Real-World Safety of Niraparib for Maintenance Treatment of Ovarian Cancer in Canada.

Niraparib was recently funded in Canada for the maintenance treatment of ovarian cancer following platinum-based chemotherapy. However, the drug's safety profile in the real world remains uncertain. We conducted a cohort study to describe the patient population using niraparib and the proportion that experienced adverse events between June 2019 and December 2022 in four Canadian provinces (Ontario, Alberta, British Columbia [BC], and Quebec). We used administrative data and electronic medical records from Ontario Health, Alberta Health Services, and BC Cancer, and registry data from Exactis Innovation. We summarized baseline characteristics using descriptive statistics and reported safety outcomes using cumulative incidence. We identified 514 patients receiving niraparib. Mean age was 67 years and most were initiated on a daily dose of 100 or 200 mg/day. Grade 3/4 anemia, neutropenia, and thrombocytopenia occurred in 11-16% of the cohort. In Ontario, the three-month cumulative incidence of grade 3/4 thrombocytopenia was 11.6% (95% CI, 8.3-15.4%), neutropenia was 7.1% (95% CI, 4.6-10.4%), and anemia was 11.3% (95% CI, 8.0-15.2%). Cumulative incidences in the remaining provinces were similar. Initial daily dose and proportions of hematological adverse events were low in the real world and may be related to cautious prescribing and close monitoring by clinicians.

Performance of Large Language Models on Medical Oncology Examination Questions.

Importance: Large language models (LLMs) recently developed an unprecedented ability to answer questions. Studies of LLMs from other fields may not generalize to medical oncology, a high-stakes clinical setting requiring rapid integration of new information. Objective: To evaluate the accuracy and safety of LLM answers on medical oncology examination questions. Design, Setting, and Participants: This cross-sectional study was conducted between May 28 and October 11, 2023. The American Society of Clinical Oncology (ASCO) Oncology Self-Assessment Series on ASCO Connection, the European Society of Medical Oncology (ESMO) Examination Trial questions, and an original set of board-style medical oncology multiple-choice questions were presented to 8 LLMs. Main Outcomes and Measures: The primary outcome was the percentage of correct answers. Medical oncologists evaluated the explanations provided by the best LLM for accuracy, classified the types of errors, and estimated the likelihood and extent of potential clinical harm. Results: Proprietary LLM 2 correctly answered 125 of 147 questions (85.0%; 95% CI, 78.2%-90.4%; P < .001 vs random answering). Proprietary LLM 2 outperformed an earlier version, proprietary LLM 1, which correctly answered 89 of 147 questions (60.5%; 95% CI, 52.2%-68.5%; P < .001), and the best open-source LLM, Mixtral-8x7B-v0.1, which correctly answered 87 of 147 questions (59.2%; 95% CI, 50.0%-66.4%; P < .001). The explanations provided by proprietary LLM 2 contained no or minor errors for 138 of 147 questions (93.9%; 95% CI, 88.7%-97.2%). Incorrect responses were most commonly associated with errors in information retrieval, particularly with recent publications, followed by erroneous reasoning and reading comprehension. If acted upon in clinical practice, 18 of 22 incorrect answers (81.8%; 95% CI, 59.7%-94.8%) would have a medium or high likelihood of moderate to severe harm. Conclusions and Relevance: In this cross-sectional study of the performance of LLMs on medical oncology examination questions, the best LLM answered questions with remarkable performance, although errors raised safety concerns. These results demonstrated an opportunity to develop and evaluate LLMs to improve health care clinician experiences and patient care, considering the potential impact on capabilities and safety.

Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence.

Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphologic features were quantified to enhance patient risk stratification within DNA mismatch repair (MMR) groups using deep learning. Using a quantitative segmentation algorithm (QuantCRC) that identifies 15 distinct morphologic features, we analyzed 402 resected stage III colon carcinomas [191 deficient (d)-MMR; 189 proficient (p)-MMR] from participants in a phase III trial of FOLFOX-based adjuvant chemotherapy. Results were validated in an independent cohort (176 d-MMR; 1,094 p-MMR). Association of morphologic features with clinicopathologic variables, MMR, KRAS, BRAFV600E, and time-to-recurrence (TTR) was determined. Multivariable Cox proportional hazards models were developed to predict TTR. Tumor morphologic features differed significantly by MMR status. Cancers with p-MMR had more immature desmoplastic stroma. Tumors with d-MMR had increased inflammatory stroma, epithelial tumor-infiltrating lymphocytes (TIL), high-grade histology, mucin, and signet ring cells. Stromal subtype did not differ by BRAFV600E or KRAS status. In p-MMR tumors, multivariable analysis identified tumor-stroma ratio (TSR) as the strongest feature associated with TTR [HRadj 2.02; 95% confidence interval (CI), 1.14-3.57; P = 0.018; 3-year recurrence: 40.2% vs. 20.4%; Q1 vs. Q2-4]. Among d-MMR tumors, extent of inflammatory stroma (continuous HRadj 0.98; 95% CI, 0.96-0.99; P = 0.028; 3-year recurrence: 13.3% vs. 33.4%, Q4 vs. Q1) and N stage were the most robust prognostically. Association of TSR with TTR was independently validated. In conclusion, QuantCRC can quantify morphologic differences within MMR groups in routine tumor sections to determine their relative contributions to patient prognosis, and may elucidate relevant pathophysiologic mechanisms driving prognosis. SIGNIFICANCE: A deep learning algorithm can quantify tumor morphologic features that may reflect underlying mechanisms driving prognosis within MMR groups. TSR was the most robust morphologic feature associated with TTR in p-MMR colon cancers. Extent of inflammatory stroma and N stage were the strongest prognostic features in d-MMR tumors. TIL density was not independently prognostic in either MMR group.

Machine Learning to Allocate Palliative Care Consultations During Cancer Treatment.

PURPOSE: For patients with advanced cancer, early consultations with palliative care (PC) specialists reduce costs, improve quality of life, and prolong survival. However, capacity limitations prevent all patients from receiving PC shortly after diagnosis. We evaluated whether a prognostic machine learning system could promote early PC, given existing capacity. METHODS: Using population-level administrative data in Ontario, Canada, we assembled a cohort of patients with incurable cancer who received palliative-intent systemic therapy between July 1, 2014, and December 30, 2019. We developed a machine learning system that predicted death within 1 year of each treatment using demographics, cancer characteristics, treatments, symptoms, laboratory values, and history of acute care admissions. We trained the system in patients who started treatment before July 1, 2017, and evaluated the potential impact of the system on PC in subsequent patients. RESULTS: Among 560,210 treatments received by 54,628 patients, death occurred within 1 year of 45.2% of treatments. The machine learning system recommended the same number of PC consultations observed with usual care at the 60.0% 1-year risk of death, with a first-alarm positive predictive value of 69.7% and an outcome-level sensitivity of 74.9%. Compared with usual care, system-guided care could increase early PC by 8.5% overall (95% CI, 7.5 to 9.5; P < .001) and by 15.3% (95% CI, 13.9 to 16.6; P < .001) among patients who live 6 months beyond their first treatment, without requiring more PC consultations in total or substantially increasing PC among patients with a prognosis exceeding 2 years. CONCLUSION: Prognostic machine learning systems could increase early PC despite existing resource constraints. These results demonstrate an urgent need to deploy and evaluate prognostic systems in real-time clinical practice to increase access to early PC.

High grade adverse event reporting and enrolment in gynecologic oncology clinical trials.

OBJECTIVE: The primary objective is to assess factors associated with treatment related high grade (CTCAE grade ≥ 3) adverse event (AE) reporting among participants in gynecologic oncology clinical trials. METHODS: All AEs recorded in the Princess Margaret Clinical Trial adverse event database between 01/2016 and 12/2018 were evaluated. Gynecologic oncology clinical trials assessing systemic therapy were included. Inferential statistics on risk factors of related grade ≥ 3 adverse event reporting and GEE logistic models with Odds Ratios (OR) were performed. Multivariable analysis adjusting for age, clinical trial phase, sponsor, and therapy type. RESULTS: The gynecology cancer clinical trials accrued 317 unique patients (359 nested on trials) in 42 systemic therapy trials. In the period, 17,175 related AEs were reported in the gynecological cancer trials, 7.4% were grade ≥ 3. On multivariable analysis, no odds differences of grade ≥ 3 related AEs were detected according to study phase. Patients in immunotherapy clinical trials had lower odds of related grade ≥ 3 AEs than patients on targeted or other therapy (adjusted OR [aOR] 0.43; 95% CI 0.24-0.75). There was greater odds of related grade ≥ 3 AEs in clinical trials assessing combination vs single therapeutics (aOR 2.26, 95% CI 1.34-3.80). Patients aged ≥65 (aOR 1.77; 95% CI 1.08-2.89) had greater odds of related grade ≥ 3 AEs than patients aged 50 to 65 years. When compared to other disease sites, the odds of having a grade  ≥ 3 related AE reported in gynecology clinical trials was no different. CONCLUSIONS: In this cohort, factors influencing the odds of related grade ≥ 3 AE reporting in gynecologic trials included type of therapy and age. The study phase did not correlate with odds of high-grade AE reporting.

Circulating Oncometabolite 2-hydroxyglutarate as a Potential Biomarker for Isocitrate Dehydrogenase (IDH1/2) Mutant Cholangiocarcinoma.

Isocitrate dehydrogenase (IDH) enzymes catalyze the decarboxylation of isocitrate to alpha-ketoglutarate (αKG). IDH1/2 mutations preferentially convert αKG to R-2-hydroxyglutarate (R2HG), resulting in R2HG accumulation in tumor tissues. We investigated circulating 2-hydroxyglutate (2HG) as potential biomarkers for patients with IDH-mutant (IDHmt) cholangiocarcinoma (CCA). R2HG and S-2-hydroxyglutarate (S2HG) levels in blood and tumor tissues were analyzed in a discovery cohort of patients with IDHmt glioma and CCA. Results were validated in cohorts of patients with CCA and clear-cell renal cell carcinoma. The R2HG/S2HG ratio (rRS) was significantly elevated in tumor tissues, but not in blood for patients with IDHmt glioma, while circulating rRS was elevated in patients with IDHmt CCA. There were overlap distributions of circulating R2HG and total 2HG in patients with both IDHmt and wild-type (IDHwt) CCA, while there was minimal overlap in rRS values between patients with IDHmt and IDHwt CCA. Using the rRS cut-off value of 1.5, the sensitivity of rRS was 90% and specificity was 96.8%. Circulating rRS is significantly increased in patients with IDHmt CCA compare with patients with IDHwt CCA. Circulating rRS is a sensitive and specific surrogate biomarker for IDH1/2 mutations in CCA. It can potentially be used as a tool for monitoring IDH-targeted therapy.

Causal relationships between risk of venous thromboembolism and 18 cancers: a bidirectional Mendelian randomization analysis.

BACKGROUND: People with cancer experience high rates of venous thromboembolism (VTE). Risk of subsequent cancer is also increased in people experiencing their first VTE. The causal mechanisms underlying this association are not completely understood, and it is unknown whether VTE is itself a risk factor for cancer. METHODS: We used data from large genome-wide association study meta-analyses to perform bidirectional Mendelian randomization analyses to estimate causal associations between genetic liability to VTE and risk of 18 different cancers. RESULTS: We found no conclusive evidence that genetic liability to VTE was causally associated with an increased incidence of cancer, or vice versa. We observed an association between liability to VTE and pancreatic cancer risk [odds ratio for pancreatic cancer: 1.23 (95% confidence interval: 1.08-1.40) per log-odds increase in VTE risk, P = 0.002]. However, sensitivity analyses revealed this association was predominantly driven by a variant proxying non-O blood group, with inadequate evidence to suggest a causal relationship. CONCLUSIONS: These findings do not support the hypothesis that genetic liability to VTE is a cause of cancer. Existing observational epidemiological associations between VTE and cancer are therefore more likely to be driven by pathophysiological changes which occur in the setting of active cancer and anti-cancer treatments. Further work is required to explore and synthesize evidence for these mechanisms.

Adjuvant and neoadjuvant therapies for hepatocellular carcinoma.

Immune-oncology-based regimens have shown efficacy in advanced HCC and have been implemented as standard of care as first-line therapy. Their efficacy, including high response rates, and safety justify their evaluation in earlier disease stages. Following negative results for adjuvant sorafenib in the global STORM trial in 2015, 4 global phase 3 trials, featuring different immune checkpoint inhibitor combinations, entered in parallel the race in the adjuvant setting. The IMbrave050 trial, comparing adjuvant atezolizumab in combination with bevacizumab to active surveillance following curative-intent resection or ablation, was the first to report, fast-tracking the results of the first interim analysis and demonstrating an improvement in recurrence-free survival. The trial has provoked a discussion on the horizon of expectations from adjuvant treatment and the clinical relevance of efficacy endpoints. Moreover, major pathological responses reported from early phase 2 data in the neoadjuvant setting provide a strong rationale for the evaluation of these concepts in phase 3 trials. In this review, we summarize current evidence and outline future directions for systemic therapies in early-stage HCC.

Machine Learning-Based Early Warning Systems for Acute Care Utilization During Systemic Therapy for Cancer.

BACKGROUND: Emergency department visits and hospitalizations frequently occur during systemic therapy for cancer. We developed and evaluated a longitudinal warning system for acute care use. METHODS: Using a retrospective population-based cohort of patients who started intravenous systemic therapy for nonhematologic cancers between July 1, 2014, and June 30, 2020, we randomly separated patients into cohorts for model training, hyperparameter tuning and model selection, and system testing. Predictive features included static features, such as demographics, cancer type, and treatment regimens, and dynamic features, such as patient-reported symptoms and laboratory values. The longitudinal warning system predicted the probability of acute care utilization within 30 days after each treatment session. Machine learning systems were developed in the training and tuning cohorts and evaluated in the testing cohort. Sensitivity analyses considered feature importance, other acute care endpoints, and performance within subgroups. RESULTS: The cohort included 105,129 patients who received 1,216,385 treatment sessions. Acute care followed 182,444 (15.0%) treatments within 30 days. The ensemble model achieved an area under the receiver operating characteristic curve of 0.742 (95% CI, 0.739-0.745) and was well calibrated in the test cohort. Important predictive features included prior acute care use, treatment regimen, and laboratory tests. If the system was set to alarm approximately once every 15 treatments, 25.5% of acute care events would be preceded by an alarm, and 47.4% of patients would experience acute care after an alarm. The system underestimated risk for some treatment regimens and potentially underserved populations such as females and non-English speakers. CONCLUSIONS: Machine learning warning systems can detect patients at risk for acute care utilization, which can aid in preventive intervention and facilitate tailored treatment. Future research should address potential biases and prospectively evaluate impact after system deployment.

Causal relationships between risk of venous thromboembolism and 18 cancers: a bidirectional Mendelian randomisation analysis.

Background: People with cancer experience high rates of venous thromboembolism (VTE). Additionally, risk of subsequent cancer is increased in people experiencing their first VTE. The causal mechanisms underlying this association are not completely understood, and it is unknown whether VTE is itself a risk factor for cancer. Methods: We used data from large genome-wide association study meta-analyses to perform bi-directional Mendelian randomisation analyses to estimate causal associations between genetically-proxied lifetime risk of VTE and risk of 18 different cancers. Results: We found no conclusive evidence that genetically-proxied lifetime risk of VTE was causally associated with an increased incidence of cancer, or vice-versa. We observed an association between VTE and pancreatic cancer risk (odds ratio for pancreatic cancer 1.23 (95% confidence interval 1.08 - 1.40) per log-odds increase in risk of VTE, P = 0.002). However, sensitivity analyses revealed this association was predominantly driven by a variant proxying non-O blood group, with inadequate evidence from Mendelian randomisation to suggest a causal relationship. Conclusions: These findings do not support the hypothesis that genetically-proxied lifetime risk of VTE is a cause of cancer. Existing observational epidemiological associations between VTE and cancer are therefore more likely to be driven by pathophysiological changes which occur in the setting of active cancer and anti-cancer treatments. Further work is required to explore and synthesise evidence for these mechanisms.

Systemic inflammatory prognostic scores in advanced pancreatic adenocarcinoma.

BACKGROUND: Systemic inflammatory scores may aid prognostication and patient selection for trials. We compared five scores in advanced pancreatic adenocarcinoma (PDAC). METHODS: Unresectable/metastatic PDAC patients enrolled in the Comprehensive Molecular Characterisation of Advanced Pancreatic Ductal Adenocarcinoma for Better Treatment Selection trial (NCT02750657) were included. Patients had pre-treatment biopsies for whole genome and RNA sequencing. CD8 immunohistochemistry was available in a subset. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio, Prognostic Nutritional Index, Gustave Roussy Immune Score (GRIm-S), and Memorial Sloan Kettering Prognostic Score (MPS) were calculated. Overall survival (OS) was estimated using Kaplan-Meier methods. Associations between inflammatory scores, clinical/genomic characteristics, and OS were analysed. RESULTS: We analysed 263 patients. High-risk NLR, GRIm-S and MPS were poorly prognostic. The GRIm-S had the highest predictive ability: median OS 6.4 vs. 10 months for high risk vs. low-risk (P < 0.001); HR 2.26 (P < 0.001). ECOG ≥ 1, the basal-like subtype, and low-HRDetect were additional poor prognostic factors (P < 0.01). Inflammatory scores did not associate with RNA-based classifiers or homologous recombination repair deficiency genotypes. High-risk MPS (P = 0.04) and GRIm-S (P = 0.02) patients had lower median CD8 + tumour-infiltrating lymphocytes. CONCLUSIONS: Inflammatory scores incorporating NLR have prognostic value in advanced PDAC. Understanding immunophenotypes of poor-risk patients and using these scores in trials will advance the field.

Whole-genome sequencing of 20 cholangiocarcinoma cases reveals unique profiles in patients with cirrhosis and primary sclerosing cholangitis.

Background: Cholangiocarcinoma (CCA) is a molecularly heterogenous disease that is often fatal. Whole genome sequencing (WGS) can provide additional knowledge of mutational spectra compared with panel sequencing. We describe the molecular landscape of CCA using whole-genome sequencing and compare the mutational landscape between short-term and long-term survivors. Methods: We explored molecular differences between short-term and long-term survivors by performing WGS on 20 patient samples from our biliary tract cancer database. Short-term survivors were enriched for cases with underlying primary sclerosing cholangitis (PSC) and patients with cirrhosis. All samples underwent tumour epithelial enrichment using laser capture microdissection (LCM). Results: Dominant single base substitution (SBS) signatures across the cohort included SBS1 and SBS5, with the latter more prevalent in long-term survivors. SBS17 was evident in 3 cases, all of whom had underlying ulcerative colitis (UC) with PSC. Additional rare signatures included SBS3 in a patient treated for prior mantle cell lymphoma and SBS26/SBS6 in a patient with a tumor mutational burden of 33 mutations/Mb and a pathogenic MLH1 germline mutation. Somatic TP53 inactivating mutations were present in 8/10 (80%) short-term survivors and in none of the long-term survivors. Additional mutations occurred in KRAS, SMAD4, CDKN2A, and chromatin remodelling genes. The long-term survivor group harboured predicted fusions in FGFR (n=2) and pathogenic mutations in BRAF and IDH1 (n=2). Conclusions: TP53 alterations are associated with poor outcomes in patients with CCA. Patients with underlying inflammatory/autoimmune conditions may be enriched for unique tumour mutational signatures.

Molecular characterisation of pancreatic ductal adenocarcinoma with NTRK fusions and review of the literature.

AIMS: The majority of pancreatic ductal adenocarcinomas (PDACs) harbour oncogenic mutations in KRAS with variants in TP53, CDKN2A and SMAD4 also prevalent. The presence of oncogenic fusions including NTRK fusions are rare but important to identify. Here we ascertain the prevalence of NTRK fusions and document their genomic characteristics in a large series of PDAC. METHODS: Whole genome sequencing and RNAseq were performed on a series of patients with resected or locally advanced/metastatic PDAC collected between 2008 and 2020 at a single institution. A subset of specimens underwent immunohistochemistry (IHC) analysis. Clinical and molecular characterisation and IHC sensitivity and specificity were evaluated. RESULTS: 400 patients were included (resected n=167; locally advanced/metastatic n=233). Three patients were identified as harbouring an NTRK fusion, two EML4-NTRK3 (KRAS-WT) and a single novel KANK1-NTRK3 fusion. The latter occurring in the presence of a subclonal KRAS mutation. Typical PDAC drivers were present including mutations in TP53 and CDKN2A. Substitution base signatures and tumour mutational burden were similar to typical PDAC. The prevalence of NTRK fusions was 0.8% (3/400), while in KRAS wild-type tumours, it was 6.25% (2/32). DNA prediction alone documented six false-positive cases. RNA analysis correctly identified the in-frame fusion transcripts. IHC analysis was negative in the KANK1-NTRK3 fusion but positive in a EML4-NTRK3 case, highlighting lower sensitivity of IHC. CONCLUSION: NTRK fusions are rare; however, with emerging therapeutic options targeting these fusions, detection is vital. Reflex testing for KRAS mutations and subsequent RNA-based screening could help identify these cases in PDAC.

Demographics, pattern of practice and clinical outcomes in rectal squamous cell carcinoma.

AIM: The aim of this study was to describe the baseline clinical features, treatment patterns and outcomes in rectal squamous cell carcinoma (SCC). METHOD: This is a retrospective study of patients with rectal SCC treated at the Princess Margaret Cancer Centre (Toronto, Canada) between 1 January 1995 and 31 December 2020. Clinical factors associated with locoregional failure (LRF), distant metastases (DM), disease-free survival (DFS) and overall survival (OS), such as age, sex, HIV status, T-category, nodal status, grade and primary treatment, were investigated with univariate analysis (UVA). RESULTS: Twenty nine patients with rectal SCC were analysed with a median follow-up of 7.4 years (range 0.3-20.4 years). The median age at diagnosis was 52 years, with the majority presenting with clinical T3 disease or higher (n = 21, 72%) and positive regional lymph nodes (n = 16, 55%), while more than quarter of patients (28%) had metastatic disease. Definitive chemoradiation was the treatment modality of choice in more than half of all cases (n = 17, 59%) with a response rate of 100%. The 10-year cumulative incidence of LRF and DM was, respectively, 12% (95% CI 1.8%-32.9%) and 31% (95% CI: 12.0%-52.6%). The 5- and 10-year OS was 82% (95% CI 66.1%-100%). UVA revealed a trend towards an association of male gender (hazard ratio = 4.65, 95% CI 0.9%-24.1; p = 0.067) and primary surgical treatment (hazard ratio = 0.76, 95% CI 0.09-6.34; p = 0.061) with DFS. CONCLUSION: Definitive chemoradiation is an effective and preferred treatment for rectal SCC allowing for sphincter preservation with complete clinical response observed in all patients.

hENT1 Expression Predicts Response to Gemcitabine and Nab-Paclitaxel in Advanced Pancreatic Ductal Adenocarcinoma.

PURPOSE: Modified FOLFIRINOX (mFFX) and gemcitabine/nab-paclitaxel (GnP) remain standard first-line options for patients with advanced pancreatic ductal adenocarcinoma (PDAC). Human equilibrative nucleoside transporter 1 (hENT1) was hypothesized to be a biomarker of gemcitabine in the adjuvant setting, with conflicting results. In this study, we explore hENT1 mRNA expression as a predictive biomarker in advanced PDAC. EXPERIMENTAL DESIGN: COMPASS was a prospective observational trial of patients with advanced PDAC. A biopsy was required prior to initiating chemotherapy, as determined by treating physician. Biopsies underwent laser capture microdissection prior to whole genome and RNA sequencing. The cut-off thresholds for hENT1 expression were determined using the maximal χ2 statistic. RESULTS: 253 patients were included in the analyses with a median follow-up of 32 months, with 138 patients receiving mFFX and 92 receiving GnP. In the intention to treat population, median overall survival (OS) was 10.0 months in hENT1high versus 7.9 months in hENT1low (P = 0.02). In patients receiving mFFX, there was no difference in overall response rate (ORR; 35% vs. 28%, P = 0.56) or median OS (10.6 vs. 10.5 months, P = 0.45). However, in patients treated with GnP, the ORR was significantly higher in hENT1high compared with hENT1low tumors (43% vs. 21%, P = 0.038). Median OS in this GnP-treated cohort was 10.6 months in hENT1high versus 6.7 months hENT1low (P < 0.001). In an interaction analysis, hENT1 was predictive of treatment response to GnP (interaction P = 0.002). CONCLUSIONS: In advanced PDAC, hENT1 mRNA expression predicts ORR and OS in patients receiving GnP.

Quantitative Pathologic Analysis of Digitized Images of Colorectal Carcinoma Improves Prediction of Recurrence-Free Survival.

BACKGROUND & AIMS: To examine whether quantitative pathologic analysis of digitized hematoxylin and eosin slides of colorectal carcinoma (CRC) correlates with clinicopathologic features, molecular alterations, and prognosis. METHODS: A quantitative segmentation algorithm (QuantCRC) was applied to 6468 digitized hematoxylin and eosin slides of CRCs. Fifteen parameters were recorded from each image and tested for associations with clinicopathologic features and molecular alterations. A prognostic model was developed to predict recurrence-free survival using data from the internal cohort (n = 1928) and validated on an internal test (n = 483) and external cohort (n = 938). RESULTS: There were significant differences in QuantCRC according to stage, histologic subtype, grade, venous/lymphatic/perineural invasion, tumor budding, CD8 immunohistochemistry, mismatch repair status, KRAS mutation, BRAF mutation, and CpG methylation. A prognostic model incorporating stage, mismatch repair, and QuantCRC resulted in a Harrell's concordance (c)-index of 0.714 (95% confidence interval [CI], 0.702-0.724) in the internal test and 0.744 (95% CI, 0.741-0.754) in the external cohort. Removing QuantCRC from the model reduced the c-index to 0.679 (95% CI, 0.673-0.694) in the external cohort. Prognostic risk groups were identified, which provided a hazard ratio of 2.24 (95% CI, 1.33-3.87, P = .004) for low vs high-risk stage III CRCs and 2.36 (95% CI, 1.07-5.20, P = .03) for low vs high-risk stage II CRCs, in the external cohort after adjusting for established risk factors. The predicted median 36-month recurrence rate for high-risk stage III CRCs was 32.7% vs 13.4% for low-risk stage III and 15.8% for high-risk stage II vs 5.4% for low-risk stage II CRCs. CONCLUSIONS: QuantCRC provides a powerful adjunct to routine pathologic reporting of CRC. A prognostic model using QuantCRC improves prediction of recurrence-free survival.

Is Biannual Surveillance for Pancreatic Cancer Sufficient in Individuals With Genetic Syndromes or Familial Pancreatic Cancer?

BACKGROUND: Individuals with a family history of pancreatic adenocarcinoma (PC) or with a germline mutation in a PC susceptibility gene are at increased risk of developing PC. These high-risk individuals (HRIs) may benefit from PC surveillance. METHODS: A PC surveillance program was developed to evaluate the detection of premalignant lesions and early-stage PCs using biannual imaging and to determine whether locally advanced or metastatic PCs develop despite biannual surveillance. From January 2013 to April 2020, asymptomatic HRIs were enrolled and followed with alternating MRI and endoscopic ultrasound every 6 months. RESULTS: Of 75 HRIs, 43 (57.3%) had a germline mutation in a PC susceptibility gene and 32 (42.7%) had a familial pancreatic cancer (FPC) pedigree. Branch-duct intraductal papillary mucinous neoplasms (BD-IPMNs) were identified in 26 individuals (34.7%), but only 2 developed progressive lesions. One patient with Peutz-Jeghers syndrome (PJS) developed locally advanced PC arising from a BD-IPMN. Whole-genome sequencing of this patient's PC and of a second patient with PJS-associated PC from the same kindred revealed biallelic inactivation of STK11 in a KRAS-independent manner. A review of 3,853 patients from 2 PC registries identified an additional patient with PJS-associated PC. All 3 patients with PJS developed advanced PC consistent with the malignant transformation of an underlying BD-IPMN in <6 months. The other surveillance patient with a progressive lesion had FPC and underwent resection of a mixed-type IPMN that harbored polyclonal KRAS mutations. CONCLUSIONS: PC surveillance identifies a high prevalence of BD-IPMNs in HRIs. Patients with PJS with BD-IPMNs may be at risk for accelerated malignant transformation.

Intra-Tumoral CD8+ T-Cell Infiltration and PD-L1 Positivity in Homologous Recombination Deficient Pancreatic Ductal Adenocarcinoma.

The immune contexture of pancreatic ductal adenocarcinoma (PDAC) is generally immunosuppressive. A role for immune checkpoint inhibitors (ICIs) in PDAC has only been demonstrated for the rare and hypermutated mismatch repair (MMR) deficient (MMR-d) subtype. Homologous recombination repair (HR) deficient (HR-d) PDAC is more prevalent and may encompass up to 20% of PDAC. Its genomic instability may promote a T-cell mediated anti-tumor response with therapeutic sensitivity to ICIs. To investigate the immunogenicity of HR-d PDAC, we used multiplex immunohistochemistry (IHC) to compare the density and spatial distribution of CD8+ cytotoxic T-cells, FOXP3+ regulatory T-cells (Tregs), and CD68+ tumor-associated macrophages (TAMs) in HR-d versus HR/MMR-intact PDAC. We also evaluated the IHC positivity of programmed death-ligand 1 (PD-L1) across the subgroups. 192 tumors were evaluated and classified as HR/MMR-intact (n=166), HR-d (n=25) or MMR-d (n=1) based on germline testing and tumor molecular hallmarks. Intra-tumoral CD8+ T-cell infiltration was higher in HR-d versus HR/MMR-intact PDAC (p<0.0001), while CD8+ T-cell densities in the peri-tumoral and stromal regions were similar in both groups. HR-d PDAC also displayed increased intra-tumoral FOXP3+ Tregs (p=0.049) and had a higher CD8+:FOXP3+ ratio (p=0.023). CD68+ TAM expression was similar in HR-d and HR/MMR-intact PDAC. Finally, 6 of the 25 HR-d cases showed a PD-L1 Combined Positive Score of >=1, whereas none of the HR/MMR-intact cases met this threshold (p<0.00001). These results provide immunohistochemical evidence for intra-tumoral CD8+ T-cell enrichment and PD-L1 positivity in HR-d PDAC, suggesting that HR-d PDAC may be amenable to ICI treatment strategies.

Phase II clinical trial of cabozantinib for the treatment of recurrent hepatocellular carcinoma after liver transplantation.

Recurrent hepatocellular carcinoma (HCC) develops in 15-20% of liver transplant recipients, and it tends to be more aggressive due to underlying immunosuppression. The multikinase inhibitor cabozantinib has been shown to be effective for the treatment of advanced HCC. However, there is no study evaluating this medication in patients with recurrent HCC. Adult patients with measurable biopsy-proven recurrent HCC are eligible for enrollment provided they are not amenable to curative treatments and no prior treatment with cabozantinib. In this study, 60 mg once daily cabozantinib will be administered orally. Participants will receive study treatment as long as they continue to experience clinical benefit or until there is unacceptable toxicity. Tumor measurements will be repeated every 8 weeks to evaluate response. The primary end point of this study will be the disease control rate at 4 months after treatment. The secondary end points will be overall survival, progression-free survival and safety profile of cabozantinib. Furthermore, potential biomarkers will be evaluated to identify their role in tumor progression. The total duration of this trial is expected to be 3 years. We anticipate that this trial will show the effectiveness and safety of cabozantinib in the treatment of post-liver transplant recurrent HCC. Cabozantinib is expected to be an effective treatment due to its activity against many protein kinases, including MET and AXL which are not inhibited by sorafenib.

Liver cancer is the sixth most diagnosed cancer worldwide with few available curative treatments. Liver transplantation (LT) is considered as one of the treatments for liver cancer especially in earlier stages of cancer. However, after LT, cancer develops again in 15­20% of the patients who undergo transplant for liver cancer. Compared with liver cancer in the nontransplant population, recurrent cancer grows faster and spreads in the body very quickly. Therefore, unfortunately, to date there are limited treatment options for these patients without significant effect on their survival. In this study, we aim to evaluate the effect of a new medication called cabozantinib on patients who develop recurrent liver cancer after their LT. Cabozantinib has been already tested in patients with liver cancer and was shown to be effective and safe in nontransplant patients. However, this is the first study to evaluate the effect of cabozantinib in liver transplant recipients with recurrent liver cancer. Clinical Trial Registration: NCT04204850 (ClinicalTrials.gov).