Molecular pathology in epidemiologic studies: a primer on key considerations

Browser-based Data Annotation, Active Learning, and Real-Time Distribution of Artificial Intelligence Models: From Tumor Tissue Microarrays to COVID-19 Radiology

BACKGROUND

Artificial intelligence (AI) is fast becoming the tool of choice for scalable and reliable analysis of medical images. However, constraints in sharing medical data outside the institutional or geographical space, as well as difficulties in getting AI models and modeling platforms to work across different environments, have led to a "reproducibility crisis" in digital medicine.

METHODS

This study details the implementation of a web platform that can be used to mitigate these challenges by orchestrating a digital pathology AI pipeline, from raw data to model inference, entirely on the local machine. We discuss how this federated platform provides governed access to data by consuming the Application Program Interfaces exposed by cloud storage services, allows the addition of user-defined annotations, facilitates active learning for training models iteratively, and provides model inference computed directly in the web browser at practically zero cost. The latter is of particular relevance to clinical workflows because the code, including the AI model, travels to the user's data, which stays private to the governance domain where it was acquired.

RESULTS

We demonstrate that the web browser can be a means of democratizing AI and advancing data socialization in medical imaging backed by consumer-facing cloud infrastructure such as Box.com. As a case study, we test the accompanying platform end-to-end on a large dataset of digital breast cancer tissue microarray core images. We also showcase how it can be applied in contexts separate from digital pathology by applying it to a radiology dataset containing COVID-19 computed tomography images.

CONCLUSIONS

The platform described in this report resolves the challenges to the findable, accessible, interoperable, reusable stewardship of data and AI models by integrating with cloud storage to maintain user-centric governance over the data. It also enables distributed, federated computation for AI inference over those data and proves the viability of client-side AI in medical imaging.

AVAILABILITY

The open-source application is publicly available at , with a short video demonstration at .

Calcium Intake and Risk of Colorectal Cancer According to Tumor-infiltrating T Cells

Calcium intake has been associated with a lower risk of colorectal cancer. Calcium signaling may enhance T-cell proliferation and differentiation, and contribute to T-cell-mediated antitumor immunity. In this prospective cohort study, we investigated the association between calcium intake and colorectal cancer risk according to tumor immunity status to provide additional insights into the role of calcium in colorectal carcinogenesis. The densities of tumor-infiltrating T-cell subsets [CD3⁺, CD8⁺ , CD45RO (PTPRC) ⁺ , or FOXP3⁺ cell] were assessed using IHC and computer-assisted image analysis in 736 cancer cases that developed among 136,249 individuals in two cohorts. HRs and 95% confidence intervals (CI) were calculated using Cox proportional hazards regression. Total calcium intake was associated with a multivariable HR of 0.55 (comparing ≥1,200 vs. <600 mg/day; 95% CI, 0.36-0.84; P _trend = 0.002) for CD8⁺ T-cell-low but not for CD8⁺ T-cell-high tumors (HR = 1.02; 95% CI, 0.67-1.55; P _trend = 0.47). Similarly, the corresponding HRs (95% CIs) for calcium for low versus high T-cell-infiltrated tumors were 0.63 (0.42-0.94; P _trend = 0.01) and 0.89 (0.58-1.35; P _trend = 0.20) for CD3⁺ ; 0.58 (0.39-0.87; P _trend = 0.006) and 1.04 (0.69-1.58; P _trend = 0.54) for CD45RO⁺ ; and 0.56 (0.36-0.85; P _trend = 0.006) and 1.10 (0.72-1.67; P _trend = 0.47) for FOXP3⁺ , although the differences by subtypes defined by T-cell density were not statistically significant. These potential differential associations generally appeared consistent regardless of sex, source of calcium intake, tumor location, and tumor microsatellite instability status. Our findings suggest a possible role of calcium in cancer immunoprevention via modulation of T-cell function.

Calcium intake and risk of colorectal cancer according to expression status of calcium-sensing receptor (CASR)

OBJECTIVE

Although evidence suggests an inverse association between calcium intake and the risk of colorectal cancer, the mechanisms remain unclear. The calcium-sensing receptor (CASR) is expressed abundantly in normal colonic epithelium and may influence carcinogenesis. We hypothesized that calcium intake might be associated with lower risk of CASR-positive, but not CASR-negative, colorectal cancer.

DESIGN

We assessed tumour CASR protein expression using immunohistochemistry in 779 incident colon and rectal cancer cases that developed among 136 249 individuals in the Nurses' Health Study and Health Professionals Follow-Up Study. Duplication method Cox proportional hazards regression analysis was used to assess associations of calcium intake with incidence of colorectal adenocarcinoma subtypes by CASR status.

RESULTS

Total calcium intake was inversely associated with the risk of developing colorectal cancer (p_trend=0.01, comparing ≥1200 vs <600 mg/day: multivariable HR=0.75, 95% CI 0.60 to 0.95). For the same comparison, higher total calcium intake was associated with a lower risk of CASR-positive tumours (p_trend=0.003, multivariable HR=0.67, 95% CI 0.51 to 0.86) but not with CASR-negative tumours (p_trend=0.67, multivariable HR=1.15, 95% CI 0.75 to 1.78; p_{heterogeneity}=0.06 between the CASR subtypes). The stronger inverse associations of calcium intake with CASR-positive but not CASR-negative tumours generally appeared consistent regardless of sex, tumour location and source of calcium.

CONCLUSIONS

Our molecular pathological epidemiology data suggest a causal relationship between higher calcium intake and lower colorectal cancer risk, and a potential role of CASR in mediating antineoplastic effect of calcium.

Considerations for Pharmacoepidemiological Studies of Drug-Cancer Associations

In this MiniReview, we provide general considerations for the planning and conduct of pharmacoepidemiological studies of associations between drug use and cancer development. We address data sources, study design, assessment of drug exposure, ascertainment of cancer outcomes, confounder adjustment and future perspectives. Aspects of data sources include assessment of complete history of drug use and data on dose and duration of drug use, allowing estimates of cumulative exposure. Outcome data from formal cancer registries are preferable, but cancer data from other sources, for example, patient or pathology registries, medical records or claims are also suitable. The two principal designs for observational studies evaluating drug-cancer associations are the cohort and case-control designs. A key challenge in studies of drug-cancer associations is the exposure assessment due to the typically long period of cancer development. We present methods to examine early and late effects of drug use on cancer development and discuss the need for employing 'lag-time' in order to avoid reverse causation. We emphasize that a new-user study design should always be considered. We also underline the need for 'dose-response' analyses, as drug-cancer associations are likely to be dose-dependent. Generally, studies of drug-cancer associations should explore risk of site-specific cancer, rather than cancer overall. Additional differentiation may also be crucial for organ-specific cancer with various distinct histological subtypes (e.g., lung or ovary cancer). We also highlight the influence of confounding factors and discuss various methods to address confounding, while emphasizing that the choices of methods depend on the design and specific objectives of the individual study. In some studies, use of active comparator(s) may be preferable. Pharmacoepidemiological studies of drug-cancer associations are expected to evolve considerably in the coming years, due to the increasing availability of long-term data on drug exposures and cancer outcomes, the increasing conduct of multinational studies, allowing studies of rare cancers and subtypes of cancer, and methodological improvements specifically addressing cancer and other long-term outcomes.

The Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) Screening Trial Pathology Tissue Resource

BACKGROUND

Pathology tissue specimens with associated epidemiologic and clinical data are valuable for cancer research. The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial undertook a large-scale effort to create a public resource of pathology tissues from PLCO participants who developed a cancer during the trial.

METHODS

Formalin-fixed paraffin-embedded tissue blocks were obtained from pathology laboratories on a loan basis for central processing of tissue microarrays, with additional free-standing tissue cores collected for nucleic acid extraction.

RESULTS

Pathology tissue specimens were obtained for prostate cancer (n = 1,052), lung cancer (n = 434), colorectal cancer (n = 675) and adenoma (n = 658), ovarian cancer and borderline tumors (n = 212), breast cancer (n = 870), and bladder cancer (n = 204). The process of creating this resource was complex, involving multidisciplinary teams with expertise in pathology, epidemiology, information technology, project management, and specialized laboratories.

CONCLUSIONS

Creating the PLCO tissue resource required a multistep process, including obtaining medical records and contacting pathology departments where pathology materials were stored after obtaining necessary patient consent and authorization. The potential to link tissue biomarkers to prospectively collected epidemiologic information, screening and clinical data, and matched blood or buccal samples offers valuable opportunities to study etiologic heterogeneity, mechanisms of carcinogenesis, and biomarkers for early detection and prognosis.

IMPACT

The methods and protocols developed for this effort, and the detailed description of this resource provided here, will be useful for those seeking to use PLCO pathology tissue specimens for their research and may also inform future tissue collection efforts in other settings. Cancer Epidemiol Biomarkers Prev; 25(12); 1635-42. ©2016 AACR.

High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium

Automated methods are needed to facilitate high-throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large-scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37-0.87) and study (kappa range = 0.39-0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p-value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000-4,500 cells: kappa = 0.78) than those with lower counts (50-500 cells: kappa = 0.41; p-value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre- and post-analytical quality control procedures are necessary in order to ensure satisfactory performance.

Molecular Testing and the Pathologist's Role in Clinical Trials of Breast Cancer

Molecular characterization of breast cancer is pivotal for identifying new molecular targets and determining the appropriate treatment choices. Advances in molecular profiling technology have given greater insight into this heterogeneous disease, over and above hormone receptor and human epidermal growth factor receptor 2 status. Agents targeting recently characterized molecular biomarkers are under clinical development; the success of these targeted agents is likely to depend on identifying the patient population most likely to benefit. Therefore, clinical trials of breast cancer often require prescreening for, or stratification by, relevant molecular markers or exploratory analyses of biomarkers that can predict or monitor the response to treatment. Consequently, the role of the pathologist has become increasingly important. The key considerations for pathologists include tissue availability, ownership of archival tissue, type of diagnostic/biomarker test required, method of sample processing, concordance between different tests and testing centers, and tumor heterogeneity. In the present review, we explore how pathology is used in current clinical trials of breast cancer and describe the various technologies available for molecular testing. Furthermore, the factors required for the successful application of pathology in clinical trials of breast cancer and the issues that can arise and how these can be circumvented are discussed.

Integration of molecular pathology, epidemiology and social science for global precision medicine

The precision medicine concept and the unique disease principle imply that each patient has unique pathogenic processes resulting from heterogeneous cellular genetic and epigenetic alterations and interactions between cells (including immune cells) and exposures, including dietary, environmental, microbial and lifestyle factors. As a core method field in population health science and medicine, epidemiology is a growing scientific discipline that can analyze disease risk factors and develop statistical methodologies to maximize utilization of big data on populations and disease pathology. The evolving transdisciplinary field of molecular pathological epidemiology (MPE) can advance biomedical and health research by linking exposures to molecular pathologic signatures, enhancing causal inference and identifying potential biomarkers for clinical impact. The MPE approach can be applied to any diseases, although it has been most commonly used in neoplastic diseases (including breast, lung and colorectal cancers) because of availability of various molecular diagnostic tests. However, use of state-of-the-art genomic, epigenomic and other omic technologies and expensive drugs in modern healthcare systems increases racial, ethnic and socioeconomic disparities. To address this, we propose to integrate molecular pathology, epidemiology and social science. Social epidemiology integrates the latter two fields. The integrative social MPE model can embrace sociology, economics and precision medicine, address global health disparities and inequalities, and elucidate biological effects of social environments, behaviors and networks. We foresee advancements of molecular medicine, including molecular diagnostics, biomedical imaging and targeted therapeutics, which should benefit individuals in a global population, by means of an interdisciplinary approach of integrative MPE and social health science.

Proceedings of the second international molecular pathological epidemiology (MPE) meeting

Disease classification system increasingly incorporates information on pathogenic mechanisms to predict clinical outcomes and response to therapy and intervention. Technological advancements to interrogate omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, interactomics, etc.) provide widely open opportunities in population-based research. Molecular pathological epidemiology (MPE) represents integrative science of molecular pathology and epidemiology. This unified paradigm requires multidisciplinary collaboration between pathology, epidemiology, biostatistics, bioinformatics, and computational biology. Integration of these fields enables better understanding of etiologic heterogeneity, disease continuum, causal inference, and the impact of environment, diet, lifestyle, host factors (including genetics and immunity), and their interactions on disease evolution. Hence, the Second International MPE Meeting was held in Boston in December 2014, with aims to: (1) develop conceptual and practical frameworks; (2) cultivate and expand opportunities; (3) address challenges; and (4) initiate the effort of specifying guidelines for MPE. The meeting mainly consisted of presentations of method developments and recent data in various malignant neoplasms and tumors (breast, prostate, ovarian and colorectal cancers, renal cell carcinoma, lymphoma, and leukemia), followed by open discussion sessions on challenges and future plans. In particular, we recognized need for efforts to further develop statistical methodologies. This meeting provided an unprecedented opportunity for interdisciplinary collaboration, consistent with the purposes of the Big Data to Knowledge, Genetic Associations and Mechanisms in Oncology, and Precision Medicine Initiative of the US National Institute of Health. The MPE meeting series can help advance transdisciplinary population science and optimize training and education systems for twenty-first century medicine and public health.

Feasibility of linking population-based cancer registries and cancer center biorepositories

PURPOSE

Biospecimen-based research offers tremendous promise as a way to increase understanding of the molecular epidemiology of cancers. Population-based cancer registries can augment this research by providing more clinical detail and long-term follow-up information than is typically available from biospecimen annotations. In order to demonstrate the feasibility of this concept, we performed a pilot linkage between the California Cancer Registry (CCR) and the University of California, Davis Cancer Center Biorepository (UCD CCB) databases to determine if we could identify patients with records in both databases.

METHODS

We performed a probabilistic data linkage between 2180 UCD CCB biospecimen records collected during the years 2005-2009 and all CCR records for cancers diagnosed from 1988-2009 based on standard data linkage procedures.

RESULTS

The 1040 UCD records with a unique medical record number, tissue site, and pathology date were linked to 3.3 million CCR records. Of these, 844 (81.2%) were identified in both databases. Overall, record matches were highest (100%) for cancers of the cervix and testis/other male genital system organs. For the most common cancers, matches were highest for cancers of the lung and respiratory system (93%), breast (91.7%), and colon and rectum (89.5%), and lower for prostate (72.9%).

CONCLUSIONS

This pilot linkage demonstrated that information on existing biospecimens from a cancer center biorepository can be linked successfully to cancer registry data. Linkages between existing biorepositories and cancer registries can foster productive collaborations and provide a foundation for virtual biorepository networks to support population-based biospecimen research.

Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium

Breast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large-scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65-70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa = 0.76), followed by human epidermal growth factor receptor 2 (Kappa = 0.69) and progesterone receptor (Kappa = 0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose-response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96-98%), but yielded many false positives (positive predictive value = 30-32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large-scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker-specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results.

Low cdc27 and high securin expression predict short survival for breast cancer patients

Cell cycle regulators cdc27 and securin participate in control of the mitotic checkpoint and survey the mitotic spindle to maintain chromosomal integrity. This is achieved by their functions in metaphase-anaphase transition, DNA damage repair, enhancement of mitotic arrest and apoptosis. We report on the roles of cdc27 and securin in aneuploidy and prognosis of breast cancer. The study comprises 429 breast cancer patients with up to 22 years of follow-up. DNA content was determined by image cytometry, and immunopositivity for cdc27 and securin was based on tissue microarrays. An inverse association between cdc27 and securin expression was observed in both image cytometric and immunohistochemical analyses. Low cdc27 and high securin expression identified patients with significant difference in disease outcome. Cdc27 and securin immunoexpression identified patients at risk of early cancer death within five years from diagnosis. In multivariate analysis, the combination of cdc27 and securin immunohistochemistry was the strongest predictor of cancer death after lymph node status. We demonstrate, for the first time in human breast cancer, the prognostic value of cdc27 and securin immunohistochemistry. Cdc27 and securin appear promising biomarkers for applications in predicting disease progression, prognostication of individual patients and potential in anti-mitotic drug development.

Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays

Although the molecular classification and prognostic assessment of breast tumors based on gene expression profiling is well established, a number of proteomic studies that propose potential breast cancer biomarkers has not yet led to any new diagnostic, prognostic or predictive test in wide clinical use. This review examines the current status of breast cancer biomarkers, discusses sample types (including plasma, tumor tissue, nipple aspirate and ductal lavage, as well as cell culture models) and different electrophoretic and mass spectrometry methods that have been widely used for the discovery of proteomic biomarkers in breast cancer, and also considers several approaches to biomarker validation. The pathway leading from the initial proteomic discovery and validation process to translation into a clinically useful test is also discussed.

Citations of scientific results and conflicts of interest: the case of mammography screening

Introduction

In 2001, a Cochrane review of mammography screening questioned whether screening reduces breast cancer mortality, and a more comprehensive review in Lancet, also in 2001, reported considerable overdiagnosis and overtreatment. This led to a heated debate and a recent review of the evidence by UK experts intended to be independent.

Objective

To explore if general medical and specialty journals differed in accepting the results and methods of three Cochrane reviews on mammography screening.

Methods

We identified articles citing the Lancet review from 2001 or updated versions of the Cochrane review (last search 20 April 2012). We explored which results were quoted, whether the methods and results were accepted (explicit agreement or quoted without caveats), differences between general and specialty journals, and change over time.

Results

We included 171 articles. The results for overdiagnosis were not quoted in 87% (148/171) of included articles and the results for breast cancer mortality were not quoted in 53% (91/171) of articles. 11% (7/63) of articles in general medical journals accepted the results for overdiagnosis compared with 3% (3/108) in specialty journals (p=0.05). 14% (9/63) of articles in general medical journals accepted the methods of the review compared with 1% (1/108) in specialty journals (p=0.001). Specialty journals were more likely to explicitly reject the estimated effect on breast cancer mortality 26% (28/108), compared with 8% (5/63) in general medical journals, p=0.02.

Conclusions

Articles in specialty journals were more likely to explicitly reject results from the Cochrane reviews, and less likely to accept the results and methods, than articles in general medical journals. Several specialty journals are published by interest groups and some authors have vested interests in mammography screening.

Interdisciplinary education to integrate pathology and epidemiology: towards molecular and population-level health science

In recent decades, epidemiology, public health, and medical sciences have been increasingly compartmentalized into narrower disciplines. The authors recognize the value of integration of divergent scientific fields in order to create new methods, concepts, paradigms, and knowledge. Herein they describe the recent emergence of molecular pathological epidemiology (MPE), which represents an integration of population and molecular biologic science to gain insights into the etiologies, pathogenesis, evolution, and outcomes of complex multifactorial diseases. Most human diseases, including common cancers (such as breast, lung, prostate, and colorectal cancers, leukemia, and lymphoma) and other chronic diseases (such as diabetes mellitus, cardiovascular diseases, hypertension, autoimmune diseases, psychiatric diseases, and some infectious diseases), are caused by alterations in the genome, epigenome, transcriptome, proteome, metabolome, microbiome, and interactome of all of the above components. In this era of personalized medicine and personalized prevention, we need integrated science (such as MPE) which can decipher diseases at the molecular, genetic, cellular, and population levels simultaneously. The authors believe that convergence and integration of multiple disciplines should be commonplace in research and education. We need to be open-minded and flexible in designing integrated education curricula and training programs for future students, clinicians, practitioners, and investigators.

Interdisciplinary education to integrate pathology and epidemiology: towards molecular and population-level health science

In recent decades, epidemiology, public health, and medical sciences have been increasingly compartmentalized into narrower disciplines. The authors recognize the value of integration of divergent scientific fields in order to create new methods, concepts, paradigms, and knowledge. Herein they describe the recent emergence of molecular pathological epidemiology (MPE), which represents an integration of population and molecular biologic science to gain insights into the etiologies, pathogenesis, evolution, and outcomes of complex multifactorial diseases. Most human diseases, including common cancers (such as breast, lung, prostate, and colorectal cancers, leukemia, and lymphoma) and other chronic diseases (such as diabetes mellitus, cardiovascular diseases, hypertension, autoimmune diseases, psychiatric diseases, and some infectious diseases), are caused by alterations in the genome, epigenome, transcriptome, proteome, metabolome, microbiome, and interactome of all of the above components. In this era of personalized medicine and personalized prevention, we need integrated science (such as MPE) which can decipher diseases at the molecular, genetic, cellular, and population levels simultaneously. The authors believe that convergence and integration of multiple disciplines should be commonplace in research and education. We need to be open-minded and flexible in designing integrated education curricula and training programs for future students, clinicians, practitioners, and investigators.

Biospecimens and biorepositories: from afterthought to science

Biospecimens are recognized as critical components of biomedical research, from basic studies to clinical trials and epidemiologic investigations. Biorepositories have existed in various forms for more than 150 years, from early small collections in pathology laboratories to modern automated facilities managing millions of samples. As collaborative science has developed, it has been recognized that biospecimens must be of consistent quality. Recent years have seen a proliferation of best practices and the recognition of the field of "biospecimen science." The future of this field will depend on the development of more evidence-based practices in both the research and clinical settings. As the field matures, educating a new generation of biospecimen/biobanking scientists will be an important need.

Pathology of breast and ovarian cancers among BRCA1 and BRCA2 mutation carriers: results from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA)

BACKGROUND

Previously, small studies have found that BRCA1 and BRCA2 breast tumors differ in their pathology. Analysis of larger datasets of mutation carriers should allow further tumor characterization.

METHODS

We used data from 4,325 BRCA1 and 2,568 BRCA2 mutation carriers to analyze the pathology of invasive breast, ovarian, and contralateral breast cancers.

RESULTS

There was strong evidence that the proportion of estrogen receptor (ER)-negative breast tumors decreased with age at diagnosis among BRCA1 (P-trend = 1.2 × 10(-5)), but increased with age at diagnosis among BRCA2, carriers (P-trend = 6.8 × 10(-6)). The proportion of triple-negative tumors decreased with age at diagnosis in BRCA1 carriers but increased with age at diagnosis of BRCA2 carriers. In both BRCA1 and BRCA2 carriers, ER-negative tumors were of higher histologic grade than ER-positive tumors (grade 3 vs. grade 1; P = 1.2 × 10(-13) for BRCA1 and P = 0.001 for BRCA2). ER and progesterone receptor (PR) expression were independently associated with mutation carrier status [ER-positive odds ratio (OR) for BRCA2 = 9.4, 95% CI: 7.0-12.6 and PR-positive OR = 1.7, 95% CI: 1.3-2.3, under joint analysis]. Lobular tumors were more likely to be BRCA2-related (OR for BRCA2 = 3.3, 95% CI: 2.4-4.4; P = 4.4 × 10(-14)), and medullary tumors BRCA1-related (OR for BRCA2 = 0.25, 95% CI: 0.18-0.35; P = 2.3 × 10(-15)). ER-status of the first breast cancer was predictive of ER-status of asynchronous contralateral breast cancer (P = 0.0004 for BRCA1; P = 0.002 for BRCA2). There were no significant differences in ovarian cancer morphology between BRCA1 and BRCA2 carriers (serous: 67%; mucinous: 1%; endometrioid: 12%; clear-cell: 2%). CONCLUSIONS/IMPACT: Pathologic characteristics of BRCA1 and BRCA2 tumors may be useful for improving risk-prediction algorithms and informing clinical strategies for screening and prophylaxis.

Cancer immunology--analysis of host and tumor factors for personalized medicine

Immune cells in the tumor microenvironment have an important role in regulating tumor progression. Therefore, stimulating immune reactions to tumors can be an attractive therapeutic and prevention strategy. Cancer cells and host cells constantly interact with each other in the tumor microenvironment; thus, cancer immunology is an interdisciplinary area where integrated analysis of both host and tumor factors is needed. Cancer represents a heterogeneous group of diseases with different genetic and epigenetic alterations; therefore, molecular classification of cancer (for example lung, prostate and breast cancers) is an important component in clinical decision making. However, most studies on antitumor immunity and clinical outcome lack analysis of tumor molecular biomarkers. In this Review, we discuss colorectal cancer as a prototypical example of cancer. Common molecular classifiers of colon cancer include KRAS, BRAF and PIK3CA mutations, microsatellite instability, LINE-1 methylation, and CpG island methylator phenotype. Since tumor molecular features and immune reactions are inter-related, a comprehensive assessment of these factors is critical. Examining the effects of tumor-host interactions on clinical outcome and prognosis represents an evolving interdisciplinary field of molecular pathological epidemiology. Pathological immunity evaluation may provide information on prognosis and help identify patients who are more likely to benefit from immunotherapy.

Risk factors by molecular subtypes of breast cancer across a population-based study of women 56 years or younger

Differences in incidence, prognosis, and treatment response suggest gene expression patterns may discern breast cancer subtypes with unique risk factor profiles; however, previous results were based predominantly on older women. In this study, we examined similar relationships in women ≤ 56 years, classified by immunohistochemical staining for estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 for 890 breast cancer cases and 3,432 frequency-matched population-based controls. Odds ratios (OR) and 95% confidence intervals (CI) for tumor subtypes were calculated using multivariate polytomous regression models. A total of 455 (51.1%) tumors were considered luminal A, 72 (8.1%) luminal B, 117 (13.1%) non-luminal HER-2/neu+, and 246 (27.6%) triple negative. Triple negative tumors were associated with breast feeding duration (per 6 months: OR = 0.76, 95% CI 0.64-0.90). Among premenopausal women, increasing body size was more strongly associated with luminal B (OR = 1.73, 95% CI 1.07-2.77) and triple negative tumors (OR = 1.67, 95% CI 1.22-2.28). A history of benign breast disease was associated only with increased risk of luminal A tumors (OR = 1.89, 95% CI 1.43-2.50). A family history of breast cancer was a risk factor for luminal A tumors (OR = 1.93, 95% CI 1.38-2.70) regardless of age, and triple negative tumors with higher risks for women <45 (OR = 5.02, 95% CI 2.82-8.92; P for age interaction = 0.005). We found that little-to-no breastfeeding and high BMI were associated with increased risk of triple negative breast cancer. That some risk factors differ by molecular subtypes suggests etiologic heterogeneity in breast carcinogenesis among young women.

Molecular pathological epidemiology of colorectal neoplasia: an emerging transdisciplinary and interdisciplinary field

Colorectal cancer is a complex disease resulting from somatic genetic and epigenetic alterations, including locus-specific CpG island methylation and global DNA or LINE-1 hypomethylation. Global molecular characteristics such as microsatellite instability (MSI), CpG island methylator phenotype (CIMP), global DNA hypomethylation, and chromosomal instability cause alterations of gene function on a genome-wide scale. Activation of oncogenes including KRAS, BRAF and PIK3CA affects intracellular signalling pathways and has been associated with CIMP and MSI. Traditional epidemiology research has investigated various factors in relation to an overall risk of colon and/or rectal cancer. However, colorectal cancers comprise a heterogeneous group of diseases with different sets of genetic and epigenetic alterations. To better understand how a particular exposure influences the carcinogenic and pathologic process, somatic molecular changes and tumour biomarkers have been studied in relation to the exposure of interest. Moreover, an investigation of interactive effects of tumour molecular changes and the exposures of interest on tumour behaviour (prognosis or clinical outcome) can lead to a better understanding of tumour molecular changes, which may be prognostic or predictive tissue biomarkers. These new research efforts represent 'molecular pathologic epidemiology', which is a multidisciplinary field of investigations of the inter-relationship between exogenous and endogenous (eg, genetic) factors, tumoural molecular signatures and tumour progression. Furthermore, integrating genome-wide association studies (GWAS) with molecular pathological investigation is a promising area (GWAS-MPE approach). Examining the relationship between susceptibility alleles identified by GWAS and specific molecular alterations can help elucidate the function of these alleles and provide insights into whether susceptibility alleles are truly causal. Although there are challenges, molecular pathological epidemiology has unique strengths, and can provide insights into the pathogenic process and help optimise personalised prevention and therapy. In this review, we overview this relatively new field of research and discuss measures to overcome challenges and move this field forward.

Challenges to developing proteomic-based breast cancer diagnostics

Over the past decade, multiple genetic and histological approaches have accelerated development of new breast cancer diagnostics and treatment paradigms. Multiple distinct genetic subtypes of breast cancers have been defined, and this has progressively led toward more personalized medicine in regard to treatment options. There still remains a deficiency in the development of molecular diagnostic assays that can be used for breast cancer detection and pretherapy clinical decisions. In particular, the type of cancer-specific biomarker typified by a serum or tissue-derived protein. Progress in this regard has been minimal, especially in comparison to the rapid advancements in genetic and histological assays for breast cancers. In this review, some potential reasons for this large gap in developing protein biomarkers will be discussed, as well as new strategies for improving these approaches. Improvements in the study design of protein biomarker discovery strategies in relation to the genetic subtypes and histology of breast cancers is also emphasized. The current successes in use of genetic and histological assays for breast cancer diagnostics are summarized, and in that context, the current limitations of the types of breast cancer-related clinical samples available for protein biomarker assay development are discussed. Based on these limitations, research strategies emphasizing identification of glycoprotein biomarkers in blood and MALDI mass spectrometry imaging of tissues are described.