The Genomics of Colorectal Cancer in Populations with African and European Ancestry

In the United States, Black patients have the highest rates of colorectal cancer with worse overall mortality compared to other ethnic groups (1). They also present at an earlier age with more advanced stage of disease (2). Blacks with early-onset colorectal cancer (diagnosed before the age of 50) additionally have more frequent proximal colon cancers and persistently low 5-year survival rates (3). Although the higher incidence of colorectal cancer in Blacks can be attributed to increased rates of obesity, decreased physical activity, and lower rates of colorectal cancer screening, these factors do not fully account for the observed clinical differences. In addition, Blacks are less likely to be offered surgical treatment, radiation, or chemotherapy, regardless of colorectal cancer stage at diagnosis (2). They also have low rates of participation in clinical trials due to lower rates of recruitment by physicians, mistrust of researchers and research institutions, limited knowledge of clinical trial opportunities, and long-standing effects of structural racism (4–6). It remains unknown if actionable genomic targets are equally prevalent in Blacks with colorectal cancer, making them eligible for targeted clinical trials.

The genomic contribution to disparities in colorectal cancer incidence and outcomes in Blacks is poorly understood due to limited genomic studies of minority patients and lack of associated clinical data (1, 7). Large-scale efforts to perform population-based genomic sequencing such as The Cancer Genome Atlas and AACR Project GENIE vastly underrepresent minorities (8–11). Further investigation into cancer-related genomic differences in Blacks by tumor location and age of onset might contribute to a better understanding of the observed health disparities and represents an unmet medical need (1, 11, 12).

To address this knowledge gap, we undertook the largest study of colorectal cancer–related genomic alterations and their impact on clinical outcomes in people of African ancestry. It is important to note that the terms “Blacks” and “whites,” as used here, refer to how they are noted in the literature, largely based on self-identified racial categorization. When we refer to data from prior studies, we will use these self-reported race terms. Our data lack self-reported race, and therefore we use ancestry-informative markers to classify patients with colorectal cancer by genetic ancestry. Here we utilize AFR and EUR to denote individuals of African and European ancestry, respectively. It has been shown that the use of genomic ancestry markers, rather than self-reported race, better captures genotypic profiles associated with disease risk, especially in complex phenotypes such as cancer (13–15). Furthermore, it has been shown that the use of genetic ancestry compared with self-reported race is more precise, leading to improved clinical predictions, and more accurately captures the associations between disease status and genetic risk (13, 16). Although we recognize that genetic ancestry fails to account for social determinants of health, which are the major drivers of health disparities, the goal of this study was to test whether genomic ancestry was associated with distinct patterns of somatic mutations, potentially giving mechanistic insights into outcome differences. We examined key mutations commonly associated with colorectal cancer across over 39,000 patients with AFR and EUR genetic ancestries.

We analyzed a total of 46,140 colorectal adenocarcinomas that were submitted for clinical genomic profiling as part of routine clinical care. Using a random forest classifier trained on the 1000 genomes phase III super populations and applied to ancestry-informative markers in our data set, we found that 5,460 (12%) colorectal cancers were from individuals of African ancestry (AFR) and 33,873 (73%) were of European ancestry (EUR), 2,307 (5%) were of East Asian or South Asian ancestry, and 4,500 (10%) were admixed American ancestry (Fig. 1A; Supplementary Table S1). We restricted the cohort to 5,301 AFR and 33,770 EUR, with ≥50% AFR and EUR admixture, respectively, for further analysis (Fig. 1B). Males were overrepresented in EUR (56%, n = 18,766) relative to AFR (51%, n = 2,709; Table 1). AFR were younger at age of biopsy or surgery compared with EUR (median age 58 vs. 61 years, Q ≤ 0.0001; Table 1). AFR had a higher frequency of tissue biopsies from the colon (44% AFR vs. 39% EUR, Q < 0.0001), whereas EUR had increased prevalence of biopsy from the rectum (8.2% AFR vs. 11% EUR, Q < 0.0001) and lung (5.4% AFR vs. 7.3% EUR, Q < 0.0001; Table 1). We found that of the 5,301 AFR, 95% were of American Southwest or Caribbean ancestry, 0.19% were East African, and 4.3% were West African, using the 1000 Genomes phase III populations as a reference. We did not find any statistically significant associations between genetic alterations and increasing percentage of AFR ancestry within the AFR group.

Microsatellite instability (MSI) status was evaluated for 93% of AFR and EUR, and AFR had a lower frequency of MSI-high tumors compared with EUR (3.9% vs. 5.5%, Q < 0.0001; Table 1). Tumor mutational burden (TMB) greater than 10 mutations per megabase (mut/Mb) was similar in AFR compared with EUR (8.0% vs. 8.8%, Q = 0.29; Table 1). The frequency of POLE/POLD1 mutations was similar in both ancestral groups (0.54% AFR vs. 0.42% EUR, Q = 0.22; Table 1). For the purposes of this study, we focused our analysis on mutational differences between 4,548 AFR and 28,629 EUR with microsatellite stable (MSS), POLE/POLD1-negative colorectal cancer with TMB < 10 mut/Mb (Supplementary Fig. S1; Supplementary Table S2).

AFR had significantly more frequent alterations in KRAS [60% vs. 50%; odds ratio (OR), 1.49; 95% confidence interval (CI), 1.39–1.58; Q < 0.0001; Fig. 2A; Table 2; Supplementary Table S3]. KRAS^G12D was the most common KRAS mutation in both AFR and EUR, with AFR having more frequent G12D mutations compared with EUR (17% vs. 14%, Q < 0.0001; Table 2). In addition, AFR had more frequent G13 mutations than EUR, respectively (11% vs. 8.3%, Q < 0.0001; Table 2). Interestingly, AFR had less frequent alterations in BRAF (4.7% vs. 8.5%; OR, 0.53; 95% CI, 0.46–0.61; Fig. 2A; Table 2; Supplementary Table S3). Specifically, BRAF^V600X mutations were less frequent in AFR than EUR (2.0% vs. 6.0%, Q < 0.0001), whereas there were no significant differences in the prevalence of BRAF class 2 (0.68% vs. 0.56%, Q = 0.80) and BRAF class 3 (1.5% vs. 1.3%, Q = 0.88) mutations (Table 2). Interestingly, AFR had more frequent alterations in the MAPK pathway (ARAF, BRAF, HRAS, KRAS, MAP2K1, MAP2K2, NF1, NRAS, and RAF1; 70% vs. 64%, Q < 0.0001).

AFR had more frequent PIK3CA mutations compared with EUR (20% vs. 17%, Q < 0.0001; Fig. 2A; Supplementary Table S3). In both ancestry groups, mutations were most commonly seen in exon 10. AFR also had more frequent alterations in the PI3K pathway (AKT, MTOR, PIK3CA/B, PIK3R1/2, PTEN, and TSC1/2; 31% vs. 28%, Q < 0.0001). Furthermore, AFR and EUR had similar rates of KRAS wild-type (KRAS^WT)/PIK3CA^mut tumors (4.9% vs. 5.6%; Q = 0.39).

AFR had more frequent alterations in APC (84% vs. 80%; OR, 1.3; 95% CI, 1.19–1.41) and FAM123B, a tumor suppressor gene involved in WNT signaling (8.0% vs. 5.8%; OR, 1.42; 95% CI, 1.26–1.60; refs. 17, 18). AFR had lower frequency of RNF43 (2.1% vs. 2.9%; OR, 0.72; 95% CI, 0.58–0.89), which is also a component of the WNT signaling pathway (Fig. 2A; Supplementary Table S3). In addition, AFR had more frequent comutations in APC^mut/KRAS^mut (51% vs. 41%; Q < 0.0001; OR, 1.52; 95% CI, 1.42–1.61) and APC^mut/KRAS^mut/TP53^mut (35% vs. 28%; Q < 0.0001; OR, 1.39; 95% CI, 1.31–1.49; Fig. 2B). Lastly, there were no differences in CTNBB1 (3.9% vs. 3.4%; Q = 0.48; OR, 1.15; 95% CI, 0.97–1.35). WNT signaling (APC, CTNNB1, RNF43, GSK3B, AXIN1, and LRP6) was more aberrant in AFR (89% AFR vs. 85% EUR; Q < 0.0001; OR, 1.34; 95% CI, 1.21–1.47).

We also found no significant differences in the frequency of known actionable kinase driver amplifications or fusions involving HER2, MET, NTRK, ALK, ROS1, and RET (Table 3).

We further analyzed a random sampling of proximal/right-sided and distal/left-sided cases in AFR (n = 121 proximal and n = 133 distal cases) and EUR (n = 132 proximal and n = 178 distal cases). Similar to prior studies, overall, the right colon had more predominant MSI-H tumors (15% vs. 2.3%, Q < 0.0001) and a higher median TMB (4.3 vs. 3.5, Q < 0.0001). Among MSS, POLE/POLD1-negative samples with TMB < 10 (AFR n = 103 proximal, 124 distal; EUR n = 100 proximal, n = 172 distal), the right colon had more frequent KRAS (66% right vs. 48% left, Q = 0.003), BRAF (12% right vs. 3.0% left, Q = 0.006), and FAM123B (13% right vs. 4.4% left, Q = 0.02) alterations. Overall, Tp53 alterations (67% right vs. 80% left, Q = 0.04) were seen more frequently in the left colon. Among EUR, there were more frequent RNF43 (10% right vs. 0.58% left, Q = 0.01), BRAF (20% right vs. 3.5% left, Q = 0.0008), and MAPK (83% right vs. 52% left, Q < 0.0001) alterations in the right colon. There were no significant mutational differences in the left and right colon among AFR (Fig. 3).

Patients with a date of genomic testing prior to age 50 were considered to have early-onset colorectal cancer. AFR had a higher frequency of early-onset colorectal cancer compared with EUR (24% vs. 20%, Q < 0.0001), and consistent with prior studies, EUR had more frequent early-onset colorectal cancer in the rectum compared with the colon (8.5% AFR vs. 13% EUR, Q = 0.0002; ref. 19), but both AFR and EUR with early-onset colorectal cancer had similar frequencies of MSI-H tumors (3.9% AFR vs. 3.8% EUR, Q = 0.81). AFR with early-onset colorectal cancer had more frequent KRAS mutations (AFR 58% vs. EUR 48%, Q < 0.0001), whereas EUR with early-onset colorectal cancer had significantly higher BRAF^V600 mutations (AFR 1.7% vs. EUR 4.2%, Q = 0.001; Supplementary Table S4). Among EUR, striking differences were found among early-onset versus average-onset EUR, with increasing BRAF^V600 mutations with advancing age. Among both ancestries, TP53 alterations were more common in younger patients. FAM123B increased with advancing age in both groups. Surprisingly, APC alterations showed opposite trends, with AFR having more frequent APC alterations with advancing age and EUR with decreased APC alterations with older age (Fig. 4; Supplementary Table S5).

To investigate the clinical outcomes of genomic differences by race, we reanalyzed the publicly available, clinically annotated data set of patients with colorectal cancer from Memorial Sloan Kettering Cancer Center (MSKCC) who underwent next-generation sequencing (20, 21). We analyzed 1,046 cases, with 7.2% (n = 76) and 92.7% (n = 970) of Blacks and whites (self-reported race), respectively. Blacks were younger (53 vs. 56 years old, P = 0.04), but both ethnic groups had similar frequencies of MSI-H tumors (7% vs. 9%, P = 0.67), early-onset colorectal cancer (34% vs. 32%, P = 0.70) and equal distribution of proximal and distal colorectal cancer (Supplementary Table S6). There was a similar distribution of cases across all stages by race, with stage 4 representing approximately 60% of the cases (Supplementary Table S6).

In this small sample, among MSS colorectal cancer cases, Blacks (n = 71) showed a trend toward more frequent KRAS mutations (52% vs. 41%, P = 0.08; Supplementary Table S7). BRAF mutations showed a trend toward being more frequent in whites but were not statistically different (5.6% vs. 13%, P = 0.09; Supplementary Table S7).

From this data set, which represents a tertiary care cancer center, we noted significantly worse overall survival in Blacks (29 vs. 39 months, P = 0.002). Among MSS cases, there were no differences in overall survival in early-stage disease; however, Black patients with stage 3 and 4 colorectal cancer had significantly worse outcomes (P = 0.00029; Fig. 5A). To analyze factors associated with overall survival, we performed a multivariate analysis to study the effect of age, race, colon site, and stage while stratifying by MSI status. Patients who were over the age of 50 with Black race, advanced stage, and right-sided tumors had worse overall survival than white patients (Fig. 5B). We then evaluated overall survival by mutational status between Blacks and whites with MSS colorectal cancer. Blacks had worse overall survival regardless of KRAS (P = 0.00069) or PIK3CA (P = 0.013; Supplementary Fig. S2A and S2B) mutation status. As previously reported, a BRAF mutation portends worse overall survival (22). Interestingly, Blacks with BRAF^WT tumors had similarly poor overall survival comparable with whites with a BRAF mutation (P < 0.0001; Supplementary Fig. S2C). Paradoxically, as noted in prior studies, APC mutations were protective (11, 23), and we observed that whites and Blacks with APC mutations had improved survival compared with patients with APC^WT tumors (P < 0.0001; Supplementary Fig. S2D).

Colorectal cancer incidence and mortality are significantly higher in Blacks compared with whites, even after controlling for socioeconomic factors (2, 24). Prior studies have highlighted the importance of education of providers and patients to improve colorectal cancer outcomes. Providers who serve minority patients are less likely to recommend colorectal cancer screening to Black patients and fail to recognize the underlying fear of colonoscopies as well as mistrust in the medical system held by many Black patients (25). Although education to providers and patients on the importance of colorectal cancer screening is of highest priority, we sought to further understand the genomics of colorectal cancer in individuals of African ancestry as other potential factors related to cancer survival.

To our knowledge, this is the most comprehensive study of colorectal cancer–related genomic alterations and their impact on clinical outcomes in AFR. Many genomic databases have large numbers of cases with missing self-reported race, and up to one third of oncology trials that lead to FDA drug approvals did not report race (20, 21, 26). By using genomic ancestry, we were able to partially overcome this issue, recognizing that using ancestry does not fully address race as a social construct. The patient cohort that we studied comprises 73% European and 12% African descendants determined by genotypic ancestry, which roughly mirrors the demographics of the U.S. population (https://www.census.gov/).

We found that AFR had significantly higher KRAS mutations compared with EUR (60% vs. 50%), which has been associated with increased disease progression and decreased survival (27). KRAS mutations are well-established biomarkers of resistance to anti-EGFR–directed therapies, including cetuximab and panitumumab (28, 29). We note for the first time that AFR had more frequent G12D and G13 alterations, suggesting that AFR would benefit from new emerging drug therapies targeting these alterations (30). We report equal frequencies of KRAS^G12C; however, in the recent FDA approval of sotorasib in KRAS^G12C-mutated solid tumors, Blacks were underrepresented, comprising only 4.7% (vs. 76% in whites) of the study population (31). Overall, our findings suggest that Blacks would benefit from greater enrollment in clinical trials of highly specific KRAS inhibitors.

AFR had significantly lower frequencies of BRAF^V600X (2% vs. 6%) compared with EUR, which is associated with worse overall survival (32). To our surprise, we found that Blacks with BRAF^WT had similar survival rates with whites harboring BRAF^V600E alterations. These findings are likely due to the known worse cancer mortality rates in Blacks, which is a complex interplay between biology and social determinants of health. Although patients with BRAF^V600E mutations have poor prognosis, this trend may change with newly approved combinatorial MEK + BRAF + EGFR inhibitor therapy (33).

We found that AFR have more frequent aberrations in WNT signaling, with higher APC mutations and comutations in APC^mut/KRAS^mut/TP53^mut. APC mutations exhibit a complex biology that appears to be protective compared with wild-type colorectal cancer. In addition, comutations in APC^mut/KRAS^mut/TP53^mut portend poor survival comparable with those with APC^WT (11, 23). We confirmed that APC mutations are protective in both AFR and EUR individuals in the survival analysis of the MSKCC cohort. In a large colorectal cancer study, upregulation of WNT signaling was found to correlate with low tumor-infiltrating lymphocytes (associated with higher risk of metastases), resistance to immunotherapies, and worse overall survival in both MSS and MSI colorectal cancer (8). Inhibitors targeting the WNT pathway may theoretically alter the tumor microenvironment and render colorectal cancer susceptible to immunotherapies.

Early-onset colorectal cancer (age <50) has been steadily rising in the past two decades, with Blacks having higher incidence than whites, particularly in the colon compared with the rectum (19). Similar to national trends, we see a higher proportion of young-onset colorectal cancer in AFR compared with EUR (24% vs. 20%; ref. 19). We show that AFR and EUR with young-onset colorectal cancer had similar rates of MSI-H and TMB-high tumors. The most striking finding is that young AFR had the lowest frequency of APC mutations, which increase with age. Interestingly, we observed the opposite trend among young EUR who have the highest frequency of APC mutations that declines as they age. Emerging data show that APC^WT tumors have worse overall survival and occur in younger age groups (23, 34). Our findings along with previous studies confirm that AFR with early-onset colorectal cancer have more frequent APC^WT tumors, which could partially explain their worse clinical outcomes (35). KRAS mutations and alterations in the MAPK pathway were significantly higher in young AFR and continued to rise with age, highlighting the need to improve access to targeted treatment options in younger minority populations.

An analysis of genomic differences between AFR and EUR with colorectal cancer has not been previously reported using a data set of this magnitude. Our study generated new knowledge about the molecular drivers of colorectal cancer in AFR, based on comprehensive profiling of genomic alterations with demonstrated prognostic and predictive relevance, including MSI status, TMB, alterations within primary tumor location, age of onset of colorectal cancer, and comutation status. Limitations of the Foundation Medicine data set include lack of stage at diagnosis, survival and treatment history, and normal tissue to assess germline variants. In addition, variants of unknown significance, which are often found more frequently in minority populations, were not included in the analysis (36, 37). Our data are limited in that tumor location (right vs. left) had to be manually extracted by a pathologist, and hence only a subset of the data were studied with tumor sidedness. Variables that reflect social determinants of health, including but not limited to economic stability, education, health care, the neighborhood/built environment, and the social/community context, are not available in this data set but are important variables needed to study health outcomes.

We found that patients with colorectal cancer with genotypic AFR ancestry had lower MSI-H tumors and therefore would benefit less from immune checkpoint inhibitors (38). Without having social determinants of health available in the Foundation Medicine data set, it is unclear whether differences in rates of MSI are a result of socioeconomics, reflecting variability in access to genomic testing based on ancestry. We did not observe any differences in actionable alterations in colorectal cancer, including MET, ERBB2, PTEN, AKT1, ALK, ROS1, RET, or NTRK1–3 alterations. AFR had more frequent PIK3CA mutations and more frequent alterations in the PIK3CA signaling pathway, and hence could benefit from adjuvant aspirin use, which has been shown to be protective for colorectal cancer and overall survival (39). Individuals of AFR have been underrepresented in cancer clinical trials, but given our findings and the similarities they share with EUR, they must be recruited to biomarker-driven targeted clinical trials for approved or investigational drugs given the equal frequency of targetable genomic alterations.

Finally, prior studies have established that genetics and ancestry contribute only 30% to premature death, whereas social determinants of health with race as a proxy account for the additional 70% (40, 41). We have described the genomic landscape of colorectal cancer in AFR and have identified targetable alterations that will affect clinical management, but we also recognize the limited role of how genomics affects health outcomes. To date, our study is one of the few to investigate correlations between genomic alterations and clinical outcomes among Blacks with colorectal cancer. We acknowledge the limitation of the MSKCC data in that these include only a small number of Blacks with colorectal cancer, but again highlight the need to include minority patients in genomic studies. It was striking to observe no survival differences by race in early-stage disease but worse survival among Blacks at later stages, even though care was provided at a single tertiary care center. When analyzing MSS colorectal cancer by race and specific genomic alterations, Blacks had worse overall survival despite mutation status. Earlier attempts to understand genomic differences that could contribute to disparities in survival have been limited by small sample sizes and testing of limited numbers of genes using selected panels (12, 42–44). Additionally, there are multiple barriers to studying tumor genomics and conducting research that is inclusive of non-white populations, including but not limited to minimal recruitment to clinical research/trials by researchers, limited resources/funding for genomic research at institutions that serve minority populations as well as low patient participation due to mistrust of physicians and health care systems (4–6). The study findings point to the effects of long-standing structural racism, which directly affect social determinants of health, such as economic stability, education, and access to health care, and lead to worse survival (45). This study also highlights the need to include social determinants of health in genomic research to interpret findings that relate genomics to health outcomes and identifies missed opportunities for equal access to novel therapies.

From 2013 to 2021, we analyzed 46,140 deidentified and consented for research colorectal adenocarcinoma patient samples using the FoundationOne and FoundationOne CDx assays (46). Comprehensive genomic profiling (CGP) was performed by an adaptor-ligation/hybrid capture–based assay of coding DNA extracted from formalin-fixed, paraffin-embedded primary or metastatic colorectal cancer tumors and sequenced to high, uniform median coverage (>500×). We selected the coding exons of up to 324 cancer-related genes and introns of genes that are frequently involved in genomic rearrangements, and analyzed them for base substitutions, short insertions and deletions (indels), copy-number alterations, and rearrangements (Supplementary Tables S8–S10). TMB and MSI were assessed as previously described (47, 48). Approval for this study, including a waiver of informed consent and a Health Insurance Portability and Accountability Act waiver of authorization, was obtained from the Western Institutional Review Board (protocol no. 20152817).

A second publicly available data set from MSK-IMPACT was used to analyze somatic mutational differences in colorectal cancer adenocarcinoma by self-reported race (African American n = 76 and Whites n = 970; Supplementary Methods; ref. 11).

As self-reported race was not available, genetic ancestry was determined for each patient sample. For each profiling platform (FoundationOne and FoundationOne CDx), more than 40,000 SNP sites sequenced by CGP were identified. To remove biases due to linkage disequilibrium (LD), LD pruning was performed using PLINK (using the –indep flag with a window size of 50, a step size of 5, and a variance inflation factor threshold of 2). A random forest classifier was trained on the 1000 Genomes samples to identify ancestral populations [AFR, AMR = admixed American (Hispanic), EAS = East Asian, EUR, and SAS = South Asian] using genetic variation at the SNP sites. Genetic variation was defined by 10 features that captured allele-count variation as determined by principal component analysis. This classifier was applied to CGP patient samples to assign them to one of the ancestral populations. A separate classifier to distinguish 11 ancestral populations, including AFR from the Americas, West Africa, and East Africa, was trained using the 1000 Genomes data with a different set of population labels.

For each platform (F1, F1CDx), admixture analysis was performed using ADMIXTURE-1.3.0 (49), and the 1000 Genomes phase III data were used as a reference population to learn five population signatures; these signatures were stored in a P-file. ADMIXTURE was then run in projection mode on the CGP samples using the reference P-file.

A board-certified pathologist (S.M. Ali) reviewed pathology reports and subclassified colorectal cancer cases as right-sided/proximal (tumors arising proximal to but not including the splenic flexure, cecum, ascending, and transverse colon) versus left-sided/distal (tumors arising from the splenic flexure, descending, sigmoid colon, and rectum).

Categorical variables reported as frequencies or percentages were compared between groups via the Fisher exact test with false discovery rate correction. The latter was performed by the Benjamini–Hochberg procedure to correct P values for multiple tests, and the resulting Q values were presented. Statistical significance was defined as a Q < 0.05. The OR and 95% CI were computed to assess genomic associations. Binomial logistic regression was performed in Python using the glm function in the statsmodels toolbox on patient samples with at least 50% AFR ancestry, as determined by admixture analysis.

All data relevant to the study are included in the article or as Supplementary Information.

J.K. Lee reports personal fees from Foundation Medicine and Roche during the conduct of the study. R.W. Madison reports personal fees from Foundation Medicine and other support from Roche Holding AG during the conduct of the study. J.Y. Newberg reports personal fees from Foundation Medicine and Roche during the conduct of the study. S.J. Klempner reports personal fees from Foundation Medicine, Astellas, Merck, Bristol Myers Squibb, Daiichi Sankyo, Sanofi-Aventis, Eli Lilly, Pieris, and Natera, and other support from Turning Point Therapeutics outside the submitted work. G.M. Frampton is an employee of Foundation Medicine and a shareholder in Roche AG. J.S. Ross reports personal fees from Foundation Medicine during the conduct of the study. J.M. Venstrom reports other support from Foundation Medicine and Roche during the conduct of the study, as well as other support from Roche outside the submitted work. A.B. Schrock reports personal fees from Foundation Medicine and Roche during the conduct of the study. S. Das reports grants from Science Foundation Ireland outside the submitted work. A. Verma reports personal fees and other support from Stelexis, and grants from Janssen, Bristol Myers Squibb, Prelude, and Curis outside the submitted work. S.M. Ali reports other support from EQRx and personal fees from Elevation Oncology, Pillar Biosciences, Droplet Biosciences, and IN8bio outside the submitted work; genomics patents with Foundation Medicine pending and issued; and is a former employee of Foundation Medicine (no longer holds equity interest). No disclosures were reported by the other authors.

P.A. Myer: Conceptualization, data curation, formal analysis, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. J.K. Lee: Data curation, formal analysis, methodology, writing–review and editing. R.W. Madison: Data curation, formal analysis, methodology, writing–review and editing. K. Pradhan: Data curation, formal analysis, methodology. J.Y. Newberg: Data curation, methodology. C.R. Isasi: Methodology, writing–review and editing. S.J. Klempner: Writing–review and editing. G.M. Frampton: Data curation, methodology, writing–review and editing. J.S. Ross: Data curation, methodology. J.M. Venstrom: Project administration, writing–review and editing. A.B. Schrock: Data curation, formal analysis, methodology, writing–review and editing. S. Das: Methodology, writing–review and editing. L. Augenlicht: Methodology, writing–review and editing. A. Verma: Writing–review and editing. J.M. Greally: Methodology, writing–review and editing. S.M. Raj: Methodology, writing–review and editing. S. Goel: Writing–review and editing. S.M. Ali: Resources, formal analysis, supervision, visualization, methodology, writing–review and editing.

We acknowledge the mentorship of Dr. I. David Goldman, Dr. Roman Perez-Soler, and Dr. Allan Wolkoff. This work was supported by NIH grant 5K12CA13278312 and the Paul Calabresi K12 Career Development Award (awarded to P.A. Myer).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.