Quantifying the Expanding Landscape of Clinical Actionability for Patients with Cancer

There is a continuing debate about the proportion of cancer patients that benefit from precision oncology, attributable in part to conflicting views as to which molecular alterations are clinically actionable. To quantify the expansion of clinical actionability since 2017, we annotated 47,271 solid tumors sequenced with the MSK-IMPACT clinical assay using two temporally distinct versions of the OncoKB knowledge base deployed 5 years apart. Between 2017 and 2022, we observed an increase from 8.9% to 31.6% in the fraction of tumors harboring a standard care (level 1 or 2) predictive biomarker of therapy response and an almost halving of tumors carrying nonactionable drivers (44.2% to 22.8%). In tumors with limited or no clinical actionability, TP53 (43.2%), KRAS (19.2%), and CDKN2A (12.2%) were the most frequently altered genes.

SIGNIFICANCE

Although clear progress has been made in expanding the availability of precision oncology-based treatment paradigms, our results suggest a continued unmet need for innovative therapeutic strategies, particularly for cancers with currently undruggable oncogenic drivers. See related commentary by Horak and Fröhling, p. 18. This article is featured in Selected Articles from This Issue, p. 5.

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.

Abstract 1189: OncoKB, MSK’s precision oncology knowledge base

OncoKB, Memorial Sloan Kettering Cancer Center’s (MSK) precision oncology knowledge base (www.oncokb.org), is a comprehensive database that annotates the oncogenic effects and clinical actionability of somatic alterations in cancer. OncoKB supports variant interpretation by the cBioPortal for Cancer Genomics and is used to annotate >12,000 MSK patient sequencing reports annually. Since its introduction in 2016, OncoKB has expanded to include 5685 alterations in 682 genes, and in October 2021, it became the first somatic knowledge base to be partially recognized by the FDA. The scope of the OncoKB FDA recognition includes clinically actionable variants that map to an FDA level of evidence, the processes of variant curation, and policies regarding database oversight, personnel training and transparency of data sources and operations. This recognition credentials OncoKB as providing accurate, reliable and clinically meaningful information to the medical and scientific communities.

The OncoKB Therapeutic (Tx) Levels of Evidence categorize variants based on their tumor type-specific predictive value of sensitivity or resistance to matched standard care or investigational targeted therapies. To date, OncoKB includes 43 Level 1 genes (included in the FDA drug label), 23 Level 2 genes (included in professional guidelines), 25 Level 3A genes (predictive of drug response in well-powered clinical studies), 23 Level 4 genes (predictive of drug response based on compelling biological evidence), and 11 R1 or R2 resistance genes. Initially focused on solid tumors, OncoKB was expanded to include hematologic disease annotation in 2019 and introduced Diagnostic (Dx) and Prognostic (Px) levels of evidence. All three level of evidence systems (Tx, Dx and Px) are consistent with the guidelines for evidence-based categorization of somatic variants published as a joint consensus recommendation by AMP/ASCO/CAP.

OncoKB is governed by a Clinical Genomics Annotation Committee, composed of MSK physicians and scientists who ensure that the information captured is accurate and current, and an external advisory board composed of leaders in the clinical oncology and genomics communities who oversee OncoKB updates and progress. OncoKB curation rules and processes are transparent and documented in the OncoKB Curation Standard Operating Procedure, which is publicly available via the website. User feedback to OncoKB content is encouraged via the website and through cBioPortal. Queries or suggestions by OncoKB users are addressed by the OncoKB team within 72 hours.

OncoKB offers licenses for academic, commercial and hospital use, with which users can programmatically access the web API. Future work includes coverage of additional cancer-associated genes, annotation of germline alterations that are predictive of drug response and/or associated with increased heritable cancer risk and the development of a clinical trial matching system.

Citation Format: Sarah P. Suehnholz, Moriah Nissan, Hongxin Zhang, Ritika Kundra, Calvin Lu, Benjamin Xu, Maria E. Arcila, Marc Ladanyi, Michael F. Berger, Ahmet Zehir, Aijaz Syed, Julia E. Rudolph, Ross L. Levine, Ahmet Dogan, Jianjiong Gao, David B. Solit, Nikolaus Schultz, Debyani Chakravarty. OncoKB, MSK’s precision oncology knowledge base [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1189.

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.

Abstract 2620: Ignoring left truncation in overall survival within real-world genomic-phenomic data leads to inflated survival estimates

Studies linking genomic and phenomic data are subject to selection biases, including delayed entry or immortal time bias. Delayed entry can be problematic for time-to-event analyses, but utilization of appropriate statistical methods to account for delayed entry are underutilized. Delayed entry commonly occurs when genomic sequencing results are obtained after the start time for survival estimation.

To evaluate the impact of left truncation on overall survival (OS) estimates, we explored outcomes in patients with de novo stage IV non-small cell lung cancer (NSCLC) and colorectal cancer (CRC) from the AACR GENIE Biopharma Collaborative, who had genomic sequencing within a specified timeframe. We analyzed OS from diagnosis and from start of the most common first-line regimen, carboplatin/pemetrexed for NSCLC (N = 212 patients) and FOLFOX for CRC (N = 369 patients). We compared median OS using standard Kaplan-Meier methods to median OS using left truncation methods to account for delayed entry. All NSCLC and CRC patients underwent genomic sequencing after their diagnosis date. Among NSCLC patients on carboplatin/pemetrexed, 41% and among CRC patients on FOLFOX, 14% had sequencing determined after starting first-line regimen. The survfit function in R package survival was used, and the absolute differences and percent differences in median OS estimates were calculated.

Failure to account for delayed entry leads to an overestimation of OS, regardless of cohort and start date. Adjusting survival outcomes using left truncation methods reduces the influence of some aspects of selection bias and results in better estimates of time to event outcomes. Analyses from these cohorts can provide meaningful insights about survival outcomes outside the clinical trial setting and may support trial design and reliable selection of control arms. As such, it is imperative that analytic methods to account for the inflated survival estimates are incorporated.

EstimateCRC Stage IV (N = 658)NSCLC Stage IV (N = 722)Unadjusted Median (IQR) Overall Survival from Diagnosis (Years)3.2 (2.9, 3.4)2.3 (2.0, 2.5)Median (IQR) Overall Survival from Diagnosis in Years, Adjusting for Delayed Entry2.1 (1.9, 2.4)1.3 (1.1, 1.6)Difference in Medians (Years)1.11.0% Difference in Medians34%44%EstimateCRC Stage IV (N = 369)NSCLC Stage IV (N = 212)Unadjusted Median (IQR) Overall Survival from Most Common First-Line Regimen (Years)2.9 (2.6, 3.4)1.3 (1.0, 1.6)Median (IQR) Overall Survival from Most Common First-Line Regimen in Years, Adjusting for Delayed Entry2.1 (1.8, 2.5)0.9 (0.7, 1.2)Difference in Medians (Years)0.80.4% Difference in Medians28%31%

Citation Format: Samantha Brown, Jessica A. Lavery, Eva M. Lepisto, Caroline McCarthy, Hira Rizvi, Celeste Yu, Kenneth L. Kehl, Shawn M. Sweeney, Julia E. Rudolph, Nikolaus Schultz, Ritika Kundra, Brooke Mastrogiacomo, Phillipe Bedard, Jeremy L. Warner, Gregory J. Riely, Deborah Schrag, Katherine S. Panageas, The AACR Project GENIE Consortium. Ignoring left truncation in overall survival within real-world genomic-phenomic data leads to inflated survival estimates [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2620.

Abstract 2619: Defining real-world recurrence in the AACR Project GENIE Biopharma Collaborative Data

Obtaining information regarding cancer recurrence from a retrospective, EHR-based dataset poses several challenges primarily due to the lack of structured data. Patients are at risk for cancer recurrence beginning at a time point at which they are characterized as having no evidence of disease. The absence of cancer may be indicated on a radiology report or a medical oncologist assessment, requiring manual review and interpretation of potentially ambiguous free text. Further, the recurrence event itself can be defined based on several distinct data sources including pathology, imaging, clinician assessments, or tumor markers. The likelihood of ascertaining recurrence is dependent on the frequency and type of surveillance performed and varies based on tumor type and based on clinicians' thresholds for pursuing workup of borderline or suspicious findings; if follow up assessments are infrequent, there are fewer opportunities to detect recurrence. Given these challenges, there is currently no standardized approach to evaluating cancer recurrence in EHR data, impeding analyses of rare molecular tumor subtypes in multi-institutional linked clinico-genomic databases.

For this analysis, we leveraged the AACR Project GENIE Biopharma Collaborative data based on the PRISSMM curation model to develop an algorithm for identifying recurrence among patients diagnosed with stage I-III non-small cell lung cancer or with stage I-III colorectal cancer. This algorithm involves using curated pathology report data to identify a definitive surgery as the time at which patients have completed curative intent treatment. Subsequent imaging reports, pathology reports, medical oncologist assessments and tumor marker data are then evaluated in order to characterize the timing of specific recurrence events.

We will present the real-world recurrence algorithm, its underlying rationale and discuss applications of recurrence endpoints. Beyond enabling estimates of recurrence-free survival, identifying cancer recurrence will allow for estimation of progression-free survival among stage I-III patients in addition to estimation of PFS among de novo stage IV patients. Estimating PFS in a large cohort of patients with linked phenomic and genomic data has historically been a limitation of these types of datasets. Overcoming this limitation will allow for precision medicine advances in oncology by facilitating data pooling across institutions and enabling examination of rare molecular subtypes in relation to clinically meaningful endpoints.

Citation Format: Jessica A. Lavery, Samantha Brown, Eva Lepisto, Michele L. Lenoue-Newton, Caroline McCarthy, Hira Rizvi, Celeste Yu, Kenneth L. Kehl, Shawn M. Sweeney, Julia E. Rudolph, Nikolaus Schultz, Brooke Mastrogiacomo, Ritika Kundra, Jeremy Warner, Philippe Bedard, Gregory J. Riely, Katherine S. Panageas, Deborah Schrag, AACR Project GENIE Consortium. Defining real-world recurrence in the AACR Project GENIE Biopharma Collaborative Data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2619.

Abstract 3208: OncoKB, a precision oncology knowledgebase

OncoKB is a precision oncology knowledge base that annotates the oncogenic effects and clinical actionability of somatic alterations in cancer. Initially focused on solid tumors, OncoKB was introduced in 2016 with >200 genes and almost 3000 somatic alterations via a public website (oncokb.org) and through the cBioPortal for Cancer Genomics. OncoKB now contains annotations for &gt5000 alterations in 642 genes. This includes 30 Level 1 alterations (included in the FDA drug label; a growth of 114% since 2016), 15 Level 2 alterations (included in the NCCN guidelines; 50% growth), and 38 Level 3A alterations (predictive of drug response in well-powered clinical studies; 65% growth). OncoKB now also supports hematologic malignancies with two new levels of evidence systems that encompass diagnostic and prognostic implications (in addition to therapeutic implications) and 288 heme-specific alterations in 156 newly curated cancer-associated genes. At MSK, OncoKB is used for the annotation of 1000 molecular patient reports per month. To assess the clinical utility of OncoKB and changes in the frequency of actionable alterations, we performed a comparison between the AACR Project GENIE cohort from 2017 and the most recent one (Table 1). With an increased number of tumor types and greater inclusion of hematologic malignancies, the overall potential actionability rate increased by 3.6 percentage points. A shift in access to targeted cancer therapies is also observed, where Level 1 or 2 alterations increased over 5 percentage points and Level 3 alterations decreased by ~3 percentage points, perhaps reaping the benefits of recent successful phase III trials.

August 2017 (v1.1)December 2019 (v7.2)AACR Project GENIE cohort size18,80480,248Tumor types with >100 samples3151Hematologic malignancies included29Level 1 or 2 annotation7.3%12.9%Level 3A annotation6.4%4.7%Level 3B annotation17.8%17.5%Total potential actionability31.5%35.1%

While only a subset of patients with targetable alterations will benefit from treatments, there is ample evidence that targeted cancer therapies can have profound and durable clinical activity. Knowledgebases such as OncoKB have become a key component to support clinical decision making, and there is a continued need to expand their capabilities while maintaining a nuanced approach to annotation.

Citation Format: Sarah Suehnholz, Hongxin Zhang, Moriah Nissan, Ritika Kundra, Jing Su, Lindsay LaFave, Kinisha Gala, Chad Vanderbilt, Maria Arcila, Marc Ladanyi, Michael Berger, Ahmet Zehir, Julia E. Rudolph, Paul Sabbatini, Ross Levine, Ahmet Dogan, Jianjiong Gao, David B. Solit, Nikolaus Schultz, Debyani Chakravarty. OncoKB, a precision oncology knowledgebase [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3208.

Progression-free survival estimates in non-small cell lung cancer when RECIST is unavailable: Project GENIE’s integration of genomic, therapeutic and phenomic data

9622

Background: Molecular tumor profiling has become an integral component of oncology practice but linked genomic-phenomic data remain scarce. Recurrence, treatment response and progression are not structured consistently in medical records and this deficit has been a roadblock to discovery of biomarkers that are associated with favorable outcomes. Methods: The Genomics Evidence Neoplasia Information Exchange (GENIE) consortium is an AACR sponsored project to link and share genomic and phenomic data to promote discovery in precision medicine. 3 cancer centers that routinely perform somatic tumor profiling for advanced cancers agreed to curate anti-neoplastic treatment exposures and outcomes including recurrence, progression, response and survival using a standard method. 6 cancer types (lung, colorectal, breast, prostate, pancreas and bladder) were selected and a REDCAP database captures anti-neoplastic treatments, and specific elements from pathology, radiology and oncology reports. Curators abstract data using data fields that rely on the PRISSMM standard. “Real world” progression free survival (PFS) was identified based on curation of: 1) the text of radiologists’ reports for CT, PET/CT, PET and MRI scans (PFSI) and 2) medical oncologists’ notes (PFSM). PFSI and PFSM were estimated from the start of 1st line anti-neoplastic systemic therapy until progression or death for all patients with molecularly characterized non-small cell lung cancer (NSCLC). Results: Genomic sequencing was performed between 2015 and 2017 for 748 patients with NSCLC treated at three major cancer centers. Median age at diagnosis was 66 years (interquartile range 58, 73) and 43% were male. As shown in the table, when RECIST assessments are unavailable, estimates of PFS vary based on whether they are derived from radiologists’ or oncologists’ interpretations. Conclusions: Radiologists’ reports and oncologists’ reports provide different PFS estimates. Cohort studies should specify the method used to define “real world” endpoints. Project GENIE will have 1800 NSCLC patients with curated endpoints by the ASCO meeting. [Table: see text]

OncoKB: A Precision Oncology Knowledge Base

PURPOSE

With prospective clinical sequencing of tumors emerging as a mainstay in cancer care, there is an urgent need for a clinical support tool that distills the clinical implications associated with specific mutation events into a standardized and easily interpretable format. To this end, we developed OncoKB, an expert-guided precision oncology knowledge base.

METHODS

OncoKB annotates the biological and oncogenic effect and the prognostic and predictive significance of somatic molecular alterations. Potential treatment implications are stratified by the level of evidence that a specific molecular alteration is predictive of drug response based on US Food and Drug Administration (FDA) labeling, National Comprehensive Cancer Network (NCCN) guidelines, disease-focused expert group recommendations and the scientific literature.

RESULTS

To date, over 3000 unique mutations, fusions, and copy number alterations in 418 cancer-associated genes have been annotated. To test the utility of OncoKB, we annotated all genomic events in 5983 primary tumor samples in 19 cancer types. Forty-one percent of samples harbored at least one potentially actionable alteration, of which 7.5% were predictive of clinical benefit from a standard treatment. OncoKB annotations are available through a public web resource (http://oncokb.org/) and are also incorporated into the cBioPortal for Cancer Genomics to facilitate the interpretation of genomic alterations by physicians and researchers.

CONCLUSION

OncoKB, a comprehensive and curated precision oncology knowledge base, offers oncologists detailed, evidence-based information about individual somatic mutations and structural alterations present in patient tumors with the goal of supporting optimal treatment decisions.

Three Drosophila beta-tubulin sequences: a developmentally regulated isoform (beta 3), the testis-specific isoform (beta 2), and an assembly-defective mutation of the testis-specific isoform (B2t8) reveal both an ancient divergence in metazoan isotypes and structural constraints for beta-tubulin function

The genomic DNA sequence and deduced amino acid sequence are presented for three Drosophila melanogaster beta-tubulins: a developmentally regulated isoform beta 3-tubulin, the wild-type testis-specific isoform beta 2-tubulin, and an ethyl methanesulfonate-induced assembly-defective mutation of the testis isoform, B2t8. The testis-specific beta 2-tubulin is highly homologous to the major vertebrate beta-tubulins, but beta 3-tubulin is considerably diverged. Comparison of the amino acid sequences of the two Drosophila isoforms to those of other beta-tubulins indicates that these two proteins are representative of an ancient sequence divergence event which at least preceded the split between lines leading to vertebrates and invertebrates. The intron/exon structures of the genes for beta 2- and beta 3-tubulin are not the same. The structure of the gene for the variant beta 3-tubulin isoform, but not that of the testis-specific beta 2-tubulin gene, is similar to that of vertebrate beta-tubulins. The mutation B2t8 in the gene for the testis-specific beta 2-tubulin defines a single amino acid residue required for normal assembly function of beta-tubulin. The sequence of the B2t8 gene is identical to that of the wild-type gene except for a single nucleotide change resulting in the substitution of lysine for glutamic acid at residue 288. This position falls at the junction between two major structural domains of the beta-tubulin molecule. Although this hinge region is relatively variable in sequence among different beta-tubulins, the residue corresponding to glu 288 of Drosophila beta 2-tubulin is highly conserved as an acidic amino acid not only in all other beta-tubulins but in alpha-tubulins as well.

Regulation of tubulin gene expression during embryogenesis in Drosophila melanogaster

Four different tubulins have been identified that are expressed during embryogenesis in Drosophila melanogaster. Two alpha-tubulin subunits (alpha 1 and alpha 2) and one beta-tubulin subunit (beta 1) are expressed throughout embryonic development. A second beta-tubulin subunit (beta 3) is expressed only for a short period in mid-embryonic development. Synthesis of beta 3-tubulin in vitro in a rabbit reticulocyte translation system is directed by RNA extracted from embryos only at the stage when the protein is expressed. Thus we conclude that the mRNA encoding beta 3-tubulin is transcribed only during the brief period of beta 3-tubulin synthesis. The expression of beta 3-tubulin is accompanied by a coordinate transient increase in the level of synthesis of the embryonic alpha-tubulins, thereby maintaining an approximately equimolar synthesis of alpha- and beta-tubulin subunits throughout embryogenesis.

Pregnancy in renal transplant patients: a review

A questionnaire survey and review of the literature show that pregnancy can be well tolerated in most women with renal transplants. Fifty-two per cent of the renal transplant recipients who became pregnant had full-term infants with no serious complications. With therapeutic abortions, excluded, 71% of the 308 pregnancies permitted to continue resulted in full-term infants. Rejection episodes were occasionally a serious problem, occurring in 9% of the pregnancies. Mechanical interference with renal excretion or preventing vaginal delivery occurred in 5.6% of the cases. Hypertension and proteinuria, often existing prior to pregnancy, became frequently increased during pregnancy. Infections not associated with rejection were common but easily controlled in most cases. Prematurity was frequent but related to renal function and the time interval from transplant to conception. The most serious infant complications were related to prematurity. Unknown is the future of these infants and their progeny because of their intrauterine exposure to immunosuppressive drugs.