Sergio Latorre, M.D.

Internal Medicine | Clinical Reasoning | Interested in Hematology | 🇨🇴

[Sergio Latorre, M.D. @Sergiolato]

"Yo vivo de preguntar, saber no puede ser lujo".
Escaramujo, Silvio Rodríguez.

[Dr. Chokri Ben Lamine @abouabdrahman0]

🧬 EHA2026 | Precision Hematology

🎤 Speaker: Sören Lehmann
🧪 Topic: Treatment strategies in TP53-mutated AML/MDS

🚨 TP53-mutated AML/MDS remains a major therapeutic challenge

💊 VEN + HMA has become a common standard approach in TP53-mutated AML

📌 Reported with VEN/HMA:
CR rates ≈ 43–67%
EFS ≈ 6–7 months
OS ≈ 5–7 months

⚠️ VIALE-A signal:
VEN/AZA improved response in TP53-mutated AML vs AZA alone
But OS remained short and similar ≈ 6 months

🧠 Key message:
VEN/HMA increases remission rate
But remissions are often short
And survival remains poor

💉 HMA monotherapy in TP53-mutated disease:
AZA CR ≈ 20–40%
Decitabine CR ≈ 30%
Median OS often ≈ 7 months

📌 In MDS:
Lower TP53 VAF = better survival
Platelets >50 ×10⁹/L = better OS

🔥 Intensive chemotherapy in TP53-mutated MN is limited

3+7 chemotherapy:
CR ≈ 20–48%
Median OS ≈ 4–9 months
3-year survival ≈ 1–8%

⚠️ CPX-351 does not appear to overcome TP53-mutated chemoresistance

📊 Meta-analysis message:
IC, HMA, and VEN/HMA may produce CR/CRi
But OS remains clustered around very short medians

🧪 Metronomic VEN/AZA is emerging in TP53-mutated AML/HR-MDS

📌 Presented regimen:
Azacitidine 0.2 mg/kg SC weekly
Venetoclax 400 mg PO days 1, 8, 15, 22
28-day cycles

📊 Cohort: VEN/AZA-naïve HR-MDS n=26 + AML n=14
Median age ≈ 76.5 years
82% had biallelic TP53 mutations

✅ Outcomes:
AML OS ≈ 11.6 months
HR-MDS OS ≈ 9.9 months
48% of responders became MRD negative
Low toxicity reported

⚠️ Neutropenic fever ≈ 15%
No therapy-related fatalities
100-day mortality ≈ 7.5%

🎯 Clinical takeaway:
For TP53-mutated AML/MDS, higher response does not always mean durable survival

💡 Precision treatment must focus on:
depth of response 🧬
MRD negativity 🔍
toxicity balance ⚖️
bridging to transplant when feasible 🧫
novel TP53-directed combinations 🚀

#EHA2026 #PrecisionHematology #TP53 #AML #MDS #HRMDS #Venetoclax #Azacitidine #HMA #MyeloidMalignancies #Hematology #KFSHRC #ESH

[Henry C Fung| MM, lymphoma, leukemia & CART @HenrychihangFu1]

Landmark trials deserve landmark scrutiny.

The PERSEUS trial has transformed frontline treatment for transplant-eligible multiple myeloma and established a new standard of care.

But practice-changing does not mean scientific discussion should stop.

As clinicians and educators, we should distinguish observed evidence from model-based projections, MRD negativity from cure, and trial populations from real-world patients.

This PSNS Myth vs Truth summarizes six common misconceptions surrounding PERSEUS.

The goal is not to criticize a landmark trial.

The goal is to better understand it.

Great science welcomes thoughtful discussion.

#MultipleMyeloma #Medwatch

[Dr. Chokri Ben Lamine @abouabdrahman0]

DLBCL algorithms - pearls #EHA2026

[Joaquín Díaz-Schmidt @iHematologo]

I think this is the best evidence we’re going to get, and it probably settles the issue. We need to move forward with different strategies.

Congratulations to the authors!

https://t.co/xPzZ7mRDdr

#EHA2026

[Sabrina Ranero Ferrari @SabrinaRanero]

HD-MTX for CNS prophylaxis in high-risk DLBCL? The answer is finally here.

3-yr CNS relapse: 6.6% (HD-MTX) vs 6.7% (no HD-MTX). Adjusted HR 0.95 (0.62–1.44).

Isolated CNS relapse: HR 0.81 (0.50–1.33).

n=1,923 UHR patients. No difference. None.

Time to move on. #EHA2026

[Papa Heme @Papa_Heme]

Been a while since I did a podcast. Fun times as always with WolverHeme

[Sergio Latorre, M.D. @Sergiolato]

Una joya que me encontré en la decanatura de la facultad hoy

[Meral Beksac @mbeksac56]

#COMY26 RESPONSE TO MYELOMA from the Blade criteria to the emerging 2026 IMS/IMWG criteria by @myelomaMD

[Henry C Fung| MM, lymphoma, leukemia & CART @HenrychihangFu1]

PFS is not always OS.

One of the biggest misconceptions in oncology is assuming that delaying progression automatically means patients live longer.

Many strategies consistently improve:
• PFS
• depth of response
• MRD negativity

…but OS often barely moves.

Examples:
• Upfront vs delayed AutoSCT
• Lenalidomide maintenance
• HD melphalan intensification
• Many maintenance strategies in lymphoma

Why?

Because:
• crossover therapies matter
• salvage therapies work
• toxicity matters
• resistant clones survive
• biology evolves

A longer remission is meaningful.
But it is not always a longer life.

The critical question is no longer:
“Did the curve separate?”

The question is:
“What happened to OS?”

#MultipleMyeloma #lymphoma #CART #Hemtwitter

Dr. Fun + G

[Sergio Latorre, M.D. @Sergiolato]

LOW-DOSE CYTARABINE AND VENETOCLAX PLUS AZACITIDINE... by Xiao Han
EHA Library; Jun 11 2026; 4206679
https://t.co/jQ63bE1FAC

[Henry C Fung| MM, lymphoma, leukemia & CART @HenrychihangFu1]

The future question in myeloma may not be:

“Transplant or no transplant?”

It may be:

“Which biology deserves a different first strike?”

Modern quadruplets and AutoSCT produce deep responses, but emerging real-world genomic-risk data suggest that biologically ultra-high-risk disease, especially TP53-abnormal myeloma, may still relapse early despite MRD negativity.

If “high-risk” patients already have median PFS ~2–3 years…

what happens in true bi-allelic TP53 disease?

A cartoon reflection on del17p / TP53-mutated myeloma, modern quadruplets, and the emerging frontline CAR-T debate. 🧬☕🚀

Inspired by recent IMS/IMWG genomic-risk real-world outcomes data from the MSKCC cohort.

#MultipleMyeloma #Hemetwitter #CAR_T

[Henry C Fung| MM, lymphoma, leukemia & CART @HenrychihangFu1]

TP53 in MDS is not binary.
It’s a spectrum.

And we’re oversimplifying it !

Large SCT dataset confirms:

• TP53-mut = adverse
• “Biallelic” = worst

Important work.
But the biology is more nuanced.

Monoallelic TP53?

Bad.
But how bad?

We all have long-term SCT survivors →
👉 suggests residual function / heterogeneity

True biallelic (multi-hit)?

• Functional TP53 loss
• Genomic instability
• Near chemo-resistant state

👉 Very difficult to rescue, even with SCT

But here’s the issue:

“Biallelic” often defined as VAF >50%

👉 Useful shortcut
👉 Not biology

VAF >50% ≠ biallelic

VAF reflects:

• Tumor purity
• Clonal dominance
• Copy number

A dominant monoallelic clone
can look like “50%”

More importantly:

👉 What if TP53 is a 10% subclone?

Different biology:

• Less genomic dominance
• Less clonal fitness

👉 GVL may still control this

So TP53-mut disease is not one entity:

• Monoallelic dominant
• True multi-hit
• Small subclonal TP53

➡️ Different biology
➡️ Different outcomes

Retrospective studies like this are critical
—but limited by simplified definitions

TP53 is a spectrum
VAF is not allelic state

True biallelic = genomic collapse

Subclonal TP53 → may be GVL-sensitive

See figure 👇

Dr Fun + G

#MDS #AML #Hemetwitter

[Jordi Martí @xarxatic]

Vamos perdiendo la batalla más importante en las aulas... la capacidad de leer un texto largo sin desconectar. No es que los alumnos no sepan leer; es que han perdido el hábito de la paciencia. Sin lectura no hay comprensión, y sin comprensión solo hay la NADA. 🧵va...

[Sergio Latorre, M.D. @Sergiolato]

When the System Is the Treatment: Rethinking Nonpharmacologic Breakthroughs in Oncology | Journal of Clinical Oncology https://t.co/Jm3ehkAwd5

[Timothée Olivier, MD @Timothee_MD]

Check out the full paper for more detailed insights !
https://t.co/2a0hnlqyNa
Thanks for following, thanks to my co-authors 🙏@vkprasadlab
Also covered here : https://t.co/CxT5UTILI8
Subscribe for free to my letter!
16/16

[Dr Rudra Narayan Swain M.D , D.M @rudra09s025]

👉🏻👉🏻📖Our work on stem cell mobilisation in multiple myeloma is now published in Leukemia & Lymphoma!

Full text:
https://t.co/DxncQdso1I

📊 Highlights:
• 80 NDMM patients undergoing ASCT
• 97% successful mobilisation with G-CSF + plerixafor
• No difference in CD34 yield between ≤4 vs >4 months lenalidomide
• Even extended exposure (>10 months in ~30%) did not compromise outcomes
• Practical, cost-effective strategy for resource-limited settings

⚠️ Caution: prolonged DARA-VRd may be associated with lower CD34 yields and higher plerixafor need—plan mobilisation carefully.

💡 Implication:
We may not need early collection or lenalidomide interruption in the plerixafor era—a major shift for real-world practice.

🔗 DOI: https://t.co/jGUhcyTMar
Thank you @DrPMPGI @LeukemiaLymph
#hematology #ASCT #myeloma #stemcell @MM_Hub @TheEBMT @SikshyaO @MirghSumeet @IndMyAcGp

[Luciano J Costa @End_myeloma]

I believe second milestone is clear. we must do the work to finish the journey. #mmsm @ESHaematology @CoMMitTrials

[Bread & Butter Haematology @MorphologyAmigo]

Thanks @UpToDate
My Honest Take on Thrombophilia Testing 😀
#hematology #hemepath #MedTwitter #MedEd @HaemBytes

[William Aird @WilliamAird4]

The Fe Paradox: High Serum Fe in Fe Deficiency

Poll results are in (≈1,200 votes).

Iron deficient patient with:
Ferritin 20
Serum Fe 388
TSAT >100%

The PCP stopped oral Fe because the Fe looked “too high.”

Interesting split in responses.

Let’s walk through the physiology.

[Dr. Chokri Ben Lamine @abouabdrahman0]

🩸📊 Statistical Tools in Hematology — Retrospective vs Clinical Trials

🔎 1️⃣ Retrospective Studies (Real-World / Registry / Cohort)

1️⃣ 📉 Kaplan–Meier → OS / PFS description
2️⃣ ⚖️ Log-rank test → Group comparison
3️⃣ 🧮 Cox regression (multivariable) → Adjust confounders (HR reporting)
4️⃣ 🎯 Propensity Score Matching (PSM) → Reduce selection bias
5️⃣ ⚙️ IPTW weighting → Alternative to PSM
6️⃣ 📊 Fine–Gray competing risks → Relapse vs NRM (post-SCT mandatory)
7️⃣ 📍 Logistic regression → Response (CR/MRD negativity)
8️⃣ 📉 Chi-square / Fisher’s exact → Categorical comparison
9️⃣ 📐 Linear / mixed models → Longitudinal labs (counts, ferritin)
🔟 📈 Time-dependent covariates → GVHD, DLI, maintenance

💡 Key Point:
Retrospective = 🔥 hypothesis-generating, but always control for bias & confounding.

⸻

🧪 2️⃣ Prospective Clinical Trials (Phase I–III)

1️⃣ 📊 Power & sample size calculation → Designed before enrollment
2️⃣ 🎲 Randomization → Eliminates selection bias
3️⃣ 📦 Intention-to-Treat (ITT) → Primary efficacy analysis
4️⃣ 📋 Per-protocol analysis → Secondary supportive analysis
5️⃣ 📈 Kaplan–Meier + Cox model → Time-to-event endpoints
6️⃣ 📉 Stratified Cox → Risk-stratified randomization
7️⃣ 📊 Interim analysis → DSMB monitoring
8️⃣ 🚦 O’Brien–Fleming / Pocock boundaries → Early stopping rules
9️⃣ 📍 Subgroup interaction testing → True heterogeneity assessment
🔟 📈 Restricted Mean Survival Time (RMST) → If PH violated

💡 Key Point:
Clinical trials = 🥇 highest level of evidence when well powered & executed.

⸻

🔬 3️⃣ Modern Advanced Hematology Analyses (Both Designs)

🔹 🧬 MRD kinetic modeling
🔹 🔎 ROC/AUC for diagnostic thresholds
🔹 🧠 Machine learning (risk prediction AML/SCT)
🔹 📊 Cumulative incidence curves in transplant
🔹 🔁 Mixed-effects models (CAR-T cytopenias dynamics)

⸻

🧠 Practical Take-Home

📌 SCT relapse analysis → Always use competing-risk models
📌 Observational study → Must adjust with PSM/IPTW
📌 Phase III RCT → ITT + stratified Cox mandatory

🔥 A strong adult hematology paper typically includes:
Kaplan–Meier + Cox ± Fine–Gray ± Multivariable adjustment

#Hematology #ClinicalTrials #RealWorldData #AML #StemCellTransplant #KFSHRC