AV performed the quantitation of the microscopic features and was involved in the project design, statistical analysis, and writing of the manuscript. JF is responsible for the statistical analysis. JB performed sequencing of
The authors have declared that no competing interests exist.
In melanoma, morphology-based classification systems have not been able to provide relevant information for selecting treatments for patients whose tumors have metastasized. The recent identification of causative genetic alterations has revealed mutations in signaling pathways that offer targets for therapy. Identifying morphologic surrogates that can identify patients whose tumors express such alterations (or functionally equivalent alterations) would be clinically useful for therapy stratification and for retrospective analysis of clinical trial data.
We defined and assessed a panel of histomorphologic measures and correlated them with the mutation status of the oncogenes
Refined morphological classification of primary melanomas can be used to improve existing melanoma classifications by forming subgroups that are genetically more homogeneous and likely to differ in important clinical variables such as outcome and pattern of metastasis. We expect this information to improve classification and facilitate stratification for therapy as well as retrospective analysis of existing trial data.
Boris Bastian and colleagues present a refined morphological classification of primary melanomas that can be used to improve existing melanoma classifications by defining genetically homogeneous subgroups.
Skin cancers—the most commonly diagnosed cancers worldwide—are usually caused by exposure to ultraviolet (UV) radiation in sunlight. UV radiation damages the DNA in skin cells and can introduce permanent genetic changes (mutations) into the skin cells that allow them to divide uncontrollably to form a tumor, a disorganized mass of cells. Because there are many different cell types in the skin, there are many types of skin cancer. The most dangerous type—melanoma—develops when genetic changes occur in melanocytes, the cells that produce the skin pigment melanin. Although only 4% of skin cancers are melanomas, 80% of skin cancer deaths are caused by melanomas. The first signs of a melanoma are often a change in the appearance or size of a mole (a pigmented skin blemish that is also called a nevus) or a newly arising pigmented lesion that looks different from the other moles (an “ugly duckling”). If this early sign is noticed and the melanoma is diagnosed before it has spread from the skin into other parts of the body, surgery can sometimes provide a cure. But, for more advanced melanomas, the outlook is generally poor. Although radiation therapy, chemotherapy, or immunotherapy (drugs that stimulate the immune system to kill the cancer cells) can prolong the life expectancy of some patients, these treatments often fail to remove all of the cancer cells.
Now, however, scientists have identified some of the genetic alterations that cause melanoma. For example, they know that many melanomas carry mutations in either the
The researchers examined several histomorphological features in more than 300 melanoma samples and used statistical methods to correlate these features with the mutation status of
These findings suggest that an improved classification of melanomas that combines an analysis of known genetic factors with histomorphological features might divide melanomas into subgroups that are likely to differ in terms of their clinical outcome and responses to targeted therapies when they become available. Additional studies are needed to investigate whether the histomorphological features identified here can be readily assessed in clinical settings and whether different observers will agree on the scoring of these features. The classification model defined by the researchers also needs to be validated and refined in independent groups of patients. Nevertheless, these findings represent an important first step toward helping clinicians improve outcomes for patients with melanoma.
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Cutaneous melanomas can vary significantly in their clinical and histopathological appearance, which has lead to the development and refinement of morphologically based classification systems. The current World Health Organization (WHO) classification of skin tumors [
In order to establish whether a disease is composed of distinct subtypes it is necessary to integrate clinicopathological features with the underlying biologic factors and identify disease groups that are homogeneous in their etiology, clinical behavior, and management requirements. These subtypes, if found, would then form the basis of a clinically useful classification system. In cancer, genetic alterations can serve as biomarkers for classification purposes, in particular if they are causally linked to the disease process. With the advent of therapeutics targeted to genes and signaling pathways, these oncogenic mutations are gaining direct importance for clinical management. But knowledge of the critical constitutional and somatic genetic factors is currently incomplete. Since the clinical characteristics of a tumor must be, at least in part, genetically determined, elucidation of the phenotypic consequences of currently known genetic factors will reveal aspects of their function and highlight gaps in knowledge, which may guide future discovery. More immediately, clinicopathological features that are associated with specific genetic factors provide an attractive basis on which to build a refined classification system that is practical to implement and useful for patient management.
Recently, we described distinct patterns of chromosomal aberrations and mutations in oncogenes such as
A total of 302 cases of primary cutaneous melanoma that have been part of previous genetic analyses were included in this study [
All histopathological evaluations were carried out on routinely stained HE sections, processed through the UCSF Dermatopathology Service. Cases were classified into SSM, LMM, NM, and, ALM according to the WHO classification, or not classifiable (NC) if they did not fit unequivocally in any of these categories [
All sections of tissue that were available for each specimen were examined, and a semiquantitative assessment of each histological feature for a tumor was obtained from the combined analysis of all of the sections. Except where stated otherwise, the morphologic assessments were made in areas of radial growth, excluding areas of vertical growth. Radial growth phase (RGP) and vertical growth phase (VGP) were defined as described by Clark and Elder [
The proportion of intraepidermal melanocytes present above the basal layer, irrespective of whether suprabasal melanocytes were arranged singly or as nests, was graded from 0 to 3 (
(A) Scatter of intraepidermal melanocytes.
(B) Nesting of intraepidermal melanocytes.
(C) Cytoplasmic pigmentation of neoplastic melanocytes.
(D) Cell shapes.
Intraepidermal melanocytes were defined as arranged in nests rather than single cells if they formed clusters of five or more cells no matter where they were located, e.g., whether within the basal epidermis or in higher layers of the epidermis (
Pigmentation was defined as melanin accumulation within the constituent melanocytes and was scored on a five-point scale using 20× and 40× objectives (
The contour of the epidermis involved by the RGP of the melanoma was compared to the adjacent normal epidermis (
Lateral circumscription was assessed by examining the transition of the intraepidermal growth portion of the tumor to normal skin at the tumor periphery. The area with the most gradual transition in any of the tissue pieces (
The degree of solar elastosis was classified on an 11-category scale ranging from 0 to 3+ by examining the normal skin surrounding the melanoma using the 10× and 20× lenses, as described previously [
Size and shape of tumor cells and their nuclei were assessed in the most cellular portion of the tumor using a 20× lens. Nuclei of small lymphocytes, which we determined ranged in size from 4 to 5 μm, were used as a size reference. Visual assessment of size was quantified on a scale from 1 to 3. Tumor cells were considered 1, small; 2, medium; or 3, large, if the greatest diameter was <8 μm, between 8 and 10 μm, and >10 μm, respectively (
Cell shape was visually assessed at 20× in the most cellular portion of the tumor using (
Sizes and shapes of cells were also determined quantitatively in 264 samples of the cohort using photomicrographs taken with a 20× lens and averaging ten random cells within a representative area (
Ulceration was defined as the presence of a full-thickness epidermal defect; evidence of host response (i.e., fibrin deposition, neutrophils); and thinning, effacement, or reactive hyperplasia of the surrounding epidermis [
Tumor thickness was measured in millimeters according to Breslow [
The univariate analyses for binary outcomes (e.g.,
Clinical information,
Clinical Characteristics of Patients and Melanomas by Mutation Status of
All features were visually scored in the 302 tumors by one of us (AV). Interobserver agreement was determined by having an additional observer (JB) score a randomly selected subset of 50 cases. The scorers were blinded to knowledge of the genetic status of the tumors until the scoring was complete. Kappa statistics [
Morphologic Features of Melanomas by Mutation Status of
Variables significantly associated with
Comparison of Morphological Variables Depending on the Mutation Status of
Given the significant univariate correlations, we employed several multivariate statistical approaches to define the most powerful combinations of variables for prediction of mutation status. These analyses produced a consistent set of variables that included the morphological features pigmentation, upward scatter, nesting of intraepidermal melanocytes, and cell shape. In addition, a younger age at diagnosis was independently associated with
Prediction trees for
(A) Terminal nodes display heatmaps showing samples by mutation status, ordered and coded as in
(B) The prediction tree for
Although intuitive to interpret, single classification trees are inherently unstable in the sense that slight perturbations in the dataset used for tree building may lead to large changes in the tree topology, e.g., several trees with approximately the same discrimination power can be found. Resampling-based algorithms are well-known solutions for improving the accuracy of single classification trees. We used the Random Forest classifier, a method that grows many classification trees, each based on a different subset containing on average 66% of the samples. The resulting collection of individual trees is referred to as “random forest.” The final prediction for each sample is the outcome of the majority vote of those trees in the forest that did not include that sample in their development. The unbiased estimate of the error is given by 1, prediction accuracy [
While the elementary morphological characteristics that contribute to the WHO classification system have a very strong relationship to
In contrast to
We did not have sufficient outcome information of the patients in our cohort to test for differences between the melanoma groups formed by the classification tree of
In this study we demonstrate that histopathological features of the primary tumor, many of which have been part of the WHO classification, provide substantial information on the mutation status of an important melanoma gene,
Results of genotype-phenotype studies can be applied in two directions. Starting with the genetics, the resulting phenotypes may allow generation of hypotheses concerning the function of a genetic factor. For example, our observation of increased melanin pigment in the tumor cells in melanomas with
In the other direction, phenotypic characteristics that are conventionally assessed in the clinic can be used to develop a practical classification system in which the classes are genetically homogeneous, and thus more likely to provide information relevant to patient management. Such a system may provide valuable, outcome-related information from its classes even before the genetic factors of each class are completely understood. Such a classification system is likely to have an important clinical impact. To achieve this goal the practicability and interobserver reproducibility of the criteria that we defined need to be assessed in a clinical setting and the classification algorithms validated on independent cohorts of cases. Such efforts have been initiated and shown good to excellent interobserver agreements in scoring the features used in this study among expert pathologists (to be published separately).
Among the few variables in our study that overlapped with data recorded in large patient registries, age was the variable that best predicted
The presence of
Disease classification evolves from descriptions of a combination of symptoms (syndromes), to more refined definitions that integrate underlying causes. The increase in knowledge of underlying causative genetic alterations in melanoma offers an opportunity to reassess the syndromic classification scheme that emerged from the Sydney classification [
In the distant future when genetic knowledge is complete and targeted therapeutic options are numerous, the appropriate clinical work up of a patient is likely to be quite different from current practice. While initial diagnosis and aspects of staging will require histopathology, more subtle distinctions will employ molecular analysis. Reaching this goal requires following a complex, uncertain path of incremental advances built on new discoveries and continuous refinement of perceived relationships among disparate types of knowledge. Here we have investigated the relationship of elementary histopathological characteristics of melanomas to the mutation status of two genes,
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(A) Epidermal contour, (B) lateral circumscription of tumors, (C) cell size.
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The data are shown by mutation status of
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IQR, interquartile range.
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Alternative assessments of pigmentation as well as morphometric assessments of cellular and nuclear sizes and shapes show similar associations with the mutation status of
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Base prediction rate (defined as proportion of the minority class) were 47, 26, 44, and 31% for the entire cohort in each of the four pair-wise comparisons, respectively. Multivariate Logistic Regressions were fit with the variables selected using the single tree (tree) and using the Bayesian information criterion (BIC). The misclassification rate is shown for the entire cohort as well as for samples for which none of the variables are missing. For methods that allow extraction of the independently associated variables those that were found to be significant are shown in the final column.
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Total percentages that do not add up to 100% have missing values.
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Site of first metastasis in patients who are older and younger than 55 y of age recorded in the Southern German Tumor Registry, with Chi-squared
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acral lentiginous melanoma
chronic sun-induced damage
lentigo maligna melanoma
not classifiable
nodular melanoma
odds ratio
radial growth phase
superficial spreading melanoma
World Health Organization