Implications of Selection Bias Due to Delayed Study Entry in Clinical Genomic Studies

Effectiveness of PARP Inhibitor Maintenance Therapy in Ovarian Cancer by BRCA1/2 and a Scar-Based HRD Signature in Real-World Practice

PURPOSE

The purpose of the study was to compare the effectiveness of PARP inhibitor maintenance therapy (mPARPi) in real-world practice by biomarker status [BRCA1/2 alterations (BRCAalt) and a homologous recombination deficiency signature (HRDsig)] in advanced ovarian cancer.

EXPERIMENTAL DESIGN

Patients with ovarian cancer receiving first-line platinum-based chemotherapy and either mPARPi or no maintenance were included. Patient data were obtained by a US-based de-identified ovarian cancer Clinico-Genomic Database, from ∼280 US cancer clinics (01/2015-03/2023). Real-world progression-free survival (rwPFS) and overall survival (rwOS) were compared by biomarker status using Cox models, weighted by propensity scores.

RESULTS

Of 673 patients, 160 received mPARPi [31.2% BRCAalt and 51.9% HRDsig(+)] and 513 no maintenance [15.6% BRCAalt and 34.1% HRDsig(+)]. BRCAalt patients receiving mPARPi versus no maintenance had favorable rwPFS [HR, 0.48; 95% confidence interval (CI), 0.26-0.87; P = 0.0154], as did BRCA wild-type (WT; HR, 0.76; 95% CI, 0.57-1.01; P = 0.0595). Favorable rwOS was not observed with mPARPi for BRCAalt or BRCA-WT. HRDsig(+) patients receiving mPARPi versus no maintenance had favorable rwPFS (HR, 0.36; 95% CI, 0.24-0.55; P < 0.001) and numerically favorable rwOS (HR, 0.46; 95% CI, 0.21-1.02; P = 0.0561). No differences were observed for HRDsig(-). mPARPi treatment interaction was observed for HRDsig(+) versus HRDsig(-) (rwPFS P < 0.001/rwOS P = 0.016) but not for BRCAalt versus BRCA-WT. Patients with BRCA-WT/HRDsig(+) receiving mPARPi had favorable rwPFS (HR, 0.40; 95% CI, 0.22-0.72; P = 0.003), whereas no difference was observed for BRCA-WT/HRDsig(-).

CONCLUSIONS

HRDsig predicted benefit of mPARPi better than BRCAalt. Patients with HRDsig(+) status experienced favorable outcomes, even if they had BRCA-WT status. In contrast, patients with HRDsig(-) status did not show significant benefit from mPARPi treatment. HRDsig might predict benefit from mPARPi regardless of BRCAalt status.

Pan-tumor validation of a NGS fraction-based MSI analysis as a predictor of response to Pembrolizumab

Microsatellite instability high (MSI-H) and mismatch repair deficient (dMMR) tumor status have been demonstrated to predict patient response to immunotherapies. We developed and validated a next-generation sequencing (NGS)-based companion diagnostic (CDx) to detect MSI-H solid tumors via a comprehensive genomic profiling (CGP) assay, FoundationOne®CDx (F1CDx). To determine MSI status, F1CDx calculates the fraction of unstable microsatellite loci across >2000 loci using a fraction-based (FB) analysis. Across solid tumor types, F1CDx demonstrated a high analytical concordance with both PCR (n = 264) and IHC (n = 279) with an overall percent agreement (OPA) of 97.7% and 97.8%, respectively. As part of a retrospective bridging clinical study from KEYNOTE-158 Cohort K and KEYNOTE-164, patients with MSI-H tumors as determined by F1CDx demonstrated an objective response rate (ORR) of 43.0% to pembrolizumab. In real-world cancer patients from a deidentified clinicogenomic database, F1CDx was at least equivalent in assessing clinical outcome following immunotherapy compared with MMR IHC. Demonstrated analytical and clinical performance of F1CDx led to the pan-tumor FDA approval in 2022 of F1CDx to identify MSI-H solid tumor patients for treatment with pembrolizumab. F1CDx is an accurate, reliable, and FDA-approved method for the identification of MSI-H tumors for treatment with pembrolizumab.

Association of Genetic Alterations with Prognosis in Extramammary Paget's Disease: Insights into the Involvement of somatic CDKN2A variants in Poor Prognosis

BACKGROUND

Previous studies reported the mutational landscape in extramammary Paget's disease (EMPD); however, the prognostic implications of genetic alterations remain unexplored. While CDKN2A loss is known to be associated with tumor progression or poor prognosis in some types of cancer, its significance in EMPD has not been investigated.

OBJECTIVES

To examine the association between common genetic alterations and prognosis in EMPD.

METHODS

This is a retrospective cohort study analyzing EMPD cases registered until January 2024 in the Center for Cancer Genomics and Advanced Therapeutics database, which is a nationwide database recording clinical data and comprehensive genomic profiling (CGP) test results in Japan.

RESULTS

A total of 167 cases were recorded in the database, with CDKN2A loss being the most frequent genetic variant. Survival analysis was conducted on 127 cases. Survival from chemotherapy initiation was analyzed with adjusting for length bias inherent in the database using the Kaplan-Meier estimator, an established adjustment method. Cases with BRCA2-mutant tumors (n=18) had a worse prognosis than those with BRCA2-wild-type tumors (n=109; HR=2.97, 95% CI 1.46-6.01, p=0.003). Additionally, CDKN2A-mutant group (n=72) had a significantly worse prognosis than those with CDKN2A-wild-type group (n=55; HR=1.81, 95% CI 1.06-3.07, p=0.029). Most CDKN2A variants were pathogenic, primarily characterized by loss, while most BRCA2 variants were variants of uncertain significance. In the analysis of survival from CGP enrollment based on Eastern Cooperative Oncology Group performance status (ECOG-PS), cases with ECOG-PS 1 at the time of CGP enrollment had significantly poorer prognosis than those with ECOG-PS 0 (p=0.034; median survival time, 531 vs. 259 days).

CONCLUSIONS

Somatic CDKN2A variant, mainly exhibiting loss, may be associated with poor prognosis in EMPD. Cases with BRCA2-mutant cancer might also have a worse prognosis in EMPD. In addition, CGP testing before PS deteriorates is preferable, considering the observed median survival of individuals undergoing CGP tests in an ECOG-PS-1 condition was less than 9 months.

Biomarker Inference and the Timing of Next-Generation Sequencing in a Multi-Institutional, Cross-Cancer Clinicogenomic Data Set

PURPOSE

Observational clinicogenomic data sets, consisting of tumor next-generation sequencing (NGS) data linked to clinical records, are commonly used for cancer research. However, in real-world practice, oncologists frequently request NGS in search of treatment options for progressive cancer. The extent and impact of this dynamic on analysis of clinicogenomic research data are not well understood.

METHODS

We analyzed clinicogenomic data for patients with non-small cell lung, colorectal, breast, prostate, pancreatic, or urothelial cancers in the American Association for Cancer Research Biopharmaceutical Consortium cohort. Associations between baseline and time-varying clinical characteristics and time from diagnosis to NGS were measured. To explore the impact of informative cohort entry on biomarker inference, statistical interactions between selected biomarkers and time to NGS with respect to overall survival were calculated.

RESULTS

Among 7,182 patients, time from diagnosis to NGS varied significantly by clinical factors, including cancer type, calendar year of sequencing, institution, and age and stage at diagnosis. NGS rates also varied significantly by dynamic clinical status variables; in an adjusted model, compared with patients with stable disease at any given time after diagnosis, patients with progressive disease by imaging or oncologist assessment had higher NGS rates (hazard ratio for NGS, 1.61 [95% CI, 1.45 to 1.78] and 2.32 [95% CI, 2.01 to 2.67], respectively). Statistical interactions between selected biomarkers and time to NGS with respect to survival, potentially indicating biased biomarker inference results, were explored.

CONCLUSION

To evaluate the appropriateness of a data set for a particular research question, it is crucial to measure associations between dynamic cancer status and the timing of NGS, as well as to evaluate interactions involving biomarkers of interest and NGS timing with respect to survival outcomes.

Genomic Characterization and Clinical Outcomes of Patients with Peritoneal Metastases from the AACR GENIE Biopharma Collaborative Colorectal Cancer Registry

Peritoneal metastases (PM) are common in metastatic colorectal cancer (mCRC). We aimed to characterize patients with mCRC and PM from a clinical and molecular perspective using the American Association of Cancer Research Genomics Evidence Neoplasia Information Exchange (GENIE) Biopharma Collaborative (BPC) registry. Patients' tumor samples underwent targeted next-generation sequencing. Clinical characteristics and treatment outcomes were collected retrospectively. Overall survival (OS) from advanced disease and progression-free survival (PFS) from start of cancer-directed drug regimen were estimated and adjusted for the left truncation bias. A total of 1,281 patients were analyzed, 244 (19%) had PM at time of advanced disease. PM were associated with female sex [OR: 1.67; 95% confidence interval (CI): 1.11-2.54; P = 0.014] and higher histologic grade (OR: 1.72; 95% CI: 1.08-2.71; P = 0.022), while rectal primary tumors were less frequent in patients with PM (OR: 0.51; 95% CI: 0.29-0.88; P < 0.001). APC occurred less frequently in patients with PM (N = 151, 64% vs. N = 788, 79%) while MED12 alterations occurred more frequently in patients with PM (N = 20, 10% vs. N = 32, 4%); differences in MED12 were not significant when restricting to oncogenic and likely oncogenic variants according to OncoKB. Patients with PM had worse OS (HR: 1.45; 95% CI: 1.16-1.81) after adjustment for independently significant clinical and genomic predictors. PFS from initiation of first-line treatment did not differ by presence of PM. In conclusion, PM were more frequent in females and right-sided primary tumors. Differences in frequencies of MED12 and APC alterations were identified between patients with and without PM. PM were associated with shorter OS but not with PFS from first-line treatment.

SIGNIFICANCE

Utilizing the GENIE BPC registry, this study found that PM in patients with colorectal cancer occur more frequently in females and right-sided primary tumors and are associated with worse OS. In addition, we found a lower frequency of APC alterations and a higher frequency in MED12 alterations in patients with PM.

Effects of KRAS Genetic Interactions on Outcomes in Cancers of the Lung, Pancreas, and Colorectum

BACKGROUND

KRAS is among the most commonly mutated oncogenes in cancer, and previous studies have shown associations with survival in many cancer contexts. Evidence from both clinical observations and mouse experiments further suggests that these associations are allele- and tissue-specific. These findings motivate using clinical data to understand gene interactions and clinical covariates within different alleles and tissues.

METHODS

We analyze genomic and clinical data from the AACR Project GENIE Biopharma Collaborative for samples from lung, colorectal, and pancreatic cancers. For each of these cancer types, we report epidemiological associations for different KRAS alleles, apply principal component analysis (PCA) to discover groups of genes co-mutated with KRAS, and identify distinct clusters of patient profiles with implications for survival.

RESULTS

KRAS mutations were associated with inferior survival in lung, colon, and pancreas, although the specific mutations implicated varied by disease. Tissue- and allele-specific associations with smoking, sex, age, and race were found. Tissue-specific genetic interactions with KRAS were identified by PCA, which were clustered to produce five, four, and two patient profiles in lung, colon, and pancreas. Membership in these profiles was associated with survival in all three cancer types.

CONCLUSIONS

KRAS mutations have tissue- and allele-specific associations with inferior survival, clinical covariates, and genetic interactions.

IMPACT

Our results provide greater insight into the tissue- and allele-specific associations with KRAS mutations and identify clusters of patients that are associated with survival and clinical attributes from combinations of genetic interactions with KRAS mutations.

Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal

International cancer registries make real-world genomic and clinical data available, but their joint analysis remains a challenge. AACR Project GENIE, an international cancer registry collecting data from 19 cancer centers, makes data from >130,000 patients publicly available through the cBioPortal for Cancer Genomics (https://genie.cbioportal.org). For 25,000 patients, additional real-world longitudinal clinical data, including treatment and outcome data, are being collected by the AACR Project GENIE Biopharma Collaborative using the PRISSMM data curation model. Several thousand of these cases are now also available in cBioPortal. We have significantly enhanced the functionalities of cBioPortal to support the visualization and analysis of this rich clinico-genomic linked dataset, as well as datasets generated by other centers and consortia. Examples of these enhancements include (i) visualization of the longitudinal clinical and genomic data at the patient level, including timelines for diagnoses, treatments, and outcomes; (ii) the ability to select samples based on treatment status, facilitating a comparison of molecular and clinical attributes between samples before and after a specific treatment; and (iii) survival analysis estimates based on individual treatment regimens received. Together, these features provide cBioPortal users with a toolkit to interactively investigate complex clinico-genomic data to generate hypotheses and make discoveries about the impact of specific genomic variants on prognosis and therapeutic sensitivities in cancer.

SIGNIFICANCE

Enhanced cBioPortal features allow clinicians and researchers to effectively investigate longitudinal clinico-genomic data from patients with cancer, which will improve exploration of data from the AACR Project GENIE Biopharma Collaborative and similar datasets.

Pan-Cancer Analysis of Postdiagnosis Exercise and Mortality

PURPOSE

The impact of postdiagnosis exercise on cause-specific mortality in cancer survivors and whether this differs on the basis of cancer site is unclear.

METHODS

We performed an analysis of 11,480 patients with cancer enrolled in the Prostate, Lung, Colorectal, and Ovarian cancer screening trial. Patients with a confirmed diagnosis of cancer completing a standardized survey quantifying exercise after diagnosis were included. The primary outcome was all-cause mortality (ACM); secondary end points were cancer mortality and mortality from other causes. Cox models were used to estimate the cause-specific hazard ratios (HRs) for ACM, cancer, and noncancer mortality as a function of meeting exercise guidelines versus not meeting guidelines with adjustment for important clinical covariates.

RESULTS

After a median follow-up of 16 years from diagnosis, 4,665 deaths were documented (1,940 due to cancer and 2,725 due to other causes). In multivariable analyses, exercise consistent with guidelines was associated with a 25% reduced risk of ACM compared with nonexercise (HR, 0.75; 95% CI, 0.70 to 0.80). Compared with nonexercise, exercise consistent with guidelines was associated with a significant reduction in cancer mortality (HR, 0.79; 95% CI, 0.72 to 0.88) and mortality from other causes (HR, 0.72; 95% CI, 0.66 to 0.78). The inverse relationship between exercise and cause-specific mortality varied by exercise dose. Exercise consistent with guidelines was associated with a reduced hazard of ACM for multiple cancer sites. Reduction in cancer mortality for exercisers was only observed in head and neck and renal cancer.

CONCLUSION

In this pan-cancer sample of long-term cancer survivors, exercise consistent with guidelines was associated with substantial ACM benefit driven by both reductions in cancer and noncancer mortality. The cause-specific impact of exercise differed as a function of cancer site.

The GENIE BPC NSCLC Cohort: A Real-World Repository Integrating Standardized Clinical and Genomic Data for 1,846 Patients with Non-Small Cell Lung Cancer

PURPOSE

We describe the clinical and genomic landscape of the non-small cell lung cancer (NSCLC) cohort of the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) Biopharma Collaborative (BPC).

EXPERIMENTAL DESIGN

A total of 1,846 patients with NSCLC whose tumors were sequenced from 2014 to 2018 at four institutions participating in AACR GENIE were randomly chosen for curation using the PRISSMM data model. Progression-free survival (PFS) and overall survival (OS) were estimated for patients treated with standard therapies.

RESULTS

In this cohort, 44% of tumors harbored a targetable oncogenic alteration, with EGFR (20%), KRAS G12C (13%), and oncogenic fusions (ALK, RET, and ROS1; 5%) as the most frequent. Median OS (mOS) on first-line platinum-based therapy without immunotherapy was 17.4 months [95% confidence interval (CI), 14.9-19.5 months]. For second-line therapies, mOS was 9.2 months (95% CI, 7.5-11.3 months) for immune checkpoint inhibitors (ICI) and 6.4 months (95% CI, 5.1-8.1 months) for docetaxel ± ramucirumab. In a subset of patients treated with ICI in the second-line or later setting, median RECIST PFS (2.5 months; 95% CI, 2.2-2.8) and median real-world PFS based on imaging reports (2.2 months; 95% CI, 1.7-2.6) were similar. In exploratory analysis of the impact of tumor mutational burden (TMB) on survival on ICI treatment in the second-line or higher setting, TMB z-score harmonized across gene panels was associated with improved OS (univariable HR, 0.85; P = 0.03; n = 247 patients).

CONCLUSIONS

The GENIE BPC cohort provides comprehensive clinicogenomic data for patients with NSCLC, which can improve understanding of real-world patient outcomes.

PTPN11 variant may be a prognostic indicator of IDH-wildtype glioblastoma in a comprehensive genomic profiling cohort

PURPOSE

Glioblastoma (GBM) is the most common type of primary malignant brain tumor and has a poor prognosis. Identifying novel targets and stratification strategies is urgently needed to improve patient survival. The present study aimed to identify clinically relevant genomic alterations in IDH-wildtype GBM using data from comprehensive genomic profiling (CGP) assays performed nationwide in Japan.

METHODS

The CGP assay results of 392 IDH-wildtype GBM cases performed between October 2019 and February 2023 obtained from the Center for Cancer Genomics and Advanced Therapeutics were retrospectively analyzed.

RESULTS

The median patient age was 52.5 years, and 207 patients (53%) were male. In the 286 patients for whom survival information was available, a protein-tyrosine phosphatase non-receptor type 11 (PTPN11) variant detected in 20 patients (6.8%) was extracted as the gene associated with significantly shorter overall survival (p = 0.002). Multivariate analysis demonstrated that the PTPN11 variant and poor performance status were independent prognostic indicators. In contrast, no prognostic impact was observed in the cohort in The Cancer Genome Atlas data. The discrepancy in the prognostic impact of the PTPN11 variant from these two pools might have resulted from differences in the biases affecting the survival of patients who underwent a CGP assay, including left-truncation and right-censored bias. However, survival simulation done to adjust for these biases showed that the prognostic impact of the PTPN11 variant was also significant.

CONCLUSIONS

The PTPN11 variant was a negative prognostic indicator of IDH-wildtype GBM in the patient cohort with the CGP assay.

Tumor Mutational Burden in Real-World Patients With Pancreatic Cancer: Genomic Alterations and Predictive Value for Immune Checkpoint Inhibitor Effectiveness

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is largely considered a nonimmunogenic malignancy; however, approximately 1%, of patients may have tumors with deficient mismatch repair, high microsatellite instability, or high tumor mutational burden (TMB ≥10 mutations/Mb), which may be predictive of response to immune checkpoint inhibitor (ICI) therapy. We sought to analyze outcomes of patients with high-TMB and pathogenic genomic alterations observed in this population.

METHODS

This study included patients with PDAC who underwent comprehensive genomic profiling (CGP) at Foundation Medicine (Cambridge, MA). Clinical data were obtained from a US-wide real-world clinicogenomic pancreatic database. We report genomic alterations in those with high and low TMB, and compare outcomes on the basis of receipt of single-agent ICI or therapy regimens not containing ICI.

RESULTS

We evaluated 21,932 patients with PDAC who had tissue CGP data available, including 21,639 (98.7%) with low-TMB and 293 (1.3%) with high-TMB. Among patients with high-TMB, a greater number of alterations were observed in BRCA2, BRAF, PALB2, and genes of the mismatch repair pathway, whereas fewer alterations were observed in KRAS. Among patients who received an ICI (n = 51), those with high-TMB had more favorable median overall survival when compared with the low-TMB subset (25.7 v 5.2 months; hazard ratio, 0.32; 95% CI, 0.11 to 0.91; P = .034).

CONCLUSION

Longer survival was observed in patients with high-TMB receiving ICI compared with those with low-TMB. This supports the role of high-TMB as a predictive biomarker for efficacy of ICI therapy in PDAC. Additionally, we report higher rates of BRAF and BRCA2 mutations and lower rates of KRAS mutation among patients with PDAC and high-TMB, which to our knowledge, is a novel finding.

Mismatch repair deficiency, next-generation sequencing-based microsatellite instability, and tumor mutational burden as predictive biomarkers for immune checkpoint inhibitor effectiveness in frontline treatment of advanced stage endometrial cancer

OBJECTIVE

Molecular profiling is developing to inform treatment in endometrial cancer. Using real world evidence, we sought to evaluate frontline immune checkpoint inhibitor vs chemotherapy effectiveness in advanced endometrial cancer, stratified by Tumor Mutational Burden (TMB) ≥10 mut/MB and microsatellite instability (MSI).

METHODS

Patients with advanced endometrial cancer in the US-based de-identified Flatiron Health-Foundation Medicine Clinico-Genomic Database were included. Data originated from patients treated between January 2011- March 2022 at 280 US clinics. Next-generation sequencing assays were performed via FoundationOne or FoundationOneCDx. Longitudinal clinical data were derived from electronic health records. Immune checkpoint inhibitor treatment included pembrolizumab, dostarlimab, and nivolumab monotherapies. Time to next treatment, time to treatment discontinuation, and overall survival were assessed with the log-rank test and Cox proportional hazard models with adjusted hazard ratios (aHR) for known prognostic factors. We used the Likelihood ratio test to compare biomarker performance.

RESULTS

A total of 343 patients received chemotherapy and 28 received immune checkpoint inhibitor monotherapy as frontline treatment. Patients who received monotherapy were more likely to be stage III at diagnosis (immune checkpoint inhibitor: 54.6% vs chemotherapy: 15.0%; p<0.001) and more likely to test MSI-high via next-generation sequencing (immune checkpoint inhibitor: 53.6% vs chemotherapy: 19.2%; p<0.001). In MSI-high cancers, single-agent immune checkpoint inhibitor had a more favorable time to next treatment (aHR: 0.18, p=0.001) and overall survival (aHR 0.29, p=0.045). Additional analyses on 70 unique tumor specimens revealed mismatch repair deficiency (dMMR) via immunohistochemistry and MSI-high via next-generation sequencing concordance (91%), with nominal improvement of MSI over dMMR to predict time to treatment discontinuation (p=0.030), time to next treatment (p=0.032), and overall survival (p=0.22). MSI status was concordant with tumor mutational burden ≥10 in 94.3% of cases.

CONCLUSION

Immune checkpoint inhibitors may have improved efficacy over chemotherapy in frontline treatment for advanced endometrial cancer defined by MSI-high using next-generation sequencing as a nominally better predictor of outcomes than dMMR with immunohistochemistry. This provides the biologic rationale of active phase III trials.

Genomic Biomarkers Associated with Response to Induction Chemotherapy in Patients with Localized Pancreatic Ductal Adenocarcinoma

PURPOSE

There is increasing use of neoadjuvant chemotherapy in the management of localized pancreatic ductal adenocarcinoma (PDAC), yet there are few validated biomarkers to guide therapy selection. We aimed to determine whether somatic genomic biomarkers predict response to induction FOLFIRINOX or gemcitabine/nab-paclitaxel.

EXPERIMENTAL DESIGN

This single-institution cohort study included consecutive patients (N = 322) with localized PDAC (2011-2020) who received at least one cycle of FOLFIRINOX (N = 271) or gemcitabine/nab-paclitaxel (N = 51) as initial treatment. We assessed somatic alterations in four driver genes (KRAS, TP53, CDKN2A, and SMAD4) by targeted next-generation sequencing, and determined associations between these alterations and (1) rate of metastatic progression during induction chemotherapy, (2) surgical resection, and (3) complete/major pathologic response.

RESULTS

The alteration rates in driver genes KRAS, TP53, CDKN2A, and SMAD4 were 87.0%, 65.5%, 26.7%, and 19.9%, respectively. For patients receiving first-line FOLFIRINOX, SMAD4 alterations were uniquely associated with metastatic progression (30.0% vs. 14.5%; P = 0.009) and decreased rate of surgical resection (37.1% vs. 66.7%; P < 0.001). For patients receiving induction gemcitabine/nab-paclitaxel, alterations in SMAD4 were not associated with metastatic progression (14.3% vs. 16.2%; P = 0.866) nor decreased rate of surgical resection (33.3% vs. 41.9%; P = 0.605). Major pathologic response was rare (6.3%) and not associated with type of chemotherapy regimen.

CONCLUSIONS

SMAD4 alterations were associated with more frequent development of metastasis and lower probability of reaching surgical resection during neoadjuvant FOLFIRINOX but not gemcitabine/nab-paclitaxel. Confirmation in a larger, diverse patient cohort will be important before prospective evaluation of SMAD4 as a genomic biomarker to guide treatment selection.

Tumor Genomic Profile Is Associated With Arterial Thromboembolism Risk in Patients With Solid Cancer

Background

Patients with cancer have an increased risk for arterial thromboembolism (ATE). Scant data exist about the impact of cancer-specific genomic alterations on the risk for ATE.

Objectives

The aim of this study was to determine whether individual solid tumor somatic genomic alterations influence the incidence of ATE.

Methods

A retrospective cohort study was conducted using tumor genetic alteration data from adults with solid cancers who underwent Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets testing between 2014 and 2016. The primary outcome, ATE, was defined as myocardial infarction, coronary revascularization, ischemic stroke, peripheral arterial occlusion, or limb revascularization and identified through systematic electronic medical record assessments. Patients were followed from date of tissue-matched blood control accession to first ATE event or death, for up to 1 year. Cause-specific Cox proportional hazards regression was used to determine HRs of ATE for individual genes adjusted for pertinent clinical covariates.

Results

Among 11,871 eligible patients, 74% had metastatic disease, and there were 160 ATE events. A significantly increased risk for ATE independent of tumor type was noted for the KRAS oncogene (HR: 1.98; 95% CI: 1.34-2.94; multiplicity-adjusted P = 0.015) and the STK11 tumor suppressor gene (HR: 2.51; 95% CI: 1.44-4.38; multiplicity-adjusted P = 0.015).

Conclusions

In a large genomic tumor-profiling registry of patients with solid cancers, alterations in KRAS and STK11 were associated with an increased risk for ATE independent of cancer type. Further investigation is needed to elucidate the mechanism by which these mutations contribute to ATE in this high-risk population.

Elucidating Analytic Bias Due to Informative Cohort Entry in Cancer Clinico-genomic Datasets

BACKGROUND

Oncologists often order genomic testing to inform treatment for worsening cancer. The resulting correlation between genomic testing timing and prognosis, or "informative entry," can bias observational clinico-genomic research. The efficacy of existing approaches to this problem in clinico-genomic cohorts is poorly understood.

METHODS

We simulated clinico-genomic cohorts followed from an index date to death. Subgroups in each cohort who underwent genomic testing before death were "observed." We varied data generation parameters under four scenarios: (i) independent testing and survival times; (ii) correlated testing and survival times for all patients; (iii) correlated testing and survival times for a subset of patients; and (iv) testing and mortality exclusively following progression events. We examined the behavior of conditional Kendall tau (Tc) statistics, Cox entry time coefficients, and biases in overall survival (OS) estimation and biomarker inference across scenarios.

RESULTS

Scenario #1 yielded null Tc and Cox entry time coefficients and unbiased OS inference. Scenario #2 yielded positive Tc, negative Cox entry time coefficients, underestimated OS, and biomarker associations biased toward the null. Scenario #3 yielded negative Tc, positive Cox entry time coefficients, and underestimated OS, but biomarker estimates were less biased. Scenario #4 yielded null Tc and Cox entry time coefficients, underestimated OS, and biased biomarker estimates. Transformation and copula modeling did not provide unbiased results.

CONCLUSIONS

Approaches to informative clinico-genomic cohort entry, including Tc and Cox entry time statistics, are sensitive to heterogeneity in genotyping and survival time distributions.

IMPACT

Novel methods are needed for unbiased inference using observational clinico-genomic data.

Efficacy of Targeted Trials and Signaling Pathway Landscape in Advanced Gastrointestinal Cancers From SCRUM-Japan GI-SCREEN: A Nationwide Genomic Profiling Program

PURPOSE

Genomic profiling programs have been implemented to apply next-generation sequencing (NGS) for facilitating trial enrollment. SCRUM-Japan GI-SCREEN is a large-scale genomic profiling program in advanced gastrointestinal cancers using a validated genomic assay with the goal of facilitating enrollment in targeted clinical trials, generating real-world data, and performing clinicogenomic analysis for biomarker discovery.

PATIENTS AND METHODS

Genotyping of tumor tissue samples from 5,743 patients with advanced gastrointestinal cancers enrolled in GI-SCREEN was centrally performed with NGS. Patients were enrolled in matched trials of targeted agents affiliated with GI-SCREEN on the basis of genotyping results.

RESULTS

A total of 11 gastrointestinal cancers were included, with colorectal cancer being the most common. The median age ranged from 59 to 70.5 years across cancer types. Patients enrolled after initiation of first-line treatment had significantly longer overall survival (OS) than that before treatment initiation with a median survival time difference of 8.9 months and a hazard ratio (HR) ranging from 0.25 to 0.73 across cancer types, demonstrating an immortal time bias. One hundred and forty-nine patients received matched therapies in clinical trials on the basis of their identified alterations. Among patients with colorectal cancer harboring actionable alterations, the median OS was significantly longer in patients who received matched therapies in trials than in those who did not (HR, 0.52; 95% CI, 0.26 to 1.01; P = .049). Cancer-specific pathway alterations were significantly associated with shorter survival and related to primary resistance to matched trial therapies.

CONCLUSION

Our genomic profiling program led to patient enrollment in targeted clinical trials and improved survival of patients with colorectal cancer who received matched therapies in clinical trials. To avoid immortal time bias, precautions are needed when using data from patients who have undergone NGS testing after initiation of the evaluated treatment line.

Selection bias due to delayed comprehensive genomic profiling in Japan

Patients with advanced cancer undergo comprehensive genomic profiling in Japan only after treatment options have been exhausted. Patients with a very poor prognosis were not able to undergo profiling tests, resulting in a selection bias called length bias, which makes accurate survival analysis impossible. The actual impact of length bias on the overall survival of patients who have undergone profiling tests is unclear, yet appropriate methods for adjusting for length bias have not been developed. To assess the length bias in overall survival, we established a simulation-based model for length bias adjustment. This study utilized clinicogenomic data of 8813 patients with advanced cancer who underwent profiling tests at hospitals throughout Japan between June 2019 and April 2022. Length bias was estimated by the conditional Kendall τ statistics and was significantly positive for 13 of the 15 cancer subtypes, suggesting a worse prognosis for patients who underwent profiling tests in early timing. The median overall survival time in colorectal, breast, and pancreatic cancer from the initial survival-prolonging chemotherapy with adjustment for length bias was 937 (886-991), 1225 (1152-1368), and 585 (553-617) days, respectively (median; 95% credible interval). Adjusting for length bias made it possible to analyze the prognostic relevance of oncogenic mutations and treatments. In total, 12 tumor-specific oncogenic mutations correlating with poor survival were detected after adjustment. There was no difference in survival between FOLFIRINOX (leucovorin, fluorouracil, irinotecan, and oxaliplatin) or gemcitabine with nab-paclitaxel-treated groups as first-line chemotherapy for pancreatic cancer. Adjusting for length bias is an essential part of utilizing real-world clinicogenomic data.

A data processing pipeline for the AACR project GENIE biopharma collaborative data with the {genieBPC} R package

MOTIVATION

Data from the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE BPC) represent comprehensive clinical data linked to high-throughput sequencing data, providing a multi-institution, pan-cancer, publicly available data repository. GENIE BPC data provide detailed demographic, clinical, treatment, genomic and outcome data for patients with cancer. These data result in a unique observational database of molecularly characterized tumors with comprehensive clinical annotation that can be used for health outcomes and precision medicine research in oncology. Due to the inherently complex structure of the multiple phenomic and genomic datasets, the use of these data requires a robust process for data integration and preparation in order to build analytic models.

RESULTS

We present the {genieBPC} package, a user-friendly data processing pipeline to facilitate the creation of analytic cohorts from the GENIE BPC data that are ready for clinico-genomic modeling and analyses.

AVAILABILITY AND IMPLEMENTATION

{genieBPC} is available on CRAN and GitHub.

Durable responders in advanced NSCLC with elevated TMB and treated with 1L immune checkpoint inhibitor: a real-world outcomes analysis

BACKGROUND

For patients with advanced non-small cell lung carcinoma (NSCLC), immune checkpoint inhibitor (ICPI) and chemotherapy (chemo) ICPI represent two distinct first-line standard-of-care regimens without clear and established biomarkers to inform the optimal choice for individual patients. Here, we examined the complementary roles of tumor mutational burden (TMB) and programmed death ligand-1 (PD-L1) immunohistochemistry (IHC) to inform first-line therapy using a large real-world (rw) data set.

MATERIALS AND METHODS

The study included patients with NSCLC from an rw de-identified clinico-genomic database. All patients underwent genomic testing using Foundation Medicine's tissue comprehensive genomic profiling assay and PD-L1 IHC assay scored for tumor cell staining (TS).

RESULTS

Of 2165 patients included in the analysis, 150 exhibited durable benefit from first-line ICPI regimens (these patients were enriched for PD-L1 TS ≥50, non-squamous histology, and TMB ≥20 mutations/megabase (muts/Mb)). Comparing low TMB (<10 muts/Mb), high TMB (10-19 muts/Mb), and very high TMB (≥20 muts/Mb) receiving ICPI alone, we observed a stepwise increase in median rwPFS (real world-progression free survival) (6.5, 7.5, 17.2 months) and rwOS (real world-overall survival) (10.1, 11.8, 26.9 months) as TMB increased. In the low PD-L1 (TS <50%) cohort, TMB <20 muts/Mb showed a more favorable rwPFS (HR: 0.56 (95% CI: 0.40 to 0.79)) and rwOS (HR 0.74 (95% CI: 0.58 to 0.96)) on chemoICPI when compared with ICPI alone while the point estimate in rwPFS favored monoICPI in the TMB ≥20 muts/Mb cohort, the CI is wide and does not reach statistical significance (HR: 1.68 (95% CI: 0.52 to 5.48)).

CONCLUSION

This study provides evidence that higher TMB cut-offs, such as 20 muts/Mb, can identify patients with prolonged benefit from ICPI. TMB ≥20 muts/Mb is a potential biomarker that may identify patients in whom an ICPI without chemo could be considered, even in the setting of lower PD-L1 levels. Prospective validation of these findings could increase access to chemo-sparing regimens for the first-line treatment of advanced NSCLC.

SPOP Mutations as a Predictive Biomarker for Androgen Receptor Axis-Targeted Therapy in De Novo Metastatic Castration-Sensitive Prostate Cancer

PURPOSE

Intensification of androgen deprivation therapy (ADT) with either docetaxel or androgen receptor axis-targeted therapies (ARAT) are the current standard of care for patients with metastatic castration-sensitive prostate cancer (mCSPC). However, biomarkers guiding treatment selection are lacking. We hypothesized that ADT intensification with ARAT, but not with docetaxel, would be associated with improved outcomes in patients with de novo (dn)-mCSPC harboring SPOP mutations.

EXPERIMENTAL DESIGN

Patient-level data from a deidentified nationwide (U.S.-based) prostate cancer clinico-genomic database between January 2011 and December 2021 were extracted. Eligibility criteria: diagnosis of metastatic disease within 30 days of original prostate cancer diagnosis, genomic profiling of a tissue biopsy collected within 90 days of original diagnosis, and initiation of ARAT or docetaxel within 120 days of initial diagnosis. The log-rank test and Cox proportional hazards models were used to compare time to castration-resistant prostate cancer (TTCRPC) and overall survival (OS) for patients with and without SPOP mutations undergoing ADT intensification with ARAT or docetaxel.

RESULTS

In the ARAT cohort, presence of SPOP mutation compared with wild-type was associated with more favorable TTCRPC [not reached (NR) vs. 16.7 months; adjusted HR (aHR), 0.20; 95% confidence interval (CI), 0.06-0.63; P = 0.006] and OS (NR vs. 27.2 months; aHR, 0.19; 95% CI, 0.05-0.79; P = 0.022). In contrast, SPOP mutation status was not associated with TTCRPC or OS in docetaxel-treated cohort.

CONCLUSIONS

In real-world settings, SPOP mutations were associated with improved outcomes to ADT plus ARAT (but not ADT plus docetaxel) in patients with dn-mCSPC. This may serve as a predictive biomarker to guide treatment selection for patients with mCSPC.

Genomic analysis of early-stage lung cancer reveals a role for TP53 mutations in distant metastasis

Patients with non-small cell lung cancer (NSCLC) who have distant metastases have a poor prognosis. To determine which genomic factors of the primary tumor are associated with metastasis, we analyzed data from 759 patients originally diagnosed with stage I-III NSCLC as part of the AACR Project GENIE Biopharma Collaborative consortium. We found that TP53 mutations were significantly associated with the development of new distant metastases. TP53 mutations were also more prevalent in patients with a history of smoking, suggesting that these patients may be at increased risk for distant metastasis. Our results suggest that additional investigation of the optimal management of patients with early-stage NSCLC harboring TP53 mutations at diagnosis is warranted in light of their higher likelihood of developing new distant metastases.

Real-world Validation of TMB and Microsatellite Instability as Predictive Biomarkers of Immune Checkpoint Inhibitor Effectiveness in Advanced Gastroesophageal Cancer

Patients with advanced gastroesophageal cancer (mEG) and tumor mutational burden ≥10 mut/Mb (TMB ≥ 10) have more favorable outcomes on immune checkpoint inhibitor (ICPI) monotherapy compared with chemotherapy in subgroup analyses of randomized controlled trials. We sought to evaluate the robustness of these associations in real-world settings where patients and practices are more diverse. A total of 362 2 L and 692 1 L patients, respectively received ICPI (n = 99, 33) or chemotherapy (n = 263, 659) across approximately 280 U.S. academic or community-based cancer clinics March 2014-July 2021. Deidentified data were captured into a real-world clinico-genomic database. All patients underwent Foundation Medicine testing. Time to next treatment (TTNT) and overall survival (OS) comparing ICPI versus chemotherapy were adjusted for treatment assignment imbalances using propensity scores. 2L: TMB ≥ 10 had more favorable TTNT [median 24 vs. 4.1 months; HR: 0.19; 95% confidence interval (CI): 0.09-0.44; P = 0.0001] and OS (median 43.1 vs. 6.2 months; HR: 0.24; 95% CI: 0.011-0.54; P = 0.0005), TMB < 10 did not (P > 0.05). 1L: TMB ≥ 10 had more favorable TTNT (not reached vs. median 4.1 months; HR: 0.13; 95% CI: 0.03-0.48; P = 0.0024) and OS (not reached vs. median 17.1 months; HR: 0.30; 95% CI: 0.08-1.14; P = 0.078), TMB < 10 had less favorable TTNT (median 2.8 vs. 6.5 months; HR: 2.36; 95% CI: 1.25-4.45; P = 0.008) and OS (median 4.5 vs. 13.1 months; HR: 1.82, 95% CI: 0.87-3.81; P = 0.11). TMB ≥ 10 robustly identifies patients with mEG with more favorable outcomes on 2 L ICPI monotherapy versus chemotherapy. 1 L data are more limited, but effects are consistent with 2L.

Significance

Using real-world data, we sought to evaluate robustness of these clinical associations using the same assay platform and biomarker cut-off point used in both clinical trials and pan-tumor CDx approvals for later treatment lines. TMB ≥ 10 robustly identified patients with mEG with more favorable outcomes on ICPI monotherapy versus chemotherapy and suggests this subset of patients could be targeted for further trial development.

Alterations in Somatic Driver Genes Are Associated with Response to Neoadjuvant FOLFIRINOX in Patients with Localized Pancreatic Ductal Adenocarcinoma

BACKGROUND

There is increased use of neoadjuvant fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX) in the management of localized pancreatic ductal adenocarcinoma (PDAC), yet there are few validated biomarkers of treatment response.

STUDY DESIGN

Consecutive patients (n = 196) with resectable, borderline resectable or locally advanced PDAC (2012-2019) receiving FOLFIRINOX as initial treatment and with targeted sequencing of a pretreatment biopsy were identified in a prospective institutional database. Genomic alterations were determined in the 4 driver mutations (KRAS, TP53, CDKN2A, SMAD4), and associations between genomic alterations and clinical outcomes were assessed.

RESULTS

Alterations in KRAS (n = 172, 87.8%) and TP53 (n = 131, 66.8%) were common; alterations in CDKN2A (n = 49, 25.0%) and SMAD4 (n = 36, 18.4%) were less frequently observed. A total of 105 patients (53.6%) were able to undergo resection, of whom 8 (7.6%) had a complete/near-complete pathologic response. There were no somatic alterations associated with major pathologic response. Alterations in SMAD4 were associated with a lower rate of surgical resection (27.8% vs 59.4%, p < 0.001); this was additionally observed in a multivariable regression model accounting for resectability status (OR 0.35, 95% confidence interval [CI] 0.15-0.85). Thirty-three patients (16.8%) developed metastatic disease while on neoadjuvant therapy. SMAD4 alterations were associated with a significant risk of metastatic progression on therapy when controlling for resectability status (OR 3.31, 95% CI 1.44-7.60).

CONCLUSIONS

SMAD4 alterations are associated with more frequent development of metastasis during neoadjuvant FOLFIRINOX and lower probability of reaching surgical resection. Evaluation of alternative chemotherapy regimens in patients with SMAD4 alterations will be important to distinguish whether this represents a prognostic or predictive biomarker.

Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer

Approximately 30% of breast cancer (BC) patients suffer from disease relapse after definitive treatment. Monitoring BC at baseline and disease progression using comprehensive genomic profiling would facilitate the prediction of prognosis. We retrospectively studied 101 BC patients ultimately experiencing relapse and/or metastases. The baseline and circulating tumor DNA‐monitoring cohorts included patients with baseline tumor tissue and serial plasma samples, respectively. Samples were analyzed with targeted next‐generation sequencing of 425 cancer‐relevant genes. Of 35 patients in the baseline cohort, patients with TP53 mutations (P < 0.01), or CTCF/GNAS mutations (P < 0.01) displayed inferior disease‐free survival, and patients harboring TP53 (P = 0.06) or NOTCH1 (P = 0.06) mutations showed relatively poor overall survival (OS), compared to patients with wild‐type counterparts. Of the 59 patients with serial plasma samples, 11 patients who were newly detected with TP53 mutations had worse OS than patients whose TP53 mutational status remained negative (P < 0.01). These results indicate that an inferior prognosis of advanced breast cancer was potentially associated with baseline TP53, CTCF, and NOTCH1 alterations. Newly identified TP53 mutations after relapse and/or metastasis was another potential prognostic biomarker of poor prognosis.

Computational methods and translational applications for targeted next-generation sequencing platforms

During the past decade, next-generation sequencing (NGS) technologies have become widely adopted in cancer research and clinical care. Common applications within the clinical setting include patient stratification into relevant molecular subtypes, identification of biomarkers of response and resistance to targeted and systemic therapies, assessment of heritable cancer risk based on known pathogenic variants, and longitudinal monitoring of treatment response. The need for efficient downstream processing and reliable interpretation of sequencing data has led to the development of novel algorithms and computational pipelines, as well as structured knowledge bases that link genomic alterations to currently available drugs and ongoing clinical trials. Cancer centers around the world use different types of targeted solid-tissue and blood based NGS assays to analyze the genomic and transcriptomic profile of patients as part of their routine clinical care. Recently, cross-institutional collaborations have led to the creation of large pooled datasets that can offer valuable insights into the genomics of rare cancers.

Artificial intelligence-aided clinical annotation of a large multi-cancer genomic dataset

To accelerate cancer research that correlates biomarkers with clinical endpoints, methods are needed to ascertain outcomes from electronic health records at scale. Here, we train deep natural language processing (NLP) models to extract outcomes for participants with any of 7 solid tumors in a precision oncology study. Outcomes are extracted from 305,151 imaging reports for 13,130 patients and 233,517 oncologist notes for 13,511 patients, including patients with 6 additional cancer types. NLP models recapitulate outcome annotation from these documents, including the presence of cancer, progression/worsening, response/improvement, and metastases, with excellent discrimination (AUROC > 0.90). Models generalize to cancers excluded from training and yield outcomes correlated with survival. Among patients receiving checkpoint inhibitors, we confirm that high tumor mutation burden is associated with superior progression-free survival ascertained using NLP. Here, we show that deep NLP can accelerate annotation of molecular cancer datasets with clinically meaningful endpoints to facilitate discovery.