underwent RNA sequencing; 17 samples were available for posttreatment assessment.RESULTSTwenty-one patients were enrolled. The overall response rate was 8 of 21 (38%). No patients with progressive disease. At 1-year, response-free survival and overall survival was 83% and 89%, respectively. The most frequent grade 3 toxicity was hypertension (33%, 7 of 21). Sequencing revealed strong concordance between pre- and posttreatment samples within individual tumors, suggesting tumors harbor stable core profiles. However, a reduction in private mutations followed treatment, suggesting a selective process favoring enrichment of driver mutations.CONCLUSIONNeoadjuvant pazopanib is safe and active in ccRCC. Future genomic analyses may enable the segregation of driver and passenger mutations. Furthermore, tumor infiltrating immune cells persist during therapy, suggesting that pazopanib can be combined with immune checkpoint inhibitors without dampening the immune response.FUNDINGSupport was provided by Novartis and GlaxoSmithKline as part of an investigator-initiated study.

Receiver operating characteristic curves and confidence bands for support vector machines

Many problems that appear in biomedical decision-making, such as diagnosing disease and predicting response to treatment, can be expressed as binary classification problems. The support vector machine (SVM) is a popular classification technique that is robust to model misspecification and effectively handles high-dimensional data. The relative costs of false positives and false negatives can vary across application domains. The receiving operating characteristic (ROC) curve provides a visual representation of the trade-off between these two types of errors. Because the SVM does not produce a predicted probability, an ROC curve cannot be constructed in the traditional way of thresholding a predicted probability. However, a sequence of weighted SVMs can be used to construct an ROC curve. Although ROC curves constructed using weighted SVMs have great potential for allowing ROC curves analyses that cannot be done by thresholding predicted probabilities, their theoretical properties have heretofore been underdeveloped. We propose a method for constructing confidence bands for the SVM ROC curve and provide the theoretical justification for the SVM ROC curve by showing that the risk function of the estimated decision rule is uniformly consistent across the weight parameter. We demonstrate the proposed confidence band method using simulation studies. We present a predictive model for treatment response in breast cancer as an illustrative example.

B Cells and T Follicular Helper Cells Mediate Response to Checkpoint Inhibitors in High Mutation Burden Mouse Models of Breast Cancer

This study identifies mechanisms mediating responses to immune checkpoint inhibitors using mouse models of triple-negative breast cancer. By creating new mammary tumor models, we find that tumor mutation burden and specific immune cells are associated with response. Further, we developed a rich resource of single-cell RNA-seq and bulk mRNA-seq data of immunotherapy-treated and non-treated tumors from sensitive and resistant murine models. Using this, we uncover that immune checkpoint therapy induces T follicular helper cell activation of B cells to facilitate the anti-tumor response in these models. We also show that B cell activation of T cells and the generation of antibody are key to immunotherapy response and propose a new biomarker for immune checkpoint therapy. In total, this work presents resources of new preclinical models of breast cancer with large mRNA-seq and single-cell RNA-seq datasets annotated for sensitivity to therapy and uncovers new components of response to immune checkpoint inhibitors.

HIF-1α, TWIST-1 and ITGB-1, associated with Tumor Stiffness, as Novel Predictive Markers for the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer

Purpose

To investigate the relationship between hypoxia-inducible factor 1-alpha (HIF-1α), Twist family BHLH transcription factor 1 (TWIST-1), and β1 integrin (ITGB-1) expression and tumor stiffness, and evaluate performance of HIF-1α, TWIST-1, and ITGB-1 alone and in combination with Ki-67 for predicting pathological responses to neoadjuvant chemotherapy (NACT) in breast cancer (BC).

Patients and Methods

This was a prospective cohort study of 104 BC patients receiving NACT. Tumor stiffness and oxygen score (OS) were evaluated before NACT by shear-wave elastography and optical imaging; HIF-1α, TWIST-1, ITGB-1, and Ki-67 expression were quantitatively assessed by immunohistochemistry of paraffin-embedded tumor samples obtained by core needle biopsy. Indexes were compared among different residual cancer burden (RCB) groups, and associations of HIF-1α, TWIST-1, ITGB-1, and Ki-67 with tumor stiffness and OS were examined. The value of HIF-1α, TWIST-1, ITGB-1, and Ki-67, and a possible new combined index (predRCB) for predicting NACT responses was assessed by receiver operating characteristic (ROC) curves.

Results

HIF-1α, TWIST-1, and ITGB-1 expression were positively correlated with tumor stiffness and negatively with OS. Area under the ROC curves (AUCs) measuring the performance of HIF-1α, TWIST-1, ITGB-1, and Ki-67 for predicting responses to NACT were 0.81, 0.85, 0.79, and 0.80 for favorable responses, and 0.83, 0.86, 0.84, and 0.85 for resistant responses, respectively. PredRCB showed better prediction than the other individual indexes for favorable responses (AUC = 0.88) and resistant responses (AUC = 0.92).

Conclusion

HIF-1α, TWIST-1, ITGB-1, and Ki-67 performed well in predicting favorable responses and resistance to NACT, and predRCB improved the predictive power of the individual indexes. These results support individualized treatment of BC patients receiving NACT.

Using RNA Sequencing to Characterize the Tumor Microenvironment

RNA sequencing (RNA-seq) is an integral tool in immunogenomics, allowing for interrogation of the transcriptome of a tumor and its microenvironment. Analytical methods to deconstruct the genomics data can then be applied to infer gene expression patterns associated with the presence of various immunocyte populations. High quality RNA-seq is possible from formalin-fixed, paraffin-embedded (FFPE), fresh-frozen, and fresh tissue, with a wide variety of sequencing library preparation methods, sequencing platforms, and downstream bioinformatics analyses currently available. Selection of an appropriate library preparation method is largely determined by tissue type, quality of RNA, and quantity of RNA. Downstream of sequencing, many analyses can be applied to the data, including differential gene expression analysis, immune gene signature analysis, gene pathway analysis, T/B-cell receptor inference, HLA inference, and viral transcript quantification. In this chapter, we will describe our workflow for RNA-seq from bulk tissue to evaluable data, including extraction of RNA, library preparation methods, sequencing of libraries, alignment and quality assurance of data, and initial downstream analyses of RNA-seq data to extract relevant immunogenomics features. Systems biology methods that draw additional insights by integrating these features are covered further in Chapters 28 - 30 .

Aggressive Mammary Cancers Lacking Lymphocytic Infiltration Arise in Irradiated Mice and Can Be Prevented by Dietary Intervention

Because the incidence of breast cancer increases decades after ionizing radiation exposure, aging has been implicated in the evolution of the tumor microenvironment and tumor progression. Here, we investigated radiation-induced carcinogenesis using a model in which the mammary glands of 10-month-old BALB/c mice were transplanted with Trp53-null mammary tissue 3 days after exposure to low doses of sparsely ionizing γ-radiation or densely ionizing particle radiation. Mammary transplants in aged, irradiated hosts gave rise to significantly more tumors that grew more rapidly than those in sham-irradiated mice, with the most pronounced effects seen in mice irradiated with densely ionizing particle radiation. Tumor transcriptomes identified a characteristic immune signature of these aggressive cancers. Consistent with this, fast-growing tumors exhibited an immunosuppressive tumor microenvironment with few infiltrating lymphocytes, abundant immunosuppressive myeloid cells, and high COX-2 and TGFβ. Only irradiated hosts gave rise to tumors lacking cytotoxic CD8⁺ lymphocytes (defined here as immune desert), which also occurred in younger irradiated hosts. These data suggest that host irradiation may promote immunosuppression. To test this, young chimera mice were fed chow containing a honeybee-derived compound with anti-inflammatory and immunomodulatory properties, caffeic acid phenethyl ester (CAPE). CAPE prevented the detrimental effects of host irradiation on tumor growth rate, immune signature, and immunosuppression. These data indicated that low-dose radiation, particularly densely ionizing exposure of aged mice, promoted more aggressive cancers by suppressing antitumor immunity. Dietary intervention with a nontoxic immunomodulatory agent could prevent systemic effects of radiation that fuel carcinogenesis, supporting the potential of this strategy for cancer prevention.

Genetic determinants of the molecular portraits of epithelial cancers

The ability to characterize and predict tumor phenotypes is crucial to precision medicine. In this study, we present an integrative computational approach using a genome-wide association analysis and an Elastic Net prediction method to analyze the relationship between DNA copy number alterations and an archive of gene expression signatures. Across breast cancers, we are able to quantitatively predict many gene signatures levels within individual tumors with high accuracy based upon DNA copy number features alone, including proliferation status and Estrogen-signaling pathway activity. We can also predict many other key phenotypes, including intrinsic molecular subtypes, estrogen receptor status, and TP53 mutation. This approach is also applied to TCGA Pan-Cancer, which identify repeatedly predictable signatures across tumor types including immune features in lung squamous and basal-like breast cancers. These Elastic Net DNA predictors could also be called from DNA-based gene panels, thus facilitating their use as biomarkers to guide therapeutic decision making.

Effective precision medicine strategies rely on the ability to predict tumour behaviour based on molecular characteristics. Here, the authors build models to predict multiple distinct gene expression patterns using DNA copy number alterations

BMI, irAE, and gene expression signatures associate with resistance to immune-checkpoint inhibition and outcomes in renal cell carcinoma

BACKGROUND

Clinical variables may correlate with lack of response to treatment (primary resistance) or clinical benefit in patients with clear cell renal cell carcinoma (ccRCC) treated with anti-programmed death 1/ligand one antibodies.

METHODS

In this multi-institutional collaboration, clinical characteristics of patients with primary resistance (defined as progression on initial computed tomography scan) were compared to patients with clinical benefit using Two sample t-test and Chi-square test (or Fisher's Exact test). The Kaplan-Meier method was used to estimate the distribution of progression-free survival (PFS) and overall survival (OS) in all patients and the subsets of patients with clinical benefit or primary resistance. Cox's regression model was used to evaluate the correlation between survival endpoints and variables of interest. To explore clinical factors in a larger, independent patient sample, The Cancer Genome Atlas (TCGA) was analyzed. RNAseq gene expression data as well as demographic and clinical information were downloaded for primary tumors of 517 patients included within TCGA-ccRCC.

RESULTS

Of 90 patients, 38 (42.2%) had primary resistance and 52 (57.8%) had clinical benefit. Compared with the cohort of patients with initial benefit, primary resistance was more likely to occur in patients with worse ECOG performance status (p = 0.03), earlier stage at diagnosis (p = 0.04), had no prior nephrectomy (p = 0.04) and no immune-related adverse events (irAE) (p = 0.02). In patients with primary resistance, improved OS was significantly correlated with lower International Metastatic RCC Database Consortium risk score (p = 0.02) and lower neutrophil:lymphocyte ratio (p = 0.04). In patients with clinical benefit, improved PFS was significantly associated with increased BMI (p = 0.007) and irAE occurrence (p = 0.02) while improved OS was significantly correlated with overweight BMI (BMI 25-30; p = 0.03) and no brain metastasis (p = 0.005). The cohort TCGA-ccRCC was examined for the correlations between gene expression patterns, clinical factors, and survival outcomes observing associations of T-cell inflammation and angiogenesis signatures with histologic grade, pathologic stage and OS.

CONCLUSIONS

Clinical characteristics including performance status, BMI and occurrence of an irAE associate with outcomes in patients with ccRCC treated with immunotherapy. The inverse association of angiogenesis gene signature with ccRCC histologic grade highlight opportunities for adjuvant combination VEGFR2 tyrosine kinase inhibitor and immune-checkpoint inhibition.

Integrative proteogenomic analyses of human tumours identifies ADNP as a novel oncogenic mediator of cell cycle progression in high-grade serous ovarian cancer with poor prognosis

Background

Despite toxic side effects and limited durable response, the current standard-of-care treatment for high grade serous ovarian cancer (HGSOC) remains platinum/taxane-based chemotherapy. Given that the overall prognosis has not improved drastically over the past several decades, there is a critical need to understand the underlying mechanisms that lead to tumour development and progression.

Methods

We utilized an integrative proteogenomic analysis of HGSOC tumours applying a poor prognosis gene expression signature (PPS) as a conceptual framework to analyse orthogonal genomic and proteomic data from the TCGA (n = 488) and CPTAC (n = 169) studies. Genes identified through in silico analyses were assessed in vitro studies to demonstrate their impact on proliferation and cell cycle progression.

Findings

These analyses identified DNA amplification and overexpression of the transcription factor ADNP (Activity Dependent Neuroprotector Homeobox) in poorly prognostic tumours. Validation studies confirmed the prognostic capacity of ADNP and suggested an oncogenic role for this protein given the association between ADNP expression and pro-proliferative signalling. In vitro studies confirmed ADNP as a novel and essential mediator of cell proliferation through dysregulation of cell cycle checkpoints.

Interpretation

We identified ADNP as being amplified and overexpressed in poor prognosis HGSOC in silico analyses and demonstrated that ADNP is a novel and essential oncogene in HGSOC which mediates proliferation through dysregulation of cell cycle checkpoints in vitro.

Funding

The National Cancer Institute of the National Institutes of Health, the V Foundation for Cancer Research and the New Jersey Commission for Cancer Research.