An analysis of genetic heterogeneity in untreated cancers

Abstract

Genetic intratumoural heterogeneity is a natural consequence of imperfect DNA replication. Any two randomly selected cells, whether normal or cancerous, are therefore genetically different. Here, we review the different forms of genetic heterogeneity in cancer and re-analyse the extent of genetic heterogeneity within seven types of untreated epithelial cancers, with particular regard to its clinical relevance. We find that the homogeneity of predicted functional mutations in driver genes is the rule rather than the exception. In primary tumours with multiple samples, 97% of driver-gene mutations in 38 patients were homogeneous. Moreover, among metastases from the same primary tumour, 100% of the driver mutations in 17 patients were homogeneous. With a single biopsy of a primary tumour in 14 patients, the likelihood of missing a functional driver-gene mutation that was present in all metastases was 2.6%. Furthermore, all functional driver-gene mutations detected in these 14 primary tumours were present among all their metastases. Finally, we found that individual metastatic lesions responded concordantly to targeted therapies in 91% of 44 patients. These analyses indicate that the cells within the primary tumours that gave rise to metastases are genetically homogeneous with respect to functional driver-gene mutations, and we suggest that future efforts to develop combination therapies have the potential to be curative.

Fig. 1:. Clonal sweeps give rise to driver gene mutation homogeneity.

Subclonal cells containing different driver genes emerge over time. Subclones of cells with different driver gene mutations are colored yellow, orange, red, or green. a | Driver gene mutation heterogeneity in a small lesion. b | Lesion grows with the expansion of both the yellow and the red subclones. Some subclones may progress, others remain stable or regress. c | The red subclone sweeps through the lesion and eradicates the preexisting driver gene heterogeneity harbored by the yellow subclone. New driver gene mutations in another subclone (green) may be acquired during the growth of the lesion.

Fig. 2:. Three forms of heterogeneity within a single patient.

Subclonal cells containing different driver genes emerge over time. Subclones of cells with different driver gene mutations are colored yellow, orange, or green. a | Intraprimary heterogeneity: Subclones containing different driver gene mutations expand in parallel. b | Intermetastatic heterogeneity: Cells with different driver gene mutations disseminate and colonize distant sites, leading to driver gene heterogeneity among the founding cells of different metastases. c | Intrametastatic heterogeneity: Mutations in the founding cells of a metastasis clonally expand so that they are present in all cells of the metastasis. However, additional driver gene mutations can be acquired during the growth process of the metastatic lesion. Whether intrametastatic heterogeneity can arise from the dissemination of new clones from one metastatic lesion to another is the subject of ongoing research^,,.

Fig. 3:. Majority of primary tumors are surgically resectable at the time of diagnosis.

a | Estimated incidence of selected solid cancers in the United States in 2018. *Solid cancer types with more than 10,000 estimated new cases per year were selected. Selected cancer types represent approximately 81% of all new cancer cases in the US. Hematological cancers, cancer types with less than 10,000 estimated new cases per year, and cancers for which surgery is not routinely recommended (i.e. small-cell lung cancers), or for which the primary tumor often cannot be completely resected (i.e. glioblastoma) were excluded. b | Fraction of resectable primary tumors across cancer types in the US. Approximately 70% (984,506/1,400,960) of newly diagnosed cases of these solid cancer types (panel a) and approximately 57% (984,506/1,735,350) of all newly diagnosed cancer cases are resectable.

Fig. 4:. Intratumoral heterogeneity in untreated primary tumors and among metastases.

Intraprimary heterogeneity analysis based on 96 samples from 38 subjects (13 ovarian, 10 colorectal^,,,, 9 breast, 4 pancreatic, 1 gastric, and 1 endometrial cancers; Supplementary Methods S1). Intermetastatic heterogeneity analysis based on 67 metastases samples of 17 subjects (6 pancreatic, 4 endometrial^,, 3 colorectal, 2 breast, 1 gastric, 1 prostate cancers). a | Driver gene mutations present in all samples from a single primary tumor were more frequently predicted to be functional than those present in only a subset of the samples from a primary tumor (54% vs. 11%, P < 0.001). The fraction of subclonal functional driver gene mutations (11%) was not significantly different from the fraction of clonal or subclonal functional passenger gene mutations in the same tumor (3.3% and 2.3%). b | Mutations in driver genes that were present among all metastases samples of a subject were more frequently predicted to be functional than those present only in a subset of metastases samples (65% vs. 0%, P < 0.001). The fraction of subclonal functional driver gene mutations (0%) was not significantly different from the fraction of clonal and subclonal functional passenger gene mutations in the same samples (4.1% and 6.6%). c | On average 69% and 66% of the mutations per patient were clonal (homogeneous) among primary tumor samples and among metastases, respectively. Mutations in putative driver genes were significantly more homogeneous among primary tumor samples (90%, P < 0.001) and among metastases (84%, P < 0.0048) than mutations in all genes (sum of passenger genes and driver genes). Likely functional driver gene mutations were even more homogeneous among primary tumor samples (98%, P < 0.0042) and among metastases (100%, P < 0.0018) than other categories of mutations. Two-sided Fisher’s exact tests were used in panels a and b. Two-sided Wilcoxon rank-sum tests were used in panel c. Thick black bars denote 90% confidence interval. Numbers in brackets denote number of variants in each group. ** P < 0.01; *** P < 0.001.

Fig. 5:. Subclonal driver gene mutations did not lead to worse patient outcomes in patients with non-small-cell lung carcinomas.

Analysis based on data of Jamal et al.. a | No statistically significant difference in disease-free survival between patients that harbored subclonal driver mutations (n = 62) and those that did not harbor any subclonal driver gene mutations (n = 38), according to the originally provided driver and heterogeneity classification. Shaded areas denote 90% confidence interval. The hazard ratio of subjects with subclonal driver gene mutations was 0.51 (95% CI: 0.24 − 1.1; P = 0.088, likelihood ratio test). b | When the LiFD algorithm for identifying functional driver gene mutations was applied, the number of patients that harbored subclonal driver gene mutations was 32 and the number of patients that did not harbor any functional driver gene mutations was 68. No statistically significant difference in disease-free survival between patients that harbored subclonal functional driver gene mutations and those that did not harbor subclonal functional driver gene mutations was observed. The hazard ratio of subjects with subclonal functional driver gene mutations was 1.4 (95% CI: 0.61 − 3.0; P = 0.46, likelihood ratio test). In panel b, a different heterogeneity classification was performed than in panel a (Supplementary Methods S1).

Fig. 6:. Lesions of individual patients respond similarly to targeted therapy.

Patients are represented by humanoid cartoons. Circles within the humanoids represent responding, stable, or growing lesions (green, blue, and red, respectively). A lesion was considered to respond if it shrank by at least 30% in diameter; a lesion was considered to grow if its diameter increased by at least 10%; and a lesion was considered to be stable if it did not grow by at least 10% or shrink by at least 30%. a | At least one lesion responded in 27 of 33 melanoma patients^,. In 23 patients (gray humanoids), no lesion grew. In four patients (yellow humanoids), one of the lesions grew while the others responded, i.e., a heterogeneous response was observed. In six patients (red humanoids), no lesion responded. b | Examples of different types of responses to targeted therapy. All lesions responded in patient M37. One lesion responded less well than three other lesions in patient M40. None of the lesions responded in patient M29. One lesion responded, two lesions remained stable, and a fourth lesion grew in patient M08. c | At least on lesion responded in 8 of 11 thyroid cancer patients. In eight patients (gray humanoids), no lesions grew. In three patients (red humanoids), no lesion responded. Additional information about these patients’ responses are provided in (Supplementary Tables S4–S5). In 91% (40/44) of the patients analyzed (with melanomas or thyroid cancers), all lesions responded similarly to targeted therapy.