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.

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.

Lung resection after initial nonoperative treatment for non-small cell lung cancer

OBJECTIVES

The study objectives were to assess the outcomes of lung resection in patients with non-small cell lung cancer previously treated with nonoperative treatment and to identify prognostic factors associated with survival.

METHODS

Patients who underwent surgery (2010-2022) after initial nonoperative treatment at a single institution were identified from a prospectively maintained database. Exclusion criteria included metachronous cancer, planned neoadjuvant therapy, and surgery for diagnostic or palliative indications. Cox models were constructed for overall survival and event-free survival. Survival of patients with stage IV disease was compared with survival of a nonstudy cohort who did not undergo surgery.

RESULTS

In total, 120 patients met the inclusion criteria. Initial clinical stage was early stage in 16%, locoregionally advanced in 25%, and metastatic in 59% of patients. The indication for surgery was recurrence in 18%, local persistent disease in 23%, oligoprogression in 22%, and local control of oligometastatic disease in 38% of patients. Grade 3 or greater complications occurred in 5% of patients; 90-day mortality was 3%. Three-year event-free survival and overall survival were 39% and 73%, respectively. Male sex and lymphovascular invasion were associated with shorter event-free survival and overall survival; younger age and prior radiation therapy were associated with shorter overall survival. Patients with stage IV disease who received salvage lung resection had better overall survival than similar patients who received subsequent systemic therapy and no surgery.

CONCLUSIONS

In this selected, heterogeneous population, lung resection after initial nonoperative treatment for non-small cell lung cancer was safe. Surgery as local consolidative therapy was associated with encouraging outcomes and should be considered for these patients.

Genomic mapping of metastatic organotropism in lung adenocarcinoma

We analyzed 2,532 lung adenocarcinomas (LUAD) to identify the clinicopathological and genomic features associated with metastasis, metastatic burden, organotropism, and metastasis-free survival. Patients who develop metastasis are younger and male, with primary tumors enriched in micropapillary or solid histological subtypes and with a higher mutational burden, chromosomal instability, and fraction of genome doublings. Inactivation of TP53, SMARCA4, and CDKN2A are correlated with a site-specific shorter time to metastasis. The APOBEC mutational signature is more prevalent among metastases, particularly liver lesions. Analyses of matched specimens show that oncogenic and actionable alterations are frequently shared between primary tumors and metastases, whereas copy number alterations of unknown significance are more often private to metastases. Only 4% of metastases harbor therapeutically actionable alterations undetected in their matched primaries. Key clinicopathological and genomic alterations in our cohort were externally validated. In summary, our analysis highlights the complexity of clinicopathological features and tumor genomics in LUAD organotropism.

Abstract 4260: Understanding genomic and social determinants of cancer immunotherapy outcome across ancestry

Compared with previous standards of care, the use of immune checkpoint inhibitors (ICI) has brought significant improvements in survival and quality of life for lung cancer patients. However, only a small proportion of these patients respond durably. People with different ancestries differ probabilistically in genetic factors, environmental exposures, and socio-economic conditions. Whether patients of different ancestry benefit equally from ICIs remains unclear.

We studied the impact of genomic ancestry, tumor genomics, and social determinants of health (SDH) factors and factors that are impacted from SDH including recorded race/ethnicity, inferred low-income status from patient zip codes, exposure to smoking, and BMI on ICI response, defined by cancer progression-free survival (PFS, minimum 6 months FU), for non-small cell lung cancer (NSCLC) patients with MSK-IMPACT targeted panel sequencing. This FDA approved assay includes matched tumor-white blood cell sequencing to distinguish germline from somatic variants and has been applied to 1,802 NSCLC patients who received ICI treatment, including 81 and 117 patients with at least 80% of African (AFR) and East Asian (EAS) ancestry, respectively. Moreover, 173 samples were derived from admixed patients with more than one major ancestry.

We first used a natural language processing (NLP) model to obtain PFS from free-text clinical notes. A multivariable cox proportional hazard model was then used to associate PFS with ancestry, race, smoking status, ICI drug regimen, PD-L1 status, disease stage, tumor mutational burden (TMB), inferred income, and BMI. Neither genetic ancestry nor self-reported race/ethnicity was associated with the PFS. Moreover, ICI drug regimen types, low-income status, and BMI were not associated with PFS in our cohort. TMB-high was associated with longer PFS across all ancestries, although TMB was lower in patients with EAS ancestry (Median 7.9 vs. 5.3 mut/Mb, p&lt;0.001).

These results suggest that the benefits of ICI extend across ancestry, race, and income lines in a single institution, arguing for more equitable patient access to these medications. We also show that TMB is a generalizable biomarker for ICI outcome across ancestries. However, more diverse patient populations are needed to understand whether there is ancestry-specificity in other ICI outcome biomarkers.

Citation Format: Christopher J. Fong, Michele Waters, Karl Pichotta, Justin Jee, Devika R. Jutagir, David Ma, Tomin Perea-Chamblee, Susie Kim, Kanika Arora, Brooke Mastrogiacomo, Thinh Tran, Steven Maron, Mirella Altoe, Anisha Luthra, Joseph Kholodenko, Arfath Patha, Doori Rose, Michael F. Berger, Gregory J. Riely, Nikolaus Schultz, Sanna Goyert, Adam Schoenfeld, Francesca Gany, Jian Carrot-Zhang. Understanding genomic and social determinants of cancer immunotherapy outcome across ancestry. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4260.

Abstract 4259: Identification of anti-neoplastic therapy given before initial visit at a referral center using natural language processing applied to medical oncology initial consultation notes

Anticancer therapy changes tumor physiology and genomics, making it a key variable in cancer studies. Although antineoplastics given at a single institution may be available in research-ready format, treatment at external institutions prior to receiving care at academic medical centers, common among patients at these centers, is often only described in free-text clinical notes, necessitating manual curation for downstream analysis. To overcome this bottleneck, we trained and validated natural language processing (NLP) models using initial consult notes to identify whether patients had received treatment at external institutions and studied the impact of these putative treatments on tumor genomics.

Training data were derived from the AACR Project GENIE Biopharma Collaborative (BPC) for 2,663 patients at Memorial Sloan Kettering (MSK) across four cancer types. For each patient, we selected initial visits with medical and radiation oncologists based on an a priori note prioritization scheme and determined “ground-truth” prior external medications based on manually curated BPC administration records, whitelisting MSK-given medications. We trained logistic regression and clinical longformer models to identify external treatment receipt and evaluated model performance with 5-fold cross-validation. The clinical longformer model performed best across evaluation metrics, with an average area under the receiver operating characteristic curve of 0.972, macro-averaged precision/recall of 0.854/0.902 and macro-averaged F1 score of 0.876. Re-review of discrepant cases suggested that 75% of “false positives” may be due to curation error.

We used our model to infer treatment status in a pan-cancer cohort with tumor genomic profiling using our institutional sequencing platform. Out of 48,447 patients, 11,900 were predicted to have received external treatment. Patients with putative external treatment had higher alteration frequencies in resistance-related genes than untreated patients and comparable to known pre-treated patients, including ESR1 in patients with breast cancer, AR in patients with prostate cancer, and EGFR T790M in patients with EGFR-mutated non-small cell lung cancer. Patients with putative external treatments, similar to known pre-treated patients, had shorter survival compared to treatment-naïve patients of the same cancer type.

NLP can abstract external treatment status from clinical notes. When applied at scale, our model could help mitigate confounding variables and identify relationships between clinicogenomic variables and anticancer therapy.

Citation Format: Thinh N. Tran, Karl B. Pichotta, Si-Yang Liu, Christopher Fong, Anisha Luthra, Brooke Mastrogiacomo, Steven Maron, Deborah Schrag, Sohrab P. Shah, Pedram Razavi, Bob T. Li, Gregory J. Riely, Nikolaus Schultz, Justin Jee. Identification of anti-neoplastic therapy given before initial visit at a referral center using natural language processing applied to medical oncology initial consultation notes. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4259.

Abstract 5721: Automated annotation for large-scale clinicogenomic models of lung cancer treatment response and overall survival

The digitization of health records and prompt availability of tumor DNA sequencing results offer a chance to study the determinants of cancer outcomes with unprecedented richness; however, abstraction of key attributes from free text presents a major limitation to large-scale analyses. Using natural language processing (NLP), we derived sites of metastasis, prior treatment at outside institutions, programmed death ligand 1 (PD-L1) levels, and smoking status from records of patients with tumor sequencing to create a richly annotated clinicogenomic cohort. We sought to define whether combining features would improve models of overall survival (OS) and treatment response as validated in a multi-institution, manually curated cohort. We leveraged the manually curated AACR GENIE Biopharma Collaborative (BPC) dataset to train NLP algorithms to abstract the aforementioned features from overlapping records available at Memorial Sloan Kettering (MSK). All models achieved precision and recall &gt; 0.85. We deployed these algorithms to records of all MSK patients with non-small cell lung cancer (NSCLC) and tumor profiling with our FDA-authorized institutional targeted sequencing platform (N=7,015). These labels were combined with genomic, demographic, histopathologic, internal treatment and staging data to train random survival forests (RSF) to predict OS and time-to-next-treatment (TTNT) for molecularly targeted and immunotherapies. RSFs trained on the MSK NSCLC cohort were validated with the curated, non-MSK BPC NSCLC cohort (N=977). The addition of NLP-derived variables to genomic features enhanced RSF predictive power for OS (c-index, 10x bootstrap 95%CI: 0.58, 0.57-0.59 vs 0.75, 0.74-0.76 combined) and targeted and immunotherapy TTNT. The size of the MSK NSCLC cohort enabled discovery of associations between metastatic sites, PD-L1 status, genomics, and TTNTs not apparent in the smaller BPC cohort. We measured the added predictive value of variables not available in BPC with MSK-only cross-validation analyses. White blood cell differential counts and additional tissue genomic features including tumor mutational burden and fraction genome altered added minimally, while circulating tumor DNA sequencing added prognostic power for OS over other factors including disease burden

Using NLP we present a large NSCLC cohort with rich clinicoradiographic annotation, leading to superior models of patient outcomes. Our data uncovers associations not observed in smaller, manually curated cohorts and provides a foundation for further research in therapy choice and prognostication.

Citation Format: Justin Jee, Chris Fong, Karl Pichotta, Thinh Tran, Anisha Luthra, Mirella Altoe, Steven Maron, Ronglai Shen, Si-Yang Liu, Michele Waters, Joseph Kholodenko, Brooke Mastrogiacomo, Susie Kim, A Rose Brannon, Michael F. Berger, Axel Martin, Jason Chang, Anton Safonov, Jorge S. Reis-Filho, Deborah Schrag, Sohrab P. Shah, Pedram Razavi, Bob T. Li, Gregory J. Riely, Nikolaus Schultz. Automated annotation for large-scale clinicogenomic models of lung cancer treatment response and overall survival. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5721.

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.

A prospective study of dysgeusia and related symptoms in patients with multiple myeloma after autologous hematopoietic cell transplantation

BACKGROUND

Dysgeusia is a common but understudied complication in patients undergoing autologous hematopoietic cell transplantation (auto-HCT). We assessed the feasibility of using chemical gustometry (CG) to measure dysgeusia and explored its associations with symptom burden, nutrition, chemotherapy pharmacokinetics (PK), and the oral microbiome.

METHODS

We conducted a single-center, prospective feasibility study (NCT03276481) of patients with multiple myeloma undergoing auto-HCT. CG was performed longitudinally testing five flavors (sweet, sour, salty, bitter, umami) to calculate a total taste score (maximum score, 30). We measured caloric intake and patient-reported symptoms, assessing their correlation with oral microbiota composition and salivary and blood melphalan PK exposure.

RESULTS

Among all 45 patients, 39 (87%) completed at least four (>60%) and 22 (49%) completed all six CG assessments. Median total CG scores remained stable over time but were lowest at day +7 (27, range 24-30) with recovery by day +100. Symptom burden was highest by day +10 (area under the curve, 2.9; range, 1.0-4.6) corresponding with the lowest median overall caloric intake (1624 kcal; range, 1345-2267). Higher serum/salivary melphalan levels correlated with higher patient-reported dysgeusia and lower caloric intake. Oral microbiota α-diversity was stable early and increased slightly by day +100.

CONCLUSIONS

Assessment of dysgeusia by CG is feasible after auto-HCT. Most dysgeusia, symptom burden, and lowest caloric intake occurred during the blood count nadir. Higher melphalan concentrations correlated with more dysgeusia and poorer caloric intake. Future studies will aim to modulate melphalan exposure by PK-targeted dosing and characterize patient taste preferences to personalize diets for improved nutritional intake.

LAY SUMMARY

Taste changes after cancer treatments are very common. We used chemical gustometry (taste testing) to study taste changes and to better understand why patients with multiple myeloma experience this symptom after autologous hematopoietic cell transplantation. We found that taste testing was feasible, taste changes peaked when blood counts were lowest, and most patients recovered their taste by 100 days after transplantation. Taste changes correlated with lower food intake and with higher levels of chemotherapy in the body. Future work will focus on using personalized chemotherapy doses to reduce taste changes and to match patients' individual taste preferences with their diets.

A germline SNP in BRMS1 predisposes patients with lung adenocarcinoma to metastasis and can be ameliorated by targeting c-fos

About 50% of patients with early-stage, surgically resected lung cancer will develop distant metastasis. There remains an unmet need to identify patients likely to develop recurrence and to design innovative therapies to decrease this risk. Two primary isoforms of BRMS1, v1 and v2, are present in humans. Using next-generation sequencing of BRMS1 on matched human noncancerous lung tissue and non-small cell lung cancer (NSCLC) specimens, we identified single-nucleotide polymorphism (SNP) rs1052566 that results in an A273V mutation of BRMS1v2. This SNP is homozygous (BRMS1v2^A273V/A273V) in 8% of the population and correlates with aggressive biology in lung adenocarcinoma (LUAD). Mechanistically, we show that BRMS1v2 A273V abolishes the metastasis suppressor function of BRMS1v2 and promotes robust cell invasion and metastases by activation of c-fos-mediated gene-specific transcriptional regulation. BRMS1v2 A273V increases cell invasion in vitro and increases metastases in both tail-vein injection xenografts and LUAD patient-derived organoid (PDO) intracardiac injection metastasis in vivo models. Moreover, we show that BRMS1v2 A273V fails to interact with nuclear Src, thereby activating intratumoral c-fos in vitro. Higher c-fos results in up-regulation of CEACAM6, which drives metastases in vitro and in vivo. Using both xenograft and PDO metastasis models, we repurposed T5224 for treatment, a c-fos pharmacologic inhibitor investigated in clinical trials for arthritis, and observed suppression of metastases in BRMS1v2^A273V/A273V LUAD in mice. Collectively, we elucidate the mechanism of BRMS1v2^A273V/A273V-induced metastases and offer a putative therapeutic strategy for patients with LUAD who have this germline alteration.

Abstract 1158: A.I.-assisted clinical data curation to determine genomic biomarkers of cancer metastasis

While progression to metastatic disease is the main cause of cancer death, little is known about the genomic mechanisms that drive metastasis. Rapidly growing clinical genomic data sets have the potential to identify genomic biomarkers of cancer metastasis, however, manual curation of clinical data is quickly emerging as a bottleneck. To overcome this challenge, we have developed a natural language processing (NLP) pipeline to identify organs affected by metastasis from radiology reports of patients with cancer. To develop our NLP models, we leveraged the AACR GENIE Biopharma Collaborative lung and colorectal cancer datasets generated in part at Memorial Sloan Kettering Cancer Center (MSK), containing curated labels of ten metastatic disease sites derived from 31,445 corresponding free-text radiology reports (2,310 patients). Using these data, we trained three machine learning models for identifying metastatic events from clinical text, using logistic regression, convolutional neural networks (CNN), and Bidirectional Encoder Representations from Transformers (BERT). We split patients into a training set (80% of patients) and validation set (20%). The BERT model yielded superior performance across evaluation metrics, with an average per metastatic disease site area under the receiver operating characteristic curve (AUC) of 0.981, average accuracy of 97.3%, macro-average precision/recall of 85.1/85.6, and micro-average precision/recall of 87.5/89.6. We applied our method to radiology reports from 52,000 patients with tumors prospectively profiled using the MSK-IMPACT clinical sequencing cohort. A comparison with the MSK-MET cohort, which contains metastatic events derived from billing codes in a subset of 25,000 patients, showed strong concordance (79.7% of metastatic events matched), with the NLP-based method identified an average of 1.4 additional metastatic sites per patient, an expected result given the incomplete nature of the billing code data. Analyzing genomic and clinical data in this cohort, we confirmed that chromosomal instability, as inferred by the fraction of genome altered (FGA), is strongly correlated with metastatic burden (defined as the number of distinct organs affected by metastases) in several tumor types, including prostate adenocarcinoma, lung adenocarcinoma and HR-positive breast ductal carcinoma, and we identified this trend in 10 additional cancer types not previously identified, including lobular HR-positive breast carcinoma and esophageal adenocarcinoma.We demonstrate that mining of electronic health records can be used to extract rich, structured clinical information. Our models, applied at scale, offer a unique resource for the investigation of the biological basis for metastatic spread. We hope our automated clinical data extractions can enable further large-scale studies of associations between genomic biomarkers and metastatic behavior.

Citation Format: Anisha Luthra, Karl Pichotta, Brooke Mastrogiacomo, Samantha McCarthy, Steven Maron, Jianjiong Gao, Justin Jee, Christopher J. Fong, Nikolaus Schultz. A.I.-assisted clinical data curation to determine genomic biomarkers of cancer metastasis [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 1158.

Abstract 3142: BRMS1 alters the tumor inflammatory signature and immune infiltration in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths. Metastatic cancer is highly fatal which contributes to at least 90% of cancer-associated morbidities and mortalities. During metastasis, tumor cells alter their signaling cascade and interact with the immune cells in the tumor microenvironment Therefore, it is necessary to characterize and understand how these cancer cells influence tumor-infiltrating immune cells to facilitate and even enhance their ability to metastasize. BRMS1 is a metastasis suppressor gene which is frequently downregulated in LUAD. We, and others, have shown that loss of BRMS1 results in distant metastatic disease. Given the strong correlation between immune suppression, metastasis, and therapy resistance we sought to elucidate if BRMS1 contributes to these processes. Specifically, we hypothesized that LUAD cells downregulate BRMS1 which influences the immune cell composition in the tumor. This creates an immune-suppressive tumor microenvironment aiding in metastasis.

Methods: To test our hypothesis, we generated KrasG12D P53fl/fl Brms1-/- mice which spontaneously develop tumors with Ad-Cre intratracheal inoculation. To elucidate the role of BRMS1 in the context of cancer, we performed RNA sequencing on tumors from Brms1 wild type and knockout background. We used MCP counter, an in silico cellular deconvolution algorithm on our bulk RNA seq data to identify various subsets of tumor-infiltrating immune cells. Furthermore, our observations were validated by immunofluorescence and flow cytometry.

Results: Differential gene expression analysis using RNA seq data revealed a reduction in CXCL9, CCL5, CLEC1B, CCL9, CCL7 and other proinflammatory molecules in the Brms1-/- tumors. Gene ontology and gene set enrichment analysis highlighted a diminished immune response signature in Brms1-/- tumors. Hallmarks like interferon and IL6 signaling, complement and inflammation are highly enriched in Brms1+/+ tumors. MCP counter also suggested a significant increase in NK cells and reduction in CD8+ T cell population in the Brms1-/- tumors. To validate our observation, we performed immunofluorescence and flow cytometry to assess the number of infiltrating immune cells in the tumor microenvironment. Immunofluorescence data showed reduced cytotoxic T cells (CD8+ T cells) in Brms1-/- mice. The flow cytometric analysis also revealed a reduction in the proliferative potential of these CD8+ T cells along with the increased presence of myeloid-derived suppressor cells in the Brms1-/- mice.

Conclusion: Our data suggest for the first time that reduced BRMS1 expression which is generally observed in multiple tumors not only influences metastasis but also alters tumor-infiltrating immune cell composition. Thus, BRMS1 downregulation presents as one of the potential immune evasion mechanisms that could be targeted for an improved therapy outcome in LAUD.

Citation Format: Manendra B. Lankadasari, Yuan Liu, Di He, Samhita Bapat, Brooke Mastrogiacomo, Harry B. Lengel, David R. Jones. BRMS1 alters the tumor inflammatory signature and immune infiltration in lung adenocarcinoma [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 3142.

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.

Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients

Metastatic progression is the main cause of death in cancer patients, whereas the underlying genomic mechanisms driving metastasis remain largely unknown. Here, we assembled MSK-MET, a pan-cancer cohort of over 25,000 patients with metastatic diseases. By analyzing genomic and clinical data from this cohort, we identified associations between genomic alterations and patterns of metastatic dissemination across 50 tumor types. We found that chromosomal instability is strongly correlated with metastatic burden in some tumor types, including prostate adenocarcinoma, lung adenocarcinoma, and HR+/HER2+ breast ductal carcinoma, but not in others, including colorectal cancer and high-grade serous ovarian cancer, where copy-number alteration patterns may be established early in tumor development. We also identified somatic alterations associated with metastatic burden and specific target organs. Our data offer a valuable resource for the investigation of the biological basis for metastatic spread and highlight the complex role of chromosomal instability in cancer progression.

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.

Intraoperative opioid exposure, tumour genomic alterations, and survival differences in people with lung adenocarcinoma

BACKGROUND

Opioids have been linked to worse oncologic outcomes in surgical patients. Studies in certain cancer types have identified associations between survival and intra-tumoural opioid receptor gene alterations, but no study has investigated whether the tumour genome interacts with opioid exposure to affect survival. We sought to determine whether intraoperative opioid exposure is associated with recurrence-specific survival and overall survival in early-stage lung adenocarcinoma, and whether selected tumour genomics are associated with this relationship. Associations between ketamine and dexmedetomidine and outcomes were also studied.

METHODS

Surgical patients (N=740) with pathological stage I-III lung adenocarcinoma and next-generation sequencing data were retrospectively reviewed from a prospectively maintained database.

RESULTS

On multivariable analysis, ketamine administration was protective for recurrence-specific survival (hazard ratio = 0.44, 95% confidence interval 0.24-0.80; P=0.007), compared with no adjunct. Higher intraoperative oral morphine milligram equivalents were significantly associated with worse overall survival (hazard ratio=1.09/10 morphine milligram equivalents, 95% confidence interval 1.02-1.17; P=0.010). Significant interaction effects were found between morphine milligram equivalents and fraction genome altered and morphine milligram equivalents and CDKN2A, such that higher fraction genome altered or CDKN2A alterations were associated with worse overall survival at higher morphine milligram equivalents (P=0.044 and P=0.052, respectively). In contrast, alterations in the Wnt (P=0.029) and Hippo (P=0.040) oncogenic pathways were associated with improved recurrence-specific survival at higher morphine milligram equivalents, compared with unaltered pathways.

CONCLUSIONS

Intraoperative opioid exposure is associated with worse overall survival, whereas ketamine exposure is associated with improved recurrence-specific survival in patients with early-stage lung adenocarcinoma. This is the first study to investigate tumour-specific genomic interactions with intraoperative opioid administration to modify survival associations.

Pan-cancer evaluation of homologous repair deficiency somatic mutations and response to first-line anti-neoplastic therapy

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Background: Homologous recombination is a major mechanism of defective DNA repair, but it remains uncertain whether homologous repair deficient (HRD) tumors have favorable prognosis or are more/less likely to respond to treatment than tumors lacking such mutations. Objective: To determine whether lung (NSCLC) and colorectal (CRC) HRD+ tumors have better survival or response to chemotherapy than HRD- tumors. Methods: Patients with de novo stage IV NSCLC or CRC who had next generation sequencing (NGS) between 2015-2018 from one of four cancer centers were identified. Records were curated using the PRISSMM framework to ascertain treatment, overall survival (OS) and progression free survival based on imaging (PFS-I) and oncologists’ notes (PFS-M). Each NSCLC or CRC tumor was categorized as HRD+ if NGS revealed an oncogenic/likely oncogenic mutation in: ATM, BAP1, BARD1, BLM, BRCA1, BRCA2, BRIP1, CHEK2, FAM175A, FANCA, FANCC, NBN, PALB2, RAD50, RAD51, RAD51C, RTEL1, or MRE11A based on the OncoKB database. The tumor was categorized as HRD- if no oncogenic mutation in any of these genes was evident and HRD indeterminate (HRD?) if no mutation was identified but the panel did not include all genes. OS, PFS-I and PFS-M from start of first line therapy were reported by HRD status. The percentage with a good response to first line therapy (≥2x the median) and exceptional response (≥3x the median) was estimated for each endpoint. Results: For NSCLC 4% were HRD+, 59% HRD- and 37% HRD?. For CRC there were 5% HRD+, 60% HRD- and 35% HRD?. There were no significant differences for any survival endpoint between patients who were HRD+ vs HRD- in univariable analyses. The proportion of good and exceptional responders to first line systemic chemotherapy also did not vary by HRD status, though patients with HRD+ CRC were potentially more likely to be exceptional responders. Similarly, no differences between HRD+ and HRD- tumors were apparent for the subgroup receiving platinum containing therapy. Conclusions: NSCLC and CRC patients with somatic mutations in HRD oncogenic genes did not differ from patients lacking such a mutation with respect to OS or PFS. CRC patients with HRD+ tumors may be more likely to be exceptional responders, but sample sizes are limited. By May, the analysis will include breast and pancreatic cancer cases.[Table: see text]

KRAS G12C Mutation Is Associated with Increased Risk of Recurrence in Surgically Resected Lung Adenocarcinoma

PURPOSE

KRAS ^G12C is the most common KRAS mutation in primary lung adenocarcinoma. Phase I clinical trials have demonstrated encouraging clinical activity of KRAS ^G12C inhibitors in the metastatic setting. We investigated disease-free survival (DFS) and tumor genomic features in patients with surgically resected KRAS ^G12C-mutant lung adenocarcinoma.

EXPERIMENTAL DESIGN

Patients who underwent resection of stage I-III lung adenocarcinoma and next-generation sequencing (NGS) were evaluated. Exclusion criteria were receipt of induction therapy, incomplete resection, and low-quality NGS. Mutations were classified as KRAS wild-type (KRAS ^wt), G12C (KRAS ^G12C), or non-G12C (KRAS ^other). DFS was compared between groups using the log-rank test; factors associated with DFS were assessed using Cox regression. Mutual exclusivity and cooccurrence, tumor clonality, and mutational signatures were assessed.

RESULTS

In total, 604 patients were included: 374 KRAS ^wt (62%), 95 KRAS ^G12C (16%), and 135 KRAS ^other (22%). Three-year DFS was not different between KRAS-mutant and KRAS ^wt tumors. However, 3-year DFS was worse in patients with KRAS ^G12C than KRAS ^other tumors (log-rank P = 0.029). KRAS ^G12C tumors had more lymphovascular invasion (51% vs. 37%; P = 0.032) and higher tumor mutation burden [median (interquartile range), 7.0 (5.3-10.8) vs. 6.1 (3.5-9.7); P = 0.021], compared with KRAS ^other tumors. KRAS ^G12C mutation was independently associated with worse DFS on multivariable analysis. Our DFS findings were externally validated in an independent The Cancer Genome Atlas cohort.

CONCLUSIONS

KRAS ^G12C mutations are associated with worse DFS after complete resection of stage I-III lung adenocarcinoma. These tumors harbor more aggressive clinicopathologic and genomic features than other KRAS-mutant tumors. We identified a high-risk group for whom KRAS ^G12C inhibitors may be investigated to improve survival.

The Underlying Tumor Genomics of Predominant Histologic Subtypes in Lung Adenocarcinoma

INTRODUCTION

The purpose of the study is to genomically characterize the biology and related therapeutic opportunities of prognostically important predominant histologic subtypes in lung adenocarcinoma (LUAD).

METHODS

We identified 604 patients with stage I to III LUAD who underwent complete resection and targeted next-generation sequencing using the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets platform. Tumors were classified according to predominant histologic subtype and grouped by architectural grade (lepidic [LEP], acinar or papillary [ACI/PAP], and micropapillary or solid [MIP/SOL]). Associations among clinicopathologic factors, genomic features, mutational signatures, and recurrence were evaluated within subtypes and, when appropriate, quantified using competing-risks regression, with adjustment for pathologic stage and extent of resection.

RESULTS

MIP/SOL tumors had higher tumor mutational burden (p < 0.001), fraction of genome altered (p = 0.001), copy number amplifications (p = 0.021), rate of whole-genome doubling (p = 0.008), and number of oncogenic pathways altered ( p < 0.001) as compared with LEP and ACI/PAP tumors. Across all tumors, mutational signatures attributed to APOBEC activity were associated with the highest risk of postresection recurrence: SBS2 (p = 0.021) and SBS13 (p = 0.005). Three oncogenic pathways (p53, Wnt, Myc) were altered with statistical significance in MIP/SOL tumors. Compared with LEP and ACI/PAP tumors, MIP/SOL tumors had a higher frequency of targetable BRAF-V600E mutations (p = 0.046). Among ACI/PAP tumors, alterations in the cell cycle (p < 0.001) and PI3K (p = 0.002) pathways were associated with recurrence; among MIP/SOL tumors, only PI3K alterations were associated with recurrence (p = 0.049).

CONCLUSIONS

These results provide the first in-depth assessment of tumor genomic profiling of predominant LUAD histologic subtypes, their associations with recurrence, and their correlation with targetable driver alterations in patients with surgically resected LUAD.