Cancer heterogeneity, long recognized as an important clinical determinant of patient outcomes, was poorly understood at a molecular level. Genomic studies have significantly improved our understanding of heterogeneity, and have pointed to ways in which heterogeneity might be understood and defeated for therapeutic effect. Recent studies have evaluated intratumoral heterogeneity within the primary tumor, as well as heterogeneity observed between primary and metastasis. The existence of clonal heterogeneity in the primary and metastasis also affects response to therapy, since the Darwinian pressures of systemic therapy result in clonal selection for initially rare variants. Novel technologies (such as measurements of circulating tumor cells and circulating tumor DNA) may allow physicians to monitor the emergence of clonal subtypes and intervene at an early point to improve patient prognosis.
Cancer is a heterogeneous disease. Practically from the moment pathologists first looked at human cancers under the microscope, they saw that differing histologic appearances could define distinct subtypes of cancers from the same primary site of origin. These histology-based definitions of cancer subtypes have been modified and refined over time to elucidate both prognosis and prediction of response to specific treatments. Molecular data have revealed how radically different cancers from one primary site can be, and using this information, we have refined and in many cases revolutionized cancer classification systems. Oncologists can now use knowledge of the intertumor heterogeneity of each cancer type to treat patients with more personalized and targeted therapies that lead to better outcomes.
However, heterogeneity in cancer is not limited to differences between different patients, but also occurs within a single patient (Figure 1). This intrapatient or intratumoral heterogeneity can present great challenges for cancer treatment. When oncologists first began to treat patients with systemic therapy, they noticed that some metastatic deposits grew while others shrank. Such mixed responses still occur, suggesting that intratumoral heterogeneity is a living force that follows classic Darwinian patterns. However, it is still currently unclear what level of heterogeneity for a drug target is required to thwart the drug’s successful elimination of the cancer.
Studies of intratumoral heterogeneity have proliferated in recent years. These studies have developed from two converging sources. First, recent years have seen the creation of numerous agents that target specific biologic pathways, each with its own particular resistance mechanisms. Secondly, the “omics” revolution has revealed the true nature of intratumoral heterogeneity at multiple levels—and such epigenomic, genomic, and proteomic analyses have enriched our understanding of why cancers grow and kill.
Doctors and patients deal with tumor heterogeneity on a daily basis. A pathologist notes that some tumor cells in a breast cancer are amplified for human epidermal growth factor receptor 2 (HER2) by fluorescence in situ hybridization (FISH), while others express normal HER2 copy numbers. A medical oncologist, treating a patient with metastatic colorectal cancer with epidermal growth factor receptor (EGFR)-targeted therapy, observes growth of one liver metastasis while another shrinks.
This heterogeneity at the molecular, cellular, tissue, and organ levels bedevils clinical cancer care because it allows cancers to evolve and evade available therapeutics. Wherever significant heterogeneity exists, it serves as a potential threat to the life of the patient. However, we currently understand very little about what type of heterogeneity is the most threatening.
What have we learned regarding tumor heterogeneity in recent years? What are its sources and how does it manifest itself? How does it affect the natural history of human cancers—and their unnatural history in the face of cancer therapeutics? This paper will discuss recent observations derived from molecular and clinical/translational studies that help elucidate the mystery of tumor heterogeneity.
Defining Clinically Relevant Heterogeneity
Some level of heterogeneity exists in all cancers, but defining what the most clinically relevant cell populations are in a cancer will depend heavily on its definition and on the setting of critical thresholds. Immunohistochemical assays of HER2 have suggested the existence of heterogeneity in anywhere from < 1% to 30% of tumors, although a carefully performed recent analysis suggests a rate of 5% for FISH. However, the frequency of a positive result will depend on both the definition of a single “positive cell,” as well as the threshold set for the percentage of positive cells in the population that will qualify the result as positive.
What percentage of cells needs to be positive for a specific biomarker or mutation to consider the cancer “positive” and likely to respond to a specific therapy? Clearly a breast cancer with 1% estrogen receptor (ER) positivity is not likely to respond to hormonal therapy to the same extent as one with greater than 95% of cells positive, yet both are considered ER-positive breast cancers and candidates for hormonal therapy. The heterogeneity present in the low ER expresser is clinically relevant and will make it more likely that additional therapeutic modalities will be required for successful treatment. The original study by Harvey et al examining the relationship between ER expression levels and response to hormonal therapy showed the best disease-free survival (DFS) curves for patients with the highest levels of ER expression (Allred scores of 7–8). However, because the patients with mid- to low-level ER expression (Allred scores of 3–5) still had better DFS than those with < 1% or no expression (Allred scores of < 2), it was recommended that the threshold for offering treatment be an Allred score of 3 or greater (corresponding to at least 1% of cells with weak staining). While few studies have been able to demonstrate that the relative degree of benefit of hormonal therapy is in a direct or linear relationship to the level of hormone receptor expression, current American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) hormone receptor testing guidelines for breast cancer recommend the positive threshold of 1% of cells weakly positive but also recommend as standard the reporting of the percentage and intensity of cells staining positive (not just a positive or negative result).
The distribution of heterogeneous cell populations may be clinically significant as well. For HER2 testing by in situ hybridization (ISH), amplified cells can be present diffusely (as in the classic HER2-amplified breast cancer) or as a minor population in intermixed or clustered patterns.[4,5] Although there are limited data to suggest significant differences in outcomes between clustered and intermixed minority HER2-amplified cells, the 2013 ASCO/CAP HER2 testing guideline update for breast cancer recommends counting clustered cell populations separately. As Figure 2 illustrates, these clustered HER2-positive minority populations may be evident on immunohistochemistry and missed on ISH testing unless the entire area is hybridized and scanned under the fluorescent microscope. Interestingly, the majority of breast cancers with results in the equivocal range or close to the threshold for positivity have the intermixed form of HER2 heterogeneity, still considered to be of uncertain clinical significance.
Sometimes heterogeneity for the same biomarker can have different implications in different cancer types. Heterogeneity for HER2 gene amplification is more common in gastroesophageal and gastric cancers than in breast cancers.[6-8] Therefore, current recommendations for HER2 testing of gastric cancers (based on results of the ToGA trial) have been modified from the ASCO/CAP guidelines on HER2 testing in breast cancer to allow for smaller clusters of positive cells to be interpreted as a positive result in gastric biopsies.[4,9]
Ideally, the definitions and thresholds of these heterogeneous markers will be clinically validated so they can optimally predict outcomes and treatment benefit. However, because borderline cases are often not a large proportion of clinical trial populations, we are often left to use limited evidence or nonclinical data to make decisions about these definitions. In addition, it is possible that for some drug targets, heterogeneity may not be as clinically significant as for others. For example, the death of the targeted population might elicit a host immune response such that newly exposed antigens could result in an immune attack on the remainder of the cancer cells.
1. Perez EA, Press MF, Dueck AC, et al. Immunohistochemistry and fluorescence in situ hybridization assessment of HER2 in clinical trials of adjuvant therapy for breast cancer (NCCTG N9831, BCIRG 006, and BCIRG 005). Breast Cancer Res Treat. 2013;138:99-108.
2. Harvey JM, Clark GM, Osborne CK, Allred DC. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J Clin Oncol. 1999;17:1474-81.
3. Hammond ME, Hayes DF, Dowsett M, et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer (unabridged version). Arch Path Lab Med. 2010;134:e48-72.
4. Wolff AC, Hammond ME, Hicks DG, et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. J Clin Oncol. 2013;31:3997-4013.
5. Allison KH, Dintzis SM, Schmidt RA. Frequency of HER2 heterogeneity by fluorescence in situ hybridization according to CAP expert panel recommendations: time for a new look at how to report heterogeneity. Am J Clin Pathol. 2011;136:864-71.
6. Hofmann M, Stoss O, Shi D, et al. Assessment of a HER2 scoring system for gastric cancer: results from a validation study. Histopathology. 2008;52:797-805.
7. Albarello L, Pecciarini L, Doglioni C. HER2 testing in gastric cancer. Adv Anat Pathol. 2011;18:53-9.
8. Pirrelli M, Caruso M, Di Maggio M, et al. Are biopsy specimens predictive of HER2 status in gastric cancer patients? Digest Dis Sci. 2013;58:397-404.
9. Wolff A, Hammond M, Schwartz J, et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. J Clin Oncol. 2007;25:118-45.
10. van Kessel CS, Samim M, Koopman M, et al. Radiological heterogeneity in response to chemotherapy is associated with poor survival in patients with colorectal liver metastases. Eur J Cancer. 2013;49:2486-93.
11. Kandoth C, McLellan MD, Vandin F, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013;502:333-9.
12. Lawrence MS, Stojanov P, Polak P, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013;499:214-8.
13. Stransky N, Egloff AM, Tward AD, et al. The mutational landscape of head and neck squamous cell carcinoma. Science. 2011;333:1157-60.
14. Gerlinger M, Horswell S, Larkin J, et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat Genet. 2014;46:225-33.
15. Wang Y, Waters J, Leung ML, et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature. 2014;512:155-60.
16. Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883-92.
17. Harbst K, Lauss M, Cirenajwis H, et al. Molecular and genetic diversity in the metastatic process of melanoma. J Pathol. 2014;233:39-50.
18. Penault-Llorca F, Coudry RA, Hanna WM, et al. Experts’ opinion: Recommendations for retesting breast cancer metastases for HER2 and hormone receptor status. Breast. 2013;22:200-2.
19. Jeselsohn R, Yelensky R, Buchwalter G, et al. Emergence of constitutively active estrogen receptor-alpha mutations in pretreated advanced estrogen receptor-positive breast cancer. Clin Cancer Res. 2014;20:1757-67.
20. Barone I, Brusco L, Fuqua SA. Estrogen receptor mutations and changes in downstream gene expression and signaling. Clin Cancer Res. 2010;16:2702-8.
21. Fuqua SA, Gu G, Rechoum Y. Estrogen receptor (ER) alpha mutations in breast cancer: hidden in plain sight. Breast Cancer Res Treat. 2014;144:11-9.
22. Sighoko D, Liu J, Hou N, et al. Discordance in hormone receptor status among primary, metastatic, and second primary breast cancers: biological difference or misclassification? Oncologist. 2014;19:592-601.
23. Chen ZY, Zhong WZ, Zhang XC, et al. EGFR mutation heterogeneity and the mixed response to EGFR tyrosine kinase inhibitors of lung adenocarcinomas. Oncologist. 2012;17:978-85.
24. Diaz LA Jr, Williams RT, Wu J, et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature. 2012;486:537-40.
25. Watanabe T, Kobunai T, Yamamoto Y, et al.5Heterogeneity of KRAS status may explain the subset of discordant KRAS status between primary and metastatic colorectal cancer. Dis Colon Rectum. 2011;54:1170-8.
26. Landau DA, Carter SL, Stojanov P, et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell. 2013;152:714-26.
27. Ding L, Ley TJ, Larson DE, et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature. 2012;481:506-10.
28. Pfeifer H, Lange T, Wystub S, et al. Prevalence and dynamics of bcr-abl kinase domain mutations during imatinib treatment differ in patients with newly diagnosed and recurrent bcr-abl positive acute lymphoblastic leukemia. Leukemia. 2012;26:1475-81.
29. Hochhaus A, Saglio G, Larson RA, et al. Nilotinib is associated with a reduced incidence of BCR-ABL mutations vs imatinib in patients with newly diagnosed chronic myeloid leukemia in chronic phase. Blood. 2013;121:3703-8.
30. Newman AM, Bratman SV, To J, et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med. 2014;20:548-54.
31. Stephens PJ, Tarpey PS, Davies H, et al. The landscape of cancer genes and mutational processes in breast cancer. Nature. 2012;486:400-4.
32. Campbell PJ, Yachida S, Mudie LJ, et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature. 2010;467:1109-13.