Purpose:

Developing new therapeutics for any of the more than 100 sarcoma subtypes presents a challenge. After progression from standard therapies, patients with sarcoma may be referred for enrollment in early-phase trials. This study aimed to investigate whether enrollment in biomarker-matched early-phase clinical trials leads to better outcomes for patients with advanced sarcoma.

Experimental Design:

In this retrospective analysis, investigational treatment characteristics and longitudinal survival outcomes were analyzed in patients with biopsy-confirmed sarcoma enrolled in early-phase trials at MD Anderson Cancer Center from May 2006 to July 2021.

Results:

Five hundred eighty-seven patients were included [405 soft tissue, 122 bone, 60 gastrointestinal stromal tumor (GIST); median of three prior lines of therapy]. Most common subtypes were leiomyosarcoma (17.2%), liposarcoma (14.0%), and GIST (10.2%). Molecular testing was available for 511 patients (87.1%); 221 patients (37.6%) were treated in matched trials. Overall response rate was 13.1% matched compared with 4.9% in unmatched (P < 0.001); the clinical benefit rate at 6 months was 43.9% vs. 19.9% (P < 0.001). Progression-free survival was longer for patients in matched trials (median, 5.5 vs. 2.4 months; P < 0.001), and overall survival was also superior for patients in matched trials (median, 21.5 vs. 12.3 months; P < 0.001). The benefit of enrollment in matched trials was maintained when patients with GIST were excluded from the analysis.

Conclusions:

Enrollment in biomarker-matched early-phase trials is associated with improved outcomes in heavily pretreated patients with metastatic sarcoma. Molecular testing of tumors from patients with advanced sarcoma and enrollment in matched trials is a reasonable therapeutic strategy.

Translational Relevance

Developing new therapeutics for sarcomas is challenging due to their rarity and heterogeneity. Because of the limited efficacy of approved systemic treatments, patients with sarcoma are often enrolled in early-phase clinical trials. For these patients, molecular profiling can lead to promising novel therapies. A review of patients with sarcoma from a large phase I program demonstrated that 37% were treated on biomarker-matched treatments. Patients who were enrolled in matched trials achieved higher response rates and progression-free and overall survival when compared with outcomes for those in unmatched studies. Therefore, molecular biomarker tests, including next-generation sequencing, of tumors from patients with advanced sarcoma and enrollment in matched trials can be used as a therapeutic strategy.

Sarcoma is an umbrella term used to designate a heterogeneous group of mesenchymal malignancies, representing less than 1% of cancers diagnosed yearly in adults in the United States (1). More than 70 distinct types have been defined by the World Health Organization, comprising a collection of tumors originating from muscular, adipose, bone, cartilage, and vascular tissues, among others (2).

Traditionally, the pathologic classification of sarcoma has been based on its growth pattern and/or tissue differentiation suggesting lineage (2). More recently, with technological advances and decreased cost, cytogenetic and molecular tests have been rapidly integrated into the diagnostic workup for sarcomas (3). Indeed, the current soft-tissue and bone sarcoma classification system has been designed by carefully correlating recurrent genomic alterations with histopathological subtypes (2). The relevance of molecular characterization for diagnosis was highlighted by an elegant multicenter analysis by Italiano and colleagues, in which the diagnosis of 53 of 384 (14%) patients was changed after molecular test results (3). In this context, genomic characterization is likely to be recommended progressively earlier during the disease course.

Reflecting the heterogeneity of sarcomas, a wide array of biological pathways is involved in the initiation and progression of these malignancies (4). Understanding the prevalence and function of individual molecular alterations in specific sarcoma subtypes is critical for the development of novel potential targets for personalized therapies. Targeted therapies can produce significant tumor responses by disrupting driver mutations, providing highly effective and tailored treatments, exemplified by advances in gastrointestinal stromal tumors (GIST), lung cancer, melanoma, colon cancer, and others (5). Interestingly, targeted therapy for solid tumors was pioneered in sarcomas: imatinib was one of the first genome-directed drugs to demonstrate benefits in solid tumors, leading to FDA approval in 2001, just 3 years following the identification of recurrent KIT mutations in GIST (6). Unfortunately, advancements in genomically targeted treatment have stalled for other sarcoma subtypes owing to their rarity and diversity. Scarce genome-directed drugs approved by the FDA specifically for sarcomas are those blocking KIT/PDGFRA in GIST, EZH2 in epithelioid sarcomas, characterized by loss of INI1/SMARCB1, ALK in inflammatory myofibroblastic tumors, and CSF1R in malignant tenosynovial giant cell tumor (7–9).

Nonetheless, recent reports have highlighted the importance of next-generation sequencing for identifying potential targets for treating advanced soft-tissue and bone sarcomas. For example, previous data from our group analyzing 102 consecutive patients with sarcoma showed that 61% carried potentially actionable genetic alterations (10). In that report, 16% of the patients eventually received genomically informed therapy; encouragingly, 50% of the patients achieved clinical benefit (10). Moreover, the development of antibody–drug conjugates and adoptive cellular therapy, including preliminary data on sarcoma, has emphasized the potential relevance of other biomarkers beyond genomic data (11).

Because of the limited efficacy of currently approved systemic therapies, patients with sarcoma are often enrolled in early-phase clinical trials investigating drugs with novel mechanisms of action (12). Phase I studies traditionally aim to establish the optimal dose of new drugs for future phase II trials; however, the demonstration of early efficacy signals has become a relevant objective, especially in the expansion arms of such trials (13). Although in vitro and in vivo studies with novel targeted agents have provided a rationale for a precision oncology approach in treating sarcomas, the clinical benefit of enrolling patients in molecularly matched early-phase trials remains unknown. The present retrospective SAMBA 101 (SArcoma Matched Biomarker Analysis 101) study aimed to investigate whether enrollment in biomarker-matched early-phase clinical trials leads to better outcomes for patients with advanced sarcoma.

Study design and participants

We retrospectively reviewed the medical records of consecutive patients with metastatic or unresectable advanced bone and soft-tissue sarcomas treated in early-phase clinical trials in the Department of Investigational Cancer Therapeutics at The University of Texas MD Anderson Cancer Center (Houston, TX) from May 1, 2006, to July 1, 2021. No age limits were applied in this study. Pathology was previously reviewed at MD Anderson by a pathologist with expertise in soft-tissue and bone sarcomas, and molecular testing using Clinical Laboratory Improvement Amendments–certified next-generation sequencing was ordered according to the treating physician's preference as part of routine clinical care. MD Anderson's proprietary molecular testing and genomic sequencing were used in addition to commercially available platforms.

Biomarker-matched trials were defined as those in which patients received treatment in trials in which specific genetic alterations found in their tumors through genomic sequencing or specific proteins expressed as identified by IHC were part of the inclusion criteria. Trial choice and enrollment were dependent on availability at the time of referral and opening in the different dose-escalation or -expansion cohorts of early-phase clinical trials. The MD Anderson Institutional Review Board independently reviewed and approved each clinical trial in which patients presented within this analysis were enrolled. The patients provided written informed consent before treatment with investigational therapy. This retrospective study was approved by the MD Anderson Institutional Review Board. All procedures conformed with the ethical standards of the institutional research committee and with the Declaration of Helsinki.

Procedures

Patient charts were reviewed for baseline characteristics such as age at enrollment in the study, sex, tumor histology, molecular findings, and the number of prior therapies. We also collected data regarding clinical trial therapy, including the date of initiation of investigational treatment, whether the trial required a biomarker for enrollment, the patient's best and current response to therapy, and the date of study discontinuation. We also recorded the date of the last known follow-up period or the date of death.

Outcomes

The overall response rate was measured using RECIST as part of the individual clinical trials. Progression-free survival (PFS) was calculated from the first dose of the experimental therapy to the date of study discontinuation due to progression or the last follow-up. Patients who received both matched and unmatched therapies were classified in the matched cohort. For patients who participated in multiple matched or unmatched clinical trials, we considered the one with the longest PFS for the analysis. Clinical benefit rate was defined as the sum of patients achieving either a complete response, a partial response, or stable disease for at least 4 or 6 months (two separate analyses are presented). Overall survival was measured from the first early-phase trial treatment date to the date of death or the last follow-up. For patients who participated in more than one study, overall survival was calculated from enrollment in the first trial. Analyses were performed for the whole cohort, for the aggregate of patients excluding patients with GIST, and for specific cohorts of non-GIST soft-tissue sarcoma and bone sarcoma.

We also calculated the intrapatient PFS ratio (PFSr) for patients enrolled in more than one clinical trial, including at least one matched and unmatched study. PFSr was calculated by dividing the PFS in a matched study by the PFS in an unmatched study, regardless of which therapy was first. For patients that participated in more than one matched or unmatched trial, we also considered the trial with the longest PFS for this analysis.

Statistical analysis

All statistical analyses were performed using SPSS version 28. We performed Kaplan–Meier analysis to estimate the PFS and overall survival between patients treated in molecularly matched trials versus molecularly unmatched trials. Survival was compared using log-rank test. The overall response and clinical benefit rates were compared using the χ2 test. Statistical significance was set at P values < 0.05. Associations between survival and clinicopathologic characteristics were evaluated by univariate and multivariate Cox models. We selected variables with P < 0.1 in univariate analysis to be included in the multivariate model.

Data availability

The data generated in this study are available upon reasonable request to the corresponding author.

Study population

Between May 2006 and July 2021, 587 patients with advanced sarcomas were enrolled and accounted for 863 inclusions in early-phase clinical trials at MD Anderson. The baseline clinical characteristics of the patients are summarized in Table 1. A minority of patients (n = 277; 47.2%) were female, and patients had a median of three prior lines of therapy (range, 0–9). Most patients (n = 405; 69.0%) were diagnosed with non-GIST soft-tissue sarcoma, 122 (20.8%) with primary sarcoma of the bone, and the remaining 60 (10.2%) with GIST. The most common soft-tissue sarcoma subtypes were leiomyosarcoma (n = 101; 17.2%), liposarcoma (n = 82; 14.0%), and undifferentiated pleomorphic sarcoma (n = 45; 7.7%). The most commonly treated bone sarcomas were chondrosarcoma (n = 45; 7.7%), osteosarcoma (n = 39; 6.6%), and Ewing sarcoma (n = 27; 4.6%). Most of the patients participated in a single clinical trial (n = 410, 69.8%).

Table 1.

Baseline patient characteristics.

Matched trialsNonmatched trialsTotal
Number of patients—n (%) 221 (37.6%) 366 (62.4%) 587 (100%) 
Male sex—n (%) 121 (54.7%) 189 (51.6%) 310 (52.8%) 
Soft-tissue sarcoma—n (%) 142 (65.0%) 263 (71.5%) 405 (69.0%) 
 Leiomyosarcoma 28 (12.8%) 73 (19.9%) 101 (17.2%) 
 Liposarcoma 53 (23.9%) 29 (7.8%) 82 (14.0%) 
 Undifferentiated pleomorphic sarcoma 8 (4.0%) 37 (10.2%) 45 (7.7%) 
 Sarcoma, NOS 10 (4.4%) 24 (6.6%) 34 (5.8%) 
 Synovial sarcoma 10 (4.4%) 10 (2.7%) 20 (3.4%) 
 Angiosarcoma 4 (1.8%) 11 (3.0%) 15 (2.5%) 
 Alveolar soft part sarcoma 12 (3.3%) 12 (2.0%) 
 Rhabdomyosarcoma 3 (1.3%) 8 (2.2%) 11 (1.9%) 
 Malignant peripheral nerve sheath tumor 2 (1.8%) 8 (1.7%) 10 (1.7%) 
 Clear-cell sarcoma 7 (3.1%) 3 (0.8%) 10 (1.7%) 
 Othera 17 (7.5%) 48 (13.0%) 65 (10.9%) 
Bone sarcoma—n (%) 24 (10.6%) 98 (27.1%) 122 (20.8%) 
 Chondrosarcoma 12 (5.3%) 33 (9.1%) 45 (7.7%) 
 Osteosarcoma 3 (1.3%) 34 (9.4%) 37 (6.6%) 
 Ewing sarcoma 4 (1.8%) 23 (6.4%) 27 (4.6%) 
 Chordoma 4 (1.8%) 5 (1.4%) 9 (1.5%) 
 Otherb 1 (0.4%) 3 (0.6%) 4 (0.5%) 
GIST—n (%) 55 (24.9%) 5 (1.4%) 60 (10.2%) 
Prior lines of therapy—median (range) 2.0 (0–9) 3.0 (0–9) 3.0 (0–9) 
Number of prior lines of therapy: 
 0 34 (15.3%) 41 (11.2%) 75 (12.7%) 
 1 43 (19.4%) 48 (13.1%) 91 (15.5%) 
 2 35 (15.8%) 67 (18.3%) 102 (17.4%) 
 ≥3 109 (49.7%) 210 (57.3%) 319 (54.3%) 
Number of trial participation per patient 
 1 146 (66.1%) 264 (72.1%) 410 (69.8%) 
 2 49 (22.2%) 67 (18.3%) 116 (19.8%) 
 3 15 (6.8%) 22 (6.0%) 37 (6.3%) 
 4 6 (2.7%) 8 (2.2%) 14 (2.4%) 
 5 5 (2.3%) 1 (0.4%) 6 (1.0%) 
 6 4 (1.1%) 4 (0.7%) 
Matched trialsNonmatched trialsTotal
Number of patients—n (%) 221 (37.6%) 366 (62.4%) 587 (100%) 
Male sex—n (%) 121 (54.7%) 189 (51.6%) 310 (52.8%) 
Soft-tissue sarcoma—n (%) 142 (65.0%) 263 (71.5%) 405 (69.0%) 
 Leiomyosarcoma 28 (12.8%) 73 (19.9%) 101 (17.2%) 
 Liposarcoma 53 (23.9%) 29 (7.8%) 82 (14.0%) 
 Undifferentiated pleomorphic sarcoma 8 (4.0%) 37 (10.2%) 45 (7.7%) 
 Sarcoma, NOS 10 (4.4%) 24 (6.6%) 34 (5.8%) 
 Synovial sarcoma 10 (4.4%) 10 (2.7%) 20 (3.4%) 
 Angiosarcoma 4 (1.8%) 11 (3.0%) 15 (2.5%) 
 Alveolar soft part sarcoma 12 (3.3%) 12 (2.0%) 
 Rhabdomyosarcoma 3 (1.3%) 8 (2.2%) 11 (1.9%) 
 Malignant peripheral nerve sheath tumor 2 (1.8%) 8 (1.7%) 10 (1.7%) 
 Clear-cell sarcoma 7 (3.1%) 3 (0.8%) 10 (1.7%) 
 Othera 17 (7.5%) 48 (13.0%) 65 (10.9%) 
Bone sarcoma—n (%) 24 (10.6%) 98 (27.1%) 122 (20.8%) 
 Chondrosarcoma 12 (5.3%) 33 (9.1%) 45 (7.7%) 
 Osteosarcoma 3 (1.3%) 34 (9.4%) 37 (6.6%) 
 Ewing sarcoma 4 (1.8%) 23 (6.4%) 27 (4.6%) 
 Chordoma 4 (1.8%) 5 (1.4%) 9 (1.5%) 
 Otherb 1 (0.4%) 3 (0.6%) 4 (0.5%) 
GIST—n (%) 55 (24.9%) 5 (1.4%) 60 (10.2%) 
Prior lines of therapy—median (range) 2.0 (0–9) 3.0 (0–9) 3.0 (0–9) 
Number of prior lines of therapy: 
 0 34 (15.3%) 41 (11.2%) 75 (12.7%) 
 1 43 (19.4%) 48 (13.1%) 91 (15.5%) 
 2 35 (15.8%) 67 (18.3%) 102 (17.4%) 
 ≥3 109 (49.7%) 210 (57.3%) 319 (54.3%) 
Number of trial participation per patient 
 1 146 (66.1%) 264 (72.1%) 410 (69.8%) 
 2 49 (22.2%) 67 (18.3%) 116 (19.8%) 
 3 15 (6.8%) 22 (6.0%) 37 (6.3%) 
 4 6 (2.7%) 8 (2.2%) 14 (2.4%) 
 5 5 (2.3%) 1 (0.4%) 6 (1.0%) 
 6 4 (1.1%) 4 (0.7%) 

Abbreviations: GIST, gastrointestinal stromal tumor; NOS, not otherwise specified.

aOther subtypes of soft-tissue sarcomas included sclerosing epithelioid fibrosarcoma (n = 8), solitary fibrous tumor (n = 7), PECOMA (n = 6), desmoid tumor (n = 5), desmosplastic small round cell tumor (n = 5), epithelioid sarcoma (n = 4), myxofibrosarcoma (n = 4), small round cell sarcoma NOS (n = 4), extraskeletal chondrosarcoma (n = 4), and epithelioid hemangioendothelioma (n = 3). Inflammatory myofibroblastic tumor (n = 2), endometrial stromal sarcoma (n = 2), ossifying fibromyxoid tumor (n = 2), extraosseous osteosarcoma (n = 2), dermatofibrosarcoma protuberans (n = 1), adamantinoma (n = 1), giant cell soft-tissue tumor (n = 1), low-grade fibromyxoid sarcoma (n = 1), metastatic phyllodes tumor (n = 1), plexiform fibrohistiocytic tumor (n = 1), and histiocytic sarcoma (n = 1).

bOther subtypes of bone sarcomas include epithelioid sarcoma (n = 1), undifferentiated pleomorphic bone sarcoma (n = 1, enrolled in matched trial), sclerosing epithelioid fibrosarcoma (n = 1), and giant cell tumor of bone (n = 1).

Next-generation sequencing data were available for 473 patients (80.6%), including 347 patients (85.6%) within the soft-tissue sarcoma cohort, 87 (71.3%) with bone sarcoma, and 39 (65.0%) with GIST. Of these patients, 398 had at least one alteration detected by next-generation sequencing (84.1% of those tested), whereas no mutations were detected using the molecular panel employed for the remaining 75 patients (15.9%). An additional 38 (6.5%) patients had limited molecular profiling performed: KIT/PDGFRA analysis in GIST (n = 20), analysis of MDM2 amplification in liposarcoma (n = 3) or undifferentiated pleomorphic sarcoma (n = 1), fusion workup for diagnostic inclusion or exclusion in Ewing sarcoma (n = 4), synovial sarcoma (n = 4), desmoplastic small round cell tumor (n = 1), myxofibrosarcoma (n = 1), small round cell sarcoma (n = 1), clear cell sarcoma (n = 1), and angiosarcoma (n = 1), or other limited molecular testing in epithelioid sarcoma (n = 1). Therefore, 511 patients (87.1%) underwent molecular testing before trial enrollment (Fig. 1).

Figure 1.

Flow diagram depicting patient distribution, percentage of patients with molecular testing, and type of phase 1 therapy received. GIST, gastrointestinal stromal tumor; MD Anderson, MD Anderson Cancer Center; NGS, next-generation sequencing; UPS, undifferentiated pleomorphic sarcoma. (Created with BioRender.com.)

Figure 1.

Flow diagram depicting patient distribution, percentage of patients with molecular testing, and type of phase 1 therapy received. GIST, gastrointestinal stromal tumor; MD Anderson, MD Anderson Cancer Center; NGS, next-generation sequencing; UPS, undifferentiated pleomorphic sarcoma. (Created with BioRender.com.)

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A total of 75 patients were enrolled in early-phase trials with no prior systemic therapy. These patients most commonly had a diagnosis of chemoresistant histologies with limited availability of standard-of-care options at the time of inclusion: liposarcoma (n = 26), chondrosarcoma (n = 20), clear cell sarcoma (n = 6), alveolar soft part sarcoma (n = 4), epithelioid sarcoma (n = 3), chordoma (n = 3), epithelioid hemangioendothelioma (n = 2), leiomyosarcoma (n = 2), Ewing sarcoma (n = 1), extraskeletal myxoid chondrosarcoma (n = 1), inflammatory myofibroblastic tumor (n = 1), low-grade fibromyxoid sarcoma (n = 1), undifferentiated pleomorphic sarcoma (n = 1), osteosarcoma (n = 1), plexiform fibrohistiocytic tumor (n = 1), synovial sarcoma (n = 1), and sclerosing epithelioid fibrosarcoma (n = 1). Thirty-four of those patients (45.3%) were enrolled in biomarker-matched studies.

Patients included in biomarker-matched trials

In total, 221 patients (37.6% of the total) were treated in biomarker-matched trials (Fig. 1). Of the patients included in the matched trials, 64.2% (n = 142) were diagnosed with soft-tissue sarcoma, 24.9% (n = 55) with GIST, and the remaining 10.9% (n = 24) with sarcoma of the bone.

The most frequent subtypes of soft-tissue sarcoma enrolled in matched trials included liposarcoma (n = 53), leiomyosarcoma (n = 28), synovial sarcoma (n = 10), sarcoma not otherwise specified (n = 10), and undifferentiated pleomorphic sarcoma (n = 8).

Among the patients with bone sarcomas, 12 were diagnosed with chondrosarcoma, 4 with Ewing sarcoma, 4 with chordoma, and 3 with osteosarcoma.

Supplementary Figure S1 illustrates the most common drug mechanisms in patients treated in the biomarker-matched study.

Patient outcomes for the whole cohort

Objective responses were observed in 13.1% (n = 29/221) of the matched patients compared with 4.9% (n = 18/366) of those in unmatched trials (P < 0.001). Objective responses were observed for patients matched to treatments with KIT/PDGFR alpha (n = 8 as single agent; n = 1 in combination), MDM2 (n = 4 as single agent), PIK3CA/mTOR (n = 3 in combination), NTRK (n = 3 as single agent), MET (n = 1 as single agent; n = 2 in combination), MAGE-A4 (n = 2 as single agent), aurora kinase (n = 1 as single agent), PARP (n = 1 as single agent), EED (n = 1 as single agent), ALK (n = 1 as single agent), and IGF1R (n = 1, in combination).

The clinical benefit rate at 6 months for biomarker-matched trials was 43.9% versus 19.9% for unmatched studies (P < 0.001), and the clinical benefit rate at 4 months was 54.8% versus 30.3%, respectively (P < 0.001). Figure 2 summarizes the characteristics of the patients who achieved a response or clinical benefit for at least 6 months.

Figure 2.

Mechanism of matched drug along with histology and time on treatment for patients who received biomarker-matched therapies and achieved a clinical benefit, excluding GIST (CR, PR, or SD for at least 6 months). Orange bars represent CR or PR, blue bars SD. ADC, antibody–drug conjugate; CR, complete response; GIST, gastrointestinal stromal tumor; i(inh), inhibitor; LGFS, low-grade fibromyxoid sarcoma; NOS, not otherwise specified; PR, partial response; SD, stable disease; SEF, sclerosing epithelioid fibrosarcoma; SFT, solitary fibrous tumor; UPS, undifferentiated pleomorphic sarcoma.

Figure 2.

Mechanism of matched drug along with histology and time on treatment for patients who received biomarker-matched therapies and achieved a clinical benefit, excluding GIST (CR, PR, or SD for at least 6 months). Orange bars represent CR or PR, blue bars SD. ADC, antibody–drug conjugate; CR, complete response; GIST, gastrointestinal stromal tumor; i(inh), inhibitor; LGFS, low-grade fibromyxoid sarcoma; NOS, not otherwise specified; PR, partial response; SD, stable disease; SEF, sclerosing epithelioid fibrosarcoma; SFT, solitary fibrous tumor; UPS, undifferentiated pleomorphic sarcoma.

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PFS was significantly longer for patients enrolled in matched trials (median = 5.5 vs. 2.4 months; P < 0.001; Fig. 3A). Overall survival was also superior in patients enrolled in matched trials (median = 21.5 vs. 12.3 months; P < 0.001; Fig. 3B).

Figure 3.

Kaplan–Meier plots of PFS (A) and overall survival (B) in all patients, and PFS (C) and overall survival (D) excluding patients diagnosed with GIST from the analysis.

Figure 3.

Kaplan–Meier plots of PFS (A) and overall survival (B) in all patients, and PFS (C) and overall survival (D) excluding patients diagnosed with GIST from the analysis.

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Patient outcomes for subgroups

For patients treated in biomarker-matched trials, the overall response rate was not different between patients with GIST (18.1%; n = 10/55) and those with non-GIST sarcomas (11.4%; n = 19/166; P = 0.200). On the other hand, the clinical benefit rate was higher for GIST than for non-GIST sarcomas (4 months: 72.7% vs. 48.8%, P = 0.002; 6 months: 65.4% vs. 36.7%, P < 0.001).

The benefits of the overall response rate (11.4% vs. 5.0%, P = 0.007) and clinical benefit rate (4 months: 48.8% vs. 30.5%, P < 0.001; 6 months: 36.7% vs. 20.2%, P < 0.001) for matched trials were maintained when patients with GIST were excluded from the analysis of the whole cohort. Supplementary Table S1 summarizes the response and clinical benefit outcomes of the selected subgroups. In addition, PFS and overall survival benefit were also maintained after exclusion of patients diagnosed with GIST. Median PFS was 4.1 versus 2.5 months (P < 0.001), and median overall survival was 20.8 versus 11.7 months (P < 0.001; Fig. 3C and D).

Survival outcomes were also improved for patients enrolled in matched versus unmatched trials when considering only patients with non-GIST soft-tissue sarcoma (Fig. 4A and B; median PFS 4.6 vs. 2.7 months, P < 0.001; median overall survival 20.8 vs. 12.8 months, P < 0.001). Regarding patients with bone sarcoma, there was a trend toward improved survival for enrollment in matched trials, although this was not statistically significant (Fig. 4C and D; median PFS 2.0 vs. 2.2 months, P = 0.232; median overall survival 19.6 vs. 8.8 months, P = 0.512).

Figure 4.

Kaplan–Meier plots of PFS (A) and overall survival (B) in the non-GIST soft-tissue sarcoma cohort, and PFS (C) and overall survival (D) in the bone sarcoma cohort.

Figure 4.

Kaplan–Meier plots of PFS (A) and overall survival (B) in the non-GIST soft-tissue sarcoma cohort, and PFS (C) and overall survival (D) in the bone sarcoma cohort.

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Patients enrolled with no prior systemic therapy had significantly higher PFS (median, 6.8 months for no prior lines, 4.1 months for one prior line, 3.0 months for two prior lines, and 2.6 months for three or more prior lines; P < 0.001) and overall survival (median, 36.9 months for no prior lines, 19.8 months for one prior line, 15.7 months for two prior lines, and 12.4 months for three or more prior lines; P < 0.001).

In total, 75 patients in the matched group participated in more than one clinical trial, including 43 who participated in at least one matched and one unmatched study. The median PFS for matched trials in this population was 6.4 months, compared with 3.7 months for unmatched patients (P = 0.004). The median PFSr for these patients was 1.75 (range, 0.2–24.8), and most patients (60.5%; n = 26) had a PFSr of at least 1.33.

Factors associated with PFS

In univariate analysis excluding patients with GIST, factors significantly associated with longer PFS were specific histologies (P < 0.001), soft-tissue sarcoma (vs. bone sarcoma; P = 0.023), number of prior lines of therapy (P < 0.001), number of trials the patient enrolled in (P < 0.001), and enrollment in matched studies (P < 0.001; Table 2). In multivariate analysis excluding patients with GIST, factors significantly associated with longer PFS were number of prior lines of therapy (P < 0.001), number of trials the patient enrolled in (P < 0.001), and enrollment in matched studies (P = 0.006; Table 2).

Table 2.

Association between PFS and patient characteristics for advanced patients with sarcomas enrolled in early-phase trials, excluding patients with GIST.

nUnivariate Cox HR (95% CI)Univariate Cox P valueMultivariate Cox HR (95% CI)Multivariate Cox P value
Sex      
 Female 258 Ref  Ref  
 Male 269 1.18 (0.99–1.41) 0.063 1.16 (0.96–1.40) 0.12 
Type of sarcoma      
 Soft tissue 405 Ref  Ref  
 Bone 122 1.28 (1.04–1.59) 0.023 1.05 (0.62–1.78) 0.84 
Sarcoma subtype   <0.001  0.160 
 UPS 45 Ref  Ref  
 Chondrosarcoma 45 1.07 (0.70–1.64)  1.71 (0.89–3.29) 0.10 
 Ewing 27 0.82 (0.50–1.35)  0.89 (0.43–1.84) 0.75 
 Leiomyosarcoma 101 0.89 (0.62–1.28)  1.13 (0.77–1.65) 0.53 
 Liposarcoma 82 0.65 (0.45–0.96)  1.14 (0.76–1.72) 0.52 
 Osteosarcoma 37 1.60 (1.03–2.49)  1.63 (0.85–3.13) 0.14 
 Other 153 0.80 (0.57–1.13)  1.15 (0.80–1.65) 0.46 
 Sarcoma NOS 34 1.12 (0.71–1.78)  1.40 (0.88–2.25) 0.16 
Prior therapies   <0.001  <0.001 
 0 75 Ref  Ref  
 1 84 1.56 (1.12–2.20)  1.56 (1.11–2.21) 0.012 
 2 90 1.97 (1.42–2.73)  2.09 (1.48–2.96) <0.001 
 3+ 278 2.25 (1.70–2.97)  2.39 (1.74–3.27) <0.001 
Number of trials   <0.001  <0.001 
 1 364 Ref  Ref  
 2 104 0.69 (0.56–0.87)  0.71 (0.57–0.89) 0.004 
 3+ 59 0.47 (0.35–0.63)  0.48 (0.36–0.65) <0.001 
Matched trial      
 No 361 Ref  Ref  
 Yes 166 0.64 (0.52–0.77) <0.001 0.74 (0.60–0.92) 0.006 
IO trial      
 No 344 Ref    
 Yes 183 0.86 (0.72–1.04) 0.12 — — 
nUnivariate Cox HR (95% CI)Univariate Cox P valueMultivariate Cox HR (95% CI)Multivariate Cox P value
Sex      
 Female 258 Ref  Ref  
 Male 269 1.18 (0.99–1.41) 0.063 1.16 (0.96–1.40) 0.12 
Type of sarcoma      
 Soft tissue 405 Ref  Ref  
 Bone 122 1.28 (1.04–1.59) 0.023 1.05 (0.62–1.78) 0.84 
Sarcoma subtype   <0.001  0.160 
 UPS 45 Ref  Ref  
 Chondrosarcoma 45 1.07 (0.70–1.64)  1.71 (0.89–3.29) 0.10 
 Ewing 27 0.82 (0.50–1.35)  0.89 (0.43–1.84) 0.75 
 Leiomyosarcoma 101 0.89 (0.62–1.28)  1.13 (0.77–1.65) 0.53 
 Liposarcoma 82 0.65 (0.45–0.96)  1.14 (0.76–1.72) 0.52 
 Osteosarcoma 37 1.60 (1.03–2.49)  1.63 (0.85–3.13) 0.14 
 Other 153 0.80 (0.57–1.13)  1.15 (0.80–1.65) 0.46 
 Sarcoma NOS 34 1.12 (0.71–1.78)  1.40 (0.88–2.25) 0.16 
Prior therapies   <0.001  <0.001 
 0 75 Ref  Ref  
 1 84 1.56 (1.12–2.20)  1.56 (1.11–2.21) 0.012 
 2 90 1.97 (1.42–2.73)  2.09 (1.48–2.96) <0.001 
 3+ 278 2.25 (1.70–2.97)  2.39 (1.74–3.27) <0.001 
Number of trials   <0.001  <0.001 
 1 364 Ref  Ref  
 2 104 0.69 (0.56–0.87)  0.71 (0.57–0.89) 0.004 
 3+ 59 0.47 (0.35–0.63)  0.48 (0.36–0.65) <0.001 
Matched trial      
 No 361 Ref  Ref  
 Yes 166 0.64 (0.52–0.77) <0.001 0.74 (0.60–0.92) 0.006 
IO trial      
 No 344 Ref    
 Yes 183 0.86 (0.72–1.04) 0.12 — — 

Abbreviations: IO, immunotherapy; NOS, not otherwise specified; Ref, Reference; UPS, undifferentiated pleomorphic sarcoma.

The results from this single-center retrospective analysis identified that enrollment in early-phase trials informed by molecular biomarkers was associated with a statistically significant improvement in the response rate, PFS, and overall survival in a heavily pretreated population of 587 patients diagnosed with advanced sarcoma. We found sustained clinical benefit in patients with advanced sarcoma receiving agents targeting KIT, PDGFR alpha, MDM2, PIK3CA/mTOR, NTRK, MET, MAGE-A4, aurora kinase, PARP, EED/EZH2, and ALK among other targets. Importantly, the benefit of enrollment in biomarker-matched trials was maintained even after the exclusion of patients diagnosed with GIST, a population in which the role of targeted therapy has already been established.

Our findings are consistent with reports of the benefits of molecularly matched treatments in early-phase trials. An earlier retrospective analysis (IMPACT initiative) by MD Anderson evaluated 1,283 patients enrolled in phase I trials over a 4-year period (14). Notably, the availability of targeted drugs was limited to those targeting mTOR, PIK3CA, BRAF, MEK, EGFR, KIT, RET, and non-specific multikinase inhibitors. However, the results were remarkable for improved response rate and survival for patients with identified actionable alterations and those who received matched treatment (14). In that analysis, 35 patients diagnosed with sarcoma were included, and only 11% had an actionable alteration; the limited availability of drugs and limited molecular testing performed at that time likely influenced this small number (14). In 2017, the results for 1,436 additional patients within the IMPACT initiative were published in an era of increased availability of targeted agents and confirmed the benefit of molecularly matched therapies (15). Twenty-one patients with sarcoma were enrolled in the updated dataset, including 15 (72%) treated in the matched studies (15).

Likewise, a single-center retrospective analysis including 98 patients with advanced solid tumors conducted at the INCLIVA Precision Medicine Unit (MAST study) confirmed that those enrolled in molecularly matched studies had longer PFS (16). Moreover, a meta-analysis of single-arm phase II studies published in 2015 demonstrated that trials with a personalized approach consistently and independently correlated with a higher overall response rate, PFS, and overall survival (17). In this analysis, only 23 of the 570 trials included patients with sarcoma (17). Furthermore, a recent study of comprehensive molecular profiling in rare cancers—the MASTER trial—showed that genomic and transcriptomic data facilitated an evidence-based management strategy including experimental therapy beyond current guidelines in 88% of the cases (18). Notably, only 32% of patients received the recommended treatments, mostly off-label use, although some patients were treated in clinical trials. In the cohort of patients with soft-tissue sarcoma treated with biomarker-guided therapy recommended by the tumor board, 35% of patients experienced PFS to targeted therapy at least 30% longer than that on prior treatment, suggesting a clinically meaningful benefit of biomarker-matched therapy in these patients. Importantly, a detailed description of the histology represented within the soft-tissue sarcoma group was not provided, and follow-up data to evaluate PFS were available for only a small fraction of patients with sarcoma receiving targeted therapy in this trial (n = 111); response evaluation was possible for even fewer patients with sarcoma enrolled (n = 61). Of note, molecular findings led to a histologic diagnosis reconsideration in 4.4% of cases in the whole study, including 27 of the 439 patients with sarcoma (6.2%), highlighting the importance of early integration of molecular profiling in selected patients with mesenchymal tumors.

Our study results also agree with other reports highlighting the value of molecular testing in identifying potential targets for treating sarcomas. The most extensive analysis included 7,494 patients diagnosed with 44 sarcoma histologies (81% soft tissue, 14% bone) who underwent sequencing with Foundation Medicine (19). Of the 118 patients in this series treated at Memorial Sloan Kettering for whom clinical data were available, 47% had actionable molecular alterations and 29% of patients in the whole cohort were enrolled in a matched trial or received off-label treatment of an FDA-approved drug. An additional report by Lucchesi and colleagues analyzed data on 584 sarcomas in the AACR Genie Database and found that 41% of cases harbored a specific genomic alteration that could impact therapy (20). Similarly, a single-center report from the Moffitt Cancer Center identified that 49% of 114 consecutive patients with sarcoma carried an actionable mutation; 15 patients received therapies guided by genomic results, with 26% deriving clinical benefit (21). More recently, a report from two centers in France suggested the benefit of molecular- and histology-driven treatments in 214 patients with advanced sarcomas included in early-phase trials (22). In this analysis, 25% of patients were included in trials with molecular screening and 62% in studies that were histology driven.

On the other hand, most of the above-cited analyses provide limited follow-up data of patients, including restricted information regarding the targets leading to clinical benefit and tumor types in those patients. Given the difficulty of performing specific biomarker-guided trials in rare cancers, generating clinical evidence to support new drug development is difficult for sarcomas. Therefore, a retrospective analysis of large databases, such as ours, can contribute to the identification of actionable alterations in specific subtypes of mesenchymal malignancies. In addition, in the absence of clinical trial availability and in the context of ultra-rare neoplasms, in which clinical decisions are often based solely on case series or small trials, retrospective analyses can empower patients and physicians to pursue specific off-label targeted therapies in the context of potentially actionable next-generation result (23).

Our analysis also confirms previous data that demonstrated encouraging results with targeted therapies in sarcomas, especially MET in clear cell sarcoma, ALK in inflammatory myofibroblastic tumors, MDM2 and CDK4 in well-differentiated/dedifferentiated liposarcomas, MAGE-A4 in synovial sarcoma, EZH2 in epithelioid sarcoma, and NTRK across multiple histologies (24–27). Of note, however, patients with these molecular alterations represent a minority of those with advanced sarcoma, and encouraging efficacy was also observed in our study for therapies targeting PARP, aurora kinase, and mTOR, among other targets.

Despite our study's heterogeneous population with multiple soft-tissue and bone sarcoma subtypes, the median PFS of 4.6 months and median overall survival of 20.8 months achieved by molecularly-matched phase I clinical trials compare favorably to historical data for pretreated patients with sarcoma (28). In a previous meta-analysis of 10 randomized trials evaluating salvage therapy in soft-tissue sarcomas (two targeted therapies and eight cytotoxic drugs), the pooled PFS for the experimental arms was 3.9 versus 2.3 months for the control groups. The median overall survival was 13.4 months in the experimental arms compared with 10.1 months in the control arms (28).

Interestingly, regarding patients with sarcoma enrolled in early-phase trials, the outcomes have considerably improved in the past years. For illustration, among patients with sarcoma enrolled in phase I studies at the Royal Marsden Hospital from 1998 to 2009, the overall response rate was 2.4%, median PFS was 2.1 months, and median overall survival was 7.6 months (29). In a multicenter European study evaluating 178 patients with sarcoma in early-phase trials from 2005–2007, the response rate was 3% (30). Alternately, in the above-cited recent report from France, among 225 patients included in early-phase trials from 2012 to 2020, outcomes were overall comparable with those in our cohort—the overall response rate was 9.5%, disease control rate was 61%, median PFS was 2.8 months, and median overall survival was 12.3 months (22). The increased awareness of sarcoma subtypes’ molecular and biological context, with higher percentage of trials with molecular and/or histology screening alongside the increasing availability of novel therapies are reasonable explanations for the improved outcomes in analysis encompassing more recent years, such as ours.

In addition, increased enrollment in phase I trials earlier in the disease course can also contribute to such disparities in outcomes. Our data show improved PFS and overall survival for patients enrolled with no prior therapies, which is concordant with prior studies (22). The median PFS of 6.8 months and overall survival of 36.9 months in the subgroup of treatment-naïve patients in our cohort is comparable with the PFS (median, 6.5 months) and overall survival (median, 32.5 months) observed for this subgroup in the recent French study (22).

Notwithstanding the promising PFS and overall survival observed in patients enrolled in matched trials, the overall response rate in this subgroup was still low (13%). The WINTHER trial demonstrated the feasibility of transcriptomic-based treatments for patients with advanced cancer (31). In that study, transcriptomic analysis expanded eligibility for targeted agents, and 26% that received treatment directed by the transcriptomic analysis achieved a clinical benefit (31). Of note, only 4 patients with sarcoma were included—2 liposarcoma, 2 rhabdomyosarcoma—and none achieved a response (31). Moreover, the elegant I-PREDICT study has shed light on the importance of customized multidrug regimens for individualized combination therapies to improve the drug matching score (32). In this trial, 49% of consented patients received matched treatments, and those with a high matching score > 50% achieved higher PFS and overall survival: impressively, 75% of patients with high matching score achieved a PFS ratio of over 1.3 compared with prior therapy (33). Four patients with sarcoma were enrolled, and 3 received at least one matched treatment. However, none had a high matching score, and only one achieved clinical benefit—a patient diagnosed with dedifferentiated liposarcoma matched to palbociclib. The fact that most patients received matched therapy with single-agent drugs, that inclusion in dose-escalation cohorts was possible, and that molecular profiling performed was mostly based on DNA sequencing might be reasonable explanations for the suboptimal response rate in our analysis.

Our analysis has notable limitations, including its retrospective design and bias toward selecting patients with better performance status inherent to the population of patients participating in early-phase clinical trials. Furthermore, the relatively small number of patients receiving specific targeted therapies precludes conclusions regarding the activity of individual agents in particular tumor subtypes. In addition, the limited number of patients with bone sarcoma enrolled in matched trials can lead to uncertainty regarding the generalizability of these results to this specific patient population. This subgroup did not derive benefit from matched agents in our subanalysis. Moreover, specific pathologic details were not annotated, which precludes further histologic classification in the liposarcoma family. In addition, reflecting the long period of inclusion, heterogeneous molecular tests were utilized, some of which might not be part of the current standard of care. However, to our knowledge, this is the largest analysis assessing the outcomes of patients with sarcoma enrolled in phase I clinical trials in the era of targeted and immunotherapy and specifically investigating the outcomes of patients with sarcoma in biomarker-matched versus unmatched trials. The significance of our sample size can be appraised by comparison with the previously discussed multicenter prospective MASTER trial among 2,340 patients with rare tumors assessed for eligibility during the first 6 years, wherein only 61 patients with sarcoma were evaluable for response (18). An additional potential limitation of retrospective analyses evaluating matched versus unmatched treatments is the selection of patients with actionable alterations that have more indolent cancer behavior, and imbalance is notable between groups—patients treated in matched trials are enriched for MDM2 amplification for soft-tissue sarcoma and IDH for bone sarcomas, for illustration. However, prior investigations have consistently suggested that the benefit is associated with the receipt of targeted agents rather than the presence of actionable mutations. For example, in the MAST study, the PFS on prior therapy was not different for patients who subsequently received matched versus unmatched treatments (16). In addition, Pishvaian and colleagues demonstrated that, in pancreatic cancer, the benefit of matched therapies was still present when compared with patients who received unmatched treatments but had an actionable alteration (34). Furthermore, intrapatient PFSr analysis in our study showed that most patients derived more benefit from matched trials compared with unmatched trials, and multivariate analysis demonstrated that enrollment in matched trials was an independent factor leading to improved PFS.

Conclusion

Enrollment in molecularly matched early-phase trials was associated with improved overall survival and progression-free outcomes in patients with advanced bone and soft-tissue sarcomas; these benefits were maintained when patients with GIST were excluded from the analysis. Our findings suggest that molecular testing of tumors from patients with advanced sarcoma is warranted, including next-generation sequencing in selected patients, and enrollment in biomarker-matched trials can be used as a therapeutic strategy. Further research is required to identify novel therapies for advanced soft-tissue and bone sarcomas, and biomarker-driven molecularly matched basket trials for patients with sarcoma are critically needed.

R. Carmagnani Pestana reports personal fees from Servier, Bayer, Pfizer, Merck, BMS, Amgen, Manual de Oncologia Brasil (MOC), Oncologia Brasil, and Associacao Brasileira de Linfoma e Leucemia (ABRALE) outside the submitted work. D.S. Hong reports research(Inst)/grant funding (Inst) from AbbVie, Adaptimmune, Adlai-Nortye, Amgen, AstraZeneca, Bayer, Bristol-Myers Squibb, Daiichi-Sankyo, Deciphera, Eisai, Eli Lilly, Endeavor, Erasca, F. Hoffmann-La Roche, Fate Therapeutics, Genentech, Genmab, Immunogenesis, Infinity, Kyowa Kirin, Merck, Mirati, Navier, NCI-CTEP, Novartis, Numab, Pfizer, Pyramid Bio, Revolution Medicine, SeaGen, STCube, Takeda, TCR2, Turning Point Therapeutics, and VM Oncology; travel, accommodations, and expenses from AACR, ASCO, Bayer, Genmab, SITC, and Telperian; consulting, speaker, or advisory role for AbbVie, Acuta, Adaptimmune, Alkermes, Alpha Insights, Amgen, Aumbiosciences, Axiom, Baxter, Bayer, Boxer Capital, BridgeBio, COG, COR2ed, Cowen, Ecor1, Erasca, F. Hoffmann-La Roche, Genentech, Gennao Bio, Gilead, GLG, Group H, Guidepoint, HCW Precision Oncology, Immunogenesis, Janssen, Liberium, MedaCorp, Medscape, Numab, Oncologia Brasil, ORI Capital, Pfizer, Pharma Intelligence, POET Congress, Prime Oncology, RAIN, SeaGen, STCube, Takeda, Tavistock, Trieza Therapeutics, Turning Point Therapeutics, WebMD, YingLing Pharma, and Ziopharm; and other ownership interests in Molecular Match (advisor), OncoResponse (founder, advisor), and Telperian (founder, advisor). A. Naing reports grants from NCI, EMD Serono, MedImmune, Healios Onc, Nutrition, Atterocor/Millendo, Amplimmune, Karyopharm Therapeutics, Incyte, Novartis, Regeneron, Bristol-Myers Squibb, Pfizer, Neon Therapeutics, Calithera Biosciences, TopAlliance Biosciences, Eli Lilly, Kymab, Arcus Biosciences, NeoImmuneTech, Surface Oncology, Monopteros Therapeutics, BioNTech SE, Seven & Eight Biopharma, and SOTIO Biotech AG; grants and nonfinancial support from ARMO BioSciences; grants and personal fees from Merck Sharp & Dohme Corp, CytomX Therapeutics, PsiOxus Therapeutics, and Immune-Onc Therapeutics; and personal fees from Deka Biosciences, NGM Bio, STCube Pharmaceuticals, OncoSec KEYNOTE-695, Genome & Company, Nouscom, OncoNano, Servier, Lynx Health, AbbVie, AKH Inc, The Lynx Group, Society for Immunotherapy of Cancer (SITC), Korean Society of Medical Oncology (KSMO), Scripps Cancer Care Symposium, ASCO Direct Oncology Highlights, European Society for Medical Oncology (ESMO), and CME Outfitters outside the submitted work. In addition, A. Naing's spouse reports research funding from The Texas Medical Center Digestive Diseases Center, Jeffery Modell Foundation, Immune Deficiency Foundation, Baxalta US Inc, and Chao Physician-Scientist Foundation; consultant/advisory board participation with Takeda, Pharming Healthcare Inc, and Horizon Therapeutics USA, Inc.; and ad hoc consultancy with Alfaisal University. S.A. Piha-Paul reports other support from AbbVie, ABM Therapeutics, Acepodia, Alkermes, Aminex Therapeutics, Amphivena Therapeutics, BioMarin Pharmaceutical, Boehringer Ingelheim, Bristol Myers Squibb, Cerulean Pharma, Chugai Pharmaceutical Co., Ltd, Curis, Cyclacel Pharmaceuticals, Daiichi Sankyo, Eli Lilly, ENB Therapeutics, Epigenetix, Five Prime Therapeutics, F-Star Beta Limited, F-Star Therapeutics, Gene Quantum, Genmab A/S, Gilead Sciences, GlaxoSmithKline, Helix BioPharma Corp., Hengrui Pharmaceuticals, Co., Ltd., HiberCell, Immorna Biotherapeutics, Immunomedics, Incyte Corp., Jacobio Pharmaceuticals Co., Ltd., Jiangsu Simcere Pharmaceutical Co., Ltd., Lytix Biopharma AS, Medimmune, Medivation, Merck Sharp and Dohme, Nectin Therapeutics, Ltd., Novartis Pharmaceuticals, Pieris Pharmaceuticals, Pfizer, Phanes Therapeutics, Principia Biopharma, Puma Biotechnology, Purinomia Biotech, Rapt Therapeutics, Replimune, Seattle Genetics, Silverback Therapeutics, Synlogic Therapeutics, Taiho Oncology, Tesaro, TransThera Bio, and ZielBio, and grants from NCI/NIH: P30CA016672 — Core Grant (CCSG Shared Resources) outside the submitted work; in addition, S.A. Piha-Paul has worked as a consultant for CRC Oncology. J. Rodon reports personal fees from Peptomyc, Kelun Pharmaceuticals/Klus Pharma, Ellipses Pharma, Molecular Partners, IONCTURA, Clarion Healthcare, Debiopharm, Monte Rosa Therapeutics, Cullgen, Macrogenics, NovellusDX, Oncology One, Envision Pharma, Columbus Venture Partners, Sardona Therapeutics, Avoro Capital Advisors, Chinese University of Hong Kong, Boxer Capital, LLC, and Tang Advisors, LLC; grants and personal fees from Pfizer, Aadi Bioscience, Merus, and Black Diamond; personal fees and other support from Vall d'Hebron Institute of Oncology/Ministero De Empleo Y Seguridad Social; grants from Blueprint Medicines, Merck Sharp & Dohme, Hummingbird, Yingli, Vall d'Hebron Institute of Oncology/Cancer Core Europe, Novartis, Spectrum Pharmaceuticals, Symphogen, BioAtla, GenMab, CytomX, Kelun-Biotech, Takeda-Millenium, GlaxoSmithKline, Taiho, Roche Pharmaceuticals, Bicycle Therapeutics, Curis, Bayer, Nuvation, ForeBio, BioMed Valley Discoveries, Loxo Oncology, Hutchinson MediPharma, Cellestia, Deciphera, Ideaya, Amgen, Tango Therapeutics, Mirati, and Linneaus Therapeutics; and other support from European Society for Medical Oncology outside the submitted work. T.A. Yap reports grants and other support from University of Texas MD Anderson Cancer Center; grants and personal fees from Acrivon, Artios, AstraZeneca, Bayer, BeiGene, Clovis, EMD Serono, F-Star, ImmuneSensor, Merck, Pfizer, and Repare; grants from BioNTech, Blueprint, BMS, Constellation, Cyteir, Eli Lilly, Forbius, GlaxoSmithKline, Genentech, Haihe, Ionis, Ipsen, Jounce, Karyopharm, KSQ, Kyowa, Mirati, Novartis, Ribon Therapeutics, Regeneron, Rubius, Sanofi, Scholar Rock, Seattle Genetics, Tesaro, Vivace, and Zenith; personal fees from AbbVie, Adagene, Almac, Aduro, Amphista, Athena, Atrin, Avoro, Axiom, Baptist Health Systems, Boxer, Bristol Myers Squibb, C4 Therapeutics, Calithera, Cancer Research UK, Cybrexa, Diffusion, Genmab, Glenmark, GLG, Globe Life Sciences, GSK, Guidepoint, Idience, Ignyta, I-Mab, Institut Gustave Roussy, Intellisphere, Janssen, Kyn, MEI Pharma, Mereo, Natera, Nexys, Novocure, OHSU, OncoSec, Ono Pharma, Pegascy, PER, Piper-Sandler, Prolynx, resTORbio, Roche, Schrodinger, Theragnostics, Varian, Versant, Vivliome, Xinthera, Zai Labs, and ZielBio; and other support from Seagen outside the submitted work. D.D. Karp reports grants from NIH and NCI Core Grant; grants and other support from Myriad Drug Companies; personal fees from Black Beret Life Sciences and Affigen; and grants, personal fees, and other support from Phosplatin outside the submitted work. A.M. Tsimberidou reports grants from OBI Pharmaceuticals, IMMATICS, TEMPUS, Parker Institute for Cancer Immunotherapy, Tvardi, Tachyon, Agenus, and Novocure and personal fees and other support from VinceRx, Diaccurate, Nex-I, BrYet, BioEclipse, and Macrogenics during the conduct of the study. R. Ratan reports personal fees from Bayer and Epizyme; grants, personal fees, and nonfinancial support from Springworks; and grants from C4 Therapeutics outside the submitted work. V. Ravi reports grants from Novartis, grants and personal fees from Aadi Bioscience, and personal fees from Daiichi Sankyo outside the submitted work. C.L. Roland reports grants from Bristol Myers Squibb outside the submitted work. S.R. Patel reports personal fees from Daiichi Sankyo, Deciphera, Adaptimmune, and Rain Therapeutics and grants from Rain Therapeutics and Blueprint medicines outside the submitted work. J.A. Livingston reports grants from REPARE Therapeutics and personal fees from Foghorn Therapeutics outside the submitted work. N. Somaiah reports personal fees from Boehringer Ingelheim, Epizyme, and Aadi Biosciences outside the submitted work. A.P. Conley reports grants from Chordoma Foundation, Eli Lilly, Roche, NCI, Nant Pharma, and EpicentRx; grants and personal fees from Inhibrx; personal fees from Aadi Bioscience, Deciphera Pharmaceuticals, Applied Clinical Intelligence, and Guidepoint Global; and other support from OncLive and Medscape outside the submitted work. F. Meric-Bernstam reports personal fees from AbbVie, Aduro BioTech Inc., Alkermes, AstraZeneca, Daiichi Sankyo Co. Ltd., DebioPharm, Ecor1 Capital, eFFECTOR Therapeutics, F. Hoffmann-La Roche Ltd., GT Apeiron, Genentech, HarbingerHealth, IBM Watson, Infinity Pharmaceuticals, Jackson Laboratory, Kolon Life Science, Lengo Therapeutics, Menarini Group, OrigiMed, PACT Pharma, Parexel International, Pfizer, Protai Bio Ltd, Samsung Bioepis, Seattle Genetics, Tallac Therapeutics, Tyra Biosciences, Xencor, Zymeworks, Black Diamond, Biovica, Eisai, FogPharma, Immunomedics, Inflection Biosciences, Karyopharm Therapeutics, Loxo Oncology, Mersana Therapeutics, OnCusp Therapeutics, Puma Biotechnology Inc., Seattle Genetics, Sanofi, Silverback Therapeutics, Spectrum Pharmaceuticals, Zentalis, and Chugai Biopharmaceuticals; grants from Aileron Therapeutics, AstraZeneca, Bayer Healthcare Pharmaceutical, Calithera Biosciences, Curis, CytomX Therapeutics, Daiichi Sankyo Co. Ltd., Debiopharm International, eFFECTOR Therapeutics, Genentech, Guardant Health, Klus Pharma, Takeda Pharmaceutical, Novartis, Puma Biotechnology, and Taiho Pharmaceutical Co.; and nonfinancial support from European Organisation for Research and Treatment of Cancer (EORTC) and European Society for Medical Oncology (ESMO) outside the submitted work. V. Subbiah reports research funding/grant support for clinical trials from AbbVie, Agensys, Alfasigma, Altum, Amgen, Bayer, BERG Health, Blueprint Medicines, Boston Biomedical, Boston Pharmaceuticals, Celgene, D3 Bio, Dragonfly Therapeutics, Exelixis, Fujifilm, GlaxoSmithKline, Idera Pharmaceuticals, Incyte, Inhibrx, Loxo Oncology, MedImmune, MultiVir, NanoCarrier, National Comprehensive Cancer Network, NCI-CTEP, Northwest Biotherapeutics, Novartis, PharmaMar, Pfizer, Relay Therapeutics, Roche/Genentech, Takeda, Turning Point Therapeutics, UT MD Anderson Cancer Center, and Vegenics; travel support from ASCO, ESMO, Helsinn Healthcare, Incyte, Novartis and PharmaMar; consultancy/advisory board participation for Helsinn Healthcare, Jazz Pharmaceuticals, Incyte, Loxo Oncology/Eli Lilly, MedImmune, Novartis, QED Therapeutics, Relay Therapeutics, Daiichi Sankyo, and R-Pharm US; and a relationship with Medscape. No disclosures were reported by the other authors.

R. Carmagnani Pestana: Conceptualization, visualization, methodology, writing–original draft, writing–review and editing. J.T. Moyers: Conceptualization, data curation, formal analysis, validation, writing–review and editing. J. Roszik: Formal analysis, investigation, methodology, writing–review and editing. S. Sen: Resources, data curation, visualization, writing–review and editing. D.S. Hong: Supervision, validation, investigation, writing–review and editing. A. Naing: Resources, investigation, writing–review and editing. C.E. Herzog: Resources, investigation, writing–review and editing. S. Fu: Resources, investigation, writing–review and editing. S.A. Piha-Paul: Resources, investigation, writing–review and editing. J. Rodon: Resources, investigation, writing–review and editing. T.A. Yap: Resources, investigation, writing–review and editing. D.D. Karp: Resources, investigation, writing–review and editing. A.M. Tsimberidou: Resources, investigation, writing–review and editing. S. Pant: Resources, investigation, writing–review and editing. M.A. Zarzour: Resources, investigation, writing–review and editing. R. Ratan: Resources, investigation, writing–review and editing. V. Ravi: Resources, investigation, writing–review and editing. R.S. Benjamin: Supervision, investigation, writing–review and editing. A.J. Lazar: Resources, investigation. W.-L. Wang: Resources, investigation. N. Daw: Resources, investigation. J.B. Gill: Resources, investigation. D.J. Harrison: Resources, investigation. V.O. Lewis: Resources, supervision, investigation. C.L. Roland: Resources, investigation. S.R. Patel: Resources, supervision, investigation, writing–review and editing. J.A. Livingston: Resources, investigation, writing–review and editing. N. Somaiah: Resources, investigation, writing–review and editing. J.A. Ludwig: Resources, investigation, writing–review and editing. A.P. Conley: Resources, validation, investigation, writing–review and editing. N. Hamerschlak: Supervision, writing–review and editing. R. Gorlick: Resources, investigation, writing–review and editing. F. Meric-Bernstam: Resources, supervision, validation, investigation, writing–review and editing. V. Subbiah: Conceptualization, data curation, formal analysis, supervision, validation, methodology, writing–original draft, writing–review and editing.

This study was supported in part by the NIH/NCI under award number P30CA016672 (used by the Biostatistics Resource Group).

V. Subbiah is an Andrew Sabin Family Foundation Fellow at the University of Texas, MD Anderson Cancer Center. V. Subbiah acknowledges the support of the Jacquelyn A. Brady Fund. V. Subbiah is supported by NIH grants (no. R01CA242845 and R01CA273168). The MD Anderson Cancer Center Department of Investigational Cancer Therapeutics is supported by the Cancer Prevention and Research Institute of Texas (RP1100584), the Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy (1U01 CA180964), NCATS Grant UL1 TR000371 (Center for Clinical and Translational Sciences), and the MD Anderson Cancer Center Support Grant (P30 CA016672).

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).

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