Assessment of Temporal Selection Bias in Genomic Testing in a Cohort of Patients With Cancer

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.

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.

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.

Evaluation and Comparison of Real-World Databases for Conducting Research in Patients With Colorectal Cancer

PURPOSE

Evaluating whether patient populations in clinico-genomic oncology databases are comparable with whom in other databases without genomic component is important.

METHODS

Four databases were compared for colorectal cancer (CRC) cases and stage IV CRC cases: American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE-BPC), The Cancer Genome Atlas (TCGA), SEER-Medicare, and MarketScan Commercial and Medicare Supplemental claims databases. These databases were also compared with the SEER registry database which serves as national benchmarks. Demographics, clinical characteristics, and overall survival were compared in patients with newly diagnosed CRC and patients with stage IV CRC across databases. Treatment patterns were further compared in patients with stage IV CRC.

RESULTS

A total of 65,976 patients with CRC and 13,985 patients with stage IV CRC were identified. GENIE-BPC had the youngest patient population (mean age [years]: CRC, 54.1; stage IV CRC, 52.7). SEER-Medicare had the oldest patient population (CRC, 77.7; stage IV CRC, 77.3). Most patients were male and of White race across databases. GENIE-BPC had the highest proportion of patients with stage IV CRC (48.4% v other databases 13.8%-25.4%) and patients receiving treatments (95.7% v 37.6%-59.1%). Infusional fluorouracil, leucovorin, and oxaliplatin with or without bevacizumab was the most common regimen across databases accounting for 47.3%-78.5% of patients receiving first line of therapy. The median survival from diagnosis was 36, 94, 44 months (CRC) and 23, 36, 15 months (stage IV CRC) for patients in GENIE-BPC after left truncation, TCGA, and SEER-Medicare databases, respectively.

CONCLUSION

Compared with other databases, GENIE-BPC had the youngest patients with CRC with the most advanced disease and the largest proportion of patients receiving treatment. Investigators should consider adjustments when extrapolating results from clinico-genomic databases to the general CRC population.

PCSK6 and Survival in Idiopathic Pulmonary Fibrosis

Rationale: Idiopathic pulmonary fibrosis (IPF) is a devastating disease characterized by limited treatment options and high mortality. A better understanding of the molecular drivers of IPF progression is needed. Objectives: To identify and validate molecular determinants of IPF survival. Methods: A staged genome-wide association study was performed using paired genomic and survival data. Stage I cases were drawn from centers across the United States and Europe and stage II cases from Vanderbilt University. Cox proportional hazards regression was used to identify gene variants associated with differential transplantation-free survival (TFS). Stage I variants with nominal significance (P < 5 × 10-5) were advanced for stage II testing and meta-analyzed to identify those reaching genome-wide significance (P < 5 × 10-8). Downstream analyses were performed for genes and proteins associated with variants reaching genome-wide significance. Measurements and Main Results: After quality controls, 1,481 stage I cases and 397 stage II cases were included in the analysis. After filtering, 9,075,629 variants were tested in stage I, with 158 meeting advancement criteria. Four variants associated with TFS with consistent effect direction were identified in stage II, including one in an intron of PCSK6 (proprotein convertase subtilisin/kexin type 6) reaching genome-wide significance (hazard ratio, 4.11 [95% confidence interval, 2.54-6.67]; P = 9.45 × 10-9). PCSK6 protein was highly expressed in IPF lung parenchyma. PCSK6 lung staining intensity, peripheral blood gene expression, and plasma concentration were associated with reduced TFS. Conclusions: We identified four novel variants associated with IPF survival, including one in PCSK6 that reached genome-wide significance. Downstream analyses suggested that PCSK6 protein plays a potentially important role in IPF progression.

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.

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.

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.

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

Importance

Real-world data sets that combine clinical and genomic data may be subject to left truncation (when potential study participants are not included because they have already passed the milestone of interest at the time of study recruitment). The lapse between diagnosis and molecular testing can present analytic challenges and threaten the validity and interpretation of survival analyses.

Observations

Effects of ignoring left truncation when estimating overall survival are illustrated using data from the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE BPC), and a straightforward risk-set adjustment approach is described. Ignoring left truncation results in overestimation of overall survival: unadjusted median survival estimates from diagnosis among patients with stage IV non-small cell lung cancer or stage IV colorectal cancer were overestimated by more than 1 year.

Conclusions and Relevance

Clinicogenomic data are a valuable resource for evaluation of real-world cancer outcomes and should be analyzed using appropriate methods to maximize their potential. Analysts must become adept at application of appropriate statistical methods to ensure valid, meaningful, and generalizable research findings.

Clinical Inflection Point Detection on the Basis of EHR Data to Identify Clinical Trial-Ready Patients With Cancer

PURPOSE

To inform precision oncology, methods are needed to use electronic health records (EHRs) to identify patients with cancer who are experiencing clinical inflection points, consistent with worsening prognosis or a high propensity to change treatment, at specific time points. Such patients might benefit from real-time screening for clinical trials.

METHODS

Using serial unstructured imaging reports for patients with solid tumors or lymphoma participating in a single-institution precision medicine study, we trained a deep neural network natural language processing (NLP) model to dynamically predict patients' prognoses and propensity to start new palliative-intent systemic therapy within 30 days. Model performance was evaluated using Harrell's c-index (for prognosis) and the area under the receiver operating characteristic curve (AUC; for new treatment and new clinical trial enrollment). Associations between model outputs and manual annotations of cancer progression were also evaluated using the AUC.

RESULTS

A deep NLP model was trained and evaluated using 302,688 imaging reports for 16,780 patients. In a held-out test set of 34,770 reports for 1,952 additional patients, the model predicted survival with a c-index of 0.76 and initiation of new treatment with an AUC of 0.77. Model-generated prognostic scores were associated with annotation of cancer progression on the basis of manual EHR review (n = 1,488 reports for 110 patients with lung or colorectal cancer) with an AUC of 0.78, and predictions of new treatment were associated with annotation of cancer progression on the basis of manual EHR review with an AUC of 0.84.

CONCLUSION

Training a deep NLP model to identify clinical inflection points among patients with cancer is feasible. This approach could identify patients who may benefit from real-time targeted clinical trial screening interventions at health system scale.

Penalized regression for left-truncated and right-censored survival data

High‐dimensional data are becoming increasingly common in the medical field as large volumes of patient information are collected and processed by high‐throughput screening, electronic health records, and comprehensive genomic testing. Statistical models that attempt to study the effects of many predictors on survival typically implement feature selection or penalized methods to mitigate the undesirable consequences of overfitting. In some cases survival data are also left‐truncated which can give rise to an immortal time bias, but penalized survival methods that adjust for left truncation are not commonly implemented. To address these challenges, we apply a penalized Cox proportional hazards model for left‐truncated and right‐censored survival data and assess implications of left truncation adjustment on bias and interpretation. We use simulation studies and a high‐dimensional, real‐world clinico‐genomic database to highlight the pitfalls of failing to account for left truncation in survival modeling.

Correlation Between Surrogate End Points and Overall Survival in a Multi-institutional Clinicogenomic Cohort of Patients With Non-Small Cell Lung or Colorectal Cancer

IMPORTANCE

Contemporary observational cancer research requires associating genomic biomarkers with reproducible end points; overall survival (OS) is a key end point, but interpretation can be challenging when multiple lines of therapy and prolonged survival are common. Progression-free survival (PFS), time to treatment discontinuation (TTD), and time to next treatment (TTNT) are alternative end points, but their utility as surrogates for OS in real-world clinicogenomic data sets has not been well characterized.

OBJECTIVE

To measure correlations between candidate surrogate end points and OS in a multi-institutional clinicogenomic data set.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective cohort study was conducted of patients with non-small cell lung cancer (NSCLC) or colorectal cancer (CRC) whose tumors were genotyped at 4 academic centers from January 1, 2014, to December 31, 2017, and who initiated systemic therapy for advanced disease. Patients were followed up through August 31, 2020 (NSCLC), and October 31, 2020 (CRC). Statistical analyses were conducted on January 5, 2021.

EXPOSURES

Candidate surrogate end points included TTD; TTNT; PFS based on imaging reports only; PFS based on medical oncologist ascertainment only; PFS based on either imaging or medical oncologist ascertainment, whichever came first; and PFS defined by a requirement that both imaging and medical oncologist ascertainment have indicated progression.

MAIN OUTCOMES AND MEASURES

The primary outcome was the correlation between candidate surrogate end points and OS.

RESULTS

There were 1161 patients with NSCLC (648 women [55.8%]; mean [SD] age, 63 [11] years) and 1150 with CRC (647 men [56.3%]; mean [SD] age, 54 [12] years) identified for analysis. Progression-free survival based on both imaging and medical oncologist documentation was most correlated with OS (NSCLC: ρ = 0.76; 95% CI, 0.73-0.79; CRC: ρ = 0.73; 95% CI, 0.69-0.75). Time to treatment discontinuation was least associated with OS (NSCLC: ρ = 0.45; 95% CI, 0.40-0.50; CRC: ρ = 0.13; 95% CI, 0.06-0.19). Time to next treatment was modestly associated with OS (NSCLC: ρ = 0.60; 0.55-0.64; CRC: ρ = 0.39; 95% CI, 0.32-0.46).

CONCLUSIONS AND RELEVANCE

This cohort study suggests that PFS based on both a radiologist and a treating oncologist determining that a progression event has occurred was the surrogate end point most highly correlated with OS for analysis of observational clinicogenomic data.

YB-1: The key to personalised prostate cancer management?

Y-box-binding protein 1 (YB-1) is a DNA/RNA binding protein increasingly implicated in the regulation of cancer cell biology. Normally located in the cytoplasm, nuclear localisation in prostate cancer is associated with more aggressive, potentially treatment-resistant disease. This is attributed to the ability of YB-1 to act as a transcription factor for various target genes associated with androgen receptor signalling, survival, DNA repair, proliferation, invasion, differentiation, angiogenesis and hypoxia. This review aims to examine the clinical potential of YB-1 in the detection and therapeutic management of prostate cancer.