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Accurate and reproducible classification of tumors is essential for clinical management, cancer surveillance, and studies of pathogenesis and etiology. Tumor classification has historically been based on the primary anatomic site or organ in which the tumor occurs and on its morphologic and histologic phenotype. While pathologic criteria are useful in predicting the average behavior of a group of tumors, histopathology alone cannot accurately predict the prognosis and treatment response of individual cancers. Traditional measures such as tumor stage and grade do not take into account molecular events that influence tumor aggressiveness or changes in the tumor composition during treatment. This chapter provides a primer on approaches that use pathology and molecular biology to classify and subclassify cancers. It describes the features of carcinomas, sarcomas, and malignant neoplasms of the immune system and blood, as well as various high-throughput genomic platforms that characterize the molecular profile of tumors.

Background No study has predicted the future incidence rate and annual burden (number) of new cases in the United States of invasive and in situ female breast cancers stratified by the estrogen receptor (ER) status. Methods We constructed forecasts for women age 30 to 84 years in 2011 through 2030 using cancer incidence data from the Surveillance, Epidemiology, and End Results Program, novel age-period-cohort forecasting models, and population projections from the US Census Bureau. Results The total number of new tumors (invasive plus in situ) is expected to rise from 283 000 in 2011 to 441 000 in 2030 (plausible range 353 500 to 466 700 cases). The proportion of all new case patients age 70 to 84 years is expected to increase from 24.3% to 34.8%, while the proportion ages 50 to 69 years is expected to decrease from 54.7% to 43.6%. The proportion of ER-positive invasive cancers is expected to remain nearly the same at 62.6%, whereas the proportion of ER-positive in situ cancers is expected to increase from 19.1% to 28.9%. The proportion of ER-negative cancers (invasive and in situ) is expected to decrease from 16.8% to 8.6%. Conclusions Breast cancer overall will rise in the United States through 2030, especially for ER-positive in situ tumors among women age 70 to 84 years. In contrast, ER-negative invasive and in situ tumors will fall, for reasons that are not fully understood. These results highlight a need to optimize case management among older women, characterize the natural history of in situ cancers, and identify those factors responsible for declining ER-negative incidence.

Breast cancer is a heterogeneous disease, divisible into a variable number of clinical subtypes. A fundamental question is how many etiological classes underlie the clinical spectrum of breast cancer? An etiological subtype reflects a grouping with a common set of causes, whereas a clinical subtype represents a grouping with similar prognosis and/or prediction. Herein, we review the evidence for breast cancer etiological heterogeneity. We then evaluate the etiological evidence with mRNA profiling data. A bimodal age distribution at diagnosis with peak frequencies near ages 50 and 70 years is a fundamental characteristic of breast cancer for important tumor features, clinical characteristics, risk factor profiles, and molecular subtypes. The bimodal peak frequencies at diagnosis divide breast cancer overall into a 'mixture' of two main components in varying proportions in different cancer populations. The first breast cancer tends to arise early in life with modal age-at-diagnosis near 50 years and generally behaves aggressively. The second breast cancer occurs later in life with modal age near 70 years and usually portends a more indolent clinical course. These epidemiological and molecular data are consistent with a two-component mixture model and compatible with a hierarchal view of breast cancers arising from two main cell types of origin. Notwithstanding the potential added value of more detailed categorizations for personalized breast cancer treatment, we suggest that the development of better criteria to identify the two proposed etiologic classes would advance breast cancer research and prevention.

The non-Hodgkin lymphomas (NHLs) are a heterogeneous group of B-cell and T-cell neoplasms that vary by morphological appearance, etiology, biology, and clinical manifestations. Worldwide, the incidence of NHL began to increase circa 1950 (1,2), approximately doubling in the United States between 1975 and 2000 (3,4), and then stabilizing with an age-standardized rate near 20 per 100 000 person-years (3). The age-standardized incidence rate, however, is an age-specific weighted average that may mask important age-related trends (5,6), especially among younger persons. Additionally, whereas recent trends for NHL overall may be related to HIV/AIDS and changes in classification systems or diagnostic practice patterns (2), perinatal and family risk factors also may be important for NHL in early life. Therefore, in this issue of the Journal, Crump et al. (7) conducted a national cohort study in Sweden among young persons aged 0–37 years, who were newly diagnosed with NHL between 1973 and 2008 and followed through 2009. The authors linked Swedish birth and cancer registries data for NHL, family characteristics, and the perinatal period (variously defined as the days immediately before and after birth). Linkage to the national birth and cancer registries was nearly 100% complete. More than 99% of the original cohort remained after exclusions for missing data regarding gestational age at birth and/or birth weight. There were 936 NHL patients with 66.3 million person-years of follow-up. The overall incidence rate was 1.4 per 100 000 person-years (1.7 for men and boys and 1.1 for women and girls). Age-specific incidence rates rose with advancing age at diagnosis, like rates of epithelial carcinomas (8). For a similar period, incidence rate patterns were slightly higher among young persons in the United States than in Sweden (Figure 1), using the National Cancer Institute's Surveillance, Epidemiology, and End Results 9 Registries Database (SEER, 1973–2008) (9). There were 15 089 NHL patients with 527 million person-years of follow-up among persons of the youngest nine of 19 SEER age groups (ie, ages 00, 01–04, 05–09, … 35–39 years). The age-standardized incidence rate (adjusted to the World Standard Million population) for these nine age intervals combined was 2.37 per 100 000 person-years (3.15 for men and boys and 1.58 for women and girls) (Figure 1, A). The estimated annual percentage change for the age-standardized rate was 1.26% and 1.91% among men and boys and women and girls, respectively. Age-specific rates rose continuously with advancing aging (Figure 1, B), as in Sweden. Figure 1 Age-standardized incidence rates and age-specific incidence rates, by sex, for non-Hodgkin lymphoma among Americans aged 0–39 years. Incidence rates and 95% confidence intervals (shaded areas) are based on November 2010 data from the National ... In the Swedish study (7), relative risks of non-Hodgkin lymphoma were presented as hazard ratios (HRs) unadjusted and adjusted for sex, birth year, fetal growth, gestational age at birth, twinning, birth order, maternal age at delivery, maternal and paternal education level, and family history of NHL. Independent risk factors were family history of NHL, high fetal growth, older maternal age, low birth order, and male sex. Based on small numbers, the strongest risk factor was for family history of NHL in either a sibling (HR = 9.84) or parent (HR = 2.36) with a greater risk for a same-sex sibling or parent. High fetal growth weight that was two standard deviations or more from the reference birth weight, by gestational age and sex, was associated with increased risk of NHL (HR = 1.64). Maternal age was an important confounder for low birth order with an inverse relationship between NHL and birth order after adjusting for maternal age. Male sex was an age-specific effect modifier, with a twofold increase in NHL among boys who were aged younger than 15 years but not older. In the United States, there was also a relative excess of NHL among younger men compared with women (Figure 1, B) with a male to female incidence rate ratio of 2.7 (95% CI = 1.9 to 3.7) between ages 5–9 years and 2.3 (95% CI = 1.7 to 3.0) between ages 10–14 years. When incidence of NHL subtypes was examined (7), family history and older maternal age were associated with increased risk for diffuse B-cell subtypes. Family history was not estimable for the other subtypes. Older maternal age was associated with a borderline increased risk for T-cell tumors. A reciprocal birth cohort effect was observed with decreasing risk by year of birth for diffuse B-cell and increasing risk for other B-cell and T-cell tumors. Although it is important to determine whether the etiology of NHL differs by subtype, subtype trends in this study are somewhat uncertain because of small sample sizes and missing data (35% missing subtype, 332 of 936 subjects). Additionally, although the distinction between B-cell and T-cell tumors is generally straightforward, the categorization for B-cell and T-cell subtypes has evolved over time, raising concern for calendar period effects due to changing classification systems or improved diagnostic techniques. The authors attempted to rule out calendar period effects and to confirm birth cohort or exposure effects by accommodating different patterns of missingness. First, they distributed the subjects with missing subtype data in the observed proportions of known subjects with diffuse B-cell tumors (50%), other B-cell tumors (40%), and T-cell tumors (10%) and then recalculated the subtype trends. Second, missing subtype data were redistributed by the observed proportions of known subjects for each year of birth, age at diagnosis, and sex. Birth cohort effects persisted and remained statistically significant in these sensitivity analyses. Alternatively, population-based birth cohort effects could have been assessed with age–period–cohort models (10). Notwithstanding the well-documented identifiability issues for the age–period–cohort framework (11), cohort deviations or changes are estimable functions that have the added value of being fully adjusted for age and period effects. In the United States SEER population (Figure 2), we could show statistically significant decelerating (concave parabola) cohort deviations or changes for diffuse B-cell tumors (P < .001) and accelerating (convex parabola) deviations for T-cell tumors (P < .001) but not for other B-cell tumors (P = .28). Figure 2 Birth cohort deviations in US incidence of non-Hodgkin lymphoma (NHL) by histopathologic subtype. The National Cancer Institute's Surveillance, Epidemiology, and End Results 9 Registries Database (SEER 9, 1973–2008) was used to determine age–period–cohort ... In conclusion, the study by Crump et al. (7) is a national cohort study of perinatal and family characteristics associated with the diagnosis of NHL among children and young adults in Sweden. Limitations included the unavailability of many host and environmental risk factors for NHL such as infectious exposures, radiation, environmental contaminants, and primary or inherited immune deficiencies. Nonetheless, their large-scale and population-based design minimized selection bias and maximized generalizable conclusions for associations between NHL in early life and family history, high fetal growth weight, older maternal age, low birth order, and male sex. However, risk estimates for family history were based on small numbers of case patients with an affected sibling (n = 4) or parent (n = 10). Pooled studies with larger sample sizes will be required to further assess the potential role of gene–environmental exposures for family history of NHL. In contrast to Crump et al. (7), a large meta-analysis showed no statistically significant association between high or low birth weight and NHL (12), and the authors concluded that birth weight might not be a good metric for an association between fetal growth and lymphoma (12). Advancing maternal age appears to be a risk factor for many childhood cancers possibly due to differential gene expression in cell cycle control and DNA damage repair mechanisms among older women, age-related mutations in oocyte genes, increasing use of fertility technology by older mothers, or changes in maternal hormonal exposures with advancing age (13). The association of low birth order after adjustment for maternal age is consistent with the hypothesis of “reduced or delayed exposure” to first viral or bacterial infection in infancy due to the absence of other siblings (14); however, others have found little support for an association between NHL and birth order or family size (15,16). Finally, though male predominance in the incidence of cancer is well established (17), the age interaction or effect modification by sex before age 15 years is not easily explained.

Albain et al. (1) present provocative data from Southwest Oncology Group (SWOG) clinical trials indicating that African American patients with breast or other sex-specific cancers are less likely than white patients to survive their disease even when they receive equal treatment. The authors posit that this survival gap most likely reflects racial differences in tumor and host biology. We analyzed data from the Surveillance, Epidemiology, and End Results (SEER) program to explore racial survival disparity for breast cancer in the general population (2). Surprisingly, little disparity was explained by differences in tumor biology, such as higher prevalence of estrogen receptor–negative disease among African American women. Rather, the disparity was mostly explained by the consistently higher risk for breast cancer death among African American patients irrespective of estrogen receptor expression, especially during the first few years after diagnosis. Previously, reports from the National Surgical Adjuvant Breast and Bowel Project (NSABP) (3,4) showed that African American and white patients with breast cancer who had similar diagnosis and treatment experienced comparable benefits of treatment. Our results and those from the NSABP suggest that unequal access to quality cancer care is a major driver of disparity, not simply racial differences in tumor biology. How can one reconcile the SWOG, SEER, and NSABP studies? Media reports on the SWOG study stated that African American patients with “equal care” are more likely to “die of breast cancer” than white patients. In fact, the primary SWOG analysis demonstrates that African American patients who received the same investigational treatments were ultimately more likely to die of all causes than white patients. It is well known that African American patients with breast cancer have substantially higher competing mortality than white patients (5). Hence, use of all-cause mortality as the endpoint in the SWOG analysis, rather than breast cancer–specific survival as in the SEER and NSABP studies, may yield upwardly biased estimates of the relative rate of death from breast cancer. This potential bias is particularly relevant for tumors such as breast cancers with comparatively long times to recurrence and death. Albain et al. briefly describe a sensitivity analysis that attempts to substitute for standard analysis of breast cancer–specific survival, but it is hard to see how this can be done effectively, given that the SWOG did not have “detailed cause of death data.” It is also difficult to see how participants in breast cancer trials would receive equal health care after the trials were closed. Rather, all-cause mortality rates observed in the SWOG studies may reflect an initial period with equal cancer care provided by the trials, followed by a longer period of overall care received in the general medical community. If so, it is not clear how the SWOG study can illuminate racial differences in breast tumor biology free of the effects of racial differences in access to health care. Our working model is that biology and access to health care are both key determinants of racial disparity for breast and other cancers. Biology is certainly important (6), but consistent access to quality cancer care is a crucial issue for cancer-specific and all-cause mortality.

BACKGROUND: In the United States, a black-to-white disparity in age-standardized breast cancer mortality rates emerged in the 1980s and has widened since then. METHODS: To further explore this racial disparity, black-to-white rate ratios (RRs(BW)) for mortality, incidence, hazard of breast cancer death, and incidence-based mortality (IBM) were investigated using data from the National Cancer Institute's Surveillance, Epidemiology, and End Results program on 244 786 women who were diagnosed with breast cancer from January 1990 through December 2003 and followed through December 2004. A counterfactual approach was used to examine the expected IBM RRs(BW), assuming equal distributions for estrogen receptor (ER) expression, and/or equal hazard rates of breast cancer death, among black and white women. RESULTS: From 1990 through 2004, mortality RR(BW) was greater than 1.0 and widened over time (age-standardized breast cancer mortality rates fell from 36 to 29 per 100 000 for blacks and from 30 to 22 per 100 000 for whites). In contrast, incidence RR(BW) was generally less than 1.0. Absolute hazard rates of breast cancer death declined substantially for ER-positive tumors and modestly for ER-negative tumors but were persistently higher for blacks than whites. Equalizing the distributions of ER expression in blacks and whites decreased the IBM RR(BW) slightly. Interestingly, the black-to-white disparity in IBM RR(BW) was essentially eliminated when hazard rates of breast cancer death were matched within each ER category. CONCLUSIONS: The black-to-white disparity in age-standardized breast cancer mortality was largely driven by the higher hazard rates of breast cancer death among black women, diagnosed with the disease, irrespective of ER expression, and especially in the first few years following diagnosis. Greater emphasis should be placed on identifying the etiology of these excess hazards and developing therapeutic strategies to address them.