
==== Front
ESMO Open
ESMO Open
ESMO Open
2059-7029
Elsevier

S2059-7029(24)01428-5
10.1016/j.esmoop.2024.103659
103659
Original Research
Addressing the knowledge gap in the genomic landscape and tailored therapeutic approaches to adolescent and young adult cancers
Hayashi N. 12
Ono M. makiko.ono@jfcr.or.jp
34∗
Fukada I. 1
Yamazaki M. 14
Sato N. 1
Hosonaga M. 5
Wang X. 3
Kaneko K. 2
Arakawa H. 2
Habano E. 2
Kuga A. 2
Kataoka A. 5
Ueki A. 2
Kiyotani K. 67
Tonooka A. 89
Takeuchi K. 8910
Kogawa T. 4
Kitano S. 4
Takano T. 5
Watanabe M. 11
Mori S. 12
Takahashi S. 134
1 Department of Genomic Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Koto-ku, Tokyo
2 Department of Clinical Genetic Oncology, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Koto-ku, Tokyo
3 Department of Medical Oncology, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Koto-ku, Tokyo
4 Department of Advanced Medical Development, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Koto-ku, Tokyo
5 Breast Oncology Center, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Koto, Tokyo
6 Project for Immunogenomics, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Koto-ku, Tokyo
7 Laboratory of Immunogenomics, The Center for Intractable Diseases and ImmunoGenomics (CiDIG), National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Osaka
8 Division of Pathology, The Cancer Institute, Japanese Foundation for Cancer Research, Koto-ku, Tokyo
9 Department of Pathology, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Koto-ku, Tokyo
10 Pathology Project for Molecular Targets, The Cancer Institute, Japanese Foundation for Cancer Research, Koto-ku, Tokyo
11 Total Care Center, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Koto-ku, Tokyo
12 Project for Development of Innovative Research on Cancer Therapeutics, The Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Koto-ku, Tokyo, Japan
∗ Correspondence to: Dr Makiko Ono, Department of Medical Oncology, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan. Tel: +8135200111; Fax: +8135200141 makiko.ono@jfcr.or.jp
12 8 2024
8 2024
12 8 2024
9 8 103659© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

Adolescents and young adults (AYAs) represent a small proportion of patients with cancer. The genomic profiles of AYA patients with cancer are not well-studied, and outcomes of genome-matched therapies remain largely unknown.

Patients and methods

We investigated differences between Japanese AYA and older adult (OA) patients in genomic alterations, therapeutic evidence levels, and genome-matched therapy usage by cancer type. We also assessed treatment outcomes.

Results

AYA patients accounted for 8.3% of 876 cases. Microsatellite instability-high and/or tumor mutation burden was less common in AYA patients (1.4% versus 7.7% in OA; P = 0.05). However, BRCA1 alterations were more common in AYA patients with breast cancer (27.3% versus 1.7% in OA; P = 0.01), as were MYC alterations in AYA patients with colorectal cancer (23.5% versus 5.8% in OA; P = 0.02) and sarcoma (31.3% versus 3.4% in OA; P = 0.01). Genome-matched therapy use was similar between groups, with overall survival tending to improve in both. However, in AYA patients, the small number of patients prevented statistical significance. Comprehensive genomic profiling-guided genome-matched therapy yielded encouraging results, with progression-free survival of 9.0 months in AYA versus 3.7 months in OA patients (P = 0.59).

Conclusion

Our study suggests that tailored therapeutic approaches can benefit cancer patients regardless of age.

Highlights

• Comprehensive genomic profiling of AYA patients with cancer was conducted.

• Genomic status, genome-matched therapies, and overall survival were compared with OA patients with cancer.

• AYA patients had higher rates of BRCA1 and MYC alterations than older patients.

• Genome-matched therapy use was similar between groups, with overall survival tending to improve in both.

• There was some suggestion of the benefits of receiving drugs guided by comprehensive genomic profiling in both groups.

Key words

adolescent
young adult
older adult
genomic profiling
genome-matched therapy
treatment outcome
==== Body
pmcIntroduction

Adolescent and young adult (AYA) patients with cancer are generally defined as those between the ages of 15 and 39 years at the time of their initial cancer diagnosis.1 According to the 2023 Surveillance Epidemiology and End Results database, the incidence of cancer among AYA patients has been rising slowly; however, this population represents only 4.4% of all patients with cancer.2 This low frequency of AYA patients with cancer means that their situation is not well understood, and therefore treatment remains challenging. There are no standardized treatment guidelines, few specialized hospitals and support foundations, and few clinical trials have been conducted.3, 4, 5, 6

In recent years, cancer genomic research has become increasingly popular, largely because of advances in next-generation sequencing technology. Studying cancer driver genes may lead to the development of novel targeted drugs, potentially enhancing survival rates for patients with advanced cancer.7, 8, 9 In clinical practice, comprehensive genomic profiling (CGP) using next-generation sequencing technology can guide tailored approaches to cancer treatment at low cost. However, despite such advances in technology, the genomes of AYA patients with cancer have not been thoroughly studied, and the outcomes of genome-matched therapies for these patients remain largely unknown.

Therefore, we conducted retrospective cancer genomic analysis on AYA patients who underwent CGP. The aim of this study was to investigate the genomic landscape of AYA cancer and the treatment outcomes of genome-matched therapy in this patient subset, in comparison with that of older adult (OA) patients.

Methods

Data sources

Genomic and clinical data of 912 consecutive patients who underwent CGP at The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan between December 2019 and April 2023 were retrospectively collected. A total of 36 patients were excluded because their CGP results were not available. Patient data included age, sex, Eastern Cooperative Oncology Group Performance Status,10 primary cancer type, treatment details, type of CGP, and tumor genomic alterations. We classified patients based on their age at the time of their initial cancer diagnosis according to Adolescent and Young Adult Oncology Progress Review Group guidelines1: AYA patients, 15-39 years old; and OA patients, ≥40 years.

All patients included in this study were tested using one of the three CGP tests covered by Japanese national insurance at the time of data collection as chosen by their physician. The FoundationOne CDx Cancer Genomic Profile (Foundation Medicine, Cambridge, MA) was used to detect alterations in 324 genes using DNA extracted from formalin-fixed, paraffin-embedded tumor samples.11 The DNA libraries were selected using hybrid capture and sequenced with high uniform depth, targeting a median coverage of >500× with >99% of exons at a coverage of >100× using a HiSeq 4000 system (Illumina, San Diego, CA). The FoundationOne Liquid CDx Cancer Genomic Profile (Foundation Medicine) detects alterations in 324 genes using circulating cell-free DNA isolated from plasma derived from anticoagulated peripheral whole blood of patients with cancer using the NovaSeq 6000 system (Illumina).12 The OncoGuide NCC Oncopanel System (Sysmex, Kobe, Japan) was used to detect alterations in 124 genes using formalin-fixed, paraffin-embedded tumor samples.13 DNA sequencing was carried out using a NextSeq 550Dx system (Illumina) with a median coverage of >100×.

Frequencies of genomic alterations

Genomic alterations were classified as ‘pathogenic’, ‘likely pathogenic’, or ‘other’, by our molecular tumor board with reference to the Catalogue of Somatic Mutations in Cancer database.14 We investigated the frequencies of these genomic alterations and compared them by cancer type between AYA and OA patients.

Distribution of OncoKB therapeutic evidence levels

Pathogenic and likely pathogenic alterations were annotated with a therapeutic evidence level: 1, Food and Drug Administration (FDA)-approved drugs; 2, standard care; 3, clinical evidence; or 4, biological evidence, in accordance with the OncoKB database, which defines actionable genomic alterations.15, 16, 17 Microsatellite instability-high (MSI-H) or tumor mutation burden-high (TMB-H) cases were annotated as level 1. For other levels, the highest therapeutic evidence level was given as the therapeutic evidence level for that case. We investigated the distributions of OncoKB therapeutic evidence levels and compared them by cancer type between AYA and OA patients.

Use of genome-matched therapy

We investigated the proportion of patients with pathogenic or likely pathogenic genomic alterations who received genome-matched therapy. This included both drugs covered by Japanese national health insurance and investigational drugs. The evaluation period extended from the introduction of chemotherapy to the data cut-off date. We also compared the use of genome-matched therapy between AYA and OA patients and also by cancer type.

Treatment outcomes for genome-matched therapy

Overall survival (OS) was compared between patients treated with or without genome-matched therapy. OS was calculated from the date of specimen shipment to the date of death from any cause, and survival curves were compared between AYA and OA patients.

We also assessed objective tumor response and progression-free survival (PFS) as treatment outcomes for those who underwent genome-matched therapy guided by CGP, using the RECIST guidelines to assess objective tumor response.18 Computed tomography was used to monitor response to treatment every 8 weeks (±2 weeks), or earlier if any clinical events occurred in the patients. When clinical progressive disease was suggested, such as by a decline in Eastern Cooperative Oncology Group Performance Status, the date was set as the date of progressive disease. PFS was calculated from the date of introducing treatment guided by CGP to the date of disease progression.

Germline findings

When presumed germline pathogenic variants (PGPVs) were identified by our molecular tumor board, patients underwent comprehensive genetic counseling by experts (i.e. medical geneticists and genetic counselors). They then underwent diagnostic genetic testing to determine whether the PGPV was of germline origin. If the test result was positive, the variant was determined to be a germline pathogenic variant (GPV).

Statistical analysis

We used chi-square and Mann–Whitney U tests to compare findings in AYA patients and OA patients. PFS and OS were estimated using the Kaplan–Meier method and the log-rank test. Data were censored on 31 March 2024. Patients who were lost to follow-up were censored at the date of last contact or follow-up. The level of significance was set at P < 0.05 for univariate and multivariate analyses and was two-sided. All analyses were carried out using EZR (www.r-project.org).19

Ethics approval and consent to participate

All procedures were carried out in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1964 and later versions. This observational study was approved by the institutional review board of the Japanese Foundation for Cancer Research (approval number 2021-GA-1075). Patient consent for this study was in the form of an opt-out form.

Results

Patient characteristics

In this study of 876 patients, 73 (8.3%) were AYA, with a median (range) age of 34 (15-39) years, compared with 60 (40-89) years for OA (Table 1). Two AYA patients (2.7%) had a poor Eastern Cooperative Oncology Group Performance Status of ≥3, compared with 9 (1.0%) for OA. Colorectal cancer was the most common cancer in both the AYA and OA patients [17 (23.3%) versus 190 (23.6%)]. Sarcoma and breast cancer affected significantly higher proportions of AYA patients than OA patients [16 (21.9%) versus 29 (3.6%), P < 0.01 and 11 (15.1%) versus 60 (7.5%), P = 0.04, respectively]. The proportions of patients undergoing tissue-based CGP tests were higher in the AYA group than in the OA group [tissue-based; 69 (94.5%) versus 671 (83.6%) and liquid-based; 4 (5.5%) versus 32 (16.4%), P = 0.01]. Cancers occurring in less than five patients, such as pleural mesothelioma, peritoneal mesothelioma, paraganglioma, melanoma, and thymic, urachal, adrenocortical, germ cell, kidney, and peripheral nerve cancers, were grouped as ‘rare cancers’ for this analysis.Table 1 Patient characteristics

Characteristics	Adolescent and young adult (n = 73)	Older adult (n = 803)	
Age, median (range)	34 (15-39)	60 (40-89)	
Sex, n (%)			
 Male	33 (45.2)	375 (47)	
 Female	40 (54.8)	428 (53)	
ECOG PS, n (%)			
 0-2	71 (98.3)	794 (99)	
 3-4	2 (2.7)	9 (1.0)	
Cancer type, n (%)			
 Colorectal	17 (23.3)	190 (23.6)	
 Sarcoma	16 (21.9)	28 (3.5)	
 Breast	11 (15.1)	60 (7.5)	
 Ovarian	8 (11.0)	62 (7.7)	
 Stomach	6 (8.2)	34 (4.2)	
 Head and neck	4 (5.5)	33 (4.1)	
 Rare cancersa	3 (4.1)	19 (2.4)	
 Lung	2 (2.7)	31 (3.9)	
 Urothelial	2 (2.7)	11 (1.4)	
 Pancreatic	2 (2.7)	130 (16)	
 Cervical	1 (1.4)	19 (2.4)	
 Cancer of unknown primary origin	1 (1.4)	14 (1.7)	
 Biliary tract	0 (0.0)	72 (9.0)	
 Esophageal	0 (0.0)	30 (3.7)	
 Endometrial	0 (0.0)	27 (3.4)	
 Prostate	0 (0.0)	17 (2.1)	
 Thyroid	0 (0.0)	14 (1.7)	
 Hepatocellular	0 (0.0)	6 (0.7)	
 Skin	0 (0.0)	5 (0.6)	
Type of CGP, n (%)			
 Tissue based	69 (94.5)	671 (83.6)	
 Liquid based	4 (5.5)	132 (16.4)	
CGP, comprehensive genomic profiling; ECOG PS, Eastern Cooperative Oncology Group Performance Status.

a Rare cancers are those for which there are less than five patients in this study. These include cancers such as pleural mesothelioma; peritoneal mesothelioma; paraganglioma; melanoma; and thymic, urachal, adrenocortical, germ cell, kidney, and peripheral nerve cancers.

Overview of genomic alterations in AYA and OA tumors

A total of 4498 genomic alterations were detected in the tumors of 876 patients, of which 3132 (69.6%) were classified as pathogenic or likely pathogenic (Figure 1A). MSI-H or TMB-H status was detected in 63 tumors (7.2%). After excluding duplicate genomic alterations, we focused on 33 genes listed in all three comprehensive genomic profiles with either a mutation frequency of ≥5% or a cancer-type-specific mutation frequency of ≥10% in this cohort. The details of the frequencies of alterations in these tumor genes, including MSI-H/TMB-H, are shown in Figure 1B. These were largely similar between AYA and OA patients. In addition, MYC alteration was significantly more common in AYA patients with colorectal cancer (AYA: 23.5% versus OA: 5.8%, P = 0.02) and sarcoma (AYA: 31.3% versus OA: 3.4%, P = 0.01). Tumor BRCA1 alterations were more common in AYA patients (5.5% versus OA: 2.0%, P = 0.08), particularly in breast cancer tumors (AYA: 27.3% versus OA: 1.7%, P = 0.01). Conversely, MTAP loss and MSI-H/TMB-H tended to be less common in AYA patients (AYA: 4.1% versus OA: 10.8%, P = 0.07 and AYA: 1.4% versus OA: 7.7% P = 0.05, respectively).Figure 1 Genomic landscape. (A) Flowchart of the overall patient disposition in this study. A total of 4498 genomic alterations were detected in 876 patients, of which 3132 (69.6%) were considered pathogenic. Thirty-seven genes were selected for further investigation. (B) The frequency of driver gene alterations by cancer type in AYA and OA patients. Thirty-three genes listed in all three comprehensive genomic profiles and meeting one of the following criteria were selected: (i) mutation frequency of ≥5% or (ii) a cancer-type-specific mutation frequency of ≥10% in this cohort. AYA and OA patients had similar frequencies of MSI-H/TMB-H. However, AYA patients with breast cancer had significantly higher rates of BRCA1 mutations (P = 0.01), and MYC alterations were significantly more common in AYA patients with colorectal cancer and sarcoma (P = 0.02 and 0.01, respectively). AYA, adolescent and young adult; CUP, cancer of unknown primary origin; HCC, hepatocellular carcinoma; MSI-H, microsatellite instability-high; NE, not evaluated; NS, not significant; OA, older adult; TMB-H, tumor mutation burden-high.

Comparison of germline findings between AYA and OA patients

Genetic testing revealed that a significantly higher proportion of AYA patients (10.9%) than OA patients (4.2%) harbored GPVs (P = 0.02). The most common GPVs in AYA patients were BRCA1 variants (AYA: 5.5% versus OA: 1.6%, P = 0.05) while BRCA2 variants were more common in OA patients (AYA: 1.4% versus OA: 2.5%, P > 0.99).

Distribution of OncoKB therapeutic evidence levels in AYA and OA patients

Of the 3132 pathogenic genomic alterations identified, 932 (20.7%) had a therapeutic evidence level. When assigning the highest evidence level to each case, similar proportions of AYA patients (65.8%) and OA patients (67.6%) had therapeutic evidence level >4. There were no significant differences in the distribution of therapeutic evidence levels between AYA and OA patients, either overall or by cancer type (Figure 2A).Figure 2 Patient-based analysis. (A) Distribution of OncoKB therapeutic evidence levels in AYA and OA patients. There were no significant differences in the distribution of OncoKB therapeutic evidence levels assigned between AYA and OA patients, neither overall nor by cancer type. The numbers represent the following therapeutic evidence levels: 1, FDA-approved drugs; 2, standard care; 3, clinical evidence; and 4, biological evidence. (B) Use of genome-matched therapy in AYA and OA patients. AYA patients had a tendency to more likely receive genome-matched therapy (20.5% and 12.9%, P = 0.07). Regarding cancer type, there was no significant difference between AYA and OA patients. (C) Overall survival according to the use of genome-matched therapy in AYA and OA patients. Overall survival was 33.0 months (95% CI 6.8-not achieved) in patients with genome-matched therapy compared with 13.0 months (95% CI 8.8-17.8) in those with nongenome-matched therapy in AYA patients (P = 0.10), while the corresponding values were 14.7 months (95% CI 11.8-20.9) and 12.5 months (95% CI 11.0-14.2) in OA patients (P = 0.08), respectively. AYA, adolescent and young adult; CUP, cancer of unknown primary; HCC, hepatocellular carcinoma; MSI-H, microsatellite instability-high; OA, older adult; TMB-H, tumor mutation burden-high.

Use of genome-matched therapy in AYA and OA patients

Of the 876 patients included in this study, 119 (13.6%) received genome-matched therapy. Throughout the treatment duration, there was a tendency for a higher proportion of AYA patients (15/73, 20.5%) to receive genome-matched therapy, including both drugs approved by Japanese national health insurance and investigational drugs, than OA patients (104/803, 12.9%; P = 0.07). However, stratified by cancer type, there were no differences between the two groups (Figure 2B and Supplementary Table S1, available at https://doi.org/10.1016/j.esmoop.2024.103659).

Overall survival in patients with and without genome-matched therapy

At the data cut-off date, 513 (58.6%) of the 876 patients were censored, including 46/73 (63.0%) AYA patients and 467/803 (58.2%) OA patients. Overall, the median OS in patients receiving genome-matched therapy was significantly longer than in those not receiving such treatment [17.5 months; 95% confidence interval (CI) 11.9-22.2 versus 12.6 months; 95% CI 11.2-14.1; P = 0.03].

In AYA patients, the median OS in patients treated with genome-matched therapy was 33.0 months (n = 15; 95% CI 6.8-not achieved) compared with 13.0 months in those treated without genome-matched therapy (n = 58; 95% CI 8.8-17.8; P = 0.10). In OA patients, the corresponding median OS periods were 14.7 months (n = 104; 95% CI 11.8-20.9) in those treated with genome-matched therapy and 12.5 months (n = 699; 95% CI 11.0-14.2) in those treated without genome-matched therapy (P = 0.08; Figure 2C).

Other treatment outcomes for genome-matched therapy guided by CGP

For 48 [AYA: 6 (8.2%) and OA: 42 (5.2%); P = 0.28] of the 876 patients investigated, it was their new opportunity to receive genome-matched therapy guided by CGP. Outcomes were assessed in 42 of these patients, as 6 could not be evaluated owing to being treated elsewhere (Figure 3A). There was no significant difference in the partial response rate between the two groups (AYA: 40.0% versus OA: 24.3%, P = 0.59). Disease control for >6 months was achieved in 3 AYA patients (60.0%) and 10 OA patients (27.0%; P = 0.16; see Figure 3B for details). There was no significant difference in PFS (AYA: 9.0 months versus OA: 3.7 months, P = 0.59) between the two groups.Figure 3 Outcomes of genome-matched therapy guided by comprehensive genomic profiling. (A) Flowchart of patient disposition according to the use of genome-matched therapy in AYA and OA patients. Forty-eight patients received genome-matched therapy guided by comprehensive genomic profiling. Six patients were excluded owing to being treated elsewhere. (B) Objective response and progression-free survival in AYA and OA patients. Arrows indicate continued response; disease control for >6 months was achieved in 3 AYA patients (60.0%) and 10 OA patients (27.0%). The table details each cancer type and treatment. ALK, anaplastic lymphoma kinase; AYA, adolescent and young adult; BRAF, v-raf murine sarcoma viral oncogene homolog B1; CDK, cyclin-dependent kinase; CGP, comprehensive genomic profiling; ERBB2, v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2; FGFR, fibroblast growth factor receptor; KRAS, Kirsten rat sarcoma virus; MEK, mitogen-activated protein kinase kinase; OA, older adult; PARP, poly(ADP-ribose) polymerase; PI3K, phosphoinositide 3-kinase.

Discussion

This comparison of genomic profiles between AYA and OA patients with cancer focused on the usefulness of tailored therapeutic approaches by assessing treatment efficacy and prognosis. Despite differences between AYA and OA patients with cancer in their genomic backgrounds, the groups had similar opportunities to receive genome-matched treatment.

This study found that AYA patients had a tendency toward a lower frequency of MSI-H/TMB-H alterations than OA patients. TMB-H is an indicator of the generation of immunogenic neopeptides and can result from various factors, including lifestyle and environmental influences.20 However, as they are younger, AYA patients are likely to have had less exposure to lifestyle or environmental factors that contribute to TMB-H, such as tobacco smoke, than OA patients, leading to a lower frequency of TMB-H in these patients.21,22 This suggests that cancer in AYA patients is not primarily driven by the accumulation of genomic alterations from lifestyle or environmental exposure but rather by the presence of strong driver genes.

AYA patients tended to have a higher frequency of BRCA1 alterations, with patients with breast cancer in this group having significantly higher rates. AYA patients with colorectal cancer and sarcoma had significantly more MYC alterations. Previous research comparing AYA and OA cancer genomes has not primarily focused on MYC alterations21; however, Marx et al.23 noted a high incidence of MYC alterations and a low incidence of APC alterations in AYA patients with colorectal cancer, which aligns with our current findings. Some reports on patients with MYC alterations have shown that such alterations are mutually exclusive with PIK3CA, PTEN, APC, or BRAF mutations, highlighting the potent role of MYC as a cancer driver gene.24, 25, 26 These findings suggest that MYC alteration in AYA cancer may be particularly noteworthy. The increase in MYC alteration among AYA patients with sarcomas could be attributed to patient bias, as bone sarcomas are more common in AYA patients.27,28 Similarly, the tendency toward a lower frequency of MTAP loss observed might be owing to the small number of AYA patients included in this study, and particularly cases of pancreatic and lung cancers.29

Genetic predisposition to cancer has been reported to be more common in AYA patients than in OA patients.30, 31, 32 In this study, AYA patients tended to have significantly higher rates of GPVs, with breast cancer cases having significantly higher rates of BRCA1 alterations, supporting the crucial role that genetic predisposition may play a part in the development of cancer in AYA patients. Several GPVs that cause hereditary tumors also serve as biomarkers for genome-targeted therapies, such as poly (ADP-ribose) polymerase inhibitors.33, 34, 35, 36 Examining the cancer genome in AYA patients holds significant clinical promise from both therapeutic and genetic perspectives.

Overall, genome-matched therapy significantly improved OS in our study population (P = 0.03). While there was a tendency toward an improvement in OS for the OA group (n = 803; P = 0.08), the small sample size likely precluded a similar result being seen in the AYA group (n = 73, P = 0.10). In addition, genome-matched therapy tended to improve tumor response and PFS in both groups. Overall, those treatment outcomes were better for AYA patients. For OA patients with cancer, previous reports have described improvements in survival in association with genome-matched therapy.7, 8, 9,37, 38, 39 However, to the best of our knowledge, this is the first report assessing treatment outcomes, including survival, in AYA patients, and further studies with larger sample sizes are warranted.

In this study, there was a tendency for AYA patients to be more likely to receive genome-matched therapy. This is probably owing to delays in drug approval within the Japanese health insurance system. For example, in the OncoKB database, BRAF plus mitogen-activated extracellular signal-regulated kinase inhibitors for BRAF V600E mutations have a therapeutic evidence level of 1. While these drugs have been approved by the FDA, they are not yet covered by insurance in Japan, making them difficult to access for patients with BRAF V600E mutations.40 If these drugs were approved, more patients, particularly those with thyroid cancer, could receive genome-matched therapy. This issue disproportionately affects OA compared with AYA patients because all patients with thyroid cancer in this study were OAs. A similar ‘drug lag’ issue applies to the use of AKT inhibitors to treat breast cancer with PIK3CA alterations.40 For CGP to be effective, it is also necessary to establish a patient support system that allows the rapid introduction of these drugs.41

Unfortunately, unlike previous reports, there was only a trend toward a significant difference in OS among OA patients regarding genome-matched therapy. It has been noted that for some cancer types, such as gastric cancer and esophageal cancer, a TMB-H cut-off of >10 is not a useful biomarker for selecting patients who will benefit from immune checkpoint inhibitors.42 The large number of patients with these cancer types in this cohort may have affected the observed survival benefit of genome-matched therapy. This should be considered in the analyses of pan-cancer types in future work.

This study had several limitations. First, it was a retrospective study conducted at a single institute. Second, there was patient selection bias as including cancer types with a high number of actionable mutations increases the likelihood of carrying out genome-matched therapy. Previous studies have shown that the primary cancers in AYA patients are predominantly brain tumors, thyroid cancer, breast cancer, soft-tissue sarcoma, and testicular germ cell tumors.2,43, 44, 45 In this study, the inclusion of a relatively high number of patients with breast and ovarian cancer allowed for the use of PARP inhibitors. Importantly, this study included only a very small sample of AYA patients with cancer, which may have prevented the detection of statistical significance in some cases. Further study on the genomes of AYA patients with cancer will be conducted using the Japan-wide CGP database system.

This study analyzed the genomic profiles of AYA patients and demonstrated the outcomes of genome-matched therapy, shedding light on future tailored therapeutic approaches for AYA patients. Another positive finding in this study is that although there were some differences in genomic backgrounds between AYA and OA patients, both groups had similar rates of receiving genome-matched therapy. Overall, there were some indications of improved survival and tumor response outcomes from genome-matched therapies in our study population. However, the small sample size is likely to have contributed to the lack of OS benefit seen with genome-matched therapy, so future studies in larger study populations are warranted.

Supplementary data

Supplementary Table

Acknowledgements

We thank the medical staff of the Genomic Medicine Department at The Cancer Institute Hospital of the Japanese Foundation for Cancer Research for their support during this study. We also thank Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.

Funding

None declared.

Disclosure

MO has received research support from Pfizer, Astellas, and Eisai. AU has received research support from Pfizer outside of the submitted work. KK reports employment with Cancer Precision Medicine, Inc. TT has received honoraria from Daiichi Sankyo, Chugai, and Eli Lilly. KT has received consultancy fees from Nichirei Bioscience, Nippon Shinyaku, and Meiji Seika Pharma; research support from Fujirebio and Daiichi Sankyo; honoraria from Eli Lilly, Chugai, Kyowa Kirin, and Janssen; and royalties from Sysmex and Nichirei Bioscience outside of the submitted work. TK has received grants and personal fees from Eisai, Gilead Sciences, Daiichi-Sankyo, AstraZeneca, Eli Lilly, and Guardant, outside the submitted work; personal fees from Pfizer, Taiho, Astellas, and Chugai; and grants from Ono Pharmaceutical Co., Ltd. SK has received grants and personal fees from AstraZeneca, Pfizer, Nippon Boehringer Ingelheim, MSD, Eisai, Ono Pharmaceutical Co., Ltd., GSK, Daiichi-Sankyo, Chugai, and Takeda; personal fees from Taiho, Novartis, Sumitomo Pharma, Bristol-Myers Squibb, Rakuten Medical, ImmuniT Research Inc., Merck KGaA, United Immunity, and PMDA (Pharmaceuticals and Medical Devices Agency); grants from Astellas, Takara Bio Inc., Incyte, Eli Lilly/LOXO Oncology, and AbbVie, all outside the submitted work. ST has received research support from Ono Pharmaceutical Co., Ltd., Bristol-Myers Squibb, MSD, AstraZeneca, Chugai, and Bayer; and honoraria from Chugai outside of the submitted work. The remaining authors have declared no conflicts of interest.

Data sharing

The datasets generated and/or analyzed during this study are available from the corresponding author on reasonable request.
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