Analysis of Breast Cancer Mortality in the US-1975 to 2019

IMPORTANCE: Breast cancer mortality in the US declined between 1975 and 2019. The association of changes in metastatic breast cancer treatment with improved breast cancer mortality is unclear. OBJECTIVE: To simulate the relative associations of breast cancer screening, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer with improved breast cancer mortality. DESIGN, SETTING, AND PARTICIPANTS: Using aggregated observational and clinical trial data on the dissemination and effects of screening and treatment, 4 Cancer Intervention and Surveillance Modeling Network (CISNET) models simulated US breast cancer mortality rates. Death due to breast cancer, overall and by estrogen receptor and ERBB2 (formerly HER2) status, among women aged 30 to 79 years in the US from 1975 to 2019 was simulated. EXPOSURES: Screening mammography, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer. MAIN OUTCOMES AND MEASURES: Model-estimated age-adjusted breast cancer mortality rate associated with screening, stage I to III treatment, and metastatic treatment relative to the absence of these exposures was assessed, as was model-estimated median survival after breast cancer metastatic recurrence. RESULTS: The breast cancer mortality rate in the US (age adjusted) was 48/100 000 women in 1975 and 27/100 000 women in 2019. In 2019, the combination of screening, stage I to III treatment, and metastatic treatment was associated with a 58% reduction (model range, 55%-61%) in breast cancer mortality. Of this reduction, 29% (model range, 19%-33%) was associated with treatment of metastatic breast cancer, 47% (model range, 35%-60%) with treatment of stage I to III breast cancer, and 25% (model range, 21%-33%) with mammography screening. Based on simulations, the greatest change in survival after metastatic recurrence occurred between 2000 and 2019, from 1.9 years (model range, 1.0-2.7 years) to 3.2 years (model range, 2.0-4.9 years). Median survival for estrogen receptor (ER)-positive/ERBB2-positive breast cancer improved by 2.5 years (model range, 2.0-3.4 years), whereas median survival for ER-/ERBB2- breast cancer improved by 0.5 years (model range, 0.3-0.8 years). CONCLUSIONS AND RELEVANCE: According to 4 simulation models, breast cancer screening and treatment in 2019 were associated with a 58% reduction in US breast cancer mortality compared with interventions in 1975. Simulations suggested that treatment for stage I to III breast cancer was associated with approximately 47% of the mortality reduction, whereas treatment for metastatic breast cancer was associated with 29% of the reduction and screening with 25% of the reduction

Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy.

Genomic changes observed across treatment may result from either clonal evolution or geographically disparate sampling of heterogeneous tumors. Here we use computational modeling based on analysis of fifteen primary breast tumors and find that apparent clonal change between two tumor samples can frequently be explained by pre-treatment heterogeneity, such that at least two regions are necessary to detect treatment-induced clonal shifts. To assess for clonal replacement, we devise a summary statistic based on whole-exome sequencing of a pre-treatment biopsy and multi-region sampling of the post-treatment surgical specimen and apply this measure to five breast tumors treated with neoadjuvant HER2-targeted therapy. Two tumors underwent clonal replacement with treatment, and mathematical modeling indicates these two tumors had resistant subclones prior to treatment and rates of resistance-related genomic changes that were substantially larger than previous estimates. Our results provide a needed framework to incorporate primary tumor heterogeneity in investigating the evolution of resistance

Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups.

The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data. Long-term follow-up is especially important for those with oestrogen-receptor (ER)-positive breast cancers, which can recur up to two decades after initial diagnosis1-6. It is therefore essential to identify patients who have a high risk of late relapse7-9. Here we present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related death and death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. We apply this model to 3,240 patients with breast cancer, including 1,980 for whom molecular data are available, and delineate spatiotemporal patterns of relapse across different categories of molecular information (namely immunohistochemical subtypes; PAM50 subtypes, which are based on gene-expression patterns10,11; and integrative or IntClust subtypes, which are based on patterns of genomic copy-number alterations and gene expression12,13). We identify four late-recurring integrative subtypes, comprising about one quarter (26%) of tumours that are both positive for ER and negative for human epidermal growth factor receptor 2, each with characteristic tumour-driving alterations in genomic copy number and a high risk of recurrence (mean 47-62%) up to 20 years after diagnosis. We also define a subgroup of triple-negative breast cancers in which cancer rarely recurs after five years, and a separate subgroup in which patients remain at risk. Use of the integrative subtypes improves the prediction of late, distant relapse beyond what is possible with clinical covariates (nodal status, tumour size, tumour grade and immunohistochemical subtype). These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials.Cancer Research UK (CRUK) travel grant (SWAH/047) 282 to visit Prof. Curtis’ Lab. C.R. is supported by award MTM2015-71217-R. Ca.C. is 283 supported by CRUK, ECMC and NIHR. C.C. is supported by the National Institutes 284 of Health through the NIH Director’s Pioneer Award (DP1-CA238296) and the Breast 285 Cancer Research Foundation

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Genetic and immunohistochemical profiling of small cell and large cell neuroendocrine carcinomas of the breast.

Neuroendocrine carcinomas (NEC) of the breast are exceedingly rare tumors, which are classified in the WHO system as small cell (SCNEC) and large cell (LCNEC) carcinoma based on indistinguishable features from their lung counterparts. In contrast to lung and enteropancreatic NEC, the genomics of breast NEC have not been well-characterized. In this study, we examined the clinicopathologic, immunohistochemical, and genetic features of 13 breast NEC (7 SCNEC, 4 LCNEC, 2 NEC with ambiguous small versus large cell morphology [ANEC]). Co-alterations of TP53 and RB1 were identified in 86% (6/7) SCNEC, 100% (2/2) ANEC, and 50% (2/4) LCNEC. The one SCNEC without TP53/RB1 alteration had other p53 pathway aberrations (MDM2 and MDM4 amplification) and was immunohistochemically RB negative. PIK3CA/PTEN pathway alterations and ZNF703 amplifications were each identified in 46% (6/13) NEC. Two tumors (1 SCNEC, 1 LCNEC) were CDH1 mutated. By immunohistochemistry, 100% SCNEC (6/6) and ANEC (2/2) and 50% (2/4) LCNEC (83% NEC) showed RB loss, compared to 0% (0/8) grade 3 neuroendocrine tumors (NET) (p < 0.001) and 38% (36/95) grade 3 invasive ductal carcinomas of no special type (IDC-NST) (p = 0.004). NEC were also more often p53 aberrant (60% vs 0%, p = 0.013), ER negative (69% vs 0%, p = 0.005), and GATA3 negative (67% vs 0%, p = 0.013) than grade 3 NET. Two mixed NEC had IDC-NST components, and 69% (9/13) of tumors were associated with carcinoma in situ (6 neuroendocrine DCIS, 2 non-neuroendocrine DCIS, 1 non-neuroendocrine LCIS). NEC and IDC-NST components of mixed tumors were clonally related and immunophenotypically distinct, lacking ER and GATA3 expression in NEC relative to IDC-NST, with RB loss only in NEC of one ANEC. The findings provide insight into the pathogenesis of breast NEC, underscore their classification as a distinct tumor type, and highlight genetic similarities to extramammary NEC, including highly prevalent p53/RB pathway aberrations in SCNEC

Abstract P4-07-01: Tumor expression and microenvironment in HER2-positive breast cancer before and on HER2-targeted therapy: Analysis of microarray expression data from the TRIO-US B07 trial

Abstract Background: Neoadjuvant HER2-targeted therapy in combination with chemotherapy is a standard treatment approach for early-stage HER2-positive breast cancer. Proposed biomarkers to predict pathologic complete response (pCR), and thereby inform which patients may benefit from de-escalation of therapy, include expression-based subtyping and immune enrichment scores. Little is known about how tumors and their microenvironment may change with HER2-targeted therapy alone, and whether these changes may predict outcome. Methods: The TRIO-US B07 phase II trial randomized 128 participants with stage I-III HER2-positive breast cancer to trastuzumab (N=34), lapatinib (N=36), or the combination (N=58) for three weeks, followed by six cycles of docetaxel and carboplatin with continued HER2-targeted therapy. Fresh-frozen core biopsies of the tumor prior to therapy (N=110) and after 14-21 days of HER2-targeted therapy alone (N=89) were collected, and RNA was extracted and subjected to Agilent Whole Human Genome 44K 2-color chip. The pre-treatment tumor RNA was normalized against a mixed breast tumor reference, and the on-treatment tumor RNA against the matched pre-treatment sample. Absolute intrinsic molecular subtyping was used to determine intrinsic subtype, the iC10 expression-based classifier to determine integrative subtype, gene set enrichment analysis (GSEA) to assess signature changes across treatment, single-sample GSEA to compare individual gene signature scores between tumors, and CIBERSORT to quantify immune cell populations before and on treatment. Results: Primary trial results have been reported previously and showed a pCR rate of 47% with trastuzumab, 25% with lapatinib, and 52% with the combination. Prior to treatment, 56% of tumors classified as the HER2-enriched intrinsic subtype and 78% as the iC5 integrative subtype. HER2-enriched tumors trended toward a higher rate of pCR relative to other intrinsic subtypes (50% vs 33%, P=0.12), as did iC5 tumors relative to other integrative subtypes (48% vs 25%, P=0.08). However, in multivariate analysis, HER2 FISH ratio (P=0.04) and hormone receptor status (P=0.02), each associated themselves with intrinsic and integrative subtype, proved the most valuable in predicting pCR, with little information added by expression-based subtyping. Immune cell signatures correlated with pCR in the trastuzumab-containing arms only. Of 65 gene signatures tested, 47 changed across HER2-targeted therapy with false discovery rate < 0.1, driven by decreasing tumor proliferation, increasing immune cell signatures, and increasing stromal cell/epithelial mesenchymal transition signatures. Quantification of immune cell populations suggested the immune changes were both anti-tumor (CD8+ T cells) and pro-tumor (M2 macrophages). Intrinsic subtype changed in 54% of tumors (79% of these converting to normal-like) and integrative subtype changed in 26%. Change in subtype, proliferation, or immune infiltration with targeted therapy did not correlate with pCR. A higher proportion of tumors treated with trastuzumab alone maintained their proliferation (42%), compared with lapatinib alone (20%; P=0.16) or the combination (16%; P=0.04). Conclusions: In the TRIO-US B07 study, the biomarkers most predictive of response to neoadjuvant HER2-targeted therapy were hormone receptor status in combination with HER2 FISH ratio. Multiple changes in the tumor and its microenvironment occurred with HER2-targeted therapy, but these changes did not predict pCR. Tumors treated with lapatinib tended to decrease proliferation more than tumors treated with trastuzumab, despite trastuzumab being more effective in preventing recurrence, an observation with implications for window of opportunity studies. Citation Format: Jennifer L. Caswell-Jin, Katherine L. McNamara, Judy Dering, Hsiao-Wang Chen, Robert Dichmann, Alejandra Perez, Ravindranath Patel, Eran Kotler, Jason J. Zoeller, Joan S. Brugge, Michael F. Press, Dennis J. Slamon, Christina Curtis, Sara A. Hurvitz. Tumor expression and microenvironment in HER2-positive breast cancer before and on HER2-targeted therapy: Analysis of microarray expression data from the TRIO-US B07 trial [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-07-01

Spatial proteomic characterization of HER2-positive breast tumors through neoadjuvant therapy predicts response

The addition of HER2-targeted agents to neoadjuvant chemotherapy has dramatically improved pathological complete response (pCR) rates in early-stage, HER2-positive breast cancer. Nonetheless, up to 50% of patients have residual disease after treatment, while others are likely overtreated. Here, we performed multiplex spatial proteomic characterization of 122 samples from 57 HER2-positive breast tumors from the neoadjuvant TRIO-US B07 clinical trial sampled pre-treatment, after 14-21 d of HER2-targeted therapy and at surgery. We demonstrated that proteomic changes after a single cycle of HER2-targeted therapy aids the identification of tumors that ultimately undergo pCR, outperforming pre-treatment measures or transcriptomic changes. We further developed and validated a classifier that robustly predicted pCR using a single marker, CD45, measured on treatment, and showed that CD45-positive cell counts measured via conventional immunohistochemistry perform comparably. These results demonstrate robust biomarkers that can be used to enable the stratification of sensitive tumors early during neoadjuvant HER2-targeted therapy, with implications for tailoring subsequent therapy

Molecular definition of a metastatic lung cancer state reveals a targetable CD109-Janus kinase-Stat axis.

Lung cancer is the leading cause of cancer deaths worldwide, with the majority of mortality resulting from metastatic spread. However, the molecular mechanism by which cancer cells acquire the ability to disseminate from primary tumors, seed distant organs, and grow into tissue-destructive metastases remains incompletely understood. We combined tumor barcoding in a mouse model of human lung adenocarcinoma with unbiased genomic approaches to identify a transcriptional program that confers metastatic ability and predicts patient survival. Small-scale in vivo screening identified several genes, including Cd109, that encode novel pro-metastatic factors. We uncovered signaling mediated by Janus kinases (Jaks) and the transcription factor Stat3 as a critical, pharmacologically targetable effector of CD109-driven lung cancer metastasis. In summary, by coupling the systematic genomic analysis of purified cancer cells in distinct malignant states from mouse models with extensive human validation, we uncovered several key regulators of metastatic ability, including an actionable pro-metastatic CD109-Jak-Stat3 axis